09_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "#|hide\n",
    "%pip install -Uqq pandas matplotlib html5lib ds_tut\n",
    "\n",
    "import ds_tut\n",
    "ds_tut.setup()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Tutorial\n",
    "\n",
    "Further documentation:\n",
    "\n",
    "* [Cheat Sheet](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf)\n",
    "* [Three Part Tutorial from Greg Reda](http://gregreda.com/2013/10/26/intro-to-pandas-data-structures/)\n",
    "* [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do)\n",
    "* [Official Pandas Documentation](http://pandas.pydata.org/pandas-docs/stable/)\n",
    "* [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do)\n",
    "\n",
    "More advanced:\n",
    "\n",
    "* [Modern Pandas](https://tomaugspurger.github.io/modern-1-intro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.options.display.max_columns = 50\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Series\n",
    "\n",
    "Series are one-dimensional objects like lists, numpy arrays or table columns. Each series has an index that can be used to find elements. By default this is an integer from 0 to n-1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = [3, \"foo\", 3.14, 42, -23, \"bar\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       3\n",
      "1     foo\n",
      "2    3.14\n",
      "3      42\n",
      "4     -23\n",
      "5     bar\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series(values, index=index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a       3\n",
       "b     foo\n",
       "c    3.14\n",
       "d      42\n",
       "e     -23\n",
       "f     bar\n",
       "dtype: object"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[\"d\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a       3\n",
       "b     foo\n",
       "c    3.14\n",
       "dtype: object"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[[\"a\", \"b\", \"c\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16    6\n",
       "17    7\n",
       "18    8\n",
       "19    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.arange(10), index=np.arange(10, 20))\n",
    "s[s > 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DataFrames\n",
    "\n",
    "Are two-dimensional data structures similar to tables or Excel sheets. Basically a collection of Series. We use the table of the [largest cities in Europe from wikipedia](https://de.wikipedia.org/wiki/Liste_der_gr%C3%B6%C3%9Ften_St%C3%A4dte_Europas) as an example here.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities_url = \"https://en.wikipedia.org/wiki/List_of_largest_cities\"\n",
    "tables = pd.read_html(cities_url, header=0, flavor=\"bs4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 83 entries, 0 to 82\n",
      "Data columns (total 13 columns):\n",
      " #   Column                           Non-Null Count  Dtype \n",
      "---  ------                           --------------  ----- \n",
      " 0   City[a]                          82 non-null     object\n",
      " 1   Country                          82 non-null     object\n",
      " 2   UN 2018 population estimates[b]  82 non-null     object\n",
      " 3   City proper[c]                   82 non-null     object\n",
      " 4   City proper[c].1                 82 non-null     object\n",
      " 5   City proper[c].2                 82 non-null     object\n",
      " 6   City proper[c].3                 82 non-null     object\n",
      " 7   Urban area[8]                    82 non-null     object\n",
      " 8   Urban area[8].1                  82 non-null     object\n",
      " 9   Urban area[8].2                  82 non-null     object\n",
      " 10  Metropolitan area[d]             82 non-null     object\n",
      " 11  Metropolitan area[d].1           82 non-null     object\n",
      " 12  Metropolitan area[d].2           82 non-null     object\n",
      "dtypes: object(13)\n",
      "memory usage: 8.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index                               132\n",
       "City[a]                            4711\n",
       "Country                            4621\n",
       "UN 2018 population estimates[b]    4681\n",
       "City proper[c]                     5302\n",
       "City proper[c].1                   4727\n",
       "City proper[c].2                   4462\n",
       "City proper[c].3                   4945\n",
       "Urban area[8]                      4690\n",
       "Urban area[8].1                    4381\n",
       "Urban area[8].2                    4493\n",
       "Metropolitan area[d]               5269\n",
       "Metropolitan area[d].1             5101\n",
       "Metropolitan area[d].2             5236\n",
       "dtype: int64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.memory_usage(deep=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method Chaining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = (df.dropna()\n",
    "        .rename(columns=df.iloc[0]).drop(df.index[0])\n",
    "        .rename(columns=str.lower)\n",
    "        .reset_index()      \n",
    "        .rename(columns={\"city[a]\": \"name\", \"index\": \"rank\"})      \n",
    "        .assign(country=lambda x: pd.Categorical(x[\"country\"]),\n",
    "                name=lambda x: pd.Categorical(x[\"name\"]),\n",
    "                population_est=lambda x: x[\"un 2018 population estimates[b]\"].astype(np.int32),\n",
    "                definition=lambda x: pd.Categorical(x[\"definition\"]),\n",
    "                density=lambda x: x[\"density (/km2)\"].iloc[:, 0].str.replace(\",\", \"\").str.extract(r'(\\d+)').astype(np.float32),\n",
    "                rank=lambda x: x[\"rank\"] - 1\n",
    "        )\n",
    ")[[\"rank\", \"name\", \"country\", \"definition\", \"population_est\", \"density\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 81 entries, 0 to 80\n",
      "Data columns (total 6 columns):\n",
      " #   Column          Non-Null Count  Dtype   \n",
      "---  ------          --------------  -----   \n",
      " 0   rank            81 non-null     int64   \n",
      " 1   name            81 non-null     category\n",
      " 2   country         81 non-null     category\n",
      " 3   definition      81 non-null     category\n",
      " 4   population_est  81 non-null     int32   \n",
      " 5   density         74 non-null     float32 \n",
      "dtypes: category(3), float32(1), int32(1), int64(1)\n",
      "memory usage: 6.9 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>name</th>\n",
       "      <th>country</th>\n",
       "      <th>definition</th>\n",
       "      <th>population_est</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Metropolis prefecture</td>\n",
       "      <td>37468000</td>\n",
       "      <td>6169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>Municipal corporation</td>\n",
       "      <td>28514000</td>\n",
       "      <td>11289.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Shanghai</td>\n",
       "      <td>China</td>\n",
       "      <td>Municipality</td>\n",
       "      <td>25582000</td>\n",
       "      <td>3922.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Municipality</td>\n",
       "      <td>21650000</td>\n",
       "      <td>8055.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Mexico City</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>City-state</td>\n",
       "      <td>21581000</td>\n",
       "      <td>6202.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank         name country             definition  population_est  density\n",
       "0     1        Tokyo   Japan  Metropolis prefecture        37468000   6169.0\n",
       "1     2        Delhi   India  Municipal corporation        28514000  11289.0\n",
       "2     3     Shanghai   China           Municipality        25582000   3922.0\n",
       "3     4    São Paulo  Brazil           Municipality        21650000   8055.0\n",
       "4     5  Mexico City  Mexico             City-state        21581000   6202.0"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>name</th>\n",
       "      <th>country</th>\n",
       "      <th>definition</th>\n",
       "      <th>population_est</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Manila</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>Capital city</td>\n",
       "      <td>13482000</td>\n",
       "      <td>41399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>Capital city</td>\n",
       "      <td>19578000</td>\n",
       "      <td>26349.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>India</td>\n",
       "      <td>Municipal corporation</td>\n",
       "      <td>14681000</td>\n",
       "      <td>21935.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>Municipal corporation</td>\n",
       "      <td>19980000</td>\n",
       "      <td>20694.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>Commune</td>\n",
       "      <td>10901000</td>\n",
       "      <td>20460.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank     name      country             definition  population_est  density\n",
       "16    17   Manila  Philippines           Capital city        13482000  41399.0\n",
       "8      9    Dhaka   Bangladesh           Capital city        19578000  26349.0\n",
       "15    16  Kolkata        India  Municipal corporation        14681000  21935.0\n",
       "6      7   Mumbai        India  Municipal corporation        19980000  20694.0\n",
       "27    28    Paris       France                Commune        10901000  20460.0"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"density\", ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = df.set_index(\"rank\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>definition</th>\n",
       "      <th>population_est</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>Municipal corporation</td>\n",
       "      <td>28514000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>China</td>\n",
       "      <td>Municipality</td>\n",
       "      <td>25582000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Municipality</td>\n",
       "      <td>21650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>City-state</td>\n",
       "      <td>21581000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country             definition  population_est\n",
       "rank                                               \n",
       "2      India  Municipal corporation        28514000\n",
       "3      China           Municipality        25582000\n",
       "4     Brazil           Municipality        21650000\n",
       "5     Mexico             City-state        21581000"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.loc[2:5, \"country\": \"population_est\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>definition</th>\n",
       "      <th>population_est</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>Capital city</td>\n",
       "      <td>19578000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>France</td>\n",
       "      <td>Commune</td>\n",
       "      <td>10901000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country    definition  population_est\n",
       "rank                                          \n",
       "9     Bangladesh  Capital city        19578000\n",
       "28        France       Commune        10901000"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.loc[ds.name.isin((\"Paris\", \"Dhaka\")), \"country\": \"population_est\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multidimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>name</th>\n",
       "      <th>country</th>\n",
       "      <th>definition</th>\n",
       "      <th>population_est</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Metropolis prefecture</td>\n",
       "      <td>37468000</td>\n",
       "      <td>6169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>Municipal corporation</td>\n",
       "      <td>28514000</td>\n",
       "      <td>11289.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Shanghai</td>\n",
       "      <td>China</td>\n",
       "      <td>Municipality</td>\n",
       "      <td>25582000</td>\n",
       "      <td>3922.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Municipality</td>\n",
       "      <td>21650000</td>\n",
       "      <td>8055.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Mexico City</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>City-state</td>\n",
       "      <td>21581000</td>\n",
       "      <td>6202.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank         name country             definition  population_est  density\n",
       "0     1        Tokyo   Japan  Metropolis prefecture        37468000   6169.0\n",
       "1     2        Delhi   India  Municipal corporation        28514000  11289.0\n",
       "2     3     Shanghai   China           Municipality        25582000   3922.0\n",
       "3     4    São Paulo  Brazil           Municipality        21650000   8055.0\n",
       "4     5  Mexico City  Mexico             City-state        21581000   6202.0"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = df.set_index([\"country\", \"definition\", \"rank\"]).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>population_est</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>definition</th>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">City (sub-provincial)</th>\n",
       "      <th>22</th>\n",
       "      <td>Guangzhou</td>\n",
       "      <td>12638000</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Shenzhen</td>\n",
       "      <td>11908000</td>\n",
       "      <td>6111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Chengdu</td>\n",
       "      <td>8813000</td>\n",
       "      <td>1116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Nanjing</td>\n",
       "      <td>8245000</td>\n",
       "      <td>1103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Wuhan</td>\n",
       "      <td>8176000</td>\n",
       "      <td>1282.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Xi'an</td>\n",
       "      <td>7444000</td>\n",
       "      <td>887.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Hangzhou</td>\n",
       "      <td>7236000</td>\n",
       "      <td>570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Shenyang</td>\n",
       "      <td>6921000</td>\n",
       "      <td>639.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Harbin</td>\n",
       "      <td>6115000</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Qingdao</td>\n",
       "      <td>5381000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Dalian</td>\n",
       "      <td>5300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Jinan</td>\n",
       "      <td>5052000</td>\n",
       "      <td>849.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Municipality</th>\n",
       "      <th>3</th>\n",
       "      <td>Shanghai</td>\n",
       "      <td>25582000</td>\n",
       "      <td>3922.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Beijing</td>\n",
       "      <td>19618000</td>\n",
       "      <td>1334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Chongqing</td>\n",
       "      <td>14838000</td>\n",
       "      <td>389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Tianjin</td>\n",
       "      <td>13215000</td>\n",
       "      <td>1163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Prefecture-level city</th>\n",
       "      <th>49</th>\n",
       "      <td>Dongguan</td>\n",
       "      <td>7360000</td>\n",
       "      <td>3384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Foshan</td>\n",
       "      <td>7236000</td>\n",
       "      <td>1870.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Suzhou</td>\n",
       "      <td>6339000</td>\n",
       "      <td>1263.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Special administrative region</th>\n",
       "      <th>48</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>7429000</td>\n",
       "      <td>6611.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         name  population_est  density\n",
       "definition                    rank                                    \n",
       "City (sub-provincial)         22    Guangzhou        12638000   1950.0\n",
       "                              25     Shenzhen        11908000   6111.0\n",
       "                              40      Chengdu         8813000   1116.0\n",
       "                              41      Nanjing         8245000   1103.0\n",
       "                              42        Wuhan         8176000   1282.0\n",
       "                              47        Xi'an         7444000    887.0\n",
       "                              50     Hangzhou         7236000    570.0\n",
       "                              52     Shenyang         6921000    639.0\n",
       "                              60       Harbin         6115000    200.0\n",
       "                              75      Qingdao         5381000      NaN\n",
       "                              76       Dalian         5300000      NaN\n",
       "                              80        Jinan         5052000    849.0\n",
       "Municipality                  3      Shanghai        25582000   3922.0\n",
       "                              8       Beijing        19618000   1334.0\n",
       "                              14    Chongqing        14838000    389.0\n",
       "                              20      Tianjin        13215000   1163.0\n",
       "Prefecture-level city         49     Dongguan         7360000   3384.0\n",
       "                              51       Foshan         7236000   1870.0\n",
       "                              58       Suzhou         6339000   1263.0\n",
       "Special administrative region 48    Hong Kong         7429000   6611.0"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.loc[\"China\",:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = (10, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 428,
       "width": 822
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.density.hist(figsize=size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 463,
       "width": 833
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot(x=\"name\", y=\"population_est\", figsize=size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 463,
       "width": 846
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot.scatter(x=\"density\", y=\"population_est\", figsize=size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population_est</th>\n",
       "      <th>cities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>China</td>\n",
       "      <td>194846000</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>United States</td>\n",
       "      <td>74865000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>India</td>\n",
       "      <td>115074000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Japan</td>\n",
       "      <td>71807000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>40915000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>27138000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Russia</td>\n",
       "      <td>17793000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>26604000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>25162000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Spain</td>\n",
       "      <td>11991000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>5486000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>6907000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  population_est  cities\n",
       "6           China       194846000      20\n",
       "34  United States        74865000       9\n",
       "11          India       115074000       9\n",
       "15          Japan        71807000       4\n",
       "3          Brazil        40915000       3\n",
       "20       Pakistan        27138000       2\n",
       "23         Russia        17793000       2\n",
       "17         Mexico        26604000       2\n",
       "9           Egypt        25162000       2\n",
       "28          Spain        11991000       2\n",
       "26   South Africa         5486000       1\n",
       "24   Saudi Arabia         6907000       1"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = (df[[\"country\", \"population_est\", \"name\"]]\n",
    "      .groupby(\"country\", observed=False)\n",
    "      .agg({\"population_est\": \"sum\", \"name\": \"count\"})\n",
    "      .rename(columns={\"name\": \"cities\"})\n",
    "      .reset_index()\n",
    "      .sort_values(by=\"cities\", ascending=False)\n",
    "      .head(12))\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How often do which values occur in a column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cities\n",
       "2     5\n",
       "9     2\n",
       "1     2\n",
       "20    1\n",
       "4     1\n",
       "3     1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.cities.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetch the inhabitants per country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries_url = \"https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_population\"\n",
    "tables = pd.read_html(countries_url, header=0, flavor=\"bs4\", decimal=\",\", thousands=\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc = tables[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1,34.5'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"1,34.5\".replace(\",.\", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc = (dc.rename(columns=str.lower)\n",
    "   .assign(\n",
    "       population=lambda x: x[\"population\"].str.replace(r\"[.,]\", \"\", regex=True).astype(np.int64),\n",
    "       country=lambda x: pd.Categorical(x[\"country / dependency\"])\n",
    "   )\n",
    ")[[\"country\", \"population\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>World</td>\n",
       "      <td>8068346000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>1411750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>1392329000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United States</td>\n",
       "      <td>335576000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>279118866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  population\n",
       "0          World  8068346000\n",
       "1          China  1411750000\n",
       "2          India  1392329000\n",
       "3  United States   335576000\n",
       "4      Indonesia   279118866"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>1392329000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United States</td>\n",
       "      <td>335576000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Niger</td>\n",
       "      <td>25369415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  population\n",
       "2           India  1392329000\n",
       "3   United States   335576000\n",
       "56          Niger    25369415"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc.query(\"population > 10**6 and country in ('Niger', 'United States', 'India')\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population_est</th>\n",
       "      <th>cities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>China</td>\n",
       "      <td>194846000</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>United States</td>\n",
       "      <td>74865000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>India</td>\n",
       "      <td>115074000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Japan</td>\n",
       "      <td>71807000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>40915000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>27138000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Russia</td>\n",
       "      <td>17793000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>26604000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>25162000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Spain</td>\n",
       "      <td>11991000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>5486000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>6907000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  population_est  cities\n",
       "6           China       194846000      20\n",
       "34  United States        74865000       9\n",
       "11          India       115074000       9\n",
       "15          Japan        71807000       4\n",
       "3          Brazil        40915000       3\n",
       "20       Pakistan        27138000       2\n",
       "23         Russia        17793000       2\n",
       "17         Mexico        26604000       2\n",
       "9           Egypt        25162000       2\n",
       "28          Spain        11991000       2\n",
       "26   South Africa         5486000       1\n",
       "24   Saudi Arabia         6907000       1"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join - like in SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>cities</th>\n",
       "      <th>in_cities</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>China</td>\n",
       "      <td>20</td>\n",
       "      <td>194846000</td>\n",
       "      <td>1411750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United States</td>\n",
       "      <td>9</td>\n",
       "      <td>74865000</td>\n",
       "      <td>335576000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>9</td>\n",
       "      <td>115074000</td>\n",
       "      <td>1392329000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>71807000</td>\n",
       "      <td>124340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>3</td>\n",
       "      <td>40915000</td>\n",
       "      <td>203062512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2</td>\n",
       "      <td>27138000</td>\n",
       "      <td>241499431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Russia</td>\n",
       "      <td>2</td>\n",
       "      <td>17793000</td>\n",
       "      <td>146424729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>2</td>\n",
       "      <td>26604000</td>\n",
       "      <td>129202482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2</td>\n",
       "      <td>25162000</td>\n",
       "      <td>105481000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2</td>\n",
       "      <td>11991000</td>\n",
       "      <td>48345223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>1</td>\n",
       "      <td>5486000</td>\n",
       "      <td>62027503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>1</td>\n",
       "      <td>6907000</td>\n",
       "      <td>32175224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  cities  in_cities       total\n",
       "0           China      20  194846000  1411750000\n",
       "1   United States       9   74865000   335576000\n",
       "2           India       9  115074000  1392329000\n",
       "3           Japan       4   71807000   124340000\n",
       "4          Brazil       3   40915000   203062512\n",
       "5        Pakistan       2   27138000   241499431\n",
       "6          Russia       2   17793000   146424729\n",
       "7          Mexico       2   26604000   129202482\n",
       "8           Egypt       2   25162000   105481000\n",
       "9           Spain       2   11991000    48345223\n",
       "10   South Africa       1    5486000    62027503\n",
       "11   Saudi Arabia       1    6907000    32175224"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm = (pd.merge(dt, dc, on=\"country\")[[\"country\", \"cities\", \"population_est\", \"population\"]]\n",
    "        .rename(columns={\"population_est\": \"in_cities\", \"population\": \"total\"}))\n",
    "dm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dm[\"city_ratio\"] = dm.in_cities / dm.total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>cities</th>\n",
       "      <th>in_cities</th>\n",
       "      <th>total</th>\n",
       "      <th>city_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>China</td>\n",
       "      <td>20</td>\n",
       "      <td>194846000</td>\n",
       "      <td>1411750000</td>\n",
       "      <td>0.138017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United States</td>\n",
       "      <td>9</td>\n",
       "      <td>74865000</td>\n",
       "      <td>335576000</td>\n",
       "      <td>0.223094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>9</td>\n",
       "      <td>115074000</td>\n",
       "      <td>1392329000</td>\n",
       "      <td>0.082649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>71807000</td>\n",
       "      <td>124340000</td>\n",
       "      <td>0.577505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>3</td>\n",
       "      <td>40915000</td>\n",
       "      <td>203062512</td>\n",
       "      <td>0.201490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2</td>\n",
       "      <td>27138000</td>\n",
       "      <td>241499431</td>\n",
       "      <td>0.112373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Russia</td>\n",
       "      <td>2</td>\n",
       "      <td>17793000</td>\n",
       "      <td>146424729</td>\n",
       "      <td>0.121516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>2</td>\n",
       "      <td>26604000</td>\n",
       "      <td>129202482</td>\n",
       "      <td>0.205909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2</td>\n",
       "      <td>25162000</td>\n",
       "      <td>105481000</td>\n",
       "      <td>0.238545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2</td>\n",
       "      <td>11991000</td>\n",
       "      <td>48345223</td>\n",
       "      <td>0.248029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>1</td>\n",
       "      <td>5486000</td>\n",
       "      <td>62027503</td>\n",
       "      <td>0.088445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>1</td>\n",
       "      <td>6907000</td>\n",
       "      <td>32175224</td>\n",
       "      <td>0.214668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  cities  in_cities       total  city_ratio\n",
       "0           China      20  194846000  1411750000    0.138017\n",
       "1   United States       9   74865000   335576000    0.223094\n",
       "2           India       9  115074000  1392329000    0.082649\n",
       "3           Japan       4   71807000   124340000    0.577505\n",
       "4          Brazil       3   40915000   203062512    0.201490\n",
       "5        Pakistan       2   27138000   241499431    0.112373\n",
       "6          Russia       2   17793000   146424729    0.121516\n",
       "7          Mexico       2   26604000   129202482    0.205909\n",
       "8           Egypt       2   25162000   105481000    0.238545\n",
       "9           Spain       2   11991000    48345223    0.248029\n",
       "10   South Africa       1    5486000    62027503    0.088445\n",
       "11   Saudi Arabia       1    6907000    32175224    0.214668"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Often used Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(index=[\"A\", \"B\", \"C\"])\n",
    "df[\"name\"] = [\"foo\", \"bar\", \"baz\"]\n",
    "df[\"value\"] = [23, 42, 28]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>foo</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>bar</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>baz</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  value\n",
       "A  foo     23\n",
       "B  bar     42\n",
       "C  baz     28"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>foo</td>\n",
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       "      <th>1</th>\n",
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       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>baz</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  index name  value\n",
       "0     A  foo     23\n",
       "1     B  bar     42\n",
       "2     C  baz     28"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>variable</th>\n",
       "      <th>new_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>name</td>\n",
       "      <td>foo</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>name</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>name</td>\n",
       "      <td>baz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>value</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>value</td>\n",
       "      <td>42</td>\n",
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       "      <th>5</th>\n",
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       "      <td>value</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  index variable new_value\n",
       "0     A     name       foo\n",
       "1     B     name       bar\n",
       "2     C     name       baz\n",
       "3     A    value        23\n",
       "4     B    value        42\n",
       "5     C    value        28"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm = df.reset_index().melt(id_vars=[\"index\"], value_name=\"new_value\")\n",
    "dm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>value</th>\n",
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       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>foo</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>bar</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>baz</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "variable name value\n",
       "index              \n",
       "A         foo    23\n",
       "B         bar    42\n",
       "C         baz    28"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.pivot(columns=\"variable\", values=\"new_value\", index=\"index\")"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
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