diff --git a/db-course/000-Connect.ipynb b/db-course/000-Connect.ipynb deleted file mode 100644 index 4eaa7cb..0000000 --- a/db-course/000-Connect.ipynb +++ /dev/null @@ -1,584 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "dj.config[\"database.host\"] = \"127.0.0.1\"\n", - "dj.config[\"database.user\"] = \"root\"\n", - "dj.config[\"database.password\"] = \"simple\"" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "dj.config.save_local()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-08-29 19:01:01,193][INFO]: Connecting root@127.0.0.1:3306\n", - "[2023-08-29 19:01:01,648][INFO]: Connected root@127.0.0.1:3306\n" - ] - } - ], - "source": [ - "schema = dj.Schema(\"university\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Person(dj.Manual):\n", - " definition = \"\"\"\n", - " person : int\n", - " ---\n", - " first_name : varchar(30)\n", - " last_name : varchar(30)\n", - " date_of_birth : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "`university`.`person` (1 tuples)\n", - "Proceed? [yes, No]: no\n" - ] - } - ], - "source": [ - "Person.drop()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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person

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1JaneSmith2022-02-02
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Total: 1

\n", - " " - ], - "text/plain": [ - "*person first_name last_name date_of_birth \n", - "+--------+ +------------+ +-----------+ +------------+\n", - "1 Jane Smith 2022-02-02 \n", - " (Total: 1)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Person()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "Person.insert1((1, \"Jane\", \"Smith\", \"2022-02-02\"), skip_duplicates=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Person()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "from faker import Faker\n", - "\n", - "faker = Faker()\n", - "from tqdm import tqdm" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████| 1000/1000 [00:00<00:00, 5399.02it/s]\n" - ] - } - ], - "source": [ - "Person.insert(\n", - " (i, faker.first_name(), faker.last_name(), faker.date_of_birth())\n", - " for i in tqdm(range(1000, 2000))\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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1JaneSmith2022-02-02
1000RobertSmith1947-11-18
1001DanWarren2019-08-07
1002MelissaJackson2003-12-26
1003BradBuck1984-12-18
1004JosephMaxwell1947-09-09
1005JonathanTaylor1943-12-10
1006CarrieRichards1908-05-29
1007OliviaColeman1941-02-17
1008ElizabethPena1981-08-18
1009BenjaminFernandez1913-08-10
1010JesseWilliams2004-04-02
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...

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Total: 1001

\n", - " " - ], - "text/plain": [ - "*person first_name last_name date_of_birth \n", - "+--------+ +------------+ +-----------+ +------------+\n", - "1 Jane Smith 2022-02-02 \n", - "1000 Robert Smith 1947-11-18 \n", - "1001 Dan Warren 2019-08-07 \n", - "1002 Melissa Jackson 2003-12-26 \n", - "1003 Brad Buck 1984-12-18 \n", - "1004 Joseph Maxwell 1947-09-09 \n", - "1005 Jonathan Taylor 1943-12-10 \n", - "1006 Carrie Richards 1908-05-29 \n", - "1007 Olivia Coleman 1941-02-17 \n", - "1008 Elizabeth Pena 1981-08-18 \n", - "1009 Benjamin Fernandez 1913-08-10 \n", - "1010 Jesse Williams 2004-04-02 \n", - " ...\n", - " (Total: 1001)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Person()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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first_namelast_namedate_of_birth
person
1JaneSmith2022-02-02
1000RobertSmith1947-11-18
1001DanWarren2019-08-07
1002MelissaJackson2003-12-26
1003BradBuck1984-12-18
............
1995DianaMclean1947-04-27
1996ChristopherWaters1965-10-30
1997SamanthaRodriguez2007-02-16
1998MatthewLucas1962-10-24
1999AlexisJohnson1971-04-08
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1001 rows × 3 columns

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" - ], - "text/plain": [ - " first_name last_name date_of_birth\n", - "person \n", - "1 Jane Smith 2022-02-02\n", - "1000 Robert Smith 1947-11-18\n", - "1001 Dan Warren 2019-08-07\n", - "1002 Melissa Jackson 2003-12-26\n", - "1003 Brad Buck 1984-12-18\n", - "... ... ... ...\n", - "1995 Diana Mclean 1947-04-27\n", - "1996 Christopher Waters 1965-10-30\n", - "1997 Samantha Rodriguez 2007-02-16\n", - "1998 Matthew Lucas 1962-10-24\n", - "1999 Alexis Johnson 1971-04-08\n", - "\n", - "[1001 rows x 3 columns]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Person.fetch(format=\"frame\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "benv", - "language": "python", - "name": "benv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/000-ConnectCursors.ipynb b/db-course/000-ConnectCursors.ipynb deleted file mode 100644 index a8daa72..0000000 --- a/db-course/000-ConnectCursors.ipynb +++ /dev/null @@ -1,322 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Optional: Learn to use cursors \n", - "\n", - "Cursors are the usual way of issuing database queries and processing their results.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pymysql\n", - "\n", - "creds = {\"user\": \"root\", \"password\": \"simple\", \"host\": \"127.0.0.1\"}\n", - "\n", - "# establish a database connection\n", - "conn = pymysql.connect(\n", - " host=creds[\"host\"],\n", - " user=creds[\"user\"],\n", - " passwd=creds[\"password\"],\n", - " autocommit=True,\n", - ")\n", - "cursor = conn.cursor(cursor=pymysql.cursors.DictCursor)\n", - "cursor.execute(\"CREATE SCHEMA IF NOT EXISTS university\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "s = \"\"\"\n", - "Yesterday, \n", - "all my troubles seemed so far away.\n", - "Now it seems as though they're here to stay.\n", - "Oh, how I long for yesterday.\n", - "\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "ename": "ProgrammingError", - "evalue": "(1007, \"Can't create database 'university'; database exists\")", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mProgrammingError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m cursor\u001b[39m.\u001b[39;49mexecute(\u001b[39m'\u001b[39;49m\u001b[39mCREATE schema university\u001b[39;49m\u001b[39m'\u001b[39;49m)\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/cursors.py:153\u001b[0m, in \u001b[0;36mCursor.execute\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[39mpass\u001b[39;00m\n\u001b[1;32m 151\u001b[0m query \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmogrify(query, args)\n\u001b[0;32m--> 153\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_query(query)\n\u001b[1;32m 154\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_executed \u001b[39m=\u001b[39m query\n\u001b[1;32m 155\u001b[0m \u001b[39mreturn\u001b[39;00m result\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/cursors.py:322\u001b[0m, in \u001b[0;36mCursor._query\u001b[0;34m(self, q)\u001b[0m\n\u001b[1;32m 320\u001b[0m conn \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_db()\n\u001b[1;32m 321\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_clear_result()\n\u001b[0;32m--> 322\u001b[0m conn\u001b[39m.\u001b[39;49mquery(q)\n\u001b[1;32m 323\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_do_get_result()\n\u001b[1;32m 324\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrowcount\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/connections.py:558\u001b[0m, in \u001b[0;36mConnection.query\u001b[0;34m(self, sql, unbuffered)\u001b[0m\n\u001b[1;32m 556\u001b[0m sql \u001b[39m=\u001b[39m sql\u001b[39m.\u001b[39mencode(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mencoding, \u001b[39m\"\u001b[39m\u001b[39msurrogateescape\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 557\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_execute_command(COMMAND\u001b[39m.\u001b[39mCOM_QUERY, sql)\n\u001b[0;32m--> 558\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_affected_rows \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_read_query_result(unbuffered\u001b[39m=\u001b[39;49munbuffered)\n\u001b[1;32m 559\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_affected_rows\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/connections.py:822\u001b[0m, in \u001b[0;36mConnection._read_query_result\u001b[0;34m(self, unbuffered)\u001b[0m\n\u001b[1;32m 820\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 821\u001b[0m result \u001b[39m=\u001b[39m MySQLResult(\u001b[39mself\u001b[39m)\n\u001b[0;32m--> 822\u001b[0m result\u001b[39m.\u001b[39;49mread()\n\u001b[1;32m 823\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result \u001b[39m=\u001b[39m result\n\u001b[1;32m 824\u001b[0m \u001b[39mif\u001b[39;00m result\u001b[39m.\u001b[39mserver_status \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/connections.py:1200\u001b[0m, in \u001b[0;36mMySQLResult.read\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1198\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mread\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m 1199\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1200\u001b[0m first_packet \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnection\u001b[39m.\u001b[39;49m_read_packet()\n\u001b[1;32m 1202\u001b[0m \u001b[39mif\u001b[39;00m first_packet\u001b[39m.\u001b[39mis_ok_packet():\n\u001b[1;32m 1203\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_read_ok_packet(first_packet)\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/connections.py:772\u001b[0m, in \u001b[0;36mConnection._read_packet\u001b[0;34m(self, packet_type)\u001b[0m\n\u001b[1;32m 770\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result\u001b[39m.\u001b[39munbuffered_active \u001b[39mis\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m 771\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result\u001b[39m.\u001b[39munbuffered_active \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m--> 772\u001b[0m packet\u001b[39m.\u001b[39;49mraise_for_error()\n\u001b[1;32m 773\u001b[0m \u001b[39mreturn\u001b[39;00m packet\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/protocol.py:221\u001b[0m, in \u001b[0;36mMysqlPacket.raise_for_error\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[39mif\u001b[39;00m DEBUG:\n\u001b[1;32m 220\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39merrno =\u001b[39m\u001b[39m\"\u001b[39m, errno)\n\u001b[0;32m--> 221\u001b[0m err\u001b[39m.\u001b[39;49mraise_mysql_exception(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_data)\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/err.py:143\u001b[0m, in \u001b[0;36mraise_mysql_exception\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[39mif\u001b[39;00m errorclass \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 142\u001b[0m errorclass \u001b[39m=\u001b[39m InternalError \u001b[39mif\u001b[39;00m errno \u001b[39m<\u001b[39m \u001b[39m1000\u001b[39m \u001b[39melse\u001b[39;00m OperationalError\n\u001b[0;32m--> 143\u001b[0m \u001b[39mraise\u001b[39;00m errorclass(errno, errval)\n", - "\u001b[0;31mProgrammingError\u001b[0m: (1007, \"Can't create database 'university'; database exists\")" - ] - } - ], - "source": [ - "cursor.execute(\"CREATE schema university\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "ename": "ProgrammingError", - "evalue": "(1146, \"Table 'university.person' doesn't exist\")", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mProgrammingError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[5], line 13\u001b[0m\n\u001b[1;32m 2\u001b[0m cursor\u001b[39m.\u001b[39mexecute(\u001b[39m\"\"\"\u001b[39m\n\u001b[1;32m 3\u001b[0m \u001b[39mCREATE TABLE person(\u001b[39m\n\u001b[1;32m 4\u001b[0m \u001b[39mperson_id int not NULL,\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[39m\"\"\"\u001b[39m\n\u001b[1;32m 11\u001b[0m )\n\u001b[1;32m 12\u001b[0m cursor\u001b[39m.\u001b[39mexecute(\u001b[39m\"\u001b[39m\u001b[39mDROP TABLE person\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> 13\u001b[0m cursor\u001b[39m.\u001b[39;49mexecute(\n\u001b[1;32m 14\u001b[0m \u001b[39m \u001b[39;49m\u001b[39m\"\"\"\u001b[39;49;00m\n\u001b[1;32m 15\u001b[0m \u001b[39m INSERT INTO person \u001b[39;49;00m\n\u001b[1;32m 16\u001b[0m \u001b[39m VALUES\u001b[39;49;00m\n\u001b[1;32m 17\u001b[0m \u001b[39m (2, \"Jane\", \"Doe\", \"2002-02-02\"),\u001b[39;49;00m\n\u001b[1;32m 18\u001b[0m \u001b[39m (3, \"John\", \"Smith\", \"2003-03-01\"),\u001b[39;49;00m\n\u001b[1;32m 19\u001b[0m \u001b[39m (4, \"John\", \"Wick\", \"1979-12-02\")\u001b[39;49;00m\n\u001b[1;32m 20\u001b[0m \u001b[39m\"\"\"\u001b[39;49;00m)\n\u001b[1;32m 21\u001b[0m cursor\u001b[39m.\u001b[39mexecute(\u001b[39m\"\"\"\u001b[39m\n\u001b[1;32m 22\u001b[0m \u001b[39m SELECT * FROM person\u001b[39m\n\u001b[1;32m 23\u001b[0m \u001b[39m\"\"\"\u001b[39m)\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/cursors.py:153\u001b[0m, in \u001b[0;36mCursor.execute\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[39mpass\u001b[39;00m\n\u001b[1;32m 151\u001b[0m query \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmogrify(query, args)\n\u001b[0;32m--> 153\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_query(query)\n\u001b[1;32m 154\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_executed \u001b[39m=\u001b[39m query\n\u001b[1;32m 155\u001b[0m \u001b[39mreturn\u001b[39;00m result\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/cursors.py:322\u001b[0m, in \u001b[0;36mCursor._query\u001b[0;34m(self, q)\u001b[0m\n\u001b[1;32m 320\u001b[0m conn \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_db()\n\u001b[1;32m 321\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_clear_result()\n\u001b[0;32m--> 322\u001b[0m conn\u001b[39m.\u001b[39;49mquery(q)\n\u001b[1;32m 323\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_do_get_result()\n\u001b[1;32m 324\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrowcount\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/connections.py:558\u001b[0m, in \u001b[0;36mConnection.query\u001b[0;34m(self, sql, unbuffered)\u001b[0m\n\u001b[1;32m 556\u001b[0m sql \u001b[39m=\u001b[39m sql\u001b[39m.\u001b[39mencode(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mencoding, \u001b[39m\"\u001b[39m\u001b[39msurrogateescape\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 557\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_execute_command(COMMAND\u001b[39m.\u001b[39mCOM_QUERY, sql)\n\u001b[0;32m--> 558\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_affected_rows \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_read_query_result(unbuffered\u001b[39m=\u001b[39;49munbuffered)\n\u001b[1;32m 559\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_affected_rows\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/connections.py:822\u001b[0m, in \u001b[0;36mConnection._read_query_result\u001b[0;34m(self, unbuffered)\u001b[0m\n\u001b[1;32m 820\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 821\u001b[0m result \u001b[39m=\u001b[39m MySQLResult(\u001b[39mself\u001b[39m)\n\u001b[0;32m--> 822\u001b[0m result\u001b[39m.\u001b[39;49mread()\n\u001b[1;32m 823\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result \u001b[39m=\u001b[39m result\n\u001b[1;32m 824\u001b[0m \u001b[39mif\u001b[39;00m result\u001b[39m.\u001b[39mserver_status \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/connections.py:1200\u001b[0m, in \u001b[0;36mMySQLResult.read\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1198\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mread\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m 1199\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1200\u001b[0m first_packet \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnection\u001b[39m.\u001b[39;49m_read_packet()\n\u001b[1;32m 1202\u001b[0m \u001b[39mif\u001b[39;00m first_packet\u001b[39m.\u001b[39mis_ok_packet():\n\u001b[1;32m 1203\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_read_ok_packet(first_packet)\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/connections.py:772\u001b[0m, in \u001b[0;36mConnection._read_packet\u001b[0;34m(self, packet_type)\u001b[0m\n\u001b[1;32m 770\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result\u001b[39m.\u001b[39munbuffered_active \u001b[39mis\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m 771\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result\u001b[39m.\u001b[39munbuffered_active \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m--> 772\u001b[0m packet\u001b[39m.\u001b[39;49mraise_for_error()\n\u001b[1;32m 773\u001b[0m \u001b[39mreturn\u001b[39;00m packet\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/protocol.py:221\u001b[0m, in \u001b[0;36mMysqlPacket.raise_for_error\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[39mif\u001b[39;00m DEBUG:\n\u001b[1;32m 220\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39merrno =\u001b[39m\u001b[39m\"\u001b[39m, errno)\n\u001b[0;32m--> 221\u001b[0m err\u001b[39m.\u001b[39;49mraise_mysql_exception(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_data)\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/err.py:143\u001b[0m, in \u001b[0;36mraise_mysql_exception\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[39mif\u001b[39;00m errorclass \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 142\u001b[0m errorclass \u001b[39m=\u001b[39m InternalError \u001b[39mif\u001b[39;00m errno \u001b[39m<\u001b[39m \u001b[39m1000\u001b[39m \u001b[39melse\u001b[39;00m OperationalError\n\u001b[0;32m--> 143\u001b[0m \u001b[39mraise\u001b[39;00m errorclass(errno, errval)\n", - "\u001b[0;31mProgrammingError\u001b[0m: (1146, \"Table 'university.person' doesn't exist\")" - ] - } - ], - "source": [ - "cursor.execute(\"USE university\")\n", - "cursor.execute(\n", - " \"\"\"\n", - "CREATE TABLE person(\n", - "person_id int not NULL,\n", - "first_name varchar(30) NOT NULL,\n", - "last_name varchar(30) NOT NULL,\n", - "date_of_birth date NOT NULL,\n", - "primary key(person_id)\n", - ")\n", - "\"\"\"\n", - ")\n", - "cursor.execute(\"DROP TABLE person\")\n", - "cursor.execute(\n", - " \"\"\"\n", - " INSERT INTO person \n", - " VALUES\n", - " (2, \"Jane\", \"Doe\", \"2002-02-02\"),\n", - " (3, \"John\", \"Smith\", \"2003-03-01\"),\n", - " (4, \"John\", \"Wick\", \"1979-12-02\")\n", - "\"\"\"\n", - ")\n", - "cursor.execute(\n", - " \"\"\"\n", - " SELECT * FROM person\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for rec in cursor:\n", - " print(rec)\n", - "cursor.fetchall()\n", - "# insert\n", - "cursor.execute(\n", - " \"\"\"\n", - " INSERT INTO person \n", - " (person_id, first_name, last_name, date_of_birth) VALUES\n", - " (%s, %s, %s, %s)\n", - "\"\"\",\n", - " (5, faker.first_name(), faker.last_name(), faker.date_of_birth()),\n", - ")\n", - "from tqdm import tqdm\n", - "\n", - "for i in tqdm(range(1000, 1200)):\n", - " cursor.execute(\n", - " \"\"\"\n", - " INSERT INTO \n", - " person (person_id, first_name, last_name, date_of_birth) \n", - " VALUES (%s, %s, %s, %s)\n", - " \"\"\",\n", - " (i, faker.first_name(), faker.last_name(), faker.date_of_birth()),\n", - " )\n", - "cursor.execute(\n", - " \"\"\"\n", - "SELECT * FROM person\n", - "\"\"\"\n", - ")\n", - "cursor.fetchall()\n", - "cursor.execute(\n", - " \"\"\"\n", - "DROP TABLE dimitri_test.fake_person\n", - "\"\"\"\n", - ")\n", - "import datetime\n", - "\n", - "cursor.execute(\"\"\"USE dimitri_test\"\"\")\n", - "cursor.execute(\"\"\"SELECT * FROM fake_person\"\"\")\n", - "cursor.fetchone()\n", - "cursor.fetchall()\n", - "cursor.execute(\"\"\"SELECT * FROM dimitri_test.fake_person\"\"\")\n", - "for rec in cursor:\n", - " print(rec)\n", - "import datetime\n", - "\n", - "faker.date_between(datetime.date(2018, 2, 3), \"today\")\n", - "cursor.execute(\n", - " \"\"\"\n", - "drop table fake_death\n", - "\"\"\"\n", - ")\n", - "cursor.execute(\n", - " \"\"\"\n", - "CREATE TABLE dimitri_test.fake_death(\n", - " person_id int not null,\n", - " date_of_death date NOT NULL,\n", - " primary key(person_id), \n", - " foreign key (person_id) REFERENCES dimitri_test.fake_person (person_id))\n", - "\"\"\"\n", - ")\n", - "cursor.execute(\n", - " \"\"\"\n", - "CREATE TABLE hotel_reserviation(\n", - " \n", - " hotel varchar(20) not null\n", - " room int not null,\n", - " reservation_date date,\n", - " person_id int not null,\n", - " \n", - " unique index (person_id, reservation_date), \n", - " primary key (hotel, room, reservation_date),\n", - " foreign key (person_id) references fake_person(person_id)\n", - "\"\"\"\n", - ")\n", - "cursor.execute(\n", - " \"\"\"\n", - "CREATE TABLE bank_account (\n", - " \n", - " bank_id int not null, \n", - " account int not null,\n", - " \n", - " primary key(bank_id, account)\n", - "\n", - "\"\"\"\n", - ")\n", - "\n", - "cursor.execute(\n", - " \"\"\"\n", - "CREATE TABLE bank_account_owner (\n", - " \n", - " bank_id int not null, \n", - " account int not null,\n", - " person_id int not null,\n", - " \n", - " primary key(bank_id, account, person_id),\n", - " foreign key (person_id) references fake_person(person_id),\n", - " foreign key (bank_id, account) references fake_person(bank_id, account),\n", - "\n", - "\"\"\"\n", - ")\n", - "\n", - "cursor.execute(\n", - " \"\"\"\n", - "SELECT * FROM fake_person\n", - "\"\"\"\n", - ")\n", - "cursor.fetchone()\n", - "cursor.execute(\"\"\"INSERT into fake_death (date_of_death) values ('2020-10-09')\"\"\")\n", - "cursor.execute(\n", - " \"\"\"INSERT into fake_death (person_id, date_of_death) values (1000, '2020-09-09')\"\"\"\n", - ")\n", - "persons = cursor.execute(\n", - " \"\"\"SELECT person_id, date_of_birth FROM dimitri_test.fake_person\"\"\"\n", - ")\n", - "for rec in cursor.fetchall():\n", - " cursor.execute(\n", - " \"\"\"\n", - " INSERT INTO dimitri_test.fake_death (person_id, date_of_death) VALUES (%s, %s)\n", - " \"\"\",\n", - " (\n", - " rec[\"person_id\"],\n", - " faker.date_between(\n", - " rec[\"date_of_birth\"],\n", - " rec[\"date_of_birth\"] + datetime.timedelta(days=40000),\n", - " ),\n", - " ),\n", - " )\n", - "\n", - "\n", - "cursor.execute(\n", - " \"\"\"\n", - "SELECT first_name, floor(DATEDIFF(date_of_death, date_of_birth)/365.25) as died_at\n", - "FROM dimitri_test.fake_person NATURAL JOIN dimitri_test.fake_death\"\"\"\n", - ")\n", - "\n", - "for rec in cursor:\n", - " print(rec)\n", - "cursor.execute(\n", - " \"\"\"\n", - "DROP TABLE dimitri_test.fake_death\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/000-ConnectSQL.ipynb b/db-course/000-ConnectSQL.ipynb deleted file mode 100644 index ecd1ed6..0000000 --- a/db-course/000-ConnectSQL.ipynb +++ /dev/null @@ -1,255 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pymysql\n", - "\n", - "pymysql.install_as_MySQLdb()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext sql\n", - "%config SqlMagic.autocommit=True" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "connection_string = \"mysql://root:simple@127.0.0.1\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "%sql $connection_string" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "1 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "CREATE SCHEMA IF NOT EXISTS university" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql \n", - "\n", - "USE university " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "CREATE TABLE person (\n", - " person_id int NOT NULL,\n", - " first_name varchar(30) NOT NULL,\n", - " last_name varchar(30) NOT NULL,\n", - " PRIMARY KEY(person_id)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "2 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "INSERT INTO person VALUES (2, \"Jane\", \"Doe\"), (3, \"Alice\", \"Cooper\") " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "3 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
person_idfirst_namelast_name
1AliceCooper
2JaneDoe
3AliceCooper
" - ], - "text/plain": [ - "[(1, 'Alice', 'Cooper'), (2, 'Jane', 'Doe'), (3, 'Alice', 'Cooper')]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "SELECT * FROM person" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/001-FakeIt.ipynb b/db-course/001-FakeIt.ipynb deleted file mode 100644 index f243640..0000000 --- a/db-course/001-FakeIt.ipynb +++ /dev/null @@ -1,774 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Faker for testing databases\n", - "Learn to use the `faker` module to allow testing database designs." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "from faker import Faker" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "faker = Faker()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(faker.credit_card_full())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "faker.date_of_birth()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "faker.phone_number()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(faker.paragraph())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "faker.license_plate()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "faker.street_address()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "faker.coordinate()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "faker.words(4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Learn about Cursors\n", - "Cursors are the usual way of issuing database queries and processing their results." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "creds = {\"user\": \"root\", \"password\": \"simple\", \"host\": \"127.0.0.1\"}" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['user', 'password', 'host']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(creds)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import pymysql\n", - "\n", - "# establish a database connection\n", - "conn = pymysql.connect(\n", - " host=creds[\"host\"],\n", - " user=creds[\"user\"],\n", - " passwd=creds[\"password\"],\n", - " autocommit=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "cursor = conn.cursor(cursor=pymysql.cursors.DictCursor)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cursor.execute(\"CREATE SCHEMA IF NOT EXISTS university\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s = \"\"\"\n", - "Yesterday, \n", - "all my troubles seemed so far away.\n", - "Now it seems as though they're here to stay.\n", - "Oh, how I long for yesterday.\n", - "\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cursor.execute(\"USE university\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - "CREATE TABLE person(\n", - "person_id int not NULL,\n", - "first_name varchar(30) NOT NULL,\n", - "last_name varchar(30) NOT NULL,\n", - "date_of_birth date NOT NULL,\n", - "primary key(person_id)\n", - ")\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\"DROP TABLE person\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - " INSERT INTO person \n", - " VALUES\n", - " (2, \"Jane\", \"Doe\", \"2002-02-02\"),\n", - " (3, \"John\", \"Smith\", \"2003-03-01\"),\n", - " (4, \"John\", \"Wick\", \"1979-12-02\")\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - " SELECT * FROM person\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'person_id': 2, 'first_name': 'Jane', 'last_name': 'Doe', 'date_of_birth': datetime.date(2002, 2, 2)}\n", - "{'person_id': 3, 'first_name': 'John', 'last_name': 'Smith', 'date_of_birth': datetime.date(2003, 3, 1)}\n", - "{'person_id': 4, 'first_name': 'John', 'last_name': 'Wick', 'date_of_birth': datetime.date(1979, 12, 2)}\n", - "{'person_id': 5, 'first_name': 'Gregory', 'last_name': 'Morris', 'date_of_birth': datetime.date(1954, 9, 4)}\n" - ] - } - ], - "source": [ - "for rec in cursor:\n", - " print(rec)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cursor.fetchall()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# insert\n", - "cursor.execute(\n", - " \"\"\"\n", - " INSERT INTO person \n", - " (person_id, first_name, last_name, date_of_birth) VALUES\n", - " (%s, %s, %s, %s)\n", - "\"\"\",\n", - " (5, faker.first_name(), faker.last_name(), faker.date_of_birth()),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "from tqdm import tqdm" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|█████████████████████████████████████████| 200/200 [00:01<00:00, 164.26it/s]\n" - ] - } - ], - "source": [ - "for i in tqdm(range(1000, 1200)):\n", - " cursor.execute(\n", - " \"\"\"\n", - " INSERT INTO \n", - " person (person_id, first_name, last_name, date_of_birth) \n", - " VALUES (%s, %s, %s, %s)\n", - " \"\"\",\n", - " (i, faker.first_name(), faker.last_name(), faker.date_of_birth()),\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "204" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - "SELECT * FROM person\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cursor.fetchall()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - "DROP TABLE dimitri_test.fake_person\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import datetime" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\"\"\"USE dimitri_test\"\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\"\"\"SELECT * FROM fake_person\"\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.fetchone()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.fetchall()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\"\"\"SELECT * FROM dimitri_test.fake_person\"\"\")\n", - "for rec in cursor:\n", - " print(rec)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import datetime" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "faker.date_between(datetime.date(2018, 2, 3), \"today\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - "drop table fake_death\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - "CREATE TABLE dimitri_test.fake_death(\n", - " person_id int not null,\n", - " date_of_death date NOT NULL,\n", - " primary key(person_id), \n", - " foreign key (person_id) REFERENCES dimitri_test.fake_person (person_id))\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - "CREATE TABLE hotel_reserviation(\n", - " \n", - " hotel varchar(20) not null\n", - " room int not null,\n", - " reservation_date date,\n", - " person_id int not null,\n", - " \n", - " unique index (person_id, reservation_date), \n", - " primary key (hotel, room, reservation_date),\n", - " foreign key (person_id) references fake_person(person_id)\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - "CREATE TABLE bank_account (\n", - " \n", - " bank_id int not null, \n", - " account int not null,\n", - " \n", - " primary key(bank_id, account)\n", - "\n", - "\"\"\"\n", - ")\n", - "\n", - "cursor.execute(\n", - " \"\"\"\n", - "CREATE TABLE bank_account_owner (\n", - " \n", - " bank_id int not null, \n", - " account int not null,\n", - " person_id int not null,\n", - " \n", - " primary key(bank_id, account, person_id),\n", - " foreign key (person_id) references fake_person(person_id),\n", - " foreign key (bank_id, account) references fake_person(bank_id, account),\n", - "\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - "SELECT * FROM fake_person\n", - "\"\"\"\n", - ")\n", - "cursor.fetchone()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\"\"\"INSERT into fake_death (date_of_death) values ('2020-10-09')\"\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\n", - " \"\"\"INSERT into fake_death (person_id, date_of_death) values (1000, '2020-09-09')\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "persons = cursor.execute(\n", - " \"\"\"SELECT person_id, date_of_birth FROM dimitri_test.fake_person\"\"\"\n", - ")\n", - "for rec in cursor.fetchall():\n", - " cursor.execute(\n", - " \"\"\"\n", - " INSERT INTO dimitri_test.fake_death (person_id, date_of_death) VALUES (%s, %s)\n", - " \"\"\",\n", - " (\n", - " rec[\"person_id\"],\n", - " faker.date_between(\n", - " rec[\"date_of_birth\"],\n", - " rec[\"date_of_birth\"] + datetime.timedelta(days=40000),\n", - " ),\n", - " ),\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - "SELECT first_name, floor(DATEDIFF(date_of_death, date_of_birth)/365.25) as died_at\n", - "FROM dimitri_test.fake_person NATURAL JOIN dimitri_test.fake_death\"\"\"\n", - ")\n", - "\n", - "for rec in cursor:\n", - " print(rec)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - "DROP TABLE dimitri_test.fake_death\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Terminology\n", - "\n", - "Translation from relational terminology into database programming\n", - "\n", - "* Tuple -> Row\n", - "* Attribute -> Field/column\n", - "* Attribute value -> cell\n", - "* Relation -> Table\n", - "* Domain -> data type" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "benv", - "language": "python", - "name": "benv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/002-Tables.ipynb b/db-course/002-Tables.ipynb deleted file mode 100644 index de4914d..0000000 --- a/db-course/002-Tables.ipynb +++ /dev/null @@ -1,152 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tables\n", - "\n", - "The only data structure in relational databases is a table. All data are organized as tables. Tables cannot be nested. Tables have named columns. Each column has a datatype.\n", - "\n", - "## Schema\n", - "A schema is a set of logically related tables, their definitions, including data integrity constraints.\n", - "\n", - "## \"Scholarly\" Terminology \n", - "* Tables = relation\n", - "* Column = attribute\n", - "* Datatype = domain\n", - "* Row = tuple\n", - "* Field = attribute value\n", - "\n", - "## Relations\n", - "Relational databases come from 19th-century set theory concepts. \n", - "In that theory, a *relation* is a subset of a cartesian product of several sets.\n", - "Operations on such sets yield nontrivial insights.\n", - "\n", - "## First normal form\n", - " https://en.wikipedia.org/wiki/First_normal_form\n", - "\n", - "* All data are in relational tables\n", - "* No repeated columns \n", - "* No value in the table can contain another table (tables are not nested)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Second and Third Normal Form\n", - "\n", - "* https://en.wikipedia.org/wiki/Second_normal_form\n", - "* https://en.wikipedia.org/wiki/Third_normal_form\n", - "* All secondary attributes apply to the entity itself and to the whole entity\n", - "* \"All attributes describe the key, the whole key, and nothing but the key\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Types\n", - "https://dev.mysql.com/doc/refman/8.0/en/data-types.html\n", - "\n", - "* `int [unsigned]`, `smallint [unsigned]`, `tinyint`, `bigint`\n", - "* `char(n)`, `varchar(n)`\n", - "* `decimal(m, n)` same as `numeric`\n", - "* `enum`\n", - "* `float`, `double` - don't use in primary keys\n", - "* `date`\n", - "\n", - "\n", - "## Entity Integrity\n", - "\n", - "\n", - "Entity integrity is a set of database design principles and practices to ensure 1:1 correspondence between real-world entities and their digital representations in the database. Entity integrity may require a complex set of enterprise rules.\n", - "\n", - "1. Each table represents a well-defined entity type from the real world. We reflect this in the table name. The name of the table reflects the entity class represented by each row in the table.\n", - "2. For each entity type, enforce 1:1 correspondence between the real-world entity and its representation in the table. How can you do it?\n", - "3. In the real-world, we need to permanently associate a persistent identifier with each entity of the class. The database cannot do it by itself.\n", - "4. The database can use the permanent identifier to enforce uniqueness in the table using a uniqueness constraint.\n", - "5. A **primary key** is a unique, non-nullable index that is designated as the primary way to identify entities in a table. Each tables must have a carefully chosen primary key. \n", - "6. Secondary unique indexes can be nullable.\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "schema = dj.Schema(\"test\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Car(dj.Manual):\n", - " definition = \"\"\"\n", - " vin : char(17)\n", - " ---\n", - " make : varchar(16)\n", - " year : year\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Classroom(dj.Manual):\n", - " definition = \"\"\"\n", - " building_code : char(3)\n", - " room_number : smallint unsigned \n", - " ---\n", - " capacity : smallint unsigned\n", - " \"\"\"" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/003-ForeignKeys-HW.ipynb b/db-course/003-ForeignKeys-HW.ipynb deleted file mode 100644 index 20139d3..0000000 --- a/db-course/003-ForeignKeys-HW.ipynb +++ /dev/null @@ -1,41 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Homework 3\n", - "\n", - "Design tables for the following types of entities.\n", - "* Make sure that the tables have a proper primary key that correctly enforces *entity integrity*.\n", - "* Make sure that the table has all the required columnts with appropriate data types and null constraints\n", - "* Include an insert statement to populate a few entries into each table but you do not need to completely fill the tables.\n", - "* Execute the entire assignment in one notebook, print it and submit the PDF to the instructor by Slack.\n", - "\n", - "You can use DataJoint, SQL Jupypter Magic, or `pymysql` to interact with the database.\n", - "\n", - "1. Courses at the University of St Thomas, including the department name, and a course description.\n", - "2. Birthdays and emails for the students in this class.\n", - "3. Boston Marathon champions for each year for men's and women's open divisions. Include the runner's nationality and their time.\n", - "4. US State capitals, state birds and flowers\n", - "5. HTML colors including their names, R, G, and B values.\n", - "6. Students' grades for each homework assignment in this course. Do include the student's email, assignment number, and the grade, but you do not need to include any course information.\n", - "\n", - "Ensure that the tables are in First Normal Form." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "language_info": { - "name": "python" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/003-ForeignKeys-SQL.ipynb b/db-course/003-ForeignKeys-SQL.ipynb deleted file mode 100644 index f2a059c..0000000 --- a/db-course/003-ForeignKeys-SQL.ipynb +++ /dev/null @@ -1,698 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import pymysql\n", - "\n", - "pymysql.install_as_MySQLdb()\n", - "\n", - "%load_ext sql\n", - "%config SqlMagic.autocommit=True" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "connection_string = \"mysql://root:simple@127.0.0.1\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "%sql $connection_string" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "6 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Database
information_schema
mysql
performance_schema
person
sys
test
" - ], - "text/plain": [ - "[('information_schema',),\n", - " ('mysql',),\n", - " ('performance_schema',),\n", - " ('person',),\n", - " ('sys',),\n", - " ('test',)]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql \n", - "\n", - "show schemas" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "1 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "CREATE SCHEMA IF NOT EXISTS person2" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "use person2" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Tables_in_person2
" - ], - "text/plain": [ - "[]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "SHOW TABLES" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "CREATE TABLE person (\n", - " person_id INT NOT NULL,\n", - " first_name VARCHAR(30) NOT NULL,\n", - " last_name VARCHAR(30) NOT NULL,\n", - " PRIMARY KEY(person_id) \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "1 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "INSERT person (person_id, first_name, last_name) VALUES (1, \"Bob\", \"Builder\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql \n", - "CREATE TABLE department (\n", - " dept_code char(4) NOT NULL, \n", - " dept_name varchar(200) NOT NULL,\n", - " PRIMARY KEY (dept_code)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "CREATE TABLE student (\n", - " person_id INT NOT NULL,\n", - " dept_code CHAR(4) NOT NULL,\n", - " PRIMARY KEY(person_id),\n", - " FOREIGN KEY(person_id) REFERENCES person (person_id),\n", - " FOREIGN KEY(dept_code) REFERENCES department (dept_code)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "create table title ( \n", - " title_code char(8) not null,\n", - " full_title varchar(120) not null,\n", - " PRIMARY KEY(title_code) \n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
title_codefull_title
" - ], - "text/plain": [ - "[]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql \n", - "SELECT * FROM title;" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "create table employee(\n", - "\tperson_id INT NOT NULL,\n", - "\ttitle_code char(8) NOT NULL,\n", - "\tPrimary key(person_id),\n", - "\tForeign Key(person_id) REFERENCES person(person_id),\n", - "\tForeign Key(title_code) REFERENCES title(title_code)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "drop table employee" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "CREATE TABLE language (\n", - " lang_code char(8) NOT NULL,\n", - " language varchar(20) NOT NULL,\n", - " PRIMARY KEY(lang_code)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "6 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "INSERT language (lang_code, language) VALUES\n", - " (\"Eng\", \"English\"),\n", - " (\"Nav\", \"Navajo\"),\n", - " (\"Fr\", \"French\"),\n", - " (\"It\", \"Italian\"),\n", - " (\"Sp\", \"Spanish\"),\n", - " (\"Ar\", \"Arabic\") " - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "6 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
lang_codelanguage
ArArabic
EngEnglish
FrFrench
ItItalian
NavNavajo
SpSpanish
" - ], - "text/plain": [ - "[('Ar', 'Arabic'),\n", - " ('Eng', 'English'),\n", - " ('Fr', 'French'),\n", - " ('It', 'Italian'),\n", - " ('Nav', 'Navajo'),\n", - " ('Sp', 'Spanish')]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "SELECT * FROM language" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "\n", - "CREATE TABLE language_skill (\n", - " person_id int NOT NULL,\n", - " language_code char(8) NOT NULL,\n", - " skill_level enum(\"beginner\", \"intermediate\", \"fluent\", \"native\") NOT NULL,\n", - " PRIMARY KEY(person_id),\n", - " FOREIGN KEY(language_code) REFERENCES language(language_code),\n", - " FOREIGN KEY(person_id) REFERENCES person(person_id)\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/003-ForeignKeys.ipynb b/db-course/003-ForeignKeys.ipynb deleted file mode 100644 index 163925e..0000000 --- a/db-course/003-ForeignKeys.ipynb +++ /dev/null @@ -1,1241 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Referential integrity\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Referential integrity\n", - "\n", - "- Correcting matching of corresponding entities across the schema\n", - "- Relies on entity integrity\n", - "- Enforced by foreign keys\n", - "\n", - "## Foreign keys\n", - "\n", - "A foreign key is a column or several columns in the child table referencing the primary key column(s) in the parent table.\n", - "\n", - "- More generally, foreign keys can reference other sets of columns than the primary key. However, in common practice and in this class foreign keys will always reference the primary key in the referenced table.\n", - "\n", - "## Effects of a foreign key constraint\n", - "\n", - "1. Restrict inserts into the child table if there is no match in parent.\n", - "2. Restrict deletes (and updates of primary key values) from the parent table when there is a match in child.\n", - "3. An index is created in the child table to speed up searches on the foreign key.\n", - "\n", - "As a result, the child table is prevented from having values in its foreign keys columns in the absence of entries in the parent table with matching primary key values.\n", - "\n", - "Importantly, unlike other types of links in other data models, no actual link is created between individual rows of both tables. Referential integrity is maintained by restricting dta manipulations.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Diagramming notation\n", - "\n", - "- Entity-relationship diagram\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Examples\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-09-12 23:54:22,515][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", - "[2023-09-12 23:54:22,522][INFO]: Connected root@fakeservices.datajoint.io:3306\n" - ] - } - ], - "source": [ - "import datajoint as dj\n", - "\n", - "schema = dj.Schema(\"person\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Title(dj.Lookup):\n", - " definition = \"\"\"\n", - " title_code : char(8)\n", - " ---\n", - " full_title : varchar(120)\n", - " \"\"\"\n", - "\n", - " contents = [\n", - " (\"SW-Dev1\", \"Software Developer 1\"),\n", - " (\"SW-Dev2\", \"Software Developer 2\"),\n", - " (\"SW-Dev3\", \"Software Developer 3\"),\n", - " (\"Web-Dev1\", \"Web Developer 1\"),\n", - " (\"Web-Dev2\", \"Web Developer 2\"),\n", - " (\"Web-Dev3\", \"Web Developer 3\"),\n", - " (\"HR-Mgr\", \"Human Resources Manager\"),\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

title_code

\n", - " \n", - "
\n", - "

full_title

\n", - " \n", - "
HR-MgrHuman Resources Manager
SW-Dev1Software Developer 1
SW-Dev2Software Developer 2
SW-Dev3Software Developer 3
Web-Dev1Web Developer 1
Web-Dev2Web Developer 2
Web-Dev3Web Developer 3
\n", - " \n", - "

Total: 7

\n", - " " - ], - "text/plain": [ - "*title_code full_title \n", - "+------------+ +------------+\n", - "HR-Mgr Human Resource\n", - "SW-Dev1 Software Devel\n", - "SW-Dev2 Software Devel\n", - "SW-Dev3 Software Devel\n", - "Web-Dev1 Web Developer \n", - "Web-Dev2 Web Developer \n", - "Web-Dev3 Web Developer \n", - " (Total: 7)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Title()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Person(dj.Manual):\n", - " definition = \"\"\"\n", - " person_id : int \n", - " ---\n", - " first_name : varchar(30)\n", - " last_name : varchar(30)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "Title\n", - "\n", - "\n", - "Title\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "Person.insert1((1, \"Bob\", \"Builder\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Department(dj.Lookup):\n", - " definition = \"\"\"\n", - " dept_code : char(4) \n", - " --- \n", - " dept_name : varchar(200) \n", - " \"\"\"\n", - "\n", - " contents = (\n", - " (\"BIOL\", \"Biology\"),\n", - " (\"MATH\", \"Mathematics\"),\n", - " (\"STAT\", \"Statistics\"),\n", - " (\"ENG\", \"English\"),\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Student(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Person\n", - " ---\n", - " -> Department\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Employee(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Person\n", - " ---\n", - " -> Title\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "Department\n", - "\n", - "\n", - "Department\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Department->Student\n", - "\n", - "\n", - "\n", - "\n", - "Employee\n", - "\n", - "\n", - "Employee\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Title\n", - "\n", - "\n", - "Title\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Title->Employee\n", - "\n", - "\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person->Employee\n", - "\n", - "\n", - "\n", - "\n", - "Person->Student\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Foreign keys have 4 effects\n", - "\n", - "0. The primary key of the parent becomes part of the child definition (the foreign key)\n", - "1. Restrict inserts into child table if no match in parent\n", - "2. Restrict deletes from parent if there is a matching child\n", - "3. Create an index in child to make searches fast on the value of the FK value.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

dept_code

\n", - " \n", - "
\n", - "

dept_name

\n", - " \n", - "
BIOLBiology
ENGEnglish
MATHMathematics
STATStatistics
\n", - " \n", - "

Total: 4

\n", - " " - ], - "text/plain": [ - "*dept_code dept_name \n", - "+-----------+ +------------+\n", - "BIOL Biology \n", - "ENG English \n", - "MATH Mathematics \n", - "STAT Statistics \n", - " (Total: 4)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Department()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "
\n", - "

person_id

\n", - " \n", - "
\n", - "

dept_code

\n", - " \n", - "
\n", - " \n", - "

Total: 0

\n", - " " - ], - "text/plain": [ - "*person_id dept_code \n", - "+-----------+ +-----------+\n", - "\n", - " (Total: 0)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Student()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "
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person_id

\n", - " \n", - "
\n", - "

first_name

\n", - " \n", - "
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last_name

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1BobBuilder
\n", - " \n", - "

Total: 1

\n", - " " - ], - "text/plain": [ - "*person_id first_name last_name \n", - "+-----------+ +------------+ +-----------+\n", - "1 Bob Builder \n", - " (Total: 1)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Person()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "Student.insert1((1, \"BIOL\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Language(dj.Lookup):\n", - " definition = \"\"\"\n", - " lang_code : char(8)\n", - " ---\n", - " language : varchar(20)\n", - " \"\"\"\n", - "\n", - " contents = [\n", - " (\"Eng\", \"English\"),\n", - " (\"Nav\", \"Navajo\"),\n", - " (\"Fr\", \"French\"),\n", - " (\"It\", \"Italian\"),\n", - " (\"Sp\", \"Spanish\"),\n", - " (\"Ar\", \"Arabic\"),\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

lang_code

\n", - " \n", - "
\n", - "

language

\n", - " \n", - "
ArArabic
EngEnglish
FrFrench
ItItalian
NavNavajo
SpSpanish
\n", - " \n", - "

Total: 6

\n", - " " - ], - "text/plain": [ - "*lang_code language \n", - "+-----------+ +----------+\n", - "Ar Arabic \n", - "Eng English \n", - "Fr French \n", - "It Italian \n", - "Nav Navajo \n", - "Sp Spanish \n", - " (Total: 6)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Language()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class LanguageSkill(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Person\n", - " -> Language\n", - " ---\n", - " skill_level : enum(\"beginner\", \"intermediate\", \"fluent\", \"native\")\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "
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person_id

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lang_code

\n", - " \n", - "
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skill_level

\n", - " \n", - "
\n", - " \n", - "

Total: 0

\n", - " " - ], - "text/plain": [ - "*person_id *lang_code skill_level \n", - "+-----------+ +-----------+ +------------+\n", - "\n", - " (Total: 0)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "LanguageSkill()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "LanguageSkill.insert1((1, \"Sp\", \"fluent\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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person_id

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lang_code

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skill_level

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first_name

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last_name

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\n", - "

language

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1EngbeginnerBobBuilderEnglish
1SpfluentBobBuilderSpanish
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Total: 2

\n", - " " - ], - "text/plain": [ - "*person_id *lang_code skill_level first_name last_name language \n", - "+-----------+ +-----------+ +------------+ +------------+ +-----------+ +----------+\n", - "1 Eng beginner Bob Builder English \n", - "1 Sp fluent Bob Builder Spanish \n", - " (Total: 2)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "LanguageSkill * Person * Language" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "Department\n", - "\n", - "\n", - "Department\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Department->Student\n", - "\n", - "\n", - "\n", - "\n", - "LanguageSkill\n", - "\n", - "\n", - "LanguageSkill\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Employee\n", - "\n", - "\n", - "Employee\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Language\n", - "\n", - "\n", - "Language\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Language->LanguageSkill\n", - "\n", - "\n", - "\n", - "\n", - "Title\n", - "\n", - "\n", - "Title\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Title->Employee\n", - "\n", - "\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person->LanguageSkill\n", - "\n", - "\n", - "\n", - "\n", - "Person->Employee\n", - "\n", - "\n", - "\n", - "\n", - "Person->Student\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/003-Indexes.ipynb b/db-course/003-Indexes.ipynb deleted file mode 100644 index 7ec0d44..0000000 --- a/db-course/003-Indexes.ipynb +++ /dev/null @@ -1,1193 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Indexes\n", - "\n", - "Table indexes are data structures that allow fast lookups by an indexed attribute or combination of attributes.\n", - "\n", - "In DataJoint, indexes are created by one of the three mechanisms:\n", - "\n", - "1. Primary key\n", - "2. Foreign key\n", - "3. Explicitly defined indexes\n", - "\n", - "The first two mechanisms are obligatory. Every table has a primary key, which serves as an unique index. Therefore, restrictions by a primary key are very fast. Foreign keys create additional indexes unless a suitable index already exists.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's test this principle. Let's create a table with a 10,000 entries and compare lookup times:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2022-10-04 18:21:37,285][INFO]: Connecting dimitri@db.ust-data-sci.net:3306\n", - "[2022-10-04 18:21:37,668][INFO]: Connected dimitri@db.ust-data-sci.net:3306\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Proceed to delete entire schema `dimitri_indexes`? [yes, No]: yes\n" - ] - } - ], - "source": [ - "import datajoint as dj\n", - "\n", - "schema = dj.schema(\"dimitri_indexes\")\n", - "schema.drop() # drop previous schema definition (if any) and create anew\n", - "schema = dj.schema(\"dimitri_indexes\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's say a mouse in the lab has a lab-specific ID but it also has a separate id issued by the animal facility.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Mouse(dj.Manual):\n", - " definition = \"\"\"\n", - " mouse_id : int # lab-specific ID\n", - " ---\n", - " tag_id : int # animal facility ID\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "\n", - "\n", - "def populate_mice(table, n=200_000):\n", - " \"\"\"insert a bunch of mice\"\"\"\n", - " table.insert(\n", - " (\n", - " (random.randint(1, 1000_000_000), random.randint(1, 1000_000_000))\n", - " for i in range(n)\n", - " ),\n", - " skip_duplicates=True,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "populate_mice(Mouse())" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - "

mouse_id

\n", - " lab-specific ID\n", - "
\n", - "

tag_id

\n", - " animal facility ID\n", - "
216940882878
3245388150272
3812732593055
12227984921897
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...

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Total: 199977

\n", - " " - ], - "text/plain": [ - "*mouse_id tag_id \n", - "+----------+ +-----------+\n", - "2169 40882878 \n", - "3245 388150272 \n", - "3812 732593055 \n", - "12227 984921897 \n", - "13451 899146841 \n", - "19943 990904474 \n", - "33091 159537843 \n", - "42354 182413700 \n", - "56467 407326699 \n", - "60850 286034489 \n", - "62052 827330832 \n", - "64187 967534575 \n", - " ...\n", - " (Total: 199977)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Mouse()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "37.4 ms ± 1.83 ms per loop (mean ± std. dev. of 3 runs, 6 loops each)\n" - ] - } - ], - "source": [ - "%%timeit -n6 -r3\n", - "\n", - "# efficient! Uses the primary key\n", - "(Mouse() & {\"mouse_id\": random.randint(0, 999_999)}).fetch()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "84.4 ms ± 11.8 ms per loop (mean ± std. dev. of 3 runs, 6 loops each)\n" - ] - } - ], - "source": [ - "%%timeit -n6 -r3\n", - "\n", - "# inefficient! Requires a full table scan\n", - "(Mouse() & {\"tag_id\": random.randint(0, 999_999)}).fetch()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The indexed searches are much faster!\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To make searches faster on fields other than the primary key or a foreign key, you can add a secondary index explicitly.\n", - "\n", - "Regular indexes are declared as `index(attr1, ..., attrN)` on a separate line anywhere in the table declaration (below the primary key divide).\n", - "\n", - "Indexes can be declared with unique constraint as `unique index (attr1, ..., attrN)`.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's redeclare the table with a unique index on `tag_id`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Mouse2(dj.Manual):\n", - " definition = \"\"\"\n", - " mouse_id : int # lab-specific ID\n", - " ---\n", - " tag_id : int # animal facility ID\n", - " unique index (tag_id)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "populate_mice(Mouse2())" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - "

mouse_id

\n", - " lab-specific ID\n", - "
\n", - "

tag_id

\n", - " animal facility ID\n", - "
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37770837731911
44386906135317
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41527814253746
78085560861784
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41427779376695
\n", - "

...

\n", - "

Total: 199972

\n", - " " - ], - "text/plain": [ - "*mouse_id tag_id \n", - "+-----------+ +--------+\n", - "104267471 4179 \n", - "377708377 31911 \n", - "443869061 35317 \n", - "128843269 36413 \n", - "210025330 43358 \n", - "184517392 46214 \n", - "473829023 46327 \n", - "395488175 47271 \n", - "415278142 53746 \n", - "780855608 61784 \n", - "209041487 67300 \n", - "414277793 76695 \n", - " ...\n", - " (Total: 199972)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Mouse2()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now both types of searches are equally efficient!\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "36.9 ms ± 1.23 ms per loop (mean ± std. dev. of 3 runs, 6 loops each)\n" - ] - } - ], - "source": [ - "%%timeit -n6 -r3\n", - "\n", - "# efficient! Uses the primary key\n", - "(Mouse2() & {\"mouse_id\": random.randint(0, 999_999)}).fetch()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "36.4 ms ± 273 µs per loop (mean ± std. dev. of 3 runs, 6 loops each)\n" - ] - } - ], - "source": [ - "%%timeit -n6 -r3\n", - "\n", - "# efficient! Uses the secondary index on tag_id\n", - "(Mouse2() & {\"tag_id\": random.randint(0, 999_999)}).fetch()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now imagine that rats in the `Rat` table are identified by the combination of lab the `lab_name` and `rat_id` in each lab:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Rat(dj.Manual):\n", - " definition = \"\"\"\n", - " lab_name : char(16) \n", - " rat_id : int unsigned # lab-specific ID\n", - " ---\n", - " date_of_birth = null : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "def populate_rats(table):\n", - " lab_names = (\"Cajal\", \"Kandel\", \"Moser\", \"Wiesel\")\n", - " for date_of_birth in (None, \"2019-10-01\", \"2019-10-02\", \"2019-10-03\", \"2019-10-04\"):\n", - " table.insert(\n", - " (\n", - " (random.choice(lab_names), random.randint(1, 1_000_000), date_of_birth)\n", - " for i in range(100_000)\n", - " ),\n", - " skip_duplicates=True,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "populate_rats()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - "

lab_name

\n", - " \n", - "
\n", - "

rat_id

\n", - " lab-specific ID\n", - "
\n", - "

date_of_birth

\n", - " \n", - "
Cajal12019-10-03
Cajal172019-10-01
Cajal442019-10-04
Cajal562019-10-03
Cajal662019-10-01
Cajal692019-10-03
Cajal762019-10-01
Cajal93None
Cajal109None
Cajal1132019-10-04
Cajal144None
Cajal1622019-10-03
\n", - "

...

\n", - "

Total: 470097

\n", - " " - ], - "text/plain": [ - "*lab_name *rat_id date_of_birth \n", - "+----------+ +--------+ +------------+\n", - "Cajal 1 2019-10-03 \n", - "Cajal 17 2019-10-01 \n", - "Cajal 44 2019-10-04 \n", - "Cajal 56 2019-10-03 \n", - "Cajal 66 2019-10-01 \n", - "Cajal 69 2019-10-03 \n", - "Cajal 76 2019-10-01 \n", - "Cajal 93 None \n", - "Cajal 109 None \n", - "Cajal 113 2019-10-04 \n", - "Cajal 144 None \n", - "Cajal 162 2019-10-03 \n", - " ...\n", - " (Total: 470097)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Rat()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that despite the fact that `rat_id` is in the index, search by `rat_id` alone are not helped by the index because it is not first in the index. This is similar to search for a word in a dictionary that orders words alphabetically. Searching by the first letters of a word is easy but searching by the last few letters of a word requires scanning the whole dictionary.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this table, the primary key is a unique index on the combination `(lab_id, rat_id)`. Therefore searches on these attributes or on `lab_id` alone are fast. But this index cannot help searches on `rat_id` alone:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "140 ms ± 32.1 ms per loop (mean ± std. dev. of 10 runs, 2 loops each)\n" - ] - } - ], - "source": [ - "%%timeit -n2 -r10\n", - "\n", - "# inefficient! Requires full table scan.\n", - "(Rat() & {\"rat_id\": 300}).fetch()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "36.7 ms ± 981 µs per loop (mean ± std. dev. of 10 runs, 2 loops each)\n" - ] - } - ], - "source": [ - "%%timeit -n2 -r10\n", - "\n", - "# efficient! Uses the primary key\n", - "(Rat() & {\"rat_id\": 300, \"lab_name\": \"Cajal\"}).fetch()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "127 ms ± 21 ms per loop (mean ± std. dev. of 10 runs, 2 loops each)\n" - ] - } - ], - "source": [ - "%%timeit -n2 -r10\n", - "\n", - "# inefficient! Requires a full table scan\n", - "len(Rat & {\"rat_id\": 500})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pattern searches in strings can benefit from an index when the starting characters are specified.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "298 ms ± 20 ms per loop (mean ± std. dev. of 2 runs, 2 loops each)\n" - ] - } - ], - "source": [ - "%%timeit -n2 -r2\n", - "\n", - "# efficient! Uses the primary key\n", - "len(Rat & 'lab_name LIKE \"Caj%\"')" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "489 ms ± 127 ms per loop (mean ± std. dev. of 2 runs, 2 loops each)\n" - ] - } - ], - "source": [ - "%%timeit -n2 -r2\n", - "\n", - "# inefficient! requires a full table scan\n", - "len(Rat & 'lab_name LIKE \"%jal\"')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, searching by the date requires an inefficient full-table scan:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "384 ms ± 67.9 ms per loop (mean ± std. dev. of 6 runs, 3 loops each)\n" - ] - } - ], - "source": [ - "%%timeit -n3 -r6\n", - "\n", - "len(Rat & 'date_of_birth > \"2019-10-02\"')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To speed up searches by the `rat_id` and `date_of_birth`, we can explicit indexes to `Rat`:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Rat2(dj.Manual):\n", - " definition = \"\"\"\n", - " lab_name : char(16) \n", - " rat_id : int unsigned # lab-specific ID\n", - " ---\n", - " date_of_birth = null : date\n", - "\n", - " index(rat_id)\n", - " index(date_of_birth)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "populate_rats(Rat2())" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "37.6 ms ± 2.64 ms per loop (mean ± std. dev. of 6 runs, 3 loops each)\n" - ] - } - ], - "source": [ - "%%timeit -n3 -r6\n", - "\n", - "# efficient! uses index on rat_id\n", - "(Rat2() & {\"rat_id\": 300}).fetch()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "262 ms ± 41.8 ms per loop (mean ± std. dev. of 2 runs, 2 loops each)\n" - ] - } - ], - "source": [ - "%%timeit -n2 -r2\n", - "\n", - "# efficient! uses index on date_of_birth\n", - "len(Rat2 & 'date_of_birth > \"2019-10-02\"')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Quiz: How many indexes does the table `Rat` have?\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Rat.describe();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Answer\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Three: primary key, rat_id, date_of_birth\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Indexes in SQL\n" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import pymysql\n", - "\n", - "pymysql.install_as_MySQLdb()\n", - "\n", - "with open(\"cred.json\") as f:\n", - " creds = json.load(f)\n", - "\n", - "connection_string = \"mysql://{user}:{password}@{host}\".format(**creds)\n", - "\n", - "%load_ext sql\n", - "%config SqlMagic.autocommit=True\n", - "%sql $connection_string" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://dimitri:***@db.ust-data-sci.net\n", - "(pymysql.err.ProgrammingError) (1007, \"Can't create database 'dimitri_indexes'; database exists\")\n", - "[SQL: create database dimitri_indexes]\n", - "(Background on this error at: https://sqlalche.me/e/14/f405)\n" - ] - } - ], - "source": [ - "%%sql\n", - "\n", - "create database dimitri_indexes" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://dimitri:***@db.ust-data-sci.net\n", - "5 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Tables_in_dimitri_indexes
mouse
mouse2
rat
rat2
~log
" - ], - "text/plain": [ - "[('mouse',), ('mouse2',), ('rat',), ('rat2',), ('~log',)]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "SHOW TABLES in dimitri_indexes;" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://dimitri:***@db.ust-data-sci.net\n", - "(pymysql.err.OperationalError) (1050, \"Table 'mouse' already exists\")\n", - "[SQL: CREATE TABLE dimitri_indexes.mouse(\n", - "mouse_id int NOT NULL,\n", - "tag_id int NOT NULL,\n", - "primary key(mouse_id)\n", - ")]\n", - "(Background on this error at: https://sqlalche.me/e/14/e3q8)\n" - ] - } - ], - "source": [ - "%%sql\n", - "\n", - "CREATE TABLE dimitri_indexes.mouse(\n", - "mouse_id int NOT NULL,\n", - "tag_id int NOT NULL,\n", - "primary key(mouse_id)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://dimitri:***@db.ust-data-sci.net\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "drop table dimitri_indexes.mouse" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://dimitri:***@db.ust-data-sci.net\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "CREATE TABLE dimitri_indexes.mouse(\n", - "mouse_id int NOT NULL,\n", - "tag_id int NOT NULL,\n", - "primary key(mouse_id),\n", - "index (tag_id)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://dimitri:***@db.ust-data-sci.net\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "CREATE UNIQUE INDEX mouse_idx ON dimitri_indexes.mouse (tag_id)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://dimitri:***@db.ust-data-sci.net\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "DROP INDEX mouse_idx ON dimitri_indexes.mouse;" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "benv", - "language": "python", - "name": "benv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/003-UUID.ipynb b/db-course/003-UUID.ipynb deleted file mode 100644 index f1f3c37..0000000 --- a/db-course/003-UUID.ipynb +++ /dev/null @@ -1,1787 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# UUIDs\n", - "## Background\n", - "Universally Unique Identifiers (UUIDs) provide convenient mechanisms for identifying pieces of information (objects) inside an information system. Various conventions exist. However, general patterns have been established and formalized as RFC 4122.\n", - "\n", - "Comprised of hex digits, UUIDs have the pattern `8-4-4-4-12`, e.g. `e45ba2cc-39db-11e9-8e62-7470fdf23ef1`.\n", - "\n", - "It adds up to 36 characters (32 hex + 4 hyphens), or 16 bytes of information (128 bits).\n", - "\n", - "Python provides a [UUID module](https://docs.python.org/3/library/uuid.html) in its standard library." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import uuid" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function uuid1 in module uuid:\n", - "\n", - "uuid1(node=None, clock_seq=None)\n", - " Generate a UUID from a host ID, sequence number, and the current time.\n", - " If 'node' is not given, getnode() is used to obtain the hardware\n", - " address. If 'clock_seq' is given, it is used as the sequence number;\n", - " otherwise a random 14-bit sequence number is chosen.\n", - "\n" - ] - } - ], - "source": [ - "help(uuid.uuid1)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "UUID('f9a46b8c-6d43-11ee-8c98-0242ac120002')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "uuid.uuid1()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[UUID('fa69277e-6d43-11ee-8c98-0242ac120002'),\n", - " UUID('fa6928e6-6d43-11ee-8c98-0242ac120002'),\n", - " UUID('fa6929d6-6d43-11ee-8c98-0242ac120002'),\n", - " UUID('fa692ac6-6d43-11ee-8c98-0242ac120002'),\n", - " UUID('fa692b8e-6d43-11ee-8c98-0242ac120002'),\n", - " UUID('fa692c56-6d43-11ee-8c98-0242ac120002'),\n", - " UUID('fa692d1e-6d43-11ee-8c98-0242ac120002'),\n", - " UUID('fa692de6-6d43-11ee-8c98-0242ac120002')]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# use the current hardware address and time\n", - "[uuid.uuid1() for _ in range(8)]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[UUID('faf96eb0-6d43-11ee-8c98-0242ac120002'),\n", - " UUID('faf96fb4-6d43-11ee-8c98-0242ac120002'),\n", - " UUID('faf97054-6d43-11ee-8c98-0242ac120002'),\n", - " UUID('faf970e0-6d43-11ee-8c98-0242ac120002'),\n", - " UUID('faf97162-6d43-11ee-8c98-0242ac120002')]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# use the current hardware address and time\n", - "[uuid.uuid1() for _ in range(5)]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[UUID('fc601494-6d43-11ee-8001-000000000003'),\n", - " UUID('fc60155a-6d43-11ee-8001-000000000003'),\n", - " UUID('fc6015b2-6d43-11ee-8001-000000000003'),\n", - " UUID('fc6015e3-6d43-11ee-8001-000000000003'),\n", - " UUID('fc60160a-6d43-11ee-8001-000000000003')]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# use fixed values\n", - "[uuid.uuid1(3, 1) for _ in range(5)]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function uuid1 in module uuid:\n", - "\n", - "uuid1(node=None, clock_seq=None)\n", - " Generate a UUID from a host ID, sequence number, and the current time.\n", - " If 'node' is not given, getnode() is used to obtain the hardware\n", - " address. If 'clock_seq' is given, it is used as the sequence number;\n", - " otherwise a random 14-bit sequence number is chosen.\n", - "\n" - ] - } - ], - "source": [ - "help(uuid.uuid1)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function uuid3 in module uuid:\n", - "\n", - "uuid3(namespace, name)\n", - " Generate a UUID from the MD5 hash of a namespace UUID and a name.\n", - "\n" - ] - } - ], - "source": [ - "help(uuid.uuid3)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function uuid5 in module uuid:\n", - "\n", - "uuid5(namespace, name)\n", - " Generate a UUID from the SHA-1 hash of a namespace UUID and a name.\n", - "\n" - ] - } - ], - "source": [ - "help(uuid.uuid5)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "top = uuid.UUID(\"00000000-0000-0000-0000-000000000000\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "UUID('00000000-0000-0000-0000-000000000000')" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "top" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "UUID('913e0174-a390-5c08-b50a-623690546dd5')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "topic = uuid.uuid5(top, \"Neuroscience\")\n", - "topic" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "UUID('b5804c3f-57b1-54e3-8176-3b45aa443a97')" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subject1 = uuid.uuid5(topic, \"Habenula\")\n", - "subject1" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(UUID('345b4a08-7955-5b86-8646-f0826799afe9'),\n", - " UUID('b5804c3f-57b1-54e3-8176-3b45aa443a97'),\n", - " UUID('58571fff-c6bd-583f-88ac-ef0b8ff2981f'),\n", - " UUID('b5804c3f-57b1-54e3-8176-3b45aa443a97'),\n", - " UUID('6340129b-3a59-5354-aec6-5df769ae2ce7'))" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "top = uuid.UUID(\"00000000-0000-0000-0000-000000000000\")\n", - "topic = uuid.uuid5(top, \"Neuroscience\")\n", - "subject1 = uuid.uuid5(topic, \"Habenula\")\n", - "subject2 = uuid.uuid5(topic, \"Entorhinal cortex\")\n", - "subject3 = uuid.uuid5(topic, \"Habenula\")\n", - "\n", - "topic = uuid.uuid5(top, \"Philosophy\")\n", - "subject4 = uuid.uuid5(topic, \"Habenula\")\n", - "\n", - "topic, subject1, subject2, subject3, subject4" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "UUID('3d9d9035-dec3-5fc8-b66c-38cd8537acbe')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "uuid.uuid5(subject4, \"study\" * 1000000)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function uuid4 in module uuid:\n", - "\n", - "uuid4()\n", - " Generate a random UUID.\n", - "\n" - ] - } - ], - "source": [ - "help(uuid.uuid4)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[UUID('e97fb242-f513-491b-b628-cdc10745bed7'),\n", - " UUID('6240cec4-bf4d-434e-b284-53722fd2c9b7'),\n", - " UUID('789fcb88-ea0f-4a1e-a670-914a82ba7e07'),\n", - " UUID('efb07c22-1a94-4b5f-bc6e-7653321176c0'),\n", - " UUID('706203b8-de8b-4802-b83b-d6cf1c8e671f'),\n", - " UUID('660d7e08-2e03-49b8-a4c1-5dd124750c69'),\n", - " UUID('3dd8e385-0c68-4ae5-b5f5-c075b2b3aa5a'),\n", - " UUID('d1cc073e-d1d1-449b-82f9-f57b509dfe7d'),\n", - " UUID('8356b5b5-90d0-489c-8fa9-7e6352a3cca6'),\n", - " UUID('32d015c0-c3b1-4f65-a9c7-57ac1b1e6ca1'),\n", - " UUID('a108f427-5ce6-456e-84f8-e4620ed489db'),\n", - " UUID('b4e58cd8-e0c2-47be-ab5b-d839f27bda1b')]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[uuid.uuid4() for _ in range(12)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## UUIDs in DataJoint" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.14.1'" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import datajoint as dj\n", - "\n", - "dj.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-10-17 23:22:36,428][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", - "[2023-10-17 23:22:36,446][INFO]: Connected root@fakeservices.datajoint.io:3306\n" - ] - } - ], - "source": [ - "schema = dj.Schema(\"uuid\")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Message(dj.Manual):\n", - " definition = \"\"\"\n", - " message_id : uuid # internal message id\n", - " ---\n", - " message_body : varchar(1000) \n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "message_id : uuid # internal message id\n", - "---\n", - "message_body : varchar(1000) \n", - "\n" - ] - } - ], - "source": [ - "print(Message.describe())" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'`message_id` binary(16) NOT NULL COMMENT \":uuid:internal message id\"'" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# For the curious: Internally, DataJoint represents uuids as BINARY(16)\n", - "Message.heading[\"message_id\"].sql" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "Message.insert1((uuid.uuid1(), \"Hello, world!\"))\n", - "Message.insert1((uuid.uuid1(), \"Cogito ergo sum\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

message_id

\n", - " internal message id\n", - "
\n", - "

message_body

\n", - " \n", - "
681a997e-6d44-11ee-8c98-0242ac120002Hello, world!
68214b2a-6d44-11ee-8c98-0242ac120002Cogito ergo sum
71a3e82e-6d44-11ee-8c98-0242ac120002I will be back
71aa3580-6d44-11ee-8c98-0242ac120002Must destroy humans.
82f8a010-6d44-11ee-8c98-0242ac120002I will be back
82ffbb84-6d44-11ee-8c98-0242ac120002Must destroy humans.
83f36c84-6d44-11ee-8c98-0242ac120002I will be back
83f890ba-6d44-11ee-8c98-0242ac120002Must destroy humans.
84dd3aa8-6d44-11ee-8c98-0242ac120002I will be back
84e27202-6d44-11ee-8c98-0242ac120002Must destroy humans.
\n", - " \n", - "

Total: 10

\n", - " " - ], - "text/plain": [ - "*message_id message_body \n", - "+------------+ +------------+\n", - "681a997e-6d44- Hello, world! \n", - "68214b2a-6d44- Cogito ergo su\n", - "71a3e82e-6d44- I will be back\n", - "71aa3580-6d44- Must destroy h\n", - "82f8a010-6d44- I will be back\n", - "82ffbb84-6d44- Must destroy h\n", - "83f36c84-6d44- I will be back\n", - "83f890ba-6d44- Must destroy h\n", - "84dd3aa8-6d44- I will be back\n", - "84e27202-6d44- Must destroy h\n", - " (Total: 10)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Message()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "Message.insert1((uuid.uuid1(), \"I will be back\"))\n", - "Message.insert1((uuid.uuid1(), \"Must destroy humans.\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "b\"\\xa4\\xfd\\xe8\\x94\\x0f@@\\x95\\xa7 '5$\\xf8\\x06\\x97\"" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "b = uuid.uuid4().bytes\n", - "b" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "UUID('a4fde894-0f40-4095-a720-273524f80697')" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "uuid.UUID(bytes=b)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

message_id

\n", - " internal message id\n", - "
\n", - "

message_body

\n", - " \n", - "
681a997e-6d44-11ee-8c98-0242ac120002Hello, world!
68214b2a-6d44-11ee-8c98-0242ac120002Cogito ergo sum
71a3e82e-6d44-11ee-8c98-0242ac120002I will be back
71aa3580-6d44-11ee-8c98-0242ac120002Must destroy humans.
82f8a010-6d44-11ee-8c98-0242ac120002I will be back
82ffbb84-6d44-11ee-8c98-0242ac120002Must destroy humans.
83f36c84-6d44-11ee-8c98-0242ac120002I will be back
83f890ba-6d44-11ee-8c98-0242ac120002Must destroy humans.
84dd3aa8-6d44-11ee-8c98-0242ac120002I will be back
84e27202-6d44-11ee-8c98-0242ac120002Must destroy humans.
\n", - " \n", - "

Total: 10

\n", - " " - ], - "text/plain": [ - "*message_id message_body \n", - "+------------+ +------------+\n", - "681a997e-6d44- Hello, world! \n", - "68214b2a-6d44- Cogito ergo su\n", - "71a3e82e-6d44- I will be back\n", - "71aa3580-6d44- Must destroy h\n", - "82f8a010-6d44- I will be back\n", - "82ffbb84-6d44- Must destroy h\n", - "83f36c84-6d44- I will be back\n", - "83f890ba-6d44- Must destroy h\n", - "84dd3aa8-6d44- I will be back\n", - "84e27202-6d44- Must destroy h\n", - " (Total: 10)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Message()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "Message.insert1((uuid.uuid4(), \"Hasta la vista baby\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

message_id

\n", - " internal message id\n", - "
\n", - "

message_body

\n", - " \n", - "
553e2582-bc00-4c3e-a316-47c754f0677fHasta la vista baby
681a997e-6d44-11ee-8c98-0242ac120002Hello, world!
68214b2a-6d44-11ee-8c98-0242ac120002Cogito ergo sum
71a3e82e-6d44-11ee-8c98-0242ac120002I will be back
71aa3580-6d44-11ee-8c98-0242ac120002Must destroy humans.
82f8a010-6d44-11ee-8c98-0242ac120002I will be back
82ffbb84-6d44-11ee-8c98-0242ac120002Must destroy humans.
83f36c84-6d44-11ee-8c98-0242ac120002I will be back
83f890ba-6d44-11ee-8c98-0242ac120002Must destroy humans.
84dd3aa8-6d44-11ee-8c98-0242ac120002I will be back
84e27202-6d44-11ee-8c98-0242ac120002Must destroy humans.
\n", - " \n", - "

Total: 11

\n", - " " - ], - "text/plain": [ - "*message_id message_body \n", - "+------------+ +------------+\n", - "553e2582-bc00- Hasta la vista\n", - "681a997e-6d44- Hello, world! \n", - "68214b2a-6d44- Cogito ergo su\n", - "71a3e82e-6d44- I will be back\n", - "71aa3580-6d44- Must destroy h\n", - "82f8a010-6d44- I will be back\n", - "82ffbb84-6d44- Must destroy h\n", - "83f36c84-6d44- I will be back\n", - "83f890ba-6d44- Must destroy h\n", - "84dd3aa8-6d44- I will be back\n", - "84e27202-6d44- Must destroy h\n", - " (Total: 11)" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Message()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Comment(dj.Manual):\n", - " definition = \"\"\"\n", - " comment_id : uuid\n", - " --- \n", - " -> Message\n", - " comment_body : varchar(1000)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CREATE TABLE `comment` (\n", - " `comment_id` binary(16) NOT NULL COMMENT ':uuid:',\n", - " `message_id` binary(16) NOT NULL COMMENT ':uuid:internal message id',\n", - " `comment_body` varchar(1000) NOT NULL,\n", - " PRIMARY KEY (`comment_id`),\n", - " KEY `message_id` (`message_id`),\n", - " CONSTRAINT `comment_ibfk_1` FOREIGN KEY (`message_id`) REFERENCES `message` (`message_id`) ON DELETE RESTRICT ON UPDATE CASCADE\n", - ") ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci\n" - ] - } - ], - "source": [ - "# For the curious: This is how the table was declared in SQL\n", - "print(schema.connection.query(\"show create table `uuid`.`comment`\").fetchall()[0][1])" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "Message\n", - "\n", - "\n", - "Message\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Comment\n", - "\n", - "\n", - "Comment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Message->Comment\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "keys = Message.fetch(\"KEY\")" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'message_id': UUID('553e2582-bc00-4c3e-a316-47c754f0677f')},\n", - " {'message_id': UUID('681a997e-6d44-11ee-8c98-0242ac120002')},\n", - " {'message_id': UUID('68214b2a-6d44-11ee-8c98-0242ac120002')},\n", - " {'message_id': UUID('71a3e82e-6d44-11ee-8c98-0242ac120002')},\n", - " {'message_id': UUID('71aa3580-6d44-11ee-8c98-0242ac120002')},\n", - " {'message_id': UUID('82f8a010-6d44-11ee-8c98-0242ac120002')},\n", - " {'message_id': UUID('82ffbb84-6d44-11ee-8c98-0242ac120002')},\n", - " {'message_id': UUID('83f36c84-6d44-11ee-8c98-0242ac120002')},\n", - " {'message_id': UUID('83f890ba-6d44-11ee-8c98-0242ac120002')},\n", - " {'message_id': UUID('84dd3aa8-6d44-11ee-8c98-0242ac120002')},\n", - " {'message_id': UUID('84e27202-6d44-11ee-8c98-0242ac120002')}]" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "keys" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "Comment.insert1(dict(keys[0], comment_id=uuid.uuid1(), comment_body=\"thank you\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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message_body

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553e2582-bc00-4c3e-a316-47c754f0677ff768fb02-6d44-11ee-8c98-0242ac120002Hasta la vista babythank you
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Total: 1

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comment_id

\n", - " \n", - "
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message_body

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comment_body

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553e2582-bc00-4c3e-a316-47c754f0677ff768fb02-6d44-11ee-8c98-0242ac120002Hasta la vista babythank you
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Total: 1

\n", - " " - ], - "text/plain": [ - "*message_id *comment_id message_body comment_body \n", - "+------------+ +------------+ +------------+ +------------+\n", - "553e2582-bc00- f768fb02-6d44- Hasta la vista thank you \n", - " (Total: 1)" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Message * Comment & keys[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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message_id

\n", - " internal message id\n", - "
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message_body

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681a997e-6d44-11ee-8c98-0242ac120002Hello, world!
68214b2a-6d44-11ee-8c98-0242ac120002Cogito ergo sum
71a3e82e-6d44-11ee-8c98-0242ac120002I will be back
\n", - " \n", - "

Total: 3

\n", - " " - ], - "text/plain": [ - "*message_id message_body \n", - "+------------+ +------------+\n", - "681a997e-6d44- Hello, world! \n", - "68214b2a-6d44- Cogito ergo su\n", - "71a3e82e-6d44- I will be back\n", - " (Total: 3)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Message & keys[1:4]" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "Comment.insert1(dict(keys[1], comment_id=uuid.uuid1(), comment_body=\"thank you\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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comment_id

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message_id

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18d4c99c-6d45-11ee-8c98-0242ac120002681a997e-6d44-11ee-8c98-0242ac120002thank you
f768fb02-6d44-11ee-8c98-0242ac120002553e2582-bc00-4c3e-a316-47c754f0677fthank you
\n", - " \n", - "

Total: 2

\n", - " " - ], - "text/plain": [ - "*comment_id message_id comment_body \n", - "+------------+ +------------+ +------------+\n", - "18d4c99c-6d45- 681a997e-6d44- thank you \n", - "f768fb02-6d44- 553e2582-bc00- thank you \n", - " (Total: 2)" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Comment()" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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message_id

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message_body

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553e2582-bc00-4c3e-a316-47c754f0677fHasta la vista baby
681a997e-6d44-11ee-8c98-0242ac120002Hello, world!
\n", - " \n", - "

Total: 2

\n", - " " - ], - "text/plain": [ - "*message_id message_body \n", - "+------------+ +------------+\n", - "553e2582-bc00- Hasta la vista\n", - "681a997e-6d44- Hello, world! \n", - " (Total: 2)" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Message & Comment" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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message_id

\n", - " internal message id\n", - "
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message_body

\n", - " \n", - "
68214b2a-6d44-11ee-8c98-0242ac120002Cogito ergo sum
71a3e82e-6d44-11ee-8c98-0242ac120002I will be back
71aa3580-6d44-11ee-8c98-0242ac120002Must destroy humans.
82f8a010-6d44-11ee-8c98-0242ac120002I will be back
82ffbb84-6d44-11ee-8c98-0242ac120002Must destroy humans.
83f36c84-6d44-11ee-8c98-0242ac120002I will be back
83f890ba-6d44-11ee-8c98-0242ac120002Must destroy humans.
84dd3aa8-6d44-11ee-8c98-0242ac120002I will be back
84e27202-6d44-11ee-8c98-0242ac120002Must destroy humans.
\n", - " \n", - "

Total: 9

\n", - " " - ], - "text/plain": [ - "*message_id message_body \n", - "+------------+ +------------+\n", - "68214b2a-6d44- Cogito ergo su\n", - "71a3e82e-6d44- I will be back\n", - "71aa3580-6d44- Must destroy h\n", - "82f8a010-6d44- I will be back\n", - "82ffbb84-6d44- Must destroy h\n", - "83f36c84-6d44- I will be back\n", - "83f890ba-6d44- Must destroy h\n", - "84dd3aa8-6d44- I will be back\n", - "84e27202-6d44- Must destroy h\n", - " (Total: 9)" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Message - Comment" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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681a997e-6d44-11ee-8c98-0242ac12000218d4c99c-6d45-11ee-8c98-0242ac120002Hello, world!thank you
553e2582-bc00-4c3e-a316-47c754f0677ff768fb02-6d44-11ee-8c98-0242ac120002Hasta la vista babythank you
\n", - " \n", - "

Total: 2

\n", - " " - ], - "text/plain": [ - "*message_id *comment_id message_body comment_body \n", - "+------------+ +------------+ +------------+ +------------+\n", - "681a997e-6d44- 18d4c99c-6d45- Hello, world! thank you \n", - "553e2582-bc00- f768fb02-6d44- Hasta la vista thank you \n", - " (Total: 2)" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Message * Comment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/004-DatabaseHotel.ipynb b/db-course/004-DatabaseHotel.ipynb deleted file mode 100644 index 5027878..0000000 --- a/db-course/004-DatabaseHotel.ipynb +++ /dev/null @@ -1,2305 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6e26075b", - "metadata": {}, - "source": [ - "# Hotel Database\n", - "## Assignment\n", - "\n", - "Design, populate, and query a database for a hotel reservation system with the following business rules:\n", - "\n", - "1. The hotel has a number of rooms of two types: Deluxe and Suite\n", - "2. For every night, some rooms are made available for reservation for a specific price.\n", - "3. A guest can make a reservation for an avavilable room for one night. The reservation must include credit card payment info. At most one reservation can be made per night per room.\n", - "4. A guest can check into a room that has been reserved. An attempt to check in without a reservation will generate an error.\n", - "5. A guest can check out only after checking in. An attempt to check out multiple times or check out without checking in will generate an error.\n", - "\n", - "Your Python code should provide the following:\n", - "\n", - "1. A section to create the tables. The design must be in 3rd normal form following the conventions discussed in class and enforcing the business rules above.\n", - "\n", - "2. Provide code to populate rooms and room availability with prices.\n", - "\n", - "3. The function `reserve_room(room, date, guest_name, credit_card)` to make a reservation. A script that populates at least 300 reservations (e.g. use `faker`)\n", - "\n", - "4. The functions `checkin(room, date)` and `checkout(room, date)` to check guests in and out. Write a script that invokes `checkin` and `checkout` for a buncha guests. Demonstrate that that the functions enforces the rules of the business.\n", - "\n", - "5. Write a query to list all guests who have stayed in a given room in 2021.\n", - "\n", - "6. Write a query to list all dates on which a specific guest stayed at the hotel.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "33e46762", - "metadata": {}, - "source": [ - "# Define the database" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "fc703fb8", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-10-31 21:29:48,070][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", - "[2023-10-31 21:29:48,100][INFO]: Connected root@fakeservices.datajoint.io:3306\n" - ] - } - ], - "source": [ - "import datajoint as dj\n", - "\n", - "schema = dj.Schema(\"hotel\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "62d97e04", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Room(dj.Manual):\n", - " definition = \"\"\"\n", - " room : int\n", - " --- \n", - " room_type : enum('Deluxe', 'Suite')\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "280e7440", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class RoomAvailable(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Room\n", - " date : date \n", - " ---\n", - " price : decimal(6, 2)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "dc952e39", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Guest(dj.Manual):\n", - " definition = \"\"\"\n", - " guest_id : int unsigned\n", - " --- \n", - " guest_name : varchar(60)\n", - " index(guest_name)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "14929f5b", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Reservation(dj.Manual):\n", - " definition = \"\"\"\n", - " -> RoomAvailable\n", - " ---\n", - " -> Guest\n", - " credit_card : varchar(80)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "61f03f74", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class CheckIn(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Reservation\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c163c97c", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class CheckOut(dj.Manual):\n", - " definition = \"\"\"\n", - " -> CheckIn\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c98936a8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "CheckOut\n", - "\n", - "\n", - "CheckOut\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Room\n", - "\n", - "\n", - "Room\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "RoomAvailable\n", - "\n", - "\n", - "RoomAvailable\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Room->RoomAvailable\n", - "\n", - "\n", - "\n", - "\n", - "CheckIn\n", - "\n", - "\n", - "CheckIn\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "CheckIn->CheckOut\n", - "\n", - "\n", - "\n", - "\n", - "Reservation\n", - "\n", - "\n", - "Reservation\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Reservation->CheckIn\n", - "\n", - "\n", - "\n", - "\n", - "RoomAvailable->Reservation\n", - "\n", - "\n", - "\n", - "\n", - "Guest\n", - "\n", - "\n", - "Guest\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Guest->Reservation\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "id": "3bab6ec9", - "metadata": {}, - "source": [ - "# Populate Room Availability" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "64060872", - "metadata": {}, - "outputs": [], - "source": [ - "import faker\n", - "import random\n", - "import datetime\n", - "import tqdm\n", - "\n", - "fake = faker.Faker()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "281c8ca7", - "metadata": {}, - "outputs": [], - "source": [ - "# populate rooms\n", - "Room.insert((i, \"Deluxe\" if i % 2 else \"Suite\") for i in range(80))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "86bc4436", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 45/45 [00:01<00:00, 23.68it/s]\n" - ] - } - ], - "source": [ - "# Populate Room availability: 45 days starting on start_date\n", - "start_date = datetime.date(2023, 11, 1)\n", - "days = 45\n", - "\n", - "for day in tqdm.tqdm(range(days)):\n", - " price = random.randint(40, 350)\n", - " RoomAvailable.insert(\n", - " dict(key, date=start_date + datetime.timedelta(days=day), price=price)\n", - " for key in Room.fetch(\"KEY\")\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "4deda107", - "metadata": {}, - "source": [ - "# Functions " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "48470620", - "metadata": {}, - "outputs": [], - "source": [ - "class HotelException(Exception):\n", - " pass\n", - "\n", - "\n", - "class RoomUnavailable(HotelException):\n", - " pass\n", - "\n", - "\n", - "class RoomAlreadyReserved(HotelException):\n", - " pass\n", - "\n", - "\n", - "class AlreadyChecked(HotelException):\n", - " pass\n", - "\n", - "\n", - "class NoReservation(HotelException):\n", - " pass\n", - "\n", - "\n", - "class NotCheckedIn(HotelException):\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "5603309c", - "metadata": {}, - "outputs": [], - "source": [ - "def reserve_room(room, date, guest_name, credit_card):\n", - " # lookup guest by name\n", - " keys = (Guest & {\"guest_name\": guest_name}).fetch(\"KEY\")\n", - "\n", - " if keys:\n", - " # if multiple found, use the first, for example\n", - " key = keys[0]\n", - " else:\n", - " # if not registered before, create a new guest id\n", - " key = dict(guest_id=random.randint(0, 2**32 - 1))\n", - " Guest.insert1(dict(key, guest_name=guest_name))\n", - "\n", - " try:\n", - " Reservation.insert1(dict(key, room=room, date=date, credit_card=credit_card))\n", - " except dj.errors.DuplicateError:\n", - " raise RoomAlreadyReserved(room, date.isoformat()) from None\n", - " except dj.errors.IntegrityError:\n", - " raise RoomUnavailable(room, date.isoformat()) from None" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "fd3b9e30", - "metadata": {}, - "outputs": [], - "source": [ - "def check_in(room, date):\n", - " try:\n", - " CheckIn.insert1(dict(room=room, date=date))\n", - " except dj.errors.DuplicateError:\n", - " raise AlreadyChecked(room, date.isoformat()) from None\n", - " except dj.errors.IntegrityError:\n", - " raise NoReservation(room, date.isoformat()) from None" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "09e340e2", - "metadata": {}, - "outputs": [], - "source": [ - "def check_out(room, date):\n", - " try:\n", - " CheckOut.insert1(dict(room=room, date=date))\n", - " except dj.errors.DuplicateError:\n", - " raise AlreadyChecked(room, date.isoformat()) from None\n", - " except dj.errors.IntegrityError:\n", - " raise NotCheckedIn(room, date.isoformat()) from None" - ] - }, - { - "cell_type": "markdown", - "id": "60740817", - "metadata": {}, - "source": [ - "# Operations" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "8dbad565", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 13%|█▎ | 13/100 [00:04<00:31, 2.74it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RoomAlreadyReserved(41, '2023-11-09')\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 16%|█▌ | 16/100 [00:05<00:29, 2.82it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RoomAlreadyReserved(51, '2023-11-29')\n", - "RoomAlreadyReserved(35, '2023-11-08')\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 17%|█▋ | 17/100 [00:05<00:24, 3.42it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RoomAlreadyReserved(27, '2023-12-04')\n" - 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\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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room

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date

\n", - " \n", - "
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guest_id

\n", - " \n", - "
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credit_card

\n", - " \n", - "
02023-11-0338864046303539029767348314 02/24 538
02023-11-044222389012676270844746 02/25 520
02023-11-0635759303894982559346440216 04/24 375
02023-11-1441268773644512428001432 01/31 528
02023-11-16603044597375976386451502 09/32 3794
02023-11-1733254095063567555750016091 06/33 0971
02023-11-2228078682742251945256257864 02/25 362
02023-11-2513170537383597251137404879 03/31 428
02023-11-2820712954194037273286797 05/31 3831
02023-12-0120531456346011323937289809 11/26 4405
02023-12-126024417024303603515452016 07/28 986
02023-12-145024013066590640810134656 10/31 310
\n", - "

...

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Total: 917

\n", - " " - ], - "text/plain": [ - "*room *date guest_id credit_card \n", - "+------+ +------------+ +------------+ +------------+\n", - "0 2023-11-03 3886404630 35390297673483\n", - "0 2023-11-04 4222389012 676270844746 0\n", - "0 2023-11-06 3575930389 49825593464402\n", - "0 2023-11-14 4126877364 4512428001432 \n", - "0 2023-11-16 603044597 37597638645150\n", - "0 2023-11-17 3325409506 35675557500160\n", - "0 2023-11-22 2807868274 22519452562578\n", - "0 2023-11-25 1317053738 35972511374048\n", - "0 2023-11-28 2071295419 4037273286797 \n", - "0 2023-12-01 2053145634 60113239372898\n", - "0 2023-12-12 602441702 43036035154520\n", - "0 2023-12-14 502401306 65906408101346\n", - " ...\n", - " (Total: 917)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# make a bunch of random reservations\n", - "\n", - "number_of_guests = 100\n", - "max_nights = 20\n", - "\n", - "for i in tqdm.tqdm(range(number_of_guests)):\n", - " guest = fake.name()\n", - " credit_card = \" \".join(\n", - " (\n", - " fake.credit_card_number(),\n", - " fake.credit_card_expire(),\n", - " fake.credit_card_security_code(),\n", - " )\n", - " )\n", - "\n", - " for j in range(random.randint(1, max_nights)):\n", - " date = fake.date_between_dates(\n", - " start_date, start_date + datetime.timedelta(days=45)\n", - " )\n", - " room = random.randint(0, 80)\n", - " try:\n", - " reserve_room(room, date, guest, credit_card)\n", - " except HotelException as e:\n", - " print(repr(e))\n", - "\n", - "# show successful reservations\n", - "Reservation()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "dbc10353", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

guest_id

\n", - " \n", - "
\n", - "

guest_name

\n", - " \n", - "
2864601305Adam Nelson
1642893781Alicia Huerta
355112262Andres Castro
2949960533Arthur Wright
456674970Ashley Harrison
1042772978Ashley Swanson
3447296133Benjamin Jacobs
3113809067Billy Dennis
2024062282Brad Booth
2681245024Brandon Mcdonald
583335287Brian Santiago
3277354936Caitlyn Cruz
\n", - "

...

\n", - "

Total: 100

\n", - " " - ], - "text/plain": [ - "*guest_id guest_name \n", - "+------------+ +------------+\n", - "2864601305 Adam Nelson \n", - "1642893781 Alicia Huerta \n", - "355112262 Andres Castro \n", - "2949960533 Arthur Wright \n", - "456674970 Ashley Harriso\n", - "1042772978 Ashley Swanson\n", - "3447296133 Benjamin Jacob\n", - "3113809067 Billy Dennis \n", - "2024062282 Brad Booth \n", - "2681245024 Brandon Mcdona\n", - "583335287 Brian Santiago\n", - "3277354936 Caitlyn Cruz \n", - " ...\n", - " (Total: 100)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Guest()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "81a75c55", - "metadata": {}, - "outputs": [ - { - "ename": "NoReservation", - "evalue": "(2, '2023-11-02')", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNoReservation\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[23], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39m# Try check in\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m check_in(\u001b[39m2\u001b[39;49m, datetime\u001b[39m.\u001b[39;49mdate(\u001b[39m2023\u001b[39;49m, \u001b[39m11\u001b[39;49m, \u001b[39m2\u001b[39;49m))\n", - "Cell \u001b[0;32mIn[18], line 7\u001b[0m, in \u001b[0;36mcheck_in\u001b[0;34m(room, date)\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[39mraise\u001b[39;00m AlreadyChecked(room, date\u001b[39m.\u001b[39misoformat()) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[39mexcept\u001b[39;00m dj\u001b[39m.\u001b[39merrors\u001b[39m.\u001b[39mIntegrityError:\n\u001b[0;32m----> 7\u001b[0m \u001b[39mraise\u001b[39;00m NoReservation(room, date\u001b[39m.\u001b[39misoformat()) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n", - "\u001b[0;31mNoReservation\u001b[0m: (2, '2023-11-02')" - ] - } - ], - "source": [ - "# Try check in\n", - "check_in(2, datetime.date(2023, 11, 2))" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "8efc7fac", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 825/825 [00:24<00:00, 34.16it/s]\n" - ] - } - ], - "source": [ - "# checkin a bunch of people\n", - "checkins = random.sample(Reservation().fetch(\"KEY\"), k=int(0.9 * len(Reservation())))\n", - "for r in tqdm.tqdm(checkins):\n", - " try:\n", - " check_in(**r)\n", - " except AlreadyChecked as e:\n", - " print(repr(e))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "ec862447", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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room

\n", - " \n", - "
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date

\n", - " \n", - "
02023-11-03
02023-11-04
02023-11-06
02023-11-14
02023-11-16
02023-11-25
02023-11-28
02023-12-01
02023-12-12
12023-11-01
12023-11-02
12023-11-14
\n", - "

...

\n", - "

Total: 825

\n", - " " - ], - "text/plain": [ - "*room *date \n", - "+------+ +------------+\n", - "0 2023-11-03 \n", - "0 2023-11-04 \n", - "0 2023-11-06 \n", - "0 2023-11-14 \n", - "0 2023-11-16 \n", - "0 2023-11-25 \n", - "0 2023-11-28 \n", - "0 2023-12-01 \n", - "0 2023-12-12 \n", - "1 2023-11-01 \n", - "1 2023-11-02 \n", - "1 2023-11-14 \n", - " ...\n", - " (Total: 825)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CheckIn()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "2faabb54", - "metadata": {}, - "outputs": [ - { - "ename": "AlreadyChecked", - "evalue": "(70, '2023-12-07')", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAlreadyChecked\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[26], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39m# Try duplicate checkin\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m check_in(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcheckins[\u001b[39m0\u001b[39;49m])\n", - "Cell \u001b[0;32mIn[18], line 5\u001b[0m, in \u001b[0;36mcheck_in\u001b[0;34m(room, date)\u001b[0m\n\u001b[1;32m 3\u001b[0m CheckIn\u001b[39m.\u001b[39minsert1(\u001b[39mdict\u001b[39m(room\u001b[39m=\u001b[39mroom, date\u001b[39m=\u001b[39mdate))\n\u001b[1;32m 4\u001b[0m \u001b[39mexcept\u001b[39;00m dj\u001b[39m.\u001b[39merrors\u001b[39m.\u001b[39mDuplicateError:\n\u001b[0;32m----> 5\u001b[0m \u001b[39mraise\u001b[39;00m AlreadyChecked(room, date\u001b[39m.\u001b[39misoformat()) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[39mexcept\u001b[39;00m dj\u001b[39m.\u001b[39merrors\u001b[39m.\u001b[39mIntegrityError:\n\u001b[1;32m 7\u001b[0m \u001b[39mraise\u001b[39;00m NoReservation(room, date\u001b[39m.\u001b[39misoformat()) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n", - "\u001b[0;31mAlreadyChecked\u001b[0m: (70, '2023-12-07')" - ] - } - ], - "source": [ - "# Try duplicate checkin\n", - "check_in(**checkins[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "5236b125", - "metadata": {}, - "outputs": [ - { - "ename": "NotCheckedIn", - "evalue": "(2, '2023-10-02')", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNotCheckedIn\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[27], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39m# Try checkout\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m check_out(\u001b[39m2\u001b[39;49m, datetime\u001b[39m.\u001b[39;49mdate(\u001b[39m2023\u001b[39;49m, \u001b[39m10\u001b[39;49m, \u001b[39m2\u001b[39;49m))\n", - "Cell \u001b[0;32mIn[19], line 7\u001b[0m, in \u001b[0;36mcheck_out\u001b[0;34m(room, date)\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[39mraise\u001b[39;00m AlreadyChecked(room, date\u001b[39m.\u001b[39misoformat()) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[39mexcept\u001b[39;00m dj\u001b[39m.\u001b[39merrors\u001b[39m.\u001b[39mIntegrityError:\n\u001b[0;32m----> 7\u001b[0m \u001b[39mraise\u001b[39;00m NotCheckedIn(room, date\u001b[39m.\u001b[39misoformat()) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n", - "\u001b[0;31mNotCheckedIn\u001b[0m: (2, '2023-10-02')" - ] - } - ], - "source": [ - "# Try checkout\n", - "\n", - "check_out(2, datetime.date(2023, 10, 2))" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "85a3a740", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 742/742 [00:21<00:00, 34.42it/s]\n" - ] - } - ], - "source": [ - "# checkout a bunch of people\n", - "checkouts = random.sample(CheckIn().fetch(\"KEY\"), k=int(0.9 * len(CheckIn())))\n", - "for r in tqdm.tqdm(checkouts):\n", - " try:\n", - " check_out(**r)\n", - " except AlreadyChecked as e:\n", - " print(repr(e))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "5151ae4b", - "metadata": {}, - "outputs": [ - { - "ename": "AlreadyChecked", - "evalue": "(65, '2023-12-13')", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAlreadyChecked\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[29], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39m# try duplicate checkout\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m check_out(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcheckouts[\u001b[39m0\u001b[39;49m])\n", - "Cell \u001b[0;32mIn[19], line 5\u001b[0m, in \u001b[0;36mcheck_out\u001b[0;34m(room, date)\u001b[0m\n\u001b[1;32m 3\u001b[0m CheckOut\u001b[39m.\u001b[39minsert1(\u001b[39mdict\u001b[39m(room\u001b[39m=\u001b[39mroom, date\u001b[39m=\u001b[39mdate))\n\u001b[1;32m 4\u001b[0m \u001b[39mexcept\u001b[39;00m dj\u001b[39m.\u001b[39merrors\u001b[39m.\u001b[39mDuplicateError:\n\u001b[0;32m----> 5\u001b[0m \u001b[39mraise\u001b[39;00m AlreadyChecked(room, date\u001b[39m.\u001b[39misoformat()) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[39mexcept\u001b[39;00m dj\u001b[39m.\u001b[39merrors\u001b[39m.\u001b[39mIntegrityError:\n\u001b[1;32m 7\u001b[0m \u001b[39mraise\u001b[39;00m NotCheckedIn(room, date\u001b[39m.\u001b[39misoformat()) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n", - "\u001b[0;31mAlreadyChecked\u001b[0m: (65, '2023-12-13')" - ] - } - ], - "source": [ - "# try duplicate checkout\n", - "\n", - "check_out(**checkouts[0])" - ] - }, - { - "cell_type": "markdown", - "id": "4ebd1fd9", - "metadata": {}, - "source": [ - "# Queries \n", - "\n", - "## Query 1: List all guests who have stayed in room 1" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "fa181a79", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

guest_id

\n", - " \n", - "
\n", - "

guest_name

\n", - " \n", - "
1567202417Mark Lane
877452699Sandra Brewer
856648119Kimberly Lee
2107900346Nathan Atkinson
2413549786Danielle Hamilton
12668262Patrick Wells
2053145634Jeremy Phillips
3325409506Melinda Flores
4167308935Melody Snyder
2071295419Douglas Murphy
\n", - " \n", - "

Total: 10

\n", - " " - ], - "text/plain": [ - "*guest_id guest_name \n", - "+------------+ +------------+\n", - "1567202417 Mark Lane \n", - "877452699 Sandra Brewer \n", - "856648119 Kimberly Lee \n", - "2107900346 Nathan Atkinso\n", - "2413549786 Danielle Hamil\n", - "12668262 Patrick Wells \n", - "2053145634 Jeremy Phillip\n", - "3325409506 Melinda Flores\n", - "4167308935 Melody Snyder \n", - "2071295419 Douglas Murphy\n", - " (Total: 10)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Guest & (Reservation & (CheckIn & \"room=1\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "e9891efc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - "

guest_id

\n", - " \n", - "
\n", - "

guest_name

\n", - " \n", - "
1567202417Mark Lane
877452699Sandra Brewer
856648119Kimberly Lee
2107900346Nathan Atkinson
2413549786Danielle Hamilton
12668262Patrick Wells
2053145634Jeremy Phillips
3325409506Melinda Flores
4167308935Melody Snyder
2071295419Douglas Murphy
\n", - " \n", - "

Total: 10

\n", - " " - ], - "text/plain": [ - "*guest_id guest_name \n", - "+------------+ +------------+\n", - "1567202417 Mark Lane \n", - "877452699 Sandra Brewer \n", - "856648119 Kimberly Lee \n", - "2107900346 Nathan Atkinso\n", - "2413549786 Danielle Hamil\n", - "12668262 Patrick Wells \n", - "2053145634 Jeremy Phillip\n", - "3325409506 Melinda Flores\n", - "4167308935 Melody Snyder \n", - "2071295419 Douglas Murphy\n", - " (Total: 10)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Guest & (Reservation * CheckIn & \"room=1\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d24bccfb", - "metadata": {}, - "outputs": [], - "source": [ - "_.make_sql()" - ] - }, - { - "cell_type": "markdown", - "id": "d281a8f4", - "metadata": {}, - "source": [ - "## Query 2: List all nights when a guest stayed at a hotel" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "37156855", - "metadata": {}, - "outputs": [], - "source": [ - "# pick a guest\n", - "guest = random.choice(Guest().fetch(\"KEY\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "f166cbe4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'guest_id': 2807868274}" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "guest" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "63504e50", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

room

\n", - " \n", - "
\n", - "

date

\n", - " \n", - "
102023-11-17
102023-12-02
142023-11-21
182023-11-23
202023-11-19
222023-11-09
242023-11-09
252023-11-07
322023-11-20
362023-11-29
362023-12-06
372023-11-10
\n", - "

...

\n", - "

Total: 15

\n", - " " - ], - "text/plain": [ - "*room *date \n", - "+------+ +------------+\n", - "10 2023-11-17 \n", - "10 2023-12-02 \n", - "14 2023-11-21 \n", - "18 2023-11-23 \n", - "20 2023-11-19 \n", - "22 2023-11-09 \n", - "24 2023-11-09 \n", - "25 2023-11-07 \n", - "32 2023-11-20 \n", - "36 2023-11-29 \n", - "36 2023-12-06 \n", - "37 2023-11-10 \n", - " ...\n", - " (Total: 15)" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(Reservation * CheckIn & guest).proj()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f8d93df", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/db-course/004-DatabaseNations.ipynb b/db-course/004-DatabaseNations.ipynb deleted file mode 100644 index b286da2..0000000 --- a/db-course/004-DatabaseNations.ipynb +++ /dev/null @@ -1,438 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "https://www.mariadbtutorial.com/getting-started/mariadb-sample-database/" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The sql extension is already loaded. To reload it, use:\n", - " %reload_ext sql\n" - ] - } - ], - "source": [ - "import pymysql\n", - "\n", - "pymysql.install_as_MySQLdb()\n", - "\n", - "%load_ext sql\n", - "%config SqlMagic.autocommit=True\n", - "%sql mysql://root:simple@127.0.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "1 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "7 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 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"execute_result" - } - ], - "source": [ - "%%sql\n", - "-- MySQL dump 10.13 Distrib 8.0.16, for Win64 (x86_64)\n", - "--\n", - "-- Host: localhost Database: nation\n", - "-- ------------------------------------------------------\n", - "-- Server version\t5.5.5-10.4.8-MariaDB\n", - "\n", - "/*!40101 SET @OLD_CHARACTER_SET_CLIENT=@@CHARACTER_SET_CLIENT */;\n", - "/*!40101 SET @OLD_CHARACTER_SET_RESULTS=@@CHARACTER_SET_RESULTS */;\n", - "/*!40101 SET @OLD_COLLATION_CONNECTION=@@COLLATION_CONNECTION */;\n", - " SET NAMES utf8mb4 ;\n", - "/*!40103 SET @OLD_TIME_ZONE=@@TIME_ZONE */;\n", - "/*!40103 SET TIME_ZONE='+00:00' */;\n", - "/*!40014 SET @OLD_UNIQUE_CHECKS=@@UNIQUE_CHECKS, UNIQUE_CHECKS=0 */;\n", - "/*!40014 SET @OLD_FOREIGN_KEY_CHECKS=@@FOREIGN_KEY_CHECKS, FOREIGN_KEY_CHECKS=0 */;\n", - "/*!40101 SET @OLD_SQL_MODE=@@SQL_MODE, SQL_MODE='NO_AUTO_VALUE_ON_ZERO' */;\n", - "/*!40111 SET @OLD_SQL_NOTES=@@SQL_NOTES, SQL_NOTES=0 */;\n", - "\n", - "--\n", - "-- Current Database: `nation`\n", - "--\n", - "\n", - "CREATE DATABASE /*!32312 IF NOT EXISTS*/ `nation` /*!40100 DEFAULT CHARACTER SET utf8 */;\n", - "\n", - "USE `nation`;\n", - "\n", - "--\n", - "-- Table structure for table `continents`\n", - "--\n", - "\n", - "DROP TABLE IF EXISTS `continents`;\n", - "/*!40101 SET @saved_cs_client = @@character_set_client */;\n", - " SET character_set_client = utf8mb4 ;\n", - "CREATE TABLE `continents` (\n", - " `continent_id` int(11) NOT NULL AUTO_INCREMENT,\n", - " `name` varchar(255) NOT NULL,\n", - " PRIMARY KEY (`continent_id`)\n", - ") ENGINE=InnoDB AUTO_INCREMENT=8 DEFAULT CHARSET=utf8;\n", - "/*!40101 SET character_set_client = @saved_cs_client */;\n", - "\n", - "--\n", - "-- Dumping data for table `continents`\n", - "--\n", - "\n", - "LOCK TABLES `continents` WRITE;\n", - "/*!40000 ALTER TABLE `continents` DISABLE KEYS */;\n", - "INSERT INTO `continents` VALUES (1,'North America'),(2,'Asia'),(3,'Africa'),(4,'Europe'),(5,'South America'),(6,'Oceania'),(7,'Antarctica');\n", - "/*!40000 ALTER TABLE `continents` ENABLE KEYS */;\n", - "UNLOCK TABLES;\n", - "\n", - "--\n", - "-- Table structure for table `countries`\n", - "--\n", - "\n", - "DROP TABLE IF EXISTS `countries`;\n", - "/*!40101 SET @saved_cs_client = @@character_set_client */;\n", - " SET character_set_client = utf8mb4 ;\n", - "CREATE TABLE `countries` (\n", - " `country_id` int(11) NOT NULL AUTO_INCREMENT,\n", - " `name` varchar(50) DEFAULT NULL,\n", - " `area` decimal(10,2) NOT NULL,\n", - " `national_day` date DEFAULT NULL,\n", - " `country_code2` char(2) NOT NULL,\n", - " `country_code3` char(3) NOT NULL,\n", - " `region_id` int(11) NOT NULL,\n", - " PRIMARY KEY (`country_id`),\n", - " UNIQUE KEY `country_code2` (`country_code2`),\n", - " UNIQUE KEY `country_code3` (`country_code3`),\n", - " KEY `region_id` (`region_id`),\n", - " CONSTRAINT `countries_ibfk_1` FOREIGN KEY (`region_id`) REFERENCES `regions` (`region_id`)\n", - ") ENGINE=InnoDB AUTO_INCREMENT=240 DEFAULT CHARSET=utf8;\n", - "/*!40101 SET character_set_client = @saved_cs_client */;\n", - "\n", - "--\n", - "-- Dumping data for table `countries`\n", - "--\n", - "\n", - "LOCK TABLES `countries` WRITE;\n", - "/*!40000 ALTER TABLE `countries` DISABLE KEYS */;\n", - "INSERT INTO `countries` VALUES (1,'Aruba',193.00,NULL,'AW','ABW',1),(2,'Afghanistan',652090.00,'1919-08-19','AF','AFG',2),(3,'Angola',1246700.00,'1975-11-11','AO','AGO',3),(4,'Anguilla',96.00,'1967-05-30','AI','AIA',1),(5,'Albania',28748.00,'1912-11-28','AL','ALB',4),(6,'Andorra',468.00,NULL,'AD','AND',4),(7,'Netherlands Antilles',800.00,NULL,'AN','ANT',1),(8,'United Arab Emirates',83600.00,'1971-12-02','AE','ARE',5),(9,'Argentina',2780400.00,'1816-07-09','AR','ARG',6),(10,'Armenia',29800.00,'1991-09-21','AM','ARM',5),(11,'American Samoa',199.00,NULL,'AS','ASM',7),(12,'Antarctica',13120000.00,NULL,'AQ','ATA',8),(13,'French Southern territories',7780.00,NULL,'TF','ATF',8),(14,'Antigua and Barbuda',442.00,'1981-11-01','AG','ATG',1),(15,'Australia',7741220.00,NULL,'AU','AUS',9),(16,'Austria',83859.00,'1955-10-26','AT','AUT',10),(17,'Azerbaijan',86600.00,'1991-10-18','AZ','AZE',5),(18,'Burundi',27834.00,'1962-07-01','BI','BDI',11),(19,'Belgium',30518.00,'1831-07-21','BE','BEL',10),(20,'Benin',112622.00,'1960-08-01','BJ','BEN',12),(21,'Burkina Faso',274000.00,'1960-08-05','BF','BFA',12),(22,'Bangladesh',143998.00,'1971-03-26','BD','BGD',2),(23,'Bulgaria',110994.00,'1878-03-03','BG','BGR',13),(24,'Bahrain',694.00,'1971-12-16','BH','BHR',5),(25,'Bahamas',13878.00,NULL,'BS','BHS',1),(26,'Bosnia and Herzegovina',51197.00,'1992-03-01','BA','BIH',4),(27,'Belarus',207600.00,'1944-07-03','BY','BLR',13),(28,'Belize',22696.00,'1981-09-21','BZ','BLZ',14),(29,'Bermuda',53.00,NULL,'BM','BMU',15),(30,'Bolivia',1098581.00,'1825-08-06','BO','BOL',6),(31,'Brazil',8547403.00,'1822-09-07','BR','BRA',6),(32,'Barbados',430.00,'1966-11-30','BB','BRB',1),(33,'Brunei',5765.00,'1984-01-01','BN','BRN',16),(34,'Bhutan',47000.00,NULL,'BT','BTN',2),(35,'Bouvet Island',59.00,NULL,'BV','BVT',8),(36,'Botswana',581730.00,'1966-09-30','BW','BWA',17),(37,'Central African Republic',622984.00,'1960-08-13','CF','CAF',3),(38,'Canada',9970610.00,'1867-07-01','CA','CAN',15),(39,'Cocos (Keeling) Islands',14.00,NULL,'CC','CCK',9),(40,'Switzerland',41284.00,'1291-08-01','CH','CHE',10),(41,'Chile',756626.00,'1810-09-18','CL','CHL',6),(42,'China',9572900.00,NULL,'CN','CHN',18),(43,'Côte d’Ivoire',322463.00,NULL,'CI','CIV',12),(44,'Cameroon',475442.00,'1960-01-01','CM','CMR',3),(45,'The Democratic Republic of the 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Republic',48511.00,NULL,'DO','DOM',1),(62,'Algeria',2381741.00,'1962-07-05','DZ','DZA',20),(63,'Ecuador',283561.00,'1809-08-10','EC','ECU',6),(64,'Egypt',1001449.00,NULL,'EG','EGY',20),(65,'Eritrea',117600.00,'1993-05-24','ER','ERI',11),(66,'Western Sahara',266000.00,NULL,'EH','ESH',20),(67,'Spain',505992.00,NULL,'ES','ESP',4),(68,'Estonia',45227.00,'1918-02-24','EE','EST',21),(69,'Ethiopia',1104300.00,NULL,'ET','ETH',11),(70,'Finland',338145.00,'1917-12-06','FI','FIN',19),(71,'Fiji Islands',18274.00,NULL,'FJ','FJI',22),(72,'Falkland Islands',12173.00,NULL,'FK','FLK',6),(73,'France',551500.00,'1789-07-14','FR','FRA',10),(74,'Faroe Islands',1399.00,NULL,'FO','FRO',19),(75,'Federated States of Micronesia',702.00,NULL,'FM','FSM',23),(76,'Gabon',267668.00,'1960-08-17','GA','GAB',3),(77,'United 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Territory',78.00,NULL,'IO','IOT',11),(102,'Ireland',70273.00,'1916-04-24','IE','IRL',24),(103,'Iran',1648195.00,'1979-04-01','IR','IRN',2),(104,'Iraq',438317.00,'1932-10-03','IQ','IRQ',5),(105,'Iceland',103000.00,'1944-06-17','IS','ISL',19),(106,'Israel',21056.00,'1948-01-00','IL','ISR',5),(107,'Italy',301316.00,NULL,'IT','ITA',4),(108,'Jamaica',10990.00,'1962-08-06','JM','JAM',1),(109,'Jordan',88946.00,'1946-05-25','JO','JOR',5),(110,'Japan',377829.00,NULL,'JP','JPN',18),(111,'Kazakstan',2724900.00,NULL,'KZ','KAZ',2),(112,'Kenya',580367.00,'1963-12-12','KE','KEN',11),(113,'Kyrgyzstan',199900.00,'1991-08-31','KG','KGZ',2),(114,'Cambodia',181035.00,'1953-11-09','KH','KHM',16),(115,'Kiribati',726.00,'1979-07-12','KI','KIR',23),(116,'Saint Kitts and Nevis',261.00,'1983-09-19','KN','KNA',1),(117,'South 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PRIMARY KEY (`country_id`,`language_id`),\n", - " KEY `language_id` (`language_id`),\n", - " CONSTRAINT `country_languages_ibfk_1` FOREIGN KEY (`country_id`) REFERENCES `countries` (`country_id`),\n", - " CONSTRAINT `country_languages_ibfk_2` FOREIGN KEY (`language_id`) REFERENCES `languages` (`language_id`)\n", - ") ENGINE=InnoDB DEFAULT CHARSET=utf8;\n", - "/*!40101 SET character_set_client = @saved_cs_client */;\n", - "\n", - "--\n", - "-- Dumping data for table `country_languages`\n", - "--\n", - "\n", - "LOCK TABLES `country_languages` WRITE;\n", - "/*!40000 ALTER TABLE `country_languages` DISABLE KEYS */;\n", - "INSERT INTO `country_languages` VALUES 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- "/*!40000 ALTER TABLE `country_languages` ENABLE KEYS */;\n", - "UNLOCK TABLES;\n", - "\n", - "--\n", - "-- Table structure for table `country_stats`\n", - "--\n", - "\n", - "DROP TABLE IF EXISTS `country_stats`;\n", - "/*!40101 SET @saved_cs_client = @@character_set_client */;\n", - " SET character_set_client = utf8mb4 ;\n", - "CREATE TABLE `country_stats` (\n", - " `country_id` int(11) NOT NULL,\n", - " `year` int(11) NOT NULL,\n", - " `population` int(11) DEFAULT NULL,\n", - " `gdp` decimal(15,0) DEFAULT NULL,\n", - " PRIMARY KEY (`country_id`,`year`),\n", - " CONSTRAINT `country_stats_ibfk_1` FOREIGN KEY (`country_id`) REFERENCES `countries` (`country_id`)\n", - ") ENGINE=InnoDB DEFAULT CHARSET=utf8;\n", - "/*!40101 SET character_set_client = @saved_cs_client */;\n", - "\n", - "--\n", - "-- Dumping data for table `country_stats`\n", - "--\n", - "\n", - "LOCK TABLES `country_stats` WRITE;\n", - "/*!40000 ALTER TABLE `country_stats` DISABLE KEYS */;\n", - "INSERT INTO `country_stats` VALUES 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- "/*!40000 ALTER TABLE `country_stats` ENABLE KEYS */;\n", - "UNLOCK TABLES;\n", - "\n", - "--\n", - "-- Table structure for table `guests`\n", - "--\n", - "\n", - "DROP TABLE IF EXISTS `guests`;\n", - "/*!40101 SET @saved_cs_client = @@character_set_client */;\n", - " SET character_set_client = utf8mb4 ;\n", - "CREATE TABLE `guests` (\n", - " `guest_id` int(11) NOT NULL,\n", - " `name` varchar(100) NOT NULL,\n", - " PRIMARY KEY (`guest_id`)\n", - ") ENGINE=InnoDB DEFAULT CHARSET=utf8;\n", - "/*!40101 SET character_set_client = @saved_cs_client */;\n", - "\n", - "--\n", - "-- Dumping data for table `guests`\n", - "--\n", - "\n", - "LOCK TABLES `guests` WRITE;\n", - "/*!40000 ALTER TABLE `guests` DISABLE KEYS */;\n", - "INSERT INTO `guests` VALUES (1,'John'),(2,'Jane'),(3,'Jean'),(4,'Storm'),(5,'Beast');\n", - "/*!40000 ALTER TABLE `guests` ENABLE KEYS */;\n", - "UNLOCK TABLES;\n", - "\n", - "--\n", - "-- Table structure for table `languages`\n", - "--\n", - "\n", - "DROP TABLE IF EXISTS `languages`;\n", - "/*!40101 SET @saved_cs_client = @@character_set_client */;\n", - " SET character_set_client = utf8mb4 ;\n", - "CREATE TABLE `languages` (\n", - " `language_id` int(11) NOT NULL AUTO_INCREMENT,\n", - " `language` varchar(50) NOT NULL,\n", - " PRIMARY KEY (`language_id`)\n", - ") ENGINE=InnoDB AUTO_INCREMENT=458 DEFAULT CHARSET=utf8;\n", - "/*!40101 SET character_set_client = @saved_cs_client */;\n", - "\n", - "--\n", - "-- Dumping data for table `languages`\n", - "--\n", - "\n", - "LOCK TABLES `languages` WRITE;\n", - "/*!40000 ALTER TABLE `languages` DISABLE KEYS */;\n", - "INSERT INTO `languages` VALUES 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Asia',4494801.00),('Southern Africa',2674778.00),('Southern and Central Asia',10791130.00),('Southern Europe',1316392.40),('Western Africa',6138338.00),('Western Europe',1108456.50);\n", - "/*!40000 ALTER TABLE `region_areas` ENABLE KEYS */;\n", - "UNLOCK TABLES;\n", - "\n", - "--\n", - "-- Table structure for table `regions`\n", - "--\n", - "\n", - "DROP TABLE IF EXISTS `regions`;\n", - "/*!40101 SET @saved_cs_client = @@character_set_client */;\n", - " SET character_set_client = utf8mb4 ;\n", - "CREATE TABLE `regions` (\n", - " `region_id` int(11) NOT NULL AUTO_INCREMENT,\n", - " `name` varchar(100) NOT NULL,\n", - " `continent_id` int(11) NOT NULL,\n", - " PRIMARY KEY (`region_id`),\n", - " KEY `continent_id` (`continent_id`),\n", - " CONSTRAINT `regions_ibfk_1` FOREIGN KEY (`continent_id`) REFERENCES `continents` (`continent_id`)\n", - ") ENGINE=InnoDB AUTO_INCREMENT=26 DEFAULT CHARSET=utf8;\n", - "/*!40101 SET character_set_client = @saved_cs_client */;\n", - "\n", - "--\n", - "-- Dumping data for table `regions`\n", - "--\n", - "\n", - "LOCK TABLES `regions` WRITE;\n", - "/*!40000 ALTER TABLE `regions` DISABLE KEYS */;\n", - "INSERT INTO `regions` VALUES (1,'Caribbean',1),(2,'Southern and Central Asia',2),(3,'Central Africa',3),(4,'Southern Europe',4),(5,'Middle East',2),(6,'South America',5),(7,'Polynesia',6),(8,'Antarctica',7),(9,'Australia and New Zealand',6),(10,'Western Europe',4),(11,'Eastern Africa',3),(12,'Western Africa',3),(13,'Eastern Europe',4),(14,'Central America',1),(15,'North America',1),(16,'Southeast Asia',2),(17,'Southern Africa',3),(18,'Eastern Asia',2),(19,'Nordic Countries',4),(20,'Northern Africa',3),(21,'Baltic Countries',4),(22,'Melanesia',6),(23,'Micronesia',6),(24,'British Islands',4),(25,'Micronesia/Caribbean',6);\n", - "/*!40000 ALTER TABLE `regions` ENABLE KEYS */;\n", - "UNLOCK TABLES;\n", - "\n", - "/*!40103 SET TIME_ZONE=@OLD_TIME_ZONE */;\n", - "\n", - "/*!40101 SET SQL_MODE=@OLD_SQL_MODE */;\n", - "/*!40014 SET FOREIGN_KEY_CHECKS=@OLD_FOREIGN_KEY_CHECKS */;\n", - "/*!40014 SET UNIQUE_CHECKS=@OLD_UNIQUE_CHECKS */;\n", - "/*!40101 SET CHARACTER_SET_CLIENT=@OLD_CHARACTER_SET_CLIENT */;\n", - "/*!40101 SET CHARACTER_SET_RESULTS=@OLD_CHARACTER_SET_RESULTS */;\n", - "/*!40101 SET COLLATION_CONNECTION=@OLD_COLLATION_CONNECTION */;\n", - "/*!40111 SET SQL_NOTES=@OLD_SQL_NOTES */;\n", - "\n", - "-- Dump completed on 2019-10-18 11:48:19\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/004-DatabaseSales.ipynb b/db-course/004-DatabaseSales.ipynb deleted file mode 100644 index 531807d..0000000 --- a/db-course/004-DatabaseSales.ipynb +++ /dev/null @@ -1,7872 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pymysql\n", - "\n", - "pymysql.install_as_MySQLdb()\n", - "\n", - "%load_ext sql\n", - "%config SqlMagic.autocommit=True\n", - "%sql mysql://root:simple@127.0.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n", - "1 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "0 rows affected.\n", - "7 rows affected.\n", - "23 rows affected.\n", - "22 rows affected.\n", - "122 rows affected.\n", - "7 rows affected.\n", - "110 rows affected.\n", - "326 rows affected.\n", - "2996 rows affected.\n", - "273 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "/*\n", - "*********************************************************************\n", - "http://www.mysqltutorial.org\n", - "*********************************************************************\n", - "Name: MySQL Sample Database classicmodels\n", - "Link: http://www.mysqltutorial.org/mysql-sample-database.aspx\n", - "Version 3.2 (for datajoint-tutorials)\n", - "+ adapted to DataJoint conventions\n", - "Version 3.1\n", - "+ changed data type from DOUBLE to DECIMAL for amount columns\n", - "Version 3.0\n", - "+ changed DATETIME to DATE for some colunmns\n", - "Version 2.0\n", - "+ changed table type from MyISAM to InnoDB\n", - "+ added foreign keys for all tables \n", - "*********************************************************************\n", - "*/\n", - "\n", - "\n", - "DROP DATABASE IF EXISTS `classicsales`;\n", - "CREATE DATABASE `classicsales` DEFAULT CHARACTER SET latin1;\n", - "\n", - "USE `classicsales`;\n", - "\n", - "\n", - "CREATE TABLE `office` (\n", - " `office_code` varchar(10) NOT NULL,\n", - " `city` varchar(50) NOT NULL,\n", - " `phone` varchar(50) NOT NULL,\n", - " `postal_line1` varchar(50) NOT NULL,\n", - " `postal_line2` varchar(50) DEFAULT NULL,\n", - " `state` varchar(50) DEFAULT NULL,\n", - " `country` varchar(50) NOT NULL,\n", - " `postal_code` varchar(15) NOT NULL,\n", - " `territory` varchar(10) NOT NULL,\n", - " PRIMARY KEY (`office_code`)\n", - ") ENGINE=InnoDB DEFAULT CHARSET=latin1;\n", - "\n", - "\n", - "CREATE TABLE `product_line` (\n", - " `product_line` varchar(50) NOT NULL,\n", - " `textDescription` varchar(4000) DEFAULT NULL,\n", - " `htmlDescription` mediumtext,\n", - " `image` mediumblob,\n", - " PRIMARY KEY (`product_line`)\n", - ") ENGINE=InnoDB DEFAULT CHARSET=latin1;\n", - "\n", - "\n", - "CREATE TABLE `employee` (\n", - " `employee_number` int(11) NOT NULL,\n", - " `last_name` varchar(50) NOT NULL,\n", - " `first_name` varchar(50) NOT NULL,\n", - " `extension` varchar(10) NOT NULL,\n", - " `email` varchar(100) NOT NULL,\n", - " `office_code` varchar(10) NOT NULL,\n", - " `job_title` varchar(50) NOT NULL,\n", - " PRIMARY KEY (`employee_number`),\n", - " CONSTRAINT `employees_ibfk_2` FOREIGN KEY (`office_code`) REFERENCES `office` (`office_code`)\n", - ") ENGINE=InnoDB DEFAULT CHARSET=latin1;\n", - "\n", - "\n", - "CREATE TABLE `report` (\n", - " `employee_number` int(11) NOT NULL,\n", - " `reports_to` int(11) NOT NULL,\n", - " PRIMARY KEY (`employee_number`),\n", - " FOREIGN KEY (`employee_number`) REFERENCES `employee` (`employee_number`),\n", - " FOREIGN KEY (`reports_to`) REFERENCES `employee` (`employee_number`)\n", - ");\n", - "\n", - "\n", - "CREATE TABLE `customer` (\n", - " `customer_number` int(11) NOT NULL,\n", - " `customer_name` varchar(50) NOT NULL,\n", - " `contact_last_name` varchar(50) NOT NULL,\n", - " `contact_first_name` varchar(50) NOT NULL,\n", - " `phone` varchar(50) NOT NULL,\n", - " `postal_line1` varchar(50) NOT NULL,\n", - " `postal_line2` varchar(50) DEFAULT NULL,\n", - " `city` varchar(50) NOT NULL,\n", - " `state` varchar(50) DEFAULT NULL,\n", - " `postal_code` varchar(15) DEFAULT NULL,\n", - " `country` varchar(50) NOT NULL,\n", - " `sales_rep` int(11) DEFAULT NULL,\n", - " `credit_limit` decimal(10,2) DEFAULT NULL,\n", - " PRIMARY KEY (`customer_number`),\n", - " FOREIGN KEY (`sales_rep`) REFERENCES `employee` (`employee_number`)\n", - ") ENGINE=InnoDB DEFAULT CHARSET=latin1;\n", - "\n", - "\n", - "CREATE TABLE `product` (\n", - " `product_code` varchar(15) NOT NULL,\n", - " `product_name` varchar(70) NOT NULL,\n", - " `product_line` varchar(50) NOT NULL,\n", - " `product_scale` varchar(10) NOT NULL,\n", - " `vendor` varchar(50) NOT NULL,\n", - " `product_description` text NOT NULL,\n", - " `quantity_in_stock` smallint(6) NOT NULL,\n", - " `buy_price` decimal(10,2) NOT NULL,\n", - " `MSRP` decimal(10,2) NOT NULL,\n", - " PRIMARY KEY (`product_code`),\n", - " FOREIGN KEY (`product_line`) REFERENCES `product_line` (`product_line`)\n", - ") ENGINE=InnoDB DEFAULT CHARSET=latin1;\n", - "\n", - "\n", - "CREATE TABLE `order` (\n", - " `order_number` int(11) NOT NULL,\n", - " `order_date` date NOT NULL,\n", - " `required_date` date NOT NULL,\n", - " `shipped_date` date DEFAULT NULL,\n", - " `status` varchar(15) NOT NULL,\n", - " `comments` text,\n", - " `customer_number` int(11) NOT NULL,\n", - " PRIMARY KEY (`order_number`),\n", - " FOREIGN KEY (`customer_number`) REFERENCES `customer` (`customer_number`)\n", - ") ENGINE=InnoDB DEFAULT CHARSET=latin1;\n", - "\n", - "\n", - "CREATE TABLE `order__item` (\n", - " `order_number` int(11) NOT NULL,\n", - " `product_code` varchar(15) NOT NULL,\n", - " `quantity` int(11) NOT NULL,\n", - " `price` decimal(10,2) NOT NULL,\n", - " `order_line_number` smallint(6) NOT NULL,\n", - " PRIMARY KEY (`order_number`,`product_code`),\n", - " FOREIGN KEY (`order_number`) REFERENCES `order` (`order_number`),\n", - " FOREIGN KEY (`product_code`) REFERENCES `product` (`product_code`)\n", - ") ENGINE=InnoDB DEFAULT CHARSET=latin1;\n", - "\n", - "\n", - "CREATE TABLE `payment` (\n", - " `customer_number` int(11) NOT NULL,\n", - " `check_number` varchar(50) NOT NULL,\n", - " `payment_date` date NOT NULL,\n", - " `amount` decimal(10,2) NOT NULL,\n", - " PRIMARY KEY (`customer_number`, `check_number`),\n", - " FOREIGN KEY (`customer_number`) REFERENCES `customer` (`customer_number`)\n", - ") ENGINE=InnoDB DEFAULT CHARSET=latin1;\n", - "\n", - "\n", - "\n", - "/*Data for the table `office` */\n", - "insert into `office`(`office_code`,`city`,`phone`,`postal_line1`,`postal_line2`,`state`,`country`,`postal_code`,`territory`) values \n", - "('1','San Francisco','+1 650 219 4782','100 Market Street','Suite 300','CA','USA','94080','NA'),\n", - "('2','Boston','+1 215 837 0825','1550 Court Place','Suite 102','MA','USA','02107','NA'),\n", - "('3','NYC','+1 212 555 3000','523 East 53rd Street','apt. 5A','NY','USA','10022','NA'),\n", - "('4','Paris','+33 14 723 4404','43 Rue Jouffroy D\\'abbans',NULL,NULL,'France','75017','EMEA'),\n", - "('5','Tokyo','+81 33 224 5000','4-1 Kioicho',NULL,'Chiyoda-Ku','Japan','102-8578','Japan'),\n", - "('6','Sydney','+61 2 9264 2451','5-11 Wentworth Avenue','Floor #2',NULL,'Australia','NSW 2010','APAC'),\n", - "('7','London','+44 20 7877 2041','25 Old Broad Street','Level 7',NULL,'UK','EC2N 1HN','EMEA');\n", - "\n", - "\n", - "\n", - "/*Data for the table `employee` */\n", - "insert into `employee`(`employee_number`,`last_name`,`first_name`,`extension`,`email`,`office_code`,`job_title`) values \n", - "(1002,'Murphy','Diane','x5800','dmurphy@classicmodelcars.com','1','President'),\n", - "(1056,'Patterson','Mary','x4611','mpatterso@classicmodelcars.com','1','VP Sales'),\n", - "(1076,'Firrelli','Jeff','x9273','jfirrelli@classicmodelcars.com','1','VP Marketing'),\n", - "(1088,'Patterson','William','x4871','wpatterson@classicmodelcars.com','6','Sales Manager (APAC)'),\n", - "(1102,'Bondur','Gerard','x5408','gbondur@classicmodelcars.com','4','Sale Manager (EMEA)'),\n", - "(1143,'Bow','Anthony','x5428','abow@classicmodelcars.com','1','Sales Manager (NA)'),\n", - "(1165,'Jennings','Leslie','x3291','ljennings@classicmodelcars.com','1','Sales Rep'),\n", - "(1166,'Thompson','Leslie','x4065','lthompson@classicmodelcars.com','1','Sales Rep'),\n", - "(1188,'Firrelli','Julie','x2173','jfirrelli@classicmodelcars.com','2','Sales Rep'),\n", - "(1216,'Patterson','Steve','x4334','spatterson@classicmodelcars.com','2','Sales Rep'),\n", - "(1286,'Tseng','Foon Yue','x2248','ftseng@classicmodelcars.com','3','Sales Rep'),\n", - "(1323,'Vanauf','George','x4102','gvanauf@classicmodelcars.com','3','Sales Rep'),\n", - "(1337,'Bondur','Loui','x6493','lbondur@classicmodelcars.com','4','Sales Rep'),\n", - "(1370,'Hernandez','Gerard','x2028','ghernande@classicmodelcars.com','4','Sales Rep'),\n", - "(1401,'Castillo','Pamela','x2759','pcastillo@classicmodelcars.com','4','Sales Rep'),\n", - "(1501,'Bott','Larry','x2311','lbott@classicmodelcars.com','7','Sales Rep'),\n", - "(1504,'Jones','Barry','x102','bjones@classicmodelcars.com','7','Sales Rep'),\n", - "(1611,'Fixter','Andy','x101','afixter@classicmodelcars.com','6','Sales Rep'),\n", - "(1612,'Marsh','Peter','x102','pmarsh@classicmodelcars.com','6','Sales Rep'),\n", - "(1619,'King','Tom','x103','tking@classicmodelcars.com','6','Sales Rep'),\n", - "(1621,'Nishi','Mami','x101','mnishi@classicmodelcars.com','5','Sales Rep'),\n", - "(1625,'Kato','Yoshimi','x102','ykato@classicmodelcars.com','5','Sales Rep'),\n", - "(1702,'Gerard','Martin','x2312','mgerard@classicmodelcars.com','4','Sales Rep');\n", - "\n", - "insert into `report`(`employee_number`,`reports_to`) values \n", - "(1056, 1002),\n", - "(1076, 1002),\n", - "(1088, 1056),\n", - "(1102, 1056),\n", - "(1143, 1056),\n", - "(1165, 1143),\n", - "(1166, 1143),\n", - "(1188, 1143),\n", - "(1216, 1143),\n", - "(1286, 1143),\n", - "(1323, 1143),\n", - "(1337, 1102),\n", - "(1370, 1102),\n", - "(1401, 1102),\n", - "(1501, 1102),\n", - "(1504, 1102),\n", - "(1611, 1088),\n", - "(1612, 1088),\n", - "(1619, 1088),\n", - "(1621, 1056),\n", - "(1625, 1621),\n", - "(1702, 1102);\n", - "\n", - "\n", - "/*Data for the table `customer` */\n", - "insert into `customer` (`customer_number`,`customer_name`,`contact_last_name`,`contact_first_name`,`phone`,`postal_line1`,`postal_line2`,`city`,`state`,`postal_code`,`country`,`sales_rep`,`credit_limit`) values \n", - "(103,'Atelier graphique','Schmitt','Carine ','40.32.2555','54, rue Royale',NULL,'Nantes',NULL,'44000','France',1370,'21000.00'),\n", - "(112,'Signal Gift Stores','King','Jean','7025551838','8489 Strong St.',NULL,'Las Vegas','NV','83030','USA',1166,'71800.00'),\n", - "(114,'Australian Collectors, Co.','Ferguson','Peter','03 9520 4555','636 St Kilda Road','Level 3','Melbourne','Victoria','3004','Australia',1611,'117300.00'),\n", - "(119,'La Rochelle Gifts','Labrune','Janine ','40.67.8555','67, rue des Cinquante Otages',NULL,'Nantes',NULL,'44000','France',1370,'118200.00'),\n", - "(121,'Baane Mini Imports','Bergulfsen','Jonas ','07-98 9555','Erling Skakkes gate 78',NULL,'Stavern',NULL,'4110','Norway',1504,'81700.00'),\n", - "(124,'Mini Gifts Distributors Ltd.','Nelson','Susan','4155551450','5677 Strong St.',NULL,'San Rafael','CA','97562','USA',1165,'210500.00'),\n", - "(125,'Havel & Zbyszek Co','Piestrzeniewicz','Zbyszek ','(26) 642-7555','ul. Filtrowa 68',NULL,'Warszawa',NULL,'01-012','Poland',NULL,'0.00'),\n", - "(128,'Blauer See Auto, Co.','Keitel','Roland','+49 69 66 90 2555','Lyonerstr. 34',NULL,'Frankfurt',NULL,'60528','Germany',1504,'59700.00'),\n", - "(129,'Mini Wheels Co.','Murphy','Julie','6505555787','5557 North Pendale Street',NULL,'San Francisco','CA','94217','USA',1165,'64600.00'),\n", - "(131,'Land of Toys Inc.','Lee','Kwai','2125557818','897 Long Airport Avenue',NULL,'NYC','NY','10022','USA',1323,'114900.00'),\n", - "(141,'Euro+ Shopping Channel','Freyre','Diego ','(91) 555 94 44','C/ Moralzarzal, 86',NULL,'Madrid',NULL,'28034','Spain',1370,'227600.00'),\n", - "(144,'Volvo Model Replicas, Co','Berglund','Christina ','0921-12 3555','Berguvsvägen 8',NULL,'Luleå',NULL,'S-958 22','Sweden',1504,'53100.00'),\n", - "(145,'Danish Wholesale Imports','Petersen','Jytte ','31 12 3555','Vinbæltet 34',NULL,'Kobenhavn',NULL,'1734','Denmark',1401,'83400.00'),\n", - "(146,'Saveley & Henriot, Co.','Saveley','Mary ','78.32.5555','2, rue du Commerce',NULL,'Lyon',NULL,'69004','France',1337,'123900.00'),\n", - "(148,'Dragon Souvenirs, Ltd.','Natividad','Eric','+65 221 7555','Bronz Sok.','Bronz Apt. 3/6 Tesvikiye','Singapore',NULL,'079903','Singapore',1621,'103800.00'),\n", - "(151,'Muscle Machine Inc','Young','Jeff','2125557413','4092 Furth Circle','Suite 400','NYC','NY','10022','USA',1286,'138500.00'),\n", - "(157,'Diecast Classics Inc.','Leong','Kelvin','2155551555','7586 Pompton St.',NULL,'Allentown','PA','70267','USA',1216,'100600.00'),\n", - "(161,'Technics Stores Inc.','Hashimoto','Juri','6505556809','9408 Furth Circle',NULL,'Burlingame','CA','94217','USA',1165,'84600.00'),\n", - "(166,'Handji Gifts& Co','Victorino','Wendy','+65 224 1555','106 Linden Road Sandown','2nd Floor','Singapore',NULL,'069045','Singapore',1612,'97900.00'),\n", - "(167,'Herkku Gifts','Oeztan','Veysel','+47 2267 3215','Brehmen St. 121','PR 334 Sentrum','Bergen',NULL,'N 5804','Norway ',1504,'96800.00'),\n", - "(168,'American Souvenirs Inc','Franco','Keith','2035557845','149 Spinnaker Dr.','Suite 101','New Haven','CT','97823','USA',1286,'0.00'),\n", - "(169,'Porto Imports Co.','de Castro','Isabel ','(1) 356-5555','Estrada da saúde n. 58',NULL,'Lisboa',NULL,'1756','Portugal',NULL,'0.00'),\n", - "(171,'Daedalus Designs Imports','Rancé','Martine ','20.16.1555','184, chaussée de Tournai',NULL,'Lille',NULL,'59000','France',1370,'82900.00'),\n", - "(172,'La Corne D\\'abondance, Co.','Bertrand','Marie','(1) 42.34.2555','265, boulevard Charonne',NULL,'Paris',NULL,'75012','France',1337,'84300.00'),\n", - "(173,'Cambridge Collectables Co.','Tseng','Jerry','6175555555','4658 Baden Av.',NULL,'Cambridge','MA','51247','USA',1188,'43400.00'),\n", - "(175,'Gift Depot Inc.','King','Julie','2035552570','25593 South Bay Ln.',NULL,'Bridgewater','CT','97562','USA',1323,'84300.00'),\n", - "(177,'Osaka Souvenirs Co.','Kentary','Mory','+81 06 6342 5555','1-6-20 Dojima',NULL,'Kita-ku','Osaka',' 530-0003','Japan',1621,'81200.00'),\n", - "(181,'Vitachrome Inc.','Frick','Michael','2125551500','2678 Kingston Rd.','Suite 101','NYC','NY','10022','USA',1286,'76400.00'),\n", - "(186,'Toys of Finland, Co.','Karttunen','Matti','90-224 8555','Keskuskatu 45',NULL,'Helsinki',NULL,'21240','Finland',1501,'96500.00'),\n", - "(187,'AV Stores, Co.','Ashworth','Rachel','(171) 555-1555','Fauntleroy Circus',NULL,'Manchester',NULL,'EC2 5NT','UK',1501,'136800.00'),\n", - "(189,'Clover Collections, Co.','Cassidy','Dean','+353 1862 1555','25 Maiden Lane','Floor No. 4','Dublin',NULL,'2','Ireland',1504,'69400.00'),\n", - "(198,'Auto-Moto Classics Inc.','Taylor','Leslie','6175558428','16780 Pompton St.',NULL,'Brickhaven','MA','58339','USA',1216,'23000.00'),\n", - "(201,'UK Collectables, Ltd.','Devon','Elizabeth','(171) 555-2282','12, Berkeley Gardens Blvd',NULL,'Liverpool',NULL,'WX1 6LT','UK',1501,'92700.00'),\n", - "(202,'Canadian Gift Exchange Network','Tamuri','Yoshi ','(604) 555-3392','1900 Oak St.',NULL,'Vancouver','BC','V3F 2K1','Canada',1323,'90300.00'),\n", - "(204,'Online Mini Collectables','Barajas','Miguel','6175557555','7635 Spinnaker Dr.',NULL,'Brickhaven','MA','58339','USA',1188,'68700.00'),\n", - "(205,'Toys4GrownUps.com','Young','Julie','6265557265','78934 Hillside Dr.',NULL,'Pasadena','CA','90003','USA',1166,'90700.00'),\n", - "(206,'Asian Shopping Network, Co','Walker','Brydey','+612 9411 1555','Suntec Tower Three','8 Temasek','Singapore',NULL,'038988','Singapore',NULL,'0.00'),\n", - "(209,'Mini Caravy','Citeaux','Frédérique ','88.60.1555','24, place Kléber',NULL,'Strasbourg',NULL,'67000','France',1370,'53800.00'),\n", - "(211,'King Kong Collectables, Co.','Gao','Mike','+852 2251 1555','Bank of China Tower','1 Garden Road','Central Hong Kong',NULL,NULL,'Hong Kong',1621,'58600.00'),\n", - "(216,'Enaco Distributors','Saavedra','Eduardo ','(93) 203 4555','Rambla de Cataluña, 23',NULL,'Barcelona',NULL,'08022','Spain',1702,'60300.00'),\n", - "(219,'Boards & Toys Co.','Young','Mary','3105552373','4097 Douglas Av.',NULL,'Glendale','CA','92561','USA',1166,'11000.00'),\n", - "(223,'Natürlich Autos','Kloss','Horst ','0372-555188','Taucherstraße 10',NULL,'Cunewalde',NULL,'01307','Germany',NULL,'0.00'),\n", - "(227,'Heintze Collectables','Ibsen','Palle','86 21 3555','Smagsloget 45',NULL,'Århus',NULL,'8200','Denmark',1401,'120800.00'),\n", - "(233,'Québec Home Shopping Network','Fresnière','Jean ','(514) 555-8054','43 rue St. Laurent',NULL,'Montréal','Québec','H1J 1C3','Canada',1286,'48700.00'),\n", - "(237,'ANG Resellers','Camino','Alejandra ','(91) 745 6555','Gran Vía, 1',NULL,'Madrid',NULL,'28001','Spain',NULL,'0.00'),\n", - "(239,'Collectable Mini Designs Co.','Thompson','Valarie','7605558146','361 Furth Circle',NULL,'San Diego','CA','91217','USA',1166,'105000.00'),\n", - "(240,'giftsbymail.co.uk','Bennett','Helen ','(198) 555-8888','Garden House','Crowther Way 23','Cowes','Isle of Wight','PO31 7PJ','UK',1501,'93900.00'),\n", - "(242,'Alpha Cognac','Roulet','Annette ','61.77.6555','1 rue Alsace-Lorraine',NULL,'Toulouse',NULL,'31000','France',1370,'61100.00'),\n", - "(247,'Messner Shopping Network','Messner','Renate ','069-0555984','Magazinweg 7',NULL,'Frankfurt',NULL,'60528','Germany',NULL,'0.00'),\n", - "(249,'Amica Models & Co.','Accorti','Paolo ','011-4988555','Via Monte Bianco 34',NULL,'Torino',NULL,'10100','Italy',1401,'113000.00'),\n", - "(250,'Lyon Souveniers','Da Silva','Daniel','+33 1 46 62 7555','27 rue du Colonel Pierre Avia',NULL,'Paris',NULL,'75508','France',1337,'68100.00'),\n", - "(256,'Auto Associés & Cie.','Tonini','Daniel ','30.59.8555','67, avenue de l\\'Europe',NULL,'Versailles',NULL,'78000','France',1370,'77900.00'),\n", - "(259,'Toms Spezialitäten, Ltd','Pfalzheim','Henriette ','0221-5554327','Mehrheimerstr. 369',NULL,'Köln',NULL,'50739','Germany',1504,'120400.00'),\n", - "(260,'Royal Canadian Collectables, Ltd.','Lincoln','Elizabeth ','(604) 555-4555','23 Tsawassen Blvd.',NULL,'Tsawassen','BC','T2F 8M4','Canada',1323,'89600.00'),\n", - "(273,'Franken Gifts, Co','Franken','Peter ','089-0877555','Berliner Platz 43',NULL,'München',NULL,'80805','Germany',NULL,'0.00'),\n", - "(276,'Anna\\'s Decorations, Ltd','O\\'Hara','Anna','02 9936 8555','201 Miller Street','Level 15','North Sydney','NSW','2060','Australia',1611,'107800.00'),\n", - "(278,'Rovelli Gifts','Rovelli','Giovanni ','035-640555','Via Ludovico il Moro 22',NULL,'Bergamo',NULL,'24100','Italy',1401,'119600.00'),\n", - "(282,'Souveniers And Things Co.','Huxley','Adrian','+61 2 9495 8555','Monitor Money Building','815 Pacific Hwy','Chatswood','NSW','2067','Australia',1611,'93300.00'),\n", - "(286,'Marta\\'s Replicas Co.','Hernandez','Marta','6175558555','39323 Spinnaker Dr.',NULL,'Cambridge','MA','51247','USA',1216,'123700.00'),\n", - "(293,'BG&E Collectables','Harrison','Ed','+41 26 425 50 01','Rte des Arsenaux 41 ',NULL,'Fribourg',NULL,'1700','Switzerland',NULL,'0.00'),\n", - "(298,'Vida Sport, Ltd','Holz','Mihael','0897-034555','Grenzacherweg 237',NULL,'Genève',NULL,'1203','Switzerland',1702,'141300.00'),\n", - "(299,'Norway Gifts By Mail, Co.','Klaeboe','Jan','+47 2212 1555','Drammensveien 126A','PB 211 Sentrum','Oslo',NULL,'N 0106','Norway ',1504,'95100.00'),\n", - "(303,'Schuyler Imports','Schuyler','Bradley','+31 20 491 9555','Kingsfordweg 151',NULL,'Amsterdam',NULL,'1043 GR','Netherlands',NULL,'0.00'),\n", - "(307,'Der Hund Imports','Andersen','Mel','030-0074555','Obere Str. 57',NULL,'Berlin',NULL,'12209','Germany',NULL,'0.00'),\n", - "(311,'Oulu Toy Supplies, Inc.','Koskitalo','Pirkko','981-443655','Torikatu 38',NULL,'Oulu',NULL,'90110','Finland',1501,'90500.00'),\n", - "(314,'Petit Auto','Dewey','Catherine ','(02) 5554 67','Rue Joseph-Bens 532',NULL,'Bruxelles',NULL,'B-1180','Belgium',1401,'79900.00'),\n", - "(319,'Mini Classics','Frick','Steve','9145554562','3758 North Pendale Street',NULL,'White Plains','NY','24067','USA',1323,'102700.00'),\n", - "(320,'Mini Creations Ltd.','Huang','Wing','5085559555','4575 Hillside Dr.',NULL,'New Bedford','MA','50553','USA',1188,'94500.00'),\n", - "(321,'Corporate Gift Ideas Co.','Brown','Julie','6505551386','7734 Strong St.',NULL,'San Francisco','CA','94217','USA',1165,'105000.00'),\n", - "(323,'Down Under Souvenirs, Inc','Graham','Mike','+64 9 312 5555','162-164 Grafton Road','Level 2','Auckland ',NULL,NULL,'New Zealand',1612,'88000.00'),\n", - "(324,'Stylish Desk Decors, Co.','Brown','Ann ','(171) 555-0297','35 King George',NULL,'London',NULL,'WX3 6FW','UK',1501,'77000.00'),\n", - "(328,'Tekni Collectables Inc.','Brown','William','2015559350','7476 Moss Rd.',NULL,'Newark','NJ','94019','USA',1323,'43000.00'),\n", - "(333,'Australian Gift Network, Co','Calaghan','Ben','61-7-3844-6555','31 Duncan St. West End',NULL,'South Brisbane','Queensland','4101','Australia',1611,'51600.00'),\n", - "(334,'Suominen Souveniers','Suominen','Kalle','+358 9 8045 555','Software Engineering Center','SEC Oy','Espoo',NULL,'FIN-02271','Finland',1501,'98800.00'),\n", - "(335,'Cramer Spezialitäten, Ltd','Cramer','Philip ','0555-09555','Maubelstr. 90',NULL,'Brandenburg',NULL,'14776','Germany',NULL,'0.00'),\n", - "(339,'Classic Gift Ideas, Inc','Cervantes','Francisca','2155554695','782 First Street',NULL,'Philadelphia','PA','71270','USA',1188,'81100.00'),\n", - "(344,'CAF Imports','Fernandez','Jesus','+34 913 728 555','Merchants House','27-30 Merchant\\'s Quay','Madrid',NULL,'28023','Spain',1702,'59600.00'),\n", - "(347,'Men \\'R\\' US Retailers, Ltd.','Chandler','Brian','2155554369','6047 Douglas Av.',NULL,'Los Angeles','CA','91003','USA',1166,'57700.00'),\n", - "(348,'Asian Treasures, Inc.','McKenna','Patricia ','2967 555','8 Johnstown Road',NULL,'Cork','Co. Cork',NULL,'Ireland',NULL,'0.00'),\n", - "(350,'Marseille Mini Autos','Lebihan','Laurence ','91.24.4555','12, rue des Bouchers',NULL,'Marseille',NULL,'13008','France',1337,'65000.00'),\n", - "(353,'Reims Collectables','Henriot','Paul ','26.47.1555','59 rue de l\\'Abbaye',NULL,'Reims',NULL,'51100','France',1337,'81100.00'),\n", - "(356,'SAR Distributors, Co','Kuger','Armand','+27 21 550 3555','1250 Pretorius Street',NULL,'Hatfield','Pretoria','0028','South Africa',NULL,'0.00'),\n", - "(357,'GiftsForHim.com','MacKinlay','Wales','64-9-3763555','199 Great North Road',NULL,'Auckland',NULL,NULL,'New Zealand',1612,'77700.00'),\n", - "(361,'Kommission Auto','Josephs','Karin','0251-555259','Luisenstr. 48',NULL,'Münster',NULL,'44087','Germany',NULL,'0.00'),\n", - "(362,'Gifts4AllAges.com','Yoshido','Juri','6175559555','8616 Spinnaker Dr.',NULL,'Boston','MA','51003','USA',1216,'41900.00'),\n", - "(363,'Online Diecast Creations Co.','Young','Dorothy','6035558647','2304 Long Airport Avenue',NULL,'Nashua','NH','62005','USA',1216,'114200.00'),\n", - "(369,'Lisboa Souvenirs, Inc','Rodriguez','Lino ','(1) 354-2555','Jardim das rosas n. 32',NULL,'Lisboa',NULL,'1675','Portugal',NULL,'0.00'),\n", - "(376,'Precious Collectables','Urs','Braun','0452-076555','Hauptstr. 29',NULL,'Bern',NULL,'3012','Switzerland',1702,'0.00'),\n", - "(379,'Collectables For Less Inc.','Nelson','Allen','6175558555','7825 Douglas Av.',NULL,'Brickhaven','MA','58339','USA',1188,'70700.00'),\n", - "(381,'Royale Belge','Cartrain','Pascale ','(071) 23 67 2555','Boulevard Tirou, 255',NULL,'Charleroi',NULL,'B-6000','Belgium',1401,'23500.00'),\n", - "(382,'Salzburg Collectables','Pipps','Georg ','6562-9555','Geislweg 14',NULL,'Salzburg',NULL,'5020','Austria',1401,'71700.00'),\n", - "(385,'Cruz & Sons Co.','Cruz','Arnold','+63 2 555 3587','15 McCallum Street','NatWest Center #13-03','Makati City',NULL,'1227 MM','Philippines',1621,'81500.00'),\n", - "(386,'L\\'ordine Souveniers','Moroni','Maurizio ','0522-556555','Strada Provinciale 124',NULL,'Reggio Emilia',NULL,'42100','Italy',1401,'121400.00'),\n", - "(398,'Tokyo Collectables, Ltd','Shimamura','Akiko','+81 3 3584 0555','2-2-8 Roppongi',NULL,'Minato-ku','Tokyo','106-0032','Japan',1621,'94400.00'),\n", - "(406,'Auto Canal+ Petit','Perrier','Dominique','(1) 47.55.6555','25, rue Lauriston',NULL,'Paris',NULL,'75016','France',1337,'95000.00'),\n", - "(409,'Stuttgart Collectable Exchange','Müller','Rita ','0711-555361','Adenauerallee 900',NULL,'Stuttgart',NULL,'70563','Germany',NULL,'0.00'),\n", - "(412,'Extreme Desk Decorations, Ltd','McRoy','Sarah','04 499 9555','101 Lambton Quay','Level 11','Wellington',NULL,NULL,'New Zealand',1612,'86800.00'),\n", - "(415,'Bavarian Collectables Imports, Co.','Donnermeyer','Michael',' +49 89 61 08 9555','Hansastr. 15',NULL,'Munich',NULL,'80686','Germany',1504,'77000.00'),\n", - "(424,'Classic Legends Inc.','Hernandez','Maria','2125558493','5905 Pompton St.','Suite 750','NYC','NY','10022','USA',1286,'67500.00'),\n", - "(443,'Feuer Online Stores, Inc','Feuer','Alexander ','0342-555176','Heerstr. 22',NULL,'Leipzig',NULL,'04179','Germany',NULL,'0.00'),\n", - "(447,'Gift Ideas Corp.','Lewis','Dan','2035554407','2440 Pompton St.',NULL,'Glendale','CT','97561','USA',1323,'49700.00'),\n", - "(448,'Scandinavian Gift Ideas','Larsson','Martha','0695-34 6555','Åkergatan 24',NULL,'Bräcke',NULL,'S-844 67','Sweden',1504,'116400.00'),\n", - "(450,'The Sharp Gifts Warehouse','Frick','Sue','4085553659','3086 Ingle Ln.',NULL,'San Jose','CA','94217','USA',1165,'77600.00'),\n", - "(452,'Mini Auto Werke','Mendel','Roland ','7675-3555','Kirchgasse 6',NULL,'Graz',NULL,'8010','Austria',1401,'45300.00'),\n", - "(455,'Super Scale Inc.','Murphy','Leslie','2035559545','567 North Pendale Street',NULL,'New Haven','CT','97823','USA',1286,'95400.00'),\n", - "(456,'Microscale Inc.','Choi','Yu','2125551957','5290 North Pendale Street','Suite 200','NYC','NY','10022','USA',1286,'39800.00'),\n", - "(458,'Corrida Auto Replicas, Ltd','Sommer','Martín ','(91) 555 22 82','C/ Araquil, 67',NULL,'Madrid',NULL,'28023','Spain',1702,'104600.00'),\n", - "(459,'Warburg Exchange','Ottlieb','Sven ','0241-039123','Walserweg 21',NULL,'Aachen',NULL,'52066','Germany',NULL,'0.00'),\n", - "(462,'FunGiftIdeas.com','Benitez','Violeta','5085552555','1785 First Street',NULL,'New Bedford','MA','50553','USA',1216,'85800.00'),\n", - "(465,'Anton Designs, Ltd.','Anton','Carmen','+34 913 728555','c/ Gobelas, 19-1 Urb. La Florida',NULL,'Madrid',NULL,'28023','Spain',NULL,'0.00'),\n", - "(471,'Australian Collectables, Ltd','Clenahan','Sean','61-9-3844-6555','7 Allen Street',NULL,'Glen Waverly','Victoria','3150','Australia',1611,'60300.00'),\n", - "(473,'Frau da Collezione','Ricotti','Franco','+39 022515555','20093 Cologno Monzese','Alessandro Volta 16','Milan',NULL,NULL,'Italy',1401,'34800.00'),\n", - "(475,'West Coast Collectables Co.','Thompson','Steve','3105553722','3675 Furth Circle',NULL,'Burbank','CA','94019','USA',1166,'55400.00'),\n", - "(477,'Mit Vergnügen & Co.','Moos','Hanna ','0621-08555','Forsterstr. 57',NULL,'Mannheim',NULL,'68306','Germany',NULL,'0.00'),\n", - "(480,'Kremlin Collectables, Co.','Semenov','Alexander ','+7 812 293 0521','2 Pobedy Square',NULL,'Saint Petersburg',NULL,'196143','Russia',NULL,'0.00'),\n", - "(481,'Raanan Stores, Inc','Altagar,G M','Raanan','+ 972 9 959 8555','3 Hagalim Blv.',NULL,'Herzlia',NULL,'47625','Israel',NULL,'0.00'),\n", - "(484,'Iberia Gift Imports, Corp.','Roel','José Pedro ','(95) 555 82 82','C/ Romero, 33',NULL,'Sevilla',NULL,'41101','Spain',1702,'65700.00'),\n", - "(486,'Motor Mint Distributors Inc.','Salazar','Rosa','2155559857','11328 Douglas Av.',NULL,'Philadelphia','PA','71270','USA',1323,'72600.00'),\n", - "(487,'Signal Collectibles Ltd.','Taylor','Sue','4155554312','2793 Furth Circle',NULL,'Brisbane','CA','94217','USA',1165,'60300.00'),\n", - "(489,'Double Decker Gift Stores, Ltd','Smith','Thomas ','(171) 555-7555','120 Hanover Sq.',NULL,'London',NULL,'WA1 1DP','UK',1501,'43300.00'),\n", - "(495,'Diecast Collectables','Franco','Valarie','6175552555','6251 Ingle Ln.',NULL,'Boston','MA','51003','USA',1188,'85100.00'),\n", - "(496,'Kelly\\'s Gift Shop','Snowden','Tony','+64 9 5555500','Arenales 1938 3\\'A\\'',NULL,'Auckland ',NULL,NULL,'New Zealand',1612,'110000.00');\n", - "\n", - "\n", - "/*Data for the table `product_line` */\n", - "\n", - "insert into `product_line`(`product_line`,`textDescription`,`htmlDescription`,`image`) values \n", - "\n", - "('Classic Cars','Attention car enthusiasts: Make your wildest car ownership dreams come true. Whether you are looking for classic muscle cars, dream sports cars or movie-inspired miniatures, you will find great choices in this category. These replicas feature superb attention to detail and craftsmanship and offer features such as working steering system, opening forward compartment, opening rear trunk with removable spare wheel, 4-wheel independent spring suspension, and so on. The models range in size from 1:10 to 1:24 scale and include numerous limited edition and several out-of-production vehicles. All models include a certificate of authenticity from their manufacturers and come fully assembled and ready for display in the home or office.',NULL,NULL),\n", - "\n", - "('Motorcycles','Our motorcycles are state of the art replicas of classic as well as contemporary motorcycle legends such as Harley Davidson, Ducati and Vespa. Models contain stunning details such as official logos, rotating wheels, working kickstand, front suspension, gear-shift lever, footbrake lever, and drive chain. Materials used include diecast and plastic. The models range in size from 1:10 to 1:50 scale and include numerous limited edition and several out-of-production vehicles. All models come fully assembled and ready for display in the home or office. Most include a certificate of authenticity.',NULL,NULL),\n", - "\n", - "('Planes','Unique, diecast airplane and helicopter replicas suitable for collections, as well as home, office or classroom decorations. Models contain stunning details such as official logos and insignias, rotating jet engines and propellers, retractable wheels, and so on. Most come fully assembled and with a certificate of authenticity from their manufacturers.',NULL,NULL),\n", - "\n", - "('Ships','The perfect holiday or anniversary gift for executives, clients, friends, and family. These handcrafted model ships are unique, stunning works of art that will be treasured for generations! They come fully assembled and ready for display in the home or office. We guarantee the highest quality, and best value.',NULL,NULL),\n", - "\n", - "('Trains','Model trains are a rewarding hobby for enthusiasts of all ages. Whether you\\'re looking for collectible wooden trains, electric streetcars or locomotives, you\\'ll find a number of great choices for any budget within this category. The interactive aspect of trains makes toy trains perfect for young children. The wooden train sets are ideal for children under the age of 5.',NULL,NULL),\n", - "\n", - "('Trucks and Buses','The Truck and Bus models are realistic replicas of buses and specialized trucks produced from the early 1920s to present. The models range in size from 1:12 to 1:50 scale and include numerous limited edition and several out-of-production vehicles. Materials used include tin, diecast and plastic. All models include a certificate of authenticity from their manufacturers and are a perfect ornament for the home and office.',NULL,NULL),\n", - "\n", - "('Vintage Cars','Our Vintage Car models realistically portray automobiles produced from the early 1900s through the 1940s. Materials used include Bakelite, diecast, plastic and wood. Most of the replicas are in the 1:18 and 1:24 scale sizes, which provide the optimum in detail and accuracy. Prices range from $30.00 up to $180.00 for some special limited edition replicas. All models include a certificate of authenticity from their manufacturers and come fully assembled and ready for display in the home or office.',NULL,NULL);\n", - "\n", - "\n", - "/*Data for the table `product` */\n", - "\n", - "insert into `product`(`product_code`,`product_name`,`product_line`,`product_scale`,`vendor`,`product_description`,`quantity_in_stock`,`buy_price`,`MSRP`) values \n", - "\n", - "('S10_1678','1969 Harley Davidson Ultimate Chopper','Motorcycles','1:10','Min Lin Diecast','This replica features working kickstand, front suspension, gear-shift lever, footbrake lever, drive chain, wheels and steering. All parts are particularly delicate due to their precise scale and require special care and attention.',7933,'48.81','95.70'),\n", - "\n", - "('S10_1949','1952 Alpine Renault 1300','Classic Cars','1:10','Classic Metal Creations','Turnable front wheels; steering function; detailed interior; detailed engine; opening hood; opening trunk; opening doors; and detailed chassis.',7305,'98.58','214.30'),\n", - "\n", - "('S10_2016','1996 Moto Guzzi 1100i','Motorcycles','1:10','Highway 66 Mini Classics','Official Moto Guzzi logos and insignias, saddle bags located on side of motorcycle, detailed engine, working steering, working suspension, two leather seats, luggage rack, dual exhaust pipes, small saddle bag located on handle bars, two-tone paint with chrome accents, superior die-cast detail , rotating wheels , working kick stand, diecast metal with plastic parts and baked enamel finish.',6625,'68.99','118.94'),\n", - "\n", - "('S10_4698','2003 Harley-Davidson Eagle Drag Bike','Motorcycles','1:10','Red Start Diecast','Model features, official Harley Davidson logos and insignias, detachable rear wheelie bar, heavy diecast metal with resin parts, authentic multi-color tampo-printed graphics, separate engine drive belts, free-turning front fork, rotating tires and rear racing slick, certificate of authenticity, detailed engine, display stand\\r\\n, precision diecast replica, baked enamel finish, 1:10 scale model, removable fender, seat and tank cover piece for displaying the superior detail of the v-twin engine',5582,'91.02','193.66'),\n", - "\n", - "('S10_4757','1972 Alfa Romeo GTA','Classic Cars','1:10','Motor City Art Classics','Features include: Turnable front wheels; steering function; detailed interior; detailed engine; opening hood; opening trunk; opening doors; and detailed chassis.',3252,'85.68','136.00'),\n", - "\n", - "('S10_4962','1962 LanciaA Delta 16V','Classic Cars','1:10','Second Gear Diecast','Features include: Turnable front wheels; steering function; detailed interior; detailed engine; opening hood; opening trunk; opening doors; and detailed chassis.',6791,'103.42','147.74'),\n", - "\n", - "('S12_1099','1968 Ford Mustang','Classic Cars','1:12','Autoart Studio Design','Hood, doors and trunk all open to reveal highly detailed interior features. Steering wheel actually turns the front wheels. Color dark green.',68,'95.34','194.57'),\n", - "\n", - "('S12_1108','2001 Ferrari Enzo','Classic Cars','1:12','Second Gear Diecast','Turnable front wheels; steering function; detailed interior; detailed engine; opening hood; opening trunk; opening doors; and detailed chassis.',3619,'95.59','207.80'),\n", - "\n", - "('S12_1666','1958 Setra Bus','Trucks and Buses','1:12','Welly Diecast Productions','Model features 30 windows, skylights & glare resistant glass, working steering system, original logos',1579,'77.90','136.67'),\n", - "\n", - "('S12_2823','2002 Suzuki XREO','Motorcycles','1:12','Unimax Art Galleries','Official logos and insignias, saddle bags located on side of motorcycle, detailed engine, working steering, working suspension, two leather seats, luggage rack, dual exhaust pipes, small saddle bag located on handle bars, two-tone paint with chrome accents, superior die-cast detail , rotating wheels , working kick stand, diecast metal with plastic parts and baked enamel finish.',9997,'66.27','150.62'),\n", - "\n", - "('S12_3148','1969 Corvair Monza','Classic Cars','1:18','Welly Diecast Productions','1:18 scale die-cast about 10\\\" long doors open, hood opens, trunk opens and wheels roll',6906,'89.14','151.08'),\n", - "\n", - "('S12_3380','1968 Dodge Charger','Classic Cars','1:12','Welly Diecast Productions','1:12 scale model of a 1968 Dodge Charger. Hood, doors and trunk all open to reveal highly detailed interior features. Steering wheel actually turns the front wheels. Color black',9123,'75.16','117.44'),\n", - "\n", - "('S12_3891','1969 Ford Falcon','Classic Cars','1:12','Second Gear Diecast','Turnable front wheels; steering function; detailed interior; detailed engine; opening hood; opening trunk; opening doors; and detailed chassis.',1049,'83.05','173.02'),\n", - "\n", - "('S12_3990','1970 Plymouth Hemi Cuda','Classic Cars','1:12','Studio M Art Models','Very detailed 1970 Plymouth Cuda model in 1:12 scale. The Cuda is generally accepted as one of the fastest original muscle cars from the 1970s. This model is a reproduction of one of the original 652 cars built in 1970. Red color.',5663,'31.92','79.80'),\n", - "\n", - "('S12_4473','1957 Chevy Pickup','Trucks and Buses','1:12','Exoto Designs','1:12 scale die-cast about 20\\\" long Hood opens, Rubber wheels',6125,'55.70','118.50'),\n", - "\n", - "('S12_4675','1969 Dodge Charger','Classic Cars','1:12','Welly Diecast Productions','Detailed model of the 1969 Dodge Charger. This model includes finely detailed interior and exterior features. Painted in red and white.',7323,'58.73','115.16'),\n", - "\n", - "('S18_1097','1940 Ford Pickup Truck','Trucks and Buses','1:18','Studio M Art Models','This model features soft rubber tires, working steering, rubber mud guards, authentic Ford logos, detailed undercarriage, opening doors and hood, removable split rear gate, full size spare mounted in bed, detailed interior with opening glove box',2613,'58.33','116.67'),\n", - "\n", - "('S18_1129','1993 Mazda RX-7','Classic Cars','1:18','Highway 66 Mini Classics','This model features, opening hood, opening doors, detailed engine, rear spoiler, opening trunk, working steering, tinted windows, baked enamel finish. Color red.',3975,'83.51','141.54'),\n", - "\n", - "('S18_1342','1937 Lincoln Berline','Vintage Cars','1:18','Motor City Art Classics','Features opening engine cover, doors, trunk, and fuel filler cap. Color black',8693,'60.62','102.74'),\n", - "\n", - "('S18_1367','1936 Mercedes-Benz 500K Special Roadster','Vintage Cars','1:18','Studio M Art Models','This 1:18 scale replica is constructed of heavy die-cast metal and has all the features of the original: working doors and rumble seat, independent spring suspension, detailed interior, working steering system, and a bifold hood that reveals an engine so accurate that it even includes the wiring. All this is topped off with a baked enamel finish. Color white.',8635,'24.26','53.91'),\n", - "\n", - "('S18_1589','1965 Aston Martin DB5','Classic Cars','1:18','Classic Metal Creations','Die-cast model of the silver 1965 Aston Martin DB5 in silver. This model includes full wire wheels and doors that open with fully detailed passenger compartment. In 1:18 scale, this model measures approximately 10 inches/20 cm long.',9042,'65.96','124.44'),\n", - "\n", - "('S18_1662','1980s Black Hawk Helicopter','Planes','1:18','Red Start Diecast','1:18 scale replica of actual Army\\'s UH-60L BLACK HAWK Helicopter. 100% hand-assembled. Features rotating rotor blades, propeller blades and rubber wheels.',5330,'77.27','157.69'),\n", - "\n", - "('S18_1749','1917 Grand Touring Sedan','Vintage Cars','1:18','Welly Diecast Productions','This 1:18 scale replica of the 1917 Grand Touring car has all the features you would expect from museum quality reproductions: all four doors and bi-fold hood opening, detailed engine and instrument panel, chrome-look trim, and tufted upholstery, all topped off with a factory baked-enamel finish.',2724,'86.70','170.00'),\n", - "\n", - "('S18_1889','1948 Porsche 356-A Roadster','Classic Cars','1:18','Gearbox Collectibles','This precision die-cast replica features opening doors, superb detail and craftsmanship, working steering system, opening forward compartment, opening rear trunk with removable spare, 4 wheel independent spring suspension as well as factory baked enamel finish.',8826,'53.90','77.00'),\n", - "\n", - "('S18_1984','1995 Honda Civic','Classic Cars','1:18','Min Lin Diecast','This model features, opening hood, opening doors, detailed engine, rear spoiler, opening trunk, working steering, tinted windows, baked enamel finish. Color yellow.',9772,'93.89','142.25'),\n", - "\n", - "('S18_2238','1998 Chrysler Plymouth Prowler','Classic Cars','1:18','Gearbox Collectibles','Turnable front wheels; steering function; detailed interior; detailed engine; opening hood; opening trunk; opening doors; and detailed chassis.',4724,'101.51','163.73'),\n", - "\n", - "('S18_2248','1911 Ford Town Car','Vintage Cars','1:18','Motor City Art Classics','Features opening hood, opening doors, opening trunk, wide white wall tires, front door arm rests, working steering system.',540,'33.30','60.54'),\n", - "\n", - "('S18_2319','1964 Mercedes Tour Bus','Trucks and Buses','1:18','Unimax Art Galleries','Exact replica. 100+ parts. working steering system, original logos',8258,'74.86','122.73'),\n", - "\n", - "('S18_2325','1932 Model A Ford J-Coupe','Vintage Cars','1:18','Autoart Studio Design','This model features grille-mounted chrome horn, lift-up louvered hood, fold-down rumble seat, working steering system, chrome-covered spare, opening doors, detailed and wired engine',9354,'58.48','127.13'),\n", - "\n", - "('S18_2432','1926 Ford Fire Engine','Trucks and Buses','1:18','Carousel DieCast Legends','Gleaming red handsome appearance. Everything is here the fire hoses, ladder, axes, bells, lanterns, ready to fight any inferno.',2018,'24.92','60.77'),\n", - "\n", - "('S18_2581','P-51-D Mustang','Planes','1:72','Gearbox Collectibles','Has retractable wheels and comes with a stand',992,'49.00','84.48'),\n", - "\n", - "('S18_2625','1936 Harley Davidson El Knucklehead','Motorcycles','1:18','Welly Diecast Productions','Intricately detailed with chrome accents and trim, official die-struck logos and baked enamel finish.',4357,'24.23','60.57'),\n", - "\n", - "('S18_2795','1928 Mercedes-Benz SSK','Vintage Cars','1:18','Gearbox Collectibles','This 1:18 replica features grille-mounted chrome horn, lift-up louvered hood, fold-down rumble seat, working steering system, chrome-covered spare, opening doors, detailed and wired engine. Color black.',548,'72.56','168.75'),\n", - "\n", - "('S18_2870','1999 Indy 500 Monte Carlo SS','Classic Cars','1:18','Red Start Diecast','Features include opening and closing doors. Color: Red',8164,'56.76','132.00'),\n", - "\n", - "('S18_2949','1913 Ford Model T Speedster','Vintage Cars','1:18','Carousel DieCast Legends','This 250 part reproduction includes moving handbrakes, clutch, throttle and foot pedals, squeezable horn, detailed wired engine, removable water, gas, and oil cans, pivoting monocle windshield, all topped with a baked enamel red finish. Each replica comes with an Owners Title and Certificate of Authenticity. Color red.',4189,'60.78','101.31'),\n", - "\n", - "('S18_2957','1934 Ford V8 Coupe','Vintage Cars','1:18','Min Lin Diecast','Chrome Trim, Chrome Grille, Opening Hood, Opening Doors, Opening Trunk, Detailed Engine, Working Steering System',5649,'34.35','62.46'),\n", - "\n", - "('S18_3029','1999 Yamaha Speed Boat','Ships','1:18','Min Lin Diecast','Exact replica. Wood and Metal. Many extras including rigging, long boats, pilot house, anchors, etc. Comes with three masts, all square-rigged.',4259,'51.61','86.02'),\n", - "\n", - "('S18_3136','18th Century Vintage Horse Carriage','Vintage Cars','1:18','Red Start Diecast','Hand crafted diecast-like metal horse carriage is re-created in about 1:18 scale of antique horse carriage. This antique style metal Stagecoach is all hand-assembled with many different parts.\\r\\n\\r\\nThis collectible metal horse carriage is painted in classic Red, and features turning steering wheel and is entirely hand-finished.',5992,'60.74','104.72'),\n", - "\n", - "('S18_3140','1903 Ford Model A','Vintage Cars','1:18','Unimax Art Galleries','Features opening trunk, working steering system',3913,'68.30','136.59'),\n", - "\n", - "('S18_3232','1992 Ferrari 360 Spider red','Classic Cars','1:18','Unimax Art Galleries','his replica features opening doors, superb detail and craftsmanship, working steering system, opening forward compartment, opening rear trunk with removable spare, 4 wheel independent spring suspension as well as factory baked enamel finish.',8347,'77.90','169.34'),\n", - "\n", - "('S18_3233','1985 Toyota Supra','Classic Cars','1:18','Highway 66 Mini Classics','This model features soft rubber tires, working steering, rubber mud guards, authentic Ford logos, detailed undercarriage, opening doors and hood, removable split rear gate, full size spare mounted in bed, detailed interior with opening glove box',7733,'57.01','107.57'),\n", - "\n", - "('S18_3259','Collectable Wooden Train','Trains','1:18','Carousel DieCast Legends','Hand crafted wooden toy train set is in about 1:18 scale, 25 inches in total length including 2 additional carts, of actual vintage train. This antique style wooden toy train model set is all hand-assembled with 100% wood.',6450,'67.56','100.84'),\n", - "\n", - "('S18_3278','1969 Dodge Super Bee','Classic Cars','1:18','Min Lin Diecast','This replica features opening doors, superb detail and craftsmanship, working steering system, opening forward compartment, opening rear trunk with removable spare, 4 wheel independent spring suspension as well as factory baked enamel finish.',1917,'49.05','80.41'),\n", - "\n", - "('S18_3320','1917 Maxwell Touring Car','Vintage Cars','1:18','Exoto Designs','Features Gold Trim, Full Size Spare Tire, Chrome Trim, Chrome Grille, Opening Hood, Opening Doors, Opening Trunk, Detailed Engine, Working Steering System',7913,'57.54','99.21'),\n", - "\n", - "('S18_3482','1976 Ford Gran Torino','Classic Cars','1:18','Gearbox Collectibles','Highly detailed 1976 Ford Gran Torino \\\"Starsky and Hutch\\\" diecast model. Very well constructed and painted in red and white patterns.',9127,'73.49','146.99'),\n", - "\n", - "('S18_3685','1948 Porsche Type 356 Roadster','Classic Cars','1:18','Gearbox Collectibles','This model features working front and rear suspension on accurately replicated and actuating shock absorbers as well as opening engine cover, rear stabilizer flap, and 4 opening doors.',8990,'62.16','141.28'),\n", - "\n", - "('S18_3782','1957 Vespa GS150','Motorcycles','1:18','Studio M Art Models','Features rotating wheels , working kick stand. Comes with stand.',7689,'32.95','62.17'),\n", - "\n", - "('S18_3856','1941 Chevrolet Special Deluxe Cabriolet','Vintage Cars','1:18','Exoto Designs','Features opening hood, opening doors, opening trunk, wide white wall tires, front door arm rests, working steering system, leather upholstery. Color black.',2378,'64.58','105.87'),\n", - "\n", - "('S18_4027','1970 Triumph Spitfire','Classic Cars','1:18','Min Lin Diecast','Features include opening and closing doors. Color: White.',5545,'91.92','143.62'),\n", - "\n", - "('S18_4409','1932 Alfa Romeo 8C2300 Spider Sport','Vintage Cars','1:18','Exoto Designs','This 1:18 scale precision die cast replica features the 6 front headlights of the original, plus a detailed version of the 142 horsepower straight 8 engine, dual spares and their famous comprehensive dashboard. Color black.',6553,'43.26','92.03'),\n", - "\n", - "('S18_4522','1904 Buick Runabout','Vintage Cars','1:18','Exoto Designs','Features opening trunk, working steering system',8290,'52.66','87.77'),\n", - "\n", - "('S18_4600','1940s Ford truck','Trucks and Buses','1:18','Motor City Art Classics','This 1940s Ford Pick-Up truck is re-created in 1:18 scale of original 1940s Ford truck. This antique style metal 1940s Ford Flatbed truck is all hand-assembled. This collectible 1940\\'s Pick-Up truck is painted in classic dark green color, and features rotating wheels.',3128,'84.76','121.08'),\n", - "\n", - "('S18_4668','1939 Cadillac Limousine','Vintage Cars','1:18','Studio M Art Models','Features completely detailed interior including Velvet flocked drapes,deluxe wood grain floor, and a wood grain casket with separate chrome handles',6645,'23.14','50.31'),\n", - "\n", - "('S18_4721','1957 Corvette Convertible','Classic Cars','1:18','Classic Metal Creations','1957 die cast Corvette Convertible in Roman Red with white sides and whitewall tires. 1:18 scale quality die-cast with detailed engine and underbvody. Now you can own The Classic Corvette.',1249,'69.93','148.80'),\n", - "\n", - "('S18_4933','1957 Ford Thunderbird','Classic Cars','1:18','Studio M Art Models','This 1:18 scale precision die-cast replica, with its optional porthole hardtop and factory baked-enamel Thunderbird Bronze finish, is a 100% accurate rendition of this American classic.',3209,'34.21','71.27'),\n", - "\n", - "('S24_1046','1970 Chevy Chevelle SS 454','Classic Cars','1:24','Unimax Art Galleries','This model features rotating wheels, working streering system and opening doors. All parts are particularly delicate due to their precise scale and require special care and attention. It should not be picked up by the doors, roof, hood or trunk.',1005,'49.24','73.49'),\n", - "\n", - "('S24_1444','1970 Dodge Coronet','Classic Cars','1:24','Highway 66 Mini Classics','1:24 scale die-cast about 18\\\" long doors open, hood opens and rubber wheels',4074,'32.37','57.80'),\n", - "\n", - "('S24_1578','1997 BMW R 1100 S','Motorcycles','1:24','Autoart Studio Design','Detailed scale replica with working suspension and constructed from over 70 parts',7003,'60.86','112.70'),\n", - "\n", - "('S24_1628','1966 Shelby Cobra 427 S/C','Classic Cars','1:24','Carousel DieCast Legends','This diecast model of the 1966 Shelby Cobra 427 S/C includes many authentic details and operating parts. The 1:24 scale model of this iconic lightweight sports car from the 1960s comes in silver and it\\'s own display case.',8197,'29.18','50.31'),\n", - "\n", - "('S24_1785','1928 British Royal Navy Airplane','Planes','1:24','Classic Metal Creations','Official logos and insignias',3627,'66.74','109.42'),\n", - "\n", - "('S24_1937','1939 Chevrolet Deluxe Coupe','Vintage Cars','1:24','Motor City Art Classics','This 1:24 scale die-cast replica of the 1939 Chevrolet Deluxe Coupe has the same classy look as the original. Features opening trunk, hood and doors and a showroom quality baked enamel finish.',7332,'22.57','33.19'),\n", - "\n", - "('S24_2000','1960 BSA Gold Star DBD34','Motorcycles','1:24','Highway 66 Mini Classics','Detailed scale replica with working suspension and constructed from over 70 parts',15,'37.32','76.17'),\n", - "\n", - "('S24_2011','18th century schooner','Ships','1:24','Carousel DieCast Legends','All wood with canvas sails. Many extras including rigging, long boats, pilot house, anchors, etc. Comes with 4 masts, all square-rigged.',1898,'82.34','122.89'),\n", - "\n", - "('S24_2022','1938 Cadillac V-16 Presidential Limousine','Vintage Cars','1:24','Classic Metal Creations','This 1:24 scale precision die cast replica of the 1938 Cadillac V-16 Presidential Limousine has all the details of the original, from the flags on the front to an opening back seat compartment complete with telephone and rifle. Features factory baked-enamel black finish, hood goddess ornament, working jump seats.',2847,'20.61','44.80'),\n", - "\n", - "('S24_2300','1962 Volkswagen Microbus','Trucks and Buses','1:24','Autoart Studio Design','This 1:18 scale die cast replica of the 1962 Microbus is loaded with features: A working steering system, opening front doors and tailgate, and famous two-tone factory baked enamel finish, are all topped of by the sliding, real fabric, sunroof.',2327,'61.34','127.79'),\n", - "\n", - "('S24_2360','1982 Ducati 900 Monster','Motorcycles','1:24','Highway 66 Mini Classics','Features two-tone paint with chrome accents, superior die-cast detail , rotating wheels , working kick stand',6840,'47.10','69.26'),\n", - "\n", - "('S24_2766','1949 Jaguar XK 120','Classic Cars','1:24','Classic Metal Creations','Precision-engineered from original Jaguar specification in perfect scale ratio. Features opening doors, superb detail and craftsmanship, working steering system, opening forward compartment, opening rear trunk with removable spare, 4 wheel independent spring suspension as well as factory baked enamel finish.',2350,'47.25','90.87'),\n", - "\n", - "('S24_2840','1958 Chevy Corvette Limited Edition','Classic Cars','1:24','Carousel DieCast Legends','The operating parts of this 1958 Chevy Corvette Limited Edition are particularly delicate due to their precise scale and require special care and attention. Features rotating wheels, working streering, opening doors and trunk. Color dark green.',2542,'15.91','35.36'),\n", - "\n", - "('S24_2841','1900s Vintage Bi-Plane','Planes','1:24','Autoart Studio Design','Hand crafted diecast-like metal bi-plane is re-created in about 1:24 scale of antique pioneer airplane. All hand-assembled with many different parts. Hand-painted in classic yellow and features correct markings of original airplane.',5942,'34.25','68.51'),\n", - "\n", - "('S24_2887','1952 Citroen-15CV','Classic Cars','1:24','Exoto Designs','Precision crafted hand-assembled 1:18 scale reproduction of the 1952 15CV, with its independent spring suspension, working steering system, opening doors and hood, detailed engine and instrument panel, all topped of with a factory fresh baked enamel finish.',1452,'72.82','117.44'),\n", - "\n", - "('S24_2972','1982 Lamborghini Diablo','Classic Cars','1:24','Second Gear Diecast','This replica features opening doors, superb detail and craftsmanship, working steering system, opening forward compartment, opening rear trunk with removable spare, 4 wheel independent spring suspension as well as factory baked enamel finish.',7723,'16.24','37.76'),\n", - "\n", - "('S24_3151','1912 Ford Model T Delivery Wagon','Vintage Cars','1:24','Min Lin Diecast','This model features chrome trim and grille, opening hood, opening doors, opening trunk, detailed engine, working steering system. Color white.',9173,'46.91','88.51'),\n", - "\n", - "('S24_3191','1969 Chevrolet Camaro Z28','Classic Cars','1:24','Exoto Designs','1969 Z/28 Chevy Camaro 1:24 scale replica. The operating parts of this limited edition 1:24 scale diecast model car 1969 Chevy Camaro Z28- hood, trunk, wheels, streering, suspension and doors- are particularly delicate due to their precise scale and require special care and attention.',4695,'50.51','85.61'),\n", - "\n", - "('S24_3371','1971 Alpine Renault 1600s','Classic Cars','1:24','Welly Diecast Productions','This 1971 Alpine Renault 1600s replica Features opening doors, superb detail and craftsmanship, working steering system, opening forward compartment, opening rear trunk with removable spare, 4 wheel independent spring suspension as well as factory baked enamel finish.',7995,'38.58','61.23'),\n", - "\n", - "('S24_3420','1937 Horch 930V Limousine','Vintage Cars','1:24','Autoart Studio Design','Features opening hood, opening doors, opening trunk, wide white wall tires, front door arm rests, working steering system',2902,'26.30','65.75'),\n", - "\n", - "('S24_3432','2002 Chevy Corvette','Classic Cars','1:24','Gearbox Collectibles','The operating parts of this limited edition Diecast 2002 Chevy Corvette 50th Anniversary Pace car Limited Edition are particularly delicate due to their precise scale and require special care and attention. Features rotating wheels, poseable streering, opening doors and trunk.',9446,'62.11','107.08'),\n", - "\n", - "('S24_3816','1940 Ford Delivery Sedan','Vintage Cars','1:24','Carousel DieCast Legends','Chrome Trim, Chrome Grille, Opening Hood, Opening Doors, Opening Trunk, Detailed Engine, Working Steering System. Color black.',6621,'48.64','83.86'),\n", - "\n", - "('S24_3856','1956 Porsche 356A Coupe','Classic Cars','1:18','Classic Metal Creations','Features include: Turnable front wheels; steering function; detailed interior; detailed engine; opening hood; opening trunk; opening doors; and detailed chassis.',6600,'98.30','140.43'),\n", - "\n", - "('S24_3949','Corsair F4U ( Bird Cage)','Planes','1:24','Second Gear Diecast','Has retractable wheels and comes with a stand. Official logos and insignias.',6812,'29.34','68.24'),\n", - "\n", - "('S24_3969','1936 Mercedes Benz 500k Roadster','Vintage Cars','1:24','Red Start Diecast','This model features grille-mounted chrome horn, lift-up louvered hood, fold-down rumble seat, working steering system and rubber wheels. Color black.',2081,'21.75','41.03'),\n", - "\n", - "('S24_4048','1992 Porsche Cayenne Turbo Silver','Classic Cars','1:24','Exoto Designs','This replica features opening doors, superb detail and craftsmanship, working steering system, opening forward compartment, opening rear trunk with removable spare, 4 wheel independent spring suspension as well as factory baked enamel finish.',6582,'69.78','118.28'),\n", - "\n", - "('S24_4258','1936 Chrysler Airflow','Vintage Cars','1:24','Second Gear Diecast','Features opening trunk, working steering system. Color dark green.',4710,'57.46','97.39'),\n", - "\n", - "('S24_4278','1900s Vintage Tri-Plane','Planes','1:24','Unimax Art Galleries','Hand crafted diecast-like metal Triplane is Re-created in about 1:24 scale of antique pioneer airplane. This antique style metal triplane is all hand-assembled with many different parts.',2756,'36.23','72.45'),\n", - "\n", - "('S24_4620','1961 Chevrolet Impala','Classic Cars','1:18','Classic Metal Creations','This 1:18 scale precision die-cast reproduction of the 1961 Chevrolet Impala has all the features-doors, hood and trunk that open; detailed 409 cubic-inch engine; chrome dashboard and stick shift, two-tone interior; working steering system; all topped of with a factory baked-enamel finish.',7869,'32.33','80.84'),\n", - "\n", - "('S32_1268','1980’s GM Manhattan Express','Trucks and Buses','1:32','Motor City Art Classics','This 1980’s era new look Manhattan express is still active, running from the Bronx to mid-town Manhattan. Has 35 opeining windows and working lights. Needs a battery.',5099,'53.93','96.31'),\n", - "\n", - "('S32_1374','1997 BMW F650 ST','Motorcycles','1:32','Exoto Designs','Features official die-struck logos and baked enamel finish. Comes with stand.',178,'66.92','99.89'),\n", - "\n", - "('S32_2206','1982 Ducati 996 R','Motorcycles','1:32','Gearbox Collectibles','Features rotating wheels , working kick stand. Comes with stand.',9241,'24.14','40.23'),\n", - "\n", - "('S32_2509','1954 Greyhound Scenicruiser','Trucks and Buses','1:32','Classic Metal Creations','Model features bi-level seating, 50 windows, skylights & glare resistant glass, working steering system, original logos',2874,'25.98','54.11'),\n", - "\n", - "('S32_3207','1950\\'s Chicago Surface Lines Streetcar','Trains','1:32','Gearbox Collectibles','This streetcar is a joy to see. It has 80 separate windows, electric wire guides, detailed interiors with seats, poles and drivers controls, rolling and turning wheel assemblies, plus authentic factory baked-enamel finishes (Green Hornet for Chicago and Cream and Crimson for Boston).',8601,'26.72','62.14'),\n", - "\n", - "('S32_3522','1996 Peterbilt 379 Stake Bed with Outrigger','Trucks and Buses','1:32','Red Start Diecast','This model features, opening doors, detailed engine, working steering, tinted windows, detailed interior, die-struck logos, removable stakes operating outriggers, detachable second trailer, functioning 360-degree self loader, precision molded resin trailer and trim, baked enamel finish on cab',814,'33.61','64.64'),\n", - "\n", - "('S32_4289','1928 Ford Phaeton Deluxe','Vintage Cars','1:32','Highway 66 Mini Classics','This model features grille-mounted chrome horn, lift-up louvered hood, fold-down rumble seat, working steering system',136,'33.02','68.79'),\n", - "\n", - "('S32_4485','1974 Ducati 350 Mk3 Desmo','Motorcycles','1:32','Second Gear Diecast','This model features two-tone paint with chrome accents, superior die-cast detail , rotating wheels , working kick stand',3341,'56.13','102.05'),\n", - "\n", - "('S50_1341','1930 Buick Marquette Phaeton','Vintage Cars','1:50','Studio M Art Models','Features opening trunk, working steering system',7062,'27.06','43.64'),\n", - "\n", - "('S50_1392','Diamond T620 Semi-Skirted Tanker','Trucks and Buses','1:50','Highway 66 Mini Classics','This limited edition model is licensed and perfectly scaled for Lionel Trains. The Diamond T620 has been produced in solid precision diecast and painted with a fire baked enamel finish. It comes with a removable tanker and is a perfect model to add authenticity to your static train or car layout or to just have on display.',1016,'68.29','115.75'),\n", - "\n", - "('S50_1514','1962 City of Detroit Streetcar','Trains','1:50','Classic Metal Creations','This streetcar is a joy to see. It has 99 separate windows, electric wire guides, detailed interiors with seats, poles and drivers controls, rolling and turning wheel assemblies, plus authentic factory baked-enamel finishes (Green Hornet for Chicago and Cream and Crimson for Boston).',1645,'37.49','58.58'),\n", - "\n", - "('S50_4713','2002 Yamaha YZR M1','Motorcycles','1:50','Autoart Studio Design','Features rotating wheels , working kick stand. Comes with stand.',600,'34.17','81.36'),\n", - "\n", - "('S700_1138','The Schooner Bluenose','Ships','1:700','Autoart Studio Design','All wood with canvas sails. Measures 31 1/2 inches in Length, 22 inches High and 4 3/4 inches Wide. Many extras.\\r\\nThe schooner Bluenose was built in Nova Scotia in 1921 to fish the rough waters off the coast of Newfoundland. Because of the Bluenose racing prowess she became the pride of all Canadians. Still featured on stamps and the Canadian dime, the Bluenose was lost off Haiti in 1946.',1897,'34.00','66.67'),\n", - "\n", - "('S700_1691','American Airlines: B767-300','Planes','1:700','Min Lin Diecast','Exact replia with official logos and insignias and retractable wheels',5841,'51.15','91.34'),\n", - "\n", - "('S700_1938','The Mayflower','Ships','1:700','Studio M Art Models','Measures 31 1/2 inches Long x 25 1/2 inches High x 10 5/8 inches Wide\\r\\nAll wood with canvas sail. Extras include long boats, rigging, ladders, railing, anchors, side cannons, hand painted, etc.',737,'43.30','86.61'),\n", - "\n", - "('S700_2047','HMS Bounty','Ships','1:700','Unimax Art Galleries','Measures 30 inches Long x 27 1/2 inches High x 4 3/4 inches Wide. \\r\\nMany extras including rigging, long boats, pilot house, anchors, etc. Comes with three masts, all square-rigged.',3501,'39.83','90.52'),\n", - "\n", - "('S700_2466','America West Airlines B757-200','Planes','1:700','Motor City Art Classics','Official logos and insignias. Working steering system. Rotating jet engines',9653,'68.80','99.72'),\n", - "\n", - "('S700_2610','The USS Constitution Ship','Ships','1:700','Red Start Diecast','All wood with canvas sails. Measures 31 1/2\\\" Length x 22 3/8\\\" High x 8 1/4\\\" Width. Extras include 4 boats on deck, sea sprite on bow, anchors, copper railing, pilot houses, etc.',7083,'33.97','72.28'),\n", - "\n", - "('S700_2824','1982 Camaro Z28','Classic Cars','1:18','Carousel DieCast Legends','Features include opening and closing doors. Color: White. \\r\\nMeasures approximately 9 1/2\\\" Long.',6934,'46.53','101.15'),\n", - "\n", - "('S700_2834','ATA: B757-300','Planes','1:700','Highway 66 Mini Classics','Exact replia with official logos and insignias and retractable wheels',7106,'59.33','118.65'),\n", - "\n", - "('S700_3167','F/A 18 Hornet 1/72','Planes','1:72','Motor City Art Classics','10\\\" Wingspan with retractable landing gears.Comes with pilot',551,'54.40','80.00'),\n", - "\n", - "('S700_3505','The Titanic','Ships','1:700','Carousel DieCast Legends','Completed model measures 19 1/2 inches long, 9 inches high, 3inches wide and is in barn red/black. All wood and metal.',1956,'51.09','100.17'),\n", - "\n", - "('S700_3962','The Queen Mary','Ships','1:700','Welly Diecast Productions','Exact replica. Wood and Metal. Many extras including rigging, long boats, pilot house, anchors, etc. Comes with three masts, all square-rigged.',5088,'53.63','99.31'),\n", - "\n", - "('S700_4002','American Airlines: MD-11S','Planes','1:700','Second Gear Diecast','Polished finish. Exact replia with official logos and insignias and retractable wheels',8820,'36.27','74.03'),\n", - "\n", - "('S72_1253','Boeing X-32A JSF','Planes','1:72','Motor City Art Classics','10\\\" Wingspan with retractable landing gears.Comes with pilot',4857,'32.77','49.66'),\n", - "\n", - "('S72_3212','Pont Yacht','Ships','1:72','Unimax Art Galleries','Measures 38 inches Long x 33 3/4 inches High. Includes a stand.\\r\\nMany extras including rigging, long boats, pilot house, anchors, etc. Comes with 2 masts, all square-rigged',414,'33.30','54.60');\n", - "\n", - "/*Data for the table `order` */\n", - "insert into `order`(`order_number`,`order_date`,`required_date`,`shipped_date`,`status`,`comments`,`customer_number`) values \n", - "\n", - "(10100,'2003-01-06','2003-01-13','2003-01-10','Shipped',NULL,363),\n", - "\n", - "(10101,'2003-01-09','2003-01-18','2003-01-11','Shipped','Check on availability.',128),\n", - "\n", - "(10102,'2003-01-10','2003-01-18','2003-01-14','Shipped',NULL,181),\n", - "\n", - "(10103,'2003-01-29','2003-02-07','2003-02-02','Shipped',NULL,121),\n", - "\n", - "(10104,'2003-01-31','2003-02-09','2003-02-01','Shipped',NULL,141),\n", - "\n", - "(10105,'2003-02-11','2003-02-21','2003-02-12','Shipped',NULL,145),\n", - "\n", - "(10106,'2003-02-17','2003-02-24','2003-02-21','Shipped',NULL,278),\n", - "\n", - "(10107,'2003-02-24','2003-03-03','2003-02-26','Shipped','Difficult to negotiate with customer. We need more marketing materials',131),\n", - "\n", - "(10108,'2003-03-03','2003-03-12','2003-03-08','Shipped',NULL,385),\n", - "\n", - "(10109,'2003-03-10','2003-03-19','2003-03-11','Shipped','Customer requested that FedEx Ground is used for this shipping',486),\n", - "\n", - "(10110,'2003-03-18','2003-03-24','2003-03-20','Shipped',NULL,187),\n", - "\n", - "(10111,'2003-03-25','2003-03-31','2003-03-30','Shipped',NULL,129),\n", - "\n", - "(10112,'2003-03-24','2003-04-03','2003-03-29','Shipped','Customer requested that ad materials (such as posters, pamphlets) be included in the shippment',144),\n", - "\n", - "(10113,'2003-03-26','2003-04-02','2003-03-27','Shipped',NULL,124),\n", - "\n", - "(10114,'2003-04-01','2003-04-07','2003-04-02','Shipped',NULL,172),\n", - "\n", - "(10115,'2003-04-04','2003-04-12','2003-04-07','Shipped',NULL,424),\n", - "\n", - "(10116,'2003-04-11','2003-04-19','2003-04-13','Shipped',NULL,381),\n", - "\n", - "(10117,'2003-04-16','2003-04-24','2003-04-17','Shipped',NULL,148),\n", - "\n", - "(10118,'2003-04-21','2003-04-29','2003-04-26','Shipped','Customer has worked with some of our vendors in the past and is aware of their MSRP',216),\n", - "\n", - "(10119,'2003-04-28','2003-05-05','2003-05-02','Shipped',NULL,382),\n", - "\n", - "(10120,'2003-04-29','2003-05-08','2003-05-01','Shipped',NULL,114),\n", - "\n", - "(10121,'2003-05-07','2003-05-13','2003-05-13','Shipped',NULL,353),\n", - "\n", - "(10122,'2003-05-08','2003-05-16','2003-05-13','Shipped',NULL,350),\n", - "\n", - "(10123,'2003-05-20','2003-05-29','2003-05-22','Shipped',NULL,103),\n", - "\n", - "(10124,'2003-05-21','2003-05-29','2003-05-25','Shipped','Customer very concerned about the exact color of the models. There is high risk that he may dispute the order because there is a slight color mismatch',112),\n", - "\n", - "(10125,'2003-05-21','2003-05-27','2003-05-24','Shipped',NULL,114),\n", - "\n", - "(10126,'2003-05-28','2003-06-07','2003-06-02','Shipped',NULL,458),\n", - "\n", - "(10127,'2003-06-03','2003-06-09','2003-06-06','Shipped','Customer requested special shippment. The instructions were passed along to the warehouse',151),\n", - "\n", - "(10128,'2003-06-06','2003-06-12','2003-06-11','Shipped',NULL,141),\n", - "\n", - "(10129,'2003-06-12','2003-06-18','2003-06-14','Shipped',NULL,324),\n", - "\n", - "(10130,'2003-06-16','2003-06-24','2003-06-21','Shipped',NULL,198),\n", - "\n", - "(10131,'2003-06-16','2003-06-25','2003-06-21','Shipped',NULL,447),\n", - "\n", - "(10132,'2003-06-25','2003-07-01','2003-06-28','Shipped',NULL,323),\n", - "\n", - "(10133,'2003-06-27','2003-07-04','2003-07-03','Shipped',NULL,141),\n", - "\n", - "(10134,'2003-07-01','2003-07-10','2003-07-05','Shipped',NULL,250),\n", - "\n", - "(10135,'2003-07-02','2003-07-12','2003-07-03','Shipped',NULL,124),\n", - "\n", - "(10136,'2003-07-04','2003-07-14','2003-07-06','Shipped','Customer is interested in buying more Ferrari models',242),\n", - "\n", - "(10137,'2003-07-10','2003-07-20','2003-07-14','Shipped',NULL,353),\n", - "\n", - "(10138,'2003-07-07','2003-07-16','2003-07-13','Shipped',NULL,496),\n", - "\n", - "(10139,'2003-07-16','2003-07-23','2003-07-21','Shipped',NULL,282),\n", - "\n", - "(10140,'2003-07-24','2003-08-02','2003-07-30','Shipped',NULL,161),\n", - "\n", - "(10141,'2003-08-01','2003-08-09','2003-08-04','Shipped',NULL,334),\n", - "\n", - "(10142,'2003-08-08','2003-08-16','2003-08-13','Shipped',NULL,124),\n", - "\n", - "(10143,'2003-08-10','2003-08-18','2003-08-12','Shipped','Can we deliver the new Ford Mustang models by end-of-quarter?',320),\n", - "\n", - "(10144,'2003-08-13','2003-08-21','2003-08-14','Shipped',NULL,381),\n", - "\n", - "(10145,'2003-08-25','2003-09-02','2003-08-31','Shipped',NULL,205),\n", - "\n", - "(10146,'2003-09-03','2003-09-13','2003-09-06','Shipped',NULL,447),\n", - "\n", - "(10147,'2003-09-05','2003-09-12','2003-09-09','Shipped',NULL,379),\n", - "\n", - "(10148,'2003-09-11','2003-09-21','2003-09-15','Shipped','They want to reevaluate their terms agreement with Finance.',276),\n", - "\n", - "(10149,'2003-09-12','2003-09-18','2003-09-17','Shipped',NULL,487),\n", - "\n", - "(10150,'2003-09-19','2003-09-27','2003-09-21','Shipped','They want to reevaluate their terms agreement with Finance.',148),\n", - "\n", - "(10151,'2003-09-21','2003-09-30','2003-09-24','Shipped',NULL,311),\n", - "\n", - "(10152,'2003-09-25','2003-10-03','2003-10-01','Shipped',NULL,333),\n", - "\n", - "(10153,'2003-09-28','2003-10-05','2003-10-03','Shipped',NULL,141),\n", - "\n", - "(10154,'2003-10-02','2003-10-12','2003-10-08','Shipped',NULL,219),\n", - "\n", - "(10155,'2003-10-06','2003-10-13','2003-10-07','Shipped',NULL,186),\n", - "\n", - "(10156,'2003-10-08','2003-10-17','2003-10-11','Shipped',NULL,141),\n", - "\n", - "(10157,'2003-10-09','2003-10-15','2003-10-14','Shipped',NULL,473),\n", - "\n", - "(10158,'2003-10-10','2003-10-18','2003-10-15','Shipped',NULL,121),\n", - "\n", - "(10159,'2003-10-10','2003-10-19','2003-10-16','Shipped',NULL,321),\n", - "\n", - "(10160,'2003-10-11','2003-10-17','2003-10-17','Shipped',NULL,347),\n", - "\n", - "(10161,'2003-10-17','2003-10-25','2003-10-20','Shipped',NULL,227),\n", - "\n", - "(10162,'2003-10-18','2003-10-26','2003-10-19','Shipped',NULL,321),\n", - "\n", - "(10163,'2003-10-20','2003-10-27','2003-10-24','Shipped',NULL,424),\n", - "\n", - "(10164,'2003-10-21','2003-10-30','2003-10-23','Resolved','This order was disputed, but resolved on 11/1/2003; Customer doesn\\'t like the colors and precision of the models.',452),\n", - "\n", - "(10165,'2003-10-22','2003-10-31','2003-12-26','Shipped','This order was on hold because customers\\'s credit limit had been exceeded. Order will ship when payment is received',148),\n", - "\n", - "(10166,'2003-10-21','2003-10-30','2003-10-27','Shipped',NULL,462),\n", - "\n", - "(10167,'2003-10-23','2003-10-30',NULL,'Cancelled','Customer called to cancel. The warehouse was notified in time and the order didn\\'t ship. They have a new VP of Sales and are shifting their sales model. Our VP of Sales should contact them.',448),\n", - "\n", - "(10168,'2003-10-28','2003-11-03','2003-11-01','Shipped',NULL,161),\n", - "\n", - "(10169,'2003-11-04','2003-11-14','2003-11-09','Shipped',NULL,276),\n", - "\n", - "(10170,'2003-11-04','2003-11-12','2003-11-07','Shipped',NULL,452),\n", - "\n", - "(10171,'2003-11-05','2003-11-13','2003-11-07','Shipped',NULL,233),\n", - "\n", - "(10172,'2003-11-05','2003-11-14','2003-11-11','Shipped',NULL,175),\n", - "\n", - "(10173,'2003-11-05','2003-11-15','2003-11-09','Shipped','Cautious optimism. We have happy customers here, if we can keep them well stocked. I need all the information I can get on the planned shippments of Porches',278),\n", - "\n", - "(10174,'2003-11-06','2003-11-15','2003-11-10','Shipped',NULL,333),\n", - "\n", - "(10175,'2003-11-06','2003-11-14','2003-11-09','Shipped',NULL,324),\n", - "\n", - "(10176,'2003-11-06','2003-11-15','2003-11-12','Shipped',NULL,386),\n", - "\n", - "(10177,'2003-11-07','2003-11-17','2003-11-12','Shipped',NULL,344),\n", - "\n", - "(10178,'2003-11-08','2003-11-16','2003-11-10','Shipped','Custom shipping instructions sent to warehouse',242),\n", - "\n", - "(10179,'2003-11-11','2003-11-17','2003-11-13','Cancelled','Customer cancelled due to urgent budgeting issues. Must be cautious when dealing with them in the future. Since order shipped already we must discuss who would cover the shipping charges.',496),\n", - "\n", - "(10180,'2003-11-11','2003-11-19','2003-11-14','Shipped',NULL,171),\n", - "\n", - "(10181,'2003-11-12','2003-11-19','2003-11-15','Shipped',NULL,167),\n", - "\n", - "(10182,'2003-11-12','2003-11-21','2003-11-18','Shipped',NULL,124),\n", - "\n", - "(10183,'2003-11-13','2003-11-22','2003-11-15','Shipped','We need to keep in close contact with their Marketing VP. He is the decision maker for all their purchases.',339),\n", - "\n", - "(10184,'2003-11-14','2003-11-22','2003-11-20','Shipped',NULL,484),\n", - "\n", - "(10185,'2003-11-14','2003-11-21','2003-11-20','Shipped',NULL,320),\n", - "\n", - "(10186,'2003-11-14','2003-11-20','2003-11-18','Shipped','They want to reevaluate their terms agreement with the VP of Sales',489),\n", - "\n", - "(10187,'2003-11-15','2003-11-24','2003-11-16','Shipped',NULL,211),\n", - "\n", - "(10188,'2003-11-18','2003-11-26','2003-11-24','Shipped',NULL,167),\n", - "\n", - "(10189,'2003-11-18','2003-11-25','2003-11-24','Shipped','They want to reevaluate their terms agreement with Finance.',205),\n", - "\n", - "(10190,'2003-11-19','2003-11-29','2003-11-20','Shipped',NULL,141),\n", - "\n", - "(10191,'2003-11-20','2003-11-30','2003-11-24','Shipped','We must be cautions with this customer. Their VP of Sales resigned. Company may be heading down.',259),\n", - "\n", - "(10192,'2003-11-20','2003-11-29','2003-11-25','Shipped',NULL,363),\n", - "\n", - "(10193,'2003-11-21','2003-11-28','2003-11-27','Shipped',NULL,471),\n", - "\n", - "(10194,'2003-11-25','2003-12-02','2003-11-26','Shipped',NULL,146),\n", - "\n", - "(10195,'2003-11-25','2003-12-01','2003-11-28','Shipped',NULL,319),\n", - "\n", - "(10196,'2003-11-26','2003-12-03','2003-12-01','Shipped',NULL,455),\n", - "\n", - "(10197,'2003-11-26','2003-12-02','2003-12-01','Shipped','Customer inquired about remote controlled models and gold models.',216),\n", - "\n", - "(10198,'2003-11-27','2003-12-06','2003-12-03','Shipped',NULL,385),\n", - "\n", - "(10199,'2003-12-01','2003-12-10','2003-12-06','Shipped',NULL,475),\n", - "\n", - "(10200,'2003-12-01','2003-12-09','2003-12-06','Shipped',NULL,211),\n", - "\n", - "(10201,'2003-12-01','2003-12-11','2003-12-02','Shipped',NULL,129),\n", - "\n", - "(10202,'2003-12-02','2003-12-09','2003-12-06','Shipped',NULL,357),\n", - "\n", - "(10203,'2003-12-02','2003-12-11','2003-12-07','Shipped',NULL,141),\n", - "\n", - "(10204,'2003-12-02','2003-12-10','2003-12-04','Shipped',NULL,151),\n", - "\n", - "(10205,'2003-12-03','2003-12-09','2003-12-07','Shipped',' I need all the information I can get on our competitors.',141),\n", - "\n", - "(10206,'2003-12-05','2003-12-13','2003-12-08','Shipped','Can we renegotiate this one?',202),\n", - "\n", - "(10207,'2003-12-09','2003-12-17','2003-12-11','Shipped','Check on availability.',495),\n", - "\n", - "(10208,'2004-01-02','2004-01-11','2004-01-04','Shipped',NULL,146),\n", - "\n", - "(10209,'2004-01-09','2004-01-15','2004-01-12','Shipped',NULL,347),\n", - "\n", - "(10210,'2004-01-12','2004-01-22','2004-01-20','Shipped',NULL,177),\n", - "\n", - "(10211,'2004-01-15','2004-01-25','2004-01-18','Shipped',NULL,406),\n", - "\n", - "(10212,'2004-01-16','2004-01-24','2004-01-18','Shipped',NULL,141),\n", - "\n", - "(10213,'2004-01-22','2004-01-28','2004-01-27','Shipped','Difficult to negotiate with customer. We need more marketing materials',489),\n", - "\n", - "(10214,'2004-01-26','2004-02-04','2004-01-29','Shipped',NULL,458),\n", - "\n", - "(10215,'2004-01-29','2004-02-08','2004-02-01','Shipped','Customer requested that FedEx Ground is used for this shipping',475),\n", - "\n", - "(10216,'2004-02-02','2004-02-10','2004-02-04','Shipped',NULL,256),\n", - "\n", - "(10217,'2004-02-04','2004-02-14','2004-02-06','Shipped',NULL,166),\n", - "\n", - "(10218,'2004-02-09','2004-02-16','2004-02-11','Shipped','Customer requested that ad materials (such as posters, pamphlets) be included in the shippment',473),\n", - "\n", - "(10219,'2004-02-10','2004-02-17','2004-02-12','Shipped',NULL,487),\n", - "\n", - "(10220,'2004-02-12','2004-02-19','2004-02-16','Shipped',NULL,189),\n", - "\n", - "(10221,'2004-02-18','2004-02-26','2004-02-19','Shipped',NULL,314),\n", - "\n", - "(10222,'2004-02-19','2004-02-27','2004-02-20','Shipped',NULL,239),\n", - "\n", - "(10223,'2004-02-20','2004-02-29','2004-02-24','Shipped',NULL,114),\n", - "\n", - "(10224,'2004-02-21','2004-03-02','2004-02-26','Shipped','Customer has worked with some of our vendors in the past and is aware of their MSRP',171),\n", - "\n", - "(10225,'2004-02-22','2004-03-01','2004-02-24','Shipped',NULL,298),\n", - "\n", - "(10226,'2004-02-26','2004-03-06','2004-03-02','Shipped',NULL,239),\n", - "\n", - "(10227,'2004-03-02','2004-03-12','2004-03-08','Shipped',NULL,146),\n", - "\n", - "(10228,'2004-03-10','2004-03-18','2004-03-13','Shipped',NULL,173),\n", - "\n", - "(10229,'2004-03-11','2004-03-20','2004-03-12','Shipped',NULL,124),\n", - "\n", - "(10230,'2004-03-15','2004-03-24','2004-03-20','Shipped','Customer very concerned about the exact color of the models. There is high risk that he may dispute the order because there is a slight color mismatch',128),\n", - "\n", - "(10231,'2004-03-19','2004-03-26','2004-03-25','Shipped',NULL,344),\n", - "\n", - "(10232,'2004-03-20','2004-03-30','2004-03-25','Shipped',NULL,240),\n", - "\n", - "(10233,'2004-03-29','2004-04-04','2004-04-02','Shipped','Customer requested special shippment. The instructions were passed along to the warehouse',328),\n", - "\n", - "(10234,'2004-03-30','2004-04-05','2004-04-02','Shipped',NULL,412),\n", - "\n", - "(10235,'2004-04-02','2004-04-12','2004-04-06','Shipped',NULL,260),\n", - "\n", - "(10236,'2004-04-03','2004-04-11','2004-04-08','Shipped',NULL,486),\n", - "\n", - "(10237,'2004-04-05','2004-04-12','2004-04-10','Shipped',NULL,181),\n", - "\n", - "(10238,'2004-04-09','2004-04-16','2004-04-10','Shipped',NULL,145),\n", - "\n", - "(10239,'2004-04-12','2004-04-21','2004-04-17','Shipped',NULL,311),\n", - "\n", - "(10240,'2004-04-13','2004-04-20','2004-04-20','Shipped',NULL,177),\n", - "\n", - "(10241,'2004-04-13','2004-04-20','2004-04-19','Shipped',NULL,209),\n", - "\n", - "(10242,'2004-04-20','2004-04-28','2004-04-25','Shipped','Customer is interested in buying more Ferrari models',456),\n", - "\n", - "(10243,'2004-04-26','2004-05-03','2004-04-28','Shipped',NULL,495),\n", - "\n", - "(10244,'2004-04-29','2004-05-09','2004-05-04','Shipped',NULL,141),\n", - "\n", - "(10245,'2004-05-04','2004-05-12','2004-05-09','Shipped',NULL,455),\n", - "\n", - "(10246,'2004-05-05','2004-05-13','2004-05-06','Shipped',NULL,141),\n", - "\n", - "(10247,'2004-05-05','2004-05-11','2004-05-08','Shipped',NULL,334),\n", - "\n", - "(10248,'2004-05-07','2004-05-14',NULL,'Cancelled','Order was mistakenly placed. The warehouse noticed the lack of documentation.',131),\n", - "\n", - "(10249,'2004-05-08','2004-05-17','2004-05-11','Shipped','Can we deliver the new Ford Mustang models by end-of-quarter?',173),\n", - "\n", - "(10250,'2004-05-11','2004-05-19','2004-05-15','Shipped',NULL,450),\n", - "\n", - "(10251,'2004-05-18','2004-05-24','2004-05-24','Shipped',NULL,328),\n", - "\n", - "(10252,'2004-05-26','2004-06-04','2004-05-29','Shipped',NULL,406),\n", - "\n", - "(10253,'2004-06-01','2004-06-09','2004-06-02','Cancelled','Customer disputed the order and we agreed to cancel it. We must be more cautions with this customer going forward, since they are very hard to please. We must cover the shipping fees.',201),\n", - "\n", - "(10254,'2004-06-03','2004-06-13','2004-06-04','Shipped','Customer requested that DHL is used for this shipping',323),\n", - "\n", - "(10255,'2004-06-04','2004-06-12','2004-06-09','Shipped',NULL,209),\n", - "\n", - "(10256,'2004-06-08','2004-06-16','2004-06-10','Shipped',NULL,145),\n", - "\n", - "(10257,'2004-06-14','2004-06-24','2004-06-15','Shipped',NULL,450),\n", - "\n", - "(10258,'2004-06-15','2004-06-25','2004-06-23','Shipped',NULL,398),\n", - "\n", - "(10259,'2004-06-15','2004-06-22','2004-06-17','Shipped',NULL,166),\n", - "\n", - "(10260,'2004-06-16','2004-06-22',NULL,'Cancelled','Customer heard complaints from their customers and called to cancel this order. Will notify the Sales Manager.',357),\n", - "\n", - "(10261,'2004-06-17','2004-06-25','2004-06-22','Shipped',NULL,233),\n", - "\n", - "(10262,'2004-06-24','2004-07-01',NULL,'Cancelled','This customer found a better offer from one of our competitors. Will call back to renegotiate.',141),\n", - "\n", - "(10263,'2004-06-28','2004-07-04','2004-07-02','Shipped',NULL,175),\n", - "\n", - "(10264,'2004-06-30','2004-07-06','2004-07-01','Shipped','Customer will send a truck to our local warehouse on 7/1/2004',362),\n", - "\n", - "(10265,'2004-07-02','2004-07-09','2004-07-07','Shipped',NULL,471),\n", - "\n", - "(10266,'2004-07-06','2004-07-14','2004-07-10','Shipped',NULL,386),\n", - "\n", - "(10267,'2004-07-07','2004-07-17','2004-07-09','Shipped',NULL,151),\n", - "\n", - "(10268,'2004-07-12','2004-07-18','2004-07-14','Shipped',NULL,412),\n", - "\n", - "(10269,'2004-07-16','2004-07-22','2004-07-18','Shipped',NULL,382),\n", - "\n", - "(10270,'2004-07-19','2004-07-27','2004-07-24','Shipped','Can we renegotiate this one?',282),\n", - "\n", - "(10271,'2004-07-20','2004-07-29','2004-07-23','Shipped',NULL,124),\n", - "\n", - "(10272,'2004-07-20','2004-07-26','2004-07-22','Shipped',NULL,157),\n", - "\n", - "(10273,'2004-07-21','2004-07-28','2004-07-22','Shipped',NULL,314),\n", - "\n", - "(10274,'2004-07-21','2004-07-29','2004-07-22','Shipped',NULL,379),\n", - "\n", - "(10275,'2004-07-23','2004-08-02','2004-07-29','Shipped',NULL,119),\n", - "\n", - "(10276,'2004-08-02','2004-08-11','2004-08-08','Shipped',NULL,204),\n", - "\n", - "(10277,'2004-08-04','2004-08-12','2004-08-05','Shipped',NULL,148),\n", - "\n", - "(10278,'2004-08-06','2004-08-16','2004-08-09','Shipped',NULL,112),\n", - "\n", - "(10279,'2004-08-09','2004-08-19','2004-08-15','Shipped','Cautious optimism. We have happy customers here, if we can keep them well stocked. I need all the information I can get on the planned shippments of Porches',141),\n", - "\n", - "(10280,'2004-08-17','2004-08-27','2004-08-19','Shipped',NULL,249),\n", - "\n", - "(10281,'2004-08-19','2004-08-28','2004-08-23','Shipped',NULL,157),\n", - "\n", - "(10282,'2004-08-20','2004-08-26','2004-08-22','Shipped',NULL,124),\n", - "\n", - "(10283,'2004-08-20','2004-08-30','2004-08-23','Shipped',NULL,260),\n", - "\n", - "(10284,'2004-08-21','2004-08-29','2004-08-26','Shipped','Custom shipping instructions sent to warehouse',299),\n", - "\n", - "(10285,'2004-08-27','2004-09-04','2004-08-31','Shipped',NULL,286),\n", - "\n", - "(10286,'2004-08-28','2004-09-06','2004-09-01','Shipped',NULL,172),\n", - "\n", - "(10287,'2004-08-30','2004-09-06','2004-09-01','Shipped',NULL,298),\n", - "\n", - "(10288,'2004-09-01','2004-09-11','2004-09-05','Shipped',NULL,166),\n", - "\n", - "(10289,'2004-09-03','2004-09-13','2004-09-04','Shipped','We need to keep in close contact with their Marketing VP. He is the decision maker for all their purchases.',167),\n", - "\n", - "(10290,'2004-09-07','2004-09-15','2004-09-13','Shipped',NULL,198),\n", - "\n", - "(10291,'2004-09-08','2004-09-17','2004-09-14','Shipped',NULL,448),\n", - "\n", - "(10292,'2004-09-08','2004-09-18','2004-09-11','Shipped','They want to reevaluate their terms agreement with Finance.',131),\n", - "\n", - "(10293,'2004-09-09','2004-09-18','2004-09-14','Shipped',NULL,249),\n", - "\n", - "(10294,'2004-09-10','2004-09-17','2004-09-14','Shipped',NULL,204),\n", - "\n", - "(10295,'2004-09-10','2004-09-17','2004-09-14','Shipped','They want to reevaluate their terms agreement with Finance.',362),\n", - "\n", - "(10296,'2004-09-15','2004-09-22','2004-09-16','Shipped',NULL,415),\n", - "\n", - "(10297,'2004-09-16','2004-09-22','2004-09-21','Shipped','We must be cautions with this customer. Their VP of Sales resigned. Company may be heading down.',189),\n", - "\n", - "(10298,'2004-09-27','2004-10-05','2004-10-01','Shipped',NULL,103),\n", - "\n", - "(10299,'2004-09-30','2004-10-10','2004-10-01','Shipped',NULL,186),\n", - "\n", - "(10300,'2003-10-04','2003-10-13','2003-10-09','Shipped',NULL,128),\n", - "\n", - "(10301,'2003-10-05','2003-10-15','2003-10-08','Shipped',NULL,299),\n", - "\n", - "(10302,'2003-10-06','2003-10-16','2003-10-07','Shipped',NULL,201),\n", - "\n", - "(10303,'2004-10-06','2004-10-14','2004-10-09','Shipped','Customer inquired about remote controlled models and gold models.',484),\n", - "\n", - "(10304,'2004-10-11','2004-10-20','2004-10-17','Shipped',NULL,256),\n", - "\n", - "(10305,'2004-10-13','2004-10-22','2004-10-15','Shipped','Check on availability.',286),\n", - "\n", - "(10306,'2004-10-14','2004-10-21','2004-10-17','Shipped',NULL,187),\n", - "\n", - "(10307,'2004-10-14','2004-10-23','2004-10-20','Shipped',NULL,339),\n", - "\n", - "(10308,'2004-10-15','2004-10-24','2004-10-20','Shipped','Customer requested that FedEx Ground is used for this shipping',319),\n", - "\n", - "(10309,'2004-10-15','2004-10-24','2004-10-18','Shipped',NULL,121),\n", - "\n", - "(10310,'2004-10-16','2004-10-24','2004-10-18','Shipped',NULL,259),\n", - "\n", - "(10311,'2004-10-16','2004-10-23','2004-10-20','Shipped','Difficult to negotiate with customer. We need more marketing materials',141),\n", - "\n", - "(10312,'2004-10-21','2004-10-27','2004-10-23','Shipped',NULL,124),\n", - "\n", - "(10313,'2004-10-22','2004-10-28','2004-10-25','Shipped','Customer requested that FedEx Ground is used for this shipping',202),\n", - "\n", - "(10314,'2004-10-22','2004-11-01','2004-10-23','Shipped',NULL,227),\n", - "\n", - "(10315,'2004-10-29','2004-11-08','2004-10-30','Shipped',NULL,119),\n", - "\n", - "(10316,'2004-11-01','2004-11-09','2004-11-07','Shipped','Customer requested that ad materials (such as posters, pamphlets) be included in the shippment',240),\n", - "\n", - "(10317,'2004-11-02','2004-11-12','2004-11-08','Shipped',NULL,161),\n", - "\n", - "(10318,'2004-11-02','2004-11-09','2004-11-07','Shipped',NULL,157),\n", - "\n", - "(10319,'2004-11-03','2004-11-11','2004-11-06','Shipped','Customer requested that DHL is used for this shipping',456),\n", - "\n", - "(10320,'2004-11-03','2004-11-13','2004-11-07','Shipped',NULL,144),\n", - "\n", - "(10321,'2004-11-04','2004-11-12','2004-11-07','Shipped',NULL,462),\n", - "\n", - "(10322,'2004-11-04','2004-11-12','2004-11-10','Shipped','Customer has worked with some of our vendors in the past and is aware of their MSRP',363),\n", - "\n", - "(10323,'2004-11-05','2004-11-12','2004-11-09','Shipped',NULL,128),\n", - "\n", - "(10324,'2004-11-05','2004-11-11','2004-11-08','Shipped',NULL,181),\n", - "\n", - "(10325,'2004-11-05','2004-11-13','2004-11-08','Shipped',NULL,121),\n", - "\n", - "(10326,'2004-11-09','2004-11-16','2004-11-10','Shipped',NULL,144),\n", - "\n", - "(10327,'2004-11-10','2004-11-19','2004-11-13','Resolved','Order was disputed and resolved on 12/1/04. The Sales Manager was involved. Customer claims the scales of the models don\\'t match what was discussed.',145),\n", - "\n", - "(10328,'2004-11-12','2004-11-21','2004-11-18','Shipped','Customer very concerned about the exact color of the models. There is high risk that he may dispute the order because there is a slight color mismatch',278),\n", - "\n", - "(10329,'2004-11-15','2004-11-24','2004-11-16','Shipped',NULL,131),\n", - "\n", - "(10330,'2004-11-16','2004-11-25','2004-11-21','Shipped',NULL,385),\n", - "\n", - "(10331,'2004-11-17','2004-11-23','2004-11-23','Shipped','Customer requested special shippment. The instructions were passed along to the warehouse',486),\n", - "\n", - "(10332,'2004-11-17','2004-11-25','2004-11-18','Shipped',NULL,187),\n", - "\n", - "(10333,'2004-11-18','2004-11-27','2004-11-20','Shipped',NULL,129),\n", - "\n", - "(10334,'2004-11-19','2004-11-28',NULL,'On Hold','The outstaniding balance for this customer exceeds their credit limit. Order will be shipped when a payment is received.',144),\n", - "\n", - "(10335,'2004-11-19','2004-11-29','2004-11-23','Shipped',NULL,124),\n", - "\n", - "(10336,'2004-11-20','2004-11-26','2004-11-24','Shipped','Customer requested that DHL is used for this shipping',172),\n", - "\n", - "(10337,'2004-11-21','2004-11-30','2004-11-26','Shipped',NULL,424),\n", - "\n", - "(10338,'2004-11-22','2004-12-02','2004-11-27','Shipped',NULL,381),\n", - "\n", - "(10339,'2004-11-23','2004-11-30','2004-11-30','Shipped',NULL,398),\n", - "\n", - "(10340,'2004-11-24','2004-12-01','2004-11-25','Shipped','Customer is interested in buying more Ferrari models',216),\n", - "\n", - "(10341,'2004-11-24','2004-12-01','2004-11-29','Shipped',NULL,382),\n", - "\n", - "(10342,'2004-11-24','2004-12-01','2004-11-29','Shipped',NULL,114),\n", - "\n", - "(10343,'2004-11-24','2004-12-01','2004-11-26','Shipped',NULL,353),\n", - "\n", - "(10344,'2004-11-25','2004-12-02','2004-11-29','Shipped',NULL,350),\n", - "\n", - "(10345,'2004-11-25','2004-12-01','2004-11-26','Shipped',NULL,103),\n", - "\n", - "(10346,'2004-11-29','2004-12-05','2004-11-30','Shipped',NULL,112),\n", - "\n", - "(10347,'2004-11-29','2004-12-07','2004-11-30','Shipped','Can we deliver the new Ford Mustang models by end-of-quarter?',114),\n", - "\n", - "(10348,'2004-11-01','2004-11-08','2004-11-05','Shipped',NULL,458),\n", - "\n", - "(10349,'2004-12-01','2004-12-07','2004-12-03','Shipped',NULL,151),\n", - "\n", - "(10350,'2004-12-02','2004-12-08','2004-12-05','Shipped',NULL,141),\n", - "\n", - "(10351,'2004-12-03','2004-12-11','2004-12-07','Shipped',NULL,324),\n", - "\n", - "(10352,'2004-12-03','2004-12-12','2004-12-09','Shipped',NULL,198),\n", - "\n", - "(10353,'2004-12-04','2004-12-11','2004-12-05','Shipped',NULL,447),\n", - "\n", - "(10354,'2004-12-04','2004-12-10','2004-12-05','Shipped',NULL,323),\n", - "\n", - "(10355,'2004-12-07','2004-12-14','2004-12-13','Shipped',NULL,141),\n", - "\n", - "(10356,'2004-12-09','2004-12-15','2004-12-12','Shipped',NULL,250),\n", - "\n", - "(10357,'2004-12-10','2004-12-16','2004-12-14','Shipped',NULL,124),\n", - "\n", - "(10358,'2004-12-10','2004-12-16','2004-12-16','Shipped','Customer requested that DHL is used for this shipping',141),\n", - "\n", - "(10359,'2004-12-15','2004-12-23','2004-12-18','Shipped',NULL,353),\n", - "\n", - "(10360,'2004-12-16','2004-12-22','2004-12-18','Shipped',NULL,496),\n", - "\n", - "(10361,'2004-12-17','2004-12-24','2004-12-20','Shipped',NULL,282),\n", - "\n", - "(10362,'2005-01-05','2005-01-16','2005-01-10','Shipped',NULL,161),\n", - "\n", - "(10363,'2005-01-06','2005-01-12','2005-01-10','Shipped',NULL,334),\n", - "\n", - "(10364,'2005-01-06','2005-01-17','2005-01-09','Shipped',NULL,350),\n", - "\n", - "(10365,'2005-01-07','2005-01-18','2005-01-11','Shipped',NULL,320),\n", - "\n", - "(10366,'2005-01-10','2005-01-19','2005-01-12','Shipped',NULL,381),\n", - "\n", - "(10367,'2005-01-12','2005-01-21','2005-01-16','Resolved','This order was disputed and resolved on 2/1/2005. Customer claimed that container with shipment was damaged. FedEx\\'s investigation proved this wrong.',205),\n", - "\n", - "(10368,'2005-01-19','2005-01-27','2005-01-24','Shipped','Can we renegotiate this one?',124),\n", - "\n", - "(10369,'2005-01-20','2005-01-28','2005-01-24','Shipped',NULL,379),\n", - "\n", - "(10370,'2005-01-20','2005-02-01','2005-01-25','Shipped',NULL,276),\n", - "\n", - "(10371,'2005-01-23','2005-02-03','2005-01-25','Shipped',NULL,124),\n", - "\n", - "(10372,'2005-01-26','2005-02-05','2005-01-28','Shipped',NULL,398),\n", - "\n", - "(10373,'2005-01-31','2005-02-08','2005-02-06','Shipped',NULL,311),\n", - "\n", - "(10374,'2005-02-02','2005-02-09','2005-02-03','Shipped',NULL,333),\n", - "\n", - "(10375,'2005-02-03','2005-02-10','2005-02-06','Shipped',NULL,119),\n", - "\n", - "(10376,'2005-02-08','2005-02-18','2005-02-13','Shipped',NULL,219),\n", - "\n", - "(10377,'2005-02-09','2005-02-21','2005-02-12','Shipped','Cautious optimism. We have happy customers here, if we can keep them well stocked. I need all the information I can get on the planned shippments of Porches',186),\n", - "\n", - "(10378,'2005-02-10','2005-02-18','2005-02-11','Shipped',NULL,141),\n", - "\n", - "(10379,'2005-02-10','2005-02-18','2005-02-11','Shipped',NULL,141),\n", - "\n", - "(10380,'2005-02-16','2005-02-24','2005-02-18','Shipped',NULL,141),\n", - "\n", - "(10381,'2005-02-17','2005-02-25','2005-02-18','Shipped',NULL,321),\n", - "\n", - "(10382,'2005-02-17','2005-02-23','2005-02-18','Shipped','Custom shipping instructions sent to warehouse',124),\n", - "\n", - "(10383,'2005-02-22','2005-03-02','2005-02-25','Shipped',NULL,141),\n", - "\n", - "(10384,'2005-02-23','2005-03-06','2005-02-27','Shipped',NULL,321),\n", - "\n", - "(10385,'2005-02-28','2005-03-09','2005-03-01','Shipped',NULL,124),\n", - "\n", - "(10386,'2005-03-01','2005-03-09','2005-03-06','Resolved','Disputed then Resolved on 3/15/2005. Customer doesn\\'t like the craftsmaship of the models.',141),\n", - "\n", - "(10387,'2005-03-02','2005-03-09','2005-03-06','Shipped','We need to keep in close contact with their Marketing VP. He is the decision maker for all their purchases.',148),\n", - "\n", - "(10388,'2005-03-03','2005-03-11','2005-03-09','Shipped',NULL,462),\n", - "\n", - "(10389,'2005-03-03','2005-03-09','2005-03-08','Shipped',NULL,448),\n", - "\n", - "(10390,'2005-03-04','2005-03-11','2005-03-07','Shipped','They want to reevaluate their terms agreement with Finance.',124),\n", - "\n", - "(10391,'2005-03-09','2005-03-20','2005-03-15','Shipped',NULL,276),\n", - "\n", - "(10392,'2005-03-10','2005-03-18','2005-03-12','Shipped',NULL,452),\n", - "\n", - "(10393,'2005-03-11','2005-03-22','2005-03-14','Shipped','They want to reevaluate their terms agreement with Finance.',323),\n", - "\n", - "(10394,'2005-03-15','2005-03-25','2005-03-19','Shipped',NULL,141),\n", - "\n", - "(10395,'2005-03-17','2005-03-24','2005-03-23','Shipped','We must be cautions with this customer. Their VP of Sales resigned. Company may be heading down.',250),\n", - "\n", - "(10396,'2005-03-23','2005-04-02','2005-03-28','Shipped',NULL,124),\n", - "\n", - "(10397,'2005-03-28','2005-04-09','2005-04-01','Shipped',NULL,242),\n", - "\n", - "(10398,'2005-03-30','2005-04-09','2005-03-31','Shipped',NULL,353),\n", - "\n", - "(10399,'2005-04-01','2005-04-12','2005-04-03','Shipped',NULL,496),\n", - "\n", - "(10400,'2005-04-01','2005-04-11','2005-04-04','Shipped','Customer requested that DHL is used for this shipping',450),\n", - "\n", - "(10401,'2005-04-03','2005-04-14',NULL,'On Hold','Customer credit limit exceeded. Will ship when a payment is received.',328),\n", - "\n", - "(10402,'2005-04-07','2005-04-14','2005-04-12','Shipped',NULL,406),\n", - "\n", - "(10403,'2005-04-08','2005-04-18','2005-04-11','Shipped',NULL,201),\n", - "\n", - "(10404,'2005-04-08','2005-04-14','2005-04-11','Shipped',NULL,323),\n", - "\n", - "(10405,'2005-04-14','2005-04-24','2005-04-20','Shipped',NULL,209),\n", - "\n", - "(10406,'2005-04-15','2005-04-25','2005-04-21','Disputed','Customer claims container with shipment was damaged during shipping and some items were missing. I am talking to FedEx about this.',145),\n", - "\n", - "(10407,'2005-04-22','2005-05-04',NULL,'On Hold','Customer credit limit exceeded. Will ship when a payment is received.',450),\n", - "\n", - "(10408,'2005-04-22','2005-04-29','2005-04-27','Shipped',NULL,398),\n", - "\n", - "(10409,'2005-04-23','2005-05-05','2005-04-24','Shipped',NULL,166),\n", - "\n", - "(10410,'2005-04-29','2005-05-10','2005-04-30','Shipped',NULL,357),\n", - "\n", - "(10411,'2005-05-01','2005-05-08','2005-05-06','Shipped',NULL,233),\n", - "\n", - "(10412,'2005-05-03','2005-05-13','2005-05-05','Shipped',NULL,141),\n", - "\n", - "(10413,'2005-05-05','2005-05-14','2005-05-09','Shipped','Customer requested that DHL is used for this shipping',175),\n", - "\n", - "(10414,'2005-05-06','2005-05-13',NULL,'On Hold','Customer credit limit exceeded. Will ship when a payment is received.',362),\n", - "\n", - "(10415,'2005-05-09','2005-05-20','2005-05-12','Disputed','Customer claims the scales of the models don\\'t match what was discussed. I keep all the paperwork though to prove otherwise',471),\n", - "\n", - "(10416,'2005-05-10','2005-05-16','2005-05-14','Shipped',NULL,386),\n", - "\n", - "(10417,'2005-05-13','2005-05-19','2005-05-19','Disputed','Customer doesn\\'t like the colors and precision of the models.',141),\n", - "\n", - "(10418,'2005-05-16','2005-05-24','2005-05-20','Shipped',NULL,412),\n", - "\n", - "(10419,'2005-05-17','2005-05-28','2005-05-19','Shipped',NULL,382),\n", - "\n", - "(10420,'2005-05-29','2005-06-07',NULL,'In Process',NULL,282),\n", - "\n", - "(10421,'2005-05-29','2005-06-06',NULL,'In Process','Custom shipping instructions were sent to warehouse',124),\n", - "\n", - "(10422,'2005-05-30','2005-06-11',NULL,'In Process',NULL,157),\n", - "\n", - "(10423,'2005-05-30','2005-06-05',NULL,'In Process',NULL,314),\n", - "\n", - "(10424,'2005-05-31','2005-06-08',NULL,'In Process',NULL,141),\n", - "\n", - "(10425,'2005-05-31','2005-06-07',NULL,'In Process',NULL,119);\n", - "\n", - "\n", - "/*Data for the table `order__item` */\n", - "\n", - "insert into `order__item`(`order_Number`,`product_code`,`quantity`,`price`,`order_line_number`) values \n", - "\n", - "(10100,'S18_1749',30,'136.00',3),\n", - "\n", - "(10100,'S18_2248',50,'55.09',2),\n", - "\n", - "(10100,'S18_4409',22,'75.46',4),\n", - "\n", - "(10100,'S24_3969',49,'35.29',1),\n", - "\n", - "(10101,'S18_2325',25,'108.06',4),\n", - "\n", - "(10101,'S18_2795',26,'167.06',1),\n", - "\n", - "(10101,'S24_1937',45,'32.53',3),\n", - "\n", - "(10101,'S24_2022',46,'44.35',2),\n", - "\n", - "(10102,'S18_1342',39,'95.55',2),\n", - "\n", - "(10102,'S18_1367',41,'43.13',1),\n", - "\n", - "(10103,'S10_1949',26,'214.30',11),\n", - "\n", - "(10103,'S10_4962',42,'119.67',4),\n", - "\n", - "(10103,'S12_1666',27,'121.64',8),\n", - "\n", - "(10103,'S18_1097',35,'94.50',10),\n", - "\n", - "(10103,'S18_2432',22,'58.34',2),\n", - "\n", - "(10103,'S18_2949',27,'92.19',12),\n", - "\n", - "(10103,'S18_2957',35,'61.84',14),\n", - "\n", - "(10103,'S18_3136',25,'86.92',13),\n", - "\n", - "(10103,'S18_3320',46,'86.31',16),\n", - "\n", - "(10103,'S18_4600',36,'98.07',5),\n", - "\n", - "(10103,'S18_4668',41,'40.75',9),\n", - "\n", - "(10103,'S24_2300',36,'107.34',1),\n", - "\n", - "(10103,'S24_4258',25,'88.62',15),\n", - "\n", - "(10103,'S32_1268',31,'92.46',3),\n", - "\n", - "(10103,'S32_3522',45,'63.35',7),\n", - "\n", - "(10103,'S700_2824',42,'94.07',6),\n", - "\n", - "(10104,'S12_3148',34,'131.44',1),\n", - "\n", - "(10104,'S12_4473',41,'111.39',9),\n", - "\n", - "(10104,'S18_2238',24,'135.90',8),\n", - "\n", - "(10104,'S18_2319',29,'122.73',12),\n", - "\n", - "(10104,'S18_3232',23,'165.95',13),\n", - "\n", - "(10104,'S18_4027',38,'119.20',3),\n", - "\n", - "(10104,'S24_1444',35,'52.02',6),\n", - "\n", - "(10104,'S24_2840',44,'30.41',10),\n", - "\n", - "(10104,'S24_4048',26,'106.45',5),\n", - "\n", - "(10104,'S32_2509',35,'51.95',11),\n", - "\n", - "(10104,'S32_3207',49,'56.55',4),\n", - "\n", - "(10104,'S50_1392',33,'114.59',7),\n", - "\n", - "(10104,'S50_1514',32,'53.31',2),\n", - "\n", - "(10105,'S10_4757',50,'127.84',2),\n", - "\n", - "(10105,'S12_1108',41,'205.72',15),\n", - "\n", - "(10105,'S12_3891',29,'141.88',14),\n", - "\n", - "(10105,'S18_3140',22,'136.59',11),\n", - "\n", - "(10105,'S18_3259',38,'87.73',13),\n", - "\n", - "(10105,'S18_4522',41,'75.48',10),\n", - "\n", - "(10105,'S24_2011',43,'117.97',9),\n", - "\n", - "(10105,'S24_3151',44,'73.46',4),\n", - "\n", - "(10105,'S24_3816',50,'75.47',1),\n", - "\n", - "(10105,'S700_1138',41,'54.00',5),\n", - "\n", - "(10105,'S700_1938',29,'86.61',12),\n", - "\n", - "(10105,'S700_2610',31,'60.72',3),\n", - "\n", - "(10105,'S700_3505',39,'92.16',6),\n", - "\n", - "(10105,'S700_3962',22,'99.31',7),\n", - "\n", - "(10105,'S72_3212',25,'44.77',8),\n", - "\n", - "(10106,'S18_1662',36,'134.04',12),\n", - "\n", - "(10106,'S18_2581',34,'81.10',2),\n", - "\n", - "(10106,'S18_3029',41,'80.86',18),\n", - "\n", - "(10106,'S18_3856',41,'94.22',17),\n", - "\n", - "(10106,'S24_1785',28,'107.23',4),\n", - "\n", - "(10106,'S24_2841',49,'65.77',13),\n", - 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"\n", - "(496,'EU531600','2005-05-25','30253.75'),\n", - "\n", - "(496,'MB342426','2003-07-16','32077.44'),\n", - "\n", - "(496,'MN89921','2004-12-31','52166.00');" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/004-DatabaseUniversity.ipynb b/db-course/004-DatabaseUniversity.ipynb deleted file mode 100644 index a1b93e7..0000000 --- a/db-course/004-DatabaseUniversity.ipynb +++ /dev/null @@ -1,3771 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Define\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-10-31 21:34:04,539][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", - "[2023-10-31 21:34:04,552][INFO]: Connected root@fakeservices.datajoint.io:3306\n" - ] - } - ], - "source": [ - "import datajoint as dj\n", - "\n", - "schema = dj.schema(\"university\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Student(dj.Manual):\n", - " definition = \"\"\"\n", - " student_id : int unsigned # university-wide ID number\n", - " ---\n", - " first_name : varchar(40)\n", - " last_name : varchar(40)\n", - " sex : enum('F', 'M', 'U')\n", - " date_of_birth : date\n", - " home_address : varchar(120) # mailing street address\n", - " home_city : varchar(60) # mailing address\n", - " home_state : char(2) # US state acronym: e.g. OH\n", - " home_zip : char(10) # zipcode e.g. 93979-4979\n", - " home_phone : varchar(20) # e.g. 414.657.6883x0881\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Department(dj.Manual):\n", - " definition = \"\"\"\n", - " dept : varchar(6) # abbreviated department name, e.g. BIOL\n", - " ---\n", - " dept_name : varchar(200) # full department name\n", - " dept_address : varchar(200) # mailing address\n", - " dept_phone : varchar(20)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class StudentMajor(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Student\n", - " ---\n", - " -> Department\n", - " declare_date : date # when student declared her major\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "Department\n", - "\n", - "\n", - "Department\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "StudentMajor\n", - "\n", - "\n", - "StudentMajor\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Department->StudentMajor\n", - "\n", - "\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Student->StudentMajor\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "@schema\n", - "class Course(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Department\n", - " course : int unsigned # course number, e.g. 1010\n", - " ---\n", - " course_name : varchar(200) # e.g. \"Neurobiology of Sensation and Movement.\"\n", - " credits : decimal(3,1) # number of credits earned by completing the course\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Term(dj.Manual):\n", - " definition = \"\"\"\n", - " term_year : year\n", - " term : enum('Spring', 'Summer', 'Fall')\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Section(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Course\n", - " -> Term\n", - " section : char(1)\n", - " ---\n", - " auditorium : varchar(12)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class CurrentTerm(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Term\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Enroll(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Student\n", - " -> Section\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class LetterGrade(dj.Manual):\n", - " definition = \"\"\"\n", - " grade : char(2)\n", - " ---\n", - " points : decimal(3,2)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Grade(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Enroll\n", - " ---\n", - " -> LetterGrade\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "Enroll\n", - "\n", - "\n", - "Enroll\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Grade\n", - "\n", - "\n", - "Grade\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Enroll->Grade\n", - "\n", - "\n", - "\n", - "\n", - "Course\n", - "\n", - "\n", - "Course\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Section\n", - "\n", - "\n", - "Section\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Course->Section\n", - "\n", - "\n", - "\n", - "\n", - "Department\n", - "\n", - "\n", - "Department\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Department->Course\n", - "\n", - "\n", - "\n", - "\n", - "StudentMajor\n", - "\n", - "\n", - "StudentMajor\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Department->StudentMajor\n", - "\n", - "\n", - "\n", - "\n", - "Section->Enroll\n", - "\n", - "\n", - "\n", - "\n", - "Term\n", - "\n", - "\n", - "Term\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Term->Section\n", - "\n", - "\n", - "\n", - "\n", - "CurrentTerm\n", - "\n", - "\n", - "CurrentTerm\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Term->CurrentTerm\n", - "\n", - "\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Student->Enroll\n", - "\n", - "\n", - "\n", - "\n", - "Student->StudentMajor\n", - "\n", - "\n", - "\n", - "\n", - "LetterGrade\n", - "\n", - "\n", - "LetterGrade\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "LetterGrade->Grade\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from tqdm import tqdm\n", - "import faker\n", - "import random\n", - "import itertools\n", - "import datetime\n", - "\n", - "fake = faker.Faker()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def yield_students():\n", - " fake_name = {\"F\": fake.name_female, \"M\": fake.name_male}\n", - " while True: # ignore invalid values\n", - " try:\n", - " sex = random.choice((\"F\", \"M\"))\n", - " first_name, last_name = fake_name[sex]().split(\" \")[:2]\n", - " street_address, city = fake.address().split(\"\\n\")\n", - " city, state = city.split(\", \")\n", - " state, zipcode = state.split(\" \")\n", - " except ValueError:\n", - " continue\n", - " else:\n", - " yield dict(\n", - " first_name=first_name,\n", - " last_name=last_name,\n", - " sex=sex,\n", - " home_address=street_address,\n", - " home_city=city,\n", - " home_state=state,\n", - " home_zip=zipcode,\n", - " date_of_birth=str(\n", - " fake.date_time_between(start_date=\"-35y\", end_date=\"-15y\").date()\n", - " ),\n", - " home_phone=fake.phone_number()[:20],\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "Student.insert(dict(k, student_id=i) for i, k in zip(range(100, 300), yield_students()))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

student_id

\n", - " university-wide ID number\n", - "
\n", - "

first_name

\n", - " \n", - "
\n", - "

last_name

\n", - " \n", - "
\n", - "

sex

\n", - " \n", - "
\n", - "

date_of_birth

\n", - " \n", - "
\n", - "

home_address

\n", - " mailing street address\n", - "
\n", - "

home_city

\n", - " mailing address\n", - "
\n", - "

home_state

\n", - " US state acronym: e.g. OH\n", - "
\n", - "

home_zip

\n", - " zipcode e.g. 93979-4979\n", - "
\n", - "

home_phone

\n", - " e.g. 414.657.6883x0881\n", - "
100JordanRoyF2003-01-26328 Saunders AvenueWest SusanlandMI254786826309566
101LuisGriffinM1991-11-19777 Kristin StreamPort TimothyNM26753+1-573-927-1122x7498
102RhondaMeltonF1991-02-2854424 Michael FlatWest RichardPW35557270.612.6710
103RebeccaRogersF2007-01-1967607 Scott MotorwayJasmineboroughME72148+1-381-336-9496x243
104BobbyMorganM2000-08-03546 Rodriguez Vista Suite 121New KarentownTX42320+1-754-585-3248x282
105JenniferMitchellF1992-11-042424 Philip CrestJohnsonportMS16129264.221.2688
106AngelaGordonF1998-10-21119 John Cove Apt. 577BarbermouthNC87356+1-939-793-8944x442
107BradleyHenryM2001-03-092259 Klein MountainsNew JohnVT950898866328779
108KatrinaBennettF2003-08-078486 Padilla CurvePort MichaelIA95792+1-403-246-0298x8785
109KeithGatesM1995-08-032909 Barker Overpass Suite 314East JeremyUT29772743-717-2292x03200
110DestinyBenjaminF2005-04-1581809 Richards SquaresGregorymouthOR57886+1-366-248-6065x208
111CatherineMosesF1996-08-1137827 Carolyn WellsWilliamsonchesterNC97844716.464.4008x2146
\n", - "

...

\n", - "

Total: 200

\n", - " " - ], - "text/plain": [ - "*student_id first_name last_name sex date_of_birth home_address home_city home_state home_zip home_phone \n", - "+------------+ +------------+ +-----------+ +-----+ +------------+ +------------+ +------------+ +------------+ +----------+ +------------+\n", - "100 Jordan Roy F 2003-01-26 328 Saunders A West Susanland MI 25478 6826309566 \n", - "101 Luis Griffin M 1991-11-19 777 Kristin St Port Timothy NM 26753 +1-573-927-112\n", - "102 Rhonda Melton F 1991-02-28 54424 Michael West Richard PW 35557 270.612.6710 \n", - "103 Rebecca Rogers F 2007-01-19 67607 Scott Mo Jasmineborough ME 72148 +1-381-336-949\n", - "104 Bobby Morgan M 2000-08-03 546 Rodriguez New Karentown TX 42320 +1-754-585-324\n", - "105 Jennifer Mitchell F 1992-11-04 2424 Philip Cr Johnsonport MS 16129 264.221.2688 \n", - "106 Angela Gordon F 1998-10-21 119 John Cove Barbermouth NC 87356 +1-939-793-894\n", - "107 Bradley Henry M 2001-03-09 2259 Klein Mou New John VT 95089 8866328779 \n", - "108 Katrina Bennett F 2003-08-07 8486 Padilla C Port Michael IA 95792 +1-403-246-029\n", - "109 Keith Gates M 1995-08-03 2909 Barker Ov East Jeremy UT 29772 743-717-2292x0\n", - "110 Destiny Benjamin F 2005-04-15 81809 Richards Gregorymouth OR 57886 +1-366-248-606\n", - "111 Catherine Moses F 1996-08-11 37827 Carolyn Williamsonches NC 97844 716.464.4008x2\n", - " ...\n", - " (Total: 200)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Student()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "Department.insert(\n", - " dict(\n", - " dept=dept,\n", - " dept_name=name,\n", - " dept_address=fake.address(),\n", - " dept_phone=fake.phone_number()[:20],\n", - " )\n", - " for dept, name in [\n", - " [\"CS\", \"Computer Science\"],\n", - " [\"BIOL\", \"Life Sciences\"],\n", - " [\"PHYS\", \"Physics\"],\n", - " [\"MATH\", \"Mathematics\"],\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "StudentMajor.insert(\n", - " {**s, **d, \"declare_date\": fake.date_between(start_date=datetime.date(1999, 1, 1))}\n", - " for s, d in zip(\n", - " Student.fetch(\"KEY\"), random.choices(Department.fetch(\"KEY\"), k=len(Student()))\n", - " )\n", - " if random.random() < 0.75\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

student_id

\n", - " university-wide ID number\n", - "
\n", - "

dept

\n", - " abbreviated department name, e.g. BIOL\n", - "
\n", - "

declare_date

\n", - " when student declared her major\n", - "
100MATH2010-12-15
102BIOL2020-04-28
103MATH2003-02-25
104MATH2018-02-09
105BIOL2018-08-21
106PHYS2016-03-05
108BIOL2018-07-28
109PHYS2006-07-16
110PHYS2016-11-27
112BIOL2008-05-20
113CS2016-11-06
114MATH2005-02-18
\n", - "

...

\n", - "

Total: 152

\n", - " " - ], - "text/plain": [ - "*student_id dept declare_date \n", - "+------------+ +------+ +------------+\n", - "100 MATH 2010-12-15 \n", - "102 BIOL 2020-04-28 \n", - "103 MATH 2003-02-25 \n", - "104 MATH 2018-02-09 \n", - "105 BIOL 2018-08-21 \n", - "106 PHYS 2016-03-05 \n", - "108 BIOL 2018-07-28 \n", - "109 PHYS 2006-07-16 \n", - "110 PHYS 2016-11-27 \n", - "112 BIOL 2008-05-20 \n", - "113 CS 2016-11-06 \n", - "114 MATH 2005-02-18 \n", - " ...\n", - " (Total: 152)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "StudentMajor()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# from https://www.utah.edu/\n", - "Course.insert(\n", - " [\n", - " [\"BIOL\", 1006, \"World of Dinosaurs\", 3],\n", - " [\"BIOL\", 1010, \"Biology in the 21st Century\", 3],\n", - " [\"BIOL\", 1030, \"Human Biology\", 3],\n", - " [\"BIOL\", 1210, \"Principles of Biology\", 4],\n", - " [\"BIOL\", 2010, \"Evolution & Diversity of Life\", 3],\n", - " [\"BIOL\", 2020, \"Principles of Cell Biology\", 3],\n", - " [\"BIOL\", 2021, \"Principles of Cell Science\", 4],\n", - " [\"BIOL\", 2030, \"Principles of Genetics\", 3],\n", - " [\"BIOL\", 2210, \"Human Genetics\", 3],\n", - " [\"BIOL\", 2325, \"Human Anatomy\", 4],\n", - " [\"BIOL\", 2330, \"Plants & Society\", 3],\n", - " [\"BIOL\", 2355, \"Field Botany\", 2],\n", - " [\"BIOL\", 2420, \"Human Physiology\", 4],\n", - " [\"PHYS\", 2040, \"Classcal Theoretical Physics II\", 4],\n", - " [\"PHYS\", 2060, \"Quantum Mechanics\", 3],\n", - " [\"PHYS\", 2100, \"General Relativity and Cosmology\", 3],\n", - " [\"PHYS\", 2140, \"Statistical Mechanics\", 4],\n", - " [\"PHYS\", 2210, \"Physics for Scientists and Engineers I\", 4],\n", - " [\"PHYS\", 2220, \"Physics for Scientists and Engineers II\", 4],\n", - " [\"PHYS\", 3210, \"Physics for Scientists I (Honors)\", 4],\n", - " [\"PHYS\", 3220, \"Physics for Scientists II (Honors)\", 4],\n", - " [\"MATH\", 1250, \"Calculus for AP Students I\", 4],\n", - " [\"MATH\", 1260, \"Calculus for AP Students II\", 4],\n", - " [\"MATH\", 1210, \"Calculus I\", 4],\n", - " [\"MATH\", 1220, \"Calculus II\", 4],\n", - " [\"MATH\", 2210, \"Calculus III\", 3],\n", - " [\"MATH\", 2270, \"Linear Algebra\", 4],\n", - " [\"MATH\", 2280, \"Introduction to Differential Equations\", 4],\n", - " [\"MATH\", 3210, \"Foundations of Analysis I\", 4],\n", - " [\"MATH\", 3220, \"Foundations of Analysis II\", 4],\n", - " [\"CS\", 1030, \"Foundations of Computer Science\", 3],\n", - " [\"CS\", 1410, \"Introduction to Object-Oriented Programming\", 4],\n", - " [\"CS\", 2420, \"Introduction to Algorithms & Data Structures\", 4],\n", - " [\"CS\", 2100, \"Discrete Structures\", 3],\n", - " [\"CS\", 3500, \"Software Practice\", 4],\n", - " [\"CS\", 3505, \"Software Practice II\", 3],\n", - " [\"CS\", 3810, \"Computer Organization\", 4],\n", - " [\"CS\", 4400, \"Computer Systems\", 4],\n", - " [\"CS\", 4150, \"Algorithms\", 3],\n", - " [\"CS\", 3100, \"Models of Computation\", 3],\n", - " [\"CS\", 3200, \"Introduction to Scientific Computing\", 3],\n", - " [\"CS\", 4000, \"Senior Capstone Project - Design Phase\", 3],\n", - " [\"CS\", 4500, \"Senior Capstone Project\", 3],\n", - " [\"CS\", 4940, \"Undergraduate Research\", 3],\n", - " [\"CS\", 4970, \"Computer Science Bachelor\" \"s Thesis\", 3],\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "Term.insert(\n", - " dict(term_year=year, term=term)\n", - " for year in range(1999, 2019)\n", - " for term in [\"Spring\", \"Summer\", \"Fall\"]\n", - ")\n", - "\n", - "Term().fetch(order_by=(\"term_year DESC\", \"term DESC\"), as_dict=True, limit=1)[0]\n", - "\n", - "CurrentTerm().insert1(\n", - " {**Term().fetch(order_by=(\"term_year DESC\", \"term DESC\"), as_dict=True, limit=1)[0]}\n", - ")\n", - "\n", - "\n", - "def make_section(prob):\n", - " for c in (Course * Term).proj():\n", - " for sec in \"abcd\":\n", - " if random.random() < prob:\n", - " break\n", - " yield {\n", - " **c,\n", - " \"section\": sec,\n", - " \"auditorium\": random.choice(\"ABCDEF\") + str(random.randint(1, 100)),\n", - " }\n", - "\n", - "\n", - "Section.insert(make_section(0.5))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "LetterGrade.insert(\n", - " [\n", - " [\"A\", 4.00],\n", - " [\"A-\", 3.67],\n", - " [\"B+\", 3.33],\n", - " [\"B\", 3.00],\n", - " [\"B-\", 2.67],\n", - " [\"C+\", 2.33],\n", - " [\"C\", 2.00],\n", - " [\"C-\", 1.67],\n", - " [\"D+\", 1.33],\n", - " [\"D\", 1.00],\n", - " [\"F\", 0.00],\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

grade

\n", - " \n", - "
\n", - "

points

\n", - " \n", - "
A4.00
A-3.67
B3.00
B-2.67
B+3.33
C2.00
C-1.67
C+2.33
D1.00
D+1.33
F0.00
\n", - " \n", - "

Total: 11

\n", - " " - ], - "text/plain": [ - "*grade points \n", - "+-------+ +--------+\n", - "A 4.00 \n", - "A- 3.67 \n", - "B 3.00 \n", - "B- 2.67 \n", - "B+ 3.33 \n", - "C 2.00 \n", - "C- 1.67 \n", - "C+ 2.33 \n", - "D 1.00 \n", - "D+ 1.33 \n", - "F 0.00 \n", - " (Total: 11)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "LetterGrade()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 200/200 [00:40<00:00, 4.96it/s]\n" - ] - } - ], - "source": [ - "# Enrollment\n", - "terms = Term().fetch(\"KEY\")\n", - "quit_prob = 0.1\n", - "for student in tqdm(Student.fetch(\"KEY\")):\n", - " start_term = random.randrange(len(terms))\n", - " for term in terms[start_term:]:\n", - " if random.random() < quit_prob:\n", - " break\n", - " else:\n", - " sections = ((Section & term) - (Course & (Enroll & student))).fetch(\"KEY\")\n", - " if sections:\n", - " Enroll.insert(\n", - " {**student, **section}\n", - " for section in random.sample(\n", - " sections, random.randrange(min(5, len(sections)))\n", - " )\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# assign random grades\n", - "grades = LetterGrade.fetch(\"grade\")\n", - "\n", - "grade_keys = Enroll.fetch(\"KEY\")\n", - "random.shuffle(grade_keys)\n", - "grade_keys = grade_keys[: len(grade_keys) * 9 // 10]\n", - "\n", - "Grade.insert(\n", - " {**key, \"grade\": grade}\n", - " for key, grade in zip(grade_keys, random.choices(grades, k=len(grade_keys)))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Queries\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Restriction\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

student_id

\n", - " university-wide ID number\n", - "
\n", - "

first_name

\n", - " \n", - "
\n", - "

last_name

\n", - " \n", - "
\n", - "

sex

\n", - " \n", - "
\n", - "

date_of_birth

\n", - " \n", - "
\n", - "

home_address

\n", - " mailing street address\n", - "
\n", - "

home_city

\n", - " mailing address\n", - "
\n", - "

home_state

\n", - " US state acronym: e.g. OH\n", - "
\n", - "

home_zip

\n", - " zipcode e.g. 93979-4979\n", - "
\n", - "

home_phone

\n", - " e.g. 414.657.6883x0881\n", - "
104BobbyMorganM2000-08-03546 Rodriguez Vista Suite 121New KarentownTX42320+1-754-585-3248x282
151ChristopherJacksonM1997-11-301344 Willis CrestMathewsshireTX02380373-329-0653x45425
154KathrynWilliamsF1997-05-25252 Wanda HarborsRoblestownTX74304752-407-8170x7030
205KimberlyJohnsonF2008-03-097814 Brian PlazaWest NicoleTX27816248-366-8432x9592
\n", - " \n", - "

Total: 4

\n", - " " - ], - "text/plain": [ - "*student_id first_name last_name sex date_of_birth home_address home_city home_state home_zip home_phone \n", - "+------------+ +------------+ +-----------+ +-----+ +------------+ +------------+ +------------+ +------------+ +----------+ +------------+\n", - "104 Bobby Morgan M 2000-08-03 546 Rodriguez New Karentown TX 42320 +1-754-585-324\n", - "151 Christopher Jackson M 1997-11-30 1344 Willis Cr Mathewsshire TX 02380 373-329-0653x4\n", - "154 Kathryn Williams F 1997-05-25 252 Wanda Harb Roblestown TX 74304 752-407-8170x7\n", - "205 Kimberly Johnson F 2008-03-09 7814 Brian Pla West Nicole TX 27816 248-366-8432x9\n", - " (Total: 4)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Students from Texas\n", - "Student & {\"home_state\": \"TX\"}" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

student_id

\n", - " university-wide ID number\n", - "
\n", - "

first_name

\n", - " \n", - "
\n", - "

last_name

\n", - " \n", - "
\n", - "

sex

\n", - " \n", - "
\n", - "

date_of_birth

\n", - " \n", - "
\n", - "

home_address

\n", - " mailing street address\n", - "
\n", - "

home_city

\n", - " mailing address\n", - "
\n", - "

home_state

\n", - " US state acronym: e.g. OH\n", - "
\n", - "

home_zip

\n", - " zipcode e.g. 93979-4979\n", - "
\n", - "

home_phone

\n", - " e.g. 414.657.6883x0881\n", - "
104BobbyMorganM2000-08-03546 Rodriguez Vista Suite 121New KarentownTX42320+1-754-585-3248x282
151ChristopherJacksonM1997-11-301344 Willis CrestMathewsshireTX02380373-329-0653x45425
154KathrynWilliamsF1997-05-25252 Wanda HarborsRoblestownTX74304752-407-8170x7030
205KimberlyJohnsonF2008-03-097814 Brian PlazaWest NicoleTX27816248-366-8432x9592
\n", - " \n", - "

Total: 4

\n", - " " - ], - "text/plain": [ - "*student_id first_name last_name sex date_of_birth home_address home_city home_state home_zip home_phone \n", - "+------------+ +------------+ +-----------+ +-----+ +------------+ +------------+ +------------+ +------------+ +----------+ +------------+\n", - "104 Bobby Morgan M 2000-08-03 546 Rodriguez New Karentown TX 42320 +1-754-585-324\n", - "151 Christopher Jackson M 1997-11-30 1344 Willis Cr Mathewsshire TX 02380 373-329-0653x4\n", - "154 Kathryn Williams F 1997-05-25 252 Wanda Harb Roblestown TX 74304 752-407-8170x7\n", - "205 Kimberly Johnson F 2008-03-09 7814 Brian Pla West Nicole TX 27816 248-366-8432x9\n", - " (Total: 4)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Student & 'home_state=\"TX\"'" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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101LuisGriffinM1991-11-19777 Kristin StreamPort TimothyNM26753+1-573-927-1122x7498
107BradleyHenryM2001-03-092259 Klein MountainsNew JohnVT950898866328779
109KeithGatesM1995-08-032909 Barker Overpass Suite 314East JeremyUT29772743-717-2292x03200
115GreggMartinezM1999-11-024550 Torres Via Suite 368New PeterWA25601805-497-3473x8655
116DarrellWilliamsM2001-04-168310 Tyler Mountain Suite 130New ChristianSC37869001-391-364-6711
117AndrewHooverM1994-10-300147 Miles TunnelLake JenniferFM73910001-889-404-7668x714
119JeremyJonesM2006-07-2007975 Adams Viaduct Apt. 921EricksonshireWA78996914.524.1366
120AndrewKellyM2003-09-046991 Martinez Grove Apt. 504GreerburyMI20913(894)650-2939x52028
121ShannonCampbellM2006-09-207399 James Valley Apt. 637JensenviewPA38660(525)740-8550
122JasonHudsonM1999-06-2761276 Hines WellsMelanieviewMI67378+1-894-564-3945x069
123ChristopherWaltersM1999-01-04906 Kim Vista Suite 820JenniferburghKS20193245.697.6223x93388
126MaxwellLopezM1999-09-253276 Miller ForkMalikhavenNH79031001-538-414-0111x220
\n", - "

...

\n", - "

Total: 107

\n", - " " - ], - "text/plain": [ - "*student_id first_name last_name sex date_of_birth home_address home_city home_state home_zip home_phone \n", - "+------------+ +------------+ +-----------+ +-----+ +------------+ +------------+ +------------+ +------------+ +----------+ +------------+\n", - "101 Luis Griffin M 1991-11-19 777 Kristin St Port Timothy NM 26753 +1-573-927-112\n", - "107 Bradley Henry M 2001-03-09 2259 Klein Mou New John VT 95089 8866328779 \n", - "109 Keith Gates M 1995-08-03 2909 Barker Ov East Jeremy UT 29772 743-717-2292x0\n", - "115 Gregg Martinez M 1999-11-02 4550 Torres Vi New Peter WA 25601 805-497-3473x8\n", - "116 Darrell Williams M 2001-04-16 8310 Tyler Mou New Christian SC 37869 001-391-364-67\n", - "117 Andrew Hoover M 1994-10-30 0147 Miles Tun Lake Jennifer FM 73910 001-889-404-76\n", - "119 Jeremy Jones M 2006-07-20 07975 Adams Vi Ericksonshire WA 78996 914.524.1366 \n", - "120 Andrew Kelly M 2003-09-04 6991 Martinez Greerbury MI 20913 (894)650-2939x\n", - "121 Shannon Campbell M 2006-09-20 7399 James Val Jensenview PA 38660 (525)740-8550 \n", - "122 Jason Hudson M 1999-06-27 61276 Hines We Melanieview MI 67378 +1-894-564-394\n", - "123 Christopher Walters M 1999-01-04 906 Kim Vista Jenniferburgh KS 20193 245.697.6223x9\n", - "126 Maxwell Lopez M 1999-09-25 3276 Miller Fo Malikhaven NH 79031 001-538-414-01\n", - " ...\n", - " (Total: 107)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Male students from outside Texas\n", - "(Student & 'sex=\"M\"') - {\"home_state\": \"TX\"}" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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101LuisGriffinM1991-11-19777 Kristin StreamPort TimothyNM26753+1-573-927-1122x7498
104BobbyMorganM2000-08-03546 Rodriguez Vista Suite 121New KarentownTX42320+1-754-585-3248x282
151ChristopherJacksonM1997-11-301344 Willis CrestMathewsshireTX02380373-329-0653x45425
152GaryKelleyM2006-08-18468 Latoya TrailJamestonNM74191225-953-0926
154KathrynWilliamsF1997-05-25252 Wanda HarborsRoblestownTX74304752-407-8170x7030
182MatthewGarciaM1996-04-28222 Becker ShoalsSanchezshireOK71879001-238-556-4867
205KimberlyJohnsonF2008-03-097814 Brian PlazaWest NicoleTX27816248-366-8432x9592
211DavidRobinsonM1990-02-1802562 Stephanie Groves Suite 222OconnorfurtNM06795(792)937-7966x38758
214NicoleReynoldsF2003-02-075758 Robert LandLake TravisOK65089600-895-2572x051
223MichaelWeberM1990-10-318285 Ramirez Pines Suite 214TerrimouthOK55298+1-355-488-7233x224
239ZacharyHallM2003-05-0110744 Jennifer Knolls Apt. 238DavislandOK50295(677)243-0795x36598
\n", - " \n", - "

Total: 11

\n", - " " - ], - "text/plain": [ - "*student_id first_name last_name sex date_of_birth home_address home_city home_state home_zip home_phone \n", - "+------------+ +------------+ +-----------+ +-----+ +------------+ +------------+ +------------+ +------------+ +----------+ +------------+\n", - "101 Luis Griffin M 1991-11-19 777 Kristin St Port Timothy NM 26753 +1-573-927-112\n", - "104 Bobby Morgan M 2000-08-03 546 Rodriguez New Karentown TX 42320 +1-754-585-324\n", - "151 Christopher Jackson M 1997-11-30 1344 Willis Cr Mathewsshire TX 02380 373-329-0653x4\n", - "152 Gary Kelley M 2006-08-18 468 Latoya Tra Jameston NM 74191 225-953-0926 \n", - "154 Kathryn Williams F 1997-05-25 252 Wanda Harb Roblestown TX 74304 752-407-8170x7\n", - "182 Matthew Garcia M 1996-04-28 222 Becker Sho Sanchezshire OK 71879 001-238-556-48\n", - "205 Kimberly Johnson F 2008-03-09 7814 Brian Pla West Nicole TX 27816 248-366-8432x9\n", - "211 David Robinson M 1990-02-18 02562 Stephani Oconnorfurt NM 06795 (792)937-7966x\n", - "214 Nicole Reynolds F 2003-02-07 5758 Robert La Lake Travis OK 65089 600-895-2572x0\n", - "223 Michael Weber M 1990-10-31 8285 Ramirez P Terrimouth OK 55298 +1-355-488-723\n", - "239 Zachary Hall M 2003-05-01 10744 Jennifer Davisland OK 50295 (677)243-0795x\n", - " (Total: 11)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Students from TX, OK, or NM\n", - "Student & [{\"home_state\": \"OK\"}, {\"home_state\": \"NM\"}, {\"home_state\": \"TX\"}]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Millennials\n", - "millennials = Student & 'date_of_birth between \"1981-01-01\" and \"1996-12-31\"'" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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101LuisGriffinM1991-11-19777 Kristin StreamPort TimothyNM26753+1-573-927-1122x7498
102RhondaMeltonF1991-02-2854424 Michael FlatWest RichardPW35557270.612.6710
105JenniferMitchellF1992-11-042424 Philip CrestJohnsonportMS16129264.221.2688
109KeithGatesM1995-08-032909 Barker Overpass Suite 314East JeremyUT29772743-717-2292x03200
111CatherineMosesF1996-08-1137827 Carolyn WellsWilliamsonchesterNC97844716.464.4008x2146
113KarenDanielF1996-05-2192322 Bailey Valleys Apt. 263WendybergKS55048+1-236-576-8707x878
117AndrewHooverM1994-10-300147 Miles TunnelLake JenniferFM73910001-889-404-7668x714
124BriannaJonesF1994-02-19640 Boyd ParkwaysCrystalmouthID59060(644)384-4085x9816
127DanielleGouldF1996-08-11972 George PrairieDeniseportNJ27306615-935-9425x0807
130RaymondMartinezM1996-09-1377927 Ethan Row Suite 800FieldsbergGA972798698203619
135CodyMoranM1995-12-2617651 Mark WaysJordanbergCT61463+1-437-661-2153x491
136StevenRamseyM1994-09-11970 Donna CourtWest JustintownPW77529(771)499-4334
\n", - "

...

\n", - "

Total: 78

\n", - " " - ], - "text/plain": [ - "*student_id first_name last_name sex date_of_birth home_address home_city home_state home_zip home_phone \n", - "+------------+ +------------+ +-----------+ +-----+ +------------+ +------------+ +------------+ +------------+ +----------+ +------------+\n", - "101 Luis Griffin M 1991-11-19 777 Kristin St Port Timothy NM 26753 +1-573-927-112\n", - "102 Rhonda Melton F 1991-02-28 54424 Michael West Richard PW 35557 270.612.6710 \n", - "105 Jennifer Mitchell F 1992-11-04 2424 Philip Cr Johnsonport MS 16129 264.221.2688 \n", - "109 Keith Gates M 1995-08-03 2909 Barker Ov East Jeremy UT 29772 743-717-2292x0\n", - "111 Catherine Moses F 1996-08-11 37827 Carolyn Williamsonches NC 97844 716.464.4008x2\n", - "113 Karen Daniel F 1996-05-21 92322 Bailey V Wendyberg KS 55048 +1-236-576-870\n", - "117 Andrew Hoover M 1994-10-30 0147 Miles Tun Lake Jennifer FM 73910 001-889-404-76\n", - "124 Brianna Jones F 1994-02-19 640 Boyd Parkw Crystalmouth ID 59060 (644)384-4085x\n", - "127 Danielle Gould F 1996-08-11 972 George Pra Deniseport NJ 27306 615-935-9425x0\n", - "130 Raymond Martinez M 1996-09-13 77927 Ethan Ro Fieldsberg GA 97279 8698203619 \n", - "135 Cody Moran M 1995-12-26 17651 Mark Way Jordanberg CT 61463 +1-437-661-215\n", - "136 Steven Ramsey M 1994-09-11 970 Donna Cour West Justintow PW 77529 (771)499-4334 \n", - " ...\n", - " (Total: 78)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "millennials" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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100JordanRoyF2003-01-26328 Saunders AvenueWest SusanlandMI254786826309566
101LuisGriffinM1991-11-19777 Kristin StreamPort TimothyNM26753+1-573-927-1122x7498
102RhondaMeltonF1991-02-2854424 Michael FlatWest RichardPW35557270.612.6710
103RebeccaRogersF2007-01-1967607 Scott MotorwayJasmineboroughME72148+1-381-336-9496x243
105JenniferMitchellF1992-11-042424 Philip CrestJohnsonportMS16129264.221.2688
106AngelaGordonF1998-10-21119 John Cove Apt. 577BarbermouthNC87356+1-939-793-8944x442
107BradleyHenryM2001-03-092259 Klein MountainsNew JohnVT950898866328779
108KatrinaBennettF2003-08-078486 Padilla CurvePort MichaelIA95792+1-403-246-0298x8785
109KeithGatesM1995-08-032909 Barker Overpass Suite 314East JeremyUT29772743-717-2292x03200
110DestinyBenjaminF2005-04-1581809 Richards SquaresGregorymouthOR57886+1-366-248-6065x208
111CatherineMosesF1996-08-1137827 Carolyn WellsWilliamsonchesterNC97844716.464.4008x2146
112SandraKelleyF2005-06-171181 Clark Plains Apt. 191North SherryburghAS47330476.572.7761x230
\n", - "

...

\n", - "

Total: 172

\n", - " " - ], - "text/plain": [ - "*student_id first_name last_name sex date_of_birth home_address home_city home_state home_zip home_phone \n", - "+------------+ +------------+ +-----------+ +-----+ +------------+ +------------+ +------------+ +------------+ +----------+ +------------+\n", - "100 Jordan Roy F 2003-01-26 328 Saunders A West Susanland MI 25478 6826309566 \n", - "101 Luis Griffin M 1991-11-19 777 Kristin St Port Timothy NM 26753 +1-573-927-112\n", - "102 Rhonda Melton F 1991-02-28 54424 Michael West Richard PW 35557 270.612.6710 \n", - "103 Rebecca Rogers F 2007-01-19 67607 Scott Mo Jasmineborough ME 72148 +1-381-336-949\n", - "105 Jennifer Mitchell F 1992-11-04 2424 Philip Cr Johnsonport MS 16129 264.221.2688 \n", - "106 Angela Gordon F 1998-10-21 119 John Cove Barbermouth NC 87356 +1-939-793-894\n", - "107 Bradley Henry M 2001-03-09 2259 Klein Mou New John VT 95089 8866328779 \n", - "108 Katrina Bennett F 2003-08-07 8486 Padilla C Port Michael IA 95792 +1-403-246-029\n", - "109 Keith Gates M 1995-08-03 2909 Barker Ov East Jeremy UT 29772 743-717-2292x0\n", - "110 Destiny Benjamin F 2005-04-15 81809 Richards Gregorymouth OR 57886 +1-366-248-606\n", - "111 Catherine Moses F 1996-08-11 37827 Carolyn Williamsonches NC 97844 716.464.4008x2\n", - "112 Sandra Kelley F 2005-06-17 1181 Clark Pla North Sherrybu AS 47330 476.572.7761x2\n", - " ...\n", - " (Total: 172)" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Students who have taken classes\n", - "Student & Enroll" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - " e.g. 414.657.6883x0881\n", - "
159GaryBellM1990-04-1212358 Orr Spring Apt. 648JennifersidePW72056+1-210-213-1590x503
164MichaelCampbellM1988-11-11000 Gonzalez VilleHarrisstadNJ94771609-661-1877
165DonnaValdezF1990-12-3115800 Clark Branch Suite 089SmithportPR70502207.249.7876x43833
200RyanButlerM1990-12-0753510 Schaefer Crest Apt. 831West BrianviewKY37100662.488.5373
212MarieJensenF1994-06-202176 Lori FerryJohnboroughVT08649712.471.0726x5033
217TravisBurnsM1992-07-14574 Mccoy Passage Apt. 671West JohnhavenMS45598(434)616-9677x203
228AngelaMillerF1989-04-1553016 Miller Fort Apt. 962Lake StacyNE23528570-682-8086
236BradyHayesM1993-04-13946 Kevin CreekNorth MarkboroughIN87319(920)655-5330
296EdwardReillyM1995-09-21398 Davis TurnpikeLeeburghPW66797729-815-6617
\n", - " \n", - "

Total: 9

\n", - " " - ], - "text/plain": [ - "*student_id first_name last_name sex date_of_birth home_address home_city home_state home_zip home_phone \n", - "+------------+ +------------+ +-----------+ +-----+ +------------+ +------------+ +------------+ +------------+ +----------+ +------------+\n", - "159 Gary Bell M 1990-04-12 12358 Orr Spri Jenniferside PW 72056 +1-210-213-159\n", - "164 Michael Campbell M 1988-11-11 000 Gonzalez V Harrisstad NJ 94771 609-661-1877 \n", - "165 Donna Valdez F 1990-12-31 15800 Clark Br Smithport PR 70502 207.249.7876x4\n", - "200 Ryan Butler M 1990-12-07 53510 Schaefer West Brianview KY 37100 662.488.5373 \n", - "212 Marie Jensen F 1994-06-20 2176 Lori Ferr Johnborough VT 08649 712.471.0726x5\n", - "217 Travis Burns M 1992-07-14 574 Mccoy Pass West Johnhaven MS 45598 (434)616-9677x\n", - "228 Angela Miller F 1989-04-15 53016 Miller F Lake Stacy NE 23528 570-682-8086 \n", - "236 Brady Hayes M 1993-04-13 946 Kevin Cree North Markboro IN 87319 (920)655-5330 \n", - "296 Edward Reilly M 1995-09-21 398 Davis Turn Leeburgh PW 66797 729-815-6617 \n", - " (Total: 9)" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Millennials who have never enrolled\n", - "millennials - Enroll" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - "

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\n", - " e.g. 414.657.6883x0881\n", - "
104BobbyMorganM2000-08-03546 Rodriguez Vista Suite 121New KarentownTX42320+1-754-585-3248x282
115GreggMartinezM1999-11-024550 Torres Via Suite 368New PeterWA25601805-497-3473x8655
119JeremyJonesM2006-07-2007975 Adams Viaduct Apt. 921EricksonshireWA78996914.524.1366
121ShannonCampbellM2006-09-207399 James Valley Apt. 637JensenviewPA38660(525)740-8550
134ChristopherGarciaM2001-09-18246 Breanna Forges Suite 371WebbburyIN98054(862)872-7013
141MichaelMitchellM2006-10-1261423 Hunter DamNorth JoshuaMS78973(555)481-4418x3517
155AdamGloverM2007-04-23201 Sydney Points Suite 756Lake AntonioPR78259(857)204-3851x8670
159GaryBellM1990-04-1212358 Orr Spring Apt. 648JennifersidePW72056+1-210-213-1590x503
164MichaelCampbellM1988-11-11000 Gonzalez VilleHarrisstadNJ94771609-661-1877
165DonnaValdezF1990-12-3115800 Clark Branch Suite 089SmithportPR70502207.249.7876x43833
192JoshuaSanchezM2008-03-1797848 Brown Forges Suite 538Lake LaurieMO39066+1-645-316-4436
198MichaelPorterM1997-06-102238 Baker Drive Suite 933South EdwardCO33074(507)210-8063x749
\n", - "

...

\n", - "

Total: 28

\n", - " " - ], - "text/plain": [ - "*student_id first_name last_name sex date_of_birth home_address home_city home_state home_zip home_phone \n", - "+------------+ +------------+ +-----------+ +-----+ +------------+ +------------+ +------------+ +------------+ +----------+ +------------+\n", - "104 Bobby Morgan M 2000-08-03 546 Rodriguez New Karentown TX 42320 +1-754-585-324\n", - "115 Gregg Martinez M 1999-11-02 4550 Torres Vi New Peter WA 25601 805-497-3473x8\n", - "119 Jeremy Jones M 2006-07-20 07975 Adams Vi Ericksonshire WA 78996 914.524.1366 \n", - "121 Shannon Campbell M 2006-09-20 7399 James Val Jensenview PA 38660 (525)740-8550 \n", - "134 Christopher Garcia M 2001-09-18 246 Breanna Fo Webbbury IN 98054 (862)872-7013 \n", - "141 Michael Mitchell M 2006-10-12 61423 Hunter D North Joshua MS 78973 (555)481-4418x\n", - "155 Adam Glover M 2007-04-23 201 Sydney Poi Lake Antonio PR 78259 (857)204-3851x\n", - "159 Gary Bell M 1990-04-12 12358 Orr Spri Jenniferside PW 72056 +1-210-213-159\n", - "164 Michael Campbell M 1988-11-11 000 Gonzalez V Harrisstad NJ 94771 609-661-1877 \n", - "165 Donna Valdez F 1990-12-31 15800 Clark Br Smithport PR 70502 207.249.7876x4\n", - "192 Joshua Sanchez M 2008-03-17 97848 Brown Fo Lake Laurie MO 39066 +1-645-316-443\n", - "198 Michael Porter M 1997-06-10 2238 Baker Dri South Edward CO 33074 (507)210-8063x\n", - " ...\n", - " (Total: 28)" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Students who have not taken classes\n", - "Student - Enroll" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - "

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date_of_birth

\n", - " \n", - "
\n", - "

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\n", - " mailing street address\n", - "
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\n", - " mailing address\n", - "
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home_state

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\n", - "

home_zip

\n", - " zipcode e.g. 93979-4979\n", - "
\n", - "

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\n", - " e.g. 414.657.6883x0881\n", - "
131ReginaStevensF2008-03-285535 Monica RunNew DavidfurtFM61403001-642-944-8490
133JoannRhodesF2001-11-107747 Ford CapeWest AndrewlandIN26601477-404-0830x5778
150CarlHernandezM2001-05-31395 Maria AlleyNorth VirginiatownOH05388513.264.5124x544
152GaryKelleyM2006-08-18468 Latoya TrailJamestonNM74191225-953-0926
161PatriciaWilliamsF1991-08-25859 John CoveRodriguezboroughIL435985127074752
163GabrielHamiltonM2002-02-01071 Carson UnionNew ThomasGA86656(486)759-7442x320
213GregoryFarleyM1989-03-30472 Ashlee CirclesNorth SaramouthWA68135755.273.4453
215JonMoraM1996-12-2338159 Mary PlaceAshleyfurtPA21276+1-291-400-4627x0887
221KennethSimsM2005-09-2297810 Andrea LocksPort LoriburyIA19088505.239.9379x51533
223MichaelWeberM1990-10-318285 Ramirez Pines Suite 214TerrimouthOK55298+1-355-488-7233x224
224DanielleMurphyF1989-01-147458 Kimberly VillageDuaneboroughOH16974886-583-9979x61392
235BryanMyersM2002-11-0390073 Brandi IslandLake TravisWV31881745-915-3558x801
\n", - "

...

\n", - "

Total: 21

\n", - " " - ], - "text/plain": [ - "*student_id first_name last_name sex date_of_birth home_address home_city home_state home_zip home_phone \n", - "+------------+ +------------+ +-----------+ +-----+ +------------+ +------------+ +------------+ +------------+ +----------+ +------------+\n", - "131 Regina Stevens F 2008-03-28 5535 Monica Ru New Davidfurt FM 61403 001-642-944-84\n", - "133 Joann Rhodes F 2001-11-10 7747 Ford Cape West Andrewlan IN 26601 477-404-0830x5\n", - "150 Carl Hernandez M 2001-05-31 395 Maria Alle North Virginia OH 05388 513.264.5124x5\n", - "152 Gary Kelley M 2006-08-18 468 Latoya Tra Jameston NM 74191 225-953-0926 \n", - "161 Patricia Williams F 1991-08-25 859 John Cove Rodriguezborou IL 43598 5127074752 \n", - "163 Gabriel Hamilton M 2002-02-01 071 Carson Uni New Thomas GA 86656 (486)759-7442x\n", - "213 Gregory Farley M 1989-03-30 472 Ashlee Cir North Saramout WA 68135 755.273.4453 \n", - "215 Jon Mora M 1996-12-23 38159 Mary Pla Ashleyfurt PA 21276 +1-291-400-462\n", - "221 Kenneth Sims M 2005-09-22 97810 Andrea L Port Loribury IA 19088 505.239.9379x5\n", - "223 Michael Weber M 1990-10-31 8285 Ramirez P Terrimouth OK 55298 +1-355-488-723\n", - "224 Danielle Murphy F 1989-01-14 7458 Kimberly Duaneborough OH 16974 886-583-9979x6\n", - "235 Bryan Myers M 2002-11-03 90073 Brandi I Lake Travis WV 31881 745-915-3558x8\n", - " ...\n", - " (Total: 21)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Students who have taken Biology classes but no MATH courses\n", - "(Student & (Enroll & 'dept=\"BIOL\"')) - (Enroll & 'dept=\"MATH\"')" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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student_id

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\n", - "

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\n", - "

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\n", - "

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\n", - " \n", - "
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\n", - " \n", - "
\n", - "

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\n", - " mailing street address\n", - "
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\n", - " US state acronym: e.g. OH\n", - "
\n", - "

home_zip

\n", - " zipcode e.g. 93979-4979\n", - "
\n", - "

home_phone

\n", - " e.g. 414.657.6883x0881\n", - "
101LuisGriffinM1991-11-19777 Kristin StreamPort TimothyNM26753+1-573-927-1122x7498
107BradleyHenryM2001-03-092259 Klein MountainsNew JohnVT950898866328779
111CatherineMosesF1996-08-1137827 Carolyn WellsWilliamsonchesterNC97844716.464.4008x2146
118StephanieBernardF2007-04-306929 Laura BranchPaynechesterNJ987292292643896
120AndrewKellyM2003-09-046991 Martinez Grove Apt. 504GreerburyMI20913(894)650-2939x52028
122JasonHudsonM1999-06-2761276 Hines WellsMelanieviewMI67378+1-894-564-3945x069
124BriannaJonesF1994-02-19640 Boyd ParkwaysCrystalmouthID59060(644)384-4085x9816
128StevenSuttonM2005-11-1146410 Dale Tunnel Apt. 352South SamanthatonTN71881801.205.8852x7765
130RaymondMartinezM1996-09-1377927 Ethan Row Suite 800FieldsbergGA972798698203619
139DawnCarlsonF1988-11-09955 Woodard Centers Suite 680BarrettmouthMP38164312-762-0284
145BrianSinghM1997-09-14417 Ross IslandRobinsontonND94775471.331.6608
146SydneyWeberF2001-01-094670 Miller Junctions Suite 494RussellportLA54092+1-256-588-1993x731
\n", - "

...

\n", - "

Total: 48

\n", - " " - ], - "text/plain": [ - "*student_id first_name last_name sex date_of_birth home_address home_city home_state home_zip home_phone \n", - "+------------+ +------------+ +-----------+ +-----+ +------------+ +------------+ +------------+ +------------+ +----------+ +------------+\n", - "101 Luis Griffin M 1991-11-19 777 Kristin St Port Timothy NM 26753 +1-573-927-112\n", - "107 Bradley Henry M 2001-03-09 2259 Klein Mou New John VT 95089 8866328779 \n", - "111 Catherine Moses F 1996-08-11 37827 Carolyn Williamsonches NC 97844 716.464.4008x2\n", - "118 Stephanie Bernard F 2007-04-30 6929 Laura Bra Paynechester NJ 98729 2292643896 \n", - "120 Andrew Kelly M 2003-09-04 6991 Martinez Greerbury MI 20913 (894)650-2939x\n", - "122 Jason Hudson M 1999-06-27 61276 Hines We Melanieview MI 67378 +1-894-564-394\n", - "124 Brianna Jones F 1994-02-19 640 Boyd Parkw Crystalmouth ID 59060 (644)384-4085x\n", - "128 Steven Sutton M 2005-11-11 46410 Dale Tun South Samantha TN 71881 801.205.8852x7\n", - "130 Raymond Martinez M 1996-09-13 77927 Ethan Ro Fieldsberg GA 97279 8698203619 \n", - "139 Dawn Carlson F 1988-11-09 955 Woodard Ce Barrettmouth MP 38164 312-762-0284 \n", - "145 Brian Singh M 1997-09-14 417 Ross Islan Robinsonton ND 94775 471.331.6608 \n", - "146 Sydney Weber F 2001-01-09 4670 Miller Ju Russellport LA 54092 +1-256-588-199\n", - " ...\n", - " (Total: 48)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Students who have not selected a major\n", - "Student - StudentMajor" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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100JordanRoyF2003-01-26328 Saunders AvenueWest SusanlandMI254786826309566
101LuisGriffinM1991-11-19777 Kristin StreamPort TimothyNM26753+1-573-927-1122x7498
102RhondaMeltonF1991-02-2854424 Michael FlatWest RichardPW35557270.612.6710
103RebeccaRogersF2007-01-1967607 Scott MotorwayJasmineboroughME72148+1-381-336-9496x243
104BobbyMorganM2000-08-03546 Rodriguez Vista Suite 121New KarentownTX42320+1-754-585-3248x282
105JenniferMitchellF1992-11-042424 Philip CrestJohnsonportMS16129264.221.2688
106AngelaGordonF1998-10-21119 John Cove Apt. 577BarbermouthNC87356+1-939-793-8944x442
107BradleyHenryM2001-03-092259 Klein MountainsNew JohnVT950898866328779
108KatrinaBennettF2003-08-078486 Padilla CurvePort MichaelIA95792+1-403-246-0298x8785
109KeithGatesM1995-08-032909 Barker Overpass Suite 314East JeremyUT29772743-717-2292x03200
110DestinyBenjaminF2005-04-1581809 Richards SquaresGregorymouthOR57886+1-366-248-6065x208
111CatherineMosesF1996-08-1137827 Carolyn WellsWilliamsonchesterNC97844716.464.4008x2146
\n", - "

...

\n", - "

Total: 177

\n", - " " - ], - "text/plain": [ - "*student_id first_name last_name sex date_of_birth home_address home_city home_state home_zip home_phone \n", - "+------------+ +------------+ +-----------+ +-----+ +------------+ +------------+ +------------+ +------------+ +----------+ +------------+\n", - "100 Jordan Roy F 2003-01-26 328 Saunders A West Susanland MI 25478 6826309566 \n", - "101 Luis Griffin M 1991-11-19 777 Kristin St Port Timothy NM 26753 +1-573-927-112\n", - "102 Rhonda Melton F 1991-02-28 54424 Michael West Richard PW 35557 270.612.6710 \n", - "103 Rebecca Rogers F 2007-01-19 67607 Scott Mo Jasmineborough ME 72148 +1-381-336-949\n", - "104 Bobby Morgan M 2000-08-03 546 Rodriguez New Karentown TX 42320 +1-754-585-324\n", - "105 Jennifer Mitchell F 1992-11-04 2424 Philip Cr Johnsonport MS 16129 264.221.2688 \n", - "106 Angela Gordon F 1998-10-21 119 John Cove Barbermouth NC 87356 +1-939-793-894\n", - "107 Bradley Henry M 2001-03-09 2259 Klein Mou New John VT 95089 8866328779 \n", - "108 Katrina Bennett F 2003-08-07 8486 Padilla C Port Michael IA 95792 +1-403-246-029\n", - "109 Keith Gates M 1995-08-03 2909 Barker Ov East Jeremy UT 29772 743-717-2292x0\n", - "110 Destiny Benjamin F 2005-04-15 81809 Richards Gregorymouth OR 57886 +1-366-248-606\n", - "111 Catherine Moses F 1996-08-11 37827 Carolyn Williamsonches NC 97844 716.464.4008x2\n", - " ...\n", - " (Total: 177)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Students who are taking courses in the current term\n", - "Student - (Enroll & CurrentTerm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# show corresponding SQL\n", - "(Student - (Enroll & CurrentTerm)).make_sql()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Ungraded courses\n", - "Enroll - Grade" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Ungraded courses in the current term\n", - "(Enroll & CurrentTerm) - Grade" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Students who have taken classes and have chosen a major\n", - "(Student & Enroll & StudentMajor)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Students who have taken classes or have chosen a major\n", - "Student & [Enroll, StudentMajor]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Enrollment in courses from the same department as the students' major\n", - "Enroll & StudentMajor" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Join\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Grade point values\n", - "Grade * LetterGrade" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Graded enrollments with complete course and student information\n", - "Student * Enroll * Course * Section * Grade * LetterGrade" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Enrollment with major information\n", - "Enroll * StudentMajor.proj(major=\"dept\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Enrollment outside chosen major\n", - "Enroll * StudentMajor.proj(major=\"dept\") & \"major<>dept\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Enrollment not matching major\n", - "Enroll - StudentMajor" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Total grade points\n", - "(Course * Grade * LetterGrade).proj(total=\"points*credits\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Aggr\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Students in each section\n", - "Section.aggr(Enroll, n=\"count(*)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Average grade in each course\n", - "Course.aggr(Grade * LetterGrade, avg_grade=\"avg(points)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fraction graded in each section\n", - "(Section.aggr(Enroll, n=\"count(*)\") * Section.aggr(Grade, m=\"count(*)\")).proj(\n", - " \"m\", \"n\", frac=\"m/n\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Student GPA\n", - "Student.aggr(Course * Grade * LetterGrade, gpa=\"sum(points*credits)/sum(credits)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Average GPA for each major\n", - "gpa = Student.aggr(Course * Grade * LetterGrade, gpa=\"sum(points*credits)/sum(credits)\")\n", - "Department.aggr(StudentMajor * gpa, avg_gpa=\"avg(gpa)\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/004-Design-HW.ipynb b/db-course/004-Design-HW.ipynb deleted file mode 100644 index cb8a4b6..0000000 --- a/db-course/004-Design-HW.ipynb +++ /dev/null @@ -1,268 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Homework 4\n", - "\n", - "Design schemas for the following database designs.\n", - "* Make sure that the tables have a proper primary key that correctly enforces *entity integrity*.\n", - "* Make sure that the table has all the required columns with appropriate data types and null constraints\n", - "* Introduce appropriate foreign key to enforce the constraints.\n", - "* Include an insert statement to populate a few entries into each table but you do not need to completely fill the tables.\n", - "* Follow best conventions and practices we discussed in class.\n", - "* Ensure that your schema is in 3rd normal form.\n", - "* Execute the entire assignment in one notebook, print it and submit the PDF to the instructor by Slack.\n", - "\n", - "\n", - "For the general database course, you can complete the assignment in DataJoint or you can use `pymysql` or Jupyter SQL Magic to interact with the database." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Problem 1: \"Grading\" \n", - "Design a database that handles assignments and grades in this course.\n", - "\n", - "1. Students in this class.\n", - "2. Assignments in this class, including a link to its specification.\n", - "3. If assignment has been graded, store the grade for each student and each assignment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Student(dj.Manual):\n", - " definition = \"\"\"\n", - " student_id: int\n", - " ---\n", - " first_name : varchar(64)\n", - " last_name : varchar(64)\n", - " email : varchar(100) NOT NULL\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Assignment(dj.Manual):\n", - " definition = \"\"\"\n", - " assignment_id: int\n", - " ---\n", - " title: varchar(100)\n", - " specification_link: varchar(255)\n", - " due_date: date\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class GradedAssignment(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Student\n", - " -> Assignment\n", - " ---\n", - " grade: decimal(5,2)\n", - " \"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Problem 2: \"The Library\" \n", - "Design a database to track books in a library. You may want to learn how real libraries identify different copies of the same book.\n", - "\n", - "1. Books have an ISBN but multiple copies of the same title may exist.\n", - "2. The library has members. They have a name and an address.\n", - "3. A library member can check out any book, include the checkout date.\n", - "4. The book may not be checked out by two people at the same time.\n", - "5. Some books are not checked out." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class BookTitle(dj.Manual):\n", - " definition = \"\"\"\n", - " isbn: varchar(13)\n", - " ---\n", - " title: varchar(255)\n", - " author: varchar(255)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Copy(dj.Manual):\n", - " definition = \"\"\"\n", - " -> BookTitle\n", - " copy_id: int \n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Member(dj.Manual):\n", - " definition = \"\"\"\n", - " member_id: int\n", - " ---\n", - " name: varchar(255)\n", - " address: varchar(255)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Checkout(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Copy\n", - " ---\n", - " -> Member\n", - " checkout_date: date\n", - " return_date: date \n", - " \"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Problem 3: \"The Bank\"\n", - "Design a database to represent a bank, its branches, its customers and their banking accounts.\n", - "\n", - "1. A bank has branches that have a phone and a street address.\n", - "1. The Bank has customers.\n", - "2. Each customer has one \"home branch.\"\n", - "2. The bank manages bank accounts, which can be either \"savings\" or \"checking.\"\n", - "3. Each account has exactly one customer as its owner.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Branch(dj.Manual):\n", - " definition = \"\"\"\n", - " branch_id : int \n", - " ---\n", - " address : varchar(255) \n", - " phone_number : varchar(12) \n", - " \n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Customer(dj.Manual):\n", - " definition = \"\"\"\n", - " customer_id : int \n", - " ---\n", - " address : varchar(255) \n", - " phone_number : int \n", - " first_name : varchar(255) \n", - " last_name : varchar(255) \n", - " dob : date\n", - " number_of_accounts : int \n", - " -> Branch.proj(home_branch=\"branch_id\")\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Account(dj.Manual):\n", - " definition = \"\"\"\n", - " account_id : int \n", - " ---\n", - " address : varchar(255) \n", - " phone_number : int \n", - " first_name : varchar(255) \n", - " last_name : vbarchar(255) \n", - " dob : date\n", - " account_type : enum(\"savings\", \"checking\")\n", - " -> Customer\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Bank(dj.Manual):\n", - " definition = \"\"\" \n", - " bank_id: SMALLINT UNSIGNED\n", - " ---\n", - " bank_name: VARCHAR(30)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class HomeBranch(dj.Manual):\n", - " definition = \"\"\" \n", - " -> Bank\n", - " branch_id: INT UNSIGNED\n", - " ---\n", - " street_address: VARCHAR(100)\n", - " branch_phone: BIGINT\n", - " bank_email: VARCHAR(100)\n", - "\n", - "\"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Customers(dj.Manual):\n", - " definition = \"\"\" \n", - " -> HomeBranch\n", - " customer_id: SMALLINT UNSIGNED\n", - " ---\n", - " first_name: VARCHAR(30)\n", - " last_name: VARCHAR(30)\n", - " date_of_birth: DATE\n", - " customer_email: VARCHAR(100)\n", - " phone: BIGINT UNSIGNED \n", - "\"\"\"\n", - "\n", - "\n", - "@schema\n", - "class CustomerAccount(dj.Manual):\n", - " definition = \"\"\" \n", - " -> Customer\n", - " account_num: BIGINT UNSIGNED\n", - " ---\n", - " account_type: ENUM('CHECKING','SAVINGS')\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Problem 4: \"The Phone App\"\n", - "\n", - "You are designing a smart phone app. Create a database to enforce the following rules.\n", - "\n", - "1. Users can subscribe for a free account identified by their US phone number without extensions. Provide first and last name, date of birth (optional), and sex (optional).\n", - "2. Users can add one or more credits cards to their account. Store zipcode, expiration date, and the CVV.\n", - "3. The app has paid add-ons called \"Track & Field\", \"Marathon\", and \"Sprint\", each with a fixed price.\n", - "4. A user can purchase each add-on, in which case she must provide a credit card for the purchase. A user cannot purchase the same addon twice." - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/004-Design.ipynb b/db-course/004-Design.ipynb deleted file mode 100644 index 02c6a4a..0000000 --- a/db-course/004-Design.ipynb +++ /dev/null @@ -1,752 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Problem Statement: \"The Phone App\"\n", - "\n", - "You are designing a smart phone app\n", - "\n", - "1. Users can subscribe for a free account identified by their US phone number without extensions. Provide first and last name, date of birth (optional), and sex (optional).\n", - "2. Users can add one or more credits cards to their account. Store zipcode, expiration date, and the CVV.\n", - "3. The app has paid add-ons called \"Track & Field\", \"Marathon\", and \"Sprint\", each with a fixed price.\n", - "4. A user can purchase each add-on, in which case she must provide a credit card for the purchase. Include a purchase date. A user cannot purchase the same addon twice. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Design" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-10-31 21:36:21,351][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", - "[2023-10-31 21:36:21,360][INFO]: Connected root@fakeservices.datajoint.io:3306\n" - ] - } - ], - "source": [ - "import datajoint as dj\n", - "\n", - "schema = dj.Schema(\"app\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Account(dj.Manual):\n", - " definition = \"\"\"\n", - " phone : bigint unsigned \n", - " ---\n", - " first_name : varchar(30)\n", - " last_name : varchar(30)\n", - " dob=null : date\n", - " sex='' : enum('F', 'M', '')\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class CreditCard(dj.Manual):\n", - " definition = \"\"\"\n", - " card_number : bigint unsigned \n", - " ---\n", - " exp_date : date \n", - " cvv : smallint unsigned\n", - " zipcode : int unsigned \n", - " -> Account\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class AddOn(dj.Lookup):\n", - " definition = \"\"\"\n", - " addon_id : int\n", - " ---\n", - " addon_name : varchar(30)\n", - " price : decimal(5, 2) unsigned\n", - " \"\"\"\n", - " contents = (\n", - " (1, \"Track & Field\", 13.99),\n", - " (2, \"Marathon\", 26.2),\n", - " (3, \"Sprint\", 100.00),\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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Total: 0

\n", - " " - ], - "text/plain": [ - "*phone *addon_id card_number purchase_date \n", - "+-------+ +----------+ +------------+ +------------+\n", - "\n", - " (Total: 0)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Purchase()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Purchase\n", - "\n", - "\n", - "Purchase\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account->Purchase\n", - "\n", - "\n", - "\n", - "\n", - "CreditCard\n", - "\n", - "\n", - "CreditCard\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account->CreditCard\n", - "\n", - "\n", - "\n", - "\n", - "AddOn\n", - "\n", - "\n", - "AddOn\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AddOn->Purchase\n", - "\n", - "\n", - "\n", - "\n", - "CreditCard->Purchase\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Populate \n", - "Now we will populate the database with fake data" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "from tqdm import tqdm\n", - "from faker import Faker\n", - "\n", - "fake = Faker()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# insert one account\n", - "Account.insert1(\n", - " dict(\n", - " phone=fake.random_int(1_000_000_0000, 9_999_999_9999),\n", - " first_name=fake.first_name_male(),\n", - " last_name=fake.last_name(),\n", - " sex=\"M\",\n", - " dob=fake.date_of_birth(),\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# insert many male accounts\n", - "Account.insert(\n", - " dict(\n", - " phone=fake.random_int(1_000_000_0000, 9_999_999_9999),\n", - " first_name=fake.first_name_male(),\n", - " last_name=fake.last_name(),\n", - " sex=\"M\",\n", - " dob=fake.date_of_birth(),\n", - " )\n", - " for _ in range(5000)\n", - ")\n", - "\n", - "# insert many female accounts\n", - "Account.insert(\n", - " dict(\n", - " phone=fake.random_int(1_000_000_0000, 9_999_999_9999),\n", - " first_name=fake.first_name_female(),\n", - " last_name=fake.last_name(),\n", - " sex=\"F\",\n", - " dob=fake.date_of_birth(),\n", - " )\n", - " for _ in range(5000)\n", - ")\n", - "\n", - "# insert some accounts with no sex and no birthdate\n", - "Account.insert(\n", - " dict(\n", - " phone=fake.random_int(1_000_000_0000, 9_999_999_9999),\n", - " first_name=fake.first_name(),\n", - " last_name=fake.last_name(),\n", - " )\n", - " for _ in range(500)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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sex

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10022965768StevenMccullough2015-11-04M
10028316466DeborahBlankenship1912-07-08F
10037683725DebraPatrick1976-06-09F
10052698980HollyGonzalez1911-10-27F
10058125916DianaMccoy2021-08-09F
10070677966SarahRiley1969-06-15F
10082298528LisaSimmons1941-04-28F
10090941918GeorgeBrown1912-10-06M
10111873420AnnaHall2021-04-25F
10119593334DianaWoods1921-05-27F
10132136302EmilyCollins1995-12-27F
10133731468TracyFoley1921-05-22F
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...

\n", - "

Total: 10501

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob sex \n", - "+------------+ +------------+ +------------+ +------------+ +-----+\n", - "10022965768 Steven Mccullough 2015-11-04 M \n", - "10028316466 Deborah Blankenship 1912-07-08 F \n", - "10037683725 Debra Patrick 1976-06-09 F \n", - "10052698980 Holly Gonzalez 1911-10-27 F \n", - "10058125916 Diana Mccoy 2021-08-09 F \n", - "10070677966 Sarah Riley 1969-06-15 F \n", - "10082298528 Lisa Simmons 1941-04-28 F \n", - "10090941918 George Brown 1912-10-06 M \n", - "10111873420 Anna Hall 2021-04-25 F \n", - "10119593334 Diana Woods 1921-05-27 F \n", - "10132136302 Emily Collins 1995-12-27 F \n", - "10133731468 Tracy Foley 1921-05-22 F \n", - " ...\n", - " (Total: 10501)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# get account ids:\n", - "keys = Account.fetch(\"KEY\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# insert one credit card\n", - "CreditCard.insert1(\n", - " dict(\n", - " random.choice(keys),\n", - " zipcode=random.randint(10000, 99999),\n", - " card_number=int(fake.credit_card_number()),\n", - " cvv=random.randint(1, 999),\n", - " exp_date=fake.future_date(),\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# insert many credit cards\n", - "CreditCard.insert(\n", - " dict(\n", - " random.choice(keys),\n", - " zipcode=random.randint(10000, 99999),\n", - " card_number=int(fake.credit_card_number()),\n", - " cvv=random.randint(1, 999),\n", - " exp_date=fake.future_date(),\n", - " )\n", - " for _ in range(15000)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# get all possible valid purchases, eliminate duplicate purchases that are under different cards\n", - "purchases = (Account * CreditCard * AddOn - Purchase.proj()).fetch(\n", - " \"KEY\", order_by=(\"phone\", \"addon_id\")\n", - ")\n", - "unique_purchases = [purchases.pop()]\n", - "for purchase in purchases:\n", - " if (purchase[\"phone\"], purchase[\"addon_id\"]) != (\n", - " unique_purchases[-1][\"phone\"],\n", - " unique_purchases[-1][\"addon_id\"],\n", - " ):\n", - " unique_purchases.append(dict(purchase, purchase_date=fake.past_date()))\n", - "\n", - "# insert a random subset\n", - "Purchase.insert(random.sample(unique_purchases, 5000))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "schema.drop() # optionally drop the schema to clear before the next run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/004-DesignSQL.ipynb b/db-course/004-DesignSQL.ipynb deleted file mode 100644 index 9556326..0000000 --- a/db-course/004-DesignSQL.ipynb +++ /dev/null @@ -1,521 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Design\n", - "\n", - "## Problem 4. Online App\n", - "\n", - "You are designing a smart phone app\n", - "\n", - "1. Users can subscribe for free, identified by their US phone number without extensions. Provide first and last name.\n", - "2. Users can add one or more credits cards to their account. Store zipcode, expiration date, and the CVC.\n", - "3. The app has paid add-ons called \"Track & Field\", \"Marathon\", and \"Sprint\", each with a fixed price.\n", - "4. A user can purchase each add-on, in which case she must provide a credit card for the purchase. A user cannot purchase the same addon twice. Include the purchase date." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pymysql\n", - "\n", - "pymysql.install_as_MySQLdb()\n", - "\n", - "%load_ext sql\n", - "%config SqlMagic.autocommit=True\n", - "\n", - "%sql mysql://root:simple@127.0.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "1 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "CREATE SCHEMA IF NOT EXISTS app2" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "USE app2" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Tables_in_app2
" - ], - "text/plain": [ - "[]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "SHOW TABLES" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "CREATE TABLE account (\n", - " phone bigint unsigned NOT NULL,\n", - " first_name varchar(30) NOT NULL,\n", - " last_name varchar(30) NOT NULL,\n", - " dob date,\n", - " sex enum(\"M\", \"F\"),\n", - " PRIMARY KEY (phone)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "CREATE TABLE credit_card (\n", - " card_number bigint unsigned NOT NULL,\n", - " cvc smallint unsigned NOT NULL,\n", - " exp_date date NOT NULL,\n", - " zipcode int unsigned NOT NULL,\n", - " phone bigint unsigned NOT NULL,\n", - " PRIMARY KEY (card_number),\n", - " FOREIGN KEY (phone) REFERENCES account(phone)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "CREATE TABLE addon (\n", - " addon_id int NOT NULL,\n", - " addon_name varchar(30) NOT NULL,\n", - " price decimal(5,2) unsigned NOT NULL,\n", - " PRIMARY KEY(addon_id)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "(pymysql.err.OperationalError) (1050, \"Table 'purchase' already exists\")\n", - "[SQL: CREATE TABLE purchase (\n", - " phone bigint unsigned NOT NULL,\n", - " addon_id int NOT NULL,\n", - " card_number bigint unsigned NOT NULL,\n", - " PRIMARY KEY (phone, addon_id),\n", - " FOREIGN KEY (card_number) REFERENCES credit_card(card_number),\n", - " FOREIGN KEY (phone) REFERENCES account(phone),\n", - " FOREIGN KEY (addon_id) REFERENCES addon(addon_id)\n", - ")]\n", - "(Background on this error at: https://sqlalche.me/e/20/e3q8)\n" - ] - } - ], - "source": [ - "%%sql\n", - "\n", - "CREATE TABLE purchase (\n", - " phone bigint unsigned NOT NULL,\n", - " addon_id int NOT NULL,\n", - " card_number bigint unsigned NOT NULL,\n", - " PRIMARY KEY (phone, addon_id),\n", - " FOREIGN KEY (card_number) REFERENCES credit_card(card_number),\n", - " FOREIGN KEY (phone) REFERENCES account(phone),\n", - " FOREIGN KEY (addon_id) REFERENCES addon(addon_id)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "2 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "INSERT account VALUES (7133996769, 'Anne', 'Smith'), (7139993769, 'Jackie', 'Smith') " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "1 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "INSERT credit_card VALUES (12310983234238, 123, '2023-12-12', '77006', 7133996769)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "1 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "INSERT addon VALUES (1, \"Sprint\", 100.00)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "1 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "INSERT purchase VALUES (7133996769, 1, 12310983234238)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "1 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
phoneaddon_idcard_numberaddon_namepricefirst_namelast_name
7133996769112310983234238Sprint100.00AnneSmith
" - ], - "text/plain": [ - "[(7133996769, 1, 12310983234238, 'Sprint', Decimal('100.00'), 'Anne', 'Smith')]" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "SELECT * FROM purchase NATURAL JOIN addon NATURAL JOIN account " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/005-Queries-HW.ipynb b/db-course/005-Queries-HW.ipynb deleted file mode 100644 index 9bc842b..0000000 --- a/db-course/005-Queries-HW.ipynb +++ /dev/null @@ -1,1941 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Homework 5. Simple Queries\n", - "\n", - "In this project, you will design simple queries that rely on relational restriction and projection. Some restrictions may require a subquery.\n", - "\n", - "We will use the App database that was designed and populated in [004-Design](./004-Design.ipnb`). " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "import pymysql\n", - "\n", - "pymysql.install_as_MySQLdb()\n", - "\n", - "%load_ext sql\n", - "%config SqlMagic.autocommit=True\n", - "%sql mysql://root:simple@127.0.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "AddOn\n", - "\n", - "\n", - "AddOn\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Purchase\n", - "\n", - "\n", - "Purchase\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AddOn->Purchase\n", - "\n", - "\n", - "\n", - "\n", - "CreditCard\n", - "\n", - "\n", - "CreditCard\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "CreditCard->Purchase\n", - "\n", - "\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account->Purchase\n", - "\n", - "\n", - "\n", - "\n", - "Account->CreditCard\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import datajoint as dj\n", - "\n", - "schema = dj.Schema(\"app\")\n", - "schema.spawn_missing_classes()\n", - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Problem 1 (solved). All accounts last_names for persons named \"Paul\" or \"Paula\" born in the 1990s" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

phone

\n", - " \n", - "
\n", - "

first_name

\n", - " \n", - "
\n", - "

last_name

\n", - " \n", - "
\n", - "

dob

\n", - " \n", - "
\n", - "

sex

\n", - " \n", - "
64562239472PaulaRivera1997-03-25F
65595240075PaulaMaxwell1992-04-07F
70881345723PaulPowell1993-03-25M
87062540501PaulaSullivan1994-05-27F
88149453963PaulHale1995-04-16M
\n", - " \n", - "

Total: 5

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob sex \n", - "+------------+ +------------+ +-----------+ +------------+ +-----+\n", - "64562239472 Paula Rivera 1997-03-25 F \n", - "65595240075 Paula Maxwell 1992-04-07 F \n", - "70881345723 Paul Powell 1993-03-25 M \n", - "87062540501 Paula Sullivan 1994-05-27 F \n", - "88149453963 Paul Hale 1995-04-16 M \n", - " (Total: 5)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account & 'first_name in (\"Paul\", \"Paula\")' & \"year(dob) between 1990 and 1999\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Problem 2: Show the 10 youngest males whose last names start with \"Ra\"\n", - "Hint: Look into the use of wildcard pattern matching in MySQL https://dev.mysql.com/doc/refman/8.0/en/pattern-matching.html" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

phone

\n", - " \n", - "
\n", - "

first_name

\n", - " \n", - "
\n", - "

last_name

\n", - " \n", - "
\n", - "

dob

\n", - " \n", - "
\n", - "

sex

\n", - " \n", - "
10856410224RussellRamirez2013-03-15M
13536738702CharlesRamirez2006-08-12M
23530945303RaymondRamirez2012-02-24M
34265700814StevenRaymond2007-09-26M
48210412157CharlesRamirez2022-07-19M
48982711569MatthewRamirez2018-10-29M
57783081396MichaelRamsey2015-02-11M
61704905238BryanRamirez2010-05-28M
\n", - " \n", - "

Total: 8

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob sex \n", - "+------------+ +------------+ +-----------+ +------------+ +-----+\n", - "10856410224 Russell Ramirez 2013-03-15 M \n", - "13536738702 Charles Ramirez 2006-08-12 M \n", - "23530945303 Raymond Ramirez 2012-02-24 M \n", - "34265700814 Steven Raymond 2007-09-26 M \n", - "48210412157 Charles Ramirez 2022-07-19 M \n", - "48982711569 Matthew Ramirez 2018-10-29 M \n", - "57783081396 Michael Ramsey 2015-02-11 M \n", - "61704905238 Bryan Ramirez 2010-05-28 M \n", - " (Total: 8)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(\n", - " Account\n", - " & 'sex=\"M\"'\n", - " & 'last_name LIKE \"Ra%\"'\n", - " & 'dob BETWEEN \"2004-01-01\" AND \"2023-01-01\"'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([(84217041960, 'Timothy', 'Ramos', datetime.date(2023, 8, 14), 'M'),\n", - " (48210412157, 'Charles', 'Ramirez', datetime.date(2022, 7, 19), 'M'),\n", - " (48982711569, 'Matthew', 'Ramirez', datetime.date(2018, 10, 29), 'M'),\n", - " (57783081396, 'Michael', 'Ramsey', datetime.date(2015, 2, 11), 'M'),\n", - " (10856410224, 'Russell', 'Ramirez', datetime.date(2013, 3, 15), 'M'),\n", - " (23530945303, 'Raymond', 'Ramirez', datetime.date(2012, 2, 24), 'M'),\n", - " (61704905238, 'Bryan', 'Ramirez', datetime.date(2010, 5, 28), 'M'),\n", - " (34265700814, 'Steven', 'Raymond', datetime.date(2007, 9, 26), 'M'),\n", - " (13536738702, 'Charles', 'Ramirez', datetime.date(2006, 8, 12), 'M'),\n", - " (11633552162, 'Daniel', 'Ramirez', datetime.date(1998, 1, 20), 'M')],\n", - " dtype=[('phone', '\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
first_namelast_namedobsex
phone
84217041960TimothyRamos2023-08-14M
48210412157CharlesRamirez2022-07-19M
48982711569MatthewRamirez2018-10-29M
57783081396MichaelRamsey2015-02-11M
10856410224RussellRamirez2013-03-15M
23530945303RaymondRamirez2012-02-24M
61704905238BryanRamirez2010-05-28M
34265700814StevenRaymond2007-09-26M
13536738702CharlesRamirez2006-08-12M
11633552162DanielRamirez1998-01-20M
\n", - "" - ], - "text/plain": [ - " first_name last_name dob sex\n", - "phone \n", - "84217041960 Timothy Ramos 2023-08-14 M\n", - "48210412157 Charles Ramirez 2022-07-19 M\n", - "48982711569 Matthew Ramirez 2018-10-29 M\n", - "57783081396 Michael Ramsey 2015-02-11 M\n", - "10856410224 Russell Ramirez 2013-03-15 M\n", - "23530945303 Raymond Ramirez 2012-02-24 M\n", - "61704905238 Bryan Ramirez 2010-05-28 M\n", - "34265700814 Steven Raymond 2007-09-26 M\n", - "13536738702 Charles Ramirez 2006-08-12 M\n", - "11633552162 Daniel Ramirez 1998-01-20 M" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(Account & 'sex=\"M\"' & 'last_name LIKE \"Ra%\"').fetch(\n", - " order_by=\"dob DESC\", limit=10, format=\"frame\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n", - "10 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
phonefirst_namelast_namedobsex
84217041960TimothyRamos2023-08-14M
48210412157CharlesRamirez2022-07-19M
48982711569MatthewRamirez2018-10-29M
57783081396MichaelRamsey2015-02-11M
10856410224RussellRamirez2013-03-15M
23530945303RaymondRamirez2012-02-24M
61704905238BryanRamirez2010-05-28M
34265700814StevenRaymond2007-09-26M
13536738702CharlesRamirez2006-08-12M
11633552162DanielRamirez1998-01-20M
" - ], - "text/plain": [ - "[(84217041960, 'Timothy', 'Ramos', datetime.date(2023, 8, 14), 'M'),\n", - " (48210412157, 'Charles', 'Ramirez', datetime.date(2022, 7, 19), 'M'),\n", - " (48982711569, 'Matthew', 'Ramirez', datetime.date(2018, 10, 29), 'M'),\n", - " (57783081396, 'Michael', 'Ramsey', datetime.date(2015, 2, 11), 'M'),\n", - " (10856410224, 'Russell', 'Ramirez', datetime.date(2013, 3, 15), 'M'),\n", - " (23530945303, 'Raymond', 'Ramirez', datetime.date(2012, 2, 24), 'M'),\n", - " (61704905238, 'Bryan', 'Ramirez', datetime.date(2010, 5, 28), 'M'),\n", - " (34265700814, 'Steven', 'Raymond', datetime.date(2007, 9, 26), 'M'),\n", - " (13536738702, 'Charles', 'Ramirez', datetime.date(2006, 8, 12), 'M'),\n", - " (11633552162, 'Daniel', 'Ramirez', datetime.date(1998, 1, 20), 'M')]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "USE app; \n", - "SELECT * \n", - " FROM account \n", - " WHERE sex=\"M\" AND last_name LIKE \"Ra%\" \n", - " ORDER BY dob DESC LIMIT 10" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Problem 3: Show full names of the people who omitted their date of birth. Sort them alphabetically by last name / first name. Show the first 10 only.\n", - "Hint: look into the use of string https://dev.mysql.com/doc/refman/8.0/en/string-functions.html\n", - "\n", - "Hint: Comparing to NULL, use `IS NULL` or `IS NOT NULL` https://dev.mysql.com/doc/refman/8.0/en/working-with-null.html" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
full_name
phone
43491261833Adams, Heather
83134450914Allen, Brian
79135301655Allen, Jaclyn
76954265299Allison, Carrie
75682797766Alvarado, Tiffany
84436951717Alvarez, Robert
31051161174Andersen, Michelle
55950405914Anderson, David
41913101932Anderson, Jared
73383687532Anderson, Shelby
\n", - "
" - ], - "text/plain": [ - " full_name\n", - "phone \n", - "43491261833 Adams, Heather\n", - "83134450914 Allen, Brian\n", - "79135301655 Allen, Jaclyn\n", - "76954265299 Allison, Carrie\n", - "75682797766 Alvarado, Tiffany\n", - "84436951717 Alvarez, Robert\n", - "31051161174 Andersen, Michelle\n", - "55950405914 Anderson, David\n", - "41913101932 Anderson, Jared\n", - "73383687532 Anderson, Shelby" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(Account & \"dob IS NULL\").proj(full_name=\"CONCAT(last_name, ', ', first_name)\").fetch(\n", - " order_by=\"full_name\", limit=10, format=\"frame\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "10 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
full_name
Adams, Heather
Allen, Brian
Allen, Jaclyn
Allison, Carrie
Alvarado, Tiffany
Alvarez, Robert
Andersen, Michelle
Anderson, David
Anderson, Jared
Anderson, Shelby
" - ], - "text/plain": [ - "[('Adams, Heather',),\n", - " ('Allen, Brian',),\n", - " ('Allen, Jaclyn',),\n", - " ('Allison, Carrie',),\n", - " ('Alvarado, Tiffany',),\n", - " ('Alvarez, Robert',),\n", - " ('Andersen, Michelle',),\n", - " ('Anderson, David',),\n", - " ('Anderson, Jared',),\n", - " ('Anderson, Shelby',)]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "SELECT CONCAT(last_name, ', ', first_name) as full_name FROM account WHERE dob is NULL ORDER BY (full_name) LIMIT 10 " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Problem 4: Show the full names of all females born in June, also showing their age in years. Sort by last name / first name and show the first 10 only.\n", - "Hint: look into date functions https://dev.mysql.com/doc/refman/8.0/en/date-and-time-functions.html" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "10 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
full_nameage
Adams Amy39
Adams Anita23
Adams Jasmin72
Alexander Andrea19
Ali Ruth85
Allen Amanda52
Allen Julie111
Allen Kimberly1
Anderson Crystal60
Anderson Shannon50
" - ], - "text/plain": [ - "[('Adams Amy', 39),\n", - " ('Adams Anita', 23),\n", - " ('Adams Jasmin', 72),\n", - " ('Alexander Andrea', 19),\n", - " ('Ali Ruth', 85),\n", - " ('Allen Amanda', 52),\n", - " ('Allen Julie', 111),\n", - " ('Allen Kimberly', 1),\n", - " ('Anderson Crystal', 60),\n", - " ('Anderson Shannon', 50)]" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "SELECT CONCAT(last_name,\" \",first_name) as full_name, \n", - " FLOOR(DATEDIFF(NOW(),dob) / 365.25) as age FROM account Where EXTRACT(MONTH FROM dob) = 6 and sex = 'F'\n", - "ORDER BY full_name\n", - "limit 10" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
full_nameage
phone
37162199170Adams, Amy39
16124462313Adams, Anita23
72775063589Adams, Jasmin72
42738526910Alexander, Andrea19
41294256935Ali, Ruth85
68566634095Allen, Amanda52
60532809624Allen, Julie111
40455691998Allen, Kimberly1
69848440274Anderson, Crystal60
39812426076Anderson, Shannon50
\n", - "
" - ], - "text/plain": [ - " full_name age\n", - "phone \n", - "37162199170 Adams, Amy 39\n", - "16124462313 Adams, Anita 23\n", - "72775063589 Adams, Jasmin 72\n", - "42738526910 Alexander, Andrea 19\n", - "41294256935 Ali, Ruth 85\n", - "68566634095 Allen, Amanda 52\n", - "60532809624 Allen, Julie 111\n", - "40455691998 Allen, Kimberly 1\n", - "69848440274 Anderson, Crystal 60\n", - "39812426076 Anderson, Shannon 50" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(Account & 'sex = \"F\"' & \"MONTH(dob) = 6\").proj(\n", - " full_name='CONCAT(last_name, \", \", first_name)', age=\"YEAR(CURDATE()) - YEAR(dob)\"\n", - ").fetch(order_by=\"last_name, first_name\", limit=10, format=\"frame\")" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "1 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
age
0
" - ], - "text/plain": [ - "[(0,)]" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "SELECT FLOOR(DATEDIFF(CURDATE(), \"2022-10-24\") / 365.25) as age" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Problem 5: Show the full information of the youngest person who has a credit card." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "1 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
phonefirst_namelast_namedobsex
63099749199MartinSexton2023-10-17M
" - ], - "text/plain": [ - "[(63099749199, 'Martin', 'Sexton', datetime.date(2023, 10, 17), 'M')]" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql \n", - "\n", - "SELECT *\n", - "FROM account\n", - "WHERE phone IN (SELECT phone FROM credit_card)\n", - "ORDER BY dob DESC\n", - "LIMIT 1" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
dobfull_nameage_in_day
phone
630997491992023-10-17Sexton, Martin7
\n", - "
" - ], - "text/plain": [ - " dob full_name age_in_day\n", - "phone \n", - "63099749199 2023-10-17 Sexton, Martin 7" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(Account & CreditCard).aggr(\n", - " Account.proj(excluded=\"sex\"),\n", - " dob=\"MIN(dob)\",\n", - " full_name='CONCAT(last_name, \", \", first_name)',\n", - " age_in_day=\"DAY(CURDATE()) - DAY(dob)\",\n", - ").fetch(order_by=\"dob DESC\", limit=1, format=\"frame\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Problem 6: Show the full information of the oldest person who does not have a credit card." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "1 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
phonefirst_namelast_namedobsex
37263819642VictoriaPratt1907-10-28F
" - ], - "text/plain": [ - "[(37263819642, 'Victoria', 'Pratt', datetime.date(1907, 10, 28), 'F')]" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "SELECT *\n", - "FROM account\n", - "WHERE phone NOT IN (SELECT phone FROM credit_card) and dob IS NOT NULL\n", - "ORDER BY dob\n", - "LIMIT 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Problem 7: Show the first 10 customers who purchased the \"Sprint\" addon, including their age in years." - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "(pymysql.err.NotSupportedError) (1235, \"This version of MySQL doesn't yet support 'LIMIT & IN/ALL/ANY/SOME subquery'\")\n", - "[SQL: SELECT phone, first_name, last_name, sex, floor(datediff(now(), dob)/365.25) age \n", - "FROM account\n", - "WHERE phone IN (SELECT phone FROM purchase WHERE addon_id=3 ORDER BY (purchase_date) LIMIT 10)]\n", - "(Background on this error at: https://sqlalche.me/e/20/tw8g)\n" - ] - } - ], - "source": [ - "%%sql\n", - "SELECT phone, first_name, last_name, sex, floor(datediff(now(), dob)/365.25) age \n", - "FROM account\n", - "WHERE phone IN (SELECT phone FROM purchase WHERE addon_id=3 ORDER BY (purchase_date) LIMIT 10)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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first_namelast_namepurchase_dateaddon_nameage
phoneaddon_id
158756514813NormaPierce2023-09-24Sprint35
324240792513DavidGoodwin2023-09-24Sprint85
289559392583BenjaminFrazier2023-09-24Sprint110
210442294573LawrenceCollier2023-09-24Sprint31
322598168373SusanPerez2023-09-24Sprint29
267745316413CameronHo2023-09-24Sprint40
290260724293AdamCruz2023-09-24Sprint30
171821625253ScottSmith2023-09-24Sprint18
299198336813DavidValdez2023-09-24Sprint3
240465203983SusanHunter2023-09-24Sprint30
\n", - "
" - ], - "text/plain": [ - " first_name last_name purchase_date addon_name age\n", - "phone addon_id \n", - "15875651481 3 Norma Pierce 2023-09-24 Sprint 35\n", - "32424079251 3 David Goodwin 2023-09-24 Sprint 85\n", - "28955939258 3 Benjamin Frazier 2023-09-24 Sprint 110\n", - "21044229457 3 Lawrence Collier 2023-09-24 Sprint 31\n", - "32259816837 3 Susan Perez 2023-09-24 Sprint 29\n", - "26774531641 3 Cameron Ho 2023-09-24 Sprint 40\n", - "29026072429 3 Adam Cruz 2023-09-24 Sprint 30\n", - "17182162525 3 Scott Smith 2023-09-24 Sprint 18\n", - "29919833681 3 David Valdez 2023-09-24 Sprint 3\n", - "24046520398 3 Susan Hunter 2023-09-24 Sprint 30" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "((Account * Purchase * AddOn) & ('addon_name = \"Sprint\"') & (\"dob IS NOT NULL\")).aggr(\n", - " Account.proj(excluded=\"sex\"),\n", - " \"last_name\",\n", - " \"first_name\",\n", - " \"purchase_date\",\n", - " \"addon_name\",\n", - " age=\"YEAR(CURDATE()) - YEAR(dob)\",\n", - ").fetch(order_by=\"purchase_date\", limit=10, format=\"frame\")" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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phone

\n", - " \n", - "
\n", - "

addon_id

\n", - " \n", - "
\n", - "

card_number

\n", - " \n", - "
\n", - "

purchase_date

\n", - " \n", - "
\n", - " \n", - "

Total: 0

\n", - " " - ], - "text/plain": [ - "*phone *addon_id card_number purchase_date \n", - "+-------+ +----------+ +------------+ +------------+\n", - "\n", - " (Total: 0)" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Purchase() & \"purchase_date < '2023-09-01'\"" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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first_namelast_namedobsexcard_numberpurchase_dateaddon_nameprice
phoneaddon_id
267745316413CameronHo1983-11-18M3705081065936212023-09-24Sprint100.00
158756514813NormaPierce1988-12-10F3402589194449872023-09-24Sprint100.00
210442294573LawrenceCollier1992-10-28M48755982717238162023-09-24Sprint100.00
322598168373SusanPerez1994-04-08F303317663008372023-09-24Sprint100.00
324240792513DavidGoodwin1938-02-19M382974861067742023-09-24Sprint100.00
299198336813DavidValdez2020-10-30M41074082345802023-09-24Sprint100.00
289559392583BenjaminFrazier1913-07-31M41891968361601932023-09-24Sprint100.00
290260724293AdamCruz1993-02-02M49588298730952023-09-24Sprint100.00
240465203983SusanHunter1993-11-19F35044292960459132023-09-24Sprint100.00
171821625253ScottSmith2005-09-06M40025746380392023-09-24Sprint100.00
\n", - "
" - ], - "text/plain": [ - " first_name last_name dob sex card_number \\\n", - "phone addon_id \n", - "26774531641 3 Cameron Ho 1983-11-18 M 370508106593621 \n", - "15875651481 3 Norma Pierce 1988-12-10 F 340258919444987 \n", - "21044229457 3 Lawrence Collier 1992-10-28 M 4875598271723816 \n", - "32259816837 3 Susan Perez 1994-04-08 F 30331766300837 \n", - "32424079251 3 David Goodwin 1938-02-19 M 38297486106774 \n", - "29919833681 3 David Valdez 2020-10-30 M 4107408234580 \n", - "28955939258 3 Benjamin Frazier 1913-07-31 M 4189196836160193 \n", - "29026072429 3 Adam Cruz 1993-02-02 M 4958829873095 \n", - "24046520398 3 Susan Hunter 1993-11-19 F 3504429296045913 \n", - "17182162525 3 Scott Smith 2005-09-06 M 4002574638039 \n", - "\n", - " purchase_date addon_name price \n", - "phone addon_id \n", - "26774531641 3 2023-09-24 Sprint 100.00 \n", - "15875651481 3 2023-09-24 Sprint 100.00 \n", - "21044229457 3 2023-09-24 Sprint 100.00 \n", - "32259816837 3 2023-09-24 Sprint 100.00 \n", - "32424079251 3 2023-09-24 Sprint 100.00 \n", - "29919833681 3 2023-09-24 Sprint 100.00 \n", - "28955939258 3 2023-09-24 Sprint 100.00 \n", - "29026072429 3 2023-09-24 Sprint 100.00 \n", - "24046520398 3 2023-09-24 Sprint 100.00 \n", - "17182162525 3 2023-09-24 Sprint 100.00 " - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(Account * Purchase * AddOn & 'addon_name=\"Sprint\"').fetch(\n", - " order_by=\"purchase_date\", limit=10, format=\"frame\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/005-Queries.ipynb b/db-course/005-Queries.ipynb deleted file mode 100644 index 81ef6e4..0000000 --- a/db-course/005-Queries.ipynb +++ /dev/null @@ -1,4139 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Queries\n", - "\n", - "We will use the design produced in [004-Design](./004-Design.ipynb). Please execute that notebook first to define and populate the `app` schema." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Recall the design\n", - "\n", - "The following code connects to the `app` schema and generates Python classes to access its classes." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "CreditCard\n", - "\n", - "\n", - "CreditCard\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account->CreditCard\n", - "\n", - "\n", - "\n", - "\n", - "Purchase\n", - "\n", - "\n", - "Purchase\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account->Purchase\n", - "\n", - "\n", - "\n", - "\n", - "CreditCard->Purchase\n", - "\n", - "\n", - "\n", - "\n", - "AddOn\n", - "\n", - "\n", - "AddOn\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AddOn->Purchase\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import datajoint as dj\n", - "\n", - "schema = dj.Schema(\"app\")\n", - "schema.spawn_missing_classes()\n", - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

phone

\n", - " \n", - "
\n", - "

addon_id

\n", - " \n", - "
\n", - "

card_number

\n", - " \n", - "
57739576919160400630491
13185990415260400744029
95867190471160402177269
74932324173160404678041
74932324173260404678041
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25674636975260416322612
\n", - "

...

\n", - "

Total: 5000

\n", - " " - ], - "text/plain": [ - "*phone *addon_id card_number \n", - "+------------+ +----------+ +------------+\n", - "57739576919 1 60400630491 \n", - "13185990415 2 60400744029 \n", - "95867190471 1 60402177269 \n", - "74932324173 1 60404678041 \n", - "74932324173 2 60404678041 \n", - "74932324173 3 60404678041 \n", - "54717139763 3 60405261136 \n", - "66966252830 1 60406800650 \n", - "35067438860 1 60410940211 \n", - "50651554632 1 60413723457 \n", - "25674636975 1 60416322612 \n", - "25674636975 2 60416322612 \n", - " ...\n", - " (Total: 5000)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Purchase()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Queries\n", - "\n", - "## Simple queries" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

phone

\n", - " \n", - "
\n", - "

first_name

\n", - " \n", - "
\n", - "

last_name

\n", - " \n", - "
\n", - "

dob

\n", - " \n", - "
\n", - "

sex

\n", - " \n", - "
10019062770LaurenSnyder1961-08-18F
10020965828VictorRoss1952-07-12M
10040756087SusanBrown1992-12-14F
10047410808PaigeHolder1942-09-02F
10051015428WilliamEstrada1985-12-31M
10068079952NatalieBrown1950-09-09F
10092122352HaroldRivera2011-10-19M
10103783887JordanGilbert2003-03-16M
10104072787SharonShaw1926-05-13F
10116699599AlecSmith1958-07-31M
10116742023CrystalMartin1997-09-20F
10119628924PaulKing2014-03-10M
\n", - "

...

\n", - "

Total: 10501

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob sex \n", - "+------------+ +------------+ +-----------+ +------------+ +-----+\n", - "10019062770 Lauren Snyder 1961-08-18 F \n", - "10020965828 Victor Ross 1952-07-12 M \n", - "10040756087 Susan Brown 1992-12-14 F \n", - "10047410808 Paige Holder 1942-09-02 F \n", - "10051015428 William Estrada 1985-12-31 M \n", - "10068079952 Natalie Brown 1950-09-09 F \n", - "10092122352 Harold Rivera 2011-10-19 M \n", - "10103783887 Jordan Gilbert 2003-03-16 M \n", - "10104072787 Sharon Shaw 1926-05-13 F \n", - "10116699599 Alec Smith 1958-07-31 M \n", - "10116742023 Crystal Martin 1997-09-20 F \n", - "10119628924 Paul King 2014-03-10 M \n", - " ...\n", - " (Total: 10501)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'phone': 10019062770,\n", - " 'first_name': 'Lauren',\n", - " 'last_name': 'Snyder',\n", - " 'dob': datetime.date(1961, 8, 18),\n", - " 'sex': 'F'},\n", - " {'phone': 10020965828,\n", - " 'first_name': 'Victor',\n", - " 'last_name': 'Ross',\n", - " 'dob': datetime.date(1952, 7, 12),\n", - " 'sex': 'M'},\n", - " {'phone': 10040756087,\n", - " 'first_name': 'Susan',\n", - " 'last_name': 'Brown',\n", - " 'dob': datetime.date(1992, 12, 14),\n", - " 'sex': 'F'},\n", - " {'phone': 10047410808,\n", - " 'first_name': 'Paige',\n", - " 'last_name': 'Holder',\n", - " 'dob': datetime.date(1942, 9, 2),\n", - " 'sex': 'F'},\n", - " {'phone': 10051015428,\n", - " 'first_name': 'William',\n", - " 'last_name': 'Estrada',\n", - " 'dob': datetime.date(1985, 12, 31),\n", - " 'sex': 'M'}]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account.fetch(as_dict=True, limit=5)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'phone': 15366336995,\n", - " 'first_name': 'Rachel',\n", - " 'last_name': 'Abbott',\n", - " 'dob': datetime.date(2000, 4, 22),\n", - " 'sex': 'F'},\n", - " {'phone': 91972525114,\n", - " 'first_name': 'Robert',\n", - " 'last_name': 'Abbott',\n", - " 'dob': datetime.date(1929, 1, 19),\n", - " 'sex': 'M'},\n", - " {'phone': 36893728519,\n", - " 'first_name': 'William',\n", - " 'last_name': 'Abbott',\n", - " 'dob': datetime.date(2003, 6, 22),\n", - " 'sex': 'M'},\n", - " {'phone': 51992023735,\n", - " 'first_name': 'Brianna',\n", - " 'last_name': 'Acevedo',\n", - " 'dob': datetime.date(1982, 4, 2),\n", - " 'sex': 'F'},\n", - " {'phone': 38801647559,\n", - " 'first_name': 'Christian',\n", - " 'last_name': 'Acevedo',\n", - " 'dob': datetime.date(1940, 4, 10),\n", - " 'sex': 'M'}]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account.fetch(as_dict=True, order_by=(\"last_name\", \"first_name\"), limit=5)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'phone': 61348693087,\n", - " 'first_name': 'Jennifer',\n", - " 'last_name': 'Singh',\n", - " 'dob': datetime.date(2022, 8, 6),\n", - " 'sex': 'F'},\n", - " {'phone': 40205369474,\n", - " 'first_name': 'Alexander',\n", - " 'last_name': 'Thomas',\n", - " 'dob': datetime.date(2022, 8, 6),\n", - " 'sex': 'M'},\n", - " {'phone': 47397056664,\n", - " 'first_name': 'Garrett',\n", - " 'last_name': 'Mooney',\n", - " 'dob': datetime.date(2022, 7, 27),\n", - " 'sex': 'M'},\n", - " {'phone': 72207025461,\n", - " 'first_name': 'Rhonda',\n", - " 'last_name': 'Bates',\n", - " 'dob': datetime.date(2022, 7, 21),\n", - " 'sex': 'F'},\n", - " {'phone': 85504732206,\n", - " 'first_name': 'Desiree',\n", - " 'last_name': 'Perez',\n", - " 'dob': datetime.date(2022, 7, 20),\n", - " 'sex': 'F'}]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account.fetch(as_dict=True, order_by=(\"dob DESC\"), limit=5, offset=100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Restriction (selecting rows)\n", - "In SQL, this is the `WHERE` clause. In DataJoint, we use operators `&` and `-`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "
\n", - "

phone

\n", - " \n", - "
\n", - "

first_name

\n", - " \n", - "
\n", - "

last_name

\n", - " \n", - "
\n", - "

dob

\n", - " \n", - "
\n", - "

sex

\n", - " \n", - "
\n", - " \n", - "

Total: 0

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob sex \n", - "+-------+ +------------+ +-----------+ +-----+ +-----+\n", - "\n", - " (Total: 0)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account() & {\"phone\": 69235537483}" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

phone

\n", - " \n", - "
\n", - "

first_name

\n", - " \n", - "
\n", - "

last_name

\n", - " \n", - "
\n", - "

dob

\n", - " \n", - "
\n", - "

sex

\n", - " \n", - "
10923785986MichaelSoto1945-08-13M
11005781260MichaelWashington1930-08-21M
11505392467MichaelRusso1969-04-05M
11605501506MichaelLopez1927-02-09M
11842665345MichaelWright1950-02-04M
13730659059MichaelMurphyNone
14362797615MichaelJohnson1918-12-16M
14419578436MichaelFox2016-08-02M
14443705983MichaelJones2021-12-07M
14470747853MichaelGibson1929-11-08M
14979628541MichaelGill1997-07-20M
15011091753MichaelSimpson1927-03-14M
\n", - "

...

\n", - "

Total: 263

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob sex \n", - "+------------+ +------------+ +------------+ +------------+ +-----+\n", - "10923785986 Michael Soto 1945-08-13 M \n", - "11005781260 Michael Washington 1930-08-21 M \n", - "11505392467 Michael Russo 1969-04-05 M \n", - "11605501506 Michael Lopez 1927-02-09 M \n", - "11842665345 Michael Wright 1950-02-04 M \n", - "13730659059 Michael Murphy None \n", - "14362797615 Michael Johnson 1918-12-16 M \n", - "14419578436 Michael Fox 2016-08-02 M \n", - "14443705983 Michael Jones 2021-12-07 M \n", - "14470747853 Michael Gibson 1929-11-08 M \n", - "14979628541 Michael Gill 1997-07-20 M \n", - "15011091753 Michael Simpson 1927-03-14 M \n", - " ...\n", - " (Total: 263)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account() & {\"first_name\": \"Michael\"}" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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phone

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\n", - "

first_name

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\n", - "

last_name

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\n", - "

dob

\n", - " \n", - "
\n", - "

sex

\n", - " \n", - "
10923785986MichaelSoto1945-08-13M
11005781260MichaelWashington1930-08-21M
11505392467MichaelRusso1969-04-05M
11605501506MichaelLopez1927-02-09M
11842665345MichaelWright1950-02-04M
13730659059MichaelMurphyNone
14362797615MichaelJohnson1918-12-16M
14419578436MichaelFox2016-08-02M
14443705983MichaelJones2021-12-07M
14470747853MichaelGibson1929-11-08M
14979628541MichaelGill1997-07-20M
15011091753MichaelSimpson1927-03-14M
\n", - "

...

\n", - "

Total: 263

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob sex \n", - "+------------+ +------------+ +------------+ +------------+ +-----+\n", - "10923785986 Michael Soto 1945-08-13 M \n", - "11005781260 Michael Washington 1930-08-21 M \n", - "11505392467 Michael Russo 1969-04-05 M \n", - "11605501506 Michael Lopez 1927-02-09 M \n", - "11842665345 Michael Wright 1950-02-04 M \n", - "13730659059 Michael Murphy None \n", - "14362797615 Michael Johnson 1918-12-16 M \n", - "14419578436 Michael Fox 2016-08-02 M \n", - "14443705983 Michael Jones 2021-12-07 M \n", - "14470747853 Michael Gibson 1929-11-08 M \n", - "14979628541 Michael Gill 1997-07-20 M \n", - "15011091753 Michael Simpson 1927-03-14 M \n", - " ...\n", - " (Total: 263)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account & 'first_name=\"Michael\"'" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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18775649461AnneLozano2018-10-25F
21157989480AnneSpencer2004-10-23F
34561118573AnneWilson2010-12-31F
50673081066AnneMunoz2014-01-07F
62663220810AnneWilliams2007-06-20F
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Total: 5

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10306464131ZacharyVaughan2023-06-04M
11472981791KristinWard2023-06-14F
14249470562JohnContreras2023-03-05M
14900154875DanielPerry2023-04-08M
15775138364EricColon2023-04-30M
18206197063ErinMendoza2023-07-18F
19118508049RebekahHardin2023-01-25F
20022976140ShawnTorres2023-09-14M
20280673168RebeccaFord2023-02-28F
21265760789StevenWebster2023-03-11M
23861001702MichaelDavis2023-01-26M
28389500853MichaelSchmidt2023-06-24M
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...

\n", - "

Total: 70

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob sex \n", - "+------------+ +------------+ +-----------+ +------------+ +-----+\n", - "10306464131 Zachary Vaughan 2023-06-04 M \n", - "11472981791 Kristin Ward 2023-06-14 F \n", - "14249470562 John Contreras 2023-03-05 M \n", - "14900154875 Daniel Perry 2023-04-08 M \n", - "15775138364 Eric Colon 2023-04-30 M \n", - "18206197063 Erin Mendoza 2023-07-18 F \n", - "19118508049 Rebekah Hardin 2023-01-25 F \n", - "20022976140 Shawn Torres 2023-09-14 M \n", - "20280673168 Rebecca Ford 2023-02-28 F \n", - "21265760789 Steven Webster 2023-03-11 M \n", - "23861001702 Michael Davis 2023-01-26 M \n", - "28389500853 Michael Schmidt 2023-06-24 M \n", - " ...\n", - " (Total: 70)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account & \"DATEDIFF(now(), dob) < 300\"" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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10019062770LaurenSnyder1961-08-18F
10020965828VictorRoss1952-07-12M
10040756087SusanBrown1992-12-14F
10047410808PaigeHolder1942-09-02F
10051015428WilliamEstrada1985-12-31M
10068079952NatalieBrown1950-09-09F
10092122352HaroldRivera2011-10-19M
10103783887JordanGilbert2003-03-16M
10104072787SharonShaw1926-05-13F
10116699599AlecSmith1958-07-31M
10116742023CrystalMartin1997-09-20F
10119628924PaulKing2014-03-10M
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...

\n", - "

Total: 9931

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob sex \n", - "+------------+ +------------+ +-----------+ +------------+ +-----+\n", - "10019062770 Lauren Snyder 1961-08-18 F \n", - "10020965828 Victor Ross 1952-07-12 M \n", - "10040756087 Susan Brown 1992-12-14 F \n", - "10047410808 Paige Holder 1942-09-02 F \n", - "10051015428 William Estrada 1985-12-31 M \n", - "10068079952 Natalie Brown 1950-09-09 F \n", - "10092122352 Harold Rivera 2011-10-19 M \n", - "10103783887 Jordan Gilbert 2003-03-16 M \n", - "10104072787 Sharon Shaw 1926-05-13 F \n", - "10116699599 Alec Smith 1958-07-31 M \n", - "10116742023 Crystal Martin 1997-09-20 F \n", - "10119628924 Paul King 2014-03-10 M \n", - " ...\n", - " (Total: 9931)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account - \"DATEDIFF(now(), dob) < 300\"" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "millennials = Account & 'dob > \"1978-01-01\"' & 'dob < \"1997-01-01\"'" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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15523841597MichaelLane1978-10-30M
17473604755MichaelGarcia1988-01-04M
17839832842MichaelNovak1982-12-13M
19103518385MichaelLee1988-05-13M
21002247444MichaelOwens1989-09-13M
21096585246MichaelJones1980-09-28M
23404263421MichaelKramer1990-01-02M
29638831412MichaelAtkins1981-08-09M
30870173313MichaelGomez1993-08-22M
31813197078MichaelMartinez1990-06-27M
37528261688MichaelRichardson1991-02-16M
39402056546MichaelHall1984-03-18M
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...

\n", - "

Total: 38

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob sex \n", - "+------------+ +------------+ +------------+ +------------+ +-----+\n", - "15523841597 Michael Lane 1978-10-30 M \n", - "17473604755 Michael Garcia 1988-01-04 M \n", - "17839832842 Michael Novak 1982-12-13 M \n", - "19103518385 Michael Lee 1988-05-13 M \n", - "21002247444 Michael Owens 1989-09-13 M \n", - "21096585246 Michael Jones 1980-09-28 M \n", - "23404263421 Michael Kramer 1990-01-02 M \n", - "29638831412 Michael Atkins 1981-08-09 M \n", - "30870173313 Michael Gomez 1993-08-22 M \n", - "31813197078 Michael Martinez 1990-06-27 M \n", - "37528261688 Michael Richardson 1991-02-16 M \n", - "39402056546 Michael Hall 1984-03-18 M \n", - " ...\n", - " (Total: 38)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "millennials & 'first_name=\"Michael\"'" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "millennials = Account & 'dob BETWEEN \"1978-01-01\" AND \"1997-01-01\"'" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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10170646196AnthonyCamacho1992-10-11M
10177712986MarioLewis1996-06-14M
10187345213DannyEllis1993-10-07M
10324766437KarenDaniels1996-09-04F
10358655985ChristinaMiller1978-10-05F
10430100835MauriceLeach1978-06-10M
10545086383AlexisWilliams1980-08-07F
10565161854BillyPrice1988-09-12M
10582843624SabrinaKey1989-01-07F
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...

\n", - "

Total: 1624

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob sex \n", - "+------------+ +------------+ +-----------+ +------------+ +-----+\n", - "10040756087 Susan Brown 1992-12-14 F \n", - "10051015428 William Estrada 1985-12-31 M \n", - "10126431571 Jessica Hayes 1983-05-29 F \n", - "10170646196 Anthony Camacho 1992-10-11 M \n", - "10177712986 Mario Lewis 1996-06-14 M \n", - "10187345213 Danny Ellis 1993-10-07 M \n", - "10324766437 Karen Daniels 1996-09-04 F \n", - "10358655985 Christina Miller 1978-10-05 F \n", - "10430100835 Maurice Leach 1978-06-10 M \n", - "10545086383 Alexis Williams 1980-08-07 F \n", - "10565161854 Billy Price 1988-09-12 M \n", - "10582843624 Sabrina Key 1989-01-07 F \n", - " ...\n", - " (Total: 1624)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "millennials" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Projection (selecting, calculating, and renaming columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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10019062770LaurenSnyder1961-08-18F
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10051015428WilliamEstrada1985-12-31M
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10092122352HaroldRivera2011-10-19M
10103783887JordanGilbert2003-03-16M
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10116699599AlecSmith1958-07-31M
10116742023CrystalMartin1997-09-20F
10119628924PaulKing2014-03-10M
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...

\n", - "

Total: 10501

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob sex \n", - "+------------+ +------------+ +-----------+ +------------+ +-----+\n", - "10019062770 Lauren Snyder 1961-08-18 F \n", - "10020965828 Victor Ross 1952-07-12 M \n", - "10040756087 Susan Brown 1992-12-14 F \n", - "10047410808 Paige Holder 1942-09-02 F \n", - "10051015428 William Estrada 1985-12-31 M \n", - "10068079952 Natalie Brown 1950-09-09 F \n", - "10092122352 Harold Rivera 2011-10-19 M \n", - "10103783887 Jordan Gilbert 2003-03-16 M \n", - "10104072787 Sharon Shaw 1926-05-13 F \n", - "10116699599 Alec Smith 1958-07-31 M \n", - "10116742023 Crystal Martin 1997-09-20 F \n", - "10119628924 Paul King 2014-03-10 M \n", - " ...\n", - " (Total: 10501)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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phone

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...

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Total: 10501

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10019062770Snyder
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10116742023Martin
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...

\n", - "

Total: 10501

\n", - " " - ], - "text/plain": [ - "*phone last_name \n", - "+------------+ +-----------+\n", - "10019062770 Snyder \n", - "10020965828 Ross \n", - "10040756087 Brown \n", - "10047410808 Holder \n", - "10051015428 Estrada \n", - "10068079952 Brown \n", - "10092122352 Rivera \n", - "10103783887 Gilbert \n", - "10104072787 Shaw \n", - "10116699599 Smith \n", - "10116742023 Martin \n", - "10119628924 King \n", - " ...\n", - " (Total: 10501)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account.proj(\"last_name\")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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10104072787SharonShawF
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10116742023CrystalMartinF
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\n", - "

...

\n", - "

Total: 10501

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name sex \n", - "+------------+ +------------+ +-----------+ +-----+\n", - "10019062770 Lauren Snyder F \n", - "10020965828 Victor Ross M \n", - "10040756087 Susan Brown F \n", - "10047410808 Paige Holder F \n", - "10051015428 William Estrada M \n", - "10068079952 Natalie Brown F \n", - "10092122352 Harold Rivera M \n", - "10103783887 Jordan Gilbert M \n", - "10104072787 Sharon Shaw F \n", - "10116699599 Alec Smith M \n", - "10116742023 Crystal Martin F \n", - "10119628924 Paul King M \n", - " ...\n", - " (Total: 10501)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account.proj(..., \"-dob\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "full_name = Account.proj(full_name='concat(last_name, \", \", first_name)')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "d = full_name.fetch(order_by=\"full_name\", format=\"frame\")" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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......
67805643956Zuniga, Chris
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Total: 6896

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Total: 893

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10010213238StaceyStaceyMitchell2015-10-02
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10025696662JamesJamesSmall2016-12-29
10037061898DeborahDeborahFoley1929-02-06
10046294691GwendolynGwendolynTurner2020-12-19
10046839321MarciaMarciaMartinez1990-07-15
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10070547405LeahLeahPratt1986-06-06
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...

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Total: 10001

\n", - " " - ], - "text/plain": [ - "*id first_name name last_name dob \n", - "+------------+ +------------+ +-----------+ +-----------+ +------------+\n", - "10008004398 Thomas Thomas Li 2009-05-22 \n", - "10010213238 Stacey Stacey Mitchell 2015-10-02 \n", - "10012512171 James James Henderson 1920-10-09 \n", - "10017661220 Donald Donald Anderson 1955-04-16 \n", - "10025696662 James James Small 2016-12-29 \n", - "10037061898 Deborah Deborah Foley 1929-02-06 \n", - "10046294691 Gwendolyn Gwendolyn Turner 2020-12-19 \n", - "10046839321 Marcia Marcia Martinez 1990-07-15 \n", - "10068182298 Victor Victor Clark 1986-09-13 \n", - "10070173668 Ronald Ronald Duffy 1969-03-23 \n", - "10070547405 Leah Leah Pratt 1986-06-06 \n", - "10079669194 Ryan Ryan Johnson 1978-03-08 \n", - " ...\n", - " (Total: 10001)" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account().proj(..., id=\"phone\", name=\"(first_name)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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...

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Total: 2225

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last_name

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dob

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10012512171JamesHenderson1920-10-09
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10037061898DeborahFoley1929-02-06
10046294691GwendolynTurner2020-12-19
10046839321MarciaMartinez1990-07-15
10079669194RyanJohnson1978-03-08
10099464091MatthewSmith1949-05-04
10102995813DavidYoung1923-12-29
10116323806ScottHernandez1930-11-01
10120727834RobertRodriguez1951-04-10
10151756488ChelseaCox1940-12-19
10153686421AaronBradley1912-02-04
\n", - "

...

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Total: 7768

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob \n", - "+------------+ +------------+ +-----------+ +------------+\n", - "10012512171 James Henderson 1920-10-09 \n", - "10017661220 Donald Anderson 1955-04-16 \n", - "10037061898 Deborah Foley 1929-02-06 \n", - "10046294691 Gwendolyn Turner 2020-12-19 \n", - "10046839321 Marcia Martinez 1990-07-15 \n", - "10079669194 Ryan Johnson 1978-03-08 \n", - "10099464091 Matthew Smith 1949-05-04 \n", - "10102995813 David Young 1923-12-29 \n", - "10116323806 Scott Hernandez 1930-11-01 \n", - "10120727834 Robert Rodriguez 1951-04-10 \n", - "10151756488 Chelsea Cox 1940-12-19 \n", - "10153686421 Aaron Bradley 1912-02-04 \n", - " ...\n", - " (Total: 7768)" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account & CreditCard" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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first_name

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dob

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10008004398ThomasLi2009-05-22
10010213238StaceyMitchell2015-10-02
10025696662JamesSmall2016-12-29
10068182298VictorClark1986-09-13
10070173668RonaldDuffy1969-03-23
10070547405LeahPratt1986-06-06
10092945283DeannaHayes2015-08-16
10095918854RaymondAdams2018-05-22
10127762503TracyMack1970-04-14
10152397506JamesWade1963-04-15
10271564710JuanCook1951-05-25
10313226393EricMann1925-11-01
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...

\n", - "

Total: 2233

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob \n", - "+------------+ +------------+ +-----------+ +------------+\n", - "10008004398 Thomas Li 2009-05-22 \n", - "10010213238 Stacey Mitchell 2015-10-02 \n", - "10025696662 James Small 2016-12-29 \n", - "10068182298 Victor Clark 1986-09-13 \n", - "10070173668 Ronald Duffy 1969-03-23 \n", - "10070547405 Leah Pratt 1986-06-06 \n", - "10092945283 Deanna Hayes 2015-08-16 \n", - "10095918854 Raymond Adams 2018-05-22 \n", - "10127762503 Tracy Mack 1970-04-14 \n", - "10152397506 James Wade 1963-04-15 \n", - "10271564710 Juan Cook 1951-05-25 \n", - "10313226393 Eric Mann 1925-11-01 \n", - " ...\n", - " (Total: 2233)" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account - CreditCard" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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dob

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10153686421AaronBradley1912-02-04
10291521740MelissaDavis1983-04-26
10451478023DianeDavis1919-12-31
10496786948AndrewCarter2001-05-02
10734349722KennethGraves1932-10-16
10782010443AmberWalters1933-02-12
10844022864GaryRogers1927-04-10
10862836412RandySanchez2018-09-21
11194304584MatthewLucas2003-03-25
11221407194JustinNelson1953-04-11
11279950296LanceLynch1999-03-15
11545066688NatalieJensen1908-03-17
\n", - "

...

\n", - "

Total: 660

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob \n", - "+------------+ +------------+ +-----------+ +------------+\n", - "10153686421 Aaron Bradley 1912-02-04 \n", - "10291521740 Melissa Davis 1983-04-26 \n", - "10451478023 Diane Davis 1919-12-31 \n", - "10496786948 Andrew Carter 2001-05-02 \n", - "10734349722 Kenneth Graves 1932-10-16 \n", - "10782010443 Amber Walters 1933-02-12 \n", - "10844022864 Gary Rogers 1927-04-10 \n", - "10862836412 Randy Sanchez 2018-09-21 \n", - "11194304584 Matthew Lucas 2003-03-25 \n", - "11221407194 Justin Nelson 1953-04-11 \n", - "11279950296 Lance Lynch 1999-03-15 \n", - "11545066688 Natalie Jensen 1908-03-17 \n", - " ...\n", - " (Total: 660)" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# All the people that purchased AddOn #2.\n", - "Account & (Purchase & \"addon_id=2\")" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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10496786948AndrewCarter2001-05-02
11883734808PhilipBlack2014-01-10
12328381833LouisDavis1946-10-09
12966892803KathrynNorman1913-02-27
15503931605RobertAdams1921-06-04
16065195696MathewJohnson1981-11-10
16266465922MaryMcpherson2003-11-23
17386440647CalebRoth1953-04-11
18690145667CalebParks2012-04-18
19361732502RodneyWise1934-08-11
21566103221MichaelGuerrero1971-07-15
23868945012TristanLopez1992-01-20
\n", - "

...

\n", - "

Total: 66

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob \n", - "+------------+ +------------+ +-----------+ +------------+\n", - "10496786948 Andrew Carter 2001-05-02 \n", - "11883734808 Philip Black 2014-01-10 \n", - "12328381833 Louis Davis 1946-10-09 \n", - "12966892803 Kathryn Norman 1913-02-27 \n", - "15503931605 Robert Adams 1921-06-04 \n", - "16065195696 Mathew Johnson 1981-11-10 \n", - "16266465922 Mary Mcpherson 2003-11-23 \n", - "17386440647 Caleb Roth 1953-04-11 \n", - "18690145667 Caleb Parks 2012-04-18 \n", - "19361732502 Rodney Wise 1934-08-11 \n", - "21566103221 Michael Guerrero 1971-07-15 \n", - "23868945012 Tristan Lopez 1992-01-20 \n", - " ...\n", - " (Total: 66)" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Give me all accounts who have purchased both Addon 2 and 3\n", - "Account & (Purchase & \"addon_id=2\") & (Purchase & \"addon_id=3\")" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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dob

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10153686421AaronBradley1912-02-04
10166849316BenjaminGomez1929-04-24
10291521740MelissaDavis1983-04-26
10303625356RebeccaGriffith1975-01-10
10451478023DianeDavis1919-12-31
10496786948AndrewCarter2001-05-02
10535081800JohnMcmahon1929-01-30
10661381021KennethShaffer1946-09-07
10722472340HeidiCallahan1932-01-05
10734349722KennethGraves1932-10-16
10782010443AmberWalters1933-02-12
10789671183MichaelRush1913-12-31
\n", - "

...

\n", - "

Total: 1284

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob \n", - "+------------+ +------------+ +-----------+ +------------+\n", - "10153686421 Aaron Bradley 1912-02-04 \n", - "10166849316 Benjamin Gomez 1929-04-24 \n", - "10291521740 Melissa Davis 1983-04-26 \n", - "10303625356 Rebecca Griffith 1975-01-10 \n", - "10451478023 Diane Davis 1919-12-31 \n", - "10496786948 Andrew Carter 2001-05-02 \n", - "10535081800 John Mcmahon 1929-01-30 \n", - "10661381021 Kenneth Shaffer 1946-09-07 \n", - "10722472340 Heidi Callahan 1932-01-05 \n", - "10734349722 Kenneth Graves 1932-10-16 \n", - "10782010443 Amber Walters 1933-02-12 \n", - "10789671183 Michael Rush 1913-12-31 \n", - " ...\n", - " (Total: 1284)" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Give me all accounts who have purchased Addon 2 or 3\n", - "Account & (Purchase & \"addon_id=2 OR addon_id=3\")\n", - "Account & (Purchase & \"addon_id in (2, 3)\")\n", - "Account & (Purchase & [\"addon_id=3\", \"addon_id=2\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [], - "source": [ - "# Give me all accounts who have purchased Addon 2 but not 3" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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first_name

\n", - " \n", - "
\n", - "

last_name

\n", - " \n", - "
\n", - "

dob

\n", - " \n", - "
10012512171JamesHenderson1920-10-09
10017661220DonaldAnderson1955-04-16
10037061898DeborahFoley1929-02-06
10046294691GwendolynTurner2020-12-19
10046839321MarciaMartinez1990-07-15
10079669194RyanJohnson1978-03-08
10099464091MatthewSmith1949-05-04
10102995813DavidYoung1923-12-29
10116323806ScottHernandez1930-11-01
10120727834RobertRodriguez1951-04-10
10151756488ChelseaCox1940-12-19
10153686421AaronBradley1912-02-04
\n", - "

...

\n", - "

Total: 7304

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob \n", - "+------------+ +------------+ +-----------+ +------------+\n", - "10012512171 James Henderson 1920-10-09 \n", - "10017661220 Donald Anderson 1955-04-16 \n", - "10037061898 Deborah Foley 1929-02-06 \n", - "10046294691 Gwendolyn Turner 2020-12-19 \n", - "10046839321 Marcia Martinez 1990-07-15 \n", - "10079669194 Ryan Johnson 1978-03-08 \n", - "10099464091 Matthew Smith 1949-05-04 \n", - "10102995813 David Young 1923-12-29 \n", - "10116323806 Scott Hernandez 1930-11-01 \n", - "10120727834 Robert Rodriguez 1951-04-10 \n", - "10151756488 Chelsea Cox 1940-12-19 \n", - "10153686421 Aaron Bradley 1912-02-04 \n", - " ...\n", - " (Total: 7304)" - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Accounts with credit cards that have no purchases\n", - "Account & (CreditCard - Purchase)" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

phone

\n", - " \n", - "
\n", - "

first_name

\n", - " \n", - "
\n", - "

last_name

\n", - " \n", - "
\n", - "

dob

\n", - " \n", - "
10012512171JamesHenderson1920-10-09
10017661220DonaldAnderson1955-04-16
10037061898DeborahFoley1929-02-06
10046294691GwendolynTurner2020-12-19
10046839321MarciaMartinez1990-07-15
10079669194RyanJohnson1978-03-08
10099464091MatthewSmith1949-05-04
10102995813DavidYoung1923-12-29
10116323806ScottHernandez1930-11-01
10120727834RobertRodriguez1951-04-10
10151756488ChelseaCox1940-12-19
10169406225JenniferReid1954-09-30
\n", - "

...

\n", - "

Total: 5936

\n", - " " - ], - "text/plain": [ - "*phone first_name last_name dob \n", - "+------------+ +------------+ +-----------+ +------------+\n", - "10012512171 James Henderson 1920-10-09 \n", - "10017661220 Donald Anderson 1955-04-16 \n", - "10037061898 Deborah Foley 1929-02-06 \n", - "10046294691 Gwendolyn Turner 2020-12-19 \n", - "10046839321 Marcia Martinez 1990-07-15 \n", - "10079669194 Ryan Johnson 1978-03-08 \n", - "10099464091 Matthew Smith 1949-05-04 \n", - "10102995813 David Young 1923-12-29 \n", - "10116323806 Scott Hernandez 1930-11-01 \n", - "10120727834 Robert Rodriguez 1951-04-10 \n", - "10151756488 Chelsea Cox 1940-12-19 \n", - "10169406225 Jennifer Reid 1954-09-30 \n", - " ...\n", - " (Total: 5936)" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Accounts with credit cards but no purchases\n", - "(Account & CreditCard) - Purchase" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## DeMorgan's Laws" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (1792059247.py, line 1)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m Cell \u001b[0;32mIn[3], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m not (A or B) == not A and not B\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" - ] - } - ], - "source": [ - "not (A or B) == not A and not B\n", - "\n", - "not (A and B) == not A or not B\n", - "\n", - "not (A and not B) == not A or B" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/005-QueriesSQL.ipynb b/db-course/005-QueriesSQL.ipynb deleted file mode 100644 index 283c0a5..0000000 --- a/db-course/005-QueriesSQL.ipynb +++ /dev/null @@ -1,590 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Queries\n", - "\n", - "We will use the design produced in [004-Design](./004-Design.ipynb). Please execute that notebook first to define and populate the `app` schema." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Recall the design" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pymysql\n", - "\n", - "pymysql.install_as_MySQLdb()\n", - "\n", - "%load_ext sql\n", - "%config SqlMagic.autocommit=True\n", - "\n", - "connection_string = \"mysql://root:simple@127.0.0.1\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%sql $connection_string" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Queries\n", - "\n", - "## Simple queries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%sql\n", - "use app" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%sql\n", - "SHOW TABLES" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%sql\n", - "\n", - "SELECT * FROM account LIMIT 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "SELECT * FROM account ORDER BY last_name DESC, first_name DESC LIMIT 10 " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%sql\n", - "SELECT * FROM account ORDER BY dob LIMIT 10 OFFSET 100 " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Restriction (selecting rows)\n", - "In SQL restriction is done in the `WHERE` clause." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "\n", - "SELECT * \n", - " FROM account \n", - " WHERE phone = 69235537483 " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "\n", - "SELECT * \n", - " FROM account \n", - " WHERE first_name = \"Michael\"\n", - " ORDER BY dob\n", - " LIMIT 10 OFFSET 20 \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "\n", - "SELECT * FROM account WHERE first_name=\"Anne\" AND dob > \"2001-01-01\" LIMIT 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "SELECT * \n", - "FROM account \n", - "WHERE DATEDIFF(now(), dob) < 300\n", - "LIMIT 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "SELECT * \n", - "FROM account \n", - "WHERE NOT (DATEDIFF(now(), dob) < 300) \n", - "LIMIT 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "SELECT * \n", - "FROM account \n", - "WHERE dob is NULL \n", - "LIMIT 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "SELECT * FROM account WHERE dob BETWEEN \"1978-01-01\" AND \"1997-01-01\" AND first_name=\"Michael\"\n", - "LIMIT 10" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Projection (selecting, calculating, and renaming columns)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "SELECT * FROM account LIMIT 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "SELECT DISTINCT last_name, first_name FROM account\n", - "ORDER BY last_name, first_name\n", - "LIMIT 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%sql\n", - "SELECT phone, first_name, last_name FROM account\n", - "ORDER BY last_name, first_name\n", - "LIMIT 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "SELECT phone, concat(last_name, \", \", first_name) full_name FROM account\n", - "ORDER BY full_name\n", - "LIMIT 5\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "SELECT * FROM (\n", - " SELECT phone, first_name, last_name, floor(datediff(now(), dob)/365.25) age \n", - " FROM account) as q\n", - "WHERE age < 35\n", - "LIMIT 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%sql\n", - "\n", - "SELECT *, phone id \n", - "FROM account\n", - "LIMIT 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%sql\n", - "SELECT phone from account where last_name>\"S\" LIMIT 5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Restrictions by a query\n", - "In SQL, this is a query where the `WHERE` clause includes another `SELECT`` clause." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Give me all the accounts that have a credit card" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- accounts with a credit card\n", - "SELECT * \n", - "FROM account \n", - "WHERE phone IN (SELECT phone FROM credit_card)\n", - "LIMIT 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- accounts with no credit card\n", - "SELECT * \n", - "FROM account \n", - "WHERE phone NOT IN (SELECT phone FROM credit_card)\n", - "LIMIT 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "select * from purchase limit 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- All the accounts that purchased AddOn #2.\n", - "SELECT * FROM account \n", - "WHERE phone IN (SELECT phone FROM purchase WHERE addon_id=2)\n", - "LIMIT 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- All the accounts that purchased AddOn #2.\n", - "SELECT DISTINCT phone FROM purchase WHERE addon_id=2\n", - "LIMIT 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- Give me all accounts who have purchased both Addon 2 and 3\n", - "SELECT *\n", - "FROM account WHERE \n", - " phone in (SELECT phone FROM purchase WHERE addon_id=2) AND \n", - " phone in (SELECT phone FROM purchase WHERE addon_id=3)\n", - "LIMIT 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- Give me all accounts who have purchased both Addon 2 or 3\n", - "SELECT *\n", - "FROM account WHERE \n", - " phone in (SELECT phone FROM purchase WHERE addon_id=2) or \n", - " phone in (SELECT phone FROM purchase WHERE addon_id=3)\n", - "LIMIT 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- Give me all accounts who have purchased both Addon 2 or 3\n", - "SELECT *\n", - "FROM account WHERE \n", - " phone IN (SELECT phone FROM purchase WHERE addon_id in (2, 3))\n", - "LIMIT 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- Give me all accounts who have purchased both Addon 2 but not 3\n", - "SELECT *\n", - "FROM account WHERE \n", - " phone IN (SELECT phone FROM purchase WHERE addon_id = 2) AND \n", - " phone NOT IN (SELECT phone FROM purchase WHERE addon_id = 3)\n", - "LIMIT 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%sql\n", - "-- Accounts with credit cards that have no purchases\n", - "SELECT * FROM account\n", - "WHERE phone in (SELECT phone from credit_card where card_number NOT IN (SELECT card_number FROM purchase))\n", - "LIMIT 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- Accounts with credit cards but no purchases\n", - "SELECT * FROM account\n", - "WHERE phone in (SELECT phone from credit_card)\n", - "AND phone NOT IN (SELECT phone FROM purchase)\n", - "LIMIT 5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## DeMorgan's Laws" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "NOT (A OR B) == NOT A AND NOT B\n", - "NOT (A AND B) == NOT A OR NOT B\n", - "\n", - "NOT (A AND NOT B) == NOT A OR B" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/006-Joins-HW.ipynb b/db-course/006-Joins-HW.ipynb deleted file mode 100644 index b8bb2d2..0000000 --- a/db-course/006-Joins-HW.ipynb +++ /dev/null @@ -1,2759 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "import pymysql\n", - "\n", - "pymysql.install_as_MySQLdb()\n", - "%load_ext sql\n", - "%config SqlMagic.autocommit=True\n", - "%sql mysql://root:simple@127.0.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-10-31 22:53:10,315][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", - "[2023-10-31 22:53:10,987][INFO]: Connected root@fakeservices.datajoint.io:3306\n" - ] - } - ], - "source": [ - "import datajoint as dj\n", - "\n", - "sales = dj.Schema(\"classicsales\")\n", - "sales.spawn_missing_classes()\n", - "\n", - "nations = dj.Schema(\"nation\")\n", - "nations.spawn_missing_classes()\n", - "\n", - "hotel = dj.Schema(\"hotel\")\n", - "hotel.spawn_missing_classes()\n", - "\n", - "university = dj.Schema(\"university\")\n", - "university.spawn_missing_classes()\n", - "\n", - "app = dj.Schema(\"app\")\n", - "app.spawn_missing_classes()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "0\n", - "\n", - "0\n", - "\n", - "\n", - "\n", - "Report\n", - "\n", - "\n", - "Report\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "0->Report\n", - "\n", - "\n", - "\n", - "\n", - "1\n", - "\n", - "1\n", - "\n", - "\n", - "\n", - "Customer\n", - "\n", - "\n", - "Customer\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "1->Customer\n", - "\n", - "\n", - "\n", - "\n", - "ProductLine\n", - "\n", - "\n", - "ProductLine\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Product\n", - "\n", - "\n", - "Product\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "ProductLine->Product\n", - "\n", - "\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order.Item\n", - "\n", - "\n", - "Order.Item\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order->Order.Item\n", - "\n", - "\n", - "\n", - "\n", - "Payment\n", - "\n", - "\n", - "Payment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Employee\n", - "\n", - "\n", - "Employee\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Employee->0\n", - "\n", - "\n", - "\n", - "\n", - "Employee->1\n", - "\n", - "\n", - "\n", - "\n", - "Employee->Report\n", - "\n", - "\n", - "\n", - "\n", - "Customer->Order\n", - "\n", - "\n", - "\n", - "\n", - "Customer->Payment\n", - "\n", - 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Some queries may be performed using subquiries without joins.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 1 (sales)**: Show customer names along with the last names of their sales rep (omitting ones that don't have a sales rep).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 2 (sales)**: Show all employees, including the last name of their boss (omitting the top boss who reports to no one).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 3 (sales):** Show all employees whose boss' office is outside the USA.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 4 (sales):** Show all employees whose boss is in a different office.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 5 (sales):** Show all customers who have bought model trains.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 6 (sales):** Show all employees who have not sold model trains.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 7 (nations)**: Show the names of all countries in North America along with their populations in 1996\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 8 (nations)**: Show the names of countries on the continent of Oceania along with their populations in 1996\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 9 (nations)**: Show the top 10 countries by their absolute population increase between 1990 and 2010.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 10 (nations)**: Show the top 10 countries by their percent increase in per capita gdp from 1990 to 2010.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 11 (nations)**: List the top 5 most populous countries where Spanish is the official language in 2010.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 12 (nations)**: List the top 10 wealthiest (per capita) non-English speaking countries in 2015.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 13 (hotel)**: List all the reservations for 2023-11-01, including the room price, and the guest's last name. (Feel free to pick a different date.)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 14 (hotel)**: Show all guests who have checked in and not checked out.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 15 (university):** Pick one student and show his or her course enrollments in the current term.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 16 (university):** Show all students who have received As in math in the current term.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 17 (app):** List names of the buyers from the latest 10 sales of the Marathon app.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 18 (app):** List the latest purchase made on the buyers' birthday, including the name of the addon that was purchased.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "AddOn\n", - "\n", - "\n", - "AddOn\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Purchase\n", - "\n", - "\n", - "Purchase\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AddOn->Purchase\n", - "\n", - "\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account->Purchase\n", - "\n", - "\n", - "\n", - "\n", - "CreditCard\n", - "\n", - "\n", - "CreditCard\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account->CreditCard\n", - "\n", - "\n", - "\n", - "\n", - "CreditCard->Purchase\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(app)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-10-31 23:03:34,744][WARNING]: MySQL server has gone away. 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Tables_in_app
#add_on
account
credit_card
purchase
~log
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phoneaddon_namepurchase_date
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phoneaddon_namepurchase_date
21012023597Track & Field2023-10-28
" - ], - "text/plain": [ - "[(21012023597, 'Track & Field', datetime.date(2023, 10, 28))]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "% % sql\n", - "-- show the latest purchase made on a person's birthday, show the addon name\n", - "SELECT phone, addon_name, purchase_date\n", - "FROM account\n", - " NATURAL JOIN purchase\n", - " NATURAL JOIN `#add_on`\n", - "WHERE\n", - " month(purchase_date) = month(dob)\n", - " AND day(purchase_date) = day(dob)\n", - " AND purchase_date = (\n", - " SELECT purchase_date\n", - " FROM account\n", - " NATURAL JOIN purchase\n", - " NATURAL JOIN `#add_on`\n", - " WHERE\n", - " month(purchase_date) = month(dob)\n", - " AND day(purchase_date) = day(dob)\n", - " ORDER BY purchase_date DESC\n", - " LIMIT 1\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "RegionAreas\n", - "\n", - "\n", - "RegionAreas\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Countries\n", - "\n", - "\n", - "Countries\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "CountryStats\n", - "\n", - "\n", - "CountryStats\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Countries->CountryStats\n", - "\n", - "\n", - "\n", - "\n", - "CountryLanguages\n", - "\n", - "\n", - "CountryLanguages\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Countries->CountryLanguages\n", - "\n", - "\n", - "\n", - "\n", - "Continents\n", - "\n", - "\n", - "Continents\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Regions\n", - "\n", - "\n", - "Regions\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Continents->Regions\n", - "\n", - "\n", - "\n", - "\n", - "Regions->Countries\n", - "\n", - "\n", - "\n", - "\n", - "Languages\n", - "\n", - "\n", - "Languages\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Languages->CountryLanguages\n", - "\n", - "\n", - "\n", - "\n", - "Guests\n", - "\n", - "\n", - "Guests\n", - "\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(nations)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - "

region_id

\n", - " \n", - "
\n", - "

name

\n", - " \n", - "
\n", - "

continent_id

\n", - " \n", - "
1Caribbean1
2Southern and Central Asia2
3Central Africa3
4Southern Europe4
5Middle East2
6South America5
7Polynesia6
8Antarctica7
9Australia and New Zealand6
10Western Europe4
11Eastern Africa3
12Western Africa3
\n", - "

...

\n", - "

Total: 25

\n", - " " - ], - "text/plain": [ - "*region_id name continent_id \n", - "+-----------+ +------------+ +------------+\n", - "1 Caribbean 1 \n", - "2 Southern and C 2 \n", - "3 Central Africa 3 \n", - "4 Southern Europ 4 \n", - "5 Middle East 2 \n", - "6 South America 5 \n", - "7 Polynesia 6 \n", - "8 Antarctica 7 \n", - "9 Australia and 6 \n", - "10 Western Europe 4 \n", - "11 Eastern Africa 3 \n", - "12 Western Africa 3 \n", - " ...\n", - " (Total: 25)" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# problem 7\n", - "Regions()" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - "

continent_id

\n", - " \n", - "
\n", - "

region_id

\n", - " \n", - "
\n", - "

country_id

\n", - " \n", - "
\n", - "

year

\n", - " \n", - "
\n", - "

country_name

\n", - " \n", - "
\n", - "

population

\n", - " \n", - "
1111996Aruba83200
11141996Antigua and Barbuda70173
11251996Bahamas283978
11321996Barbados267049
11521996Cuba10939293
11591996Dominica70936
11611996Dominican Republic7952763
11871996Grenada101001
11971996Haiti7887304
111081996Jamaica2558637
111161996Saint Kitts and Nevis42475
111231996Saint Lucia148834
111731996Puerto Rico3724655
112141996Trinidad and Tobago1257549
112271996Saint Vincent and the Grenadines107976
114281996Belize213664
114511996Costa Rica3632362
114891996Guatemala10646674
114951996Honduras5874809
1141361996Mexico93147044
1141571996Nicaragua4741578
1141661996Panama2796291
1141931996El Salvador5689938
115291996Bermuda60129
115381996Canada29610218
115881996Greenland55900
1152241996United States269394000
\n", - " \n", - "

Total: 27

\n", - " " - ], - "text/plain": [ - "*continent_id *region_id *country_id *year country_name population \n", - "+------------+ +-----------+ +------------+ +------+ +------------+ +------------+\n", - "1 1 1 1996 Aruba 83200 \n", - "1 1 14 1996 Antigua and Ba 70173 \n", - "1 1 25 1996 Bahamas 283978 \n", - "1 1 32 1996 Barbados 267049 \n", - "1 1 52 1996 Cuba 10939293 \n", - "1 1 59 1996 Dominica 70936 \n", - "1 1 61 1996 Dominican Repu 7952763 \n", - "1 1 87 1996 Grenada 101001 \n", - "1 1 97 1996 Haiti 7887304 \n", - "1 1 108 1996 Jamaica 2558637 \n", - "1 1 116 1996 Saint Kitts an 42475 \n", - "1 1 123 1996 Saint Lucia 148834 \n", - "1 1 173 1996 Puerto Rico 3724655 \n", - "1 1 214 1996 Trinidad and T 1257549 \n", - "1 1 227 1996 Saint Vincent 107976 \n", - "1 14 28 1996 Belize 213664 \n", - "1 14 51 1996 Costa Rica 3632362 \n", - "1 14 89 1996 Guatemala 10646674 \n", - "1 14 95 1996 Honduras 5874809 \n", - "1 14 136 1996 Mexico 93147044 \n", - "1 14 157 1996 Nicaragua 4741578 \n", - "1 14 166 1996 Panama 2796291 \n", - "1 14 193 1996 El Salvador 5689938 \n", - "1 15 29 1996 Bermuda 60129 \n", - "1 15 38 1996 Canada 29610218 \n", - "1 15 88 1996 Greenland 55900 \n", - "1 15 224 1996 United States 269394000 \n", - " (Total: 27)" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(\n", - " (\n", - " Continents.proj(..., continent_name=\"name\")\n", - " * Regions.proj(..., region_name=\"name\")\n", - " * Countries.proj(..., country_name=\"name\")\n", - " * CountryStats()\n", - " )\n", - " & 'continent_name=\"North America\"'\n", - " & \"year=1996\"\n", - ").proj(\"country_name\", \"population\")" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [], - "source": [ - "dj.config[\"display.limit\"] = 30" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n", - "10 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
namepopulation
United States269394000
Mexico93147044
Canada29610218
Cuba10939293
Guatemala10646674
Dominican Republic7952763
Haiti7887304
Honduras5874809
El Salvador5689938
Nicaragua4741578
" - ], - "text/plain": [ - "[('United States', 269394000),\n", - " ('Mexico', 93147044),\n", - " ('Canada', 29610218),\n", - " ('Cuba', 10939293),\n", - " ('Guatemala', 10646674),\n", - " ('Dominican Republic', 7952763),\n", - " ('Haiti', 7887304),\n", - " ('Honduras', 5874809),\n", - " ('El Salvador', 5689938),\n", - " ('Nicaragua', 4741578)]" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "use nation;\n", - "\n", - "SELECT countries.name, country_stats.population FROM country_stats NATURAL JOIN countries \n", - "JOIN regions USING (region_id) JOIN continents USING (continent_id)\n", - "WHERE year=1996 and continents.name=\"North America\"\n", - "ORDER BY population DESC\n", - "LIMIT 10" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - "

country_id

\n", - " \n", - "
\n", - "

name

\n", - " \n", - "
\n", - "

area

\n", - " \n", - "
\n", - "

national_day

\n", - " \n", - "
\n", - "

country_code2

\n", - " \n", - "
\n", - "

country_code3

\n", - " \n", - "
\n", - "

region_id

\n", - " \n", - "
4Anguilla96.001967-05-30AIAIA1
7Netherlands Antilles800.00NoneANANT1
12Antarctica13120000.00NoneAQATA8
13French Southern territories7780.00NoneTFATF8
35Bouvet Island59.00NoneBVBVT8
39Cocos (Keeling) Islands14.00NoneCCCCK9
47Cook Islands236.00NoneCKCOK7
53Christmas Island135.00NoneCXCXR9
66Western Sahara266000.00NoneEHESH20
72Falkland Islands12173.00NoneFKFLK6
80Gibraltar6.00NoneGIGIB4
82Guadeloupe1705.00NoneGPGLP1
90French Guiana90000.00NoneGFGUF6
94Heard Island and McDonald Islands359.00NoneHMHMD8
101British Indian Ocean Territory78.00NoneIOIOT11
146Montserrat102.00NoneMSMSR1
147Martinique1102.00NoneMQMTQ1
151Mayotte373.00NoneYTMYT11
155Norfolk Island36.00NoneNFNFK9
158Niue260.00NoneNUNIU7
167Pitcairn49.00NonePNPCN7
174North Korea120538.00NoneKPPRK18
180Réunion2510.00NoneREREU11
181Romania238391.001877-05-09ROROM13
188South Georgia and the South Sandwich Islands3903.00NoneGSSGS8
189Saint Helena314.00NoneSHSHN12
190Svalbard and Jan Mayen62422.00NoneSJSJM19
196Saint Pierre and Miquelon242.00NonePMSPM15
210Tokelau12.00NoneTKTKL7
212East Timor14874.002002-05-20TPTMP16
\n", - "

...

\n", - "

Total: 36

\n", - " " - ], - "text/plain": [ - "*country_id name area national_day country_code2 country_code3 region_id \n", - "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +-----------+\n", - "4 Anguilla 96.00 1967-05-30 AI AIA 1 \n", - "7 Netherlands An 800.00 None AN ANT 1 \n", - "12 Antarctica 13120000.00 None AQ ATA 8 \n", - "13 French Souther 7780.00 None TF ATF 8 \n", - "35 Bouvet Island 59.00 None BV BVT 8 \n", - "39 Cocos (Keeling 14.00 None CC CCK 9 \n", - "47 Cook Islands 236.00 None CK COK 7 \n", - "53 Christmas Isla 135.00 None CX CXR 9 \n", - "66 Western Sahara 266000.00 None EH ESH 20 \n", - "72 Falkland Islan 12173.00 None FK FLK 6 \n", - "80 Gibraltar 6.00 None GI GIB 4 \n", - "82 Guadeloupe 1705.00 None GP GLP 1 \n", - "90 French Guiana 90000.00 None GF GUF 6 \n", - "94 Heard Island a 359.00 None HM HMD 8 \n", - "101 British Indian 78.00 None IO IOT 11 \n", - "146 Montserrat 102.00 None MS MSR 1 \n", - "147 Martinique 1102.00 None MQ MTQ 1 \n", - "151 Mayotte 373.00 None YT MYT 11 \n", - "155 Norfolk Island 36.00 None NF NFK 9 \n", - "158 Niue 260.00 None NU NIU 7 \n", - "167 Pitcairn 49.00 None PN PCN 7 \n", - "174 North Korea 120538.00 None KP PRK 18 \n", - "180 Réunion 2510.00 None RE REU 11 \n", - "181 Romania 238391.00 1877-05-09 RO ROM 13 \n", - "188 South Georgia 3903.00 None GS SGS 8 \n", - "189 Saint Helena 314.00 None SH SHN 12 \n", - "190 Svalbard and J 62422.00 None SJ SJM 19 \n", - "196 Saint Pierre a 242.00 None PM SPM 15 \n", - "210 Tokelau 12.00 None TK TKL 7 \n", - "212 East Timor 14874.00 2002-05-20 TP TMP 16 \n", - " ...\n", - " (Total: 36)" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Countries - CountryStats" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/006-Joins.ipynb b/db-course/006-Joins.ipynb deleted file mode 100644 index ac67344..0000000 --- a/db-course/006-Joins.ipynb +++ /dev/null @@ -1,1443 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Joins\n", - "\n", - "We will use the design produced in [004-Design](./004-Design.ipynb). Please execute that notebook first to define and populate the `app` schema." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Recall the design\n", - "\n", - "The following code connects to the `app` schema and generates Python classes to access its classes." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-10-18 00:23:08,510][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", - "[2023-10-18 00:23:08,678][INFO]: Connected root@fakeservices.datajoint.io:3306\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "CreditCard\n", - "\n", - "\n", - "CreditCard\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Purchase\n", - "\n", - "\n", - "Purchase\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "CreditCard->Purchase\n", - "\n", - "\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account->CreditCard\n", - "\n", - "\n", - "\n", - "\n", - "Account->Purchase\n", - "\n", - "\n", - "\n", - "\n", - "AddOn\n", - "\n", - "\n", - "AddOn\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AddOn->Purchase\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import datajoint as dj\n", - "\n", - "schema = dj.Schema(\"app\")\n", - "schema.spawn_missing_classes()\n", - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

phone

\n", - " \n", - "
\n", - "

addon_id

\n", - " \n", - "
\n", - "

card_number

\n", - " \n", - "
\n", - "

purchase_date

\n", - " \n", - "
10003214241235202976956929282023-10-16
10022078153245465643155522023-09-23
10023766171241721458276356622023-10-13
10042418609127183658672670782023-10-09
10042418609227183658672670782023-10-06
1004440096016390562181702023-09-28
1004440096026390562181702023-10-15
1004440096036390562181702023-10-03
10055219928365451973324263142023-10-06
10060050070122819981956669212023-09-28
10060050070222819981956669212023-09-23
10060050070322819981956669212023-10-06
\n", - "

...

\n", - "

Total: 5000

\n", - " " - ], - "text/plain": [ - "*phone *addon_id card_number purchase_date \n", - "+------------+ +----------+ +------------+ +------------+\n", - "10003214241 2 35202976956929 2023-10-16 \n", - "10022078153 2 4546564315552 2023-09-23 \n", - "10023766171 2 41721458276356 2023-10-13 \n", - "10042418609 1 27183658672670 2023-10-09 \n", - "10042418609 2 27183658672670 2023-10-06 \n", - "10044400960 1 639056218170 2023-09-28 \n", - "10044400960 2 639056218170 2023-10-15 \n", - "10044400960 3 639056218170 2023-10-03 \n", - "10055219928 3 65451973324263 2023-10-06 \n", - "10060050070 1 22819981956669 2023-09-28 \n", - "10060050070 2 22819981956669 2023-09-23 \n", - "10060050070 3 22819981956669 2023-10-06 \n", - " ...\n", - " (Total: 5000)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Purchase()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Queries with Joins\n", - "\n", - "## Cross Join" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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100032142413SamanthaJohnson1948-01-03FSprint100.00
100032142412SamanthaJohnson1948-01-03FMarathon26.20
100032142411SamanthaJohnson1948-01-03FTrack & Field13.99
100133045973KeithPage1981-06-02MSprint100.00
100133045972KeithPage1981-06-02MMarathon26.20
100133045971KeithPage1981-06-02MTrack & Field13.99
100197920103GregMatthews1964-06-26MSprint100.00
100197920102GregMatthews1964-06-26MMarathon26.20
100197920101GregMatthews1964-06-26MTrack & Field13.99
100220781533TammyThompson1998-02-18FSprint100.00
100220781532TammyThompson1998-02-18FMarathon26.20
100220781531TammyThompson1998-02-18FTrack & Field13.99
\n", - "

...

\n", - "

Total: 31503

\n", - " " - ], - "text/plain": [ - "*phone *addon_id first_name last_name dob sex addon_name price \n", - "+------------+ +----------+ +------------+ +-----------+ +------------+ +-----+ +------------+ +--------+\n", - "10003214241 3 Samantha Johnson 1948-01-03 F Sprint 100.00 \n", - "10003214241 2 Samantha Johnson 1948-01-03 F Marathon 26.20 \n", - "10003214241 1 Samantha Johnson 1948-01-03 F Track & Field 13.99 \n", - "10013304597 3 Keith Page 1981-06-02 M Sprint 100.00 \n", - "10013304597 2 Keith Page 1981-06-02 M Marathon 26.20 \n", - "10013304597 1 Keith Page 1981-06-02 M Track & Field 13.99 \n", - "10019792010 3 Greg Matthews 1964-06-26 M Sprint 100.00 \n", - "10019792010 2 Greg Matthews 1964-06-26 M Marathon 26.20 \n", - "10019792010 1 Greg Matthews 1964-06-26 M Track & Field 13.99 \n", - "10022078153 3 Tammy Thompson 1998-02-18 F Sprint 100.00 \n", - "10022078153 2 Tammy Thompson 1998-02-18 F Marathon 26.20 \n", - "10022078153 1 Tammy Thompson 1998-02-18 F Track & Field 13.99 \n", - " ...\n", - " (Total: 31503)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account * AddOn" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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addon_id

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last_name

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dob

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sex

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price

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100032142413SamanthaJohnson1948-01-03FSprint100.00
100032142412SamanthaJohnson1948-01-03FMarathon26.20
100032142411SamanthaJohnson1948-01-03FTrack & Field13.99
100220781533TammyThompson1998-02-18FSprint100.00
100220781532TammyThompson1998-02-18FMarathon26.20
100220781531TammyThompson1998-02-18FTrack & Field13.99
100395357763RachelGrant1910-04-13FSprint100.00
100395357762RachelGrant1910-04-13FMarathon26.20
100395357761RachelGrant1910-04-13FTrack & Field13.99
100424186093KimberlyHoffman1934-07-15FSprint100.00
100424186092KimberlyHoffman1934-07-15FMarathon26.20
100424186091KimberlyHoffman1934-07-15FTrack & Field13.99
\n", - "

...

\n", - "

Total: 15000

\n", - " " - ], - "text/plain": [ - "*phone *addon_id first_name last_name dob sex addon_name price \n", - "+------------+ +----------+ +------------+ +-----------+ +------------+ +-----+ +------------+ +--------+\n", - "10003214241 3 Samantha Johnson 1948-01-03 F Sprint 100.00 \n", - "10003214241 2 Samantha Johnson 1948-01-03 F Marathon 26.20 \n", - "10003214241 1 Samantha Johnson 1948-01-03 F Track & Field 13.99 \n", - "10022078153 3 Tammy Thompson 1998-02-18 F Sprint 100.00 \n", - "10022078153 2 Tammy Thompson 1998-02-18 F Marathon 26.20 \n", - "10022078153 1 Tammy Thompson 1998-02-18 F Track & Field 13.99 \n", - "10039535776 3 Rachel Grant 1910-04-13 F Sprint 100.00 \n", - "10039535776 2 Rachel Grant 1910-04-13 F Marathon 26.20 \n", - "10039535776 1 Rachel Grant 1910-04-13 F Track & Field 13.99 \n", - "10042418609 3 Kimberly Hoffman 1934-07-15 F Sprint 100.00 \n", - "10042418609 2 Kimberly Hoffman 1934-07-15 F Marathon 26.20 \n", - "10042418609 1 Kimberly Hoffman 1934-07-15 F Track & Field 13.99 \n", - " ...\n", - " (Total: 15000)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account * AddOn & 'sex=\"F\"'" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "CreditCard\n", - "\n", - "\n", - "CreditCard\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Purchase\n", - "\n", - "\n", - "Purchase\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "CreditCard->Purchase\n", - "\n", - "\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account->CreditCard\n", - "\n", - "\n", - "\n", - "\n", - "Account->Purchase\n", - "\n", - "\n", - "\n", - "\n", - "AddOn\n", - "\n", - "\n", - "AddOn\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AddOn->Purchase\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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purchase_date

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price

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10042418609127183658672670782023-10-09Track & Field13.99
1004440096016390562181702023-09-28Track & Field13.99
10060050070122819981956669212023-09-28Track & Field13.99
1007201928416304074001532023-09-28Track & Field13.99
10078599367141366209311713612023-10-09Track & Field13.99
10088316347160116045337915652023-10-09Track & Field13.99
1022653872413709123149206362023-10-10Track & Field13.99
1023078087112131908942081662023-09-29Track & Field13.99
10265798436149895315178782023-09-23Track & Field13.99
1027347257313764773842602342023-09-25Track & Field13.99
1028030729711800177064561552023-10-03Track & Field13.99
1048557154312131214015121622023-10-13Track & Field13.99
\n", - "

...

\n", - "

Total: 5000

\n", - " " - ], - "text/plain": [ - "*phone *addon_id card_number purchase_date addon_name price \n", - "+------------+ +----------+ +------------+ +------------+ +------------+ +-------+\n", - "10042418609 1 27183658672670 2023-10-09 Track & Field 13.99 \n", - "10044400960 1 639056218170 2023-09-28 Track & Field 13.99 \n", - "10060050070 1 22819981956669 2023-09-28 Track & Field 13.99 \n", - "10072019284 1 630407400153 2023-09-28 Track & Field 13.99 \n", - "10078599367 1 41366209311713 2023-10-09 Track & Field 13.99 \n", - "10088316347 1 60116045337915 2023-10-09 Track & Field 13.99 \n", - "10226538724 1 37091231492063 2023-10-10 Track & Field 13.99 \n", - "10230780871 1 21319089420816 2023-09-29 Track & Field 13.99 \n", - "10265798436 1 4989531517878 2023-09-23 Track & Field 13.99 \n", - "10273472573 1 37647738426023 2023-09-25 Track & Field 13.99 \n", - "10280307297 1 18001770645615 2023-10-03 Track & Field 13.99 \n", - "10485571543 1 21312140151216 2023-10-13 Track & Field 13.99 \n", - " ...\n", - " (Total: 5000)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Purchase * AddOn" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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card_number

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price

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10042418609127183658672670782023-10-09Track & Field13.99
1004440096016390562181702023-09-28Track & Field13.99
10060050070122819981956669212023-09-28Track & Field13.99
1007201928416304074001532023-09-28Track & Field13.99
10078599367141366209311713612023-10-09Track & Field13.99
10088316347160116045337915652023-10-09Track & Field13.99
1022653872413709123149206362023-10-10Track & Field13.99
1023078087112131908942081662023-09-29Track & Field13.99
10265798436149895315178782023-09-23Track & Field13.99
1027347257313764773842602342023-09-25Track & Field13.99
1028030729711800177064561552023-10-03Track & Field13.99
1048557154312131214015121622023-10-13Track & Field13.99
\n", - "

...

\n", - "

Total: 5000

\n", - " " - ], - "text/plain": [ - "*phone *addon_id card_number purchase_date addon_name price \n", - "+------------+ +----------+ +------------+ +------------+ +------------+ +-------+\n", - "10042418609 1 27183658672670 2023-10-09 Track & Field 13.99 \n", - "10044400960 1 639056218170 2023-09-28 Track & Field 13.99 \n", - "10060050070 1 22819981956669 2023-09-28 Track & Field 13.99 \n", - "10072019284 1 630407400153 2023-09-28 Track & Field 13.99 \n", - "10078599367 1 41366209311713 2023-10-09 Track & Field 13.99 \n", - "10088316347 1 60116045337915 2023-10-09 Track & Field 13.99 \n", - "10226538724 1 37091231492063 2023-10-10 Track & Field 13.99 \n", - "10230780871 1 21319089420816 2023-09-29 Track & Field 13.99 \n", - "10265798436 1 4989531517878 2023-09-23 Track & Field 13.99 \n", - "10273472573 1 37647738426023 2023-09-25 Track & Field 13.99 \n", - "10280307297 1 18001770645615 2023-10-03 Track & Field 13.99 \n", - "10485571543 1 21312140151216 2023-10-13 Track & Field 13.99 \n", - " ...\n", - " (Total: 5000)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Purchase * AddOn" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Person(dj.Manual):\n", - " definition = \"\"\"\n", - " person_id : int \n", - " --- \n", - " full_name : varchar(60)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Dependent(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Person\n", - " -> Person.proj(provider_id=\"person_id\")\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "0\n", - "\n", - "0\n", - "\n", - "\n", - "\n", - "Dependent\n", - "\n", - "\n", - "Dependent\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "0->Dependent\n", - "\n", - "\n", - "\n", - "\n", - "Purchase\n", - "\n", - "\n", - "Purchase\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person->0\n", - "\n", - "\n", - "\n", - "\n", - "Person->Dependent\n", - "\n", - "\n", - "\n", - "\n", - "CreditCard\n", - "\n", - "\n", - "CreditCard\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "CreditCard->Purchase\n", - "\n", - "\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account->Purchase\n", - "\n", - "\n", - "\n", - "\n", - "Account->CreditCard\n", - "\n", - "\n", - "\n", - "\n", - "AddOn\n", - "\n", - "\n", - "AddOn\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AddOn->Purchase\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "Person.insert(((1, \"Bob\"), (2, \"Anne\"), (3, \"Dave\"), (4, \"Carol\")))" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "Dependent.insert1((2, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "Dependent.insert1((3, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "Dependent.insert1((4, 2))" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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21AnneBob
31DaveBob
42CarolAnne
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Total: 3

\n", - " " - ], - "text/plain": [ - "*person_id *provider_id full_name provider_full_\n", - "+-----------+ +------------+ +-----------+ +------------+\n", - "2 1 Anne Bob \n", - "3 1 Dave Bob \n", - "4 2 Carol Anne \n", - " (Total: 3)" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Person * Dependent * Person.proj(\n", - " provider_id=\"person_id\", provider_full_name=\"full_name\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/006-JoinsSQL.ipynb b/db-course/006-JoinsSQL.ipynb deleted file mode 100644 index 932a148..0000000 --- a/db-course/006-JoinsSQL.ipynb +++ /dev/null @@ -1,701 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The sql extension is already loaded. To reload it, use:\n", - " %reload_ext sql\n" - ] - } - ], - "source": [ - "import pymysql\n", - "\n", - "pymysql.install_as_MySQLdb()\n", - "%load_ext sql\n", - "%config SqlMagic.autocommit=True\n", - "%sql mysql://root:simple@127.0.0.1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Joins" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "1 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "CREATE SCHEMA depend" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "use depend" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "(pymysql.err.OperationalError) (1050, \"Table 'person' already exists\")\n", - "[SQL: CREATE TABLE person (\n", - " person_id int,\n", - " full_name varchar(60),\n", - " PRIMARY KEY (person_id)\n", - ");]\n", - "(Background on this error at: https://sqlalche.me/e/20/e3q8)\n" - ] - } - ], - "source": [ - "%%sql\n", - "\n", - "CREATE TABLE person (\n", - " person_id int,\n", - " full_name varchar(60),\n", - " PRIMARY KEY (person_id)\n", - ");" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "CREATE TABLE dependent (\n", - " person_id int,\n", - " provider_id int,\n", - " PRIMARY KEY (person_id),\n", - " FOREIGN KEY (person_id) REFERENCES person(person_id), \n", - " FOREIGN KEY (provider_id) REFERENCES person(person_id))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "4 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "INSERT INTO person (person_id, full_name) VALUES \n", - " (1, \"Bob\"),\n", - " (2, \"Anne\"),\n", - " (3, \"Dave\"),\n", - " (4, \"Carol\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "4 rows affected.\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql \n", - "INSERT INTO dependent (person_id, provider_id) VALUES \n", - " (2, 1),\n", - " (3, 1),\n", - " (4, 2),\n", - " (1, 4)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "16 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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" - ], - "text/plain": [ - "[(1, 'Bob', 4), (2, 'Anne', 1), (3, 'Dave', 1), (4, 'Carol', 2)]" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "-- equijoin on a common attribute\n", - "SELECT * FROM person NATURAL JOIN dependent " - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "4 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
person_idfull_nameperson_id_1provider_idprovider_name
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"cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pymysql\n", - "\n", - "pymysql.install_as_MySQLdb()\n", - "%load_ext sql\n", - "%config SqlMagic.autocommit=True\n", - "%sql mysql://root:simple@127.0.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj\n", - "\n", - "sales = dj.Schema(\"classicsales\")\n", - "sales.spawn_missing_classes()\n", - "\n", - "nations = dj.Schema(\"nation\")\n", - "nations.spawn_missing_classes()\n", - "\n", - "hotel = dj.Schema(\"hotel\")\n", - "hotel.spawn_missing_classes()\n", - "\n", - "university = dj.Schema(\"university\")\n", - "university.spawn_missing_classes()\n", - "\n", - "app = dj.Schema(\"app\")\n", - "app.spawn_missing_classes()" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - 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"\n", - "\n", - "\n", - "\n", - "\n", - "Office->Employee\n", - "\n", - "\n", - "\n", - "\n", - "Employee->26\n", - "\n", - "\n", - "\n", - "\n", - "Employee->27\n", - "\n", - "\n", - "\n", - "\n", - "Employee->Report\n", - "\n", - "\n", - "\n", - "\n", - "Customer->Payment\n", - "\n", - "\n", - "\n", - "\n", - "Customer->Order\n", - "\n", - "\n", - "\n", - "\n", - "Purchase\n", - "\n", - "\n", - "Purchase\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "CreditCard\n", - "\n", - "\n", - "CreditCard\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "CreditCard->Purchase\n", - "\n", - "\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account->Purchase\n", - "\n", - "\n", - "\n", - "\n", - "Account->CreditCard\n", - "\n", - "\n", - "\n", - "\n", - "AddOn\n", - "\n", - "\n", - "AddOn\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AddOn->Purchase\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 80, - 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\n", - "

office_code

\n", - " \n", - "
\n", - "

city

\n", - " \n", - "
\n", - "

phone

\n", - " \n", - "
\n", - "

postal_line1

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postal_line2

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state

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country

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postal_code

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territory

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employee_count

\n", - " calculated attribute\n", - "
1San Francisco+1 650 219 4782100 Market StreetSuite 300CAUSA94080NA6
2Boston+1 215 837 08251550 Court PlaceSuite 102MAUSA02107NA2
3NYC+1 212 555 3000523 East 53rd Streetapt. 5ANYUSA10022NA2
4Paris+33 14 723 440443 Rue Jouffroy D'abbansNoneNoneFrance75017EMEA5
5Tokyo+81 33 224 50004-1 KioichoNoneChiyoda-KuJapan102-8578Japan2
6Sydney+61 2 9264 24515-11 Wentworth AvenueFloor #2NoneAustraliaNSW 2010APAC4
7London+44 20 7877 204125 Old Broad StreetLevel 7NoneUKEC2N 1HNEMEA2
\n", - " \n", - "

Total: 7

\n", - " " - ], - "text/plain": [ - "*office_code city phone postal_line1 postal_line2 state country postal_code territory employee_count\n", - "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +-----------+ +------------+ +-----------+ +------------+\n", - "1 San Francisco +1 650 219 478 100 Market Str Suite 300 CA USA 94080 NA 6 \n", - "2 Boston +1 215 837 082 1550 Court Pla Suite 102 MA USA 02107 NA 2 \n", - "3 NYC +1 212 555 300 523 East 53rd apt. 5A NY USA 10022 NA 2 \n", - "4 Paris +33 14 723 440 43 Rue Jouffro None None France 75017 EMEA 5 \n", - "5 Tokyo +81 33 224 500 4-1 Kioicho None Chiyoda-Ku Japan 102-8578 Japan 2 \n", - "6 Sydney +61 2 9264 245 5-11 Wentworth Floor #2 None Australia NSW 2010 APAC 4 \n", - "7 London +44 20 7877 20 25 Old Broad S Level 7 None UK EC2N 1HN EMEA 2 \n", - " (Total: 7)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Office.aggr(Employee, ..., employee_count=\"count(*)\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 2 (sales)**: Show all employees, including the number of direct reports they have.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n", - "6 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
employee_idn
10022
10564
10883
11026
11436
16211
" - ], - "text/plain": [ - "[(1002, 2), (1056, 4), (1088, 3), (1102, 6), (1143, 6), (1621, 1)]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "use classicsales;\n", - "select reports_to as employee_id, count(*) n from report\n", - "group by reports_to" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%sql" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "6 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
employee_numberlast_namefirst_namenumber_of_direct_reports
1002MurphyDiane2
1056PattersonMary4
1088PattersonWilliam3
1102BondurGerard6
1143BowAnthony6
1621NishiMami1
" - ], - "text/plain": [ - "[(1002, 'Murphy', 'Diane', 2),\n", - " (1056, 'Patterson', 'Mary', 4),\n", - " (1088, 'Patterson', 'William', 3),\n", - " (1102, 'Bondur', 'Gerard', 6),\n", - " (1143, 'Bow', 'Anthony', 6),\n", - " (1621, 'Nishi', 'Mami', 1)]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "SELECT employee.employee_number, last_name, first_name, count(report.employee_number) as number_of_direct_reports \n", - "FROM employee join report\n", - "ON employee.employee_number=report.reports_to \n", - "GROUP BY employee.employee_number" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

reports_to

\n", - " \n", - "
\n", - "

n

\n", - " calculated attribute\n", - "
10022
10564
10760
10883
11026
11436
11650
11660
11880
12160
12860
13230
\n", - "

...

\n", - "

Total: 23

\n", - " " - ], - "text/plain": [ - "*reports_to n \n", - "+------------+ +---+\n", - "1002 2 \n", - "1056 4 \n", - "1076 0 \n", - "1088 3 \n", - "1102 6 \n", - "1143 6 \n", - "1165 0 \n", - "1166 0 \n", - "1188 0 \n", - "1216 0 \n", - "1286 0 \n", - "1323 0 \n", - " ...\n", - " (Total: 23)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Employee.proj(..., reports_to=\"employee_number\").aggr(\n", - " Report, n=\"count(employee_number)\", keep_all_rows=True\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 3 (sales):** Show the top biggests orders in the current month along with the total amount on the order.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

order_number

\n", - " \n", - "
\n", - "

total_amount

\n", - " calculated attribute\n", - "
1036212692.19
1036345785.34
103641834.56
103658307.28
1036614379.90
1036739580.60
1036813874.75
1036928322.83
1037027083.78
1037135137.54
1037233967.73
1037346770.52
\n", - " \n", - "

Total: 12

\n", - " " - ], - "text/plain": [ - "*order_number total_amount \n", - "+------------+ +------------+\n", - "10362 12692.19 \n", - "10363 45785.34 \n", - "10364 1834.56 \n", - "10365 8307.28 \n", - "10366 14379.90 \n", - "10367 39580.60 \n", - "10368 13874.75 \n", - "10369 28322.83 \n", - "10370 27083.78 \n", - "10371 35137.54 \n", - "10372 33967.73 \n", - "10373 46770.52 \n", - " (Total: 12)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(Order & 'order_date between \"2005-01-01\" and \"2005-01-31\"').aggr(\n", - " Order.Item, total_amount=\"sum(quantity * price)\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

order_number

\n", - " \n", - "
\n", - "

order_date

\n", - " \n", - "
\n", - "

required_date

\n", - " \n", - "
\n", - "

shipped_date

\n", - " \n", - "
\n", - "

status

\n", - " \n", - "
\n", - "

comments

\n", - " \n", - "
\n", - "

customer_number

\n", - " \n", - "
101002003-01-062003-01-132003-01-10ShippedNone363
101012003-01-092003-01-182003-01-11ShippedCheck on availability.128
101022003-01-102003-01-182003-01-14ShippedNone181
101032003-01-292003-02-072003-02-02ShippedNone121
101042003-01-312003-02-092003-02-01ShippedNone141
101052003-02-112003-02-212003-02-12ShippedNone145
101062003-02-172003-02-242003-02-21ShippedNone278
101072003-02-242003-03-032003-02-26ShippedDifficult to negotiate with customer. We need more marketing materials131
101082003-03-032003-03-122003-03-08ShippedNone385
101092003-03-102003-03-192003-03-11ShippedCustomer requested that FedEx Ground is used for this shipping486
101102003-03-182003-03-242003-03-20ShippedNone187
101112003-03-252003-03-312003-03-30ShippedNone129
\n", - "

...

\n", - "

Total: 326

\n", - " " - ], - "text/plain": [ - "*order_number order_date required_date shipped_date status comments customer_numbe\n", - "+------------+ +------------+ +------------+ +------------+ +---------+ +------------+ +------------+\n", - "10100 2003-01-06 2003-01-13 2003-01-10 Shipped None 363 \n", - "10101 2003-01-09 2003-01-18 2003-01-11 Shipped Check on avail 128 \n", - "10102 2003-01-10 2003-01-18 2003-01-14 Shipped None 181 \n", - "10103 2003-01-29 2003-02-07 2003-02-02 Shipped None 121 \n", - "10104 2003-01-31 2003-02-09 2003-02-01 Shipped None 141 \n", - "10105 2003-02-11 2003-02-21 2003-02-12 Shipped None 145 \n", - "10106 2003-02-17 2003-02-24 2003-02-21 Shipped None 278 \n", - "10107 2003-02-24 2003-03-03 2003-02-26 Shipped Difficult to n 131 \n", - "10108 2003-03-03 2003-03-12 2003-03-08 Shipped None 385 \n", - "10109 2003-03-10 2003-03-19 2003-03-11 Shipped Customer reque 486 \n", - "10110 2003-03-18 2003-03-24 2003-03-20 Shipped None 187 \n", - "10111 2003-03-25 2003-03-31 2003-03-30 Shipped None 129 \n", - " ...\n", - " (Total: 326)" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "select c.customer_number,p.amount, o.* from classicsales.order o\n", - "join payment p on p.customer_number=c.customer_number\n", - "where o.order_date>='2023-11-1'\n", - "group by c.customer_number\n", - "order by p.amount descb\n", - "limit 10;" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "10 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
order_numberorder_datetotal_amount
104192005-05-1752420.07
104142005-05-0650806.85
104122005-05-0346895.48
104202005-05-2942251.51
104252005-05-3141623.44
104162005-05-1035362.26
104242005-05-3129310.30
104112005-05-0129070.38
104172005-05-1328574.90
104132005-05-0528500.78
" - ], - "text/plain": [ - "[(10419, datetime.date(2005, 5, 17), Decimal('52420.07')),\n", - " (10414, datetime.date(2005, 5, 6), Decimal('50806.85')),\n", - " (10412, datetime.date(2005, 5, 3), Decimal('46895.48')),\n", - " (10420, datetime.date(2005, 5, 29), Decimal('42251.51')),\n", - " (10425, datetime.date(2005, 5, 31), Decimal('41623.44')),\n", - " (10416, datetime.date(2005, 5, 10), Decimal('35362.26')),\n", - " (10424, datetime.date(2005, 5, 31), Decimal('29310.30')),\n", - " (10411, datetime.date(2005, 5, 1), Decimal('29070.38')),\n", - " (10417, datetime.date(2005, 5, 13), Decimal('28574.90')),\n", - " (10413, datetime.date(2005, 5, 5), Decimal('28500.78'))]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "SELECT order_number, order_date,SUM(quantity * price) AS total_amount\n", - "FROM `order`\n", - "NATURAL JOIN `order__item` \n", - "WHERE order_date BETWEEN \"2005-05-01\" AND \"2005-05-31\" \n", - "GROUP BY order_number\n", - "ORDER BY total_amount DESC\n", - "LIMIT 10 " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 4 (sales):** Show the top 5 customers by the amount of money that they have spent this month, including the amount.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n", - "5 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
customer_numbercustomer_nametotal_spent
141Euro+ Shopping Channel104780.68
382Salzburg Collectables52420.07
362Gifts4AllAges.com50806.85
282Souvenirs And Things Co.42251.51
119La Rochelle Gifts41623.44
" - ], - "text/plain": [ - "[(141, 'Euro+ Shopping Channel', Decimal('104780.68')),\n", - " (382, 'Salzburg Collectables', Decimal('52420.07')),\n", - " (362, 'Gifts4AllAges.com', Decimal('50806.85')),\n", - " (282, 'Souveniers And Things Co.', Decimal('42251.51')),\n", - " (119, 'La Rochelle Gifts', Decimal('41623.44'))]" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "use classicsales;\n", - "\n", - "SELECT customer_number, customer_name, SUM(quantity * price) AS total_spent\n", - "FROM customer \n", - "NATURAL JOIN `order` \n", - "NATURAL JOIN `order__item` \n", - "WHERE order_date BETWEEN \"2005-05-01\" AND \"2005-05-31\"\n", - "GROUP BY customer_number\n", - "ORDER BY total_spent DESC\n", - "LIMIT 5;" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n", - "5 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
customer_numbercustomer_nametotal_spent
323Down Under Souvenirs, Inc75020.13
141Euro+ Shopping Channel46895.48
209Mini Caravy35157.75
496Kelly's Gift Shop30253.75
233Québec Home Shopping Network29070.38
" - ], - "text/plain": [ - "[(323, 'Down Under Souvenirs, Inc', Decimal('75020.13')),\n", - " (141, 'Euro+ Shopping Channel', Decimal('46895.48')),\n", - " (209, 'Mini Caravy', Decimal('35157.75')),\n", - " (496, \"Kelly's Gift Shop\", Decimal('30253.75')),\n", - " (233, 'Québec Home Shopping Network', Decimal('29070.38'))]" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "use classicsales;\n", - "\n", - "SELECT customer_number, customer_name, SUM(amount) AS total_spent\n", - "FROM customer \n", - "NATURAL JOIN payment \n", - "WHERE payment_date BETWEEN \"2005-05-01\" AND \"2005-05-31\"\n", - "GROUP BY customer_number\n", - "ORDER BY total_spent DESC\n", - "LIMIT 5;" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'SELECT `customer_number`,quantity*price as `total_spent` FROM `classicsales`.`customer` NATURAL JOIN `classicsales`.`order` NATURAL JOIN `classicsales`.`order__item` WHERE ( (order_date between \"2005-05-01\" and \"2005-05-31\")) GROUP BY `customer_number`'" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "q = Customer.aggr(\n", - " Order * Order.Item & 'order_date between \"2005-05-01\" and \"2005-05-31\"',\n", - " total_spent=\"quantity*price\",\n", - ")\n", - "\n", - "q.make_sql()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 5 (app):** For each addon, show how many people have bought them.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 6 (sales):** Show the top 5 employees by the sales they have made so far this year.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n", - "10 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
employee_numbertotal_sales
1370428755.70
1165335940.47
1612167669.84
1501153417.70
1401141205.70
1611131561.38
121686950.06
133778610.44
132372025.82
116643033.35
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languagenumber_of_countries
Ambo1
Luvale1
Nsenga1
Luchazi1
Luimbe-nganguela1
" - ], - "text/plain": [ - "[('Ambo', 1),\n", - " ('Luvale', 1),\n", - " ('Nsenga', 1),\n", - " ('Luchazi', 1),\n", - " ('Luimbe-nganguela', 1)]" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql \n", - "use nation;\n", - "\n", - "SELECT language, COUNT(country_id) AS number_of_countries\n", - "FROM \n", - "languages NATURAL LEFT JOIN country_languages \n", - "GROUP BY language_id\n", - "ORDER BY number_of_countries ASC\n", - "LIMIT 5;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 8 (nations)**: Show the world population and and gdp for 2018.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "1 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
yearworld_populationworld_gdp
2018735898717483590040156453
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year

\n", - " \n", - "
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pop

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\n", - "

gdp

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1986439019409014392301386986
1987452743748416421120484297
1988478400746218927355419074
1989486914780319736253796020
1990509999028122505042391581
1991511341319923550852228522
1992519614500524972198267199
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1995553273414330449041956687
1996561291907731114314300523
1997569283389630988159774617
\n", - "

...

\n", - "

Total: 59

\n", - " " - ], - "text/plain": [ - "*year pop gdp \n", - "+------+ +------------+ +------------+\n", - "1986 4390194090 14392301386986\n", - "1987 4527437484 16421120484297\n", - "1988 4784007462 18927355419074\n", - "1989 4869147803 19736253796020\n", - "1990 5099990281 22505042391581\n", - "1991 5113413199 23550852228522\n", - "1992 5196145005 24972198267199\n", - "1993 5346893709 25430442681870\n", - "1994 5434510824 27314474000865\n", - "1995 5532734143 30449041956687\n", - "1996 5612919077 31114314300523\n", - "1997 5692833896 30988159774617\n", - " ...\n", - " (Total: 59)" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.U(\"year\").aggr(CountryStats, pop=\"sum(population)\", gdp=\"sum(gdp)\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 9 (nations)**: Show the world population and GDP for each year.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "59 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
yearworld_populationworld_gdp
196023179359921118743000628
196123643786861207075823880
196224259780141320381474549
196324783023241421729635563
196425308419941557875653944
196526192352211704098153376
196626781183131848642377850
196728479257791973083264695
196829242919032135342222280
196929903910692351180280031
197031394617182780568237614
197132094581123071111521053
197232777194523541700567535
197333449779214315738678586
197434131446174975709725480
197534731516575554874046743
197635386651236042519670542
197736036822806833786469960
197836694440928044409572881
197937372332689345097328096
1980384316169410678306772670
1981395107621211060513825737
1982401247526910953406180850
1983408735258911170875840453
1984416796826611581522639063
1985430510521912180861558858
1986439019409014392301386986
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1997569283389630988159774617
1998577195242930943637387919
1999584987335532085437021961
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2001605332633532945078583978
2002615314821134223007114511
2003623085444938417526455764
2004633542571343303064427674
2005641492361146894074035954
2006649511783150830199606871
2007657593083557257797844936
2008663739975062791531544219
2009671879759659598461591794
2010680021914265238320326026
2011688085212472477162219588
2012696047398474175104239084
2013705759047776275187662055
2014714275894078346752313687
2015719801730273642726288557
2016728413223974726157289947
2017736960575179397294374660
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" (1975, Decimal('3473151657'), Decimal('5554874046743')),\n", - " (1976, Decimal('3538665123'), Decimal('6042519670542')),\n", - " (1977, Decimal('3603682280'), Decimal('6833786469960')),\n", - " (1978, Decimal('3669444092'), Decimal('8044409572881')),\n", - " (1979, Decimal('3737233268'), Decimal('9345097328096')),\n", - " (1980, Decimal('3843161694'), Decimal('10678306772670')),\n", - " (1981, Decimal('3951076212'), Decimal('11060513825737')),\n", - " (1982, Decimal('4012475269'), Decimal('10953406180850')),\n", - " (1983, Decimal('4087352589'), Decimal('11170875840453')),\n", - " (1984, Decimal('4167968266'), Decimal('11581522639063')),\n", - " (1985, Decimal('4305105219'), Decimal('12180861558858')),\n", - " (1986, Decimal('4390194090'), Decimal('14392301386986')),\n", - " (1987, Decimal('4527437484'), Decimal('16421120484297')),\n", - " (1988, Decimal('4784007462'), Decimal('18927355419074')),\n", - " (1989, Decimal('4869147803'), Decimal('19736253796020')),\n", - " (1990, Decimal('5099990281'), Decimal('22505042391581')),\n", - " (1991, Decimal('5113413199'), Decimal('23550852228522')),\n", - " (1992, Decimal('5196145005'), Decimal('24972198267199')),\n", - " (1993, Decimal('5346893709'), Decimal('25430442681870')),\n", - " (1994, Decimal('5434510824'), Decimal('27314474000865')),\n", - " (1995, Decimal('5532734143'), Decimal('30449041956687')),\n", - " (1996, Decimal('5612919077'), Decimal('31114314300523')),\n", - " (1997, Decimal('5692833896'), Decimal('30988159774617')),\n", - " (1998, Decimal('5771952429'), Decimal('30943637387919')),\n", - " (1999, Decimal('5849873355'), Decimal('32085437021961')),\n", - " (2000, Decimal('5976444543'), Decimal('33128174554615')),\n", - " (2001, Decimal('6053326335'), Decimal('32945078583978')),\n", - " (2002, Decimal('6153148211'), Decimal('34223007114511')),\n", - " (2003, Decimal('6230854449'), Decimal('38417526455764')),\n", - " (2004, Decimal('6335425713'), Decimal('43303064427674')),\n", - " (2005, Decimal('6414923611'), Decimal('46894074035954')),\n", - " (2006, Decimal('6495117831'), Decimal('50830199606871')),\n", - " (2007, Decimal('6575930835'), Decimal('57257797844936')),\n", - " (2008, Decimal('6637399750'), Decimal('62791531544219')),\n", - " (2009, Decimal('6718797596'), Decimal('59598461591794')),\n", - " (2010, Decimal('6800219142'), Decimal('65238320326026')),\n", - " (2011, Decimal('6880852124'), Decimal('72477162219588')),\n", - " (2012, Decimal('6960473984'), Decimal('74175104239084')),\n", - " (2013, Decimal('7057590477'), Decimal('76275187662055')),\n", - " (2014, Decimal('7142758940'), Decimal('78346752313687')),\n", - " (2015, Decimal('7198017302'), Decimal('73642726288557')),\n", - " (2016, Decimal('7284132239'), Decimal('74726157289947')),\n", - " (2017, Decimal('7369605751'), Decimal('79397294374660')),\n", - " (2018, Decimal('7358987174'), Decimal('83590040156453'))]" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "SELECT year, sum(population) as world_population, sum(gdp) as world_gdp \n", - "FROM country_stats\n", - "GROUP BY year" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 10 (nations)**: Show all continents, along with their populations and GDP in 2018.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 11 (nations)**: Show all the countries in Africa with a population greater than 100,000,000 in 2018.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 12 (university)**: Show the total number of students who have taken a math class.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 13 (university)**: Show the top course by enrollment in the current term.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 14 (hotel)**: Show the top five guests by the number of nights that they have stayed a the hotel.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - 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} - ], - "source": [ - "hotel.spawn_missing_classes()\n", - "dj.Diagram(hotel)" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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479097081Amanda Dean19
173242903Jessica Watson16
327027272Austin Duncan16
335641148Alexandra Duran16
1447851276Misty Aguirre16
\n", - "
" - ], - "text/plain": [ - " guest_name n\n", - "guest_id \n", - "479097081 Amanda Dean 19\n", - "173242903 Jessica Watson 16\n", - "327027272 Austin Duncan 16\n", - "335641148 Alexandra Duran 16\n", - "1447851276 Misty Aguirre 16" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Guest.aggr(Reservation * CheckIn, ..., n=\"count(*)\").fetch(\n", - " order_by=\"n DESC\", limit=5, format=\"frame\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n", - "5 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
guest_idguest_nametotal_nights_stayed
479097081Amanda Dean19
173242903Jessica Watson16
327027272Austin Duncan16
335641148Alexandra Duran16
1447851276Misty Aguirre16
" - ], - "text/plain": [ - "[(479097081, 'Amanda Dean', 19),\n", - " (173242903, 'Jessica Watson', 16),\n", - " (327027272, 'Austin Duncan', 16),\n", - " (335641148, 'Alexandra Duran', 16),\n", - " (1447851276, 'Misty Aguirre', 16)]" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "use hotel; \n", - "\n", - "SELECT guest_id, guest_name, \n", - " count(*) AS total_nights_stayed\n", - "FROM guest\n", - "NATURAL JOIN reservation NATURAL JOIN check_in \n", - "GROUP BY guest_id\n", - "ORDER BY total_nights_stayed DESC\n", - "LIMIT 5;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 15 (nations):** Show all the regions and the average GDP per capita in each for 2018.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 16 (Sales):** Show the top five products by total sales (in dollars)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 17 (app):** Show the total sales by day over the last month.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n", - "10 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
purchase_datetotal_sales
2023-11-0727196.86
2023-11-2327056.67
2023-11-2126495.91
2023-11-1226495.91
2023-11-1726355.72
2023-11-1326215.53
2023-11-2226075.34
2023-11-1125935.15
2023-10-3125094.01
2023-11-1425094.01
" - ], - "text/plain": [ - "[(datetime.date(2023, 11, 7), Decimal('27196.86')),\n", - " (datetime.date(2023, 11, 23), Decimal('27056.67')),\n", - " (datetime.date(2023, 11, 21), Decimal('26495.91')),\n", - " (datetime.date(2023, 11, 12), Decimal('26495.91')),\n", - " (datetime.date(2023, 11, 17), Decimal('26355.72')),\n", - " (datetime.date(2023, 11, 13), Decimal('26215.53')),\n", - " (datetime.date(2023, 11, 22), Decimal('26075.34')),\n", - " (datetime.date(2023, 11, 11), Decimal('25935.15')),\n", - " (datetime.date(2023, 10, 31), Decimal('25094.01')),\n", - " (datetime.date(2023, 11, 14), Decimal('25094.01'))]" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "use app;\n", - "\n", - "SELECT purchase_date, SUM(price) AS total_sales\n", - "FROM `purchase`\n", - "NATURAL JOIN `#add_on` \n", - "WHERE purchase_date >= CURDATE() - INTERVAL 1 MONTH\n", - "GROUP BY purchase_date\n", - "ORDER BY total_sales DESC\n", - "LIMIT 10;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 18 (university):** Show all the departments and the number of students electing them for their majors.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 19 (university):** Show all departments and the number of courses they offer in the current semester.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 20 (university)** Show what fraction of student who declared \"MATH\" as their major each year.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%sql\n", - "use university;\n", - "SELECT\n", - " YEAR(student_major.declare_date) AS declare_year,\n", - " round(avg(ifnull(dept='MATH', 0) )*100, 2) as percent_math_majors\n", - "FROM student NATURAL LEFT JOIN student_major\n", - "GROUP BY declare_year\n", - "ORDER BY declare_year DESC\n", - "LIMIT 15;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 21 (university)** Show all courses offered in the current term with at least five students enrolled.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Solutions\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Problem 15** - Show the world regions and the GDP per capita in each for 2018\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "stats2018 = Countries.proj(..., country_name=\"name\") * CountryStats() & \"year=2018\"\n", - "\n", - "Regions.aggr(stats2018, \"name\", gdp_per_capita=\"sum(gdp) / sum(population)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "% % sql\n", - "-- Show the world's GDP per capita for 2018\n", - "use nation;\n", - "\n", - "SELECT sum(gdp) / sum(population) as gdp_per_capital\n", - "FROM country_stats\n", - "WHERE\n", - " year = 2018" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "% % sql use nation;\n", - "-- Show the world's GDP per capita for 2018 by region\n", - "SELECT regions.name, sum(gdp) / sum(population) as gdp_per_capita\n", - "FROM\n", - " regions\n", - " JOIN countries using (region_id)\n", - " NATURAL JOIN country_stats\n", - "WHERE\n", - " year = 2018\n", - "GROUP BY\n", - " region_id\n", - "ORDER BY gdp_per_capita DESC" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "% % sql\n", - "-- show all the regions with GDP per capita over 25,000 in 2018\n", - "use nation;\n", - "\n", - "SELECT regions.name, sum(gdp) / sum(population) as gdp_per_capita\n", - "FROM\n", - " regions\n", - " JOIN countries using (region_id)\n", - " NATURAL JOIN country_stats\n", - "WHERE\n", - " year = 2018\n", - "GROUP BY\n", - " region_id\n", - "HAVING\n", - " gdp_per_capita > 25000\n", - "ORDER BY gdp_per_capita DESC" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "% % sql\n", - "\n", - "SELECT *\n", - "FROM (\n", - " SELECT regions.name, sum(gdp) / sum(population) as gdp_per_capita\n", - " FROM\n", - " regions\n", - " JOIN countries using (region_id)\n", - " NATURAL JOIN country_stats\n", - " WHERE\n", - " year = 2018\n", - " GROUP BY\n", - " region_id\n", - " ORDER BY gdp_per_capita DESC\n", - " ) as q\n", - "WHERE\n", - " gdp_per_capita > 25000" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/007-Aggregation.ipynb b/db-course/007-Aggregation.ipynb deleted file mode 100644 index 369eaf6..0000000 --- a/db-course/007-Aggregation.ipynb +++ /dev/null @@ -1,1389 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Aggregation queries\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "import pymysql\n", - "\n", - "pymysql.install_as_MySQLdb()\n", - "%load_ext sql\n", - "%config SqlMagic.autocommit=True\n", - "%sql mysql://root:simple@127.0.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-11-01 00:25:59,466][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", - "[2023-11-01 00:25:59,482][INFO]: Connected root@fakeservices.datajoint.io:3306\n" - ] - } - ], - "source": [ - "import datajoint as dj\n", - "\n", - "sales = dj.Schema(\"classicsales\")\n", - "sales.spawn_missing_classes()\n", - "\n", - "nations = dj.Schema(\"nation\")\n", - "nations.spawn_missing_classes()\n", - "\n", - "hotel = dj.Schema(\"hotel\")\n", - "hotel.spawn_missing_classes()\n", - "\n", - "university = dj.Schema(\"university\")\n", - "university.spawn_missing_classes()\n", - "\n", - "app = dj.Schema(\"app\")\n", - "app.spawn_missing_classes()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Concepts\n", - "\n", - "Review the MySQL aggregate functions: https://dev.mysql.com/doc/refman/8.0/en/aggregate-functions.html\n", - "\n", - "Three types of queries\n", - "\n", - "1. Aggregation functions with no `GROUP BY` clause produce 1 row. \n", - "2. Aggregation functions combined with a `GROUP BY` clause. The unique key of the result is composed of the columns of the `GROUP BY` clause.\n", - "3. Most common pattern: `JOIN` or `LEFT JOIN` of a table pair in a one-to-many relationship, grouped by the primary key of the left table. This aggregates the right entity set with respect to the left entity set. \n", - "\n", - "Note that MySQL with the default settings allows mixing aggregated and non-aggregated values (See https://dev.mysql.com/doc/refman/5.7/en/sql-mode.html#sqlmode_only_full_group_by). So you have to watch avoid invalid mixes of values.\n", - "\n", - "Using `HAVING` is equivalent to using a `WHERE` clause in an outer query." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj\n", - "\n", - "schema = dj.Schema(\"app\")\n", - "schema.spawn_missing_classes()\n", - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pymysql\n", - "\n", - "pymysql.install_as_MySQLdb()\n", - "%load_ext sql\n", - "%config SqlMagic.autocommit=True\n", - "%sql mysql://root:simple@127.0.0.1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Aggregation Queries\n", - "\n", - "Queries using aggregation functions, `GROUP BY`, and `HAVING` clauses. Using `LEFT JOIN` in combination with `GROUP BY`.\n", - "\n", - "Aggregation functions: `MAX`, `MIN`, `AVG`, `SUM`, and `COUNT`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%sql\n", - "USE app" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- show the date of the last purchase \n", - "SELECT * FROM purchase ORDER BY purchase_date DESC LIMIT 1 " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- show the data of the last pruchase \n", - "SELECT max(purchase_date) last_purchase, min(purchase_date) first_purchase, phone, card_number FROM purchase" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Aggregation functions MAX, MIN, AVG, SUM, COUNT" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- show the date of birth of the youngest person\n", - "SELECT * FROM account ORDER BY dob DESC LIMIT 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- show the date of birth of the youngest person \n", - "-- This is an invalid query because it mixes aggregation and regular fields\n", - "SELECT max(dob) as dob, phone FROM account" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "SELECT * FROM account where phone=10013740006" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- show the youngest person \n", - "SELECT * FROM account WHERE dob = (SELECT max(dob) FROM account)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "# show average male age\n", - "dj.U().aggr(Account & 'sex=\"M\"', avg_age=\"floor(avg(DATEDIFF(now(), dob)) / 365.25)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "SELECT floor(avg(DATEDIFF(now(), dob)) / 365.25) as avg_age FROM account WHERE sex=\"M\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "SELECT count(*), count(phone), count(DISTINCT first_name, last_name), count(dob) FROM account;" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- show how many of purchases have been done for each addon\n", - "\n", - "SELECT addon_id, count(*) n FROM purchase GROUP BY addon_id " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "SELECT * FROM `#add_on` LIMIT 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "SELECT * FROM purchase NATURAL JOIN `#add_on` LIMIT 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- show the total money spent by each account (limit to top 10)\n", - "\n", - "SELECT phone, sum(price) as total_spending \n", - " FROM purchase NATURAL JOIN `#add_on` \n", - " GROUP BY (phone) \n", - " ORDER BY total_spending DESC LIMIT 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- show the names of people who spent less than $100\n", - "\n", - "SELECT phone, sum(price) as total_spending \n", - " FROM purchase NATURAL JOIN `#add_on` \n", - " WHERE total_spending < 100\n", - " GROUP BY (phone) \n", - " LIMIT 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- show the names of people who spent less than $100\n", - "\n", - "SELECT * FROM (\n", - " SELECT phone, first_name, last_name, sum(price) as total_spending \n", - " FROM account NATURAL JOIN purchase NATURAL JOIN `#add_on` \n", - " GROUP BY (phone)) as q \n", - "WHERE total_spending < 100\n", - "LIMIT 10\n", - "\n", - "-- almost correct but does not include people who spent nothing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql \n", - "-- explaining LEFT joins\n", - "SELECT * FROM account NATURAL LEFT JOIN purchase NATURAL LEFT JOIN `#add_on` LIMIT 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [ - "%%sql\n", - "-- show the names of people who spent less than $100\n", - "SELECT * FROM (\n", - " SELECT phone, first_name, last_name, sum(ifnull(price), 0) as total_spending \n", - " FROM account NATURAL LEFT JOIN purchase NATURAL LEFT JOIN `#add_on` \n", - " GROUP BY (phone)) as q \n", - "WHERE total_spending < 100\n", - "LIMIT 10\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Summary of principles \n", - "1. Without a `GROUP BY`, aggregation functions collapse the table into a single row.\n", - "2. With `GROUP BY`, the grouping attributes become the new primary key of the result. \n", - "3. Do not mix aggregated and non-aggregated values in the result with or without a `GROUP BY`.\n", - "4. `HAVING` plays the same role as the `WHERE` clause in a nesting outer query so it can use the output of the aggregation functions.\n", - "5. `LEFT JOIN` is often followed with a `GROUP BY` by the primary key attributes of the left table. In this scenario the entities in the right table are aggregated for each matching row in the left table.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. Always aggregate entity B grouped by entity A\n", - "2. Then GROUP BY the primary of A\n", - "3. Aggregate the attribute B but not A\n", - "4. SELECT non-aggregated attributes of A but not B\n", - "5. Use an left join if you need to include rows of A for which there is no match in B " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "dj.Diagram(sales)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "0\n", - "\n", - "0\n", - "\n", - "\n", - "\n", - "Customer\n", - "\n", - "\n", - "Customer\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "0->Customer\n", - "\n", - "\n", - "\n", - "\n", - "1\n", - "\n", - "1\n", - "\n", - "\n", - "\n", - "Report\n", - "\n", - "\n", - "Report\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "1->Report\n", - "\n", - "\n", - "\n", - "\n", - "Payment\n", - "\n", - "\n", - "Payment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Customer->Payment\n", - "\n", - "\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Customer->Order\n", - "\n", - "\n", - "\n", - "\n", - "Office\n", - "\n", - "\n", - "Office\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Employee\n", - "\n", - "\n", - "Employee\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Office->Employee\n", - "\n", - "\n", - "\n", - "\n", - "Order.Item\n", - "\n", - "\n", - "Order.Item\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order->Order.Item\n", - "\n", - "\n", - "\n", - "\n", - "ProductLine\n", - "\n", - "\n", - "ProductLine\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Product\n", - "\n", - "\n", - "Product\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "ProductLine->Product\n", - "\n", - "\n", - "\n", - "\n", - "Employee->0\n", - "\n", - "\n", - "\n", - "\n", - "Employee->1\n", - "\n", - "\n", - "\n", - "\n", - "Employee->Report\n", - "\n", - "\n", - "\n", - "\n", - "Product->Order.Item\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(sales)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

order_number

\n", - " \n", - "
\n", - "

n

\n", - " calculated attribute\n", - "
1010816
101096
1011016
101116
101122
101134
\n", - " \n", - "

Total: 6

\n", - " " - ], - "text/plain": [ - "*order_number n \n", - "+------------+ +----+\n", - "10108 16 \n", - "10109 6 \n", - "10110 16 \n", - "10111 6 \n", - "10112 2 \n", - "10113 4 \n", - " (Total: 6)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Show all the orders made in March 2003 and the total number of items on each\n", - "(Order & 'order_date between \"2003-03-01\" and \"2003-03-31\"').aggr(\n", - " Order.Item(), n=\"count(*)\", keep_all_rows=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

reports_to

\n", - " \n", - "
\n", - "

n

\n", - " calculated attribute\n", - "
10022
10564
10883
11026
11436
16211
\n", - " \n", - "

Total: 6

\n", - " " - ], - "text/plain": [ - "*reports_to n \n", - "+------------+ +---+\n", - "1002 2 \n", - "1056 4 \n", - "1088 3 \n", - "1102 6 \n", - "1143 6 \n", - "1621 1 \n", - " (Total: 6)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# SHOW ALL the employees, the number of their direct reports\n", - "\n", - "Employee.proj(reports_to=\"employee_number\").aggr(Report, n=\"count(employee_number)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n", - "23 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
employee_numberfirst_namelast_namen
1002DianeMurphy2
1056MaryPatterson4
1076JeffFirrelli0
1088WilliamPatterson3
1102GerardBondur6
1143AnthonyBow6
1165LeslieJennings0
1166LeslieThompson0
1188JulieFirrelli0
1216StevePatterson0
1286Foon YueTseng0
1323GeorgeVanauf0
1337LouiBondur0
1370GerardHernandez0
1401PamelaCastillo0
1501LarryBott0
1504BarryJones0
1611AndyFixter0
1612PeterMarsh0
1619TomKing0
1621MamiNishi1
1625YoshimiKato0
1702MartinGerard0
" - ], - "text/plain": [ - "[(1002, 'Diane', 'Murphy', 2),\n", - " (1056, 'Mary', 'Patterson', 4),\n", - " (1076, 'Jeff', 'Firrelli', 0),\n", - " (1088, 'William', 'Patterson', 3),\n", - " (1102, 'Gerard', 'Bondur', 6),\n", - " (1143, 'Anthony', 'Bow', 6),\n", - " (1165, 'Leslie', 'Jennings', 0),\n", - " (1166, 'Leslie', 'Thompson', 0),\n", - " (1188, 'Julie', 'Firrelli', 0),\n", - " (1216, 'Steve', 'Patterson', 0),\n", - " (1286, 'Foon Yue', 'Tseng', 0),\n", - " (1323, 'George', 'Vanauf', 0),\n", - " (1337, 'Loui', 'Bondur', 0),\n", - " (1370, 'Gerard', 'Hernandez', 0),\n", - " (1401, 'Pamela', 'Castillo', 0),\n", - " (1501, 'Larry', 'Bott', 0),\n", - " (1504, 'Barry', 'Jones', 0),\n", - " (1611, 'Andy', 'Fixter', 0),\n", - " (1612, 'Peter', 'Marsh', 0),\n", - " (1619, 'Tom', 'King', 0),\n", - " (1621, 'Mami', 'Nishi', 1),\n", - " (1625, 'Yoshimi', 'Kato', 0),\n", - " (1702, 'Martin', 'Gerard', 0)]" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "use classicsales;\n", - "\n", - "SELECT employee.employee_number, first_name, last_name, count(report.employee_number) as n \n", - "FROM employee LEFT JOIN report ON (employee.employee_number = report.reports_to)\n", - "GROUP BY employee.employee_number" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "39 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
employee_numberfirst_namelast_namesubordinate
1002DianeMurphy1056
1002DianeMurphy1076
1056MaryPatterson1088
1056MaryPatterson1102
1056MaryPatterson1143
1056MaryPatterson1621
1076JeffFirrelliNone
1088WilliamPatterson1611
1088WilliamPatterson1612
1088WilliamPatterson1619
1102GerardBondur1337
1102GerardBondur1370
1102GerardBondur1401
1102GerardBondur1501
1102GerardBondur1504
1102GerardBondur1702
1143AnthonyBow1165
1143AnthonyBow1166
1143AnthonyBow1188
1143AnthonyBow1216
1143AnthonyBow1286
1143AnthonyBow1323
1165LeslieJenningsNone
1166LeslieThompsonNone
1188JulieFirrelliNone
1216StevePattersonNone
1286Foon YueTsengNone
1323GeorgeVanaufNone
1337LouiBondurNone
1370GerardHernandezNone
1401PamelaCastilloNone
1501LarryBottNone
1504BarryJonesNone
1611AndyFixterNone
1612PeterMarshNone
1619TomKingNone
1621MamiNishi1625
1625YoshimiKatoNone
1702MartinGerardNone
" - ], - "text/plain": [ - "[(1002, 'Diane', 'Murphy', 1056),\n", - " (1002, 'Diane', 'Murphy', 1076),\n", - " (1056, 'Mary', 'Patterson', 1088),\n", - " (1056, 'Mary', 'Patterson', 1102),\n", - " (1056, 'Mary', 'Patterson', 1143),\n", - " (1056, 'Mary', 'Patterson', 1621),\n", - " (1076, 'Jeff', 'Firrelli', None),\n", - " (1088, 'William', 'Patterson', 1611),\n", - " (1088, 'William', 'Patterson', 1612),\n", - " (1088, 'William', 'Patterson', 1619),\n", - " (1102, 'Gerard', 'Bondur', 1337),\n", - " (1102, 'Gerard', 'Bondur', 1370),\n", - " (1102, 'Gerard', 'Bondur', 1401),\n", - " (1102, 'Gerard', 'Bondur', 1501),\n", - " (1102, 'Gerard', 'Bondur', 1504),\n", - " (1102, 'Gerard', 'Bondur', 1702),\n", - " (1143, 'Anthony', 'Bow', 1165),\n", - " (1143, 'Anthony', 'Bow', 1166),\n", - " (1143, 'Anthony', 'Bow', 1188),\n", - " (1143, 'Anthony', 'Bow', 1216),\n", - " (1143, 'Anthony', 'Bow', 1286),\n", - " (1143, 'Anthony', 'Bow', 1323),\n", - " (1165, 'Leslie', 'Jennings', None),\n", - " (1166, 'Leslie', 'Thompson', None),\n", - " (1188, 'Julie', 'Firrelli', None),\n", - " (1216, 'Steve', 'Patterson', None),\n", - " (1286, 'Foon Yue', 'Tseng', None),\n", - " (1323, 'George', 'Vanauf', None),\n", - " (1337, 'Loui', 'Bondur', None),\n", - " (1370, 'Gerard', 'Hernandez', None),\n", - " (1401, 'Pamela', 'Castillo', None),\n", - " (1501, 'Larry', 'Bott', None),\n", - " (1504, 'Barry', 'Jones', None),\n", - " (1611, 'Andy', 'Fixter', None),\n", - " (1612, 'Peter', 'Marsh', None),\n", - " (1619, 'Tom', 'King', None),\n", - " (1621, 'Mami', 'Nishi', 1625),\n", - " (1625, 'Yoshimi', 'Kato', None),\n", - " (1702, 'Martin', 'Gerard', None)]" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "SELECT employee.employee_number, first_name, last_name, report.employee_number as subordinate \n", - "FROM employee LEFT JOIN report ON (employee.employee_number = report.reports_to)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "sql" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/007-Compute-JuliaSets.ipynb b/db-course/007-Compute-JuliaSets.ipynb deleted file mode 100644 index 9f627d6..0000000 --- a/db-course/007-Compute-JuliaSets.ipynb +++ /dev/null @@ -1,953 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8607eb0e", - "metadata": {}, - "source": [ - "# Julia Sets\n", - "\n", - "The following is a quick introduction to Julia Sets" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "87bd1a0f", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "from matplotlib import pyplot as plt\n", - "import numpy as np\n", - "import datajoint as dj" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "3f5a75af", - "metadata": {}, - "outputs": [], - "source": [ - "def julia(c, size=256, center=(0.0, 0.0), zoom=1.0, iters=256):\n", - " x, y = np.meshgrid(\n", - " np.linspace(-1, 1, size) / zoom + center[0],\n", - " np.linspace(-1, 1, size) / zoom + center[1],\n", - " )\n", - " z = x + 1j * y\n", - " im = np.zeros(z.shape)\n", - " ix = np.ones(z.shape, dtype=bool)\n", - " for i in range(iters):\n", - " z[ix] = z[ix] ** 2 + c\n", - " ix = np.abs(z) < 2\n", - " im += ix\n", - " return im" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "79556895", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.imshow(julia(-0.4 + 0.6j, center=(0.34, -0.30), zoom=10000.0), cmap=\"magma\")\n", - "plt.axis(False);" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3c63ce73", - "metadata": {}, - "outputs": [], - "source": [ - "c = (\n", - " -0.4 + 0.6j,\n", - " -0.74543 + 0.11301j,\n", - " -0.75 + 0.11j,\n", - " -0.1 + 0.651j,\n", - " -0.835 - 0.2321j,\n", - " -0.70176 - 0.3842j,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "cfc85940", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "noise_level = 5.0\n", - "\n", - "fig, ax = plt.subplots(3, 2, figsize=(7.5, 12))\n", - "for c_, a in zip(c, ax.flatten()):\n", - " img = julia(c_, zoom=0.5)\n", - " img += np.random.randn(*img.shape) * noise_level\n", - " a.imshow(img, cmap=\"magma\")\n", - " a.axis(False)" - ] - }, - { - "cell_type": "markdown", - "id": "b01e70d9", - "metadata": {}, - "source": [ - "# Image processing" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b4069a34", - "metadata": {}, - "outputs": [], - "source": [ - "from skimage import data\n", - "from skimage import filters" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "39bc25c7", - "metadata": {}, - "outputs": [], - "source": [ - "from skimage.morphology import disk\n", - "from skimage import restoration" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "aa225a15", - "metadata": {}, - "outputs": [], - "source": [ - "noise_level = 50.0\n", - "img = julia(-0.4 + 0.6j, size=200)\n", - "noise_img = img + np.random.randn(*img.shape) * noise_level\n", - "median_img = filters.median(noise_img, disk(3))\n", - "tv_img = restoration.denoise_tv_chambolle(noise_img, weight=20.0)\n", - "wavelet_img = restoration.denoise_wavelet(noise_img)\n", - "gaussian_img = filters.gaussian(noise_img, sigma=1.8)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d60ecafd", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(3, 2, figsize=(6, 9))\n", - "for a, (im, title) in zip(\n", - " ax.flatten(),\n", - " (\n", - " (img, \"original\"),\n", - " (noise_img, \"original+noise\"),\n", - " (gaussian_img, \"gaussian\"),\n", - " (median_img, \"median\"),\n", - " (wavelet_img, \"wavelet\"),\n", - " (tv_img, \"tv\"),\n", - " ),\n", - "):\n", - " a.imshow(im, cmap=\"magma\", vmin=0, vmax=255)\n", - " a.axis(False)\n", - " a.set_title(title)" - ] - }, - { - "cell_type": "markdown", - "id": "b495345a", - "metadata": {}, - "source": [ - "# DataJoint Pipeline\n", - "\n", - "Now let's build a data pipeline managing Julia sets and their analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "9015c43e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-10-24 16:01:48,861][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", - "[2023-10-24 16:01:48,877][INFO]: Connected root@fakeservices.datajoint.io:3306\n" - ] - } - ], - "source": [ - "import datajoint as dj\n", - "\n", - "schema = dj.Schema(\"julia\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "62067735", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class JuliaSpec(dj.Lookup):\n", - " definition = \"\"\"\n", - " julia_spec : smallint \n", - " ---\n", - " creal : float\n", - " cimag : float\n", - " size=256 : smallint\n", - " center_real=0.0 : float \n", - " center_imag=0.0 : float\n", - " zoom=1.0 : float\n", - " noise_level=50 : float\n", - " \"\"\"\n", - "\n", - " contents = (\n", - " dict(julia_spec=0, creal=-0.4, cimag=0.6, noise_level=50),\n", - " dict(julia_spec=1, creal=-0.7453, cimag=0.11301, noise_level=50),\n", - " dict(julia_spec=2, creal=-0.75, cimag=0.11, noise_level=50),\n", - " dict(julia_spec=3, creal=-0.1, cimag=0.651, noise_level=50),\n", - " dict(julia_spec=4, creal=-0.835, cimag=-0.2321, noise_level=50),\n", - " dict(julia_spec=5, creal=-0.70176, cimag=-0.3842, noise_level=50),\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0bc39f02", - "metadata": {}, - "outputs": [], - "source": [ - "JuliaSpec.insert1(\n", - " dict(\n", - " julia_spec=10,\n", - " creal=-0.4,\n", - " cimag=0.6,\n", - " center_real=0.34,\n", - " center_imag=-0.30,\n", - " zoom=10000.0,\n", - " noise_level=50.0,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "5630641b", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class JuliaImage(dj.Computed):\n", - " definition = \"\"\"\n", - " -> JuliaSpec \n", - " ---\n", - " image : longblob\n", - " \"\"\"\n", - "\n", - " def make(self, key):\n", - " spec = (JuliaSpec & key).fetch1()\n", - " img = julia(\n", - " spec[\"creal\"] + 1j * spec[\"cimag\"],\n", - " size=spec[\"size\"],\n", - " center=(spec[\"center_real\"], spec[\"center_imag\"]),\n", - " zoom=spec[\"zoom\"],\n", - " )\n", - " img += np.random.randn(*img.shape) * spec[\"noise_level\"]\n", - " self.insert1(dict(key, image=img.astype(np.float32)))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "71f577ff", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "JuliaImage\n", - "\n", - "\n", - "JuliaImage\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "JuliaSpec\n", - "\n", - "\n", - "JuliaSpec\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "JuliaSpec->JuliaImage\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "1c8e3481", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "JuliaImage: 100%|██████████| 6/6 [00:01<00:00, 3.60it/s]\n" - ] - } - ], - "source": [ - "JuliaImage.populate(display_progress=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "560613ef", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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Total: 6

\n", - " " - ], - "text/plain": [ - "*julia_spec image \n", - "+------------+ +--------+\n", - "0 =BLOB= \n", - "1 =BLOB= \n", - "2 =BLOB= \n", - "3 =BLOB= \n", - "4 =BLOB= \n", - "5 =BLOB= \n", - " (Total: 6)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "JuliaImage()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "76a38851", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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julia_spec

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Total: 24

\n", - " " - ], - "text/plain": [ - "*julia_spec *denoise_metho denoised_i\n", - "+------------+ +------------+ +--------+\n", - "0 0 =BLOB= \n", - "1 0 =BLOB= \n", - "2 0 =BLOB= \n", - "3 0 =BLOB= \n", - "4 0 =BLOB= \n", - "5 0 =BLOB= \n", - "0 1 =BLOB= \n", - "1 1 =BLOB= \n", - "2 1 =BLOB= \n", - "3 1 =BLOB= \n", - "4 1 =BLOB= \n", - "5 1 =BLOB= \n", - " ...\n", - " (Total: 24)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "JuliaDenoised()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "b4b76369", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "keys = JuliaDenoised.fetch(\"KEY\")\n", - "img = ((JuliaDenoised & keys[21])).fetch1(\"denoised_image\")\n", - "plt.imshow(img)\n", - "plt.axis(False);" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "4917c8c2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "DenoiseMethod\n", - "\n", - "\n", - "DenoiseMethod\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "JuliaDenoised\n", - "\n", - "\n", - "JuliaDenoised\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "DenoiseMethod->JuliaDenoised\n", - "\n", - "\n", - "\n", - "\n", - "JuliaImage\n", - "\n", - "\n", - "JuliaImage\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "JuliaImage->JuliaDenoised\n", - "\n", - "\n", - "\n", - "\n", - "JuliaSpec\n", - "\n", - "\n", - "JuliaSpec\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "JuliaSpec->JuliaImage\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "97601fad", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Blob(dj.Manual):\n", - " definition = \"\"\"\n", - " id : int\n", - " ---\n", - " blob : longblob\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02ead2d5", - "metadata": {}, - "outputs": [], - "source": [ - "Blob.insert1(dict(id=1, blob=[1, 2, 3, \"Four\"]))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bbf1b7b5", - "metadata": {}, - "outputs": [], - "source": [ - "Blob.fetch(as_dict=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b2be63e8", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/db-course/008-Default.ipynb b/db-course/008-Default.ipynb deleted file mode 100644 index a69a1ff..0000000 --- a/db-course/008-Default.ipynb +++ /dev/null @@ -1,318 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "414f0fb5", - "metadata": {}, - "source": [ - "# Missing and default values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28e327d8", - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj\n", - "import faker\n", - "\n", - "fake = faker.Faker()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "64f6506d", - "metadata": {}, - "outputs": [], - "source": [ - "fake.profile()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "schema = dj.Schema(\"defaults\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1f66841e", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Person(dj.Manual):\n", - " definition = \"\"\"\n", - " person_id : int\n", - " ---\n", - " blood_group = \"unknown\" : enum('A+', 'A-', 'AB+', 'AB-', 'B+', 'B-', 'O+', 'O-', \"unknown\")\n", - " name : varchar(60)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f54ddeac", - "metadata": {}, - "outputs": [], - "source": [ - "Person.insert1(dict(person_id=3, **fake.profile()), ignore_extra_fields=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2f610d0c", - "metadata": {}, - "outputs": [], - "source": [ - "Person()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4fb4eb3f", - "metadata": {}, - "outputs": [], - "source": [ - "Person.insert1(dict(person_id=5, name=\"heywood\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "Person.insert1(dict(person_id=6, blood_group=None, name=\"Henrietta Lacks\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "4e8b7522", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

person_id

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\n", - "

blood_group

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\n", - "

name

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3O+Rachel Wilson
5unknownheywood
6unknownHenrietta Lacks
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Total: 3

\n", - " " - ], - "text/plain": [ - "*person_id blood_group name \n", - "+-----------+ +------------+ +------------+\n", - "3 O+ Rachel Wilson \n", - "5 unknown heywood \n", - "6 unknown Henrietta Lack\n", - " (Total: 3)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Person()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83844f42", - "metadata": {}, - "outputs": [], - "source": [ - "import pymysql\n", - "\n", - "conn = pymysql.connect(user=\"root\", passwd=\"simple\", host=\"127.0.0.1\")\n", - "cursor = conn.cursor()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "47686e7a", - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\"SHOW CREATE TABLE defaults.person\")\n", - "print(cursor.fetchone()[1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e2ac8cd0", - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - "INSERT defaults.person (person_id, blood_group, name) VALUES (2, \"O+\", \"anne\")\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7b1c8980", - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - "INSERT defaults.person (person_id, blood_group) VALUES (4, \"B+\")\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "3ac55ecb", - "metadata": {}, - "outputs": [ - { - "ename": "IntegrityError", - "evalue": "(1048, \"Column 'blood_group' cannot be null\")", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIntegrityError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[31], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m cursor\u001b[39m.\u001b[39;49mexecute(\u001b[39m\"\"\"\u001b[39;49m\n\u001b[1;32m 2\u001b[0m \u001b[39mINSERT defaults.person (person_id, blood_group, name) VALUES (1, NULL, \u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mbob\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39m)\u001b[39;49m\n\u001b[1;32m 3\u001b[0m \u001b[39m\"\"\"\u001b[39;49m)\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/cursors.py:153\u001b[0m, in \u001b[0;36mCursor.execute\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[39mpass\u001b[39;00m\n\u001b[1;32m 151\u001b[0m query \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmogrify(query, args)\n\u001b[0;32m--> 153\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_query(query)\n\u001b[1;32m 154\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_executed \u001b[39m=\u001b[39m query\n\u001b[1;32m 155\u001b[0m \u001b[39mreturn\u001b[39;00m result\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/cursors.py:322\u001b[0m, in \u001b[0;36mCursor._query\u001b[0;34m(self, q)\u001b[0m\n\u001b[1;32m 320\u001b[0m conn \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_db()\n\u001b[1;32m 321\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_clear_result()\n\u001b[0;32m--> 322\u001b[0m conn\u001b[39m.\u001b[39;49mquery(q)\n\u001b[1;32m 323\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_do_get_result()\n\u001b[1;32m 324\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrowcount\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/connections.py:558\u001b[0m, in \u001b[0;36mConnection.query\u001b[0;34m(self, sql, unbuffered)\u001b[0m\n\u001b[1;32m 556\u001b[0m sql \u001b[39m=\u001b[39m sql\u001b[39m.\u001b[39mencode(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mencoding, \u001b[39m\"\u001b[39m\u001b[39msurrogateescape\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 557\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_execute_command(COMMAND\u001b[39m.\u001b[39mCOM_QUERY, sql)\n\u001b[0;32m--> 558\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_affected_rows \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_read_query_result(unbuffered\u001b[39m=\u001b[39;49munbuffered)\n\u001b[1;32m 559\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_affected_rows\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/connections.py:822\u001b[0m, in \u001b[0;36mConnection._read_query_result\u001b[0;34m(self, unbuffered)\u001b[0m\n\u001b[1;32m 820\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 821\u001b[0m result \u001b[39m=\u001b[39m MySQLResult(\u001b[39mself\u001b[39m)\n\u001b[0;32m--> 822\u001b[0m result\u001b[39m.\u001b[39;49mread()\n\u001b[1;32m 823\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result \u001b[39m=\u001b[39m result\n\u001b[1;32m 824\u001b[0m \u001b[39mif\u001b[39;00m result\u001b[39m.\u001b[39mserver_status \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/connections.py:1200\u001b[0m, in \u001b[0;36mMySQLResult.read\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1198\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mread\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m 1199\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1200\u001b[0m first_packet \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnection\u001b[39m.\u001b[39;49m_read_packet()\n\u001b[1;32m 1202\u001b[0m \u001b[39mif\u001b[39;00m first_packet\u001b[39m.\u001b[39mis_ok_packet():\n\u001b[1;32m 1203\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_read_ok_packet(first_packet)\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/connections.py:772\u001b[0m, in \u001b[0;36mConnection._read_packet\u001b[0;34m(self, packet_type)\u001b[0m\n\u001b[1;32m 770\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result\u001b[39m.\u001b[39munbuffered_active \u001b[39mis\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m 771\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result\u001b[39m.\u001b[39munbuffered_active \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m--> 772\u001b[0m packet\u001b[39m.\u001b[39;49mraise_for_error()\n\u001b[1;32m 773\u001b[0m \u001b[39mreturn\u001b[39;00m packet\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/protocol.py:221\u001b[0m, in \u001b[0;36mMysqlPacket.raise_for_error\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[39mif\u001b[39;00m DEBUG:\n\u001b[1;32m 220\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39merrno =\u001b[39m\u001b[39m\"\u001b[39m, errno)\n\u001b[0;32m--> 221\u001b[0m err\u001b[39m.\u001b[39;49mraise_mysql_exception(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_data)\n", - "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/err.py:143\u001b[0m, in \u001b[0;36mraise_mysql_exception\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[39mif\u001b[39;00m errorclass \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 142\u001b[0m errorclass \u001b[39m=\u001b[39m InternalError \u001b[39mif\u001b[39;00m errno \u001b[39m<\u001b[39m \u001b[39m1000\u001b[39m \u001b[39melse\u001b[39;00m OperationalError\n\u001b[0;32m--> 143\u001b[0m \u001b[39mraise\u001b[39;00m errorclass(errno, errval)\n", - "\u001b[0;31mIntegrityError\u001b[0m: (1048, \"Column 'blood_group' cannot be null\")" - ] - } - ], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - "INSERT defaults.person (person_id, blood_group, name) VALUES (1, NULL, 'bob')\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/db-course/008-Transactions-HW.ipynb b/db-course/008-Transactions-HW.ipynb deleted file mode 100644 index d2b5091..0000000 --- a/db-course/008-Transactions-HW.ipynb +++ /dev/null @@ -1,243 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Homework 8: Transactions\n", - "\n", - "Prior to working on this homework, execute [004-DatabaseSales.ipynb](004-DatabaseSales.ipynb) to create the classic sales database.\n", - "\n", - "Create the python function `place_order`, which accepts a list of products, their quantities, the customer id, the employee id, and the payment amount. It should use a transaction to place an order. You cannot use SQL magic; use python libraries such as `pymysql` or `datajoint` (or possibly others if you prefer). The order consists of inserting an order into `Order`, all its items into `Order.Item`, and the corresponding payment into `Payment`. The function should through an error if the total on the payment does not match the total price of the items. The entire transaction should rollback should an interruption or error occur before the entire transaction completes.\n", - "\n", - "Play with the function enough to convince yourself that it implements transactions correctly. To grade, the instructor will review the code for correctness.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The sql extension is already loaded. To reload it, use:\n", - " %reload_ext sql\n" - ] - } - ], - "source": [ - "import pymysql\n", - "\n", - "pymysql.install_as_MySQLdb()\n", - "%load_ext sql\n", - "%config SqlMagic.autocommit=True\n", - "%sql mysql://root:simple@127.0.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "0\n", - "\n", - "0\n", - "\n", - "\n", - "\n", - "Report\n", - "\n", - "\n", - "Report\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "0->Report\n", - "\n", - "\n", - "\n", - "\n", - "1\n", - "\n", - "1\n", - "\n", - "\n", - "\n", - "Customer\n", - "\n", - "\n", - "Customer\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "1->Customer\n", - "\n", - "\n", - "\n", - "\n", - "ProductLine\n", - "\n", - "\n", - "ProductLine\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Product\n", - "\n", - "\n", - "Product\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "ProductLine->Product\n", - "\n", - "\n", - "\n", - "\n", - "Order.Item\n", - "\n", - "\n", - "Order.Item\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Product->Order.Item\n", - "\n", - "\n", - "\n", - "\n", - "Payment\n", - "\n", - "\n", - "Payment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order->Order.Item\n", - "\n", - "\n", - "\n", - "\n", - "Office\n", - "\n", - "\n", - "Office\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Employee\n", - "\n", - "\n", - "Employee\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Office->Employee\n", - "\n", - "\n", - "\n", - "\n", - "Employee->0\n", - "\n", - "\n", - "\n", - "\n", - "Employee->1\n", - "\n", - "\n", - "\n", - "\n", - "Employee->Report\n", - "\n", - "\n", - "\n", - "\n", - "Customer->Payment\n", - "\n", - "\n", - "\n", - "\n", - "Customer->Order\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import datajoint as dj\n", - "\n", - "sales = dj.Schema(\"classicsales\")\n", - "sales.spawn_missing_classes()\n", - "\n", - "dj.Diagram(sales)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/008-Transactions.ipynb b/db-course/008-Transactions.ipynb deleted file mode 100644 index 2de6136..0000000 --- a/db-course/008-Transactions.ipynb +++ /dev/null @@ -1,2105 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Transactions\n", - "\n", - "Some sequences of operations must be performed carefully with isolation from outside interventions and must not be left incomplete.\n", - "\n", - "- A = Atomic\n", - "- C = Consistent\n", - "- I = Isolated\n", - "- D = Durable\n", - "\n", - "\n", - "Transaction serialization: operations performed concurrently but ensuring the same effect if they were executed sequentially." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from faker import Faker\n", - "\n", - "fake = Faker()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-11-07 22:17:16,481][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", - "[2023-11-07 22:17:16,488][INFO]: Connected root@fakeservices.datajoint.io:3306\n" - ] - } - ], - "source": [ - "import datajoint as dj\n", - "\n", - "schema = dj.Schema(\"bank\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Account(dj.Manual):\n", - " definition = \"\"\"\n", - " account_number : int\n", - " ---\n", - " customer_name : varchar(60) \n", - " balance : decimal(9, 2)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "Account.insert(\n", - " dict(\n", - " account_number=fake.random.randint(10_000_000, 99_999_999),\n", - " customer_name=fake.name(),\n", - " balance=fake.random.randint(0, 100_000_00) / 100,\n", - " )\n", - " for i in range(100)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

account_number

\n", - " \n", - "
\n", - "

customer_name

\n", - " \n", - "
\n", - "

balance

\n", - " \n", - "
10067554Patrick Juarez49454.82
11158721Alisha Weiss76428.03
11191456Courtney Flynn93467.36
11302451Jeffrey Murray69825.81
11496037Brittany Velazquez8272.43
11817010Marcia Chen24325.38
12058107Rodney West88034.24
12224361Brittany Rangel33580.56
12279407Alfred Smith29637.98
12280075Desiree Fox89808.15
13477906Keith Bell48826.15
13695945Angelica Brooks51833.25
\n", - "

...

\n", - "

Total: 200

\n", - " " - ], - "text/plain": [ - "*account_numbe customer_name balance \n", - "+------------+ +------------+ +----------+\n", - "10067554 Patrick Juarez 49454.82 \n", - "11158721 Alisha Weiss 76428.03 \n", - "11191456 Courtney Flynn 93467.36 \n", - "11302451 Jeffrey Murray 69825.81 \n", - "11496037 Brittany Velaz 8272.43 \n", - "11817010 Marcia Chen 24325.38 \n", - "12058107 Rodney West 88034.24 \n", - "12224361 Brittany Range 33580.56 \n", - "12279407 Alfred Smith 29637.98 \n", - "12280075 Desiree Fox 89808.15 \n", - "13477906 Keith Bell 48826.15 \n", - "13695945 Angelica Brook 51833.25 \n", - " ...\n", - " (Total: 200)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "keys = Account.fetch(\"KEY\")\n", - "account1 = fake.random.choice(keys)\n", - "account2 = fake.random.choice(keys)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "({'account_number': 61340350}, {'account_number': 51596516})" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "account1, account2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def transfer_bad(account1, account2, amount):\n", - " current_balance = (Account & account1).fetch1(\"balance\")\n", - " if current_balance < amount:\n", - " raise RuntimeError(\"Insufficient funds\")\n", - "\n", - " Account.update1(dict(account1, balance=float(current_balance) - amount))\n", - "\n", - " b = (Account & account2).fetch1(\"balance\")\n", - "\n", - " Account.update1(dict(account2, balance=float(b) + amount))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def transfer_good(account1, account2, amount):\n", - " conn = dj.conn()\n", - " with conn.transaction:\n", - " current_balance = (Account & account1).fetch1(\"balance\")\n", - " if current_balance < amount:\n", - " raise RuntimeError(\"Insufficient funds\")\n", - "\n", - " Account.update1(dict(account1, balance=float(current_balance) - amount))\n", - "\n", - " b = (Account & account2).fetch1(\"balance\")\n", - "\n", - " Account.update1(dict(account2, balance=float(b) + amount))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

account_number

\n", - " \n", - "
\n", - "

customer_name

\n", - " \n", - "
\n", - "

balance

\n", - " \n", - "
51596516Travis White44895.36
61340350Brian Lopez11877.72
\n", - " \n", - "

Total: 2

\n", - " " - ], - "text/plain": [ - "*account_numbe customer_name balance \n", - "+------------+ +------------+ +----------+\n", - "51596516 Travis White 44895.36 \n", - "61340350 Brian Lopez 11877.72 \n", - " (Total: 2)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account & [account1, account2]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "transfer(account1, account2, 100.00)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

account_number

\n", - " \n", - "
\n", - "

customer_name

\n", - " \n", - "
\n", - "

balance

\n", - " \n", - "
51596516Travis White44995.36
61340350Brian Lopez11777.72
\n", - " \n", - "

Total: 2

\n", - " " - ], - "text/plain": [ - "*account_numbe customer_name balance \n", - "+------------+ +------------+ +----------+\n", - "51596516 Travis White 44995.36 \n", - "61340350 Brian Lopez 11777.72 \n", - " (Total: 2)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account & [account1, account2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pymysql\n", - "\n", - "pymysql.install_as_MySQLdb()\n", - "%load_ext sql\n", - "%config SqlMagic.autocommit=True\n", - "%sql mysql://root:simple@127.0.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%sql\n", - "\n", - "use bank;\n", - "\n", - "SHOW CREATE TABLE account;" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "%%sql\n", - "\n", - "SELECT * FROM account;" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%sql\n", - "\n", - "BEGIN TRANSACTION;\n", - " \n", - " UPDATE account SET balance = balance + 100\n", - " WHERE account = 98230343;\n", - " \n", - " \n", - " UPDATE account SET balance = balance - 100\n", - " WHERE account 95440048;\n", - "\n", - "COMMIT" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "import pymysql" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "conn = pymysql.connect(\n", - " user=\"root\", host=\"127.0.0.1\", password=\"simple\", autocommit=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cursor = conn.cursor()\n", - "cursor.execute(\n", - " \"\"\"\n", - " SELECT balance FROM bank.account \n", - " WHERE account_number = %s\n", - " \"\"\",\n", - " (account1[\"account_number\"],),\n", - ")\n", - "\n", - "amount = 100\n", - "\n", - "current_balance = cursor.fetchone()\n", - "\n", - "cursor.execute(\n", - " \"\"\"\n", - " UPDATE bank.account \n", - " SET balance = balance - %s\n", - " WHERE account_number = %s\n", - " \"\"\",\n", - " (\n", - " amount,\n", - " account1[\"account_number\"],\n", - " ),\n", - ")\n", - "\n", - "cursor.execute(\n", - " \"\"\"\n", - " UPDATE bank.account \n", - " SET balance = balance + %s\n", - " WHERE account_number = %s\n", - " \"\"\",\n", - " (\n", - " amount,\n", - " account2[\"account_number\"],\n", - " ),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "def transfer(cursor, account1, account2, amount):\n", - " cursor.execute(\"BEGIN TRANSACTION\")\n", - "\n", - " try:\n", - " cursor.execute(\n", - " \"\"\"\n", - " SELECT balance FROM bank.account \n", - " WHERE account_number = %s\n", - " \"\"\",\n", - " (account1[\"account_number\"],),\n", - " )\n", - "\n", - " current_balance = cursor.fetchone()\n", - "\n", - " if current_balance < amount:\n", - " raise RuntimeError(\"Insufficient funds\")\n", - "\n", - " cursor.execute(\n", - " \"\"\"\n", - " UPDATE shared_bank.account \n", - " SET balance = balance - %s\n", - " WHERE account_number = %s\n", - " \"\"\",\n", - " (\n", - " amount,\n", - " account1[\"account_number\"],\n", - " ),\n", - " )\n", - "\n", - " cursor.execute(\n", - " \"\"\"\n", - " UPDATE shared_bank.account \n", - " SET balance = balance + %s\n", - " WHERE account_number = %s\n", - " \"\"\",\n", - " (\n", - " amount,\n", - " account2[\"account_number\"],\n", - " ),\n", - " )\n", - "\n", - " except:\n", - " cursor.execute(\"CANCEL TRANSACTION\")\n", - " raise\n", - "\n", - " else:\n", - " cursor.execute(\"COMMIT\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Design Patterns\n", - "\n", - "- Sequence\n", - " * workflows\n", - "- Specialization / Generalization\n", - " * student / faculty / staff\n", - "- Hierarchies\n", - " * Ownership\n", - " * Using composite primary keys\n", - " * Secondary keys\n", - "- Parameterization\n", - " * \n", - "- Associations\n", - " * Many-to-many relationships\n", - " * Directed graphs \n", - " * Trees\n", - " * Undirected graphs\n", - "- Master-part\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "schema = dj.schema(\"dimitri_patterns\")\n", - "schema.drop()\n", - "schema = dj.schema(\"dimitri_patterns\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sequence / Workflows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# E.g. order / shipment / confirmation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Order(dj.Manual):\n", - " definition = \"\"\"\n", - " order_number : int\n", - " ---\n", - " order_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Shipment(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Order\n", - " ---\n", - " ship_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Confirm(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Shipment\n", - " ---\n", - " confirm_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Order * Shipment * Confirm" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Order2(dj.Manual):\n", - " definition = \"\"\"\n", - " order_number : int\n", - " ---\n", - " order_date : date\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Shipment2(dj.Manual):\n", - " definition = \"\"\"\n", - " shipment_id : int\n", - " ---\n", - " ->[unique] Order2\n", - " ship_date : date\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Confirm2(dj.Manual):\n", - " definition = \"\"\"\n", - " confirm_id : int\n", - " ---\n", - " -> [unique] Shipment2\n", - " confirm_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Order * Confirm" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Order * Shipment * Confirm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Two ways to model hierarchies\n", - "\n", - "## Approach 1: Simple primary keys with secondary dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Subject(dj.Manual):\n", - " definition = \"\"\"\n", - " # Experiment Subject\n", - " subject_id : int\n", - " ---\n", - " species = 'mouse' : enum('human', 'mouse', 'rat', 'worm')\n", - " sex : enum('F', 'M', 'unknown')\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Subject()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Subject.insert1(\n", - " dict(subject_id=1, species=\"human\", sex=\"unknown\"), skip_duplicates=True\n", - ")\n", - "Subject.insert1(dict(subject_id=2, species=\"mouse\", sex=\"F\"), skip_duplicates=True)\n", - "Subject.insert1(dict(subject_id=3, species=\"worm\", sex=\"M\"), skip_duplicates=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Session(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Subject\n", - " session : int\n", - " ---\n", - " session_timestamp = CURRENT_TIMESTAMP : timestamp\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Session.insert1(dict(session=1, subject_id=2), skip_duplicates=True)\n", - "Session.insert1(dict(session=2, subject_id=2), skip_duplicates=True)\n", - "Session.insert1(dict(session=3, subject_id=3), skip_duplicates=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Session()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Scan(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Session\n", - " scan_id : int\n", - " ---\n", - " laser_power : float # mW\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Scan()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Scan.insert1(dict(subject_id=2, scan_id=1, session=1, laser_power=3200))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Cell(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Scan\n", - " cell_id : int\n", - " ---\n", - " cell_type : enum('E', 'I') # excitatory or inhibitory\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Give me excitatory cells for all males" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Cell & (Subject & {\"sex\": \"M\"})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Subject2(dj.Manual):\n", - " definition = \"\"\"\n", - " # Experiment Subject\n", - " subject_id : int\n", - " ---\n", - " species = 'mouse' : enum('human', 'mouse', 'rat', 'worm')\n", - " sex : enum('F', 'M', 'unknown')\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Session2(dj.Manual):\n", - " definition = \"\"\"\n", - " session : int\n", - " ---\n", - " -> Subject2\n", - " session_timestamp = CURRENT_TIMESTAMP : timestamp\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Scan2(dj.Manual):\n", - " definition = \"\"\"\n", - " scan_id : int\n", - " ---\n", - " -> Session2\n", - " laser_power : float # mW\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Cell2(dj.Manual):\n", - " definition = \"\"\"\n", - " cell_id : int\n", - " ---\n", - " -> Scan2\n", - " cell_type : enum('E', 'I') # excitatory or inhibitory\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Cell.insert1(dict(cell_id=1, scan_id=1, cell_type=\"E\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Cell()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Give me cells for subject_id=1\n", - "\n", - "Cell2 & (Scan2 & (Session2 & \"subject_id=2\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Give me cells for all males\n", - "\n", - "(Cell2 & (Scan2 & (Session2 & (Subject2 & 'sex=\"M\"')))).make_sql()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(Cell & (Subject & 'sex=\"M\"')).make_sql()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameterization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Image(dj.Manual):\n", - " definition = \"\"\"\n", - " image_id : int\n", - " ---\n", - " image : longblob\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class EnhanceMethod(dj.Lookup):\n", - " definition = \"\"\"\n", - " enhance_method : int\n", - " ---\n", - " method_name : varchar(16)\n", - " \"\"\"\n", - "\n", - " contents = ((1, \"sharpen\"), (2, \"contrast\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class EnhancedImage(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Image\n", - " -> EnhanceMethod\n", - " ---\n", - " enhanced_image : longblob\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Association \n", - "\n", - "Books and authors\n", - "\n", - "Checking accounts and account owners" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Book(dj.Manual):\n", - " definition = \"\"\"\n", - " isbn : int\n", - " ---\n", - " title : varchar(300)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Author(dj.Manual):\n", - " definition = \"\"\"\n", - " author_id : int\n", - " ---\n", - " name : varchar(300)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class AuthorBook(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Author\n", - " -> Book\n", - " ---\n", - " order : tinyint unsigned \n", - " unique index(isbn, order)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generalization / specialization\n", - "\n", - "Employee, student, instructor" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Person(dj.Manual):\n", - " definition = \"\"\"\n", - " person_id : int\n", - " ---\n", - " date_of_birth : date\n", - " gender : enum(\"male\", \"female\", \"unknown\")\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Employee(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Person\n", - " ---\n", - " hire_date : date \n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Instructor(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Employee\n", - " ---\n", - " department : varchar(30)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Student(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Person\n", - " ---\n", - " admission_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Directed graphs " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Subordinate(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Employee\n", - " ---\n", - " -> Employee.proj(manager_id=\"person_id\")\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Neuron(dj.Manual):\n", - " definition = \"\"\"\n", - " neuron : int\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Synapse(dj.Manual):\n", - " definition = \"\"\"\n", - " synapse_id : int\n", - " ---\n", - " -> Neuron.proj(pre=\"neuron\")\n", - " -> Neuron.proj(post=\"neuron\") \n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```sql\n", - "\n", - "CREATE TABLE managed_by (\n", - " person_id : int NOT NULL,\n", - " manager_id : int NOT NULL,\n", - " \n", - " PRIMARY KEY (person_id),\n", - " \n", - " FOREIGN KEY (person_id) REFERENCES employee (person_id),\n", - " FOREIGN KEY (manager_id) reference employee (person_id))\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Undirected graphs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# direcated friendship = full directed graph capability\n", - "@schema\n", - "class Friendship(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Person.proj(friend1 = \"person_id\")\n", - " -> Person.proj(friend2 = \"person_id\")\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Order(dj.Manual):\n", - " definition = \"\"\"\n", - " order_id : int\n", - " ---\n", - " order_date : date\n", - " \"\"\"\n", - "\n", - " class Item(dj.Part):\n", - " definition = \"\"\"\n", - " -> master\n", - " order_item : int\n", - " ---\n", - " \n", - " \"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Puzzle: \n", - "Cities and states.\n", - "1. Each city belongs to one state. \n", - "2. Each state has one capital.\n", - "3. A capital is a city.\n", - "4. A capital must be in the same state. \n", - "\n", - "* Tables\n", - "* Primary keys\n", - "* Foreign keys" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class State(dj.Manual):\n", - " definition = \"\"\"\n", - " st : char(2)\n", - " ---\n", - " state : varchar(30)\n", - " \"\"\"\n", - "\n", - "\n", - "State.insert(\n", - " ((\"WA\", \"Washington\"), (\"TX\", \"Texas\"), (\"AK\", \"Alaska\"), (\"LA\", \"Louisiana\"))\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class City(dj.Manual):\n", - " definition = \"\"\"\n", - " -> State\n", - " city_name : varchar(30)\n", - " ---\n", - " capital = null : enum(\"YES\")\n", - " unique index(st, capital)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "City.delete_quick()\n", - "\n", - "City.insert1((\"WA\", \"Seattle\", None))\n", - "City.insert1((\"TX\", \"Austin\", \"YES\"))\n", - "City.insert1((\"TX\", \"Houston\", None))\n", - "City.insert1((\"WA\", \"Olympia\", \"YES\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "City()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class State2(dj.Manual):\n", - " definition = \"\"\"\n", - " state : char (2)\n", - " ---\n", - " state_name : varchar(30)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class City2(dj.Manual):\n", - " definition = \"\"\"\n", - " -> State2\n", - " city_name : varchar(30)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Capital2(dj.Manual):\n", - " definition = \"\"\"\n", - " -> State2\n", - " ---\n", - " -> City2\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "State2.delete_quick()\n", - "City2.delete_quick()\n", - "\n", - "State2.insert(\n", - " ((\"WA\", \"Washington\"), (\"TX\", \"Texas\"), (\"AK\", \"Alaska\"), (\"LA\", \"Louisiana\"))\n", - ")\n", - "\n", - "City2.insert1((\"WA\", \"Seattle\"))\n", - "City2.insert1((\"TX\", \"Austin\"))\n", - "City2.insert1((\"TX\", \"Houston\"))\n", - "City2.insert1((\"WA\", \"Olympia\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Capital2.insert1((\"TX\", \"Austin\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Capital2.insert1((\"TX\", \"Houston\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "City2()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# In SQL \n", - "\n", - "```sql\n", - "CREATE TABLE state (\n", - " state char(2) NOT NULL,\n", - " state_name varchar(30) NOT NULL,\n", - " PRIMARY KEY (state))\n", - " \n", - "CREATE TABLE city (\n", - " state char(2) NOT NULL,\n", - " city_name varchar(30) NOT NULL,\n", - " PRIMARY KEY (state, city_name),\n", - " FOREIGN KEY (state) REFERENCES state(state))\n", - "\n", - "CREATE TABLE capital (\n", - " state char(2) NOT NULL,\n", - " city_name varchar(30) NOT NULL,\n", - " PRIMARY KEY (state),\n", - " FOREIGN KEY (state, city_name) REFERENCES city (state, city_name))\n", - "```\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "```sql\n", - "CREATE TABLE state (\n", - " state char(2),\n", - " state_name varchar(30),\n", - " capital varchar(30),\n", - " PRIMARY KEY (state),\n", - " FOREIGN KEY (state, capital) REFERENCES city (state, city_name))\n", - " \n", - "CREATE TABLE city (\n", - " state char(2),\n", - " city_name varchar(30),\n", - " PRIMARY KEY (state, city_name))\n", - " FOREIGN KEY (state) REFERENCES state(state)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# In SQL \n", - "\n", - "```sql\n", - "CREATE TABLE state (\n", - " state char(2) NOT NULL,\n", - " state_name varchar(30) NOT NULL,\n", - " PRIMARY KEY (state))\n", - " \n", - "CREATE TABLE city (\n", - " city_id int NOT NULL,\n", - " state char(2) NOT NULL,\n", - " city_name varchar(30) NOT NULL,\n", - " is_capital enum('yes'),\n", - " PRIMARY KEY (state_id),\n", - " UNIQUE INDEX(state, is_capital),\n", - " FOREIGN KEY (state) REFERENCES state(state))\n", - "```\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Problem \n", - "\n", - "Model a vet clinic. \n", - "\n", - "1. Customers bring in pets. Customers are identified by their cell phones. Pets are identified by their nicknames for that customer.\n", - "\n", - "2. Pets have a date of birth, species, and date of birth.\n", - "\n", - "3. Pets have a list of vaccinations that must be performed for their species.\n", - "\n", - "4. Pets have vaccination administration, shot date. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "schema = dj.Schema(\"shared_vet\")\n", - "schema.drop()\n", - "schema = dj.Schema(\"shared_vet\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Owner(dj.Manual):\n", - " definition = \"\"\"\n", - " cell_phone : char(10) \n", - " ---\n", - " full_name : varchar(16)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Species(dj.Lookup):\n", - " definition = \"\"\"\n", - " species : varchar(30)\n", - " \"\"\"\n", - " contents = ((\"cat\",), (\"dog\",), (\"ferret\",), (\"parrot\",))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Species()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Pet(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Owner\n", - " -> Species\n", - " nickname : varchar(30)\n", - " ---\n", - " birthdate : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class RequiredVaccine(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Species\n", - " vaccine : varchar(10)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Shot(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Pet\n", - " -> RequiredVaccine\n", - " ---\n", - " shot_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Shot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```sql\n", - "create table shot (\n", - " cell_phone char(10) NOT NULL,\n", - " nickname varchar(16) NOT NULL,\n", - " species varchar(20) NOT NULL,\n", - " vaccine varchar(10) NOT NULL,\n", - " PRIMARY KEY (cell_phone, nickname, species, vaccine),\n", - " FOREIGN KEY (cell_phone, nickname, species) REFERENCES pet(cell_phone, nickname, species),\n", - " FOREIGN KEY (species, vaccine) REFERENCES required_vaccine(species, vaccine)\n", - ")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Homework homework\n", - "\n", - "Homework assignments, students, grades\n", - "\n", - "1. Homework is given with a due date.\n", - "2. Students submit homework, we record the submit date\n", - "3. Submitted homework gets a grade\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Assignment(dj.Manual):\n", - " definition = \"\"\"\n", - " assignment : int\n", - " ---\n", - " due_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Student(dj.Manual):\n", - " definition = \"\"\"\n", - " student_id : int\n", - " ---\n", - " student_name : varchar(30)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Submission(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Student\n", - " -> Assignment\n", - " ---\n", - " submit_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Grade(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Submission\n", - " ---\n", - " grade : char(1)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Messaging App (Slack, Telegram, Signal)\n", - "\n", - "1. Users can create channels. Each channel belongs to one user.\n", - "3. Channel names are globally unique\n", - "2. A user can create a post in their channels only\n", - "3. A user can be a guest in another person's channel.\n", - "4. Guest can reply to posts\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class User(dj.Manual):\n", - " definition = \"\"\"\n", - " username : varchar(12)\n", - " ---\n", - " irl_name : varchar(30)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Channel(dj.Manual):\n", - " definition = \"\"\"\n", - " channel : varchar(12)\n", - " ---\n", - " -> User\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Guest(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Channel\n", - " -> User\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Post(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Channel\n", - " post : int\n", - " ---\n", - " message : varchar(1024)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Post * Channel" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Response(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Post\n", - " -> Guest\n", - " ---\n", - " response : varchar(1024)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/db-course/009-DesignPatterns.ipynb b/db-course/009-DesignPatterns.ipynb deleted file mode 100644 index 034ea01..0000000 --- a/db-course/009-DesignPatterns.ipynb +++ /dev/null @@ -1,6521 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Design Patterns\n", - "\n", - "- Sequence\n", - " * workflows\n", - "- Specialization / Generalization\n", - " * student / faculty / staff\n", - "- Hierarchies\n", - " * Ownership\n", - " * Using composite primary keys\n", - " * Secondary keys\n", - "- Parameterization\n", - " * \n", - "- Associations\n", - " * Many-to-many relationships\n", - " * Directed graphs \n", - " * Trees\n", - " * Undirected graphs\n", - "- Master-part\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Proceed to delete entire schema `dimitri_patterns`? [yes, No]: yes\n" - ] - } - ], - "source": [ - "schema = dj.schema(\"dimitri_patterns\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sequence / Workflows" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# E.g. order / shipment / confirmation" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Order(dj.Manual):\n", - " definition = \"\"\"\n", - " order_number : int\n", - " ---\n", - " order_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Shipment(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Order\n", - " ---\n", - " ship_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Confirm(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Shipment\n", - " ---\n", - " confirm_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment->Confirm\n", - "\n", - "\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order->Shipment\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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" scan_id : int\n", - " ---\n", - " -> Session2\n", - " laser_power : float # mW\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Cell2(dj.Manual):\n", - " definition = \"\"\"\n", - " cell_id : int\n", - " ---\n", - " -> Scan2\n", - " cell_type : enum('E', 'I') # excitatory or inhibitory\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session->Scan\n", - "\n", - "\n", - "\n", - "\n", - "Cell2\n", - "\n", - "\n", - "Cell2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order2\n", - "\n", - "\n", - "Order2\n", - "\n", - 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"File \u001b[0;32m~/opt/miniconda3/envs/benv/lib/python3.10/site-packages/datajoint/table.py:334\u001b[0m, in \u001b[0;36mTable.insert1\u001b[0;34m(self, row, **kwargs)\u001b[0m\n\u001b[1;32m 327\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minsert1\u001b[39m(\u001b[38;5;28mself\u001b[39m, row, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 328\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 329\u001b[0m \u001b[38;5;124;03m Insert one data record into the table. For ``kwargs``, see ``insert()``.\u001b[39;00m\n\u001b[1;32m 330\u001b[0m \n\u001b[1;32m 331\u001b[0m \u001b[38;5;124;03m :param row: a numpy record, a dict-like object, or an ordered sequence to be inserted\u001b[39;00m\n\u001b[1;32m 332\u001b[0m \u001b[38;5;124;03m as one row.\u001b[39;00m\n\u001b[1;32m 333\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 334\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minsert\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/benv/lib/python3.10/site-packages/datajoint/table.py:430\u001b[0m, in \u001b[0;36mTable.insert\u001b[0;34m(self, rows, replace, skip_duplicates, ignore_extra_fields, allow_direct_insert)\u001b[0m\n\u001b[1;32m 414\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 415\u001b[0m query \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{command}\u001b[39;00m\u001b[38;5;124m INTO \u001b[39m\u001b[38;5;132;01m{destination}\u001b[39;00m\u001b[38;5;124m(`\u001b[39m\u001b[38;5;132;01m{fields}\u001b[39;00m\u001b[38;5;124m`) VALUES \u001b[39m\u001b[38;5;132;01m{placeholders}\u001b[39;00m\u001b[38;5;132;01m{duplicate}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 416\u001b[0m command\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mREPLACE\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m replace \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mINSERT\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 417\u001b[0m destination\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfrom_clause(),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 428\u001b[0m ),\n\u001b[1;32m 429\u001b[0m )\n\u001b[0;32m--> 430\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 431\u001b[0m \u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 432\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 433\u001b[0m \u001b[43m \u001b[49m\u001b[43mitertools\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_iterable\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 434\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalues\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrows\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 436\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 437\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m UnknownAttributeError \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 439\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m err\u001b[38;5;241m.\u001b[39msuggest(\n\u001b[1;32m 440\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTo ignore extra fields in insert, set ignore_extra_fields=True\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 441\u001b[0m )\n", - "File \u001b[0;32m~/opt/miniconda3/envs/benv/lib/python3.10/site-packages/datajoint/connection.py:340\u001b[0m, in \u001b[0;36mConnection.query\u001b[0;34m(self, query, args, as_dict, suppress_warnings, reconnect)\u001b[0m\n\u001b[1;32m 338\u001b[0m cursor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_conn\u001b[38;5;241m.\u001b[39mcursor(cursor\u001b[38;5;241m=\u001b[39mcursor_class)\n\u001b[1;32m 339\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 340\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execute_query\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcursor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuppress_warnings\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mLostConnectionError:\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m reconnect:\n", - "File \u001b[0;32m~/opt/miniconda3/envs/benv/lib/python3.10/site-packages/datajoint/connection.py:296\u001b[0m, in \u001b[0;36mConnection._execute_query\u001b[0;34m(cursor, query, args, suppress_warnings)\u001b[0m\n\u001b[1;32m 294\u001b[0m cursor\u001b[38;5;241m.\u001b[39mexecute(query, args)\n\u001b[1;32m 295\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m client\u001b[38;5;241m.\u001b[39merr\u001b[38;5;241m.\u001b[39mError \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 296\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m translate_query_error(err, query)\n", - "\u001b[0;31mMissingAttributeError\u001b[0m: Field 'subject_id' doesn't have a default value" - ] - } - ], - "source": [ - "Cell.insert1(dict(cell_id=1, scan_id=1, cell_type=\"E\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Cell()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Give me cells for subject_id=1\n", - "\n", - "Cell2 & (Scan2 & (Session2 & \"subject_id=2\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Give me cells for all males\n", - "\n", - "(Cell2 & (Scan2 & (Session2 & (Subject2 & 'sex=\"M\"')))).make_sql()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(Cell & (Subject & 'sex=\"M\"')).make_sql()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameterization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Image(dj.Manual):\n", - " definition = \"\"\"\n", - " image_id : int\n", - " ---\n", - " image : longblob\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class EnhanceMethod(dj.Lookup):\n", - " definition = \"\"\"\n", - " enhance_method : int\n", - " ---\n", - " method_name : varchar(16)\n", - " \"\"\"\n", - "\n", - " contents = ((1, \"sharpen\"), (2, \"contrast\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class EnhancedImage(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Image\n", - " -> EnhanceMethod\n", - " ---\n", - " enhanced_image : longblob\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Association \n", - "\n", - "Books and authors\n", - "\n", - "Checking accounts and account owners" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Book(dj.Manual):\n", - " definition = \"\"\"\n", - " isbn : int\n", - " ---\n", - " title : varchar(300)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Author(dj.Manual):\n", - " definition = \"\"\"\n", - " author_id : int\n", - " ---\n", - " name : varchar(300)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class AuthorBook(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Author\n", - " -> Book\n", - " ---\n", - " order : tinyint unsigned \n", - " unique index(isbn, order)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session->Scan\n", - "\n", - "\n", - "\n", - "\n", - "Cell2\n", - "\n", - "\n", - "Cell2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order2\n", - "\n", - "\n", - "Order2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment2\n", - "\n", - "\n", - "Shipment2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order2->Shipment2\n", - "\n", - "\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject->Session\n", - "\n", - "\n", - "\n", - "\n", - "Session2\n", - "\n", - "\n", - "Session2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session2->Scan2\n", - "\n", - "\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment->Confirm\n", - "\n", - "\n", - "\n", - "\n", - "Confirm2\n", - "\n", - "\n", - "Confirm2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject2\n", - "\n", - "\n", - "Subject2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject2->Session2\n", - "\n", - "\n", - "\n", - "\n", - "AuthorBook\n", - "\n", - "\n", - "AuthorBook\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Book\n", - "\n", - "\n", - "Book\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Book->AuthorBook\n", - "\n", - "\n", - "\n", - "\n", - "Author\n", - "\n", - "\n", - "Author\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Author->AuthorBook\n", - "\n", - "\n", - "\n", - "\n", - "Scan2->Cell2\n", - "\n", - "\n", - "\n", - "\n", - "Scan->Cell\n", - "\n", - "\n", - "\n", - "\n", - "Shipment2->Confirm2\n", - "\n", - "\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order->Shipment\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generalization / specialization\n", - "\n", - "Employee, student, instructor" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Person(dj.Manual):\n", - " definition = \"\"\"\n", - " person_id : int\n", - " ---\n", - " date_of_birth : date\n", - " gender : enum(\"male\", \"female\", \"unknown\")\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Employee(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Person\n", - " ---\n", - " hire_date : date \n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Instructor(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Employee\n", - " ---\n", - " department : varchar(30)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Student(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Person\n", - " ---\n", - " admission_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject->Session\n", - "\n", - "\n", - "\n", - "\n", - "Session2\n", - "\n", - "\n", - "Session2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session2->Scan2\n", - "\n", - "\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment->Confirm\n", - 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] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Directed graphs " - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Subordinate(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Employee\n", - " ---\n", - " -> Employee.proj(manager_id=\"person_id\")\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "0\n", - "\n", - "0\n", - "\n", - "\n", - "\n", - "Subordinate\n", - "\n", - "\n", - "Subordinate\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "0->Subordinate\n", - "\n", - "\n", - "\n", - "\n", - "AuthorBook\n", - "\n", - "\n", - "AuthorBook\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - 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"```sql\n", - "\n", - "CREATE TABLE managed_by (\n", - " person_id : int NOT NULL,\n", - " manager_id : int NOT NULL,\n", - " \n", - " PRIMARY KEY (person_id),\n", - " \n", - " FOREIGN KEY (person_id) REFERENCES employee (person_id),\n", - " FOREIGN KEY (manager_id) reference employee (person_id))\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Undirected graphs" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "# direcated friendship = full directed graph capability\n", - "@schema\n", - "class Friendship(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Person.proj(friend1 = \"person_id\")\n", - " -> Person.proj(friend2 = \"person_id\")\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "4\n", - "\n", - "4\n", - "\n", - "\n", - "\n", - "Friendship\n", - 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"class Order(dj.Manual):\n", - " definition = \"\"\"\n", - " order_id : int\n", - " ---\n", - " order_date : date\n", - " \"\"\"\n", - "\n", - " class Item(dj.Part):\n", - " definition = \"\"\"\n", - " -> master\n", - " order_item : int\n", - " ---\n", - " \n", - " \"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Puzzle: \n", - "Cities and states.\n", - "1. Each city belongs to one state. \n", - "2. Each state has one capital.\n", - "3. A capital is a city.\n", - "4. A capital must be in the same state. \n", - "\n", - "* Tables\n", - "* Primary keys\n", - "* Foreign keys" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class State(dj.Manual):\n", - " definition = \"\"\"\n", - " st : char(2)\n", - " ---\n", - " state : varchar(30)\n", - " \"\"\"\n", - "\n", - "\n", - "State.insert(\n", - " ((\"WA\", \"Washington\"), (\"TX\", \"Texas\"), (\"AK\", \"Alaska\"), (\"LA\", \"Louisiana\"))\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class City(dj.Manual):\n", - " definition = \"\"\"\n", - " -> State\n", - " city_name : varchar(30)\n", - " ---\n", - " capital = null : enum(\"YES\")\n", - " unique index(st, capital)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "City.delete_quick()\n", - "\n", - "City.insert1((\"WA\", \"Seattle\", None))\n", - "City.insert1((\"TX\", \"Austin\", \"YES\"))\n", - "City.insert1((\"TX\", \"Houston\", None))\n", - "City.insert1((\"WA\", \"Olympia\", \"YES\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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st

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\n", - "

city_name

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\n", - "

capital

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TXHoustonNone
TXAustinYES
WASeattleNone
WAOlympiaYES
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Total: 4

\n", - " " - ], - "text/plain": [ - "*st *city_name capital \n", - "+----+ +-----------+ +---------+\n", - "TX Houston None \n", - "TX Austin YES \n", - "WA Seattle None \n", - "WA Olympia YES \n", - " (Total: 4)" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "City()" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class State2(dj.Manual):\n", - " definition = \"\"\"\n", - " state : char (2)\n", - " ---\n", - " state_name : varchar(30)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class City2(dj.Manual):\n", - " definition = \"\"\"\n", - " -> State2\n", - " city_name : varchar(30)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Capital2(dj.Manual):\n", - " definition = \"\"\"\n", - " -> State2\n", - " ---\n", - " -> City2\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "State2.delete_quick()\n", - "City2.delete_quick()\n", - "\n", - "State2.insert(\n", - " ((\"WA\", \"Washington\"), (\"TX\", \"Texas\"), (\"AK\", \"Alaska\"), (\"LA\", \"Louisiana\"))\n", - ")\n", - "\n", - "City2.insert1((\"WA\", \"Seattle\"))\n", - "City2.insert1((\"TX\", \"Austin\"))\n", - "City2.insert1((\"TX\", \"Houston\"))\n", - "City2.insert1((\"WA\", \"Olympia\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "9\n", - "\n", - "9\n", - "\n", - "\n", - "\n", - "Friendship\n", - "\n", - "\n", - "Friendship\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "9->Friendship\n", - "\n", - "\n", - "\n", - "\n", - "10\n", - "\n", - "10\n", - "\n", - "\n", - "\n", - "10->Friendship\n", - "\n", - "\n", - "\n", - "\n", - "11\n", - "\n", - "11\n", - "\n", - "\n", - "\n", - "Synapse\n", - "\n", - "\n", - "Synapse\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "11->Synapse\n", - "\n", - "\n", - "\n", - "\n", - "12\n", - "\n", - "12\n", - "\n", - "\n", - "\n", - "12->Synapse\n", - "\n", - "\n", - "\n", - "\n", - "13\n", - "\n", - "13\n", - "\n", - "\n", - "\n", - "Subordinate\n", - "\n", - "\n", - "Subordinate\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "13->Subordinate\n", - "\n", - "\n", - "\n", - "\n", - "AuthorBook\n", - "\n", - "\n", - "AuthorBook\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan->Cell\n", - "\n", - "\n", - "\n", - "\n", - "Cell2\n", - "\n", - "\n", - "Cell2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session->Scan\n", - "\n", - "\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan2->Cell2\n", - "\n", - "\n", - "\n", - "\n", - "State2\n", - "\n", - "\n", - "State2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "City2\n", - "\n", - "\n", - "City2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "State2->City2\n", - "\n", - "\n", - "\n", - "\n", - "Capital2\n", - "\n", - "\n", - "Capital2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "State2->Capital2\n", - "\n", - "\n", - "\n", - "\n", - "City2->Capital2\n", - "\n", - "\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject->Session\n", - "\n", - "\n", - "\n", - "\n", - "Shipment2\n", - "\n", - "\n", - "Shipment2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Confirm2\n", - "\n", - "\n", - "Confirm2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment2->Confirm2\n", - "\n", - "\n", - "\n", - "\n", - "Employee\n", - "\n", - "\n", - "Employee\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Employee->13\n", - "\n", - "\n", - "\n", - "\n", - "Employee->Subordinate\n", - "\n", - "\n", - "\n", - "\n", - "Instructor\n", - "\n", - "\n", - "Instructor\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Employee->Instructor\n", - "\n", - "\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment->Confirm\n", - "\n", - "\n", - "\n", - "\n", - "Session2\n", - "\n", - "\n", - "Session2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session2->Scan2\n", - "\n", - "\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person->9\n", - "\n", - "\n", - "\n", - "\n", - "Person->10\n", - "\n", - "\n", - "\n", - "\n", - "Person->Employee\n", - "\n", - "\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person->Student\n", - "\n", - "\n", - "\n", - "\n", - "Order.Item\n", - "\n", - "\n", - "Order.Item\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order2\n", - "\n", - "\n", - "Order2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order2->Shipment2\n", - "\n", - "\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order->Shipment\n", - "\n", - "\n", - "\n", - "\n", - "Order->Order.Item\n", - "\n", - "\n", - "\n", - "\n", - "Author\n", - "\n", - "\n", - "Author\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Author->AuthorBook\n", - "\n", - "\n", - "\n", - "\n", - "Book\n", - "\n", - "\n", - "Book\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Book->AuthorBook\n", - "\n", - "\n", - "\n", - "\n", - "Neuron\n", - "\n", - "\n", - "Neuron\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Neuron->11\n", - "\n", - "\n", - "\n", - "\n", - "Neuron->12\n", - "\n", - "\n", - "\n", - "\n", - "Subject2\n", - "\n", - "\n", - "Subject2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject2->Session2\n", - "\n", - "\n", - "\n", - "\n", - "State\n", - "\n", - "\n", - "State\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "City\n", - "\n", - "\n", - "City\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "State->City\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [], - "source": [ - "Capital2.insert1((\"TX\", \"Austin\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "ename": "DuplicateError", - "evalue": "(\"Duplicate entry 'TX' for key 'PRIMARY'\", 'To ignore duplicate entries in insert, set skip_duplicates=True')", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mDuplicateError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [76]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mCapital2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minsert1\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mTX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mHouston\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/benv/lib/python3.10/site-packages/datajoint/table.py:334\u001b[0m, in \u001b[0;36mTable.insert1\u001b[0;34m(self, row, **kwargs)\u001b[0m\n\u001b[1;32m 327\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minsert1\u001b[39m(\u001b[38;5;28mself\u001b[39m, row, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 328\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 329\u001b[0m \u001b[38;5;124;03m Insert one data record into the table. For ``kwargs``, see ``insert()``.\u001b[39;00m\n\u001b[1;32m 330\u001b[0m \n\u001b[1;32m 331\u001b[0m \u001b[38;5;124;03m :param row: a numpy record, a dict-like object, or an ordered sequence to be inserted\u001b[39;00m\n\u001b[1;32m 332\u001b[0m \u001b[38;5;124;03m as one row.\u001b[39;00m\n\u001b[1;32m 333\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 334\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minsert\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/benv/lib/python3.10/site-packages/datajoint/table.py:443\u001b[0m, in \u001b[0;36mTable.insert\u001b[0;34m(self, rows, replace, skip_duplicates, ignore_extra_fields, allow_direct_insert)\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m err\u001b[38;5;241m.\u001b[39msuggest(\n\u001b[1;32m 440\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTo ignore extra fields in insert, set ignore_extra_fields=True\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 441\u001b[0m )\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m DuplicateError \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 443\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m err\u001b[38;5;241m.\u001b[39msuggest(\n\u001b[1;32m 444\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTo ignore duplicate entries in insert, set skip_duplicates=True\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 445\u001b[0m )\n", - "\u001b[0;31mDuplicateError\u001b[0m: (\"Duplicate entry 'TX' for key 'PRIMARY'\", 'To ignore duplicate entries in insert, set skip_duplicates=True')" - ] - } - ], - "source": [ - "Capital2.insert1((\"TX\", \"Houston\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

state

\n", - " \n", - "
\n", - "

city_name

\n", - " \n", - "
TXAustin
TXHouston
WAOlympia
WASeattle
\n", - " \n", - "

Total: 4

\n", - " " - ], - "text/plain": [ - "*state *city_name \n", - "+-------+ +-----------+\n", - "TX Austin \n", - "TX Houston \n", - "WA Olympia \n", - "WA Seattle \n", - " (Total: 4)" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "City2()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# In SQL \n", - "\n", - "```sql\n", - "CREATE TABLE state (\n", - " state char(2) NOT NULL,\n", - " state_name varchar(30) NOT NULL,\n", - " PRIMARY KEY (state))\n", - " \n", - "CREATE TABLE city (\n", - " state char(2) NOT NULL,\n", - " city_name varchar(30) NOT NULL,\n", - " PRIMARY KEY (state, city_name),\n", - " FOREIGN KEY (state) REFERENCES state(state))\n", - "\n", - "CREATE TABLE capital (\n", - " state char(2) NOT NULL,\n", - " city_name varchar(30) NOT NULL,\n", - " PRIMARY KEY (state),\n", - " FOREIGN KEY (state, city_name) REFERENCES city (state, city_name))\n", - "```\n" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (724225854.py, line 1)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m Input \u001b[0;32mIn [78]\u001b[0;36m\u001b[0m\n\u001b[0;31m ```sql\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" - ] - } - ], - "source": [ - "\n", - "```sql\n", - "CREATE TABLE state (\n", - " state char(2),\n", - " state_name varchar(30),\n", - " capital varchar(30),\n", - " PRIMARY KEY (state),\n", - " FOREIGN KEY (state, capital) REFERENCES city (state, city_name))\n", - " \n", - "CREATE TABLE city (\n", - " state char(2),\n", - " city_name varchar(30),\n", - " PRIMARY KEY (state, city_name))\n", - " FOREIGN KEY (state) REFERENCES state(state)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# In SQL \n", - "\n", - "```sql\n", - "CREATE TABLE state (\n", - " state char(2) NOT NULL,\n", - " state_name varchar(30) NOT NULL,\n", - " PRIMARY KEY (state))\n", - " \n", - "CREATE TABLE city (\n", - " city_id int NOT NULL,\n", - " state char(2) NOT NULL,\n", - " city_name varchar(30) NOT NULL,\n", - " is_capital enum('yes'),\n", - " PRIMARY KEY (state_id),\n", - " UNIQUE INDEX(state, is_capital),\n", - " FOREIGN KEY (state) REFERENCES state(state))\n", - "```\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Problem \n", - "\n", - "Model a vet clinic. \n", - "\n", - "1. Customers bring in pets. Customers are identified by their cell phones. \n", - "\n", - "2. Pets are identified by their nicknames for that customer.\n", - "\n", - "3. Pets have a date of birth, species, and date of birth.\n", - "\n", - "4. Pets have a list of vaccinations that must be performed for their species.\n", - "\n", - "5. Pets have vaccination administration, shot date. " - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Proceed to delete entire schema `shared_vet`? [yes, No]: yes\n" - ] - } - ], - "source": [ - "schema = dj.Schema(\"vet\")" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Owner(dj.Manual):\n", - " definition = \"\"\"\n", - " cell_phone : char(10) \n", - " ---\n", - " full_name : varchar(16)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Species(dj.Lookup):\n", - " definition = \"\"\"\n", - " species : varchar(30)\n", - " \"\"\"\n", - " contents = ((\"cat\",), (\"dog\",), (\"ferret\",), (\"parrot\",))" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "
\n", - "

species

\n", - " \n", - "
cat
dog
ferret
parrot
\n", - " \n", - "

Total: 4

\n", - " " - ], - "text/plain": [ - "*species \n", - "+---------+\n", - "cat \n", - "dog \n", - "ferret \n", - "parrot \n", - " (Total: 4)" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Species()" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Pet(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Owner\n", - " -> Species\n", - " nickname : varchar(30)\n", - " ---\n", - " birthdate : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class RequiredVaccine(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Species\n", - " vaccine : varchar(10)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Pet\n", - "\n", - "\n", - "Pet\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "RequiredVaccine\n", - "\n", - "\n", - "RequiredVaccine\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Owner\n", - "\n", - "\n", - "Owner\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Owner->Pet\n", - "\n", - "\n", - "\n", - "\n", - "Species\n", - "\n", - "\n", - "Species\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Species->Pet\n", - "\n", - "\n", - "\n", - "\n", - "Species->RequiredVaccine\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Shot(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Pet\n", - " -> RequiredVaccine\n", - " ---\n", - " shot_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Pet\n", - "\n", - "\n", - "Pet\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shot\n", - "\n", - "\n", - "Shot\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Pet->Shot\n", - "\n", - "\n", - "\n", - "\n", - "Species\n", - "\n", - "\n", - "Species\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Species->Pet\n", - "\n", - "\n", - "\n", - "\n", - "RequiredVaccine\n", - "\n", - "\n", - "RequiredVaccine\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Species->RequiredVaccine\n", - "\n", - "\n", - "\n", - "\n", - "RequiredVaccine->Shot\n", - "\n", - "\n", - "\n", - "\n", - "Owner\n", - "\n", - "\n", - "Owner\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Owner->Pet\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "
\n", - "

cell_phone

\n", - " \n", - "
\n", - "

species

\n", - " \n", - "
\n", - "

nickname

\n", - " \n", - "
\n", - "

vaccine

\n", - " \n", - "
\n", - "

shot_date

\n", - " \n", - "
\n", - " \n", - "

Total: 0

\n", - " " - ], - "text/plain": [ - "*cell_phone *species *nickname *vaccine shot_date \n", - "+------------+ +---------+ +----------+ +---------+ +-----------+\n", - "\n", - " (Total: 0)" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Shot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```sql\n", - "create table shot (\n", - " cell_phone char(10) NOT NULL,\n", - " nickname varchar(16) NOT NULL,\n", - " species varchar(20) NOT NULL,\n", - " vaccine varchar(10) NOT NULL,\n", - " PRIMARY KEY (cell_phone, nickname, species, vaccine),\n", - " FOREIGN KEY (cell_phone, nickname, species) REFERENCES pet(cell_phone, nickname, species),\n", - " FOREIGN KEY (species, vaccine) REFERENCES required_vaccine(species, vaccine)\n", - ")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Homework homework\n", - "\n", - "Homework assignments, students, grades\n", - "\n", - "1. Homework is given with a due date.\n", - "2. Students submit homework, we record the submit date\n", - "3. Submitted homework gets a grade\n" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Assignment(dj.Manual):\n", - " definition = \"\"\"\n", - " assignment : int\n", - " ---\n", - " due_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Student(dj.Manual):\n", - " definition = \"\"\"\n", - " student_id : int\n", - " ---\n", - " student_name : varchar(30)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Submission(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Student\n", - " -> Assignment\n", - " ---\n", - " submit_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Grade(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Submission\n", - " ---\n", - " grade : char(1)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Assignment\n", - "\n", - "\n", - "Assignment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Submission\n", - "\n", - "\n", - "Submission\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Assignment->Submission\n", - "\n", - "\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Student->Submission\n", - "\n", - "\n", - "\n", - "\n", - "Grade\n", - "\n", - "\n", - "Grade\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Pet\n", - "\n", - "\n", - "Pet\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shot\n", - "\n", - "\n", - "Shot\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Pet->Shot\n", - "\n", - "\n", - "\n", - "\n", - "Species\n", - "\n", - "\n", - "Species\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Species->Pet\n", - "\n", - "\n", - "\n", - "\n", - "RequiredVaccine\n", - "\n", - "\n", - "RequiredVaccine\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Species->RequiredVaccine\n", - "\n", - "\n", - "\n", - "\n", - "RequiredVaccine->Shot\n", - "\n", - "\n", - "\n", - "\n", - "Submission->Grade\n", - "\n", - "\n", - "\n", - "\n", - "Owner\n", - "\n", - "\n", - "Owner\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Owner->Pet\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 104, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Messaging App (Slack, Telegram, Signal)\n", - "\n", - "1. Users can create channels. Each channel belongs to one user.\n", - "3. Channel names are globally unique\n", - "2. A user can create a post in their channels only\n", - "3. A user can be a guest in another person's channel.\n", - "4. Guest can reply to posts\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class User(dj.Manual):\n", - " definition = \"\"\"\n", - " username : varchar(12)\n", - " ---\n", - " irl_name : varchar(30)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Channel(dj.Manual):\n", - " definition = \"\"\"\n", - " channel : varchar(12)\n", - " ---\n", - " -> User\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Guest(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Channel\n", - " -> User\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Assignment\n", - "\n", - "\n", - "Assignment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Submission\n", - "\n", - "\n", - "Submission\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Assignment->Submission\n", - "\n", - "\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Student->Submission\n", - "\n", - "\n", - "\n", - "\n", - "Grade\n", - "\n", - "\n", - "Grade\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "User\n", - "\n", - "\n", - "User\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Guest\n", - "\n", - "\n", - "Guest\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "User->Guest\n", - "\n", - "\n", - "\n", - "\n", - "Channel\n", - "\n", - "\n", - "Channel\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "User->Channel\n", - "\n", - "\n", - "\n", - "\n", - "Pet\n", - "\n", - "\n", - "Pet\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shot\n", - "\n", - "\n", - "Shot\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Pet->Shot\n", - "\n", - "\n", - "\n", - "\n", - "Species\n", - "\n", - "\n", - "Species\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Species->Pet\n", - "\n", - "\n", - "\n", - "\n", - "RequiredVaccine\n", - "\n", - "\n", - "RequiredVaccine\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Species->RequiredVaccine\n", - "\n", - "\n", - "\n", - "\n", - "RequiredVaccine->Shot\n", - "\n", - "\n", - "\n", - "\n", - "Channel->Guest\n", - "\n", - "\n", - "\n", - "\n", - "Submission->Grade\n", - "\n", - "\n", - "\n", - "\n", - "Owner\n", - "\n", - "\n", - "Owner\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Owner->Pet\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Post(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Channel\n", - " post : int\n", - " ---\n", - " message : varchar(1024)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "
\n", - "

channel

\n", - " \n", - "
\n", - "

post

\n", - " \n", - "
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Total: 0

\n", - " " - ], - "text/plain": [ - "*channel *post message username \n", - "+---------+ +------+ +---------+ +----------+\n", - "\n", - " (Total: 0)" - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Post * Channel" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Response(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Post\n", - " -> Guest\n", - " ---\n", - " response : varchar(1024)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Response\n", - "\n", - "\n", - "Response\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Assignment\n", - "\n", - "\n", - "Assignment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Submission\n", - "\n", - "\n", - "Submission\n", - "\n", - "\n", - "\n", - "\n", - "\n", - 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"metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/db-course/009-RelationalDivision.ipynb b/db-course/009-RelationalDivision.ipynb deleted file mode 100644 index ed3283a..0000000 --- a/db-course/009-RelationalDivision.ipynb +++ /dev/null @@ -1,1671 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Relational Division\n", - "\n", - "Relational division is a query of the type:\n", - "\n", - "> \"Find all entries in A that have a matching entry in B for each entry in C.\"\n", - "\n", - "For example,\n", - "\n", - "> \"Show all the job candidates who have all the skills for a job posting.\"\n", - "\n", - "> \"Show all students who have completed all the required courses for a math major.\"\n", - "\n", - "Relational division is often difficult to think through with no direct syntax in DataJoint and SQL.\n", - "Let's review a detailed example in both DataJoint and SQL\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# prepare datajoint\n", - "import datajoint as dj\n", - "from faker import Faker\n", - "\n", - "fake = Faker()\n", - "\n", - "# prepare SQL Magic\n", - "import pymysql\n", - "\n", - "pymysql.install_as_MySQLdb()\n", - "%load_ext sql\n", - "%config SqlMagic.autocommit=True\n", - "%sql mysql://root:simple@127.0.0.1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Hiring Pipeline Database\n", - "\n", - "In this database, we will represent a set of `Skill`s (e.g. programming languages). \n", - "We also have a set of job `Seeker`s, each possessing a set of skills.\n", - "The database also has `Job` postings, each requiring a specific set of skills.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-11-23 04:48:37,401][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", - "[2023-11-23 04:48:37,409][INFO]: Connected root@fakeservices.datajoint.io:3306\n" - ] - } - ], - "source": [ - "schema = dj.Schema(\"hiring\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Skill(dj.Lookup):\n", - " definition = \"\"\"\n", - " skill : varchar(24)\n", - " \"\"\"\n", - " contents = zip((\"SQL\", \"Java\", \"Python\", \"C++\", \"JavaScript\", \"R\", \"Rust\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "
\n", - "

skill

\n", - " \n", - "
C++
Java
JavaScript
Python
R
Rust
SQL
\n", - " \n", - "

Total: 7

\n", - " " - ], - "text/plain": [ - "*skill \n", - "+------------+\n", - "C++ \n", - "Java \n", - "JavaScript \n", - "Python \n", - "R \n", - "Rust \n", - "SQL \n", - " (Total: 7)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Skill()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Seeker(dj.Manual):\n", - " definition = \"\"\"\n", - " seeker_id : int\n", - " ---\n", - " name : varchar(60)\n", - " \"\"\"\n", - "\n", - " class Skill(dj.Part):\n", - " definition = \"\"\"\n", - " -> master\n", - " -> Skill\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "Seeker.insert(\n", - " ((fake.random_int(), fake.name()) for _ in range(300)), skip_duplicates=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

seeker_id

\n", - " \n", - "
\n", - "

name

\n", - " \n", - "
14Sarah Perry
49Sarah Cole
68Laura Thompson
242Jennifer Rodgers
279Erin Wilson
281Rickey Johnson
302Brandon Johnson
308Preston Stanley
314Kim Decker
373Robert King
396Adam Banks
417Chris Johns
\n", - "

...

\n", - "

Total: 294

\n", - " " - ], - "text/plain": [ - "*seeker_id name \n", - "+-----------+ +------------+\n", - "14 Sarah Perry \n", - "49 Sarah Cole \n", - "68 Laura Thompson\n", - "242 Jennifer Rodge\n", - "279 Erin Wilson \n", - "281 Rickey Johnson\n", - "302 Brandon Johnso\n", - "308 Preston Stanle\n", - "314 Kim Decker \n", - "373 Robert King \n", - "396 Adam Banks \n", - "417 Chris Johns \n", - " ...\n", - " (Total: 294)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Seeker()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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seeker_id

\n", - " \n", - "
\n", - "

skill

\n", - " \n", - "
\n", - "

name

\n", - " \n", - "
14PythonSarah Perry
14JavaScriptSarah Perry
14C++Sarah Perry
49PythonSarah Cole
49C++Sarah Cole
68SQLLaura Thompson
68JavaLaura Thompson
242RJennifer Rodgers
242PythonJennifer Rodgers
242JavaJennifer Rodgers
279RustErin Wilson
279RErin Wilson
\n", - "

...

\n", - "

Total: 601

\n", - " " - ], - "text/plain": [ - "*seeker_id *skill name \n", - "+-----------+ +------------+ +------------+\n", - "14 SQL Sarah Perry \n", - "49 JavaScript Sarah Cole \n", - "49 Java Sarah Cole \n", - "68 SQL Laura Thompson\n", - "68 Java Laura Thompson\n", - "242 Python Jennifer Rodge\n", - "242 JavaScript Jennifer Rodge\n", - "242 Java Jennifer Rodge\n", - "242 C++ Jennifer Rodge\n", - "279 Java Erin Wilson \n", - "281 SQL Rickey Johnson\n", - "281 Rust Rickey Johnson\n", - " ...\n", - " (Total: 607)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# random subsets of skills for each seeker\n", - "Seeker * Skill & \"rand() < 0.3\"" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# server-side insert\n", - "Seeker.Skill.insert(Seeker.proj() * Skill & \"RAND() < 0.3\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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seeker_id

\n", - " \n", - "
\n", - "

skill

\n", - " \n", - "
68C++
281C++
314C++
417C++
576C++
704C++
946C++
1035C++
1113C++
1207C++
1303C++
1821C++
\n", - "

...

\n", - "

Total: 603

\n", - " " - ], - "text/plain": [ - "*seeker_id *skill \n", - "+-----------+ +-------+\n", - "68 C++ \n", - "281 C++ \n", - "314 C++ \n", - "417 C++ \n", - "576 C++ \n", - "704 C++ \n", - "946 C++ \n", - "1035 C++ \n", - "1113 C++ \n", - "1207 C++ \n", - "1303 C++ \n", - "1821 C++ \n", - " ...\n", - " (Total: 603)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Seeker.Skill()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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seeker_id

\n", - " \n", - "
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name

\n", - " \n", - "
14Sarah Perry
242Jennifer Rodgers
724Ian Stevens
806Stacy Medina
831Kayla King
1016Betty Wolfe
1049Crystal Jacobs
1207Timothy Sampson
1502David Washington
1821Theresa Soto
1827Luke Perez
1830Sarah Barnett
\n", - "

...

\n", - "

Total: 90

\n", - " " - ], - "text/plain": [ - "*seeker_id name \n", - "+-----------+ +------------+\n", - "14 Sarah Perry \n", - "242 Jennifer Rodge\n", - "724 Ian Stevens \n", - "806 Stacy Medina \n", - "831 Kayla King \n", - "1016 Betty Wolfe \n", - "1049 Crystal Jacobs\n", - "1207 Timothy Sampso\n", - "1502 David Washingt\n", - "1821 Theresa Soto \n", - "1827 Luke Perez \n", - "1830 Sarah Barnett \n", - " ...\n", - " (Total: 90)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# All seekers who know SQL\n", - "Seeker & (Seeker.Skill & 'skill=\"SQL\"')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Job(dj.Manual):\n", - " definition = \"\"\"\n", - " job : varchar(12) \n", - " ---\n", - " job_description : varchar(60)\n", - " \"\"\"\n", - "\n", - " class Skill(dj.Part):\n", - " definition = \"\"\"\n", - " -> master\n", - " -> Skill\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# insert two jobs\n", - "\n", - "Job.insert1((\"job1\", \"Software Engineer I\"))\n", - "Job.Skill.insert(((\"job1\", \"Rust\"), (\"job1\", \"JavaScript\"), (\"job1\", \"Java\")))\n", - "\n", - "Job.insert1((\"job2\", \"Data Scientist II\"))\n", - "Job.Skill.insert(((\"job2\", \"SQL\"), (\"job2\", \"Python\")))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - "

job

\n", - " \n", - "
\n", - "

skill

\n", - " \n", - "
job1Java
job1JavaScript
job2Python
job1Rust
job2SQL
\n", - " \n", - "

Total: 5

\n", - " " - ], - "text/plain": [ - "*job *skill \n", - "+------+ +------------+\n", - "job1 Java \n", - "job1 JavaScript \n", - "job2 Python \n", - "job1 Rust \n", - "job2 SQL \n", - " (Total: 5)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Job.Skill()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "Job.Skill\n", - "\n", - "\n", - "Job.Skill\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Seeker.Skill\n", - "\n", - "\n", - "Seeker.Skill\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Job\n", - "\n", - "\n", - "Job\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Job->Job.Skill\n", - "\n", - "\n", - "\n", - "\n", - "Seeker\n", - "\n", - "\n", - "Seeker\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Seeker->Seeker.Skill\n", - "\n", - "\n", - "\n", - "\n", - "Skill\n", - "\n", - "\n", - "Skill\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Skill->Job.Skill\n", - "\n", - "\n", - "\n", - "\n", - "Skill->Seeker.Skill\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Query: Show seekers who qualify for Job1\n", - "\n", - "This is described as the relational division of seeker skills by job skills.\n", - "\n", - "> Show all seekers who have _all_ the skills required for job \"Job1\"\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A common way to address relational division is to reformulate the problem as a double negative:\n", - "\n", - "> Show all seekers except those who are missing any of the skills required for Job1.\n", - "\n", - "This allows us to break the problem into simpler subqueries.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "
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job

\n", - " \n", - "
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skill

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job1Java
job1JavaScript
job1Rust
\n", - " \n", - "

Total: 3

\n", - " " - ], - "text/plain": [ - "*job *skill \n", - "+------+ +------------+\n", - "job1 Java \n", - "job1 JavaScript \n", - "job1 Rust \n", - " (Total: 3)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# skills for Job1\n", - "required_skill = Job.Skill() & {\"job\": \"Job1\"}\n", - "required_skill" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

seeker_id

\n", - " \n", - "
\n", - "

job

\n", - " \n", - "
\n", - "

skill

\n", - " \n", - "
\n", - "

name

\n", - " \n", - "
14job1RustSarah Perry
14job1JavaScriptSarah Perry
14job1JavaSarah Perry
49job1RustSarah Cole
49job1JavaSarah Cole
68job1RustLaura Thompson
68job1JavaScriptLaura Thompson
68job1JavaLaura Thompson
242job1RustJennifer Rodgers
242job1JavaScriptJennifer Rodgers
242job1JavaJennifer Rodgers
279job1RustErin Wilson
\n", - "

...

\n", - "

Total: 633

\n", - " " - ], - "text/plain": [ - "*seeker_id *job *skill name \n", - "+-----------+ +------+ +------------+ +------------+\n", - "14 job1 Rust Sarah Perry \n", - "14 job1 JavaScript Sarah Perry \n", - "14 job1 Java Sarah Perry \n", - "49 job1 Rust Sarah Cole \n", - "49 job1 Java Sarah Cole \n", - "68 job1 Rust Laura Thompson\n", - "68 job1 JavaScript Laura Thompson\n", - "68 job1 Java Laura Thompson\n", - "242 job1 Rust Jennifer Rodge\n", - "242 job1 JavaScript Jennifer Rodge\n", - "242 job1 Java Jennifer Rodge\n", - "279 job1 Rust Erin Wilson \n", - " ...\n", - " (Total: 633)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# show missing skills for all candidates.\n", - "\n", - "missing_skill = (Seeker * required_skill) - Seeker.Skill()\n", - "\n", - "missing_skill" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

seeker_id

\n", - " \n", - "
\n", - "

name

\n", - " \n", - "
1270Monica Martinez
4354Joseph Nelson
5250Rebecca Mcdaniel
5495Dillon Rodriguez
6006Joshua Bell
6837Jonathan Miranda
7914Daniel Lewis
9272Lori Cooper MD
\n", - " \n", - "

Total: 8

\n", - " " - ], - "text/plain": [ - "*seeker_id name \n", - "+-----------+ +------------+\n", - "1270 Monica Martine\n", - "4354 Joseph Nelson \n", - "5250 Rebecca Mcdani\n", - "5495 Dillon Rodrigu\n", - "6006 Joshua Bell \n", - "6837 Jonathan Miran\n", - "7914 Daniel Lewis \n", - "9272 Lori Cooper MD\n", - " (Total: 8)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# No show candidates who don't have any missing skills.\n", - "\n", - "Seeker - missing_skill.proj()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

seeker_id

\n", - " \n", - "
\n", - "

name

\n", - " \n", - "
1270Monica Martinez
4354Joseph Nelson
5250Rebecca Mcdaniel
5495Dillon Rodriguez
6006Joshua Bell
6837Jonathan Miranda
7914Daniel Lewis
9272Lori Cooper MD
\n", - " \n", - "

Total: 8

\n", - " " - ], - "text/plain": [ - "*seeker_id name \n", - "+-----------+ +------------+\n", - "1270 Monica Martine\n", - "4354 Joseph Nelson \n", - "5250 Rebecca Mcdani\n", - "5495 Dillon Rodrigu\n", - "6006 Joshua Bell \n", - "6837 Jonathan Miran\n", - "7914 Daniel Lewis \n", - "9272 Lori Cooper MD\n", - " (Total: 8)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# putting this all together as a self-contained query:\n", - "# Seekers who have all required skills for Job1.\n", - "Seeker - ((Seeker.proj() * Job.Skill & {\"job\": \"Job1\"}) - Seeker.Skill)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# In SQL\n", - "\n", - "Let's do the same in sql.\n", - "\n", - "Query 1: show all seekers' missing skills for Job1.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n", - "10 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
seeker_idnamejobskill
14Sarah Perryjob1Rust
14Sarah Perryjob1JavaScript
14Sarah Perryjob1Java
49Sarah Colejob1Rust
49Sarah Colejob1Java
68Laura Thompsonjob1Rust
68Laura Thompsonjob1JavaScript
68Laura Thompsonjob1Java
242Jennifer Rodgersjob1Rust
242Jennifer Rodgersjob1JavaScript
" - ], - "text/plain": [ - "[(14, 'Sarah Perry', 'job1', 'Rust'),\n", - " (14, 'Sarah Perry', 'job1', 'JavaScript'),\n", - " (14, 'Sarah Perry', 'job1', 'Java'),\n", - " (49, 'Sarah Cole', 'job1', 'Rust'),\n", - " (49, 'Sarah Cole', 'job1', 'Java'),\n", - " (68, 'Laura Thompson', 'job1', 'Rust'),\n", - " (68, 'Laura Thompson', 'job1', 'JavaScript'),\n", - " (68, 'Laura Thompson', 'job1', 'Java'),\n", - " (242, 'Jennifer Rodgers', 'job1', 'Rust'),\n", - " (242, 'Jennifer Rodgers', 'job1', 'JavaScript')]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "USE hiring;\n", - "\n", - "SELECT *\n", - "FROM seeker NATURAL JOIN job__skill \n", - "WHERE job=\"Job1\" AND (seeker_id, skill) NOT IN (\n", - " SELECT seeker_id, skill FROM seeker__skill)\n", - "LIMIT 10" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now show all the seekers who lack missing skills:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://root:***@127.0.0.1\n", - "0 rows affected.\n", - "8 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
seeker_idname
1270Monica Martinez
4354Joseph Nelson
5250Rebecca Mcdaniel
5495Dillon Rodriguez
6006Joshua Bell
6837Jonathan Miranda
7914Daniel Lewis
9272Lori Cooper MD
" - ], - "text/plain": [ - "[(1270, 'Monica Martinez'),\n", - " (4354, 'Joseph Nelson'),\n", - " (5250, 'Rebecca Mcdaniel'),\n", - " (5495, 'Dillon Rodriguez'),\n", - " (6006, 'Joshua Bell'),\n", - " (6837, 'Jonathan Miranda'),\n", - " (7914, 'Daniel Lewis'),\n", - " (9272, 'Lori Cooper MD')]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "USE hiring;\n", - "\n", - "SELECT * FROM seeker WHERE seeker_id NOT IN (\n", - " SELECT seeker_id\n", - " FROM seeker NATURAL JOIN job__skill \n", - " WHERE job=\"Job1\" AND (seeker_id, skill) NOT IN (\n", - " SELECT seeker_id, skill FROM seeker__skill)\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/011-Final.ipynb b/db-course/011-Final.ipynb deleted file mode 100644 index b2fb249..0000000 --- a/db-course/011-Final.ipynb +++ /dev/null @@ -1,111 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Final Project\n", - "\n", - "In this project, you will write Python code to (a) define, (b) populate, and (c) query a database. \n", - "Your submission can be a single Jupyter notebook with these three clearly=defined sections. \n", - "\n", - "You can propose your own database problem. In that case, first, describe its specification and purpose in detail. \n", - "Otherwise, use the \"Hiring\" database specification below.\n", - "\n", - "The complexity of the schema must be at least 6 tables. \n", - "It must be populated with a sufficient amount of data to allow interesting queries. \n", - "You can use real data if available or generate fake data.\n", - "\n", - "Compose at least 12 nontrivial queries that use the major patterns we learned in class: selection, projection, joins, subqueries, and aggregations.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Schema specification: \"Hiring database\"\n", - "\n", - "The schema design \n", - "\n", - "1. Hiring managers have a name, phone, and email. \n", - "2. A hiring manager can create job postings, including a job title, job description, open date, and the annual salary range (min and max). \n", - "3. Each job posting has a set of required skills. You can use a set of programming languages as skills, including SQL and Python.\n", - "4. Job seekers create profiles thas a name, phone, and email. \n", - "5. Job seekers also list their skills. \n", - "6. A job seeker can create an application for a specific job. \n", - "7. A job manager can schedule an interview for a specific application for a specific date.\n", - "8. A successful interview can lead to a job offer, including the start date and a starting salary. \n", - "9. The offer is followed by an acceptance. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, - "source": [ - "## Populate\n", - "\n", - "- At least three hiring managers\n", - "- At least 20 positions\n", - "- At least six skills\n", - "- At least 30 job seekers\n", - "- At least 50 applications\n", - "- At least 30 interviews\n", - "- At least 10 job offers\n", - "- At least 5 job acceptances." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, - "source": [ - "### Queries\n", - "\n", - "If your query output is too long, limit it to 10 rows.\n", - "\n", - "1. List all open jobs requiring SQL, including the number of applicants for each. Do not include jobs for which someone has already accepted an offer.\n", - "\n", - "2. List all skills, including the number of jobs requiring that skill, and the average salary range for jobs requiring this skill.\n", - "\n", - "3. List all hiring managers, including the number of jobs they have posted, the number of interviews the have given, and the number of job offers they have issued, and the number of accepted offers.\n", - "\n", - "3. List all job seekers who have applied for two or more jobs.\n", - "\n", - "4. List the numes of all job seekers who have been given a job offer but have not yet accepted.\n", - "\n", - "5. List all job applications for which the job candidate has all the required skills. \n", - "\n", - "6. List the top three job postings with the most applications.\n", - "\n", - "Finally, write three more interesting queries, first describing them.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/db-course/Default.ipynb b/db-course/Default.ipynb deleted file mode 100644 index 4f45a3b..0000000 --- a/db-course/Default.ipynb +++ /dev/null @@ -1,919 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "414f0fb5", - "metadata": {}, - "source": [ - "# Missing and default values" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "28e327d8", - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj\n", - "import random\n", - "import faker\n", - "\n", - "fake = faker.Faker()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "0f453506", - "metadata": {}, - "outputs": [], - "source": [ - "schema = dj.schema(\"dimitri_default\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "64f6506d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'job': 'Engineer, communications',\n", - " 'company': 'Rodgers Inc',\n", - " 'ssn': '556-83-1710',\n", - " 'residence': '2975 Contreras Green\\nSouth Karen, ID 49364',\n", - " 'current_location': (Decimal('-42.4585165'), Decimal('102.313448')),\n", - " 'blood_group': 'AB+',\n", - " 'website': ['http://www.rogers.com/', 'https://delgado.com/'],\n", - " 'username': 'elizabeth34',\n", - " 'name': 'Angela Garcia',\n", - " 'sex': 'F',\n", - " 'address': '5607 Tapia Cliff\\nCrystalside, NY 36851',\n", - " 'mail': 'gboyd@gmail.com',\n", - " 'birthdate': datetime.date(1946, 11, 28)}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fake.profile()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "1f66841e", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Person(dj.Manual):\n", - " definition = \"\"\"\n", - " person_id : int\n", - " ---\n", - " blood_group = \"unknown\" : enum('A+', 'A-', 'AB+', 'AB-', 'B+', 'B-', 'O+', 'O-', \"unknown\")\n", - " name : varchar(60)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "f54ddeac", - "metadata": {}, - "outputs": [], - "source": [ - "Person.insert1(dict(person_id=3, **fake.profile()), ignore_extra_fields=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "2f610d0c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

person_id

\n", - " \n", - "
\n", - "

blood_group

\n", - " \n", - "
\n", - "

name

\n", - " \n", - "
1A-Randy Arnold
2O+Scott Montgomery
3AB-Ronald Reyes
\n", - " \n", - "

Total: 3

\n", - " " - ], - "text/plain": [ - "*person_id blood_group name \n", - "+-----------+ +------------+ +------------+\n", - "1 A- Randy Arnold \n", - "2 O+ Scott Montgome\n", - "3 AB- Ronald Reyes \n", - " (Total: 3)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Person()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "4fb4eb3f", - "metadata": {}, - "outputs": [], - "source": [ - "Person.insert1(dict(person_id=5, name=\"heywood\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "4e8b7522", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

person_id

\n", - " \n", - "
\n", - "

blood_group

\n", - " \n", - "
\n", - "

name

\n", - " \n", - "
1A-Randy Arnold
2O+Scott Montgomery
3AB-Ronald Reyes
5unknownheywood
\n", - " \n", - "

Total: 4

\n", - " " - ], - "text/plain": [ - "*person_id blood_group name \n", - "+-----------+ +------------+ +------------+\n", - "1 A- Randy Arnold \n", - "2 O+ Scott Montgome\n", - "3 AB- Ronald Reyes \n", - "5 unknown heywood \n", - " (Total: 4)" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Person()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "9e86ec83", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CREATE TABLE `person` (\n", - " `person_id` int(11) NOT NULL,\n", - " `blood_group` enum('A+','A-','AB+','AB-','B+','B-','O+','O-','unknown') NOT NULL DEFAULT 'unknown',\n", - " `name` varchar(60) NOT NULL,\n", - " PRIMARY KEY (`person_id`)\n", - ") ENGINE=InnoDB DEFAULT CHARSET=latin1\n" - ] - } - ], - "source": [ - "print(dj.conn().query(\"SHOW CREATE TABLE dimitri_default.person\").fetchone()[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "83844f42", - "metadata": {}, - "outputs": [], - "source": [ - "import pymysql" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "47686e7a", - "metadata": {}, - "outputs": [], - "source": [ - "conn = pymysql.connect(\n", - " user=\"dimitri\", passwd=dj.config[\"database.password\"], host=\"db.ust-db.link\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "6fcb89af", - "metadata": {}, - "outputs": [], - "source": [ - "cursor = conn.cursor()" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "baa9ecee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - "CREATE TABLE dimitri_default.person2 (\n", - " person_id int NOT NULL,\n", - " blood_group enum('A+','A-','AB+','AB-','B+','B-','O+','O-', 'unknown') \n", - " NOT NULL DEFAULT 'unknown',\n", - " PRIMARY KEY(person_id)\n", - ")\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "3ac55ecb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - "INSERT dimitri_default.person2 (person_id, blood_group) VALUES (1, DEFAULT)\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "e2ac8cd0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - "INSERT dimitri_default.person2 (person_id, blood_group) VALUES (2, \"O+\")\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "7b1c8980", - "metadata": {}, - "outputs": [ - { - "ename": "IntegrityError", - "evalue": "(1048, \"Column 'blood_group' cannot be null\")", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIntegrityError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m cursor.execute(\"\"\"\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mINSERT\u001b[0m \u001b[0mdimitri_default\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mperson2\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mperson_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblood_group\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0mVALUES\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNULL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \"\"\")\n", - "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/cursors.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmogrify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 148\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 149\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_executed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/cursors.py\u001b[0m in \u001b[0;36m_query\u001b[0;34m(self, q)\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_last_executed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clear_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 310\u001b[0;31m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 311\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrowcount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/connections.py\u001b[0m in \u001b[0;36mquery\u001b[0;34m(self, sql, unbuffered)\u001b[0m\n\u001b[1;32m 546\u001b[0m \u001b[0msql\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msql\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"surrogateescape\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 547\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_execute_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCOMMAND\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCOM_QUERY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msql\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 548\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_affected_rows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_query_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munbuffered\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munbuffered\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 549\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_affected_rows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/connections.py\u001b[0m in \u001b[0;36m_read_query_result\u001b[0;34m(self, unbuffered)\u001b[0m\n\u001b[1;32m 773\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 774\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMySQLResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 775\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 776\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 777\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserver_status\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/connections.py\u001b[0m in \u001b[0;36m_read_packet\u001b[0;34m(self, packet_type)\u001b[0m\n\u001b[1;32m 723\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munbuffered_active\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 724\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munbuffered_active\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 725\u001b[0;31m \u001b[0mpacket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 726\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mpacket\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 727\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/protocol.py\u001b[0m in \u001b[0;36mraise_for_error\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mDEBUG\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"errno =\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrno\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_mysql_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pymysql/err.py\u001b[0m in \u001b[0;36mraise_mysql_exception\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merrorclass\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[0merrorclass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mInternalError\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merrno\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1000\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mOperationalError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merrorclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mIntegrityError\u001b[0m: (1048, \"Column 'blood_group' cannot be null\")" - ] - } - ], - "source": [ - "cursor.execute(\n", - " \"\"\"\n", - "INSERT dimitri_default.person2 (person_id, blood_group) VALUES (3, NULL)\n", - "\"\"\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "bfe26d70", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - "

person_id

\n", - " \n", - "
\n", - "

blood_group

\n", - " \n", - "
\n", - "

name

\n", - " \n", - "
1A-Randy Arnold
2O+Scott Montgomery
3AB-Ronald Reyes
5unknownheywood
\n", - " \n", - "

Total: 4

\n", - " " - ], - "text/plain": [ - "*person_id blood_group name \n", - "+-----------+ +------------+ +------------+\n", - "1 A- Randy Arnold \n", - "2 O+ Scott Montgome\n", - "3 AB- Ronald Reyes \n", - "5 unknown heywood \n", - " (Total: 4)" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Person()" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "0b146b05", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Department(dj.Lookup):\n", - " definition = \"\"\"\n", - " dept_code : char(8)\n", - " ---\n", - " dept_name : varchar(30)\n", - " \"\"\"\n", - "\n", - " contents = [(\"BIOL\", \"Biology\"), (\"MATH\", \"Mathematics\")]" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "f2ad3646", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Student(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Person\n", - " ---\n", - " -> [nullable] Department\n", - " \"\"\"" - ] - }, - { - "cell_type": "markdown", - "id": "9effcbeb", - "metadata": {}, - "source": [ - "```sql\n", - " CREATE TABLE student (\n", - " person_id int NOT NULL,\n", - " dept_code char(8),\n", - " PRIMARY KEY (person_id),\n", - " FOREIGN KEY (person_id) REFERENCES person(person_id),\n", - " FOREIGN KEY (dept_code) REFERENCES department(dept_code) \n", - " )\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "789fa913", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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person_id

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name

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1A-Randy Arnold
2O+Scott Montgomery
3AB-Ronald Reyes
5unknownheywood
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Total: 4

\n", - " " - ], - "text/plain": [ - "*person_id blood_group name \n", - "+-----------+ +------------+ +------------+\n", - "1 A- Randy Arnold \n", - "2 O+ Scott Montgome\n", - "3 AB- Ronald Reyes \n", - "5 unknown heywood \n", - " (Total: 4)" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Person()" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "7847357a", - "metadata": {}, - "outputs": [], - "source": [ - "Student.insert1((1, \"BIOL\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "bbfd2a3b", - "metadata": {}, - "outputs": [], - "source": [ - "Student.insert1((2, \"MATH\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "140b691b", - "metadata": {}, - "outputs": [], - "source": [ - "Student.insert1((3, None))" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "3e2cf8bb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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person_id

\n", - " \n", - "
\n", - "

dept_code

\n", - " \n", - "
3None
1BIOL
2MATH
\n", - " \n", - "

Total: 3

\n", - " " - ], - "text/plain": [ - "*person_id dept_code \n", - "+-----------+ +-----------+\n", - "3 None \n", - "1 BIOL \n", - "2 MATH \n", - " (Total: 3)" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Student()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1495b9e9", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Student(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Person\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7dc6bca4", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Major(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Students\n", - " ---\n", - " -> Department\n", - " \"\"\"" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/db-course/DesignPatterns.ipynb b/db-course/DesignPatterns.ipynb deleted file mode 100644 index eff7831..0000000 --- a/db-course/DesignPatterns.ipynb +++ /dev/null @@ -1,7928 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "with open(\"cred.json\") as f:\n", - " creds = json.load(f)\n", - "\n", - "connection_string = \"mysql://{user}:{password}@{host}\".format(**creds)\n", - "\n", - "import pymysql\n", - "\n", - "pymysql.install_as_MySQLdb()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext sql\n", - "%config SqlMagic.autocommit=True\n", - "%sql $connection_string" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Transactions\n", - "\n", - "- A = Atomic\n", - "- C = Consistent\n", - "- I = Isolated\n", - "- D = Durable" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from faker import Faker" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "fake = Faker()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2022-11-08 11:21:52,567][INFO]: Connecting dimitri@db.ust-data-sci.net:3306\n", - "[2022-11-08 11:21:53,609][INFO]: Connected dimitri@db.ust-data-sci.net:3306\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Proceed to delete entire schema `dimitri_bank`? [yes, No]: yes\n" - ] - } - ], - "source": [ - "import datajoint as dj\n", - "\n", - "schema = dj.Schema(\"dimitri_bank\")\n", - "schema.drop()\n", - "schema = dj.Schema(\"dimitri_bank\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Account(dj.Manual):\n", - " definition = \"\"\"\n", - " account_number : int\n", - " ---\n", - " customer_name : varchar(60) \n", - " balance : decimal(9, 2)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "Account.insert(\n", - " dict(\n", - " account_number=fake.random.randint(10_000_000, 99_999_999),\n", - " customer_name=fake.name(),\n", - " balance=fake.random.randint(0, 100_000_00) / 100,\n", - " )\n", - " for i in range(100)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - "

account_number

\n", - " \n", - "
\n", - "

customer_name

\n", - " \n", - "
\n", - "

balance

\n", - " \n", - "
12050569Shannon Santos6679.10
12585078Elizabeth Sherman81401.22
12922010Gary Cummings82458.58
14429509Lisa Garcia62684.81
16865880Christy Jones36643.26
16891629Alfred Potter25586.46
17183102Chad Nguyen Jr.8421.74
18025005Andrew Wilson50134.35
18104044Jody Collins43370.19
18369903Ronald Stokes72475.77
20536886Elizabeth Murphy26302.92
21122881John Mcdonald66577.76
\n", - "

...

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Total: 100

\n", - " " - ], - "text/plain": [ - "*account_numbe customer_name balance \n", - "+------------+ +------------+ +----------+\n", - "12050569 Shannon Santos 6679.10 \n", - "12585078 Elizabeth Sher 81401.22 \n", - "12922010 Gary Cummings 82458.58 \n", - "14429509 Lisa Garcia 62684.81 \n", - "16865880 Christy Jones 36643.26 \n", - "16891629 Alfred Potter 25586.46 \n", - "17183102 Chad Nguyen Jr 8421.74 \n", - "18025005 Andrew Wilson 50134.35 \n", - "18104044 Jody Collins 43370.19 \n", - "18369903 Ronald Stokes 72475.77 \n", - "20536886 Elizabeth Murp 26302.92 \n", - "21122881 John Mcdonald 66577.76 \n", - " ...\n", - " (Total: 100)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "keys = Account.fetch(\"KEY\")\n", - "account1 = fake.random.choice(keys)\n", - "account2 = fake.random.choice(keys)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "({'account_number': 71167941}, {'account_number': 45255856})" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "account1, account2" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "def transfer(account1, account2, amount):\n", - " with Account.connection.transaction:\n", - " current_balance = (Account & account1).fetch1(\"balance\")\n", - " if current_balance < amount:\n", - " raise RuntimeError(\"Insufficient funds\")\n", - "\n", - " Account.update1(dict(account1, balance=float(current_balance) - amount))\n", - "\n", - " b = (Account & account2).fetch1(\"balance\")\n", - " assert False\n", - "\n", - " Account.update1(dict(account2, balance=float(b) + amount))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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account_number

\n", - " \n", - "
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customer_name

\n", - " \n", - "
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\n", - " \n", - "
45255856Chad Atkins3900.08
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Total: 2

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TableCreate Table
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account_numbercustomer_namebalance
12050569Shannon Santos6679.10
12585078Elizabeth Sherman81401.22
12922010Gary Cummings82458.58
14429509Lisa Garcia62684.81
16865880Christy Jones36643.26
16891629Alfred Potter25586.46
17183102Chad Nguyen Jr.8421.74
18025005Andrew Wilson50134.35
18104044Jody Collins43370.19
18369903Ronald Stokes72475.77
20536886Elizabeth Murphy26302.92
21122881John Mcdonald66577.76
22346493Angela Cook8726.90
23580748Phillip Phillips87117.86
24304691Gary Davis61267.87
29585828Sheila Mack3143.64
31867228Matthew Jensen Jr.10613.30
32398369Kathryn Bush86954.62
32491608Christina Peterson96449.55
32606812Shannon Melton56821.76
32800520Desiree White5503.59
35601316Brittney Wright DDS85239.41
36448533Charles Rivers28255.00
37607221Joseph Rojas67438.57
38275461Jessica Cervantes12397.28
38310940Bryan Mcmahon31183.65
38476377Kathleen Horton24121.70
41136819Mario Anderson69689.05
41353087Amber Wallace55862.85
42185083Gina Murillo47656.73
42576215Mary Barnes82977.61
43023148Benjamin Richardson19640.96
43256866Dawn Price27384.32
43704100Sara Weaver64056.63
43712744Cynthia Willis71119.29
44244863Joseph Perez34877.09
44436274Amber Fitzpatrick58606.29
45255856Chad Atkins3900.08
45277679Aaron Barnes42783.60
45812721Tim Carter4561.58
46013960Zachary Sherman97940.03
49643932Scott Davis46175.00
50312738Victoria Andrews72410.91
50354017Melissa Hobbs12830.85
51674129Laura Huber15506.18
52662843Donna Smith96704.53
52926354Kerri Elliott94942.99
54714079Michael Jones47161.03
55439872Annette Cuevas64012.92
56541462Haley Wilson8815.40
57064339Theresa Snyder20683.21
57935558Daniel Armstrong18834.38
58000819Jamie Dixon34711.82
58545212Linda Jones2458.01
59841226Jennifer Williams79297.52
61068175Bryan Green73076.54
61825741Jessica Robinson90067.20
62723410Christine Turner32029.44
63634081Daniel Contreras87923.71
64538767Melissa Turner59850.53
65644872James Avila68329.73
69386626Kelsey Cervantes79170.79
69610798Brandi Moore11125.43
70167668Brian Hoover85543.93
71167941Cody Morgan21836.97
72203243Carrie Thomas76766.21
72987179Dana Baker96049.74
73492317Adrienne Stone45186.78
74290373William Douglas65616.71
74704243Barbara Cuevas10731.71
75147177Jocelyn Gilbert23482.65
75189013Lori Jones60922.02
75353895Paul Benson26102.18
75360790Blake Cooper16122.13
75523120Paul Gibson81543.97
75863409James Hines75250.71
76936237Chad Nicholson98420.50
77149605Jonathan Bautista95316.22
77401497Elizabeth Andersen88391.06
79438170Ms. Rebecca Hall60703.58
79475809Bryan Brown Jr.59410.50
79504929Megan Carlson48345.86
80342292Rodney Strickland11927.42
80728264Reginald Roberts78922.39
82425155David Clark17295.15
84847035Timothy Williams35919.15
85232444Ashley Knight58513.22
86646935Judith Rodriguez82176.38
87356185Laura Castillo81767.62
87591115Eric Fields49400.63
89363591Sara Griffith27862.81
90446123James Gibson58628.37
91973536Karen Hunt91278.72
94335018Eric Mcgee97024.05
95440048Angela Roman75554.23
97181338Joseph Matthews23464.91
97328507Julie Baker86320.40
98197568Mr. Dylan Hernandez67592.91
98230343Nicholas Jacobson44952.48
99345844Caleb Davis40633.29
" - ], - "text/plain": [ - "[(12050569, 'Shannon Santos', Decimal('6679.10')),\n", - " (12585078, 'Elizabeth Sherman', Decimal('81401.22')),\n", - " (12922010, 'Gary Cummings', Decimal('82458.58')),\n", - " (14429509, 'Lisa Garcia', Decimal('62684.81')),\n", - " (16865880, 'Christy Jones', Decimal('36643.26')),\n", - " (16891629, 'Alfred Potter', Decimal('25586.46')),\n", - " (17183102, 'Chad Nguyen Jr.', Decimal('8421.74')),\n", - " (18025005, 'Andrew Wilson', Decimal('50134.35')),\n", - " (18104044, 'Jody Collins', Decimal('43370.19')),\n", - " (18369903, 'Ronald Stokes', Decimal('72475.77')),\n", - " (20536886, 'Elizabeth Murphy', Decimal('26302.92')),\n", - " (21122881, 'John Mcdonald', Decimal('66577.76')),\n", - " (22346493, 'Angela Cook', Decimal('8726.90')),\n", - " (23580748, 'Phillip Phillips', Decimal('87117.86')),\n", - " (24304691, 'Gary Davis', Decimal('61267.87')),\n", - " (29585828, 'Sheila Mack', Decimal('3143.64')),\n", - " (31867228, 'Matthew Jensen Jr.', Decimal('10613.30')),\n", - " (32398369, 'Kathryn Bush', Decimal('86954.62')),\n", - " (32491608, 'Christina Peterson', Decimal('96449.55')),\n", - " (32606812, 'Shannon Melton', Decimal('56821.76')),\n", - " (32800520, 'Desiree White', Decimal('5503.59')),\n", - " (35601316, 'Brittney Wright DDS', Decimal('85239.41')),\n", - " (36448533, 'Charles Rivers', Decimal('28255.00')),\n", - " (37607221, 'Joseph Rojas', Decimal('67438.57')),\n", - " (38275461, 'Jessica Cervantes', Decimal('12397.28')),\n", - " (38310940, 'Bryan Mcmahon', Decimal('31183.65')),\n", - " (38476377, 'Kathleen Horton', Decimal('24121.70')),\n", - " (41136819, 'Mario Anderson', Decimal('69689.05')),\n", - " (41353087, 'Amber Wallace', Decimal('55862.85')),\n", - " (42185083, 'Gina Murillo', Decimal('47656.73')),\n", - " (42576215, 'Mary Barnes', Decimal('82977.61')),\n", - " (43023148, 'Benjamin Richardson', Decimal('19640.96')),\n", - " (43256866, 'Dawn Price', Decimal('27384.32')),\n", - " (43704100, 'Sara Weaver', Decimal('64056.63')),\n", - " (43712744, 'Cynthia Willis', Decimal('71119.29')),\n", - " (44244863, 'Joseph Perez', Decimal('34877.09')),\n", - " (44436274, 'Amber Fitzpatrick', Decimal('58606.29')),\n", - " (45255856, 'Chad Atkins', Decimal('3900.08')),\n", - " (45277679, 'Aaron Barnes', Decimal('42783.60')),\n", - " (45812721, 'Tim Carter', Decimal('4561.58')),\n", - " (46013960, 'Zachary Sherman', Decimal('97940.03')),\n", - " (49643932, 'Scott Davis', Decimal('46175.00')),\n", - " (50312738, 'Victoria Andrews', Decimal('72410.91')),\n", - " (50354017, 'Melissa Hobbs', Decimal('12830.85')),\n", - " (51674129, 'Laura Huber', Decimal('15506.18')),\n", - " (52662843, 'Donna Smith', Decimal('96704.53')),\n", - " (52926354, 'Kerri Elliott', Decimal('94942.99')),\n", - " (54714079, 'Michael Jones', Decimal('47161.03')),\n", - " (55439872, 'Annette Cuevas', Decimal('64012.92')),\n", - " (56541462, 'Haley Wilson', Decimal('8815.40')),\n", - " (57064339, 'Theresa Snyder', Decimal('20683.21')),\n", - " (57935558, 'Daniel Armstrong', Decimal('18834.38')),\n", - " (58000819, 'Jamie Dixon', Decimal('34711.82')),\n", - " (58545212, 'Linda Jones', Decimal('2458.01')),\n", - " (59841226, 'Jennifer Williams', Decimal('79297.52')),\n", - " (61068175, 'Bryan Green', Decimal('73076.54')),\n", - " (61825741, 'Jessica Robinson', Decimal('90067.20')),\n", - " (62723410, 'Christine Turner', Decimal('32029.44')),\n", - " (63634081, 'Daniel Contreras', Decimal('87923.71')),\n", - " (64538767, 'Melissa Turner', Decimal('59850.53')),\n", - " (65644872, 'James Avila', Decimal('68329.73')),\n", - " (69386626, 'Kelsey Cervantes', Decimal('79170.79')),\n", - " (69610798, 'Brandi Moore', Decimal('11125.43')),\n", - " (70167668, 'Brian Hoover', Decimal('85543.93')),\n", - " (71167941, 'Cody Morgan', Decimal('21836.97')),\n", - " (72203243, 'Carrie Thomas', Decimal('76766.21')),\n", - " (72987179, 'Dana Baker', Decimal('96049.74')),\n", - " (73492317, 'Adrienne Stone', Decimal('45186.78')),\n", - " (74290373, 'William Douglas', Decimal('65616.71')),\n", - " (74704243, 'Barbara Cuevas', Decimal('10731.71')),\n", - " (75147177, 'Jocelyn Gilbert', Decimal('23482.65')),\n", - " (75189013, 'Lori Jones', Decimal('60922.02')),\n", - " (75353895, 'Paul Benson', Decimal('26102.18')),\n", - " (75360790, 'Blake Cooper', Decimal('16122.13')),\n", - " (75523120, 'Paul Gibson', Decimal('81543.97')),\n", - " (75863409, 'James Hines', Decimal('75250.71')),\n", - " (76936237, 'Chad Nicholson', Decimal('98420.50')),\n", - " (77149605, 'Jonathan Bautista', Decimal('95316.22')),\n", - " (77401497, 'Elizabeth Andersen', Decimal('88391.06')),\n", - " (79438170, 'Ms. Rebecca Hall', Decimal('60703.58')),\n", - " (79475809, 'Bryan Brown Jr.', Decimal('59410.50')),\n", - " (79504929, 'Megan Carlson', Decimal('48345.86')),\n", - " (80342292, 'Rodney Strickland', Decimal('11927.42')),\n", - " (80728264, 'Reginald Roberts', Decimal('78922.39')),\n", - " (82425155, 'David Clark', Decimal('17295.15')),\n", - " (84847035, 'Timothy Williams', Decimal('35919.15')),\n", - " (85232444, 'Ashley Knight', Decimal('58513.22')),\n", - " (86646935, 'Judith Rodriguez', Decimal('82176.38')),\n", - " (87356185, 'Laura Castillo', Decimal('81767.62')),\n", - " (87591115, 'Eric Fields', Decimal('49400.63')),\n", - " (89363591, 'Sara Griffith', Decimal('27862.81')),\n", - " (90446123, 'James Gibson', Decimal('58628.37')),\n", - " (91973536, 'Karen Hunt', Decimal('91278.72')),\n", - " (94335018, 'Eric Mcgee', Decimal('97024.05')),\n", - " (95440048, 'Angela Roman', Decimal('75554.23')),\n", - " (97181338, 'Joseph Matthews', Decimal('23464.91')),\n", - " (97328507, 'Julie Baker', Decimal('86320.40')),\n", - " (98197568, 'Mr. Dylan Hernandez', Decimal('67592.91')),\n", - " (98230343, 'Nicholas Jacobson', Decimal('44952.48')),\n", - " (99345844, 'Caleb Davis', Decimal('40633.29'))]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "\n", - "SELECT * FROM account;" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * mysql://dimitri:***@db.ust-data-sci.net\n" - ] - }, - { - "ename": "Exception", - "evalue": "ipython_sql does not support transactions", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [18]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_cell_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msql\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43mBEGIN TRANSACTION;\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m \u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m UPDATE account SET balance = balance + 100\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m WHERE account = 98230343;\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m \u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m \u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m UPDATE account SET balance = balance - 100\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m WHERE account 95440048;\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43mCOMMIT\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/benv/lib/python3.10/site-packages/IPython/core/interactiveshell.py:2358\u001b[0m, in \u001b[0;36mInteractiveShell.run_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m 2356\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[1;32m 2357\u001b[0m args \u001b[38;5;241m=\u001b[39m (magic_arg_s, cell)\n\u001b[0;32m-> 2358\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2359\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/opt/miniconda3/envs/benv/lib/python3.10/site-packages/sql/magic.py:215\u001b[0m, in \u001b[0;36mSqlMagic.execute\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 215\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43msql\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparsed\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msql\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muser_ns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 217\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 218\u001b[0m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 219\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, \u001b[38;5;28mstr\u001b[39m)\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;66;03m# Instead of returning values, set variables directly in the\u001b[39;00m\n\u001b[1;32m 223\u001b[0m \u001b[38;5;66;03m# users namespace. Variable names given by column names\u001b[39;00m\n\u001b[1;32m 225\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mautopandas:\n", - "File \u001b[0;32m~/opt/miniconda3/envs/benv/lib/python3.10/site-packages/sql/run.py:357\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(conn, sql, config, user_namespace)\u001b[0m\n\u001b[1;32m 355\u001b[0m first_word \u001b[38;5;241m=\u001b[39m sql\u001b[38;5;241m.\u001b[39mstrip()\u001b[38;5;241m.\u001b[39msplit()[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mlower()\n\u001b[1;32m 356\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m first_word \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbegin\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 357\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mipython_sql does not support transactions\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m first_word\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 359\u001b[0m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpostgres\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(conn\u001b[38;5;241m.\u001b[39mdialect) \u001b[38;5;129;01mor\u001b[39;00m \\\n\u001b[1;32m 360\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mredshift\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(conn\u001b[38;5;241m.\u001b[39mdialect)):\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m PGSpecial:\n", - "\u001b[0;31mException\u001b[0m: ipython_sql does not support transactions" - ] - } - ], - "source": [ - "%%sql\n", - "\n", - "BEGIN TRANSACTION;\n", - " \n", - " UPDATE account SET balance = balance + 100\n", - " WHERE account = 98230343;\n", - " \n", - " \n", - " UPDATE account SET balance = balance - 100\n", - " WHERE account 95440048;\n", - "\n", - "COMMIT" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "import pymysql" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "with open(\"cred.json\") as f:\n", - " creds = json.load(f)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "conn = pymysql.connect(**creds, autocommit=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'account_number': 71167941}" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "account1" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cursor = conn.cursor()\n", - "cursor.execute(\n", - " \"\"\"\n", - " SELECT balance FROM dimitri_bank.account \n", - " WHERE account_number = %s\n", - " \"\"\",\n", - " (account1[\"account_number\"],),\n", - ")\n", - "\n", - "amount = 100\n", - "\n", - "current_balance = cursor.fetchone()\n", - "\n", - "cursor.execute(\n", - " \"\"\"\n", - " UPDATE dimitri_bank.account \n", - " SET balance = balance - %s\n", - " WHERE account_number = %s\n", - " \"\"\",\n", - " (\n", - " amount,\n", - " account1[\"account_number\"],\n", - " ),\n", - ")\n", - "\n", - "cursor.execute(\n", - " \"\"\"\n", - " UPDATE dimitri_bank.account \n", - " SET balance = balance + %s\n", - " WHERE account_number = %s\n", - " \"\"\",\n", - " (\n", - " amount,\n", - " account2[\"account_number\"],\n", - " ),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "def transfer(cursor, account1, account2, amount):\n", - " cursor.execute(\"BEGIN TRANSACTION\")\n", - "\n", - " try:\n", - " cursor.execute(\n", - " \"\"\"\n", - " SELECT balance FROM shared_bank.account \n", - " WHERE account_number = %s\n", - " \"\"\",\n", - " (account1[\"account_number\"],),\n", - " )\n", - "\n", - " current_balance = cursor.fetchone()\n", - "\n", - " if current_balance < amount:\n", - " raise RuntimeError(\"Insufficient funds\")\n", - "\n", - " cursor.execute(\n", - " \"\"\"\n", - " UPDATE shared_bank.account \n", - " SET balance = balance - %s\n", - " WHERE account_number = %s\n", - " \"\"\",\n", - " (\n", - " amount,\n", - " account1[\"account_number\"],\n", - " ),\n", - " )\n", - "\n", - " cursor.execute(\n", - " \"\"\"\n", - " UPDATE shared_bank.account \n", - " SET balance = balance + %s\n", - " WHERE account_number = %s\n", - " \"\"\",\n", - " (\n", - " amount,\n", - " account2[\"account_number\"],\n", - " ),\n", - " )\n", - "\n", - " except:\n", - " cursor.execute(\"CANCEL TRANSACTION\")\n", - " raise\n", - "\n", - " else:\n", - " cursor.execute(\"COMMIT\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Design Patterns\n", - "\n", - "- Sequence\n", - " * workflows\n", - "- Specialization / Generalization\n", - " * student / faculty / staff\n", - "- Hierarchies\n", - " * Ownership\n", - " * Using composite primary keys\n", - " * Secondary keys\n", - "- Parameterization\n", - " * \n", - "- Associations\n", - " * Many-to-many relationships\n", - " * Directed graphs \n", - " * Trees\n", - " * Undirected graphs\n", - "- Master-part\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Proceed to delete entire schema `dimitri_patterns`? [yes, No]: yes\n" - ] - } - ], - "source": [ - "schema = dj.schema(\"dimitri_patterns\")\n", - "schema.drop()\n", - "schema = dj.schema(\"dimitri_patterns\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sequence / Workflows" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# E.g. order / shipment / confirmation" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Order(dj.Manual):\n", - " definition = \"\"\"\n", - " order_number : int\n", - " ---\n", - " order_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Shipment(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Order\n", - " ---\n", - " ship_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Confirm(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Shipment\n", - " ---\n", - " confirm_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment->Confirm\n", - "\n", - "\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order->Shipment\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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laser_power

\n", - " mW\n", - "
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\n", - " " - ], - "text/plain": [ - "*subject_id *session *scan_id laser_power \n", - "+------------+ +---------+ +---------+ +------------+\n", - "\n", - " (Total: 0)" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Scan()" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "Scan.insert1(dict(subject_id=2, scan_id=1, session=1, laser_power=3200))" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Cell(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Scan\n", - " cell_id : int\n", - " ---\n", - " cell_type : enum('E', 'I') # excitatory or inhibitory\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session->Scan\n", - "\n", - "\n", - "\n", - "\n", - "Order2\n", - "\n", - "\n", - "Order2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment2\n", - "\n", - "\n", - "Shipment2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order2->Shipment2\n", - "\n", - "\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject->Session\n", - "\n", - "\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment->Confirm\n", - "\n", - "\n", - "\n", - "\n", - "Confirm2\n", - "\n", - "\n", - "Confirm2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan->Cell\n", - "\n", - "\n", - "\n", - "\n", - "Shipment2->Confirm2\n", - "\n", - "\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order->Shipment\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "# Give me excitatory cells for all males" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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subject_id

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session

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\n", - " " - ], - "text/plain": [ - "*subject_id *session *scan_id *cell_id cell_type \n", - "+------------+ +---------+ +---------+ +---------+ +-----------+\n", - "\n", - " (Total: 0)" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Cell & (Subject & {\"sex\": \"M\"})" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Subject2(dj.Manual):\n", - " definition = \"\"\"\n", - " # Experiment Subject\n", - " subject_id : int\n", - " ---\n", - " species = 'mouse' : enum('human', 'mouse', 'rat', 'worm')\n", - " sex : enum('F', 'M', 'unknown')\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Session2(dj.Manual):\n", - " definition = \"\"\"\n", - " session : int\n", - " ---\n", - " -> Subject2\n", - " session_timestamp = CURRENT_TIMESTAMP : timestamp\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Scan2(dj.Manual):\n", - " definition = \"\"\"\n", - " scan_id : int\n", - " ---\n", - " -> Session2\n", - " laser_power : float # mW\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Cell2(dj.Manual):\n", - " definition = \"\"\"\n", - " cell_id : int\n", - " ---\n", - " -> Scan2\n", - " cell_type : enum('E', 'I') # excitatory or inhibitory\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session->Scan\n", - "\n", - "\n", - "\n", - "\n", - "Cell2\n", - "\n", - "\n", - "Cell2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order2\n", - "\n", - "\n", - "Order2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment2\n", - "\n", - "\n", - "Shipment2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order2->Shipment2\n", - "\n", - "\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject->Session\n", - "\n", - "\n", - "\n", - "\n", - "Session2\n", - "\n", - "\n", - "Session2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session2->Scan2\n", - "\n", - "\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment->Confirm\n", - "\n", - "\n", - "\n", - "\n", - "Confirm2\n", - "\n", - "\n", - "Confirm2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject2\n", - "\n", - "\n", - "Subject2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject2->Session2\n", - "\n", - "\n", - "\n", - "\n", - "Scan2->Cell2\n", - "\n", - "\n", - "\n", - "\n", - "Scan->Cell\n", - "\n", - "\n", - "\n", - "\n", - "Shipment2->Confirm2\n", - "\n", - "\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order->Shipment\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "ename": "MissingAttributeError", - "evalue": "Field 'subject_id' doesn't have a default value", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mMissingAttributeError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [53]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mCell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minsert1\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcell_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscan_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcell_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mE\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/benv/lib/python3.10/site-packages/datajoint/table.py:334\u001b[0m, in \u001b[0;36mTable.insert1\u001b[0;34m(self, row, **kwargs)\u001b[0m\n\u001b[1;32m 327\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minsert1\u001b[39m(\u001b[38;5;28mself\u001b[39m, row, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 328\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 329\u001b[0m \u001b[38;5;124;03m Insert one data record into the table. For ``kwargs``, see ``insert()``.\u001b[39;00m\n\u001b[1;32m 330\u001b[0m \n\u001b[1;32m 331\u001b[0m \u001b[38;5;124;03m :param row: a numpy record, a dict-like object, or an ordered sequence to be inserted\u001b[39;00m\n\u001b[1;32m 332\u001b[0m \u001b[38;5;124;03m as one row.\u001b[39;00m\n\u001b[1;32m 333\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 334\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minsert\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/benv/lib/python3.10/site-packages/datajoint/table.py:430\u001b[0m, in \u001b[0;36mTable.insert\u001b[0;34m(self, rows, replace, skip_duplicates, ignore_extra_fields, allow_direct_insert)\u001b[0m\n\u001b[1;32m 414\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 415\u001b[0m query \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{command}\u001b[39;00m\u001b[38;5;124m INTO \u001b[39m\u001b[38;5;132;01m{destination}\u001b[39;00m\u001b[38;5;124m(`\u001b[39m\u001b[38;5;132;01m{fields}\u001b[39;00m\u001b[38;5;124m`) VALUES \u001b[39m\u001b[38;5;132;01m{placeholders}\u001b[39;00m\u001b[38;5;132;01m{duplicate}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 416\u001b[0m command\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mREPLACE\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m replace \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mINSERT\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 417\u001b[0m destination\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfrom_clause(),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 428\u001b[0m ),\n\u001b[1;32m 429\u001b[0m )\n\u001b[0;32m--> 430\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 431\u001b[0m \u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 432\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 433\u001b[0m \u001b[43m \u001b[49m\u001b[43mitertools\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_iterable\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 434\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalues\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrows\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 436\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 437\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m UnknownAttributeError \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 439\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m err\u001b[38;5;241m.\u001b[39msuggest(\n\u001b[1;32m 440\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTo ignore extra fields in insert, set ignore_extra_fields=True\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 441\u001b[0m )\n", - "File \u001b[0;32m~/opt/miniconda3/envs/benv/lib/python3.10/site-packages/datajoint/connection.py:340\u001b[0m, in \u001b[0;36mConnection.query\u001b[0;34m(self, query, args, as_dict, suppress_warnings, reconnect)\u001b[0m\n\u001b[1;32m 338\u001b[0m cursor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_conn\u001b[38;5;241m.\u001b[39mcursor(cursor\u001b[38;5;241m=\u001b[39mcursor_class)\n\u001b[1;32m 339\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 340\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execute_query\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcursor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuppress_warnings\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mLostConnectionError:\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m reconnect:\n", - "File \u001b[0;32m~/opt/miniconda3/envs/benv/lib/python3.10/site-packages/datajoint/connection.py:296\u001b[0m, in \u001b[0;36mConnection._execute_query\u001b[0;34m(cursor, query, args, suppress_warnings)\u001b[0m\n\u001b[1;32m 294\u001b[0m cursor\u001b[38;5;241m.\u001b[39mexecute(query, args)\n\u001b[1;32m 295\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m client\u001b[38;5;241m.\u001b[39merr\u001b[38;5;241m.\u001b[39mError \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 296\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m translate_query_error(err, query)\n", - "\u001b[0;31mMissingAttributeError\u001b[0m: Field 'subject_id' doesn't have a default value" - ] - } - ], - "source": [ - "Cell.insert1(dict(cell_id=1, scan_id=1, cell_type=\"E\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Cell()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Give me cells for subject_id=1\n", - "\n", - "Cell2 & (Scan2 & (Session2 & \"subject_id=2\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Give me cells for all males\n", - "\n", - "(Cell2 & (Scan2 & (Session2 & (Subject2 & 'sex=\"M\"')))).make_sql()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(Cell & (Subject & 'sex=\"M\"')).make_sql()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameterization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Image(dj.Manual):\n", - " definition = \"\"\"\n", - " image_id : int\n", - " ---\n", - " image : longblob\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class EnhanceMethod(dj.Lookup):\n", - " definition = \"\"\"\n", - " enhance_method : int\n", - " ---\n", - " method_name : varchar(16)\n", - " \"\"\"\n", - "\n", - " contents = ((1, \"sharpen\"), (2, \"contrast\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class EnhancedImage(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Image\n", - " -> EnhanceMethod\n", - " ---\n", - " enhanced_image : longblob\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Association \n", - "\n", - "Books and authors\n", - "\n", - "Checking accounts and account owners" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Book(dj.Manual):\n", - " definition = \"\"\"\n", - " isbn : int\n", - " ---\n", - " title : varchar(300)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Author(dj.Manual):\n", - " definition = \"\"\"\n", - " author_id : int\n", - " ---\n", - " name : varchar(300)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class AuthorBook(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Author\n", - " -> Book\n", - " ---\n", - " order : tinyint unsigned \n", - " unique index(isbn, order)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session->Scan\n", - "\n", - "\n", - "\n", - "\n", - "Cell2\n", - "\n", - "\n", - "Cell2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order2\n", - "\n", - "\n", - "Order2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment2\n", - "\n", - "\n", - "Shipment2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order2->Shipment2\n", - "\n", - "\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject->Session\n", - "\n", - "\n", - "\n", - "\n", - "Session2\n", - "\n", - "\n", - "Session2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session2->Scan2\n", - "\n", - "\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - 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"" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generalization / specialization\n", - "\n", - "Employee, student, instructor" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Person(dj.Manual):\n", - " definition = \"\"\"\n", - " person_id : int\n", - " ---\n", - " date_of_birth : date\n", - " gender : enum(\"male\", \"female\", \"unknown\")\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Employee(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Person\n", - " ---\n", - " hire_date : date \n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Instructor(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Employee\n", - " ---\n", - " department : varchar(30)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Student(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Person\n", - " ---\n", - " admission_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject->Session\n", - "\n", - "\n", - "\n", - "\n", - "Session2\n", - "\n", - "\n", - "Session2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session2->Scan2\n", - "\n", - "\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment->Confirm\n", - 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] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Directed graphs " - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Subordinate(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Employee\n", - " ---\n", - " -> Employee.proj(manager_id=\"person_id\")\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "0\n", - "\n", - "0\n", - "\n", - "\n", - "\n", - "Subordinate\n", - "\n", - "\n", - "Subordinate\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "0->Subordinate\n", - "\n", - "\n", - "\n", - "\n", - "AuthorBook\n", - "\n", - "\n", - "AuthorBook\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan->Cell\n", - "\n", - "\n", - "\n", - "\n", - "Cell2\n", - "\n", - "\n", - "Cell2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session->Scan\n", - "\n", - "\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan2->Cell2\n", - "\n", - "\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject->Session\n", - "\n", - "\n", - "\n", - "\n", - "Shipment2\n", - "\n", - "\n", - "Shipment2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Confirm2\n", - "\n", - "\n", - "Confirm2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment2->Confirm2\n", - "\n", - "\n", - "\n", - "\n", - 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"```sql\n", - "\n", - "CREATE TABLE managed_by (\n", - " person_id : int NOT NULL,\n", - " manager_id : int NOT NULL,\n", - " \n", - " PRIMARY KEY (person_id),\n", - " \n", - " FOREIGN KEY (person_id) REFERENCES employee (person_id),\n", - " FOREIGN KEY (manager_id) reference employee (person_id))\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Undirected graphs" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "# direcated friendship = full directed graph capability\n", - "@schema\n", - "class Friendship(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Person.proj(friend1 = \"person_id\")\n", - " -> Person.proj(friend2 = \"person_id\")\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "4\n", - "\n", - "4\n", - "\n", - "\n", - "\n", - "Friendship\n", - "\n", - "\n", - "Friendship\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "4->Friendship\n", - "\n", - "\n", - "\n", - "\n", - "5\n", - "\n", - "5\n", - "\n", - "\n", - "\n", - "5->Friendship\n", - "\n", - "\n", - "\n", - "\n", - "6\n", - "\n", - "6\n", - "\n", - "\n", - "\n", - "Synapse\n", - "\n", - "\n", - "Synapse\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "6->Synapse\n", - "\n", - "\n", - "\n", - "\n", - "7\n", - "\n", - "7\n", - "\n", - "\n", - "\n", - "7->Synapse\n", - "\n", - "\n", - "\n", - "\n", - "8\n", - "\n", - "8\n", - "\n", - "\n", - "\n", - "Subordinate\n", - "\n", - "\n", - "Subordinate\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "8->Subordinate\n", - "\n", - "\n", - "\n", - "\n", - "AuthorBook\n", - "\n", - "\n", - "AuthorBook\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan->Cell\n", - "\n", - "\n", - "\n", - "\n", - "Cell2\n", - "\n", - "\n", - "Cell2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session->Scan\n", - "\n", - "\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan2->Cell2\n", - "\n", - "\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject->Session\n", - "\n", - "\n", - "\n", - "\n", - "Shipment2\n", - "\n", - "\n", - "Shipment2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Confirm2\n", - "\n", - "\n", - "Confirm2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment2->Confirm2\n", - "\n", - "\n", - "\n", - "\n", - "Employee\n", - "\n", - "\n", - "Employee\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Employee->8\n", - "\n", - "\n", - "\n", - "\n", - "Employee->Subordinate\n", - "\n", - "\n", - "\n", - "\n", - "Instructor\n", - "\n", - "\n", - "Instructor\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Employee->Instructor\n", - "\n", - "\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "Shipment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment->Confirm\n", - "\n", - "\n", - "\n", - "\n", - "Session2\n", - "\n", - "\n", - "Session2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session2->Scan2\n", - "\n", - "\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person->4\n", - "\n", - "\n", - "\n", - "\n", - "Person->5\n", - "\n", - "\n", - "\n", - "\n", - "Person->Employee\n", - "\n", - "\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person->Student\n", - "\n", - "\n", - "\n", - "\n", - "Order2\n", - "\n", - "\n", - "Order2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order2->Shipment2\n", - "\n", - "\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "Order\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Order->Shipment\n", - "\n", - "\n", - "\n", - "\n", - "Author\n", - "\n", - "\n", - "Author\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Author->AuthorBook\n", - "\n", - "\n", - "\n", - "\n", - "Book\n", - "\n", - "\n", - "Book\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Book->AuthorBook\n", - "\n", - "\n", - "\n", - "\n", - "Neuron\n", - "\n", - "\n", - "Neuron\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Neuron->6\n", - "\n", - "\n", - "\n", - "\n", - "Neuron->7\n", - "\n", - "\n", - "\n", - "\n", - "Subject2\n", - "\n", - "\n", - "Subject2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject2->Session2\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Order(dj.Manual):\n", - " definition = \"\"\"\n", - " order_id : int\n", - " ---\n", - " order_date : date\n", - " \"\"\"\n", - "\n", - " class Item(dj.Part):\n", - " definition = \"\"\"\n", - " -> master\n", - " order_item : int\n", - " ---\n", - " \n", - " \"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Puzzle: \n", - "Cities and states.\n", - "1. Each city belongs to one state. \n", - "2. Each state has one capital.\n", - "3. A capital is a city.\n", - "4. A capital must be in the same state. \n", - "\n", - "* Tables\n", - "* Primary keys\n", - "* Foreign keys" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class State(dj.Manual):\n", - " definition = \"\"\"\n", - " st : char(2)\n", - " ---\n", - " state : varchar(30)\n", - " \"\"\"\n", - "\n", - "\n", - "State.insert(\n", - " ((\"WA\", \"Washington\"), (\"TX\", \"Texas\"), (\"AK\", \"Alaska\"), (\"LA\", \"Louisiana\"))\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class City(dj.Manual):\n", - " definition = \"\"\"\n", - " -> State\n", - " city_name : varchar(30)\n", - " ---\n", - " capital = null : enum(\"YES\")\n", - " unique index(st, capital)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "City.delete_quick()\n", - "\n", - "City.insert1((\"WA\", \"Seattle\", None))\n", - "City.insert1((\"TX\", \"Austin\", \"YES\"))\n", - "City.insert1((\"TX\", \"Houston\", None))\n", - "City.insert1((\"WA\", \"Olympia\", \"YES\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - "

st

\n", - " \n", - "
\n", - "

city_name

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\n", - "

capital

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TXHoustonNone
TXAustinYES
WASeattleNone
WAOlympiaYES
\n", - " \n", - "

Total: 4

\n", - " " - ], - "text/plain": [ - "*st *city_name capital \n", - "+----+ +-----------+ +---------+\n", - "TX Houston None \n", - "TX Austin YES \n", - "WA Seattle None \n", - "WA Olympia YES \n", - " (Total: 4)" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "City()" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class State2(dj.Manual):\n", - " definition = \"\"\"\n", - " state : char (2)\n", - " ---\n", - " state_name : varchar(30)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class City2(dj.Manual):\n", - " definition = \"\"\"\n", - " -> State2\n", - " city_name : varchar(30)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Capital2(dj.Manual):\n", - " definition = \"\"\"\n", - " -> State2\n", - " ---\n", - " -> City2\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "State2.delete_quick()\n", - "City2.delete_quick()\n", - "\n", - "State2.insert(\n", - " ((\"WA\", \"Washington\"), (\"TX\", \"Texas\"), (\"AK\", \"Alaska\"), (\"LA\", \"Louisiana\"))\n", - ")\n", - "\n", - "City2.insert1((\"WA\", \"Seattle\"))\n", - "City2.insert1((\"TX\", \"Austin\"))\n", - "City2.insert1((\"TX\", \"Houston\"))\n", - "City2.insert1((\"WA\", \"Olympia\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "9\n", - "\n", - "9\n", - "\n", - "\n", - "\n", - "Friendship\n", - "\n", - "\n", - "Friendship\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "9->Friendship\n", - "\n", - "\n", - "\n", - "\n", - "10\n", - "\n", - "10\n", - "\n", - "\n", - "\n", - "10->Friendship\n", - "\n", - "\n", - "\n", - "\n", - "11\n", - "\n", - "11\n", - "\n", - "\n", - "\n", - "Synapse\n", - "\n", - "\n", - "Synapse\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "11->Synapse\n", - "\n", - "\n", - "\n", - "\n", - "12\n", - "\n", - "12\n", - "\n", - "\n", - "\n", - "12->Synapse\n", - "\n", - "\n", - "\n", - "\n", - "13\n", - "\n", - "13\n", - "\n", - "\n", - "\n", - "Subordinate\n", - "\n", - "\n", - "Subordinate\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "13->Subordinate\n", - "\n", - "\n", - "\n", - "\n", - "AuthorBook\n", - "\n", - "\n", - "AuthorBook\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "Scan\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "Cell\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan->Cell\n", - "\n", - "\n", - "\n", - "\n", - "Cell2\n", - "\n", - "\n", - "Cell2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "Session\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Session->Scan\n", - "\n", - "\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "Confirm\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "Scan2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Scan2->Cell2\n", - "\n", - "\n", - "\n", - "\n", - "State2\n", - "\n", - "\n", - "State2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "City2\n", - "\n", - "\n", - "City2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "State2->City2\n", - "\n", - "\n", - "\n", - "\n", - "Capital2\n", - "\n", - "\n", - "Capital2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "State2->Capital2\n", - "\n", - "\n", - "\n", - "\n", - "City2->Capital2\n", - "\n", - "\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "Subject\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Subject->Session\n", - "\n", - "\n", - "\n", - "\n", - "Shipment2\n", - "\n", - "\n", - "Shipment2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Confirm2\n", - "\n", - "\n", - "Confirm2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shipment2->Confirm2\n", - "\n", - "\n", - "\n", - "\n", - "Employee\n", - "\n", - "\n", - "Employee\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Employee->13\n", - "\n", - 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"\n", - "City\n", - "\n", - "\n", - "City\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "State->City\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [], - "source": [ - "Capital2.insert1((\"TX\", \"Austin\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "ename": "DuplicateError", - "evalue": "(\"Duplicate entry 'TX' for key 'PRIMARY'\", 'To ignore duplicate entries in insert, set skip_duplicates=True')", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mDuplicateError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [76]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mCapital2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minsert1\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mTX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mHouston\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/benv/lib/python3.10/site-packages/datajoint/table.py:334\u001b[0m, in \u001b[0;36mTable.insert1\u001b[0;34m(self, row, **kwargs)\u001b[0m\n\u001b[1;32m 327\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minsert1\u001b[39m(\u001b[38;5;28mself\u001b[39m, row, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 328\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 329\u001b[0m \u001b[38;5;124;03m Insert one data record into the table. For ``kwargs``, see ``insert()``.\u001b[39;00m\n\u001b[1;32m 330\u001b[0m \n\u001b[1;32m 331\u001b[0m \u001b[38;5;124;03m :param row: a numpy record, a dict-like object, or an ordered sequence to be inserted\u001b[39;00m\n\u001b[1;32m 332\u001b[0m \u001b[38;5;124;03m as one row.\u001b[39;00m\n\u001b[1;32m 333\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 334\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minsert\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/benv/lib/python3.10/site-packages/datajoint/table.py:443\u001b[0m, in \u001b[0;36mTable.insert\u001b[0;34m(self, rows, replace, skip_duplicates, ignore_extra_fields, allow_direct_insert)\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m err\u001b[38;5;241m.\u001b[39msuggest(\n\u001b[1;32m 440\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTo ignore extra fields in insert, set ignore_extra_fields=True\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 441\u001b[0m )\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m DuplicateError \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 443\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m err\u001b[38;5;241m.\u001b[39msuggest(\n\u001b[1;32m 444\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTo ignore duplicate entries in insert, set skip_duplicates=True\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 445\u001b[0m )\n", - "\u001b[0;31mDuplicateError\u001b[0m: (\"Duplicate entry 'TX' for key 'PRIMARY'\", 'To ignore duplicate entries in insert, set skip_duplicates=True')" - ] - } - ], - "source": [ - "Capital2.insert1((\"TX\", \"Houston\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

state

\n", - " \n", - "
\n", - "

city_name

\n", - " \n", - "
TXAustin
TXHouston
WAOlympia
WASeattle
\n", - " \n", - "

Total: 4

\n", - " " - ], - "text/plain": [ - "*state *city_name \n", - "+-------+ +-----------+\n", - "TX Austin \n", - "TX Houston \n", - "WA Olympia \n", - "WA Seattle \n", - " (Total: 4)" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "City2()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# In SQL \n", - "\n", - "```sql\n", - "CREATE TABLE state (\n", - " state char(2) NOT NULL,\n", - " state_name varchar(30) NOT NULL,\n", - " PRIMARY KEY (state))\n", - " \n", - "CREATE TABLE city (\n", - " state char(2) NOT NULL,\n", - " city_name varchar(30) NOT NULL,\n", - " PRIMARY KEY (state, city_name),\n", - " FOREIGN KEY (state) REFERENCES state(state))\n", - "\n", - "CREATE TABLE capital (\n", - " state char(2) NOT NULL,\n", - " city_name varchar(30) NOT NULL,\n", - " PRIMARY KEY (state),\n", - " FOREIGN KEY (state, city_name) REFERENCES city (state, city_name))\n", - "```\n" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (724225854.py, line 1)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m Input \u001b[0;32mIn [78]\u001b[0;36m\u001b[0m\n\u001b[0;31m ```sql\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" - ] - } - ], - "source": [ - "\n", - "```sql\n", - "CREATE TABLE state (\n", - " state char(2),\n", - " state_name varchar(30),\n", - " capital varchar(30),\n", - " PRIMARY KEY (state),\n", - " FOREIGN KEY (state, capital) REFERENCES city (state, city_name))\n", - " \n", - "CREATE TABLE city (\n", - " state char(2),\n", - " city_name varchar(30),\n", - " PRIMARY KEY (state, city_name))\n", - " FOREIGN KEY (state) REFERENCES state(state)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# In SQL \n", - "\n", - "```sql\n", - "CREATE TABLE state (\n", - " state char(2) NOT NULL,\n", - " state_name varchar(30) NOT NULL,\n", - " PRIMARY KEY (state))\n", - " \n", - "CREATE TABLE city (\n", - " city_id int NOT NULL,\n", - " state char(2) NOT NULL,\n", - " city_name varchar(30) NOT NULL,\n", - " is_capital enum('yes'),\n", - " PRIMARY KEY (state_id),\n", - " UNIQUE INDEX(state, is_capital),\n", - " FOREIGN KEY (state) REFERENCES state(state))\n", - "```\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Problem \n", - "\n", - "Model a vet clinic. \n", - "\n", - "1. Customers bring in pets. Customers are identified by their cell phones. Pets are identified by their nicknames for that customer.\n", - "\n", - "2. Pets have a date of birth, species, and date of birth.\n", - "\n", - "3. Pets have a list of vaccinations that must be performed for their species.\n", - "\n", - "4. Pets have vaccination administration, shot date. " - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Proceed to delete entire schema `shared_vet`? [yes, No]: yes\n" - ] - } - ], - "source": [ - "schema = dj.Schema(\"shared_vet\")\n", - "schema.drop()\n", - "schema = dj.Schema(\"shared_vet\")" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Owner(dj.Manual):\n", - " definition = \"\"\"\n", - " cell_phone : char(10) \n", - " ---\n", - " full_name : varchar(16)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Species(dj.Lookup):\n", - " definition = \"\"\"\n", - " species : varchar(30)\n", - " \"\"\"\n", - " contents = ((\"cat\",), (\"dog\",), (\"ferret\",), (\"parrot\",))" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "
\n", - "

species

\n", - " \n", - "
cat
dog
ferret
parrot
\n", - " \n", - "

Total: 4

\n", - " " - ], - "text/plain": [ - "*species \n", - "+---------+\n", - "cat \n", - "dog \n", - "ferret \n", - "parrot \n", - " (Total: 4)" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Species()" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Pet(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Owner\n", - " -> Species\n", - " nickname : varchar(30)\n", - " ---\n", - " birthdate : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class RequiredVaccine(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Species\n", - " vaccine : varchar(10)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Pet\n", - "\n", - "\n", - "Pet\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "RequiredVaccine\n", - "\n", - "\n", - "RequiredVaccine\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Owner\n", - "\n", - "\n", - "Owner\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Owner->Pet\n", - "\n", - "\n", - "\n", - "\n", - "Species\n", - "\n", - "\n", - "Species\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Species->Pet\n", - "\n", - "\n", - "\n", - "\n", - "Species->RequiredVaccine\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Shot(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Pet\n", - " -> RequiredVaccine\n", - " ---\n", - " shot_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Pet\n", - "\n", - "\n", - "Pet\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shot\n", - "\n", - "\n", - "Shot\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Pet->Shot\n", - "\n", - "\n", - "\n", - "\n", - "Species\n", - "\n", - "\n", - "Species\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Species->Pet\n", - "\n", - "\n", - "\n", - "\n", - "RequiredVaccine\n", - "\n", - "\n", - "RequiredVaccine\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Species->RequiredVaccine\n", - "\n", - "\n", - "\n", - "\n", - "RequiredVaccine->Shot\n", - "\n", - "\n", - "\n", - "\n", - "Owner\n", - "\n", - "\n", - "Owner\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Owner->Pet\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "
\n", - "

cell_phone

\n", - " \n", - "
\n", - "

species

\n", - " \n", - "
\n", - "

nickname

\n", - " \n", - "
\n", - "

vaccine

\n", - " \n", - "
\n", - "

shot_date

\n", - " \n", - "
\n", - " \n", - "

Total: 0

\n", - " " - ], - "text/plain": [ - "*cell_phone *species *nickname *vaccine shot_date \n", - "+------------+ +---------+ +----------+ +---------+ +-----------+\n", - "\n", - " (Total: 0)" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Shot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```sql\n", - "create table shot (\n", - " cell_phone char(10) NOT NULL,\n", - " nickname varchar(16) NOT NULL,\n", - " species varchar(20) NOT NULL,\n", - " vaccine varchar(10) NOT NULL,\n", - " PRIMARY KEY (cell_phone, nickname, species, vaccine),\n", - " FOREIGN KEY (cell_phone, nickname, species) REFERENCES pet(cell_phone, nickname, species),\n", - " FOREIGN KEY (species, vaccine) REFERENCES required_vaccine(species, vaccine)\n", - ")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Homework homework\n", - "\n", - "Homework assignments, students, grades\n", - "\n", - "1. Homework is given with a due date.\n", - "2. Students submit homework, we record the submit date\n", - "3. Submitted homework gets a grade\n" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Assignment(dj.Manual):\n", - " definition = \"\"\"\n", - " assignment : int\n", - " ---\n", - " due_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Student(dj.Manual):\n", - " definition = \"\"\"\n", - " student_id : int\n", - " ---\n", - " student_name : varchar(30)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Submission(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Student\n", - " -> Assignment\n", - " ---\n", - " submit_date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Grade(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Submission\n", - " ---\n", - " grade : char(1)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Assignment\n", - "\n", - "\n", - "Assignment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Submission\n", - "\n", - "\n", - "Submission\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Assignment->Submission\n", - "\n", - "\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Student->Submission\n", - "\n", - "\n", - "\n", - "\n", - "Grade\n", - "\n", - "\n", - "Grade\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Pet\n", - "\n", - "\n", - "Pet\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shot\n", - "\n", - "\n", - "Shot\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Pet->Shot\n", - "\n", - "\n", - "\n", - "\n", - "Species\n", - "\n", - "\n", - "Species\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Species->Pet\n", - "\n", - "\n", - "\n", - "\n", - "RequiredVaccine\n", - "\n", - "\n", - "RequiredVaccine\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Species->RequiredVaccine\n", - "\n", - "\n", - "\n", - "\n", - "RequiredVaccine->Shot\n", - "\n", - "\n", - "\n", - "\n", - "Submission->Grade\n", - "\n", - "\n", - "\n", - "\n", - "Owner\n", - "\n", - "\n", - "Owner\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Owner->Pet\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 104, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Messaging App (Slack, Telegram, Signal)\n", - "\n", - "1. Users can create channels. Each channel belongs to one user.\n", - "3. Channel names are globally unique\n", - "2. A user can create a post in their channels only\n", - "3. A user can be a guest in another person's channel.\n", - "4. Guest can reply to posts\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class User(dj.Manual):\n", - " definition = \"\"\"\n", - " username : varchar(12)\n", - " ---\n", - " irl_name : varchar(30)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Channel(dj.Manual):\n", - " definition = \"\"\"\n", - " channel : varchar(12)\n", - " ---\n", - " -> User\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Guest(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Channel\n", - " -> User\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Assignment\n", - "\n", - "\n", - "Assignment\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Submission\n", - "\n", - "\n", - "Submission\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Assignment->Submission\n", - "\n", - "\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "Student\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Student->Submission\n", - "\n", - "\n", - "\n", - "\n", - "Grade\n", - "\n", - "\n", - "Grade\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "User\n", - "\n", - "\n", - "User\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Guest\n", - "\n", - "\n", - "Guest\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "User->Guest\n", - "\n", - "\n", - "\n", - "\n", - "Channel\n", - "\n", - "\n", - "Channel\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "User->Channel\n", - "\n", - "\n", - "\n", - "\n", - "Pet\n", - "\n", - "\n", - "Pet\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Shot\n", - "\n", - "\n", - "Shot\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Pet->Shot\n", - "\n", - "\n", - "\n", - "\n", - "Species\n", - "\n", - "\n", - "Species\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Species->Pet\n", - "\n", - "\n", - "\n", - "\n", - "RequiredVaccine\n", - "\n", - "\n", - "RequiredVaccine\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Species->RequiredVaccine\n", - "\n", - "\n", - "\n", - "\n", - "RequiredVaccine->Shot\n", - "\n", - "\n", - "\n", - "\n", - "Channel->Guest\n", - "\n", - "\n", - "\n", - "\n", - "Submission->Grade\n", - "\n", - "\n", - "\n", - "\n", - "Owner\n", - "\n", - "\n", - "Owner\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Owner->Pet\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Post(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Channel\n", - " post : int\n", - " ---\n", - " message : varchar(1024)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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Modeling 1:1, 1:N, M:N relationships \n", - "2. Default values, NULLs, and autoincrement\n", - "3. UUIDs \n", - "4. Secondary unique keys" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2022-09-21 07:06:34,147][INFO]: Connecting dimitri@db.ust-data-sci.net:3306\n", - "[2022-09-21 07:06:34,856][INFO]: Connected dimitri@db.ust-data-sci.net:3306\n" - ] - } - ], - "source": [ - "schema = dj.Schema(\"dimitri_bank\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Person(dj.Manual):\n", - " definition = \"\"\"\n", - " person_id : int unsigned\n", - " ---\n", - " date_of_birth : date\n", - " full_name : varchar(30)\n", - " ssn : int\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import faker\n", - "\n", - "fake = faker.Faker()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import random" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "Person.insert(\n", - " dict(\n", - " person_id=random.randint(100_000_000, 999_999_999),\n", - " date_of_birth=fake.date_of_birth(),\n", - " full_name=fake.name(),\n", - " ssn=random.randint(100_00_0000, 999_99_9999),\n", - " )\n", - " for _ in range(3000)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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full_name

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1007369961988-01-21Ryan Carroll693662313
1010893681949-10-31Rodney Larson624997449
1011795241914-11-28Joshua Hendricks989102464
1016281591968-10-24Joshua Grant589612296
1022150101977-04-27Robert Becker757141219
1024299931986-10-16Mark Brown691647217
1026972731955-12-06Edward Jones846903546
1028726082020-03-18Rachel Newton696859337
1028842071918-12-08Nicole Goodwin499530867
1031494971931-01-16Hannah Manning692374987
1031712641931-09-20Christy Fisher520790110
1032427522022-05-01Nancy Johnson952655808
\n", - "

...

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Total: 3000

\n", - " " - ], - "text/plain": [ - "*person_id date_of_birth full_name ssn \n", - "+-----------+ +------------+ +------------+ +-----------+\n", - "100736996 1988-01-21 Ryan Carroll 693662313 \n", - "101089368 1949-10-31 Rodney Larson 624997449 \n", - "101179524 1914-11-28 Joshua Hendric 989102464 \n", - "101628159 1968-10-24 Joshua Grant 589612296 \n", - "102215010 1977-04-27 Robert Becker 757141219 \n", - "102429993 1986-10-16 Mark Brown 691647217 \n", - "102697273 1955-12-06 Edward Jones 846903546 \n", - "102872608 2020-03-18 Rachel Newton 696859337 \n", - "102884207 1918-12-08 Nicole Goodwin 499530867 \n", - "103149497 1931-01-16 Hannah Manning 692374987 \n", - "103171264 1931-09-20 Christy Fisher 520790110 \n", - "103242752 2022-05-01 Nancy Johnson 952655808 \n", - " ...\n", - " (Total: 3000)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Person()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Account(dj.Manual):\n", - " definition = \"\"\"\n", - " account : int unsigned \n", - " ---\n", - " -> Person\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "keys = Person.fetch(\"KEY\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'person_id': 591537366}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "random.choice(keys)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'person_id': 953003224, 'account': 10}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dict(random.choice(keys), account=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "Account.insert(dict(random.choice(keys), account=i) for i in range(300, 600))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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594135845005
567137392646
555140593584
365143078002
476145303761
329152318047
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...

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Total: 300

\n", - " " - ], - "text/plain": [ - "*account person_id \n", - "+---------+ +-----------+\n", - "558 104728015 \n", - "420 105467497 \n", - "366 123340380 \n", - "384 127479809 \n", - "511 128464454 \n", - "523 129280391 \n", - "594 135845005 \n", - "567 137392646 \n", - "555 140593584 \n", - "365 143078002 \n", - "476 145303761 \n", - "329 152318047 \n", - " ...\n", - " (Total: 300)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Account()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person->Account\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* A foreign key from Child to Parent creates a one-to-one relationship if the foreign key is also a unique index. \n", - "* If the foreign key is not a unique index, then this becomes a many-to-one relationship.\n", - "* A single foreign key never creates a many-to-many relationship. For such relationships you need a separate *association table*." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Modify the design so that each person could only have one account.\n", - "One person can only have one bank account." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Person2(dj.Manual):\n", - " definition = \"\"\"\n", - " person_id : int unsigned\n", - " ---\n", - " date_of_birth : date\n", - " full_name : varchar(30)\n", - " ssn : int\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Account2(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Person2\n", - " --- \n", - " balance : decimal(10, 2)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Person2\n", - "\n", - "\n", - "Person2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account2\n", - "\n", - "\n", - "Account2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person2->Account2\n", - "\n", - "\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person->Account\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Person3(dj.Manual):\n", - " definition = \"\"\"\n", - " person_id : int unsigned\n", - " ---\n", - " date_of_birth : date\n", - " full_name : varchar(30)\n", - " ssn : int\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Account3(dj.Manual):\n", - " definition = \"\"\"\n", - " account : int\n", - " --- \n", - " -> Person3\n", - " balance : decimal(10, 2)\n", - " unique index (person_id) \n", - " \"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is how you would define Account3 in SQL:\n", - "```sql\n", - "\n", - "CREATE TABLE account3 (\n", - " account int NOT NULL,\n", - " person_id int unsigned NOT NULL,\n", - " balance decimal(10, 2) NOT NULL,\n", - " PRIMARY KEY(account),\n", - " FOREIGN KEY (person_id) REFERENCE person3(person_id),\n", - " UNIQUE INDEX (person_id)\n", - ")\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person->Account\n", - "\n", - "\n", - "\n", - "\n", - "Account2\n", - "\n", - "\n", - "Account2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account3\n", - "\n", - "\n", - "Account3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person3\n", - "\n", - "\n", - "Person3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person3->Account3\n", - "\n", - "\n", - "\n", - "\n", - "Person2\n", - "\n", - "\n", - "Person2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person2->Account2\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Modify this design to allow multiple owners for each account" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Person4(dj.Manual):\n", - " definition = \"\"\"\n", - " person_id : int unsigned\n", - " ---\n", - " date_of_birth : date\n", - " full_name : varchar(30)\n", - " ssn : int\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Account4(dj.Manual):\n", - " definition = \"\"\"\n", - " account : int unsigned\n", - " --- \n", - " balance : decimal(10, 2)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class AccountPerson4(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Person4\n", - " -> Account4\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "Person4.insert(\n", - " dict(\n", - " person_id=random.randint(100_000_000, 999_999_999),\n", - " date_of_birth=fake.date_of_birth(),\n", - " full_name=fake.name(),\n", - " ssn=random.randint(100_00_0000, 999_99_9999),\n", - " )\n", - " for _ in range(3000)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "Account4.insert(\n", - " dict(\n", - " account=random.randint(100_000_000, 999_999_999),\n", - " balance=random.randint(100_000_000, 999_999_999) / 100,\n", - " )\n", - " for _ in range(3000)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "person_keys = Person4.fetch(\"KEY\")\n", - "account_keys = Account4.fetch(\"KEY\")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'person_id': 642100582, 'account': 665472440}" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "{**random.choice(person_keys), **random.choice(account_keys)}" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "AccountPerson4.insert(\n", - " (\n", - " {**random.choice(person_keys), **random.choice(account_keys)}\n", - " for _ in range(4000)\n", - " ),\n", - " skip_duplicates=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - 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}, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Person5(dj.Manual):\n", - " definition = \"\"\"\n", - " person_id : int unsigned\n", - " ---\n", - " date_of_birth : date\n", - " full_name : varchar(30)\n", - " ssn : int\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Account5(dj.Manual):\n", - " definition = \"\"\"\n", - " account : int unsigned\n", - " --- \n", - " balance : decimal(10, 2)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class AccountPerson5(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Account5\n", - " ---\n", - " -> Person5\n", - " \"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### translate `AccountPerson5` into SQL:\n", - "```sql\n", - "create table AccountPerson5 (\n", - " account int unsigned NOT NULL,\n", - " person_id int unsigned NOT NULL,\n", - " PRIMARY KEY(account),\n", - " FOREIGN KEY(account) REFERENCES Account5(account),\n", - " FOREIGN KEY(person_id) REFERENCES Person5(person_id)\n", - ")\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "Person\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "Account\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person->Account\n", - "\n", - "\n", - "\n", - "\n", - "AccountPerson4\n", - "\n", - "\n", - "AccountPerson4\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person4\n", - "\n", - "\n", - "Person4\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person4->AccountPerson4\n", - "\n", - "\n", - "\n", - "\n", - "Account2\n", - "\n", - "\n", - "Account2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person5\n", - "\n", - "\n", - "Person5\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AccountPerson5\n", - "\n", - "\n", - "AccountPerson5\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person5->AccountPerson5\n", - "\n", - "\n", - "\n", - "\n", - "Account4\n", - "\n", - "\n", - "Account4\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account4->AccountPerson4\n", - "\n", - "\n", - "\n", - "\n", - "Account3\n", - "\n", - "\n", - "Account3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account5\n", - "\n", - "\n", - "Account5\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Account5->AccountPerson5\n", - "\n", - "\n", - "\n", - "\n", - "Person3\n", - "\n", - "\n", - "Person3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person3->Account3\n", - "\n", - "\n", - "\n", - "\n", - "Person2\n", - "\n", - "\n", - "Person2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Person2->Account2\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dj.Diagram(schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Default values, nulls, and secondary indexes" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Person6(dj.Manual):\n", - " definition = \"\"\"\n", - " person_id : int unsigned\n", - " ---\n", - " date_of_birth = null : date\n", - " full_name = \"\": varchar(30)\n", - " ssn = null : int\n", - " unique index (ssn)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Account6(dj.Manual):\n", - " definition = \"\"\"\n", - " account : int unsigned\n", - " --- \n", - " -> [nullable] Person6\n", - " balance = 0.0: decimal(10, 2)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "Person6.insert1(dict(person_id=1))" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "Person6.insert1(dict(person_id=2, full_name=\"Alice Cooper\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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Total: 2

\n", - " " - ], - "text/plain": [ - "*person_id date_of_birth full_name ssn \n", - "+-----------+ +------------+ +------------+ +------+\n", - "1 None None \n", - "2 None Alice Cooper None \n", - " (Total: 2)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Person6()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Translate Person6 and Account6 into SQL \n", - "\n", - "```sql\n", - "\n", - "CREATE TABLE Person6 (\n", - " person_id int unsigned NOT NULL,\n", - " date_of_birth date,\n", - " full_name varchar(30) NOT NULL DEFAULT \"\", \n", - " ssn int,\n", - " PRIMARY KEY(person_id),\n", - " UNIQUE INDEX (ssn))\n", - "\n", - "CREATE TABLE Account6 (\n", - " account int unsigned NOT NULL auto_increment,\n", - " person_id int unsigned,\n", - " balance decimal(10, 2) NOT NULL DEFAULT 0.0, \n", - " PRIMARY KEY(account),\n", - " FOREIGN KEY (person_id) REFERENCES Person6 (person_id) )\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "benv", - "language": "python", - "name": "benv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/db-course/QuizSystem.ipynb b/db-course/QuizSystem.ipynb deleted file mode 100644 index bf343bf..0000000 --- a/db-course/QuizSystem.ipynb +++ /dev/null @@ -1,879 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "eab79078", - "metadata": {}, - "outputs": [], - "source": [ - "import datajoint as dj" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1cca12f2", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-10-02 00:53:48,908][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", - "[2023-10-02 00:53:48,920][INFO]: Connected root@fakeservices.datajoint.io:3306\n" - ] - } - ], - "source": [ - "schema = dj.Schema(\"quiz\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "74f40761", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Subject(dj.Manual):\n", - " definition = \"\"\"\n", - " subject_id : varchar(15)\n", - " ---\n", - " subject_name : varchar(60)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "cc89cfea", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class User(dj.Manual):\n", - " definition = \"\"\"\n", - " user_id : int unsigned\n", - " ---\n", - " first_name : varchar(60)\n", - " last_name : varchar(60)\n", - " birthday : date \n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "33c9e812", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Question(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Subject\n", - " question_id : int unsigned\n", - " ---\n", - " question : varchar(2000)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "ad537703", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Answer(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Question\n", - " answer_id : tinyint unsigned \n", - " ---\n", - " answer: varchar(300)\n", - " correct = null : enum('YES')\n", - " unique index(subject_id, question_id, correct)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a4c596d5", - "metadata": {}, - "outputs": [], - "source": [ - "# Make user take a quiz only once in each calendar year on any subject" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "53c247fd", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Quiz(dj.Manual):\n", - " definition = \"\"\"\n", - " -> User\n", - " -> Subject\n", - " year : year\n", - " ---\n", - " date : date\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "33b7a50c", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class QuizQuestion(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Quiz\n", - " -> Question\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "cad762f5", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class SubmittedAnswer(dj.Manual):\n", - " definition = \"\"\"\n", - " -> QuizQuestion\n", - " -> Answer\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e0c46923", - "metadata": {}, - "outputs": [], - "source": [ - "@schema\n", - "class Result(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Quiz\n", - " ---\n", - " number_correct : int(3)\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "51c6ab85", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "Question\n", - "\n", - "\n", - "Question\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Answer\n", - "\n", - "\n", - "Answer\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Question->Answer\n", - "\n", - "\n", - "\n", - "\n", - "QuizQuestion\n", - "\n", - "\n", - "QuizQuestion\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Question->QuizQuestion\n", - "\n", - "\n", - "\n", - "\n", - "SubmittedAnswer\n", - "\n", - "\n", - "SubmittedAnswer\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Answer->SubmittedAnswer\n", - "\n", - "\n", - "\n", - "\n", - "Result\n", - "\n", - "\n", - "Result\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "QuizQuestion->SubmittedAnswer\n", - "\n", - "\n", - "\n", - "\n", - "User\n", - "\n", - "\n", - "User\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Quiz\n", - "\n", - "\n", - "Quiz\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "User->Quiz\n", - 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"class User2(dj.Manual):\n", - " definition = \"\"\"\n", - " user_id : int unsigned\n", - " ---\n", - " first_name : varchar(60)\n", - " last_name : varchar(60)\n", - " birthday : date \n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Question2(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Subject2\n", - " question_id : int unsigned\n", - " ---\n", - " question : varchar(2000)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Answer2(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Question2\n", - " answer_id : tinyint unsigned \n", - " ---\n", - " answer: varchar(300)\n", - " correct = null : enum('YES')\n", - " unique index(subject_id, question_id, correct)\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Quiz2(dj.Manual):\n", - " definition = \"\"\"\n", - " -> User2\n", - " -> Subject2\n", - " year : year\n", - " ---\n", - " date : date\n", - " \"\"\"\n", - "\n", - " class Question(dj.Part):\n", - " definition = \"\"\"\n", - " -> master\n", - " -> Question2\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Submission2(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Quiz2\n", - " \"\"\"\n", - "\n", - " class Answer(dj.Part):\n", - " definition = \"\"\"\n", - " -> master\n", - " -> Answer2\n", - " \"\"\"\n", - "\n", - "\n", - "@schema\n", - "class Result2(dj.Manual):\n", - " definition = \"\"\"\n", - " -> Submission2\n", - " ---\n", - " number_correct : smallint\n", - " \"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "70cbfc4c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "Answer\n", - "\n", - "\n", - "Answer\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "SubmittedAnswer\n", - "\n", - "\n", - "SubmittedAnswer\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Answer->SubmittedAnswer\n", - "\n", - "\n", - "\n", - "\n", - "Subject2\n", - "\n", - "\n", - "Subject2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Question2\n", - "\n", - "\n", - "Question2\n", - 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DaSyDatabase Systems
MLMachine Learning
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Total: 3

\n", - " " - ], - "text/plain": [ - "*subject_id subject_name \n", - "+------------+ +------------+\n", - "DaSy Database System\n", - "ML Machine Learni\n", - "SciViz Scientific Vis\n", - " (Total: 3)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Subject2()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1a26d61d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/requirements.txt b/requirements.txt index 677496b..96ba589 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,3 +4,4 @@ scikit-image ipython-sql sqlalchemy PyWavelets +pooch diff --git a/short_tutorials/blob-detection.ipynb b/short_tutorials/blob-detection.ipynb new file mode 100644 index 0000000..fde330d --- /dev/null +++ b/short_tutorials/blob-detection.ipynb @@ -0,0 +1,345 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Blob Detection Workflow\n", + "\n", + "This example shows a compact image-analysis pipeline that detects bright blobs in two sample images using DataJoint. It demonstrates:\n", + "\n", + "- Seeding a small `Image` manual table with two entries of standard images from `skimage.data`.\n", + "- Defining multiple parameter sets for blob detection in a lookup table `BlobParamSet`\n", + "- Defining a computed master table `Detection` together with its nested part table `Detection.Blob`.\n", + "- Populating the master, which automatically inserts all part rows inside the same transaction.\n", + "- Visualizing the results by drawing detection circles on the images.\n", + "- Visually selecting the optimal parameter set for each image and saving the selection in a manual table `SelectDetection`.\n", + "\n", + "Along the way we illustrate why master-part relationships are ideal for computational workflows: the master stores aggregate results and the parts hold per-feature detail, all created atomically.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Load the required images and display them for reference.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%xmode minimal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from skimage import data\n", + "from skimage.feature import blob_doh\n", + "from skimage.color import rgb2gray\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datajoint as dj\n", + "\n", + "schema = dj.Schema('blob_detection')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@schema\n", + "class Image(dj.Manual):\n", + " definition = \"\"\"\n", + " image_id : int\n", + " ---\n", + " image_name : varchar(30)\n", + " image : longblob\n", + " \"\"\"\n", + "\n", + "Image.insert(\n", + " (\n", + " (1, \"hubble deep field\", rgb2gray(data.hubble_deep_field())),\n", + " (2, \"human mitosis\", data.human_mitosis()/255.0)\n", + " ), skip_duplicates=True\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n", + "for ax, image, title in zip(axs, *Image.fetch(\"image\", \"image_name\")):\n", + " ax.imshow(image, cmap=\"gray_r\")\n", + " ax.axis('off')\n", + " ax.axis('equal')\n", + " ax.set_title(title)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@schema\n", + "class BlobParamSet(dj.Lookup):\n", + " definition = \"\"\"\n", + " blob_paramset : int\n", + " ---\n", + " min_sigma : float\n", + " max_sigma : float\n", + " threshold : float\n", + " \"\"\"\n", + " contents = [\n", + " (1, 2.0, 6.0, 0.001),\n", + " (2, 3.0, 8.0, 0.002),\n", + " (3, 4.0, 20.0, 0.01),\n", + " ]\n", + "\n", + "\n", + "@schema\n", + "class Detection(dj.Computed):\n", + " definition = \"\"\"\n", + " -> Image\n", + " -> BlobParamSet\n", + " ---\n", + " nblobs : int\n", + " \"\"\"\n", + "\n", + " class Blob(dj.Part):\n", + " definition = \"\"\"\n", + " -> master\n", + " blob_id : int\n", + " ---\n", + " x : float\n", + " y : float\n", + " r : float\n", + " \"\"\"\n", + "\n", + " def make(self, key):\n", + " # fetch inputs\n", + " img = (Image & key).fetch1(\"image\")\n", + " params = (BlobParamSet & key).fetch1()\n", + "\n", + " # compute results\n", + " blobs = blob_doh(\n", + " img, \n", + " min_sigma=params['min_sigma'], \n", + " max_sigma=params['max_sigma'], \n", + " threshold=params['threshold'])\n", + "\n", + " # insert master and parts\n", + " self.insert1(dict(key, nblobs=len(blobs)))\n", + " self.Blob.insert(\n", + " (dict(key, blob_id=i, x=x, y=y, r=r)\n", + " for i, (x, y, r) in enumerate(blobs)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(schema)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Detection.populate(display_progress=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Detection()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parameter sets\n", + "\n", + "Define a small lookup table of blob-detection parameters.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fix, axes = plt.subplots(2, 3, figsize=(10, 6))\n", + "for ax, key in zip(axes.ravel(), Detection.fetch(\"KEY\", order_by=\"image_id, blob_paramset\")):\n", + " img = (Image & key).fetch1(\"image\")\n", + " ax.imshow(img, cmap=\"gray_r\")\n", + " ax.axis('off')\n", + " ax.axis('equal')\n", + " ax.set_title(str(key), fontsize=10)\n", + " for x, y, r in zip(*(Detection.Blob & key).fetch(\"y\", \"x\", \"r\")):\n", + " c = plt.Circle((x, y), r*1.2, color='r', alpha=0.5, fill=False)\n", + " ax.add_patch(c)\n", + "plt.suptitle(\"Detected blobs - all paramsets\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@schema\n", + "class SelectDetection(dj.Manual):\n", + " definition = \"\"\"\n", + " -> Image\n", + " ---\n", + " -> Detection\n", + " \"\"\"\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "SelectDetection.insert1(dict(image_id=1, blob_paramset=3))\n", + "SelectDetection.insert1(dict(image_id=2, blob_paramset=1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(schema)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fix, axes = plt.subplots(1, 2, figsize=(8, 4))\n", + "for ax, key in zip(axes.ravel(), SelectDetection.fetch(as_dict=True, order_by=\"image_id\")):\n", + " img = (Image & key).fetch1(\"image\")\n", + " ax.imshow(img, cmap=\"gray_r\")\n", + " ax.axis('off')\n", + " ax.axis('equal')\n", + " ax.set_title(str(key), fontsize=10)\n", + " for x, y, r in zip(*(Detection.Blob & key).fetch(\"y\", \"x\", \"r\")):\n", + " c = plt.Circle((x, y), r*1.2, color='r', alpha=0.5, fill=False)\n", + " ax.add_patch(c)\n", + "plt.suptitle(\"Selected detections\", fontsize=16)\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Detection master and part tables\n", + "\n", + "`Detection` is a computed table. When `populate()` runs, its `make()` method:\n", + "\n", + "1. Fetches the image and parameter set.\n", + "2. Runs `skimage.feature.blob_doh` to compute blobs.\n", + "3. Inserts one master row with the blob count.\n", + "4. Inserts one `Detection.Blob` part row per blob (containing coordinates and radius).\n", + "\n", + "If any insert fails, the transaction is rolled back so master and parts stay synchronized.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Results\n", + "\n", + "Populate the detection table and display both the master summary and the per-blob annotations.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Takeaways\n", + "\n", + "- Master-part tables capture the structure “one job → many detailed results”.\n", + "- Downstream analyses depend only on the master (`-> Detection`) yet can access part details when needed.\n", + "- Populating the master guarantees atomic creation of all associated parts, preserving workflow integrity.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schema.drop() # drop the schema for re-generating the tutorial from scratch." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}