Movie_Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction and Business Understanding\n",
    "\n",
    "In this project, we will be looking at movie datasets in order to give recommendations to Microsoft's new movie studio.\n",
    "\n",
    "Entertainment is a huge, global market. According to [Forbes](https://www.forbes.com/sites/rosaescandon/2020/03/12/the-film-industry-made-a-record-breaking-100-billion-last-year/?sh=5404bc6634cd), the film industry made over 100 billion dollars in 2019. While that is a significant chunk of change, there is also a significant overhead in creating entertainment content. It is important for fledgling movie studios to research what type of content does well, so that they can use this information as a guide to replicate that success. As Microsoft is interested in making movies, the following project will focus on that component of the entertainment industry.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data \n",
    "\n",
    "In order to glean information relevant to movie-making we will be looking at two datasets from https://www.imdb.com/ and https://www.boxofficemojo.com/. \n",
    "\n",
    "- IMDB stands for Internet Movie Database and is one of the most popular and comprehensive internet sources for movie and entertainment data. The dataset we are working with focuses on information about movies - the producers, the genres, the actors, the movies ratings, ect. \n",
    "\n",
    "- Box Office Mojo is a site that tracks box-office revenue, and the data set we will be using from them contains this information. \n",
    "\n",
    "By using these two datasets together, we can extract if certain aspects of a movie lead to higher box-office revenue, which is an industry standard for movie success. \n",
    "\n",
    "**Limitations** \n",
    "\n",
    "This data is only on movies, thus it can not be extrapolated to the entire entertainment industry. Additionally, as the dataset only contains the total gross, it will not include the costs of creating the movies. Finally, the dataset only looks at box office revenue, which means that the total revenue (from streaming sites or other sources) may be higher. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    " - Import datasets and do an initial viewing. \n",
    " - Ask some relevant questions! \n",
    " - Find answers to those questions, and do some analysis.\n",
    " - Conclusion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation\n",
    "First we are going to import some required packages needed to process the data. Then we will connect to our datasets and take a peek inside. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required packages\n",
    "import warnings\n",
    "import zipfile\n",
    "import seaborn as sns\n",
    "import sqlite3 as sql\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unzip data - code from https://docs.python.org/3/library/zipfile.html\n",
    "with zipfile.ZipFile('zipped_data/im.db.zip', 'r') as zip_ref:\n",
    "    zip_ref.extractall('data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IMDB Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('movie_basics',),\n",
       " ('directors',),\n",
       " ('known_for',),\n",
       " ('movie_akas',),\n",
       " ('movie_ratings',),\n",
       " ('persons',),\n",
       " ('principals',),\n",
       " ('writers',)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create connection to the database and cursor object:\n",
    "conn = sql.connect('data/im.db')\n",
    "cur = conn.cursor()\n",
    "\n",
    "# find table names\n",
    "cur.execute(\"\"\" SELECT name \n",
    "                FROM sqlite_master \n",
    "                WHERE type = 'table';\"\"\")\n",
    "\n",
    "# fetch the result and store it in table_names\n",
    "table_names = cur.fetchall()\n",
    "table_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we see 8 tables names - lets look into those tables now. \n",
    "\n",
    "For the `movie_basics` table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
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       "    }\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>movie_id</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>original_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0063540</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>2013</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0066787</td>\n",
       "      <td>One Day Before the Rainy Season</td>\n",
       "      <td>Ashad Ka Ek Din</td>\n",
       "      <td>2019</td>\n",
       "      <td>114.0</td>\n",
       "      <td>Biography,Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0069049</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>2018</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0069204</td>\n",
       "      <td>Sabse Bada Sukh</td>\n",
       "      <td>Sabse Bada Sukh</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100275</td>\n",
       "      <td>The Wandering Soap Opera</td>\n",
       "      <td>La Telenovela Errante</td>\n",
       "      <td>2017</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Comedy,Drama,Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146139</th>\n",
       "      <td>tt9916538</td>\n",
       "      <td>Kuambil Lagi Hatiku</td>\n",
       "      <td>Kuambil Lagi Hatiku</td>\n",
       "      <td>2019</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146140</th>\n",
       "      <td>tt9916622</td>\n",
       "      <td>Rodolpho Teóphilo - O Legado de um Pioneiro</td>\n",
       "      <td>Rodolpho Teóphilo - O Legado de um Pioneiro</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146141</th>\n",
       "      <td>tt9916706</td>\n",
       "      <td>Dankyavar Danka</td>\n",
       "      <td>Dankyavar Danka</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146142</th>\n",
       "      <td>tt9916730</td>\n",
       "      <td>6 Gunn</td>\n",
       "      <td>6 Gunn</td>\n",
       "      <td>2017</td>\n",
       "      <td>116.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146143</th>\n",
       "      <td>tt9916754</td>\n",
       "      <td>Chico Albuquerque - Revelações</td>\n",
       "      <td>Chico Albuquerque - Revelações</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146144 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_id                                primary_title  \\\n",
       "0       tt0063540                                    Sunghursh   \n",
       "1       tt0066787              One Day Before the Rainy Season   \n",
       "2       tt0069049                   The Other Side of the Wind   \n",
       "3       tt0069204                              Sabse Bada Sukh   \n",
       "4       tt0100275                     The Wandering Soap Opera   \n",
       "...           ...                                          ...   \n",
       "146139  tt9916538                          Kuambil Lagi Hatiku   \n",
       "146140  tt9916622  Rodolpho Teóphilo - O Legado de um Pioneiro   \n",
       "146141  tt9916706                              Dankyavar Danka   \n",
       "146142  tt9916730                                       6 Gunn   \n",
       "146143  tt9916754               Chico Albuquerque - Revelações   \n",
       "\n",
       "                                     original_title  start_year  \\\n",
       "0                                         Sunghursh        2013   \n",
       "1                                   Ashad Ka Ek Din        2019   \n",
       "2                        The Other Side of the Wind        2018   \n",
       "3                                   Sabse Bada Sukh        2018   \n",
       "4                             La Telenovela Errante        2017   \n",
       "...                                             ...         ...   \n",
       "146139                          Kuambil Lagi Hatiku        2019   \n",
       "146140  Rodolpho Teóphilo - O Legado de um Pioneiro        2015   \n",
       "146141                              Dankyavar Danka        2013   \n",
       "146142                                       6 Gunn        2017   \n",
       "146143               Chico Albuquerque - Revelações        2013   \n",
       "\n",
       "        runtime_minutes                genres  \n",
       "0                 175.0    Action,Crime,Drama  \n",
       "1                 114.0       Biography,Drama  \n",
       "2                 122.0                 Drama  \n",
       "3                   NaN          Comedy,Drama  \n",
       "4                  80.0  Comedy,Drama,Fantasy  \n",
       "...                 ...                   ...  \n",
       "146139            123.0                 Drama  \n",
       "146140              NaN           Documentary  \n",
       "146141              NaN                Comedy  \n",
       "146142            116.0                  None  \n",
       "146143              NaN           Documentary  \n",
       "\n",
       "[146144 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating movie_basics dataframe\n",
    "imbd_movie_basics = pd.read_sql(\"\"\"\n",
    "                                SELECT * \n",
    "                                FROM movie_basics;\n",
    "                                \"\"\", conn)\n",
    "\n",
    "imbd_movie_basics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "For the `directors` table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>movie_id</th>\n",
       "      <th>person_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0285252</td>\n",
       "      <td>nm0899854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0462036</td>\n",
       "      <td>nm1940585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0835418</td>\n",
       "      <td>nm0151540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0835418</td>\n",
       "      <td>nm0151540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0878654</td>\n",
       "      <td>nm0089502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291169</th>\n",
       "      <td>tt8999974</td>\n",
       "      <td>nm10122357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291170</th>\n",
       "      <td>tt9001390</td>\n",
       "      <td>nm6711477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291171</th>\n",
       "      <td>tt9001494</td>\n",
       "      <td>nm10123242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291172</th>\n",
       "      <td>tt9001494</td>\n",
       "      <td>nm10123248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291173</th>\n",
       "      <td>tt9004986</td>\n",
       "      <td>nm4993825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>291174 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_id   person_id\n",
       "0       tt0285252   nm0899854\n",
       "1       tt0462036   nm1940585\n",
       "2       tt0835418   nm0151540\n",
       "3       tt0835418   nm0151540\n",
       "4       tt0878654   nm0089502\n",
       "...           ...         ...\n",
       "291169  tt8999974  nm10122357\n",
       "291170  tt9001390   nm6711477\n",
       "291171  tt9001494  nm10123242\n",
       "291172  tt9001494  nm10123248\n",
       "291173  tt9004986   nm4993825\n",
       "\n",
       "[291174 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a dataframe for the directors table\n",
    "imbd_directors = pd.read_sql(\"\"\"\n",
    "                                SELECT * \n",
    "                                FROM directors;\n",
    "                                \"\"\", conn)\n",
    "\n",
    "imbd_directors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "For the `known_for` table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>person_id</th>\n",
       "      <th>movie_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nm0061671</td>\n",
       "      <td>tt0837562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nm0061671</td>\n",
       "      <td>tt2398241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nm0061671</td>\n",
       "      <td>tt0844471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nm0061671</td>\n",
       "      <td>tt0118553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nm0061865</td>\n",
       "      <td>tt0896534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638255</th>\n",
       "      <td>nm9990690</td>\n",
       "      <td>tt9090932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638256</th>\n",
       "      <td>nm9990690</td>\n",
       "      <td>tt8737130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638257</th>\n",
       "      <td>nm9991320</td>\n",
       "      <td>tt8734436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638258</th>\n",
       "      <td>nm9991320</td>\n",
       "      <td>tt9615610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638259</th>\n",
       "      <td>nm9993380</td>\n",
       "      <td>tt8743182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1638260 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         person_id   movie_id\n",
       "0        nm0061671  tt0837562\n",
       "1        nm0061671  tt2398241\n",
       "2        nm0061671  tt0844471\n",
       "3        nm0061671  tt0118553\n",
       "4        nm0061865  tt0896534\n",
       "...            ...        ...\n",
       "1638255  nm9990690  tt9090932\n",
       "1638256  nm9990690  tt8737130\n",
       "1638257  nm9991320  tt8734436\n",
       "1638258  nm9991320  tt9615610\n",
       "1638259  nm9993380  tt8743182\n",
       "\n",
       "[1638260 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting data and creating the dataframe for the known_for table\n",
    "imbd_known_for = pd.read_sql(\"\"\"\n",
    "                                SELECT * \n",
    "                                FROM known_for;\n",
    "                                \"\"\", conn)\n",
    "imbd_known_for"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the `movie_akas` table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>movie_id</th>\n",
       "      <th>ordering</th>\n",
       "      <th>title</th>\n",
       "      <th>region</th>\n",
       "      <th>language</th>\n",
       "      <th>types</th>\n",
       "      <th>attributes</th>\n",
       "      <th>is_original_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>10</td>\n",
       "      <td>Джурасик свят</td>\n",
       "      <td>BG</td>\n",
       "      <td>bg</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>11</td>\n",
       "      <td>Jurashikku warudo</td>\n",
       "      <td>JP</td>\n",
       "      <td>None</td>\n",
       "      <td>imdbDisplay</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>12</td>\n",
       "      <td>Jurassic World: O Mundo dos Dinossauros</td>\n",
       "      <td>BR</td>\n",
       "      <td>None</td>\n",
       "      <td>imdbDisplay</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>13</td>\n",
       "      <td>O Mundo dos Dinossauros</td>\n",
       "      <td>BR</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>short title</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>14</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>FR</td>\n",
       "      <td>None</td>\n",
       "      <td>imdbDisplay</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331698</th>\n",
       "      <td>tt9827784</td>\n",
       "      <td>2</td>\n",
       "      <td>Sayonara kuchibiru</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>original</td>\n",
       "      <td>None</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331699</th>\n",
       "      <td>tt9827784</td>\n",
       "      <td>3</td>\n",
       "      <td>Farewell Song</td>\n",
       "      <td>XWW</td>\n",
       "      <td>en</td>\n",
       "      <td>imdbDisplay</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331700</th>\n",
       "      <td>tt9880178</td>\n",
       "      <td>1</td>\n",
       "      <td>La atención</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>original</td>\n",
       "      <td>None</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331701</th>\n",
       "      <td>tt9880178</td>\n",
       "      <td>2</td>\n",
       "      <td>La atención</td>\n",
       "      <td>ES</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331702</th>\n",
       "      <td>tt9880178</td>\n",
       "      <td>3</td>\n",
       "      <td>The Attention</td>\n",
       "      <td>XWW</td>\n",
       "      <td>en</td>\n",
       "      <td>imdbDisplay</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>331703 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_id  ordering                                    title region  \\\n",
       "0       tt0369610        10                            Джурасик свят     BG   \n",
       "1       tt0369610        11                        Jurashikku warudo     JP   \n",
       "2       tt0369610        12  Jurassic World: O Mundo dos Dinossauros     BR   \n",
       "3       tt0369610        13                  O Mundo dos Dinossauros     BR   \n",
       "4       tt0369610        14                           Jurassic World     FR   \n",
       "...           ...       ...                                      ...    ...   \n",
       "331698  tt9827784         2                       Sayonara kuchibiru   None   \n",
       "331699  tt9827784         3                            Farewell Song    XWW   \n",
       "331700  tt9880178         1                              La atención   None   \n",
       "331701  tt9880178         2                              La atención     ES   \n",
       "331702  tt9880178         3                            The Attention    XWW   \n",
       "\n",
       "       language        types   attributes  is_original_title  \n",
       "0            bg         None         None                0.0  \n",
       "1          None  imdbDisplay         None                0.0  \n",
       "2          None  imdbDisplay         None                0.0  \n",
       "3          None         None  short title                0.0  \n",
       "4          None  imdbDisplay         None                0.0  \n",
       "...         ...          ...          ...                ...  \n",
       "331698     None     original         None                1.0  \n",
       "331699       en  imdbDisplay         None                0.0  \n",
       "331700     None     original         None                1.0  \n",
       "331701     None         None         None                0.0  \n",
       "331702       en  imdbDisplay         None                0.0  \n",
       "\n",
       "[331703 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating the dataframe for the movie_akas table\n",
    "imbd_movie_akas = pd.read_sql(\"\"\"\n",
    "                                SELECT * \n",
    "                                FROM movie_akas;\n",
    "                                \"\"\", conn)\n",
    "imbd_movie_akas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the `movie_ratings` table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "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>movie_id</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>numvotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt10356526</td>\n",
       "      <td>8.3</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt10384606</td>\n",
       "      <td>8.9</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt1042974</td>\n",
       "      <td>6.4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt1043726</td>\n",
       "      <td>4.2</td>\n",
       "      <td>50352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt1060240</td>\n",
       "      <td>6.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73851</th>\n",
       "      <td>tt9805820</td>\n",
       "      <td>8.1</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73852</th>\n",
       "      <td>tt9844256</td>\n",
       "      <td>7.5</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73853</th>\n",
       "      <td>tt9851050</td>\n",
       "      <td>4.7</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73854</th>\n",
       "      <td>tt9886934</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73855</th>\n",
       "      <td>tt9894098</td>\n",
       "      <td>6.3</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73856 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_id  averagerating  numvotes\n",
       "0      tt10356526            8.3        31\n",
       "1      tt10384606            8.9       559\n",
       "2       tt1042974            6.4        20\n",
       "3       tt1043726            4.2     50352\n",
       "4       tt1060240            6.5        21\n",
       "...           ...            ...       ...\n",
       "73851   tt9805820            8.1        25\n",
       "73852   tt9844256            7.5        24\n",
       "73853   tt9851050            4.7        14\n",
       "73854   tt9886934            7.0         5\n",
       "73855   tt9894098            6.3       128\n",
       "\n",
       "[73856 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating the dataframe for the movie_ratings table\n",
    "imbd_movie_ratings = pd.read_sql(\"\"\"\n",
    "                                SELECT * \n",
    "                                FROM movie_ratings;\n",
    "                                \"\"\", conn)\n",
    "imbd_movie_ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the `persons` table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>person_id</th>\n",
       "      <th>primary_name</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>death_year</th>\n",
       "      <th>primary_profession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nm0061671</td>\n",
       "      <td>Mary Ellen Bauder</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>miscellaneous,production_manager,producer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nm0061865</td>\n",
       "      <td>Joseph Bauer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>composer,music_department,sound_department</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nm0062070</td>\n",
       "      <td>Bruce Baum</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>miscellaneous,actor,writer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nm0062195</td>\n",
       "      <td>Axel Baumann</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>camera_department,cinematographer,art_department</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nm0062798</td>\n",
       "      <td>Pete Baxter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>production_designer,art_department,set_decorator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606643</th>\n",
       "      <td>nm9990381</td>\n",
       "      <td>Susan Grobes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>actress</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606644</th>\n",
       "      <td>nm9990690</td>\n",
       "      <td>Joo Yeon So</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>actress</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606645</th>\n",
       "      <td>nm9991320</td>\n",
       "      <td>Madeline Smith</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>actress</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606646</th>\n",
       "      <td>nm9991786</td>\n",
       "      <td>Michelle Modigliani</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>producer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606647</th>\n",
       "      <td>nm9993380</td>\n",
       "      <td>Pegasus Envoyé</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>director,actor,writer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>606648 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        person_id         primary_name  birth_year  death_year  \\\n",
       "0       nm0061671    Mary Ellen Bauder         NaN         NaN   \n",
       "1       nm0061865         Joseph Bauer         NaN         NaN   \n",
       "2       nm0062070           Bruce Baum         NaN         NaN   \n",
       "3       nm0062195         Axel Baumann         NaN         NaN   \n",
       "4       nm0062798          Pete Baxter         NaN         NaN   \n",
       "...           ...                  ...         ...         ...   \n",
       "606643  nm9990381         Susan Grobes         NaN         NaN   \n",
       "606644  nm9990690          Joo Yeon So         NaN         NaN   \n",
       "606645  nm9991320       Madeline Smith         NaN         NaN   \n",
       "606646  nm9991786  Michelle Modigliani         NaN         NaN   \n",
       "606647  nm9993380       Pegasus Envoyé         NaN         NaN   \n",
       "\n",
       "                                      primary_profession  \n",
       "0              miscellaneous,production_manager,producer  \n",
       "1             composer,music_department,sound_department  \n",
       "2                             miscellaneous,actor,writer  \n",
       "3       camera_department,cinematographer,art_department  \n",
       "4       production_designer,art_department,set_decorator  \n",
       "...                                                  ...  \n",
       "606643                                           actress  \n",
       "606644                                           actress  \n",
       "606645                                           actress  \n",
       "606646                                          producer  \n",
       "606647                             director,actor,writer  \n",
       "\n",
       "[606648 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating the dataframe for the persons table\n",
    "imbd_persons = pd.read_sql(\"\"\"\n",
    "                                SELECT * \n",
    "                                FROM persons;\n",
    "                                \"\"\", conn)\n",
    "imbd_persons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the `principals` table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>movie_id</th>\n",
       "      <th>ordering</th>\n",
       "      <th>person_id</th>\n",
       "      <th>category</th>\n",
       "      <th>job</th>\n",
       "      <th>characters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0111414</td>\n",
       "      <td>1</td>\n",
       "      <td>nm0246005</td>\n",
       "      <td>actor</td>\n",
       "      <td>None</td>\n",
       "      <td>[\"The Man\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0111414</td>\n",
       "      <td>2</td>\n",
       "      <td>nm0398271</td>\n",
       "      <td>director</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0111414</td>\n",
       "      <td>3</td>\n",
       "      <td>nm3739909</td>\n",
       "      <td>producer</td>\n",
       "      <td>producer</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0323808</td>\n",
       "      <td>10</td>\n",
       "      <td>nm0059247</td>\n",
       "      <td>editor</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0323808</td>\n",
       "      <td>1</td>\n",
       "      <td>nm3579312</td>\n",
       "      <td>actress</td>\n",
       "      <td>None</td>\n",
       "      <td>[\"Beth Boothby\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028181</th>\n",
       "      <td>tt9692684</td>\n",
       "      <td>1</td>\n",
       "      <td>nm0186469</td>\n",
       "      <td>actor</td>\n",
       "      <td>None</td>\n",
       "      <td>[\"Ebenezer Scrooge\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028182</th>\n",
       "      <td>tt9692684</td>\n",
       "      <td>2</td>\n",
       "      <td>nm4929530</td>\n",
       "      <td>self</td>\n",
       "      <td>None</td>\n",
       "      <td>[\"Herself\",\"Regan\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028183</th>\n",
       "      <td>tt9692684</td>\n",
       "      <td>3</td>\n",
       "      <td>nm10441594</td>\n",
       "      <td>director</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028184</th>\n",
       "      <td>tt9692684</td>\n",
       "      <td>4</td>\n",
       "      <td>nm6009913</td>\n",
       "      <td>writer</td>\n",
       "      <td>writer</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028185</th>\n",
       "      <td>tt9692684</td>\n",
       "      <td>5</td>\n",
       "      <td>nm10441595</td>\n",
       "      <td>producer</td>\n",
       "      <td>producer</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1028186 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          movie_id  ordering   person_id  category       job  \\\n",
       "0        tt0111414         1   nm0246005     actor      None   \n",
       "1        tt0111414         2   nm0398271  director      None   \n",
       "2        tt0111414         3   nm3739909  producer  producer   \n",
       "3        tt0323808        10   nm0059247    editor      None   \n",
       "4        tt0323808         1   nm3579312   actress      None   \n",
       "...            ...       ...         ...       ...       ...   \n",
       "1028181  tt9692684         1   nm0186469     actor      None   \n",
       "1028182  tt9692684         2   nm4929530      self      None   \n",
       "1028183  tt9692684         3  nm10441594  director      None   \n",
       "1028184  tt9692684         4   nm6009913    writer    writer   \n",
       "1028185  tt9692684         5  nm10441595  producer  producer   \n",
       "\n",
       "                   characters  \n",
       "0                 [\"The Man\"]  \n",
       "1                        None  \n",
       "2                        None  \n",
       "3                        None  \n",
       "4            [\"Beth Boothby\"]  \n",
       "...                       ...  \n",
       "1028181  [\"Ebenezer Scrooge\"]  \n",
       "1028182   [\"Herself\",\"Regan\"]  \n",
       "1028183                  None  \n",
       "1028184                  None  \n",
       "1028185                  None  \n",
       "\n",
       "[1028186 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating the dataframe for the principals table\n",
    "imbd_principals = pd.read_sql(\"\"\"\n",
    "                                SELECT * \n",
    "                                FROM principals;\n",
    "                                \"\"\", conn)\n",
    "imbd_principals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the `writers` table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>movie_id</th>\n",
       "      <th>person_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0285252</td>\n",
       "      <td>nm0899854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0438973</td>\n",
       "      <td>nm0175726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0438973</td>\n",
       "      <td>nm1802864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0462036</td>\n",
       "      <td>nm1940585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0835418</td>\n",
       "      <td>nm0310087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255868</th>\n",
       "      <td>tt8999892</td>\n",
       "      <td>nm10122246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255869</th>\n",
       "      <td>tt8999974</td>\n",
       "      <td>nm10122357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255870</th>\n",
       "      <td>tt9001390</td>\n",
       "      <td>nm6711477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255871</th>\n",
       "      <td>tt9004986</td>\n",
       "      <td>nm4993825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255872</th>\n",
       "      <td>tt9010172</td>\n",
       "      <td>nm8352242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>255873 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_id   person_id\n",
       "0       tt0285252   nm0899854\n",
       "1       tt0438973   nm0175726\n",
       "2       tt0438973   nm1802864\n",
       "3       tt0462036   nm1940585\n",
       "4       tt0835418   nm0310087\n",
       "...           ...         ...\n",
       "255868  tt8999892  nm10122246\n",
       "255869  tt8999974  nm10122357\n",
       "255870  tt9001390   nm6711477\n",
       "255871  tt9004986   nm4993825\n",
       "255872  tt9010172   nm8352242\n",
       "\n",
       "[255873 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating the dataframe for the writers table\n",
    "imbd_writers = pd.read_sql(\"\"\"\n",
    "                                SELECT * \n",
    "                                FROM writers;\n",
    "                                \"\"\", conn)\n",
    "imbd_writers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box Office Mojo (BOM) Dataset\n",
    "\n",
    "First we are going to read and create the BOM dataframe. Then we will clean it a little, and check the column value types for the BOM dataset (`bom_df`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "title              object\n",
      "studio             object\n",
      "domestic_gross    float64\n",
      "foreign_gross      object\n",
      "year                int64\n",
      "dtype: object\n",
      "title              object\n",
      "studio             object\n",
      "domestic_gross    float64\n",
      "foreign_gross     float64\n",
      "year                int64\n",
      "dtype: object\n",
      "Number of null values in 'foreign_gross' column :1350\n",
      "Number of null values in 'title' column :0\n",
      "Number of null values in 'foreign_gross' column after cleaning :0\n"
     ]
    }
   ],
   "source": [
    "# reaing the data and checking the columns data types\n",
    "bom_df = pd.read_csv('zipped_data/bom.movie_gross (1).csv.gz')\n",
    "print(bom_df.dtypes)\n",
    "\n",
    "# getting rid of commas before we make the transformation\n",
    "bom_df['foreign_gross'] = bom_df['foreign_gross'].str.replace(',', '')\n",
    "\n",
    "# transforming the string object into a float\n",
    "bom_df = bom_df.astype({'foreign_gross': np.float})\n",
    "\n",
    "# checking to see if it worked\n",
    "print(bom_df.dtypes)\n",
    "\n",
    "# check to see number of NaN values in `foreign_gross`\n",
    "print(\n",
    "    f\"Number of null values in 'foreign_gross' column :{bom_df['foreign_gross'].isnull().sum()}\")\n",
    "\n",
    "# check to see number of NaN values in `title`\n",
    "print(\n",
    "    f\"Number of null values in 'title' column :{bom_df['title'].isnull().sum()}\")\n",
    "\n",
    "# drop rows with NaN values in `foreign_gross`\n",
    "bom_df.dropna(subset=['foreign_gross'], inplace=True)\n",
    "\n",
    "# check for NaN values in `foreign_gross`\n",
    "print(\n",
    "    f\"Number of null values in 'foreign_gross' column after cleaning :{bom_df['foreign_gross'].isnull().sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>title</th>\n",
       "      <th>studio</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>BV</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652000000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alice in Wonderland (2010)</td>\n",
       "      <td>BV</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry Potter and the Deathly Hallows Part 1</td>\n",
       "      <td>WB</td>\n",
       "      <td>296000000.0</td>\n",
       "      <td>664300000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Inception</td>\n",
       "      <td>WB</td>\n",
       "      <td>292600000.0</td>\n",
       "      <td>535700000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shrek Forever After</td>\n",
       "      <td>P/DW</td>\n",
       "      <td>238700000.0</td>\n",
       "      <td>513900000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3275</th>\n",
       "      <td>I Still See You</td>\n",
       "      <td>LGF</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1500000.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3286</th>\n",
       "      <td>The Catcher Was a Spy</td>\n",
       "      <td>IFC</td>\n",
       "      <td>725000.0</td>\n",
       "      <td>229000.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3309</th>\n",
       "      <td>Time Freak</td>\n",
       "      <td>Grindstone</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>256000.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342</th>\n",
       "      <td>Reign of Judges: Title of Liberty - Concept Short</td>\n",
       "      <td>Darin Southa</td>\n",
       "      <td>93200.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3353</th>\n",
       "      <td>Antonio Lopez 1970: Sex Fashion &amp; Disco</td>\n",
       "      <td>FM</td>\n",
       "      <td>43200.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2037 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  title        studio  \\\n",
       "0                                           Toy Story 3            BV   \n",
       "1                            Alice in Wonderland (2010)            BV   \n",
       "2           Harry Potter and the Deathly Hallows Part 1            WB   \n",
       "3                                             Inception            WB   \n",
       "4                                   Shrek Forever After          P/DW   \n",
       "...                                                 ...           ...   \n",
       "3275                                    I Still See You           LGF   \n",
       "3286                              The Catcher Was a Spy           IFC   \n",
       "3309                                         Time Freak    Grindstone   \n",
       "3342  Reign of Judges: Title of Liberty - Concept Short  Darin Southa   \n",
       "3353            Antonio Lopez 1970: Sex Fashion & Disco            FM   \n",
       "\n",
       "      domestic_gross  foreign_gross  year  \n",
       "0        415000000.0    652000000.0  2010  \n",
       "1        334200000.0    691300000.0  2010  \n",
       "2        296000000.0    664300000.0  2010  \n",
       "3        292600000.0    535700000.0  2010  \n",
       "4        238700000.0    513900000.0  2010  \n",
       "...              ...            ...   ...  \n",
       "3275          1400.0      1500000.0  2018  \n",
       "3286        725000.0       229000.0  2018  \n",
       "3309         10000.0       256000.0  2018  \n",
       "3342         93200.0         5200.0  2018  \n",
       "3353         43200.0        30000.0  2018  \n",
       "\n",
       "[2037 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cleaned dataset!\n",
    "bom_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions about the Data and Data Analysis\n",
    "\n",
    "- Do certain genres result in higher grossing films?\n",
    "- Is there a relationship between specific actors and higher grossing films?\n",
    "- Do movies with higher average ratings result in higher grossing films?\n",
    "\n",
    "You may have noticed that all these questions are related to the gross of the movies. That's because in order for Microsoft's new movie studio to flourish, it needs to be able to make money, as usually production costs for movies are not cheap. Hopefully, after answering these questions we will have some recommendations for Microsoft in order to direct them on how to make high-grossing, successful films!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do certain genres result in higher grossing films?\n",
    "In order to answer this question we need to make sure our data types are the way we want them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets start by saving `bom_df` in a SQLite database, so that we can then use SQLite to join and query the `bom_df` data set and the `imbd_movie_basics` data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "bom_df.to_sql('bom_df', conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can query `bom_df` using SQLite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>title</th>\n",
       "      <th>studio</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>BV</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652000000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Alice in Wonderland (2010)</td>\n",
       "      <td>BV</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Harry Potter and the Deathly Hallows Part 1</td>\n",
       "      <td>WB</td>\n",
       "      <td>296000000.0</td>\n",
       "      <td>664300000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Inception</td>\n",
       "      <td>WB</td>\n",
       "      <td>292600000.0</td>\n",
       "      <td>535700000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Shrek Forever After</td>\n",
       "      <td>P/DW</td>\n",
       "      <td>238700000.0</td>\n",
       "      <td>513900000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>3275</td>\n",
       "      <td>I Still See You</td>\n",
       "      <td>LGF</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1500000.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>3286</td>\n",
       "      <td>The Catcher Was a Spy</td>\n",
       "      <td>IFC</td>\n",
       "      <td>725000.0</td>\n",
       "      <td>229000.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>3309</td>\n",
       "      <td>Time Freak</td>\n",
       "      <td>Grindstone</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>256000.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>3342</td>\n",
       "      <td>Reign of Judges: Title of Liberty - Concept Short</td>\n",
       "      <td>Darin Southa</td>\n",
       "      <td>93200.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2036</th>\n",
       "      <td>3353</td>\n",
       "      <td>Antonio Lopez 1970: Sex Fashion &amp; Disco</td>\n",
       "      <td>FM</td>\n",
       "      <td>43200.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2037 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index                                              title        studio  \\\n",
       "0         0                                        Toy Story 3            BV   \n",
       "1         1                         Alice in Wonderland (2010)            BV   \n",
       "2         2        Harry Potter and the Deathly Hallows Part 1            WB   \n",
       "3         3                                          Inception            WB   \n",
       "4         4                                Shrek Forever After          P/DW   \n",
       "...     ...                                                ...           ...   \n",
       "2032   3275                                    I Still See You           LGF   \n",
       "2033   3286                              The Catcher Was a Spy           IFC   \n",
       "2034   3309                                         Time Freak    Grindstone   \n",
       "2035   3342  Reign of Judges: Title of Liberty - Concept Short  Darin Southa   \n",
       "2036   3353            Antonio Lopez 1970: Sex Fashion & Disco            FM   \n",
       "\n",
       "      domestic_gross  foreign_gross  year  \n",
       "0        415000000.0    652000000.0  2010  \n",
       "1        334200000.0    691300000.0  2010  \n",
       "2        296000000.0    664300000.0  2010  \n",
       "3        292600000.0    535700000.0  2010  \n",
       "4        238700000.0    513900000.0  2010  \n",
       "...              ...            ...   ...  \n",
       "2032          1400.0      1500000.0  2018  \n",
       "2033        725000.0       229000.0  2018  \n",
       "2034         10000.0       256000.0  2018  \n",
       "2035         93200.0         5200.0  2018  \n",
       "2036         43200.0        30000.0  2018  \n",
       "\n",
       "[2037 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(\"\"\"\n",
    "                SELECT * \n",
    "                FROM bom_df;\n",
    "                \"\"\", conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're going to join the two tables based on movie title and year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "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>title</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "      <th>movie_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>946400000.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2015</td>\n",
       "      <td>tt2395427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>891800000.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4881806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Frozen</td>\n",
       "      <td>875700000.0</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>2013</td>\n",
       "      <td>tt2294629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Transformers: Age of Extinction</td>\n",
       "      <td>858600000.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2014</td>\n",
       "      <td>tt2109248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minions</td>\n",
       "      <td>823400000.0</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>2015</td>\n",
       "      <td>tt2293640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>Client 9: The Rise and Fall of Eliot Spitzer</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>2010</td>\n",
       "      <td>tt1638362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>1369.5</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4154756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>1019.4</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2015</td>\n",
       "      <td>tt0369610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>The Fate of the Furious</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>Action,Crime,Thriller</td>\n",
       "      <td>2017</td>\n",
       "      <td>tt4630562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>Chasing Mavericks</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Biography,Drama,Sport</td>\n",
       "      <td>2012</td>\n",
       "      <td>tt1629757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1199 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             title  foreign_gross  \\\n",
       "0                          Avengers: Age of Ultron    946400000.0   \n",
       "1                   Jurassic World: Fallen Kingdom    891800000.0   \n",
       "2                                           Frozen    875700000.0   \n",
       "3                  Transformers: Age of Extinction    858600000.0   \n",
       "4                                          Minions    823400000.0   \n",
       "...                                            ...            ...   \n",
       "1194  Client 9: The Rise and Fall of Eliot Spitzer         3500.0   \n",
       "1195                        Avengers: Infinity War         1369.5   \n",
       "1196                                Jurassic World         1019.4   \n",
       "1197                       The Fate of the Furious         1010.0   \n",
       "1198                             Chasing Mavericks          600.0   \n",
       "\n",
       "                          genres  year   movie_id  \n",
       "0        Action,Adventure,Sci-Fi  2015  tt2395427  \n",
       "1        Action,Adventure,Sci-Fi  2018  tt4881806  \n",
       "2     Adventure,Animation,Comedy  2013  tt2294629  \n",
       "3        Action,Adventure,Sci-Fi  2014  tt2109248  \n",
       "4     Adventure,Animation,Comedy  2015  tt2293640  \n",
       "...                          ...   ...        ...  \n",
       "1194                 Documentary  2010  tt1638362  \n",
       "1195     Action,Adventure,Sci-Fi  2018  tt4154756  \n",
       "1196     Action,Adventure,Sci-Fi  2015  tt0369610  \n",
       "1197       Action,Crime,Thriller  2017  tt4630562  \n",
       "1198       Biography,Drama,Sport  2012  tt1629757  \n",
       "\n",
       "[1199 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we don't need the movie_id column for the first question, but we will in the second question\n",
    "bom_movie_basics_joined = pd.read_sql('''\n",
    "    SELECT bom_df.title, bom_df.foreign_gross, movie_basics.genres, bom_df.year, movie_basics.movie_id\n",
    "    FROM bom_df\n",
    "        INNER JOIN movie_basics\n",
    "            /* Joining on title and year, so as to have the most accurate resulting dataset */\n",
    "        ON bom_df.title=movie_basics.original_title AND bom_df.year=movie_basics.start_year\n",
    "    ORDER BY foreign_gross DESC;\n",
    "''', conn)\n",
    "bom_movie_basics_joined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>title</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "      <th>movie_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Coco</td>\n",
       "      <td>597400000.0</td>\n",
       "      <td>Horror</td>\n",
       "      <td>2017</td>\n",
       "      <td>tt7002100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>The Artist</td>\n",
       "      <td>88800000.0</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>2011</td>\n",
       "      <td>tt1825978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>Lights Out</td>\n",
       "      <td>81600000.0</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>2016</td>\n",
       "      <td>tt5328340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>The Bounty Hunter</td>\n",
       "      <td>69300000.0</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>tt1472211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>Abduction</td>\n",
       "      <td>54000000.0</td>\n",
       "      <td>Horror,Thriller</td>\n",
       "      <td>2011</td>\n",
       "      <td>tt2447982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Truth or Dare</td>\n",
       "      <td>53900000.0</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt6869948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>Spotlight</td>\n",
       "      <td>53200000.0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>2015</td>\n",
       "      <td>tt7785302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>The Walk</td>\n",
       "      <td>51000000.0</td>\n",
       "      <td>Adventure,Biography,Drama</td>\n",
       "      <td>2015</td>\n",
       "      <td>tt3488710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>Burlesque</td>\n",
       "      <td>50100000.0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>2010</td>\n",
       "      <td>tt1586713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>Legend</td>\n",
       "      <td>41100000.0</td>\n",
       "      <td>Biography,Crime,Drama</td>\n",
       "      <td>2015</td>\n",
       "      <td>tt3569230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>The Visit</td>\n",
       "      <td>33200000.0</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>2015</td>\n",
       "      <td>tt3833746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>Sisters</td>\n",
       "      <td>18000000.0</td>\n",
       "      <td>Biography,Documentary,Music</td>\n",
       "      <td>2015</td>\n",
       "      <td>tt4793074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>Big Eyes</td>\n",
       "      <td>14800000.0</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>2014</td>\n",
       "      <td>tt4317898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>Sleepless</td>\n",
       "      <td>12100000.0</td>\n",
       "      <td>Drama,War</td>\n",
       "      <td>2017</td>\n",
       "      <td>tt4388844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>The Forest</td>\n",
       "      <td>11000000.0</td>\n",
       "      <td>Drama,Fantasy,Horror</td>\n",
       "      <td>2016</td>\n",
       "      <td>tt4982356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>The Night Before</td>\n",
       "      <td>9300000.0</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>2015</td>\n",
       "      <td>tt6353886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>Gone</td>\n",
       "      <td>6400000.0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>2012</td>\n",
       "      <td>tt2230954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>Stronger</td>\n",
       "      <td>4200000.0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>2017</td>\n",
       "      <td>tt5738152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1019</th>\n",
       "      <td>The Wall</td>\n",
       "      <td>2700000.0</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>2017</td>\n",
       "      <td>tt6845582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>The Wall</td>\n",
       "      <td>2700000.0</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>2017</td>\n",
       "      <td>tt7578246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td>Cyrus</td>\n",
       "      <td>2500000.0</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>2010</td>\n",
       "      <td>tt1336617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1160</th>\n",
       "      <td>I'm Still Here</td>\n",
       "      <td>160000.0</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>tt1701997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1178</th>\n",
       "      <td>The Tempest</td>\n",
       "      <td>68700.0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>2010</td>\n",
       "      <td>tt1683003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  title  foreign_gross                       genres  year  \\\n",
       "25                 Coco    597400000.0                       Horror  2017   \n",
       "333          The Artist     88800000.0                     Thriller  2011   \n",
       "356          Lights Out     81600000.0                  Documentary  2016   \n",
       "388   The Bounty Hunter     69300000.0                         None  2010   \n",
       "451           Abduction     54000000.0              Horror,Thriller  2011   \n",
       "454       Truth or Dare     53900000.0         Comedy,Drama,Romance  2018   \n",
       "461           Spotlight     53200000.0                        Drama  2015   \n",
       "477            The Walk     51000000.0    Adventure,Biography,Drama  2015   \n",
       "482           Burlesque     50100000.0                        Drama  2010   \n",
       "534              Legend     41100000.0        Biography,Crime,Drama  2015   \n",
       "578           The Visit     33200000.0                  Documentary  2015   \n",
       "733             Sisters     18000000.0  Biography,Documentary,Music  2015   \n",
       "776            Big Eyes     14800000.0                  Documentary  2014   \n",
       "811           Sleepless     12100000.0                    Drama,War  2017   \n",
       "831          The Forest     11000000.0         Drama,Fantasy,Horror  2016   \n",
       "858    The Night Before      9300000.0                  Documentary  2015   \n",
       "915                Gone      6400000.0                        Drama  2012   \n",
       "981            Stronger      4200000.0                        Drama  2017   \n",
       "1019           The Wall      2700000.0                  Documentary  2017   \n",
       "1020           The Wall      2700000.0                  Documentary  2017   \n",
       "1024              Cyrus      2500000.0         Comedy,Drama,Romance  2010   \n",
       "1160     I'm Still Here       160000.0                         None  2010   \n",
       "1178        The Tempest        68700.0                        Drama  2010   \n",
       "\n",
       "       movie_id  \n",
       "25    tt7002100  \n",
       "333   tt1825978  \n",
       "356   tt5328340  \n",
       "388   tt1472211  \n",
       "451   tt2447982  \n",
       "454   tt6869948  \n",
       "461   tt7785302  \n",
       "477   tt3488710  \n",
       "482   tt1586713  \n",
       "534   tt3569230  \n",
       "578   tt3833746  \n",
       "733   tt4793074  \n",
       "776   tt4317898  \n",
       "811   tt4388844  \n",
       "831   tt4982356  \n",
       "858   tt6353886  \n",
       "915   tt2230954  \n",
       "981   tt5738152  \n",
       "1019  tt6845582  \n",
       "1020  tt7578246  \n",
       "1024  tt1336617  \n",
       "1160  tt1701997  \n",
       "1178  tt1683003  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bom_movie_basics_joined[bom_movie_basics_joined[\"title\"].duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use `.shape` to find the number of duplicated rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23, 5)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bom_movie_basics_joined[bom_movie_basics_joined[\"title\"].duplicated()].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we have 1199 rows in our complete joined file, and 23 duplicated rows within that. Accounting for the duplication, that means that around 46 lines are doubled. 46/1299 equals to a bit less than 4%. Since it's impossible without incorperating more data to tell these duplicated movies apart (and make sure the right profits/genres are ascribed to the right movie), I'm going to delete all of them from our dataset.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>title</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "      <th>movie_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>946400000.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2015</td>\n",
       "      <td>tt2395427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>891800000.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4881806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Frozen</td>\n",
       "      <td>875700000.0</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>2013</td>\n",
       "      <td>tt2294629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Transformers: Age of Extinction</td>\n",
       "      <td>858600000.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2014</td>\n",
       "      <td>tt2109248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minions</td>\n",
       "      <td>823400000.0</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>2015</td>\n",
       "      <td>tt2293640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>Client 9: The Rise and Fall of Eliot Spitzer</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>2010</td>\n",
       "      <td>tt1638362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>1369.5</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4154756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>1019.4</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>2015</td>\n",
       "      <td>tt0369610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>The Fate of the Furious</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>Action,Crime,Thriller</td>\n",
       "      <td>2017</td>\n",
       "      <td>tt4630562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>Chasing Mavericks</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Biography,Drama,Sport</td>\n",
       "      <td>2012</td>\n",
       "      <td>tt1629757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1154 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             title  foreign_gross  \\\n",
       "0                          Avengers: Age of Ultron    946400000.0   \n",
       "1                   Jurassic World: Fallen Kingdom    891800000.0   \n",
       "2                                           Frozen    875700000.0   \n",
       "3                  Transformers: Age of Extinction    858600000.0   \n",
       "4                                          Minions    823400000.0   \n",
       "...                                            ...            ...   \n",
       "1194  Client 9: The Rise and Fall of Eliot Spitzer         3500.0   \n",
       "1195                        Avengers: Infinity War         1369.5   \n",
       "1196                                Jurassic World         1019.4   \n",
       "1197                       The Fate of the Furious         1010.0   \n",
       "1198                             Chasing Mavericks          600.0   \n",
       "\n",
       "                          genres  year   movie_id  \n",
       "0        Action,Adventure,Sci-Fi  2015  tt2395427  \n",
       "1        Action,Adventure,Sci-Fi  2018  tt4881806  \n",
       "2     Adventure,Animation,Comedy  2013  tt2294629  \n",
       "3        Action,Adventure,Sci-Fi  2014  tt2109248  \n",
       "4     Adventure,Animation,Comedy  2015  tt2293640  \n",
       "...                          ...   ...        ...  \n",
       "1194                 Documentary  2010  tt1638362  \n",
       "1195     Action,Adventure,Sci-Fi  2018  tt4154756  \n",
       "1196     Action,Adventure,Sci-Fi  2015  tt0369610  \n",
       "1197       Action,Crime,Thriller  2017  tt4630562  \n",
       "1198       Biography,Drama,Sport  2012  tt1629757  \n",
       "\n",
       "[1154 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bom_movie_basics_joined.drop_duplicates(\n",
    "    subset=['title', 'year'], keep=False, inplace=True)\n",
    "bom_movie_basics_joined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay! Now we have our cleaned data, leaving us with a dataset of 1154 movies, with their foreign gross, the genres associated with them, the year they were released, and the movie ID. \n",
    "\n",
    "Lets get back to our original question; Do certain genres result in higher grossing films? In order to look at this, we will need to seperate the listed genres.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "bom_movie_basics_joined['genres'] = bom_movie_basics_joined['genres'].str.split(\n",
    "    pat=',')\n",
    "bom_movie_basics_exploded = bom_movie_basics_joined.explode(\n",
    "    'genres', ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets take a look at our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "title             object\n",
      "foreign_gross    float64\n",
      "genres            object\n",
      "year               int64\n",
      "movie_id          object\n",
      "dtype: object\n"
     ]
    },
    {
     "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>title</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "      <th>movie_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>946400000.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>2015</td>\n",
       "      <td>tt2395427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>946400000.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2015</td>\n",
       "      <td>tt2395427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>946400000.0</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>2015</td>\n",
       "      <td>tt2395427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>891800000.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4881806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>891800000.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4881806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2990</th>\n",
       "      <td>The Fate of the Furious</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>Crime</td>\n",
       "      <td>2017</td>\n",
       "      <td>tt4630562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2991</th>\n",
       "      <td>The Fate of the Furious</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>2017</td>\n",
       "      <td>tt4630562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2992</th>\n",
       "      <td>Chasing Mavericks</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Biography</td>\n",
       "      <td>2012</td>\n",
       "      <td>tt1629757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2993</th>\n",
       "      <td>Chasing Mavericks</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>2012</td>\n",
       "      <td>tt1629757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2994</th>\n",
       "      <td>Chasing Mavericks</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Sport</td>\n",
       "      <td>2012</td>\n",
       "      <td>tt1629757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2995 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               title  foreign_gross     genres  year  \\\n",
       "0            Avengers: Age of Ultron    946400000.0     Action  2015   \n",
       "1            Avengers: Age of Ultron    946400000.0  Adventure  2015   \n",
       "2            Avengers: Age of Ultron    946400000.0     Sci-Fi  2015   \n",
       "3     Jurassic World: Fallen Kingdom    891800000.0     Action  2018   \n",
       "4     Jurassic World: Fallen Kingdom    891800000.0  Adventure  2018   \n",
       "...                              ...            ...        ...   ...   \n",
       "2990         The Fate of the Furious         1010.0      Crime  2017   \n",
       "2991         The Fate of the Furious         1010.0   Thriller  2017   \n",
       "2992               Chasing Mavericks          600.0  Biography  2012   \n",
       "2993               Chasing Mavericks          600.0      Drama  2012   \n",
       "2994               Chasing Mavericks          600.0      Sport  2012   \n",
       "\n",
       "       movie_id  \n",
       "0     tt2395427  \n",
       "1     tt2395427  \n",
       "2     tt2395427  \n",
       "3     tt4881806  \n",
       "4     tt4881806  \n",
       "...         ...  \n",
       "2990  tt4630562  \n",
       "2991  tt4630562  \n",
       "2992  tt1629757  \n",
       "2993  tt1629757  \n",
       "2994  tt1629757  \n",
       "\n",
       "[2995 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(bom_movie_basics_exploded.dtypes)\n",
    "bom_movie_basics_exploded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay! So now we have a full workable list of the movies and their genres. \n",
    "Let's find the average amount each genre made in the dataset. \n",
    "\n",
    "We are using the average here, as the dataset is fairly large (2995 movies) and so we are not as concerned about outliers skewing the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "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>genres</th>\n",
       "      <th>foreign_gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>1.591982e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>2.297876e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Animation</td>\n",
       "      <td>2.518275e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Biography</td>\n",
       "      <td>4.951967e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>8.502199e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Crime</td>\n",
       "      <td>4.157536e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Documentary</td>\n",
       "      <td>1.201011e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Drama</td>\n",
       "      <td>4.738446e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Family</td>\n",
       "      <td>9.907607e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>1.643918e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>History</td>\n",
       "      <td>5.273703e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Horror</td>\n",
       "      <td>5.869045e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Music</td>\n",
       "      <td>5.320197e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Musical</td>\n",
       "      <td>9.681422e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>6.290475e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Romance</td>\n",
       "      <td>3.829549e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>2.127523e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Sport</td>\n",
       "      <td>2.323193e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>8.521993e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>War</td>\n",
       "      <td>2.391725e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Western</td>\n",
       "      <td>8.071757e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         genres  foreign_gross\n",
       "0        Action   1.591982e+08\n",
       "1     Adventure   2.297876e+08\n",
       "2     Animation   2.518275e+08\n",
       "3     Biography   4.951967e+07\n",
       "4        Comedy   8.502199e+07\n",
       "5         Crime   4.157536e+07\n",
       "6   Documentary   1.201011e+07\n",
       "7         Drama   4.738446e+07\n",
       "8        Family   9.907607e+07\n",
       "9       Fantasy   1.643918e+08\n",
       "10      History   5.273703e+07\n",
       "11       Horror   5.869045e+07\n",
       "12        Music   5.320197e+07\n",
       "13      Musical   9.681422e+07\n",
       "14      Mystery   6.290475e+07\n",
       "15      Romance   3.829549e+07\n",
       "16       Sci-Fi   2.127523e+08\n",
       "17        Sport   2.323193e+07\n",
       "18     Thriller   8.521993e+07\n",
       "19          War   2.391725e+07\n",
       "20      Western   8.071757e+07"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bom_movie_basics_exploded_avg = bom_movie_basics_exploded.groupby(\n",
    "    ['genres']).foreign_gross.mean().reset_index()\n",
    "bom_movie_basics_exploded_avg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization: Average Total Gross In Movie Genres: Top 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setting universal font type for this and future graphs - from :https://datascienceparichay.com/article/change-font-type-in-matplotlib-plots/\n",
    "plt.rcParams.update({'font.family': 'serif'})\n",
    "\n",
    "# specify size of plot\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "# create bar plot of top 10 grossing genres\n",
    "sns.barplot(data=bom_movie_basics_exploded_avg.sort_values(by='foreign_gross', ascending=False).head(10),\n",
    "            x='foreign_gross',\n",
    "            y='genres',\n",
    "            ci=None,\n",
    "            palette='Greens_r')\n",
    "\n",
    "# lable and define fontsize for main and axes titles\n",
    "plt.xlabel('Average Gross (USD)', fontsize=14)\n",
    "plt.ylabel('Movie Genres', fontsize=14)\n",
    "plt.title('Average Total Gross In Movie Genres: Top 10', fontsize=17)\n",
    "plt.tick_params(axis='both', which='major', labelsize=12)\n",
    "\n",
    "# set x-axes tick labels\n",
    "ax.set_xticklabels(['0', '50 Million', '100 Million',\n",
    "                   '150 Million', '200 Million', '250 Million'])\n",
    "\n",
    "# get rid of scientific notation\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Business recommendation: Do certain genres result in higher grossing films?\n",
    "\n",
    "Based on the above graph, we see that there are genres that have on average higher grossing films\n",
    "\n",
    "The top 5 genres according to the above graph are:\n",
    " - **animation**\n",
    " - **adventure** \n",
    " - **sci-fi** \n",
    " - **fantasy**\n",
    " - **action**\n",
    " \n",
    "As such, I would advise Microsoft to focus on these 5 genres in their initial films for their new movie studio. Many of these genres can and do overlap in films, so it may be best to focus on animated movies with sub-genres of adventure, sci-fi, fantasy, or action. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is there a relationship between specific actors and higher grossing films?\n",
    "\n",
    "In order to answer this question, we are going to have to create a cohesive data set where we have the actors, the various films they acted in, and the total amount the movies made once they were put out."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do this, lets create an inner join between `bom_df`, `movie_basics`, `principles`, and `persons`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>foreign_gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>person_id</th>\n",
       "      <th>primary_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>946400000.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>nm0000375</td>\n",
       "      <td>Robert Downey Jr.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>946400000.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>nm0262635</td>\n",
       "      <td>Chris Evans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>946400000.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>nm0749263</td>\n",
       "      <td>Mark Ruffalo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>946400000.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>nm1165110</td>\n",
       "      <td>Chris Hemsworth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891800000.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>nm0695435</td>\n",
       "      <td>Chris Pratt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2913</th>\n",
       "      <td>1010.0</td>\n",
       "      <td>Action,Crime,Thriller</td>\n",
       "      <td>nm0004874</td>\n",
       "      <td>Vin Diesel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2914</th>\n",
       "      <td>1010.0</td>\n",
       "      <td>Action,Crime,Thriller</td>\n",
       "      <td>nm0005458</td>\n",
       "      <td>Jason Statham</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>1010.0</td>\n",
       "      <td>Action,Crime,Thriller</td>\n",
       "      <td>nm0425005</td>\n",
       "      <td>Dwayne Johnson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>600.0</td>\n",
       "      <td>Biography,Drama,Sport</td>\n",
       "      <td>nm4103976</td>\n",
       "      <td>Jonny Weston</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>600.0</td>\n",
       "      <td>Biography,Drama,Sport</td>\n",
       "      <td>nm0124930</td>\n",
       "      <td>Gerard Butler</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2918 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      foreign_gross                   genres  person_id       primary_name\n",
       "0       946400000.0  Action,Adventure,Sci-Fi  nm0000375  Robert Downey Jr.\n",
       "1       946400000.0  Action,Adventure,Sci-Fi  nm0262635        Chris Evans\n",
       "2       946400000.0  Action,Adventure,Sci-Fi  nm0749263       Mark Ruffalo\n",
       "3       946400000.0  Action,Adventure,Sci-Fi  nm1165110    Chris Hemsworth\n",
       "4       891800000.0  Action,Adventure,Sci-Fi  nm0695435        Chris Pratt\n",
       "...             ...                      ...        ...                ...\n",
       "2913         1010.0    Action,Crime,Thriller  nm0004874         Vin Diesel\n",
       "2914         1010.0    Action,Crime,Thriller  nm0005458      Jason Statham\n",
       "2915         1010.0    Action,Crime,Thriller  nm0425005     Dwayne Johnson\n",
       "2916          600.0    Biography,Drama,Sport  nm4103976       Jonny Weston\n",
       "2917          600.0    Biography,Drama,Sport  nm0124930      Gerard Butler\n",
       "\n",
       "[2918 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_actors_gross = pd.read_sql(\"\"\"\n",
    "    SELECT bom.foreign_gross, bas.genres, per.person_id,  per.primary_name\n",
    "    FROM bom_df AS bom\n",
    "            /*joining bom_df and movie_basics*/\n",
    "        INNER JOIN \n",
    "        movie_basics AS bas\n",
    "        ON bom.title=bas.original_title AND bom.year=bas.start_year\n",
    "            /*joining pricipals to dataset*/\n",
    "        INNER JOIN \n",
    "        principals AS pri\n",
    "        ON bas.movie_id=pri.movie_id\n",
    "            /*joining persons to dataset*/\n",
    "        INNER JOIN\n",
    "        persons AS per\n",
    "        ON pri.person_id=per.person_id\n",
    "            WHERE category='actor' OR 'actress'\n",
    "    ORDER BY foreign_gross DESC; \n",
    "    \"\"\", conn)\n",
    "\n",
    "join_actors_gross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# checking for null values - you can run this if you want, but there are no null values\n",
    "# print(pd.isnull(join_actors_gross['person_id']).values.sum())\n",
    "# pd.isnull(join_actors_gross['primary_name']).values.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay! Let's try to make a data set where we have the median foreign gross per movie that the actors/actresses appeared in.\n",
    "\n",
    "We're using the median here as there may be actors who worked in only a few movies, and we want the data to be more resistant to outliers.\n",
    "\n",
    "The total number of actors in this dataset is 1411. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>person_id</th>\n",
       "      <th>primary_name</th>\n",
       "      <th>foreign_gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nm0000092</td>\n",
       "      <td>John Cleese</td>\n",
       "      <td>11722000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nm0000093</td>\n",
       "      <td>Brad Pitt</td>\n",
       "      <td>63200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nm0000095</td>\n",
       "      <td>Woody Allen</td>\n",
       "      <td>56600000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nm0000100</td>\n",
       "      <td>Rowan Atkinson</td>\n",
       "      <td>153150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nm0000101</td>\n",
       "      <td>Dan Aykroyd</td>\n",
       "      <td>101300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1407</th>\n",
       "      <td>nm9061885</td>\n",
       "      <td>Gonzalo Moreno</td>\n",
       "      <td>597400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1408</th>\n",
       "      <td>nm9061887</td>\n",
       "      <td>Leolo Moulin</td>\n",
       "      <td>597400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1409</th>\n",
       "      <td>nm9133740</td>\n",
       "      <td>Huck Milner</td>\n",
       "      <td>634200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1410</th>\n",
       "      <td>nm9377852</td>\n",
       "      <td>Yugesh Anil</td>\n",
       "      <td>600000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1411</th>\n",
       "      <td>nm9501548</td>\n",
       "      <td>Robert Jon Mello</td>\n",
       "      <td>8100000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1412 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      person_id      primary_name  foreign_gross\n",
       "0     nm0000092       John Cleese     11722000.0\n",
       "1     nm0000093         Brad Pitt     63200000.0\n",
       "2     nm0000095       Woody Allen     56600000.0\n",
       "3     nm0000100    Rowan Atkinson    153150000.0\n",
       "4     nm0000101       Dan Aykroyd    101300000.0\n",
       "...         ...               ...            ...\n",
       "1407  nm9061885    Gonzalo Moreno    597400000.0\n",
       "1408  nm9061887      Leolo Moulin    597400000.0\n",
       "1409  nm9133740       Huck Milner    634200000.0\n",
       "1410  nm9377852       Yugesh Anil       600000.0\n",
       "1411  nm9501548  Robert Jon Mello      8100000.0\n",
       "\n",
       "[1412 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_foreign_gross_per_actor = join_actors_gross.groupby(\n",
    "    ['person_id', 'primary_name'], as_index=False).median()\n",
    "median_foreign_gross_per_actor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>person_id</th>\n",
       "      <th>primary_name</th>\n",
       "      <th>foreign_gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [person_id, primary_name, foreign_gross]\n",
       "Index: []"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for duplicate rows\n",
    "median_foreign_gross_per_actor[median_foreign_gross_per_actor['primary_name'].duplicated(\n",
    ")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Woohoo! There are no duplicate rows, so we can use `median_foreign_gross_per_actor` to create our next graph. For this question, our final dataset has 1412 actors, along with their names, ID numbers, and their median foreign gross."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization: Median Total Gross By Top 15 Actors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# our data for this graph\n",
    "top15_med_gross_actor = median_foreign_gross_per_actor.sort_values(\n",
    "    by='foreign_gross', ascending=False).head(15)\n",
    "\n",
    "# specify size of plot\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "# create bar plot\n",
    "sns.barplot(data=top15_med_gross_actor,\n",
    "            x='foreign_gross',\n",
    "            y='primary_name',\n",
    "            ci=None,\n",
    "            palette='Greens_r')\n",
    "\n",
    "# lable and define fontsize for main and axes titles\n",
    "plt.xlabel('Median Gross (USD)', fontsize=14)\n",
    "plt.ylabel('Actors', fontsize=14)\n",
    "plt.title('Median Total Gross By Top 15 Actors', fontsize=17)\n",
    "plt.tick_params(axis='both', which='major', labelsize=12)\n",
    "\n",
    "# set x-axes tick labels\n",
    "ax.set_xticklabels(['0', '50 Million', '100 Million',\n",
    "                   '150 Million', '200 Million', '250 Million'])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Business recommendation: Is there a relationship between specific actors and higher grossing films?\n",
    "\n",
    " At a glance, we do see that there is a significant difference between the median gross of films with different actors. Surprisingly, there are only a few household names in the final 15. This may relate back to our previous finding, which is that animated films are the highest grossing movie genre. In animated films, one doesn't see the actors' faces, and as such may be more 'anonymous' and unknown to the average movie watcher. On a separate note, it is surprising that this list is composed only of men.\n",
    " \n",
    "Before, the analysis seemed to indicate that animated films are a direction for Microsoft's new movie studio to check out. Let's see if any of the top 15 actors were involved in animated films.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>gross</th>\n",
       "      <th>Is Animation in Genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>Jonathan Groff</td>\n",
       "      <td>875700000.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>Pierre Coffin</td>\n",
       "      <td>823400000.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>Ian McShane</td>\n",
       "      <td>804600000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>Tyrese Gibson</td>\n",
       "      <td>771400000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>Trey Parker</td>\n",
       "      <td>770200000.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>Richard Armitage</td>\n",
       "      <td>700900000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>Martin Freeman</td>\n",
       "      <td>700450000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>Ian McKellen</td>\n",
       "      <td>700000000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>Ken Stott</td>\n",
       "      <td>700000000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>Rami Malek</td>\n",
       "      <td>687200000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>Gwilym Lee</td>\n",
       "      <td>687200000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Ben Hardy</td>\n",
       "      <td>687200000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>Craig T. Nelson</td>\n",
       "      <td>634200000.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>Huck Milner</td>\n",
       "      <td>634200000.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>Orlando Bloom</td>\n",
       "      <td>622300000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name        gross  Is Animation in Genres\n",
       "697     Jonathan Groff  875700000.0                     1.0\n",
       "1033     Pierre Coffin  823400000.0                     1.0\n",
       "516        Ian McShane  804600000.0                     0.0\n",
       "1348     Tyrese Gibson  771400000.0                     0.0\n",
       "1339       Trey Parker  770200000.0                     1.0\n",
       "1071  Richard Armitage  700900000.0                     0.0\n",
       "845     Martin Freeman  700450000.0                     0.0\n",
       "515       Ian McKellen  700000000.0                     0.0\n",
       "749          Ken Stott  700000000.0                     0.0\n",
       "1050        Rami Malek  687200000.0                     0.0\n",
       "486         Gwilym Lee  687200000.0                     0.0\n",
       "119          Ben Hardy  687200000.0                     0.0\n",
       "277    Craig T. Nelson  634200000.0                     1.0\n",
       "505        Huck Milner  634200000.0                     1.0\n",
       "991      Orlando Bloom  622300000.0                     0.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top15_in_animation = []\n",
    "for name, gross, genres in zip(join_actors_gross['primary_name'], join_actors_gross['foreign_gross'], join_actors_gross['genres']):\n",
    "    if genres is not None and 'Animation' in genres:\n",
    "        top15_in_animation.append([name, gross, genres, True])\n",
    "    else:\n",
    "        top15_in_animation.append([name, gross, genres, False])\n",
    "\n",
    "\n",
    "top15_in_animation = pd.DataFrame(\n",
    "    top15_in_animation, columns=['name', 'gross', 'genres', 'Is Animation in Genres'])\n",
    "top15_in_animation.groupby(['name'], as_index=False).median().sort_values(\n",
    "    by='gross', ascending=False).head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see above that 5/15 were in at least one animated film - around 30%. It might be worth further analysis to see if any of these actors are a good fit Microsofts upcoming movies. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do movies with higher average ratings result in higher grossing films?\n",
    "\n",
    "Our final question! Let's look at the datset where we had the `averagerating` data again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>movie_id</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>numvotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt10356526</td>\n",
       "      <td>8.3</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt10384606</td>\n",
       "      <td>8.9</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt1042974</td>\n",
       "      <td>6.4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt1043726</td>\n",
       "      <td>4.2</td>\n",
       "      <td>50352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt1060240</td>\n",
       "      <td>6.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73851</th>\n",
       "      <td>tt9805820</td>\n",
       "      <td>8.1</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73852</th>\n",
       "      <td>tt9844256</td>\n",
       "      <td>7.5</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73853</th>\n",
       "      <td>tt9851050</td>\n",
       "      <td>4.7</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73854</th>\n",
       "      <td>tt9886934</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73855</th>\n",
       "      <td>tt9894098</td>\n",
       "      <td>6.3</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73856 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_id  averagerating  numvotes\n",
       "0      tt10356526            8.3        31\n",
       "1      tt10384606            8.9       559\n",
       "2       tt1042974            6.4        20\n",
       "3       tt1043726            4.2     50352\n",
       "4       tt1060240            6.5        21\n",
       "...           ...            ...       ...\n",
       "73851   tt9805820            8.1        25\n",
       "73852   tt9844256            7.5        24\n",
       "73853   tt9851050            4.7        14\n",
       "73854   tt9886934            7.0         5\n",
       "73855   tt9894098            6.3       128\n",
       "\n",
       "[73856 rows x 3 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imbd_movie_ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay! Let's do some inner joins so we can get the total gross and the average ratings.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>foreign_gross</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>652000000.0</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>535700000.0</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>513900000.0</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>398000000.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>311500000.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>1200000.0</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>2000000.0</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1187</th>\n",
       "      <td>1500000.0</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1188</th>\n",
       "      <td>229000.0</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1189</th>\n",
       "      <td>256000.0</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1190 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      foreign_gross  averagerating\n",
       "0       652000000.0            8.3\n",
       "1       535700000.0            8.8\n",
       "2       513900000.0            6.3\n",
       "3       398000000.0            5.0\n",
       "4       311500000.0            7.0\n",
       "...             ...            ...\n",
       "1185      1200000.0            6.2\n",
       "1186      2000000.0            6.9\n",
       "1187      1500000.0            5.7\n",
       "1188       229000.0            6.2\n",
       "1189       256000.0            5.7\n",
       "\n",
       "[1190 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_ratings_gross = pd.read_sql(\"\"\"\n",
    "    SELECT bom.foreign_gross, ratings.averagerating\n",
    "    FROM bom_df AS bom\n",
    "            /*joining bom_df and movie_basics*/\n",
    "        INNER JOIN \n",
    "        movie_basics AS bas\n",
    "        ON bom.title=bas.original_title AND bom.year=bas.start_year\n",
    "            /*joining movie_ratings to dataset*/\n",
    "        INNER JOIN\n",
    "        movie_ratings AS ratings\n",
    "        ON ratings.movie_id=bas.movie_id;\n",
    "        \"\"\", conn)\n",
    "join_ratings_gross"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization: Total Movie Gross By Average Rating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our final dataset for this question has 1190 average ratings, each representing a specific movie, and also contains the foreign gross for each film. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# save our dataframe as we want it\n",
    "sorted_join_ratings_gross = join_ratings_gross.sort_values(\n",
    "    by='foreign_gross', ascending=False)\n",
    "\n",
    "# specify size of plot\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "# set axis ticks and labels\n",
    "# found inspiration for the following two lines of code from Kimberly Fessel: https://youtu.be/Smh7ujDbYyg\n",
    "plt.gca().set(xlim=(5, 9))\n",
    "plt.gca().set(ylim=(0, 1000000000))\n",
    "ax.set_yticklabels(['0', '200 Million', '400 Million',\n",
    "                   '600 Million', '800 Million', '1 Billion'])\n",
    "plt.tick_params(axis='both', which='major', labelsize=12)\n",
    "\n",
    "# add regression line - seaborn documentation:https://seaborn.pydata.org/generated/seaborn.regplot.html\n",
    "sns.regplot(sorted_join_ratings_gross.averagerating, sorted_join_ratings_gross.foreign_gross,\n",
    "            scatter_kws={'color': 'g', 'alpha': 0.2}, line_kws={'color': 'dimgray', 'alpha': 0.7, 'lw': 2}, marker='d',\n",
    "            robust=True)\n",
    "\n",
    "# specifiy axis and title labels\n",
    "plt.title('Total Movie Gross By Average Rating', fontsize=17)\n",
    "plt.xlabel('Average Rating', fontsize=14)\n",
    "plt.ylabel('Total Gross', fontsize=14)\n",
    "\n",
    "# adding note that x axis starts at 5\n",
    "plt.text(5.05, 960000000, '*Note: Average Rating displayed from 5-9')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Business recommendation: Do movies with higher average ratings result in higher grossing films?\n",
    "\n",
    "Hmm, it doesn't seem like there is a strong correlation between these ratings and the total gross. Let's check Pearson's correlation, and see if there is a significant correlation\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>foreign_gross</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>foreign_gross</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.237023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averagerating</th>\n",
       "      <td>0.237023</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               foreign_gross  averagerating\n",
       "foreign_gross       1.000000       0.237023\n",
       "averagerating       0.237023       1.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pearsons correlation\n",
    "sorted_join_ratings_gross.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anything under .25 is considered no correlation - so while there is a very, very slight positive correlation between ratings and total gross, it's not significant. Based on these results, one may conclude that you don't have to make amazing movies in order to end up with financially successful movies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "In summation, the three recommendations coming out of this analysis are:\n",
    "\n",
    " - The data indicates that there are specific genres that in the past have (on average) done very well. In particular, animation seems to have high on-average box office returns. As such, I would recommend that Microsoft focus its initial film-making energies on animated films, with sub-genres of adventure, scifi, and fantasy.\n",
    "\n",
    "- There are actors that have a high median total foreign gross. These actors should be further analyzed when the movie making process is farther along to see if they would be a good fit in any of the initial films. It should be noted that 5/15 of the actors (33.3%) have worked on at least one animation film, and as such these actors should especially be noted for possible future films.\n",
    "\n",
    "- Finally, it seems that there is no significant correlation between film ratings and box office success - as such it is important to note that the quality of the movie has little to no bearing on a movie's box office success. In short, it's okay to skimp on film quality to a certain extent.\n",
    "\n",
    "Initially focusing on animated films will enable the studio to step into a lucrative movie genre. Similarly, casting actors with a high median total gross will hopefully engender similar results - particularly actors with prior experience in animated films. Finally, it is worthwhile to note that movies do not have to be exceptional to do well, which should be kept in mind if and when costs need to be cut.\n",
    "\n",
    "-\n",
    "\n",
    "\n"
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