Film_Analysis_Project .ipynb

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   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Introduction:\" data-toc-modified-id=\"Introduction:-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Introduction:</a></span><ul class=\"toc-item\"><li><span><a href=\"#Citation\" data-toc-modified-id=\"Citation-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Citation</a></span></li><li><span><a href=\"#Overview\" data-toc-modified-id=\"Overview-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Overview</a></span></li><li><span><a href=\"#Business-Understanding:\" data-toc-modified-id=\"Business-Understanding:-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Business Understanding:</a></span></li></ul></li><li><span><a href=\"#Cleaning-the-Data:\" data-toc-modified-id=\"Cleaning-the-Data:-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Cleaning the Data:</a></span><ul class=\"toc-item\"><li><span><a href=\"#Importing-the-libraries-and-various-data-files\" data-toc-modified-id=\"Importing-the-libraries-and-various-data-files-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Importing the libraries and various data files</a></span></li><li><span><a href=\"#Assigning-genres-to-the-genres-column\" data-toc-modified-id=\"Assigning-genres-to-the-genres-column-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Assigning genres to the genres column</a></span></li><li><span><a href=\"#Making-merges-to-bring-the-data-together.\" data-toc-modified-id=\"Making-merges-to-bring-the-data-together.-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Making merges to bring the data together.</a></span></li><li><span><a href=\"#Limiting-the-data-on-runtime\" data-toc-modified-id=\"Limiting-the-data-on-runtime-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>Limiting the data on runtime</a></span></li><li><span><a href=\"#Dropping-unnecessary-columns\" data-toc-modified-id=\"Dropping-unnecessary-columns-2.5\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>Dropping unnecessary columns</a></span></li><li><span><a href=\"#Formatting-the-Remaining-Data\" data-toc-modified-id=\"Formatting-the-Remaining-Data-2.6\"><span class=\"toc-item-num\">2.6&nbsp;&nbsp;</span>Formatting the Remaining Data</a></span></li><li><span><a href=\"#Checking-for-Null-Values\" data-toc-modified-id=\"Checking-for-Null-Values-2.7\"><span class=\"toc-item-num\">2.7&nbsp;&nbsp;</span>Checking for Null Values</a></span></li><li><span><a href=\"#Removing-duplicates\" data-toc-modified-id=\"Removing-duplicates-2.8\"><span class=\"toc-item-num\">2.8&nbsp;&nbsp;</span>Removing duplicates</a></span></li></ul></li><li><span><a href=\"#Recommendations:\" data-toc-modified-id=\"Recommendations:-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Recommendations:</a></span><ul class=\"toc-item\"><li><span><a href=\"#Genre-recommendations\" data-toc-modified-id=\"Genre-recommendations-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Genre recommendations</a></span><ul class=\"toc-item\"><li><span><a href=\"#CITATION:-color-coding\" data-toc-modified-id=\"CITATION:-color-coding-3.1.1\"><span class=\"toc-item-num\">3.1.1&nbsp;&nbsp;</span>CITATION: color coding</a></span></li></ul></li><li><span><a href=\"#Release-Date\" data-toc-modified-id=\"Release-Date-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Release Date</a></span></li><li><span><a href=\"#Runtime-of-Film\" data-toc-modified-id=\"Runtime-of-Film-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>Runtime of Film</a></span></li></ul></li><li><span><a href=\"#Conclusion:\" data-toc-modified-id=\"Conclusion:-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Conclusion:</a></span><ul class=\"toc-item\"><li><span><a href=\"#Three-Business-Recommendations:\" data-toc-modified-id=\"Three-Business-Recommendations:-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>Three Business Recommendations:</a></span></li><li><span><a href=\"#Future-Work\" data-toc-modified-id=\"Future-Work-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>Future Work</a></span></li></ul></li></ul></div>"
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   "source": [
    "# Introduction: "
   ]
  },
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   "metadata": {},
   "source": [
    "## Citation\n",
    "\n",
    "Much of this project and its code come directly from https://github.com/ElyLin/MovieAnalysisProject where I was a contributor. After being a contributor to the group project, I wanted to go back and re-explore my process and analysis on my own. \n",
    "\n",
    "In my presentation, I recommend making films based on DOOM, Wolfenstein, The Elder Scrolls: Skyrim, Fallout and Dishonored, all Bethesda games that Microsoft bought the rights to in 2020. https://news.microsoft.com/2020/09/21/microsoft-to-acquire-zenimax-media-and-its-game-publisher-bethesda-softworks/"
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    "## Overview\n",
    "\n",
    "Microsoft sees all the big companies creating original video content and they want to get in on the fun. They have decided to create a new movie studio, but they don't know anything about creating movies. You are charged with exploring what types of films are currently doing the best at the box office. You must then translate those findings into actionable insights that the head of Microsoft's new movie studio can use to help decide what type of films to create."
   ]
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    "## Business Understanding:\n",
    "\n",
    "In this project, I hope to make a recommendation to Microsoft based on the Net Profit of the films. I'm using Net Profit as opposed to using Return on Investment (ROI) as my main financial data metric since Microsoft is already so large and successful that they can afford to put some investment into their films. They don't need to focus on getting a high profit from a low-budget film, they don't need to be so conservative with their investments. They can instead safely invest by following my recommendations. \n",
    "\n",
    "I will be making recommendations based on the answers to the following questions: \n",
    "\n",
    "1) What is the best genre to make a film in? \n",
    "\n",
    "2) What is the best time to release the film? \n",
    "\n",
    "3) What is the ideal runtime of a film? "
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "# Cleaning the Data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the libraries and various data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "# these are the various libraries we will need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
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   "source": [
    "# here is the raw data I will be using\n",
    "\n",
    "movie_budgets_df = pd.read_csv('zippedData/tn.movie_budgets.csv.gz')   \n",
    "# this csv is from The Numbers \"where Data and the Movie Buisness meet\"\n",
    "# https://www.the-numbers.com/\n",
    "title_basics = pd.read_csv('zippedData/imdb.title.basics.csv.gz')   \n",
    "title_ratings = pd.read_csv('zippedData/imdb.title.ratings.csv.gz') \n",
    "# those csvs are from https://www.imdb.com/, an amazon company. IMDB stands \n",
    "# for Internet Movie Data Base\n",
    "tmdb = pd.read_csv('zippedData/tmdb.movies.csv.gz')\n",
    "# this csv is from The Movie Database which prides itself on being \n",
    "# community built https://www.themoviedb.org/?language=en-US"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Assigning genres to the genres column"
   ]
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   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# firstly, the tmbd database has it's genre's encoded so we must decode the \n",
    "# genre's using the below dictionary\n",
    "\n",
    "genres={'28':\"Action\",\n",
    "'12':\"Adventure\",\n",
    "'16':\"Animation\",\n",
    "'35':\"Comedy\",\n",
    "'80':\"Crime\",\n",
    "'99':\"Documentary\",\n",
    "'18':\"Drama\",\n",
    "'10751':\"Family\",\n",
    "'14':\"Fantasy\",\n",
    "'36':\"History\",\n",
    "'27':\"Horror\",\n",
    "'10402':\"Music\",\n",
    "'9648':\"Mystery\",\n",
    "'10749':\"Romance\",\n",
    "'878':\"Science Fiction\",\n",
    "'10770':\"TV Movie\",\n",
    "'53':\"Thriller\",\n",
    "'10752':\"War\",\n",
    "'37':\"Western\"}\n",
    "\n",
    "tmdb['genre_ids'] = tmdb['genre_ids'].replace(pd.Series(genres).astype(str), \n",
    "                                              regex=True)\n",
    "tmdb['genre_ids']=tmdb['genre_ids'].str.strip('[]').str.split(', ')\n",
    "\n",
    "# creating a dictionary and using the key, values pairs to decode the genres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making merges to bring the data together. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>tconst</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",
       "      <th>averagerating</th>\n",
       "      <th>numvotes</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",
       "      <td>7.0</td>\n",
       "      <td>77</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",
       "      <td>7.2</td>\n",
       "      <td>43</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",
       "      <td>6.9</td>\n",
       "      <td>4517</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",
       "      <td>6.1</td>\n",
       "      <td>13</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",
       "      <td>6.5</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      tconst                    primary_title              original_title  \\\n",
       "0  tt0063540                        Sunghursh                   Sunghursh   \n",
       "1  tt0066787  One Day Before the Rainy Season             Ashad Ka Ek Din   \n",
       "2  tt0069049       The Other Side of the Wind  The Other Side of the Wind   \n",
       "3  tt0069204                  Sabse Bada Sukh             Sabse Bada Sukh   \n",
       "4  tt0100275         The Wandering Soap Opera       La Telenovela Errante   \n",
       "\n",
       "   start_year  runtime_minutes                genres  averagerating  numvotes  \n",
       "0        2013            175.0    Action,Crime,Drama            7.0        77  \n",
       "1        2019            114.0       Biography,Drama            7.2        43  \n",
       "2        2018            122.0                 Drama            6.9      4517  \n",
       "3        2018              NaN          Comedy,Drama            6.1        13  \n",
       "4        2017             80.0  Comedy,Drama,Fantasy            6.5       119  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# secondly, we must merge the two IMDB dataframes\n",
    "\n",
    "title_master = title_basics.merge(title_ratings, how='inner')\n",
    "title_master.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making the title and tconst ID part of the same object so I can drop \n",
    "# douplicates effectively\n",
    "\n",
    "title_master['title_and_id'] = title_master['primary_title'] +' id ' + \\\n",
    "title_master['tconst']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Unnamed: 0</th>\n",
       "      <th>genre_ids</th>\n",
       "      <th>id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_date</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>tconst</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>numvotes</th>\n",
       "      <th>title_and_id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>[Adventure, Fantasy, Family]</td>\n",
       "      <td>12444</td>\n",
       "      <td>en</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>33.533</td>\n",
       "      <td>2010-11-19</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10788</td>\n",
       "      <td>tt0926084</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>2010</td>\n",
       "      <td>146.0</td>\n",
       "      <td>Adventure,Fantasy,Mystery</td>\n",
       "      <td>7.7</td>\n",
       "      <td>425530</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1 i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>[Fantasy, Adventure, Animation, Family]</td>\n",
       "      <td>10191</td>\n",
       "      <td>en</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>28.734</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7610</td>\n",
       "      <td>tt0892769</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>2010</td>\n",
       "      <td>98.0</td>\n",
       "      <td>Action,Adventure,Animation</td>\n",
       "      <td>8.1</td>\n",
       "      <td>611299</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>[Adventure, Action, Science Fiction]</td>\n",
       "      <td>10138</td>\n",
       "      <td>en</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>28.515</td>\n",
       "      <td>2010-05-07</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>12368</td>\n",
       "      <td>tt1228705</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>2010</td>\n",
       "      <td>124.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>7.0</td>\n",
       "      <td>657690</td>\n",
       "      <td>Iron Man 2 id tt1228705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>[Action, Science Fiction, Adventure]</td>\n",
       "      <td>27205</td>\n",
       "      <td>en</td>\n",
       "      <td>Inception</td>\n",
       "      <td>27.920</td>\n",
       "      <td>2010-07-16</td>\n",
       "      <td>Inception</td>\n",
       "      <td>8.3</td>\n",
       "      <td>22186</td>\n",
       "      <td>tt1375666</td>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>148.0</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1841066</td>\n",
       "      <td>Inception id tt1375666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>[Adventure, Fantasy, Family]</td>\n",
       "      <td>32657</td>\n",
       "      <td>en</td>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "      <td>26.691</td>\n",
       "      <td>2010-02-11</td>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "      <td>6.1</td>\n",
       "      <td>4229</td>\n",
       "      <td>tt0814255</td>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "      <td>2010</td>\n",
       "      <td>118.0</td>\n",
       "      <td>Adventure,Family,Fantasy</td>\n",
       "      <td>5.9</td>\n",
       "      <td>165108</td>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                genre_ids     id  \\\n",
       "0           0             [Adventure, Fantasy, Family]  12444   \n",
       "1           1  [Fantasy, Adventure, Animation, Family]  10191   \n",
       "2           2     [Adventure, Action, Science Fiction]  10138   \n",
       "3           4     [Action, Science Fiction, Adventure]  27205   \n",
       "4           5             [Adventure, Fantasy, Family]  32657   \n",
       "\n",
       "  original_language                                     original_title  \\\n",
       "0                en       Harry Potter and the Deathly Hallows: Part 1   \n",
       "1                en                           How to Train Your Dragon   \n",
       "2                en                                         Iron Man 2   \n",
       "3                en                                          Inception   \n",
       "4                en  Percy Jackson & the Olympians: The Lightning T...   \n",
       "\n",
       "   popularity release_date                                              title  \\\n",
       "0      33.533   2010-11-19       Harry Potter and the Deathly Hallows: Part 1   \n",
       "1      28.734   2010-03-26                           How to Train Your Dragon   \n",
       "2      28.515   2010-05-07                                         Iron Man 2   \n",
       "3      27.920   2010-07-16                                          Inception   \n",
       "4      26.691   2010-02-11  Percy Jackson & the Olympians: The Lightning T...   \n",
       "\n",
       "   vote_average  vote_count     tconst  \\\n",
       "0           7.7       10788  tt0926084   \n",
       "1           7.7        7610  tt0892769   \n",
       "2           6.8       12368  tt1228705   \n",
       "3           8.3       22186  tt1375666   \n",
       "4           6.1        4229  tt0814255   \n",
       "\n",
       "                                       primary_title  start_year  \\\n",
       "0       Harry Potter and the Deathly Hallows: Part 1        2010   \n",
       "1                           How to Train Your Dragon        2010   \n",
       "2                                         Iron Man 2        2010   \n",
       "3                                          Inception        2010   \n",
       "4  Percy Jackson & the Olympians: The Lightning T...        2010   \n",
       "\n",
       "   runtime_minutes                      genres  averagerating  numvotes  \\\n",
       "0            146.0   Adventure,Fantasy,Mystery            7.7    425530   \n",
       "1             98.0  Action,Adventure,Animation            8.1    611299   \n",
       "2            124.0     Action,Adventure,Sci-Fi            7.0    657690   \n",
       "3            148.0     Action,Adventure,Sci-Fi            8.8   1841066   \n",
       "4            118.0    Adventure,Family,Fantasy            5.9    165108   \n",
       "\n",
       "                                        title_and_id  \n",
       "0  Harry Potter and the Deathly Hallows: Part 1 i...  \n",
       "1              How to Train Your Dragon id tt0892769  \n",
       "2                            Iron Man 2 id tt1228705  \n",
       "3                             Inception id tt1375666  \n",
       "4  Percy Jackson & the Olympians: The Lightning T...  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we must merge the tmdb database with the imdb database. \n",
    "# we chose to do an inner merge, here because we \n",
    "# are only interested in the movies that these dataframes have in common\n",
    "\n",
    "tmdb_and_imdb = tmdb.merge(title_master, how = 'inner')\n",
    "tmdb_and_imdb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the next dataframe we want to fold in has the column \"movie\" but we want \n",
    "# to rename it to title so the merge can happen on the title of the film, \n",
    "# so we must rename the column\n",
    "\n",
    "movie_budgets_df = movie_budgets_df.rename(columns={'movie':'title'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "      <td>12368</td>\n",
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       "      <td>8.3</td>\n",
       "      <td>22186</td>\n",
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       "      <td>1841066</td>\n",
       "      <td>Inception id tt1375666</td>\n",
       "      <td>38</td>\n",
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       "      <td>$160,000,000</td>\n",
       "      <td>$292,576,195</td>\n",
       "      <td>$835,524,642</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2010-02-11</td>\n",
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       "      <th>4</th>\n",
       "      <td>7</td>\n",
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       "      <td>Toy Story 3</td>\n",
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       "   Unnamed: 0                                genre_ids   id_x  \\\n",
       "0           1  [Fantasy, Adventure, Animation, Family]  10191   \n",
       "1           2     [Adventure, Action, Science Fiction]  10138   \n",
       "2           4     [Action, Science Fiction, Adventure]  27205   \n",
       "3           5             [Adventure, Fantasy, Family]  32657   \n",
       "4           7              [Animation, Family, Comedy]  10193   \n",
       "\n",
       "  original_language                                     original_title  \\\n",
       "0                en                           How to Train Your Dragon   \n",
       "1                en                                         Iron Man 2   \n",
       "2                en                                          Inception   \n",
       "3                en  Percy Jackson & the Olympians: The Lightning T...   \n",
       "4                en                                        Toy Story 3   \n",
       "\n",
       "   popularity release_date_x  \\\n",
       "0      28.734     2010-03-26   \n",
       "1      28.515     2010-05-07   \n",
       "2      27.920     2010-07-16   \n",
       "3      26.691     2010-02-11   \n",
       "4      24.445     2010-06-17   \n",
       "\n",
       "                                               title  vote_average  \\\n",
       "0                           How to Train Your Dragon           7.7   \n",
       "1                                         Iron Man 2           6.8   \n",
       "2                                          Inception           8.3   \n",
       "3  Percy Jackson & the Olympians: The Lightning T...           6.1   \n",
       "4                                        Toy Story 3           7.7   \n",
       "\n",
       "   vote_count  ... runtime_minutes                      genres  averagerating  \\\n",
       "0        7610  ...            98.0  Action,Adventure,Animation            8.1   \n",
       "1       12368  ...           124.0     Action,Adventure,Sci-Fi            7.0   \n",
       "2       22186  ...           148.0     Action,Adventure,Sci-Fi            8.8   \n",
       "3        4229  ...           118.0    Adventure,Family,Fantasy            5.9   \n",
       "4        8340  ...           103.0  Adventure,Animation,Comedy            8.3   \n",
       "\n",
       "   numvotes                                       title_and_id  id_y  \\\n",
       "0    611299              How to Train Your Dragon id tt0892769    30   \n",
       "1    657690                            Iron Man 2 id tt1228705    15   \n",
       "2   1841066                             Inception id tt1375666    38   \n",
       "3    165108  Percy Jackson & the Olympians: The Lightning T...    17   \n",
       "4    682218                           Toy Story 3 id tt0435761    47   \n",
       "\n",
       "   release_date_y production_budget  domestic_gross worldwide_gross  \n",
       "0    Mar 26, 2010      $165,000,000    $217,581,232    $494,870,992  \n",
       "1     May 7, 2010      $170,000,000    $312,433,331    $621,156,389  \n",
       "2    Jul 16, 2010      $160,000,000    $292,576,195    $835,524,642  \n",
       "3    Feb 12, 2010       $95,000,000     $88,768,303    $223,050,874  \n",
       "4    Jun 18, 2010      $200,000,000    $415,004,880  $1,068,879,522  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
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    }
   ],
   "source": [
    "# now we want to merge the fincial dataframe from The Numbers \n",
    "# with all the others based on Title, so we specify that \n",
    "# with the on = 'title'\n",
    "\n",
    "master_unclean = tmdb_and_imdb.merge\\\n",
    "(movie_budgets_df, on = 'title', how = 'inner')\n",
    "master_unclean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2959 entries, 0 to 2958\n",
      "Data columns (total 23 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Unnamed: 0         2959 non-null   int64  \n",
      " 1   genre_ids          2959 non-null   object \n",
      " 2   id_x               2959 non-null   int64  \n",
      " 3   original_language  2959 non-null   object \n",
      " 4   original_title     2959 non-null   object \n",
      " 5   popularity         2959 non-null   float64\n",
      " 6   release_date_x     2959 non-null   object \n",
      " 7   title              2959 non-null   object \n",
      " 8   vote_average       2959 non-null   float64\n",
      " 9   vote_count         2959 non-null   int64  \n",
      " 10  tconst             2959 non-null   object \n",
      " 11  primary_title      2959 non-null   object \n",
      " 12  start_year         2959 non-null   int64  \n",
      " 13  runtime_minutes    2864 non-null   float64\n",
      " 14  genres             2952 non-null   object \n",
      " 15  averagerating      2959 non-null   float64\n",
      " 16  numvotes           2959 non-null   int64  \n",
      " 17  title_and_id       2959 non-null   object \n",
      " 18  id_y               2959 non-null   int64  \n",
      " 19  release_date_y     2959 non-null   object \n",
      " 20  production_budget  2959 non-null   object \n",
      " 21  domestic_gross     2959 non-null   object \n",
      " 22  worldwide_gross    2959 non-null   object \n",
      "dtypes: float64(4), int64(6), object(13)\n",
      "memory usage: 554.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# let's take a look at the amount of information we have now and the types \n",
    "# of information in our columns we can see where we have duplicate columns, \n",
    "# we now have x and y versions to distinguish them from each other\n",
    "# runtime_minutes and genres are the only columns that do not have \n",
    "# 16669 non null values after this merge\n",
    "\n",
    "master_unclean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limiting the data on runtime "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2010-07-16</td>\n",
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       "      <td>Action,Adventure,Sci-Fi</td>\n",
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       "      <td>Inception id tt1375666</td>\n",
       "      <td>38</td>\n",
       "      <td>Jul 16, 2010</td>\n",
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       "      <td>$835,524,642</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>[Adventure, Fantasy, Family]</td>\n",
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       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "      <td>26.691</td>\n",
       "      <td>2010-02-11</td>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
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       "      <td>5.9</td>\n",
       "      <td>165108</td>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "      <td>17</td>\n",
       "      <td>Feb 12, 2010</td>\n",
       "      <td>$95,000,000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
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       "      <td>$1,068,879,522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                genre_ids   id_x  \\\n",
       "0           1  [Fantasy, Adventure, Animation, Family]  10191   \n",
       "1           2     [Adventure, Action, Science Fiction]  10138   \n",
       "2           4     [Action, Science Fiction, Adventure]  27205   \n",
       "3           5             [Adventure, Fantasy, Family]  32657   \n",
       "4           7              [Animation, Family, Comedy]  10193   \n",
       "\n",
       "  original_language                                     original_title  \\\n",
       "0                en                           How to Train Your Dragon   \n",
       "1                en                                         Iron Man 2   \n",
       "2                en                                          Inception   \n",
       "3                en  Percy Jackson & the Olympians: The Lightning T...   \n",
       "4                en                                        Toy Story 3   \n",
       "\n",
       "   popularity release_date_x  \\\n",
       "0      28.734     2010-03-26   \n",
       "1      28.515     2010-05-07   \n",
       "2      27.920     2010-07-16   \n",
       "3      26.691     2010-02-11   \n",
       "4      24.445     2010-06-17   \n",
       "\n",
       "                                               title  vote_average  \\\n",
       "0                           How to Train Your Dragon           7.7   \n",
       "1                                         Iron Man 2           6.8   \n",
       "2                                          Inception           8.3   \n",
       "3  Percy Jackson & the Olympians: The Lightning T...           6.1   \n",
       "4                                        Toy Story 3           7.7   \n",
       "\n",
       "   vote_count  ... runtime_minutes                      genres  averagerating  \\\n",
       "0        7610  ...            98.0  Action,Adventure,Animation            8.1   \n",
       "1       12368  ...           124.0     Action,Adventure,Sci-Fi            7.0   \n",
       "2       22186  ...           148.0     Action,Adventure,Sci-Fi            8.8   \n",
       "3        4229  ...           118.0    Adventure,Family,Fantasy            5.9   \n",
       "4        8340  ...           103.0  Adventure,Animation,Comedy            8.3   \n",
       "\n",
       "   numvotes                                       title_and_id  id_y  \\\n",
       "0    611299              How to Train Your Dragon id tt0892769    30   \n",
       "1    657690                            Iron Man 2 id tt1228705    15   \n",
       "2   1841066                             Inception id tt1375666    38   \n",
       "3    165108  Percy Jackson & the Olympians: The Lightning T...    17   \n",
       "4    682218                           Toy Story 3 id tt0435761    47   \n",
       "\n",
       "   release_date_y production_budget  domestic_gross worldwide_gross  \n",
       "0    Mar 26, 2010      $165,000,000    $217,581,232    $494,870,992  \n",
       "1     May 7, 2010      $170,000,000    $312,433,331    $621,156,389  \n",
       "2    Jul 16, 2010      $160,000,000    $292,576,195    $835,524,642  \n",
       "3    Feb 12, 2010       $95,000,000     $88,768,303    $223,050,874  \n",
       "4    Jun 18, 2010      $200,000,000    $415,004,880  $1,068,879,522  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we want to exclude movies from this master dataframe which are less \n",
    "# than 45 min and more than three hours, because we want to base the \n",
    "# recomendations on films that are typically seen in the movie theater\n",
    "# and put that information on a new dataframe called run_time\n",
    "\n",
    "run_time = master_unclean.loc[(master_unclean['runtime_minutes'] > 45) \\\n",
    "                              & (master_unclean['runtime_minutes'] < 180)]\n",
    "run_time.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping unnecessary columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here, we want to drop some columns where the information is irrelivant \n",
    "# or duplicated. I'm dripping id_x and id_y since I don't need another unqie \n",
    "# identifyer,  title, original title since I already have the title column I \n",
    "# need,  start year, year and release_date_x since I already have a datetime \n",
    "# column, genres since the genre_ids column I already have is more robust, \n",
    "# and all the rating a popularity information since I won't be using that data \n",
    "# for my analysis\n",
    "\n",
    "df = master_unclean.drop\\\n",
    "(['Unnamed: 0', 'id_x', 'original_title', 'start_year', 'id_y',\\\n",
    "'genres', 'release_date_x', 'primary_title', 'original_language', \\\n",
    "'popularity', 'vote_average', 'vote_count', 'numvotes', 'averagerating',\n",
    "  'title', 'tconst'],\n",
    " axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>genre_ids</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>title_and_id</th>\n",
       "      <th>release_date_y</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[Fantasy, Adventure, Animation, Family]</td>\n",
       "      <td>98.0</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "      <td>Mar 26, 2010</td>\n",
       "      <td>$165,000,000</td>\n",
       "      <td>$217,581,232</td>\n",
       "      <td>$494,870,992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Adventure, Action, Science Fiction]</td>\n",
       "      <td>124.0</td>\n",
       "      <td>Iron Man 2 id tt1228705</td>\n",
       "      <td>May 7, 2010</td>\n",
       "      <td>$170,000,000</td>\n",
       "      <td>$312,433,331</td>\n",
       "      <td>$621,156,389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Action, Science Fiction, Adventure]</td>\n",
       "      <td>148.0</td>\n",
       "      <td>Inception id tt1375666</td>\n",
       "      <td>Jul 16, 2010</td>\n",
       "      <td>$160,000,000</td>\n",
       "      <td>$292,576,195</td>\n",
       "      <td>$835,524,642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[Adventure, Fantasy, Family]</td>\n",
       "      <td>118.0</td>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "      <td>Feb 12, 2010</td>\n",
       "      <td>$95,000,000</td>\n",
       "      <td>$88,768,303</td>\n",
       "      <td>$223,050,874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[Animation, Family, Comedy]</td>\n",
       "      <td>103.0</td>\n",
       "      <td>Toy Story 3 id tt0435761</td>\n",
       "      <td>Jun 18, 2010</td>\n",
       "      <td>$200,000,000</td>\n",
       "      <td>$415,004,880</td>\n",
       "      <td>$1,068,879,522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 genre_ids  runtime_minutes  \\\n",
       "0  [Fantasy, Adventure, Animation, Family]             98.0   \n",
       "1     [Adventure, Action, Science Fiction]            124.0   \n",
       "2     [Action, Science Fiction, Adventure]            148.0   \n",
       "3             [Adventure, Fantasy, Family]            118.0   \n",
       "4              [Animation, Family, Comedy]            103.0   \n",
       "\n",
       "                                        title_and_id release_date_y  \\\n",
       "0              How to Train Your Dragon id tt0892769   Mar 26, 2010   \n",
       "1                            Iron Man 2 id tt1228705    May 7, 2010   \n",
       "2                             Inception id tt1375666   Jul 16, 2010   \n",
       "3  Percy Jackson & the Olympians: The Lightning T...   Feb 12, 2010   \n",
       "4                           Toy Story 3 id tt0435761   Jun 18, 2010   \n",
       "\n",
       "  production_budget domestic_gross worldwide_gross  \n",
       "0      $165,000,000   $217,581,232    $494,870,992  \n",
       "1      $170,000,000   $312,433,331    $621,156,389  \n",
       "2      $160,000,000   $292,576,195    $835,524,642  \n",
       "3       $95,000,000    $88,768,303    $223,050,874  \n",
       "4      $200,000,000   $415,004,880  $1,068,879,522  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's take a look at where our dataframe is right now\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Formatting the Remaining Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this function will take a string value with commas and dollar signs and \n",
    "# return an integer value in a format that I can do math upon \n",
    "\n",
    "def reformat(string_to_money):\n",
    "    string_to_money = string_to_money.replace(',', '').replace('$', '')\n",
    "    string_to_money = int(string_to_money)\n",
    "    return string_to_money"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reformatting the production budget, domestic gross and worldwide gross columns \n",
    "\n",
    "df['production_budget'] = df['production_budget'].map(reformat)\n",
    "df['domestic_gross'] = df['domestic_gross'].map(reformat)\n",
    "df['worldwide_gross'] =df['worldwide_gross'].map(reformat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2959 entries, 0 to 2958\n",
      "Data columns (total 7 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   genre_ids          2959 non-null   object \n",
      " 1   runtime_minutes    2864 non-null   float64\n",
      " 2   title_and_id       2959 non-null   object \n",
      " 3   release_date_y     2959 non-null   object \n",
      " 4   production_budget  2959 non-null   int64  \n",
      " 5   domestic_gross     2959 non-null   int64  \n",
      " 6   worldwide_gross    2959 non-null   int64  \n",
      "dtypes: float64(1), int64(3), object(3)\n",
      "memory usage: 184.9+ KB\n"
     ]
    }
   ],
   "source": [
    "#checking to make sure my finacial data is in integer form\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# creating total gross, net_profit and ROI as finacial metrics. I will be using \n",
    "# net profit as my main metric but I wanted to create total gross and ROI \n",
    "# for any additional analysis in my conclusion \n",
    "\n",
    "df['total_gross'] = df['domestic_gross'] + df['worldwide_gross']\n",
    "df['net_profit']= df['total_gross'] - df['production_budget'] \n",
    "df['ROI'] = (df['net_profit'] / df['production_budget']) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking for Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "genre_ids             0\n",
       "runtime_minutes      95\n",
       "title_and_id          0\n",
       "release_date_y        0\n",
       "production_budget     0\n",
       "domestic_gross        0\n",
       "worldwide_gross       0\n",
       "total_gross           0\n",
       "net_profit            0\n",
       "ROI                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seeing how many columns have null values\n",
    "\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm going to drop the rows where the \n",
    "runtime data is missing, it's a small section of my dataset and since I will be making recommendations based on runtime, I don't want to throw the data off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping rows with null values \n",
    "\n",
    "df.dropna(axis=0, inplace= True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "genre_ids            0\n",
       "runtime_minutes      0\n",
       "title_and_id         0\n",
       "release_date_y       0\n",
       "production_budget    0\n",
       "domestic_gross       0\n",
       "worldwide_gross      0\n",
       "total_gross          0\n",
       "net_profit           0\n",
       "ROI                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to check if the 34 rows dropped and we have no null values\n",
    "\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2864 entries, 0 to 2958\n",
      "Data columns (total 10 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   genre_ids          2864 non-null   object \n",
      " 1   runtime_minutes    2864 non-null   float64\n",
      " 2   title_and_id       2864 non-null   object \n",
      " 3   release_date_y     2864 non-null   object \n",
      " 4   production_budget  2864 non-null   int64  \n",
      " 5   domestic_gross     2864 non-null   int64  \n",
      " 6   worldwide_gross    2864 non-null   int64  \n",
      " 7   total_gross        2864 non-null   int64  \n",
      " 8   net_profit         2864 non-null   int64  \n",
      " 9   ROI                2864 non-null   float64\n",
      "dtypes: float64(2), int64(5), object(3)\n",
      "memory usage: 246.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>genre_ids</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>title_and_id</th>\n",
       "      <th>release_date_y</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>total_gross</th>\n",
       "      <th>net_profit</th>\n",
       "      <th>ROI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1529</th>\n",
       "      <td>[Horror]</td>\n",
       "      <td>84.0</td>\n",
       "      <td>24 Hours to Die id tt3747550</td>\n",
       "      <td>Sep 18, 2015</td>\n",
       "      <td>2000000</td>\n",
       "      <td>2583301</td>\n",
       "      <td>2791973</td>\n",
       "      <td>5375274</td>\n",
       "      <td>3375274</td>\n",
       "      <td>168.763700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1525</th>\n",
       "      <td>[Crime, Drama, Thriller]</td>\n",
       "      <td>84.0</td>\n",
       "      <td>24 Hours to Die id tt3747550</td>\n",
       "      <td>Sep 18, 2015</td>\n",
       "      <td>2000000</td>\n",
       "      <td>2583301</td>\n",
       "      <td>2791973</td>\n",
       "      <td>5375274</td>\n",
       "      <td>3375274</td>\n",
       "      <td>168.763700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2667</th>\n",
       "      <td>[Comedy]</td>\n",
       "      <td>104.0</td>\n",
       "      <td>A Bad Moms Christmas id tt6359956</td>\n",
       "      <td>Nov 1, 2017</td>\n",
       "      <td>28000000</td>\n",
       "      <td>72110659</td>\n",
       "      <td>127710550</td>\n",
       "      <td>199821209</td>\n",
       "      <td>171821209</td>\n",
       "      <td>613.647175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>[Documentary]</td>\n",
       "      <td>98.0</td>\n",
       "      <td>A Better Life id tt1554091</td>\n",
       "      <td>Jun 24, 2011</td>\n",
       "      <td>10000000</td>\n",
       "      <td>1759252</td>\n",
       "      <td>1884251</td>\n",
       "      <td>3643503</td>\n",
       "      <td>-6356497</td>\n",
       "      <td>-63.564970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2432</th>\n",
       "      <td>[Drama, Fantasy]</td>\n",
       "      <td>108.0</td>\n",
       "      <td>A Monster Calls id tt3416532</td>\n",
       "      <td>Jan 6, 2017</td>\n",
       "      <td>43000000</td>\n",
       "      <td>3740823</td>\n",
       "      <td>46414530</td>\n",
       "      <td>50155353</td>\n",
       "      <td>7155353</td>\n",
       "      <td>16.640356</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1494</th>\n",
       "      <td>[Adventure, Fantasy, War, Romance]</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Wonder Woman id tt4028068</td>\n",
       "      <td>Jun 2, 2017</td>\n",
       "      <td>150000000</td>\n",
       "      <td>412563408</td>\n",
       "      <td>821133378</td>\n",
       "      <td>1233696786</td>\n",
       "      <td>1083696786</td>\n",
       "      <td>722.464524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1495</th>\n",
       "      <td>[Adventure, Fantasy, War, Romance]</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Wonder Woman id tt4283448</td>\n",
       "      <td>Jun 2, 2017</td>\n",
       "      <td>150000000</td>\n",
       "      <td>412563408</td>\n",
       "      <td>821133378</td>\n",
       "      <td>1233696786</td>\n",
       "      <td>1083696786</td>\n",
       "      <td>722.464524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2648</th>\n",
       "      <td>[Drama, Family]</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Wonder id tt2543472</td>\n",
       "      <td>Nov 17, 2017</td>\n",
       "      <td>20000000</td>\n",
       "      <td>132422809</td>\n",
       "      <td>304604712</td>\n",
       "      <td>437027521</td>\n",
       "      <td>417027521</td>\n",
       "      <td>2085.137605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2213</th>\n",
       "      <td>[Drama]</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Woodlawn id tt4183692</td>\n",
       "      <td>Oct 16, 2015</td>\n",
       "      <td>13000000</td>\n",
       "      <td>14394097</td>\n",
       "      <td>14403703</td>\n",
       "      <td>28797800</td>\n",
       "      <td>15797800</td>\n",
       "      <td>121.521538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>[Comedy, Adventure, Fantasy]</td>\n",
       "      <td>102.0</td>\n",
       "      <td>Your Highness id tt1240982</td>\n",
       "      <td>Apr 8, 2011</td>\n",
       "      <td>50000000</td>\n",
       "      <td>21596445</td>\n",
       "      <td>26121638</td>\n",
       "      <td>47718083</td>\n",
       "      <td>-2281917</td>\n",
       "      <td>-4.563834</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>663 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               genre_ids  runtime_minutes  \\\n",
       "1529                            [Horror]             84.0   \n",
       "1525            [Crime, Drama, Thriller]             84.0   \n",
       "2667                            [Comedy]            104.0   \n",
       "656                        [Documentary]             98.0   \n",
       "2432                    [Drama, Fantasy]            108.0   \n",
       "...                                  ...              ...   \n",
       "1494  [Adventure, Fantasy, War, Romance]             60.0   \n",
       "1495  [Adventure, Fantasy, War, Romance]             75.0   \n",
       "2648                     [Drama, Family]            113.0   \n",
       "2213                             [Drama]            123.0   \n",
       "398         [Comedy, Adventure, Fantasy]            102.0   \n",
       "\n",
       "                           title_and_id release_date_y  production_budget  \\\n",
       "1529       24 Hours to Die id tt3747550   Sep 18, 2015            2000000   \n",
       "1525       24 Hours to Die id tt3747550   Sep 18, 2015            2000000   \n",
       "2667  A Bad Moms Christmas id tt6359956    Nov 1, 2017           28000000   \n",
       "656          A Better Life id tt1554091   Jun 24, 2011           10000000   \n",
       "2432       A Monster Calls id tt3416532    Jan 6, 2017           43000000   \n",
       "...                                 ...            ...                ...   \n",
       "1494          Wonder Woman id tt4028068    Jun 2, 2017          150000000   \n",
       "1495          Wonder Woman id tt4283448    Jun 2, 2017          150000000   \n",
       "2648                Wonder id tt2543472   Nov 17, 2017           20000000   \n",
       "2213              Woodlawn id tt4183692   Oct 16, 2015           13000000   \n",
       "398          Your Highness id tt1240982    Apr 8, 2011           50000000   \n",
       "\n",
       "      domestic_gross  worldwide_gross  total_gross  net_profit          ROI  \n",
       "1529         2583301          2791973      5375274     3375274   168.763700  \n",
       "1525         2583301          2791973      5375274     3375274   168.763700  \n",
       "2667        72110659        127710550    199821209   171821209   613.647175  \n",
       "656          1759252          1884251      3643503    -6356497   -63.564970  \n",
       "2432         3740823         46414530     50155353     7155353    16.640356  \n",
       "...              ...              ...          ...         ...          ...  \n",
       "1494       412563408        821133378   1233696786  1083696786   722.464524  \n",
       "1495       412563408        821133378   1233696786  1083696786   722.464524  \n",
       "2648       132422809        304604712    437027521   417027521  2085.137605  \n",
       "2213        14394097         14403703     28797800    15797800   121.521538  \n",
       "398         21596445         26121638     47718083    -2281917    -4.563834  \n",
       "\n",
       "[663 rows x 10 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to show how many duplicates we have as a slice of the current df\n",
    "\n",
    "df[df.duplicated(subset = df.drop('genre_ids', axis = 1).columns, \n",
    "                )].sort_values(by = 'title_and_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping duplicate rows that are about the same film by using the above \n",
    "# subset (which checks for everything except matching values in the \n",
    "# 'genre_ids' columns.) \n",
    "\n",
    "df.drop_duplicates(subset = df.drop('genre_ids', axis = 1).columns,\n",
    "                   inplace = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2201 entries, 0 to 2955\n",
      "Data columns (total 10 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   genre_ids          2201 non-null   object \n",
      " 1   runtime_minutes    2201 non-null   float64\n",
      " 2   title_and_id       2201 non-null   object \n",
      " 3   release_date_y     2201 non-null   object \n",
      " 4   production_budget  2201 non-null   int64  \n",
      " 5   domestic_gross     2201 non-null   int64  \n",
      " 6   worldwide_gross    2201 non-null   int64  \n",
      " 7   total_gross        2201 non-null   int64  \n",
      " 8   net_profit         2201 non-null   int64  \n",
      " 9   ROI                2201 non-null   float64\n",
      "dtypes: float64(2), int64(5), object(3)\n",
      "memory usage: 189.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# checking to make sure the duplicates were dropped. \n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>genre_ids</th>\n",
       "      <th>runtime_minutes</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[Fantasy, Adventure, Animation, Family]</td>\n",
       "      <td>98.0</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "      <td>Mar 26, 2010</td>\n",
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       "      <td>494870992</td>\n",
       "      <td>712452224</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Adventure, Action, Science Fiction]</td>\n",
       "      <td>124.0</td>\n",
       "      <td>Iron Man 2 id tt1228705</td>\n",
       "      <td>May 7, 2010</td>\n",
       "      <td>170000000</td>\n",
       "      <td>312433331</td>\n",
       "      <td>621156389</td>\n",
       "      <td>933589720</td>\n",
       "      <td>763589720</td>\n",
       "      <td>449.170424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Action, Science Fiction, Adventure]</td>\n",
       "      <td>148.0</td>\n",
       "      <td>Inception id tt1375666</td>\n",
       "      <td>Jul 16, 2010</td>\n",
       "      <td>160000000</td>\n",
       "      <td>292576195</td>\n",
       "      <td>835524642</td>\n",
       "      <td>1128100837</td>\n",
       "      <td>968100837</td>\n",
       "      <td>605.063023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[Adventure, Fantasy, Family]</td>\n",
       "      <td>118.0</td>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "      <td>Feb 12, 2010</td>\n",
       "      <td>95000000</td>\n",
       "      <td>88768303</td>\n",
       "      <td>223050874</td>\n",
       "      <td>311819177</td>\n",
       "      <td>216819177</td>\n",
       "      <td>228.230713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[Animation, Family, Comedy]</td>\n",
       "      <td>103.0</td>\n",
       "      <td>Toy Story 3 id tt0435761</td>\n",
       "      <td>Jun 18, 2010</td>\n",
       "      <td>200000000</td>\n",
       "      <td>415004880</td>\n",
       "      <td>1068879522</td>\n",
       "      <td>1483884402</td>\n",
       "      <td>1283884402</td>\n",
       "      <td>641.942201</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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2951</th>\n",
       "      <td>[Horror, Mystery, Thriller]</td>\n",
       "      <td>145.0</td>\n",
       "      <td>3 id tt2123129</td>\n",
       "      <td>Sep 16, 2011</td>\n",
       "      <td>7200000</td>\n",
       "      <td>59774</td>\n",
       "      <td>295492</td>\n",
       "      <td>355266</td>\n",
       "      <td>-6844734</td>\n",
       "      <td>-95.065750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2952</th>\n",
       "      <td>[Horror, Mystery, Thriller]</td>\n",
       "      <td>115.0</td>\n",
       "      <td>3 id tt2379653</td>\n",
       "      <td>Sep 16, 2011</td>\n",
       "      <td>7200000</td>\n",
       "      <td>59774</td>\n",
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       "    <tr>\n",
       "      <th>2953</th>\n",
       "      <td>[Horror, Mystery, Thriller]</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3 id tt5618160</td>\n",
       "      <td>Sep 16, 2011</td>\n",
       "      <td>7200000</td>\n",
       "      <td>59774</td>\n",
       "      <td>295492</td>\n",
       "      <td>355266</td>\n",
       "      <td>-6844734</td>\n",
       "      <td>-95.065750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2954</th>\n",
       "      <td>[Action, Science Fiction]</td>\n",
       "      <td>91.0</td>\n",
       "      <td>Molly id tt6522982</td>\n",
       "      <td>Oct 22, 1999</td>\n",
       "      <td>21000000</td>\n",
       "      <td>17396</td>\n",
       "      <td>17396</td>\n",
       "      <td>34792</td>\n",
       "      <td>-20965208</td>\n",
       "      <td>-99.834324</td>\n",
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       "    <tr>\n",
       "      <th>2955</th>\n",
       "      <td>[]</td>\n",
       "      <td>89.0</td>\n",
       "      <td>The Box id tt1728200</td>\n",
       "      <td>Nov 6, 2009</td>\n",
       "      <td>25000000</td>\n",
       "      <td>15051977</td>\n",
       "      <td>34356760</td>\n",
       "      <td>49408737</td>\n",
       "      <td>24408737</td>\n",
       "      <td>97.634948</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>2201 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    genre_ids  runtime_minutes  \\\n",
       "0     [Fantasy, Adventure, Animation, Family]             98.0   \n",
       "1        [Adventure, Action, Science Fiction]            124.0   \n",
       "2        [Action, Science Fiction, Adventure]            148.0   \n",
       "3                [Adventure, Fantasy, Family]            118.0   \n",
       "4                 [Animation, Family, Comedy]            103.0   \n",
       "...                                       ...              ...   \n",
       "2951              [Horror, Mystery, Thriller]            145.0   \n",
       "2952              [Horror, Mystery, Thriller]            115.0   \n",
       "2953              [Horror, Mystery, Thriller]             80.0   \n",
       "2954                [Action, Science Fiction]             91.0   \n",
       "2955                                       []             89.0   \n",
       "\n",
       "                                           title_and_id release_date_y  \\\n",
       "0                 How to Train Your Dragon id tt0892769   Mar 26, 2010   \n",
       "1                               Iron Man 2 id tt1228705    May 7, 2010   \n",
       "2                                Inception id tt1375666   Jul 16, 2010   \n",
       "3     Percy Jackson & the Olympians: The Lightning T...   Feb 12, 2010   \n",
       "4                              Toy Story 3 id tt0435761   Jun 18, 2010   \n",
       "...                                                 ...            ...   \n",
       "2951                                     3 id tt2123129   Sep 16, 2011   \n",
       "2952                                     3 id tt2379653   Sep 16, 2011   \n",
       "2953                                     3 id tt5618160   Sep 16, 2011   \n",
       "2954                                 Molly id tt6522982   Oct 22, 1999   \n",
       "2955                               The Box id tt1728200    Nov 6, 2009   \n",
       "\n",
       "      production_budget  domestic_gross  worldwide_gross  total_gross  \\\n",
       "0             165000000       217581232        494870992    712452224   \n",
       "1             170000000       312433331        621156389    933589720   \n",
       "2             160000000       292576195        835524642   1128100837   \n",
       "3              95000000        88768303        223050874    311819177   \n",
       "4             200000000       415004880       1068879522   1483884402   \n",
       "...                 ...             ...              ...          ...   \n",
       "2951            7200000           59774           295492       355266   \n",
       "2952            7200000           59774           295492       355266   \n",
       "2953            7200000           59774           295492       355266   \n",
       "2954           21000000           17396            17396        34792   \n",
       "2955           25000000        15051977         34356760     49408737   \n",
       "\n",
       "      net_profit         ROI  \n",
       "0      547452224  331.789227  \n",
       "1      763589720  449.170424  \n",
       "2      968100837  605.063023  \n",
       "3      216819177  228.230713  \n",
       "4     1283884402  641.942201  \n",
       "...          ...         ...  \n",
       "2951    -6844734  -95.065750  \n",
       "2952    -6844734  -95.065750  \n",
       "2953    -6844734  -95.065750  \n",
       "2954   -20965208  -99.834324  \n",
       "2955    24408737   97.634948  \n",
       "\n",
       "[2201 rows x 10 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After cleaning and removing null values and duplicates, this analysis will use a dataset the size of 2,864 films. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recommendations:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genre recommendations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>Mar 26, 2010</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Family</td>\n",
       "      <td>98.0</td>\n",
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       "      <td>Mar 26, 2010</td>\n",
       "      <td>165000000</td>\n",
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       "      <td>712452224</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>124.0</td>\n",
       "      <td>Iron Man 2 id tt1228705</td>\n",
       "      <td>May 7, 2010</td>\n",
       "      <td>170000000</td>\n",
       "      <td>312433331</td>\n",
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      ],
      "text/plain": [
       "   genre_ids  runtime_minutes                           title_and_id  \\\n",
       "0    Fantasy             98.0  How to Train Your Dragon id tt0892769   \n",
       "0  Adventure             98.0  How to Train Your Dragon id tt0892769   \n",
       "0  Animation             98.0  How to Train Your Dragon id tt0892769   \n",
       "0     Family             98.0  How to Train Your Dragon id tt0892769   \n",
       "1  Adventure            124.0                Iron Man 2 id tt1228705   \n",
       "\n",
       "  release_date_y  production_budget  domestic_gross  worldwide_gross  \\\n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "1    May 7, 2010          170000000       312433331        621156389   \n",
       "\n",
       "   total_gross  net_profit         ROI  \n",
       "0    712452224   547452224  331.789227  \n",
       "0    712452224   547452224  331.789227  \n",
       "0    712452224   547452224  331.789227  \n",
       "0    712452224   547452224  331.789227  \n",
       "1    933589720   763589720  449.170424  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#first we need to use .explode to make a row for each genre that each film\n",
    "#falls into and so we can compare genres side by side based on total gross, \n",
    "#net profit and ROI \n",
    "\n",
    "df_exploded = df.explode('genre_ids', ignore_index=False)\n",
    "df_exploded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5118 entries, 0 to 2955\n",
      "Data columns (total 10 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   genre_ids          5118 non-null   object \n",
      " 1   runtime_minutes    5118 non-null   float64\n",
      " 2   title_and_id       5118 non-null   object \n",
      " 3   release_date_y     5118 non-null   object \n",
      " 4   production_budget  5118 non-null   int64  \n",
      " 5   domestic_gross     5118 non-null   int64  \n",
      " 6   worldwide_gross    5118 non-null   int64  \n",
      " 7   total_gross        5118 non-null   int64  \n",
      " 8   net_profit         5118 non-null   int64  \n",
      " 9   ROI                5118 non-null   float64\n",
      "dtypes: float64(2), int64(5), object(3)\n",
      "memory usage: 439.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# seeing the shape of the exploded dataframe \n",
    "\n",
    "df_exploded.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
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       "      <td>102.0</td>\n",
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       "      <td>-9424629</td>\n",
       "      <td>-94.246290</td>\n",
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       "      <th>373</th>\n",
       "      <td></td>\n",
       "      <td>85.0</td>\n",
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       "      <td>98710023</td>\n",
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       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td></td>\n",
       "      <td>90.0</td>\n",
       "      <td>Red id tt8851190</td>\n",
       "      <td>Oct 15, 2010</td>\n",
       "      <td>60000000</td>\n",
       "      <td>90380162</td>\n",
       "      <td>196439693</td>\n",
       "      <td>286819855</td>\n",
       "      <td>226819855</td>\n",
       "      <td>378.033092</td>\n",
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       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td></td>\n",
       "      <td>88.0</td>\n",
       "      <td>Breaking In id tt7137846</td>\n",
       "      <td>May 11, 2018</td>\n",
       "      <td>6000000</td>\n",
       "      <td>46840590</td>\n",
       "      <td>51411620</td>\n",
       "      <td>98252210</td>\n",
       "      <td>92252210</td>\n",
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       "    <tr>\n",
       "      <th>2280</th>\n",
       "      <td></td>\n",
       "      <td>82.0</td>\n",
       "      <td>Diamond Ruff id tt2111292</td>\n",
       "      <td>Dec 31, 2014</td>\n",
       "      <td>500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-500000</td>\n",
       "      <td>-100.000000</td>\n",
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       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td></td>\n",
       "      <td>75.0</td>\n",
       "      <td>Graduation Day id tt2616818</td>\n",
       "      <td>May 1, 1981</td>\n",
       "      <td>250000</td>\n",
       "      <td>23894000</td>\n",
       "      <td>23894000</td>\n",
       "      <td>47788000</td>\n",
       "      <td>47538000</td>\n",
       "      <td>19015.200000</td>\n",
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       "    <tr>\n",
       "      <th>2955</th>\n",
       "      <td></td>\n",
       "      <td>89.0</td>\n",
       "      <td>The Box id tt1728200</td>\n",
       "      <td>Nov 6, 2009</td>\n",
       "      <td>25000000</td>\n",
       "      <td>15051977</td>\n",
       "      <td>34356760</td>\n",
       "      <td>49408737</td>\n",
       "      <td>24408737</td>\n",
       "      <td>97.634948</td>\n",
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       "</table>\n",
       "<p>62 rows × 10 columns</p>\n",
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      "text/plain": [
       "     genre_ids  runtime_minutes                 title_and_id release_date_y  \\\n",
       "360                        80.0         Plastic id tt1691450   Sep 26, 2014   \n",
       "361                        78.0         Plastic id tt2554700   Sep 26, 2014   \n",
       "362                       102.0         Plastic id tt2556874   Sep 26, 2014   \n",
       "373                        85.0         Monster id tt1617136   Dec 24, 2003   \n",
       "374                        90.0         Monster id tt6840904   Dec 24, 2003   \n",
       "...        ...              ...                          ...            ...   \n",
       "1954                       90.0             Red id tt8851190   Oct 15, 2010   \n",
       "1957                       88.0     Breaking In id tt7137846   May 11, 2018   \n",
       "2280                       82.0    Diamond Ruff id tt2111292   Dec 31, 2014   \n",
       "2304                       75.0  Graduation Day id tt2616818    May 1, 1981   \n",
       "2955                       89.0         The Box id tt1728200    Nov 6, 2009   \n",
       "\n",
       "      production_budget  domestic_gross  worldwide_gross  total_gross  \\\n",
       "360            10000000               0           575371       575371   \n",
       "361            10000000               0           575371       575371   \n",
       "362            10000000               0           575371       575371   \n",
       "373             5000000        34469210         64240813     98710023   \n",
       "374             5000000        34469210         64240813     98710023   \n",
       "...                 ...             ...              ...          ...   \n",
       "1954           60000000        90380162        196439693    286819855   \n",
       "1957            6000000        46840590         51411620     98252210   \n",
       "2280             500000               0                0            0   \n",
       "2304             250000        23894000         23894000     47788000   \n",
       "2955           25000000        15051977         34356760     49408737   \n",
       "\n",
       "      net_profit           ROI  \n",
       "360     -9424629    -94.246290  \n",
       "361     -9424629    -94.246290  \n",
       "362     -9424629    -94.246290  \n",
       "373     93710023   1874.200460  \n",
       "374     93710023   1874.200460  \n",
       "...          ...           ...  \n",
       "1954   226819855    378.033092  \n",
       "1957    92252210   1537.536833  \n",
       "2280     -500000   -100.000000  \n",
       "2304    47538000  19015.200000  \n",
       "2955    24408737     97.634948  \n",
       "\n",
       "[62 rows x 10 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slicing out the rows where the genre was left blank\n",
    "# (these rows didn't pop as null values although they are equivilant to null)\n",
    "\n",
    "null_genre = df_exploded.loc[df_exploded['genre_ids'] == '']\n",
    "null_genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing the rows we sliced above \n",
    "\n",
    "df_exploded.drop(labels = null_genre.index, inplace = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5056 entries, 0 to 2954\n",
      "Data columns (total 10 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   genre_ids          5056 non-null   object \n",
      " 1   runtime_minutes    5056 non-null   float64\n",
      " 2   title_and_id       5056 non-null   object \n",
      " 3   release_date_y     5056 non-null   object \n",
      " 4   production_budget  5056 non-null   int64  \n",
      " 5   domestic_gross     5056 non-null   int64  \n",
      " 6   worldwide_gross    5056 non-null   int64  \n",
      " 7   total_gross        5056 non-null   int64  \n",
      " 8   net_profit         5056 non-null   int64  \n",
      " 9   ROI                5056 non-null   float64\n",
      "dtypes: float64(2), int64(5), object(3)\n",
      "memory usage: 434.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# checking to make sure those rows were removed \n",
    "\n",
    "df_exploded.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>genre_ids</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>95.351351</td>\n",
       "      <td>9.040991e+07</td>\n",
       "      <td>1.328418e+08</td>\n",
       "      <td>3.590243e+08</td>\n",
       "      <td>4.918661e+08</td>\n",
       "      <td>4.014562e+08</td>\n",
       "      <td>504.768977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>110.701538</td>\n",
       "      <td>1.010752e+08</td>\n",
       "      <td>1.124018e+08</td>\n",
       "      <td>3.239660e+08</td>\n",
       "      <td>4.363677e+08</td>\n",
       "      <td>3.352925e+08</td>\n",
       "      <td>312.927654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>108.731959</td>\n",
       "      <td>9.148041e+07</td>\n",
       "      <td>1.071063e+08</td>\n",
       "      <td>2.980314e+08</td>\n",
       "      <td>4.051378e+08</td>\n",
       "      <td>3.136574e+08</td>\n",
       "      <td>433.717198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>98.774869</td>\n",
       "      <td>8.051963e+07</td>\n",
       "      <td>1.074798e+08</td>\n",
       "      <td>2.838668e+08</td>\n",
       "      <td>3.913466e+08</td>\n",
       "      <td>3.108270e+08</td>\n",
       "      <td>453.908157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science Fiction</th>\n",
       "      <td>107.185185</td>\n",
       "      <td>6.833975e+07</td>\n",
       "      <td>7.924039e+07</td>\n",
       "      <td>2.139633e+08</td>\n",
       "      <td>2.932037e+08</td>\n",
       "      <td>2.248640e+08</td>\n",
       "      <td>396.278346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>108.844130</td>\n",
       "      <td>7.340315e+07</td>\n",
       "      <td>7.896383e+07</td>\n",
       "      <td>2.152858e+08</td>\n",
       "      <td>2.942496e+08</td>\n",
       "      <td>2.208464e+08</td>\n",
       "      <td>261.421087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>100.841244</td>\n",
       "      <td>3.535768e+07</td>\n",
       "      <td>5.192640e+07</td>\n",
       "      <td>1.169176e+08</td>\n",
       "      <td>1.688441e+08</td>\n",
       "      <td>1.334864e+08</td>\n",
       "      <td>376.646358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>War</th>\n",
       "      <td>112.522727</td>\n",
       "      <td>3.870682e+07</td>\n",
       "      <td>4.464108e+07</td>\n",
       "      <td>9.413630e+07</td>\n",
       "      <td>1.387774e+08</td>\n",
       "      <td>1.000706e+08</td>\n",
       "      <td>218.754855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>107.146245</td>\n",
       "      <td>3.452103e+07</td>\n",
       "      <td>3.941484e+07</td>\n",
       "      <td>9.185459e+07</td>\n",
       "      <td>1.312694e+08</td>\n",
       "      <td>9.674840e+07</td>\n",
       "      <td>188.966419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>109.148148</td>\n",
       "      <td>5.058519e+07</td>\n",
       "      <td>4.334205e+07</td>\n",
       "      <td>1.039884e+08</td>\n",
       "      <td>1.473305e+08</td>\n",
       "      <td>9.674528e+07</td>\n",
       "      <td>115.771572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Music</th>\n",
       "      <td>107.239130</td>\n",
       "      <td>2.490000e+07</td>\n",
       "      <td>4.031065e+07</td>\n",
       "      <td>8.124665e+07</td>\n",
       "      <td>1.215573e+08</td>\n",
       "      <td>9.665730e+07</td>\n",
       "      <td>426.324895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV Movie</th>\n",
       "      <td>98.000000</td>\n",
       "      <td>1.791667e+07</td>\n",
       "      <td>3.478072e+07</td>\n",
       "      <td>7.832768e+07</td>\n",
       "      <td>1.131084e+08</td>\n",
       "      <td>9.519174e+07</td>\n",
       "      <td>349.416810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>102.034539</td>\n",
       "      <td>2.954697e+07</td>\n",
       "      <td>3.376534e+07</td>\n",
       "      <td>8.486340e+07</td>\n",
       "      <td>1.186287e+08</td>\n",
       "      <td>8.908178e+07</td>\n",
       "      <td>607.459950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romance</th>\n",
       "      <td>105.089069</td>\n",
       "      <td>2.428330e+07</td>\n",
       "      <td>3.439347e+07</td>\n",
       "      <td>7.555306e+07</td>\n",
       "      <td>1.099465e+08</td>\n",
       "      <td>8.566323e+07</td>\n",
       "      <td>459.039203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>101.191011</td>\n",
       "      <td>2.669610e+07</td>\n",
       "      <td>3.235570e+07</td>\n",
       "      <td>7.815538e+07</td>\n",
       "      <td>1.105111e+08</td>\n",
       "      <td>8.381498e+07</td>\n",
       "      <td>672.083194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>107.205941</td>\n",
       "      <td>2.650487e+07</td>\n",
       "      <td>3.213199e+07</td>\n",
       "      <td>7.203307e+07</td>\n",
       "      <td>1.041651e+08</td>\n",
       "      <td>7.766019e+07</td>\n",
       "      <td>414.293338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror</th>\n",
       "      <td>94.375000</td>\n",
       "      <td>1.627641e+07</td>\n",
       "      <td>2.450560e+07</td>\n",
       "      <td>5.524297e+07</td>\n",
       "      <td>7.974857e+07</td>\n",
       "      <td>6.347216e+07</td>\n",
       "      <td>1159.965015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>History</th>\n",
       "      <td>121.541667</td>\n",
       "      <td>2.824583e+07</td>\n",
       "      <td>2.866987e+07</td>\n",
       "      <td>5.874817e+07</td>\n",
       "      <td>8.741805e+07</td>\n",
       "      <td>5.917221e+07</td>\n",
       "      <td>234.379429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Documentary</th>\n",
       "      <td>92.471264</td>\n",
       "      <td>1.268425e+07</td>\n",
       "      <td>1.667170e+07</td>\n",
       "      <td>3.203684e+07</td>\n",
       "      <td>4.870855e+07</td>\n",
       "      <td>3.602429e+07</td>\n",
       "      <td>444.480798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 runtime_minutes  production_budget  domestic_gross  \\\n",
       "genre_ids                                                             \n",
       "Animation              95.351351       9.040991e+07    1.328418e+08   \n",
       "Adventure             110.701538       1.010752e+08    1.124018e+08   \n",
       "Fantasy               108.731959       9.148041e+07    1.071063e+08   \n",
       "Family                 98.774869       8.051963e+07    1.074798e+08   \n",
       "Science Fiction       107.185185       6.833975e+07    7.924039e+07   \n",
       "Action                108.844130       7.340315e+07    7.896383e+07   \n",
       "Comedy                100.841244       3.535768e+07    5.192640e+07   \n",
       "War                   112.522727       3.870682e+07    4.464108e+07   \n",
       "Crime                 107.146245       3.452103e+07    3.941484e+07   \n",
       "Western               109.148148       5.058519e+07    4.334205e+07   \n",
       "Music                 107.239130       2.490000e+07    4.031065e+07   \n",
       "TV Movie               98.000000       1.791667e+07    3.478072e+07   \n",
       "Thriller              102.034539       2.954697e+07    3.376534e+07   \n",
       "Romance               105.089069       2.428330e+07    3.439347e+07   \n",
       "Mystery               101.191011       2.669610e+07    3.235570e+07   \n",
       "Drama                 107.205941       2.650487e+07    3.213199e+07   \n",
       "Horror                 94.375000       1.627641e+07    2.450560e+07   \n",
       "History               121.541667       2.824583e+07    2.866987e+07   \n",
       "Documentary            92.471264       1.268425e+07    1.667170e+07   \n",
       "\n",
       "                 worldwide_gross   total_gross    net_profit          ROI  \n",
       "genre_ids                                                                  \n",
       "Animation           3.590243e+08  4.918661e+08  4.014562e+08   504.768977  \n",
       "Adventure           3.239660e+08  4.363677e+08  3.352925e+08   312.927654  \n",
       "Fantasy             2.980314e+08  4.051378e+08  3.136574e+08   433.717198  \n",
       "Family              2.838668e+08  3.913466e+08  3.108270e+08   453.908157  \n",
       "Science Fiction     2.139633e+08  2.932037e+08  2.248640e+08   396.278346  \n",
       "Action              2.152858e+08  2.942496e+08  2.208464e+08   261.421087  \n",
       "Comedy              1.169176e+08  1.688441e+08  1.334864e+08   376.646358  \n",
       "War                 9.413630e+07  1.387774e+08  1.000706e+08   218.754855  \n",
       "Crime               9.185459e+07  1.312694e+08  9.674840e+07   188.966419  \n",
       "Western             1.039884e+08  1.473305e+08  9.674528e+07   115.771572  \n",
       "Music               8.124665e+07  1.215573e+08  9.665730e+07   426.324895  \n",
       "TV Movie            7.832768e+07  1.131084e+08  9.519174e+07   349.416810  \n",
       "Thriller            8.486340e+07  1.186287e+08  8.908178e+07   607.459950  \n",
       "Romance             7.555306e+07  1.099465e+08  8.566323e+07   459.039203  \n",
       "Mystery             7.815538e+07  1.105111e+08  8.381498e+07   672.083194  \n",
       "Drama               7.203307e+07  1.041651e+08  7.766019e+07   414.293338  \n",
       "Horror              5.524297e+07  7.974857e+07  6.347216e+07  1159.965015  \n",
       "History             5.874817e+07  8.741805e+07  5.917221e+07   234.379429  \n",
       "Documentary         3.203684e+07  4.870855e+07  3.602429e+07   444.480798  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making a table that shows all the genres and their adverage values,\n",
    "# sorted by net profit\n",
    "\n",
    "by_genre_net = df_exploded.groupby(by='genre_ids', axis=0).mean()\\\n",
    ".sort_values('net_profit', ascending=False)\n",
    "by_genre_net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making a plot to visualize the above table so we can easily see which \n",
    "# genres dominate net profits \n",
    "\n",
    "values = by_genre_net['net_profit'] \n",
    "colors = ['grey' if (x < 2.838668e+08\t) else 'blue' for x in values ]\n",
    "\n",
    "sns.set_style(style = 'white')\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "ax = sns.barplot(data = df_exploded, x = \"net_profit\", y = \"genre_ids\", \n",
    "           order = by_genre_net.index, palette=colors)\n",
    "ax.set(xlabel=\"Average Net Profit in Hundred Millions\", ylabel = \"Genre\", \n",
    "       title = \"Net Profit by Genre\")\n",
    "\n",
    "# making sure my labels don't get cut-off\n",
    "\n",
    "plt.tight_layout();\n",
    "\n",
    "# specifying the scientific notation to units in regular English \n",
    "# investigate this blank bar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The takeaway here is that animation, adventure, fantasy and family are the top four genres by close to 70 million dollars. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CITATION: color coding \n",
    "\n",
    "adapted from: https://stackoverflow.com/questions/31074758/how-to-set-a-different-color-to-the-largest-bar-in-a-seaborn-barplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Release Date "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    4235\n",
       "True      821\n",
       "Name: genre_ids, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making a new series called release_date that only contains films if they are \n",
    "# in the Animation, Adventure, Fantasy or Family genres for further analysis \n",
    "\n",
    "release_date = df_exploded['genre_ids'].isin(['Animation', 'Adventure',\n",
    "                                              'Fantasy', 'Family'])\n",
    "\n",
    "release_date.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>genre_ids</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>title_and_id</th>\n",
       "      <th>release_date_y</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>total_gross</th>\n",
       "      <th>net_profit</th>\n",
       "      <th>ROI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>98.0</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "      <td>Mar 26, 2010</td>\n",
       "      <td>165000000</td>\n",
       "      <td>217581232</td>\n",
       "      <td>494870992</td>\n",
       "      <td>712452224</td>\n",
       "      <td>547452224</td>\n",
       "      <td>331.789227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>98.0</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "      <td>Mar 26, 2010</td>\n",
       "      <td>165000000</td>\n",
       "      <td>217581232</td>\n",
       "      <td>494870992</td>\n",
       "      <td>712452224</td>\n",
       "      <td>547452224</td>\n",
       "      <td>331.789227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Animation</td>\n",
       "      <td>98.0</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "      <td>Mar 26, 2010</td>\n",
       "      <td>165000000</td>\n",
       "      <td>217581232</td>\n",
       "      <td>494870992</td>\n",
       "      <td>712452224</td>\n",
       "      <td>547452224</td>\n",
       "      <td>331.789227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Family</td>\n",
       "      <td>98.0</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "      <td>Mar 26, 2010</td>\n",
       "      <td>165000000</td>\n",
       "      <td>217581232</td>\n",
       "      <td>494870992</td>\n",
       "      <td>712452224</td>\n",
       "      <td>547452224</td>\n",
       "      <td>331.789227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>124.0</td>\n",
       "      <td>Iron Man 2 id tt1228705</td>\n",
       "      <td>May 7, 2010</td>\n",
       "      <td>170000000</td>\n",
       "      <td>312433331</td>\n",
       "      <td>621156389</td>\n",
       "      <td>933589720</td>\n",
       "      <td>763589720</td>\n",
       "      <td>449.170424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   genre_ids  runtime_minutes                           title_and_id  \\\n",
       "0    Fantasy             98.0  How to Train Your Dragon id tt0892769   \n",
       "0  Adventure             98.0  How to Train Your Dragon id tt0892769   \n",
       "0  Animation             98.0  How to Train Your Dragon id tt0892769   \n",
       "0     Family             98.0  How to Train Your Dragon id tt0892769   \n",
       "1  Adventure            124.0                Iron Man 2 id tt1228705   \n",
       "\n",
       "  release_date_y  production_budget  domestic_gross  worldwide_gross  \\\n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "1    May 7, 2010          170000000       312433331        621156389   \n",
       "\n",
       "   total_gross  net_profit         ROI  \n",
       "0    712452224   547452224  331.789227  \n",
       "0    712452224   547452224  331.789227  \n",
       "0    712452224   547452224  331.789227  \n",
       "0    712452224   547452224  331.789227  \n",
       "1    933589720   763589720  449.170424  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slicing out the series from df_exploded and making it it's own dataframe\n",
    "\n",
    "release_date = df_exploded[release_date]\n",
    "release_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 821 entries, 0 to 2947\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   genre_ids          821 non-null    object        \n",
      " 1   runtime_minutes    821 non-null    float64       \n",
      " 2   title_and_id       821 non-null    object        \n",
      " 3   release_date_y     821 non-null    object        \n",
      " 4   production_budget  821 non-null    int64         \n",
      " 5   domestic_gross     821 non-null    int64         \n",
      " 6   worldwide_gross    821 non-null    int64         \n",
      " 7   total_gross        821 non-null    int64         \n",
      " 8   net_profit         821 non-null    int64         \n",
      " 9   ROI                821 non-null    float64       \n",
      " 10  date_time          821 non-null    datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(2), int64(5), object(3)\n",
      "memory usage: 77.0+ KB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-84-6f9bb6a9cb62>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  release_date['date_time'] = pd.to_datetime(release_date['release_date_y'])\n"
     ]
    }
   ],
   "source": [
    "# turning release_date_y into datetime format \n",
    "\n",
    "release_date['date_time'] = pd.to_datetime(release_date['release_date_y'])\n",
    "release_date.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-85-11899e521ef7>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  release_date['month_num'] = release_date['date_time'].dt.month\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre_ids</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>title_and_id</th>\n",
       "      <th>release_date_y</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
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       "      <th>net_profit</th>\n",
       "      <th>ROI</th>\n",
       "      <th>date_time</th>\n",
       "      <th>month_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>98.0</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "      <td>Mar 26, 2010</td>\n",
       "      <td>165000000</td>\n",
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       "      <td>494870992</td>\n",
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       "      <td>2010-03-26</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>98.0</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "      <td>Mar 26, 2010</td>\n",
       "      <td>165000000</td>\n",
       "      <td>217581232</td>\n",
       "      <td>494870992</td>\n",
       "      <td>712452224</td>\n",
       "      <td>547452224</td>\n",
       "      <td>331.789227</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Animation</td>\n",
       "      <td>98.0</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "      <td>Mar 26, 2010</td>\n",
       "      <td>165000000</td>\n",
       "      <td>217581232</td>\n",
       "      <td>494870992</td>\n",
       "      <td>712452224</td>\n",
       "      <td>547452224</td>\n",
       "      <td>331.789227</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Family</td>\n",
       "      <td>98.0</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "      <td>Mar 26, 2010</td>\n",
       "      <td>165000000</td>\n",
       "      <td>217581232</td>\n",
       "      <td>494870992</td>\n",
       "      <td>712452224</td>\n",
       "      <td>547452224</td>\n",
       "      <td>331.789227</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>124.0</td>\n",
       "      <td>Iron Man 2 id tt1228705</td>\n",
       "      <td>May 7, 2010</td>\n",
       "      <td>170000000</td>\n",
       "      <td>312433331</td>\n",
       "      <td>621156389</td>\n",
       "      <td>933589720</td>\n",
       "      <td>763589720</td>\n",
       "      <td>449.170424</td>\n",
       "      <td>2010-05-07</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   genre_ids  runtime_minutes                           title_and_id  \\\n",
       "0    Fantasy             98.0  How to Train Your Dragon id tt0892769   \n",
       "0  Adventure             98.0  How to Train Your Dragon id tt0892769   \n",
       "0  Animation             98.0  How to Train Your Dragon id tt0892769   \n",
       "0     Family             98.0  How to Train Your Dragon id tt0892769   \n",
       "1  Adventure            124.0                Iron Man 2 id tt1228705   \n",
       "\n",
       "  release_date_y  production_budget  domestic_gross  worldwide_gross  \\\n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "1    May 7, 2010          170000000       312433331        621156389   \n",
       "\n",
       "   total_gross  net_profit         ROI  date_time  month_num  \n",
       "0    712452224   547452224  331.789227 2010-03-26          3  \n",
       "0    712452224   547452224  331.789227 2010-03-26          3  \n",
       "0    712452224   547452224  331.789227 2010-03-26          3  \n",
       "0    712452224   547452224  331.789227 2010-03-26          3  \n",
       "1    933589720   763589720  449.170424 2010-05-07          5  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#isolating the release month from that datetime column \n",
    "\n",
    "release_date['month_num'] = release_date['date_time'].dt.month\n",
    "release_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-86-152d7827001d>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  release_date['month_name'] = release_date['date_time'].dt.month_name()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <td>Mar 26, 2010</td>\n",
       "      <td>165000000</td>\n",
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       "      <td>Adventure</td>\n",
       "      <td>98.0</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "      <td>Mar 26, 2010</td>\n",
       "      <td>165000000</td>\n",
       "      <td>217581232</td>\n",
       "      <td>494870992</td>\n",
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       "      <td>2010-03-26</td>\n",
       "      <td>3</td>\n",
       "      <td>March</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Animation</td>\n",
       "      <td>98.0</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "      <td>Mar 26, 2010</td>\n",
       "      <td>165000000</td>\n",
       "      <td>217581232</td>\n",
       "      <td>494870992</td>\n",
       "      <td>712452224</td>\n",
       "      <td>547452224</td>\n",
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       "      <td>2010-03-26</td>\n",
       "      <td>3</td>\n",
       "      <td>March</td>\n",
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       "    <tr>\n",
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       "      <td>Family</td>\n",
       "      <td>98.0</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "      <td>Mar 26, 2010</td>\n",
       "      <td>165000000</td>\n",
       "      <td>217581232</td>\n",
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       "      <td>331.789227</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>3</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>124.0</td>\n",
       "      <td>Iron Man 2 id tt1228705</td>\n",
       "      <td>May 7, 2010</td>\n",
       "      <td>170000000</td>\n",
       "      <td>312433331</td>\n",
       "      <td>621156389</td>\n",
       "      <td>933589720</td>\n",
       "      <td>763589720</td>\n",
       "      <td>449.170424</td>\n",
       "      <td>2010-05-07</td>\n",
       "      <td>5</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   genre_ids  runtime_minutes                           title_and_id  \\\n",
       "0    Fantasy             98.0  How to Train Your Dragon id tt0892769   \n",
       "0  Adventure             98.0  How to Train Your Dragon id tt0892769   \n",
       "0  Animation             98.0  How to Train Your Dragon id tt0892769   \n",
       "0     Family             98.0  How to Train Your Dragon id tt0892769   \n",
       "1  Adventure            124.0                Iron Man 2 id tt1228705   \n",
       "\n",
       "  release_date_y  production_budget  domestic_gross  worldwide_gross  \\\n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "1    May 7, 2010          170000000       312433331        621156389   \n",
       "\n",
       "   total_gross  net_profit         ROI  date_time  month_num month_name  \n",
       "0    712452224   547452224  331.789227 2010-03-26          3      March  \n",
       "0    712452224   547452224  331.789227 2010-03-26          3      March  \n",
       "0    712452224   547452224  331.789227 2010-03-26          3      March  \n",
       "0    712452224   547452224  331.789227 2010-03-26          3      March  \n",
       "1    933589720   763589720  449.170424 2010-05-07          5        May  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# putting a column for the name of the month for readability \n",
    "\n",
    "release_date['month_name'] = release_date['date_time'].dt.month_name()\n",
    "release_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>genre_ids</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Family</th>\n",
       "      <th>Fantasy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>3.639112e+08</td>\n",
       "      <td>3.594065e+08</td>\n",
       "      <td>3.652238e+08</td>\n",
       "      <td>4.168243e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>1.785756e+08</td>\n",
       "      <td>5.650822e+07</td>\n",
       "      <td>8.087768e+07</td>\n",
       "      <td>2.192492e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>3.378595e+08</td>\n",
       "      <td>2.264141e+08</td>\n",
       "      <td>1.731402e+08</td>\n",
       "      <td>3.304541e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>1.855495e+08</td>\n",
       "      <td>3.756855e+08</td>\n",
       "      <td>2.598824e+08</td>\n",
       "      <td>1.908724e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>1.220691e+08</td>\n",
       "      <td>1.907414e+08</td>\n",
       "      <td>1.482089e+08</td>\n",
       "      <td>7.999303e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>3.783961e+08</td>\n",
       "      <td>6.644173e+08</td>\n",
       "      <td>3.952794e+08</td>\n",
       "      <td>2.014382e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>5.011279e+08</td>\n",
       "      <td>7.614652e+08</td>\n",
       "      <td>5.118351e+08</td>\n",
       "      <td>4.908190e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>2.832314e+08</td>\n",
       "      <td>5.556345e+08</td>\n",
       "      <td>4.403053e+08</td>\n",
       "      <td>3.973046e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>5.635206e+08</td>\n",
       "      <td>5.934604e+08</td>\n",
       "      <td>3.419126e+08</td>\n",
       "      <td>3.425121e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>4.324922e+08</td>\n",
       "      <td>4.142573e+08</td>\n",
       "      <td>3.082870e+08</td>\n",
       "      <td>4.424158e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>1.695030e+08</td>\n",
       "      <td>1.735661e+08</td>\n",
       "      <td>1.292997e+08</td>\n",
       "      <td>9.471312e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>1.169505e+08</td>\n",
       "      <td>2.287723e+08</td>\n",
       "      <td>2.049232e+08</td>\n",
       "      <td>2.056765e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "genre_ids      Adventure     Animation        Family       Fantasy\n",
       "month_name                                                        \n",
       "April       3.639112e+08  3.594065e+08  3.652238e+08  4.168243e+08\n",
       "August      1.785756e+08  5.650822e+07  8.087768e+07  2.192492e+08\n",
       "December    3.378595e+08  2.264141e+08  1.731402e+08  3.304541e+08\n",
       "February    1.855495e+08  3.756855e+08  2.598824e+08  1.908724e+08\n",
       "January     1.220691e+08  1.907414e+08  1.482089e+08  7.999303e+07\n",
       "July        3.783961e+08  6.644173e+08  3.952794e+08  2.014382e+08\n",
       "June        5.011279e+08  7.614652e+08  5.118351e+08  4.908190e+08\n",
       "March       2.832314e+08  5.556345e+08  4.403053e+08  3.973046e+08\n",
       "May         5.635206e+08  5.934604e+08  3.419126e+08  3.425121e+08\n",
       "November    4.324922e+08  4.142573e+08  3.082870e+08  4.424158e+08\n",
       "October     1.695030e+08  1.735661e+08  1.292997e+08  9.471312e+07\n",
       "September   1.169505e+08  2.287723e+08  2.049232e+08  2.056765e+08"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#making a pivot table to aggrigate the data based on genre\n",
    "\n",
    "pivot = pd.pivot_table(release_date, values='net_profit', index='month_name', \n",
    "                       columns='genre_ids')\n",
    "pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>genre_ids</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Family</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>3.639112e+08</td>\n",
       "      <td>3.594065e+08</td>\n",
       "      <td>3.652238e+08</td>\n",
       "      <td>4.168243e+08</td>\n",
       "      <td>1.505366e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>1.785756e+08</td>\n",
       "      <td>5.650822e+07</td>\n",
       "      <td>8.087768e+07</td>\n",
       "      <td>2.192492e+08</td>\n",
       "      <td>5.352107e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>3.378595e+08</td>\n",
       "      <td>2.264141e+08</td>\n",
       "      <td>1.731402e+08</td>\n",
       "      <td>3.304541e+08</td>\n",
       "      <td>1.067868e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>1.855495e+08</td>\n",
       "      <td>3.756855e+08</td>\n",
       "      <td>2.598824e+08</td>\n",
       "      <td>1.908724e+08</td>\n",
       "      <td>1.011990e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>1.220691e+08</td>\n",
       "      <td>1.907414e+08</td>\n",
       "      <td>1.482089e+08</td>\n",
       "      <td>7.999303e+07</td>\n",
       "      <td>5.410124e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>3.783961e+08</td>\n",
       "      <td>6.644173e+08</td>\n",
       "      <td>3.952794e+08</td>\n",
       "      <td>2.014382e+08</td>\n",
       "      <td>1.639531e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>5.011279e+08</td>\n",
       "      <td>7.614652e+08</td>\n",
       "      <td>5.118351e+08</td>\n",
       "      <td>4.908190e+08</td>\n",
       "      <td>2.265247e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>2.832314e+08</td>\n",
       "      <td>5.556345e+08</td>\n",
       "      <td>4.403053e+08</td>\n",
       "      <td>3.973046e+08</td>\n",
       "      <td>1.676476e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>5.635206e+08</td>\n",
       "      <td>5.934604e+08</td>\n",
       "      <td>3.419126e+08</td>\n",
       "      <td>3.425121e+08</td>\n",
       "      <td>1.841406e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>4.324922e+08</td>\n",
       "      <td>4.142573e+08</td>\n",
       "      <td>3.082870e+08</td>\n",
       "      <td>4.424158e+08</td>\n",
       "      <td>1.597452e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>1.695030e+08</td>\n",
       "      <td>1.735661e+08</td>\n",
       "      <td>1.292997e+08</td>\n",
       "      <td>9.471312e+07</td>\n",
       "      <td>5.670819e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>1.169505e+08</td>\n",
       "      <td>2.287723e+08</td>\n",
       "      <td>2.049232e+08</td>\n",
       "      <td>2.056765e+08</td>\n",
       "      <td>7.563226e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "genre_ids      Adventure     Animation        Family       Fantasy  \\\n",
       "month_name                                                           \n",
       "April       3.639112e+08  3.594065e+08  3.652238e+08  4.168243e+08   \n",
       "August      1.785756e+08  5.650822e+07  8.087768e+07  2.192492e+08   \n",
       "December    3.378595e+08  2.264141e+08  1.731402e+08  3.304541e+08   \n",
       "February    1.855495e+08  3.756855e+08  2.598824e+08  1.908724e+08   \n",
       "January     1.220691e+08  1.907414e+08  1.482089e+08  7.999303e+07   \n",
       "July        3.783961e+08  6.644173e+08  3.952794e+08  2.014382e+08   \n",
       "June        5.011279e+08  7.614652e+08  5.118351e+08  4.908190e+08   \n",
       "March       2.832314e+08  5.556345e+08  4.403053e+08  3.973046e+08   \n",
       "May         5.635206e+08  5.934604e+08  3.419126e+08  3.425121e+08   \n",
       "November    4.324922e+08  4.142573e+08  3.082870e+08  4.424158e+08   \n",
       "October     1.695030e+08  1.735661e+08  1.292997e+08  9.471312e+07   \n",
       "September   1.169505e+08  2.287723e+08  2.049232e+08  2.056765e+08   \n",
       "\n",
       "genre_ids          total  \n",
       "month_name                \n",
       "April       1.505366e+09  \n",
       "August      5.352107e+08  \n",
       "December    1.067868e+09  \n",
       "February    1.011990e+09  \n",
       "January     5.410124e+08  \n",
       "July        1.639531e+09  \n",
       "June        2.265247e+09  \n",
       "March       1.676476e+09  \n",
       "May         1.841406e+09  \n",
       "November    1.597452e+09  \n",
       "October     5.670819e+08  \n",
       "September   7.563226e+08  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I need to make a \"total\" column temporarily so that I can use those total \n",
    "# values to order the months in the pivot table\n",
    "\n",
    "pivot['total'] = pivot['Adventure'] + pivot['Animation'] + \\\n",
    "                     pivot['Family'] +  pivot['Fantasy']\n",
    "pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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",
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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>genre_ids</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Family</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>5.011279e+08</td>\n",
       "      <td>7.614652e+08</td>\n",
       "      <td>5.118351e+08</td>\n",
       "      <td>4.908190e+08</td>\n",
       "      <td>2.265247e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>5.635206e+08</td>\n",
       "      <td>5.934604e+08</td>\n",
       "      <td>3.419126e+08</td>\n",
       "      <td>3.425121e+08</td>\n",
       "      <td>1.841406e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>2.832314e+08</td>\n",
       "      <td>5.556345e+08</td>\n",
       "      <td>4.403053e+08</td>\n",
       "      <td>3.973046e+08</td>\n",
       "      <td>1.676476e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>3.783961e+08</td>\n",
       "      <td>6.644173e+08</td>\n",
       "      <td>3.952794e+08</td>\n",
       "      <td>2.014382e+08</td>\n",
       "      <td>1.639531e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>4.324922e+08</td>\n",
       "      <td>4.142573e+08</td>\n",
       "      <td>3.082870e+08</td>\n",
       "      <td>4.424158e+08</td>\n",
       "      <td>1.597452e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>3.639112e+08</td>\n",
       "      <td>3.594065e+08</td>\n",
       "      <td>3.652238e+08</td>\n",
       "      <td>4.168243e+08</td>\n",
       "      <td>1.505366e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>3.378595e+08</td>\n",
       "      <td>2.264141e+08</td>\n",
       "      <td>1.731402e+08</td>\n",
       "      <td>3.304541e+08</td>\n",
       "      <td>1.067868e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>1.855495e+08</td>\n",
       "      <td>3.756855e+08</td>\n",
       "      <td>2.598824e+08</td>\n",
       "      <td>1.908724e+08</td>\n",
       "      <td>1.011990e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>1.169505e+08</td>\n",
       "      <td>2.287723e+08</td>\n",
       "      <td>2.049232e+08</td>\n",
       "      <td>2.056765e+08</td>\n",
       "      <td>7.563226e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>1.695030e+08</td>\n",
       "      <td>1.735661e+08</td>\n",
       "      <td>1.292997e+08</td>\n",
       "      <td>9.471312e+07</td>\n",
       "      <td>5.670819e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>1.220691e+08</td>\n",
       "      <td>1.907414e+08</td>\n",
       "      <td>1.482089e+08</td>\n",
       "      <td>7.999303e+07</td>\n",
       "      <td>5.410124e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>1.785756e+08</td>\n",
       "      <td>5.650822e+07</td>\n",
       "      <td>8.087768e+07</td>\n",
       "      <td>2.192492e+08</td>\n",
       "      <td>5.352107e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "genre_ids      Adventure     Animation        Family       Fantasy  \\\n",
       "month_name                                                           \n",
       "June        5.011279e+08  7.614652e+08  5.118351e+08  4.908190e+08   \n",
       "May         5.635206e+08  5.934604e+08  3.419126e+08  3.425121e+08   \n",
       "March       2.832314e+08  5.556345e+08  4.403053e+08  3.973046e+08   \n",
       "July        3.783961e+08  6.644173e+08  3.952794e+08  2.014382e+08   \n",
       "November    4.324922e+08  4.142573e+08  3.082870e+08  4.424158e+08   \n",
       "April       3.639112e+08  3.594065e+08  3.652238e+08  4.168243e+08   \n",
       "December    3.378595e+08  2.264141e+08  1.731402e+08  3.304541e+08   \n",
       "February    1.855495e+08  3.756855e+08  2.598824e+08  1.908724e+08   \n",
       "September   1.169505e+08  2.287723e+08  2.049232e+08  2.056765e+08   \n",
       "October     1.695030e+08  1.735661e+08  1.292997e+08  9.471312e+07   \n",
       "January     1.220691e+08  1.907414e+08  1.482089e+08  7.999303e+07   \n",
       "August      1.785756e+08  5.650822e+07  8.087768e+07  2.192492e+08   \n",
       "\n",
       "genre_ids          total  \n",
       "month_name                \n",
       "June        2.265247e+09  \n",
       "May         1.841406e+09  \n",
       "March       1.676476e+09  \n",
       "July        1.639531e+09  \n",
       "November    1.597452e+09  \n",
       "April       1.505366e+09  \n",
       "December    1.067868e+09  \n",
       "February    1.011990e+09  \n",
       "September   7.563226e+08  \n",
       "October     5.670819e+08  \n",
       "January     5.410124e+08  \n",
       "August      5.352107e+08  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sorting by those totalled values\n",
    "\n",
    "pivot.sort_values('total', inplace = True, ascending=False)\n",
    "pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>genre_ids</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Family</th>\n",
       "      <th>Fantasy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>5.011279e+08</td>\n",
       "      <td>7.614652e+08</td>\n",
       "      <td>5.118351e+08</td>\n",
       "      <td>4.908190e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>5.635206e+08</td>\n",
       "      <td>5.934604e+08</td>\n",
       "      <td>3.419126e+08</td>\n",
       "      <td>3.425121e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>2.832314e+08</td>\n",
       "      <td>5.556345e+08</td>\n",
       "      <td>4.403053e+08</td>\n",
       "      <td>3.973046e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>3.783961e+08</td>\n",
       "      <td>6.644173e+08</td>\n",
       "      <td>3.952794e+08</td>\n",
       "      <td>2.014382e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>4.324922e+08</td>\n",
       "      <td>4.142573e+08</td>\n",
       "      <td>3.082870e+08</td>\n",
       "      <td>4.424158e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>3.639112e+08</td>\n",
       "      <td>3.594065e+08</td>\n",
       "      <td>3.652238e+08</td>\n",
       "      <td>4.168243e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>3.378595e+08</td>\n",
       "      <td>2.264141e+08</td>\n",
       "      <td>1.731402e+08</td>\n",
       "      <td>3.304541e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>1.855495e+08</td>\n",
       "      <td>3.756855e+08</td>\n",
       "      <td>2.598824e+08</td>\n",
       "      <td>1.908724e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>1.169505e+08</td>\n",
       "      <td>2.287723e+08</td>\n",
       "      <td>2.049232e+08</td>\n",
       "      <td>2.056765e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>1.695030e+08</td>\n",
       "      <td>1.735661e+08</td>\n",
       "      <td>1.292997e+08</td>\n",
       "      <td>9.471312e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>1.220691e+08</td>\n",
       "      <td>1.907414e+08</td>\n",
       "      <td>1.482089e+08</td>\n",
       "      <td>7.999303e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>1.785756e+08</td>\n",
       "      <td>5.650822e+07</td>\n",
       "      <td>8.087768e+07</td>\n",
       "      <td>2.192492e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "genre_ids      Adventure     Animation        Family       Fantasy\n",
       "month_name                                                        \n",
       "June        5.011279e+08  7.614652e+08  5.118351e+08  4.908190e+08\n",
       "May         5.635206e+08  5.934604e+08  3.419126e+08  3.425121e+08\n",
       "March       2.832314e+08  5.556345e+08  4.403053e+08  3.973046e+08\n",
       "July        3.783961e+08  6.644173e+08  3.952794e+08  2.014382e+08\n",
       "November    4.324922e+08  4.142573e+08  3.082870e+08  4.424158e+08\n",
       "April       3.639112e+08  3.594065e+08  3.652238e+08  4.168243e+08\n",
       "December    3.378595e+08  2.264141e+08  1.731402e+08  3.304541e+08\n",
       "February    1.855495e+08  3.756855e+08  2.598824e+08  1.908724e+08\n",
       "September   1.169505e+08  2.287723e+08  2.049232e+08  2.056765e+08\n",
       "October     1.695030e+08  1.735661e+08  1.292997e+08  9.471312e+07\n",
       "January     1.220691e+08  1.907414e+08  1.482089e+08  7.999303e+07\n",
       "August      1.785756e+08  5.650822e+07  8.087768e+07  2.192492e+08"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing the total column once it's served it's purpose so I can proceed to \n",
    "# graph the information within this aggregated data\n",
    "\n",
    "pivot.drop(columns = 'total', inplace = True)\n",
    "pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making a plot to show the above pivot table as a stacked bar chart\n",
    "\n",
    "ax = pivot.plot(kind='bar', figsize=(7, 7), stacked=True)\n",
    "\n",
    "#ax.axhline(1500000000, color = \"blue\")\n",
    "#ax.axhline(1900000000, color = \"green\")\n",
    "\n",
    "ax.set(xlabel = \"Month\", ylabel = \"Net Profit in Billions\", \n",
    "       title = \"Average Net Profit By Genre By Month\")\n",
    "\n",
    "plt.xticks(rotation = 45);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The takeaway here is to release films in June, where the average film in this genre made over 1.9 billion dollars (shown by the green line.) If there is a delay in production due to Covid-19, I'd recommend these months as backup choices: May, March, July, November or April. \n",
    "\n",
    "Interesting find: Fantasy films do not do well in July compared to other high earning months. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Runtime of Film "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 821 entries, 0 to 2947\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   genre_ids          821 non-null    object        \n",
      " 1   runtime_minutes    821 non-null    float64       \n",
      " 2   title_and_id       821 non-null    object        \n",
      " 3   release_date_y     821 non-null    object        \n",
      " 4   production_budget  821 non-null    int64         \n",
      " 5   domestic_gross     821 non-null    int64         \n",
      " 6   worldwide_gross    821 non-null    int64         \n",
      " 7   total_gross        821 non-null    int64         \n",
      " 8   net_profit         821 non-null    int64         \n",
      " 9   ROI                821 non-null    float64       \n",
      " 10  date_time          821 non-null    datetime64[ns]\n",
      " 11  month_num          821 non-null    int64         \n",
      " 12  month_name         821 non-null    object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(6), object(4)\n",
      "memory usage: 89.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# taking a look at the shape of release_date \n",
    "\n",
    "release_date.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all movies in this data set are between 30 and 180\n",
    "# making a visualization to represent the films in this dataset based\n",
    "# on Net Profit in billions and runtime. \n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "runtime_plot = sns.regplot(data = release_date, x = 'runtime_minutes', \n",
    "                 y = 'net_profit', color = 'green',\n",
    "                 scatter_kws={'alpha':0.3})\n",
    "\n",
    "runtime_plot.set(xlabel = 'Runtime Minutes', \n",
    "                 ylabel = 'Net Profit in Billions', \n",
    "                 title = \"Net Profit of Movies by Runtime Minutes\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This doesn't tell us very much so instead let's sort runtime minutes into categorical data instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[60, 90, 120, 150, 180]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making a list of bin edges\n",
    "\n",
    "bins = [ 60, 90, 120, 150, 180]\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making a list of labels to go with those bins \n",
    "\n",
    "labels = ['Sixty', 'One hundred twenty',\n",
    "          'One hundred fifty', 'One hundred eighty']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 60,  90, 120, 150, 180])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cutting the data into those bins, using both bins and labels\n",
    "\n",
    "cut_data, bin_edges = pd.cut(release_date['runtime_minutes'], bins = bins,\n",
    "                              labels = labels, retbins = True)\n",
    "bin_edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-105-1b6b290c6298>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  release_date['runtime_bins']= cut_data\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>genre_ids</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>title_and_id</th>\n",
       "      <th>release_date_y</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>total_gross</th>\n",
       "      <th>net_profit</th>\n",
       "      <th>ROI</th>\n",
       "      <th>date_time</th>\n",
       "      <th>month_num</th>\n",
       "      <th>month_name</th>\n",
       "      <th>runtime_bins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>98.0</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "      <td>Mar 26, 2010</td>\n",
       "      <td>165000000</td>\n",
       "      <td>217581232</td>\n",
       "      <td>494870992</td>\n",
       "      <td>712452224</td>\n",
       "      <td>547452224</td>\n",
       "      <td>331.789227</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>3</td>\n",
       "      <td>March</td>\n",
       "      <td>One hundred twenty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>98.0</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "      <td>Mar 26, 2010</td>\n",
       "      <td>165000000</td>\n",
       "      <td>217581232</td>\n",
       "      <td>494870992</td>\n",
       "      <td>712452224</td>\n",
       "      <td>547452224</td>\n",
       "      <td>331.789227</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>3</td>\n",
       "      <td>March</td>\n",
       "      <td>One hundred twenty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Animation</td>\n",
       "      <td>98.0</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "      <td>Mar 26, 2010</td>\n",
       "      <td>165000000</td>\n",
       "      <td>217581232</td>\n",
       "      <td>494870992</td>\n",
       "      <td>712452224</td>\n",
       "      <td>547452224</td>\n",
       "      <td>331.789227</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>3</td>\n",
       "      <td>March</td>\n",
       "      <td>One hundred twenty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Family</td>\n",
       "      <td>98.0</td>\n",
       "      <td>How to Train Your Dragon id tt0892769</td>\n",
       "      <td>Mar 26, 2010</td>\n",
       "      <td>165000000</td>\n",
       "      <td>217581232</td>\n",
       "      <td>494870992</td>\n",
       "      <td>712452224</td>\n",
       "      <td>547452224</td>\n",
       "      <td>331.789227</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>3</td>\n",
       "      <td>March</td>\n",
       "      <td>One hundred twenty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>124.0</td>\n",
       "      <td>Iron Man 2 id tt1228705</td>\n",
       "      <td>May 7, 2010</td>\n",
       "      <td>170000000</td>\n",
       "      <td>312433331</td>\n",
       "      <td>621156389</td>\n",
       "      <td>933589720</td>\n",
       "      <td>763589720</td>\n",
       "      <td>449.170424</td>\n",
       "      <td>2010-05-07</td>\n",
       "      <td>5</td>\n",
       "      <td>May</td>\n",
       "      <td>One hundred fifty</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   genre_ids  runtime_minutes                           title_and_id  \\\n",
       "0    Fantasy             98.0  How to Train Your Dragon id tt0892769   \n",
       "0  Adventure             98.0  How to Train Your Dragon id tt0892769   \n",
       "0  Animation             98.0  How to Train Your Dragon id tt0892769   \n",
       "0     Family             98.0  How to Train Your Dragon id tt0892769   \n",
       "1  Adventure            124.0                Iron Man 2 id tt1228705   \n",
       "\n",
       "  release_date_y  production_budget  domestic_gross  worldwide_gross  \\\n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "0   Mar 26, 2010          165000000       217581232        494870992   \n",
       "1    May 7, 2010          170000000       312433331        621156389   \n",
       "\n",
       "   total_gross  net_profit         ROI  date_time  month_num month_name  \\\n",
       "0    712452224   547452224  331.789227 2010-03-26          3      March   \n",
       "0    712452224   547452224  331.789227 2010-03-26          3      March   \n",
       "0    712452224   547452224  331.789227 2010-03-26          3      March   \n",
       "0    712452224   547452224  331.789227 2010-03-26          3      March   \n",
       "1    933589720   763589720  449.170424 2010-05-07          5        May   \n",
       "\n",
       "         runtime_bins  \n",
       "0  One hundred twenty  \n",
       "0  One hundred twenty  \n",
       "0  One hundred twenty  \n",
       "0  One hundred twenty  \n",
       "1   One hundred fifty  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking a look to see if our bins are a new column and labeled\n",
    "\n",
    "release_date['runtime_bins']= cut_data\n",
    "release_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>runtime_bins</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>total_gross</th>\n",
       "      <th>net_profit</th>\n",
       "      <th>ROI</th>\n",
       "      <th>month_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sixty</td>\n",
       "      <td>84.415584</td>\n",
       "      <td>5.798247e+07</td>\n",
       "      <td>7.235175e+07</td>\n",
       "      <td>2.091152e+08</td>\n",
       "      <td>2.814670e+08</td>\n",
       "      <td>2.234845e+08</td>\n",
       "      <td>315.149988</td>\n",
       "      <td>6.948052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>One hundred twenty</td>\n",
       "      <td>102.746032</td>\n",
       "      <td>8.905279e+07</td>\n",
       "      <td>1.043638e+08</td>\n",
       "      <td>2.870365e+08</td>\n",
       "      <td>3.914003e+08</td>\n",
       "      <td>3.023476e+08</td>\n",
       "      <td>408.572564</td>\n",
       "      <td>6.599206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>One hundred fifty</td>\n",
       "      <td>132.417266</td>\n",
       "      <td>1.387950e+08</td>\n",
       "      <td>1.827653e+08</td>\n",
       "      <td>4.979914e+08</td>\n",
       "      <td>6.807567e+08</td>\n",
       "      <td>5.419617e+08</td>\n",
       "      <td>486.097270</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>One hundred eighty</td>\n",
       "      <td>161.157895</td>\n",
       "      <td>1.377368e+08</td>\n",
       "      <td>1.543482e+08</td>\n",
       "      <td>5.215815e+08</td>\n",
       "      <td>6.759298e+08</td>\n",
       "      <td>5.381929e+08</td>\n",
       "      <td>255.530136</td>\n",
       "      <td>8.789474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         runtime_bins  runtime_minutes  production_budget  domestic_gross  \\\n",
       "0               Sixty        84.415584       5.798247e+07    7.235175e+07   \n",
       "1  One hundred twenty       102.746032       8.905279e+07    1.043638e+08   \n",
       "2   One hundred fifty       132.417266       1.387950e+08    1.827653e+08   \n",
       "3  One hundred eighty       161.157895       1.377368e+08    1.543482e+08   \n",
       "\n",
       "   worldwide_gross   total_gross    net_profit         ROI  month_num  \n",
       "0     2.091152e+08  2.814670e+08  2.234845e+08  315.149988   6.948052  \n",
       "1     2.870365e+08  3.914003e+08  3.023476e+08  408.572564   6.599206  \n",
       "2     4.979914e+08  6.807567e+08  5.419617e+08  486.097270   7.000000  \n",
       "3     5.215815e+08  6.759298e+08  5.381929e+08  255.530136   8.789474  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runtime_df = release_date.groupby(by='runtime_bins').mean()\n",
    "runtime_df = runtime_df.reset_index()\n",
    "runtime_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.234845e+08\n",
       "1    3.023476e+08\n",
       "2    5.419617e+08\n",
       "3    5.381929e+08\n",
       "Name: net_profit, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# taking a look at the average net profit in hundred-thousands by bin\n",
    "\n",
    "runtime_df['net_profit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>runtime_bins</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>total_gross</th>\n",
       "      <th>net_profit</th>\n",
       "      <th>ROI</th>\n",
       "      <th>month_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sixty</td>\n",
       "      <td>84.415584</td>\n",
       "      <td>5.798247e+07</td>\n",
       "      <td>7.235175e+07</td>\n",
       "      <td>2.091152e+08</td>\n",
       "      <td>2.814670e+08</td>\n",
       "      <td>2.234845e+08</td>\n",
       "      <td>315.149988</td>\n",
       "      <td>6.948052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>One hundred twenty</td>\n",
       "      <td>102.746032</td>\n",
       "      <td>8.905279e+07</td>\n",
       "      <td>1.043638e+08</td>\n",
       "      <td>2.870365e+08</td>\n",
       "      <td>3.914003e+08</td>\n",
       "      <td>3.023476e+08</td>\n",
       "      <td>408.572564</td>\n",
       "      <td>6.599206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>One hundred eighty</td>\n",
       "      <td>161.157895</td>\n",
       "      <td>1.377368e+08</td>\n",
       "      <td>1.543482e+08</td>\n",
       "      <td>5.215815e+08</td>\n",
       "      <td>6.759298e+08</td>\n",
       "      <td>5.381929e+08</td>\n",
       "      <td>255.530136</td>\n",
       "      <td>8.789474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>One hundred fifty</td>\n",
       "      <td>132.417266</td>\n",
       "      <td>1.387950e+08</td>\n",
       "      <td>1.827653e+08</td>\n",
       "      <td>4.979914e+08</td>\n",
       "      <td>6.807567e+08</td>\n",
       "      <td>5.419617e+08</td>\n",
       "      <td>486.097270</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         runtime_bins  runtime_minutes  production_budget  domestic_gross  \\\n",
       "0               Sixty        84.415584       5.798247e+07    7.235175e+07   \n",
       "1  One hundred twenty       102.746032       8.905279e+07    1.043638e+08   \n",
       "3  One hundred eighty       161.157895       1.377368e+08    1.543482e+08   \n",
       "2   One hundred fifty       132.417266       1.387950e+08    1.827653e+08   \n",
       "\n",
       "   worldwide_gross   total_gross    net_profit         ROI  month_num  \n",
       "0     2.091152e+08  2.814670e+08  2.234845e+08  315.149988   6.948052  \n",
       "1     2.870365e+08  3.914003e+08  3.023476e+08  408.572564   6.599206  \n",
       "3     5.215815e+08  6.759298e+08  5.381929e+08  255.530136   8.789474  \n",
       "2     4.979914e+08  6.807567e+08  5.419617e+08  486.097270   7.000000  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sorting this aggregated data from highest to lowest by Net Profit \n",
    "\n",
    "runtime_df.sort_values(by = 'net_profit', inplace = True) \n",
    "runtime_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the above data \n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "ax = sns.barplot(data = release_date, x = 'runtime_bins', y = 'net_profit')\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "ax.set(xlabel = 'Runtime Bins', ylabel = 'Average Net Profit in 100K Dollars', \n",
    "       title = \"Net Profit by Runtime Bin\")\n",
    "    \n",
    "plt.tight_layout();\n",
    "# this will help my labels not get cut-off "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the important piece of information is that films between 120-150 minutes do better consistently, and movies between 150-180 movies do about as well on average but have a much higher rate of deviation. (It's more likely that films in that range will do either much better or much worse than films in the previous bin. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Three Business Recommendations: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My three business recommendations to Microsoft Studios would be: \n",
    "1) Focus on making films that are a combination of animation, fantasy, adventure, and family. There are many examples of the combination of these genres already, (Pixar Studios being the best example of having luck here.) \n",
    "\n",
    "2) Release this film in June, May, March of any genre, or July with an emphasis on non-Fantasy films (which, interestingly do much better in November.)\n",
    "\n",
    "3) Make the movie somewhere within 120-150 minutes long since movies in this range tend to make the most net profit on average with the most consistency. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Future Work "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Future work: I'd love to, if the project parameters were a bit larger, take a look at what studios tend to do best within the three recommendations I've already given. I'd also love an opportunity to work with review data and see how films that fit these three categories are normally rated and reviewed. "
   ]
  }
 ],
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