Group_4_Notebook_FINAL.ipynb

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 "cells": [
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![example](./images/banner-949931_1280.jpg)"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Movie Analysis for Microsoft Studios\n",
    "\n",
    "**Authors**: [Paul Lindquist](https://www.linkedin.com/in/paul-lindquist/), [Steven Addison](https://www.linkedin.com/in/addisonse/), [Sumedh Bhardwaj](https://www.linkedin.com/in/sumedh-bhardwaj-932767202/), [Wahaj Dar](https://www.linkedin.com/in/wahaj-dar-/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "***\n",
    "This project postulates that Microsoft is launching a movie studio. We use exploratory data analysis to pitch recommendations to their stakeholders."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Business Problem\n",
    "***\n",
    "As this is a new venture for Microsoft, we want to be cognizant of cost and profitability. We offer several, data-driven approaches to maximize return.\n",
    "\n",
    "Questions we'll answer:\n",
    "* What genres of movies are lower risk to target as an entry point into the industry?\n",
    "* Are there types of movies or specific directors and actors that yield higher revenues?\n",
    "* Who are Microsoft's competitors and can anything be modeled from their successes?\n",
    "    * Does Microsoft have a catalogue of such content at their disposal?\n",
    "* When is the most profitable time to release movies?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data\n",
    "***\n",
    "We use historical and categorical data from the box office and current movie landscape – release date, directors, actors, etc. – to examine correlations.\n",
    "\n",
    "Focuses:\n",
    "* Particular focus given to cost (budget) and gross revenue (worldwide)\n",
    "* Drawing a correlation between revenue and genres, directors, actors, release date, etc.\n",
    "* Sourced from: [Box Office Mojo](https://www.boxofficemojo.com/), [The Numbers](https://www.the-numbers.com/), [IMDB](https://www.imdb.com/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic 1: Entry Point Movies\n",
    "Since Microsoft is starting a new venture, we want to be cognizant of cost and profit. We take a look at a dataset of the top 50 movies with the highest profit margins over the last 20 years. And interestingly, 23 of the top 50 are horror movies. Perhaps this genre could be a lower-risk entry point into the movie industry?\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Data Import\n",
    "Using a table that highlights both cost ('production budget') and revenue ('worldwide gross')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import standard packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open DataFrame highlighting production budget and worldwide gross revenue\n",
    "b_and_p = pd.read_csv('./data/zippedData/tn.movie_budgets.csv.gz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Data Cleaning and Filtering\n",
    "The 'gross revenue' and 'production budget' columns need to be changed to integers to run calculations. We then run a simple '(revenue - budget) / revenue' calculation to create a sortable 'profit margin' column. Finally, we create a 'top 50' list of movies sorted by 'profit margin' with corresponding genres."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert string columns to integers and date-time columns\n",
    "# Strip dollar signs and commas from integers\n",
    "b_and_p['worldwide_gross'] = b_and_p['worldwide_gross'].str.replace(',', \n",
    "                                '').str.replace('$', '').astype(int)\n",
    "b_and_p['production_budget'] = b_and_p['production_budget'].str.replace(',', \n",
    "                                '').str.replace('$', '').astype(int)\n",
    "b_and_p['domestic_gross'] = b_and_p['domestic_gross'].str.replace(',', \n",
    "                                '').str.replace('$', '').astype(int)\n",
    "b_and_p['release_date'] = pd.to_datetime(b_and_p['release_date'])\n",
    "b_and_p.dropna(subset=['production_budget', 'worldwide_gross'])\n",
    "b_and_p.at[5189, 'movie'] = \"God's Not Dead\"\n",
    "b_and_p.at[2996, 'movie'] = \"The King's Speech\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter by movies that grossed at least $1M worldwide and were made in the last 20 years\n",
    "# Create a gross profit margin column and sort by it\n",
    "b_and_p = b_and_p[b_and_p['worldwide_gross'] >= 1000000]\n",
    "b_and_p = b_and_p[b_and_p['release_date'].dt.year >= 1999]\n",
    "b_and_p['profit_margin_percent'] = (b_and_p['worldwide_gross'] - b_and_p['production_budget']) / b_and_p['worldwide_gross'] * 100\n",
    "b_and_p['gross_margin_percent'] = b_and_p['worldwide_gross'] / b_and_p['production_budget'] * 100\n",
    "b_and_p = b_and_p.sort_values(by=['gross_margin_percent'], ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>93</td>\n",
       "      <td>2009-09-25</td>\n",
       "      <td>Paranormal Activity</td>\n",
       "      <td>450000</td>\n",
       "      <td>107918810</td>\n",
       "      <td>194183034</td>\n",
       "      <td>99.768260</td>\n",
       "      <td>43151.785333</td>\n",
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       "      <td>Horror</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>2015-07-10</td>\n",
       "      <td>The Gallows</td>\n",
       "      <td>100000</td>\n",
       "      <td>22764410</td>\n",
       "      <td>41656474</td>\n",
       "      <td>99.759941</td>\n",
       "      <td>41656.474000</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Horror</td>\n",
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       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>1999-07-14</td>\n",
       "      <td>The Blair Witch Project</td>\n",
       "      <td>600000</td>\n",
       "      <td>140539099</td>\n",
       "      <td>248300000</td>\n",
       "      <td>99.758357</td>\n",
       "      <td>41383.333333</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Horror</td>\n",
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       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>2004-05-07</td>\n",
       "      <td>Super Size Me</td>\n",
       "      <td>65000</td>\n",
       "      <td>11529368</td>\n",
       "      <td>22233808</td>\n",
       "      <td>99.707652</td>\n",
       "      <td>34205.858462</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Horror</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>2007-05-16</td>\n",
       "      <td>Once</td>\n",
       "      <td>150000</td>\n",
       "      <td>9445857</td>\n",
       "      <td>23323631</td>\n",
       "      <td>99.356875</td>\n",
       "      <td>15549.087333</td>\n",
       "      <td>Musical</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>2004-06-11</td>\n",
       "      <td>Napoleon Dynamite</td>\n",
       "      <td>400000</td>\n",
       "      <td>44540956</td>\n",
       "      <td>46122713</td>\n",
       "      <td>99.132748</td>\n",
       "      <td>11530.678250</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31</td>\n",
       "      <td>2004-08-06</td>\n",
       "      <td>Open Water</td>\n",
       "      <td>500000</td>\n",
       "      <td>30500882</td>\n",
       "      <td>55518641</td>\n",
       "      <td>99.099402</td>\n",
       "      <td>11103.728200</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>81</td>\n",
       "      <td>2006-09-29</td>\n",
       "      <td>Facing the Giants</td>\n",
       "      <td>100000</td>\n",
       "      <td>10178331</td>\n",
       "      <td>10243159</td>\n",
       "      <td>99.023739</td>\n",
       "      <td>10243.159000</td>\n",
       "      <td>Christian</td>\n",
       "      <td>Horror</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>2012-01-06</td>\n",
       "      <td>The Devil Inside</td>\n",
       "      <td>1000000</td>\n",
       "      <td>53262945</td>\n",
       "      <td>101759490</td>\n",
       "      <td>99.017291</td>\n",
       "      <td>10175.949000</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Horror</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60</td>\n",
       "      <td>2009-04-23</td>\n",
       "      <td>Home</td>\n",
       "      <td>500000</td>\n",
       "      <td>15433</td>\n",
       "      <td>44793168</td>\n",
       "      <td>98.883758</td>\n",
       "      <td>8958.633600</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Horror</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>57</td>\n",
       "      <td>2004-10-29</td>\n",
       "      <td>Saw</td>\n",
       "      <td>1200000</td>\n",
       "      <td>55968727</td>\n",
       "      <td>103880027</td>\n",
       "      <td>98.844821</td>\n",
       "      <td>8656.668917</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>48</td>\n",
       "      <td>2002-04-19</td>\n",
       "      <td>My Big Fat Greek Wedding</td>\n",
       "      <td>5000000</td>\n",
       "      <td>241438208</td>\n",
       "      <td>374890034</td>\n",
       "      <td>98.666276</td>\n",
       "      <td>7497.800680</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>29</td>\n",
       "      <td>2008-09-26</td>\n",
       "      <td>Fireproof</td>\n",
       "      <td>500000</td>\n",
       "      <td>33456317</td>\n",
       "      <td>33473297</td>\n",
       "      <td>98.506272</td>\n",
       "      <td>6694.659400</td>\n",
       "      <td>Christian</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>63</td>\n",
       "      <td>2011-04-01</td>\n",
       "      <td>Insidious</td>\n",
       "      <td>1500000</td>\n",
       "      <td>54009150</td>\n",
       "      <td>99870886</td>\n",
       "      <td>98.498061</td>\n",
       "      <td>6658.059067</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>2015-04-17</td>\n",
       "      <td>Unfriended</td>\n",
       "      <td>1000000</td>\n",
       "      <td>32789645</td>\n",
       "      <td>64364198</td>\n",
       "      <td>98.446341</td>\n",
       "      <td>6436.419800</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>65</td>\n",
       "      <td>2010-10-20</td>\n",
       "      <td>Paranormal Activity 2</td>\n",
       "      <td>3000000</td>\n",
       "      <td>84752907</td>\n",
       "      <td>177512032</td>\n",
       "      <td>98.309974</td>\n",
       "      <td>5917.067733</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>50</td>\n",
       "      <td>2017-01-20</td>\n",
       "      <td>Split</td>\n",
       "      <td>5000000</td>\n",
       "      <td>138141585</td>\n",
       "      <td>278964806</td>\n",
       "      <td>98.207659</td>\n",
       "      <td>5579.296120</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>90</td>\n",
       "      <td>2014-03-21</td>\n",
       "      <td>God's Not Dead</td>\n",
       "      <td>1150000</td>\n",
       "      <td>60755732</td>\n",
       "      <td>63777092</td>\n",
       "      <td>98.196845</td>\n",
       "      <td>5545.834087</td>\n",
       "      <td>Christian</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16</td>\n",
       "      <td>2006-05-24</td>\n",
       "      <td>An Inconvenient Truth</td>\n",
       "      <td>1000000</td>\n",
       "      <td>24146161</td>\n",
       "      <td>53365925</td>\n",
       "      <td>98.126145</td>\n",
       "      <td>5336.592500</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>49</td>\n",
       "      <td>2017-02-24</td>\n",
       "      <td>Get Out</td>\n",
       "      <td>5000000</td>\n",
       "      <td>176040665</td>\n",
       "      <td>255367951</td>\n",
       "      <td>98.042041</td>\n",
       "      <td>5107.359020</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>17</td>\n",
       "      <td>2001-03-16</td>\n",
       "      <td>Gabriela</td>\n",
       "      <td>50000</td>\n",
       "      <td>2335352</td>\n",
       "      <td>2335352</td>\n",
       "      <td>97.858995</td>\n",
       "      <td>4670.704000</td>\n",
       "      <td>Romance</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>18</td>\n",
       "      <td>2012-05-25</td>\n",
       "      <td>Les Intouchables</td>\n",
       "      <td>10800000</td>\n",
       "      <td>13182281</td>\n",
       "      <td>484873045</td>\n",
       "      <td>97.772613</td>\n",
       "      <td>4489.565231</td>\n",
       "      <td>Foreign</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>64</td>\n",
       "      <td>2016-10-21</td>\n",
       "      <td>Moonlight</td>\n",
       "      <td>1500000</td>\n",
       "      <td>27854931</td>\n",
       "      <td>65245512</td>\n",
       "      <td>97.700991</td>\n",
       "      <td>4349.700800</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>18</td>\n",
       "      <td>2012-05-25</td>\n",
       "      <td>Chernobyl Diaries</td>\n",
       "      <td>1000000</td>\n",
       "      <td>18119640</td>\n",
       "      <td>42411721</td>\n",
       "      <td>97.642161</td>\n",
       "      <td>4241.172100</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>51</td>\n",
       "      <td>2011-10-21</td>\n",
       "      <td>Paranormal Activity 3</td>\n",
       "      <td>5000000</td>\n",
       "      <td>104028807</td>\n",
       "      <td>207039844</td>\n",
       "      <td>97.585006</td>\n",
       "      <td>4140.796880</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>99</td>\n",
       "      <td>2007-02-09</td>\n",
       "      <td>Das Leben der Anderen</td>\n",
       "      <td>2000000</td>\n",
       "      <td>11284657</td>\n",
       "      <td>81197047</td>\n",
       "      <td>97.536856</td>\n",
       "      <td>4059.852350</td>\n",
       "      <td>Foreign</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>84</td>\n",
       "      <td>2014-10-03</td>\n",
       "      <td>Annabelle</td>\n",
       "      <td>6500000</td>\n",
       "      <td>84273813</td>\n",
       "      <td>256862920</td>\n",
       "      <td>97.469467</td>\n",
       "      <td>3951.737231</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>35</td>\n",
       "      <td>2005-06-24</td>\n",
       "      <td>La marche de l'empereur</td>\n",
       "      <td>3400000</td>\n",
       "      <td>77437223</td>\n",
       "      <td>133276359</td>\n",
       "      <td>97.448910</td>\n",
       "      <td>3919.892912</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>15</td>\n",
       "      <td>2010-08-27</td>\n",
       "      <td>The Last Exorcism</td>\n",
       "      <td>1800000</td>\n",
       "      <td>41034350</td>\n",
       "      <td>70165900</td>\n",
       "      <td>97.434651</td>\n",
       "      <td>3898.105556</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>15</td>\n",
       "      <td>2004-06-23</td>\n",
       "      <td>Fahrenheit 9/11</td>\n",
       "      <td>6000000</td>\n",
       "      <td>119114517</td>\n",
       "      <td>221148959</td>\n",
       "      <td>97.286897</td>\n",
       "      <td>3685.815983</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>86</td>\n",
       "      <td>2007-12-05</td>\n",
       "      <td>Juno</td>\n",
       "      <td>7000000</td>\n",
       "      <td>143495265</td>\n",
       "      <td>231450102</td>\n",
       "      <td>96.975590</td>\n",
       "      <td>3306.430029</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>53</td>\n",
       "      <td>2013-09-13</td>\n",
       "      <td>Insidious Chapter 2</td>\n",
       "      <td>5000000</td>\n",
       "      <td>83586447</td>\n",
       "      <td>161921515</td>\n",
       "      <td>96.912084</td>\n",
       "      <td>3238.430300</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>56</td>\n",
       "      <td>2016-12-21</td>\n",
       "      <td>Dangal</td>\n",
       "      <td>9500000</td>\n",
       "      <td>12391761</td>\n",
       "      <td>294654618</td>\n",
       "      <td>96.775886</td>\n",
       "      <td>3101.627558</td>\n",
       "      <td>Foreign</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>52</td>\n",
       "      <td>2005-10-28</td>\n",
       "      <td>Saw II</td>\n",
       "      <td>5000000</td>\n",
       "      <td>87025093</td>\n",
       "      <td>152925093</td>\n",
       "      <td>96.730425</td>\n",
       "      <td>3058.501860</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>59</td>\n",
       "      <td>2011-12-30</td>\n",
       "      <td>Jodaeiye Nader az Simin</td>\n",
       "      <td>800000</td>\n",
       "      <td>7098492</td>\n",
       "      <td>24426169</td>\n",
       "      <td>96.724824</td>\n",
       "      <td>3053.271125</td>\n",
       "      <td>Foreign</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>67</td>\n",
       "      <td>2013-06-07</td>\n",
       "      <td>The Purge</td>\n",
       "      <td>3000000</td>\n",
       "      <td>64473115</td>\n",
       "      <td>91266581</td>\n",
       "      <td>96.712926</td>\n",
       "      <td>3042.219367</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Foreign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>55</td>\n",
       "      <td>2016-07-22</td>\n",
       "      <td>Lights Out</td>\n",
       "      <td>5000000</td>\n",
       "      <td>67268835</td>\n",
       "      <td>148806510</td>\n",
       "      <td>96.639932</td>\n",
       "      <td>2976.130200</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Foreign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>79</td>\n",
       "      <td>2003-09-12</td>\n",
       "      <td>Lost in Translation</td>\n",
       "      <td>4000000</td>\n",
       "      <td>44585453</td>\n",
       "      <td>117085297</td>\n",
       "      <td>96.583687</td>\n",
       "      <td>2927.132425</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Foreign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>69</td>\n",
       "      <td>2012-10-12</td>\n",
       "      <td>Sinister</td>\n",
       "      <td>3000000</td>\n",
       "      <td>48086903</td>\n",
       "      <td>87727807</td>\n",
       "      <td>96.580332</td>\n",
       "      <td>2924.260233</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Foreign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>97</td>\n",
       "      <td>2010-11-26</td>\n",
       "      <td>The King's Speech</td>\n",
       "      <td>15000000</td>\n",
       "      <td>138797449</td>\n",
       "      <td>430821168</td>\n",
       "      <td>96.518277</td>\n",
       "      <td>2872.141120</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>59</td>\n",
       "      <td>2012-10-19</td>\n",
       "      <td>Paranormal Activity 4</td>\n",
       "      <td>5000000</td>\n",
       "      <td>53900335</td>\n",
       "      <td>142817992</td>\n",
       "      <td>96.499041</td>\n",
       "      <td>2856.359840</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>86</td>\n",
       "      <td>2017-07-07</td>\n",
       "      <td>A Ghost Story</td>\n",
       "      <td>100000</td>\n",
       "      <td>1594798</td>\n",
       "      <td>2769782</td>\n",
       "      <td>96.389608</td>\n",
       "      <td>2769.782000</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>74</td>\n",
       "      <td>2008-11-12</td>\n",
       "      <td>Slumdog Millionaire</td>\n",
       "      <td>14000000</td>\n",
       "      <td>141330703</td>\n",
       "      <td>384530440</td>\n",
       "      <td>96.359196</td>\n",
       "      <td>2746.646000</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>91</td>\n",
       "      <td>2018-04-13</td>\n",
       "      <td>Truth or Dare</td>\n",
       "      <td>3500000</td>\n",
       "      <td>41411015</td>\n",
       "      <td>95127344</td>\n",
       "      <td>96.320721</td>\n",
       "      <td>2717.924114</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>17</td>\n",
       "      <td>2013-08-23</td>\n",
       "      <td>You're Next</td>\n",
       "      <td>1000000</td>\n",
       "      <td>18494006</td>\n",
       "      <td>26887177</td>\n",
       "      <td>96.280755</td>\n",
       "      <td>2688.717700</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>94</td>\n",
       "      <td>2005-12-25</td>\n",
       "      <td>Wolf Creek</td>\n",
       "      <td>1100000</td>\n",
       "      <td>16186348</td>\n",
       "      <td>29005064</td>\n",
       "      <td>96.207559</td>\n",
       "      <td>2636.824000</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Christian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>41</td>\n",
       "      <td>2003-11-14</td>\n",
       "      <td>Tupac Resurrection</td>\n",
       "      <td>300000</td>\n",
       "      <td>7718961</td>\n",
       "      <td>7808524</td>\n",
       "      <td>96.158045</td>\n",
       "      <td>2602.841333</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>73</td>\n",
       "      <td>2012-06-15</td>\n",
       "      <td>Your Sister's Sister</td>\n",
       "      <td>120000</td>\n",
       "      <td>1597486</td>\n",
       "      <td>3090593</td>\n",
       "      <td>96.117250</td>\n",
       "      <td>2575.494167</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>43</td>\n",
       "      <td>2014-06-06</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "      <td>12000000</td>\n",
       "      <td>124872350</td>\n",
       "      <td>307166834</td>\n",
       "      <td>96.093328</td>\n",
       "      <td>2559.723617</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>36</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>Halloween</td>\n",
       "      <td>10000000</td>\n",
       "      <td>159342015</td>\n",
       "      <td>254900667</td>\n",
       "      <td>96.076903</td>\n",
       "      <td>2549.006670</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id release_date                     movie  production_budget  \\\n",
       "0   93   2009-09-25       Paranormal Activity             450000   \n",
       "1   80   2015-07-10               The Gallows             100000   \n",
       "2    7   1999-07-14   The Blair Witch Project             600000   \n",
       "3   10   2004-05-07             Super Size Me              65000   \n",
       "4   57   2007-05-16                      Once             150000   \n",
       "5    8   2004-06-11         Napoleon Dynamite             400000   \n",
       "6   31   2004-08-06                Open Water             500000   \n",
       "7   81   2006-09-29         Facing the Giants             100000   \n",
       "8   12   2012-01-06          The Devil Inside            1000000   \n",
       "9   60   2009-04-23                      Home             500000   \n",
       "10  57   2004-10-29                       Saw            1200000   \n",
       "11  48   2002-04-19  My Big Fat Greek Wedding            5000000   \n",
       "12  29   2008-09-26                 Fireproof             500000   \n",
       "13  63   2011-04-01                 Insidious            1500000   \n",
       "14  14   2015-04-17                Unfriended            1000000   \n",
       "15  65   2010-10-20     Paranormal Activity 2            3000000   \n",
       "16  50   2017-01-20                     Split            5000000   \n",
       "17  90   2014-03-21            God's Not Dead            1150000   \n",
       "18  16   2006-05-24     An Inconvenient Truth            1000000   \n",
       "19  49   2017-02-24                   Get Out            5000000   \n",
       "20  17   2001-03-16                  Gabriela              50000   \n",
       "21  18   2012-05-25          Les Intouchables           10800000   \n",
       "22  64   2016-10-21                 Moonlight            1500000   \n",
       "23  18   2012-05-25         Chernobyl Diaries            1000000   \n",
       "24  51   2011-10-21     Paranormal Activity 3            5000000   \n",
       "25  99   2007-02-09     Das Leben der Anderen            2000000   \n",
       "26  84   2014-10-03                 Annabelle            6500000   \n",
       "27  35   2005-06-24   La marche de l'empereur            3400000   \n",
       "28  15   2010-08-27         The Last Exorcism            1800000   \n",
       "29  15   2004-06-23           Fahrenheit 9/11            6000000   \n",
       "30  86   2007-12-05                      Juno            7000000   \n",
       "31  53   2013-09-13       Insidious Chapter 2            5000000   \n",
       "32  56   2016-12-21                    Dangal            9500000   \n",
       "33  52   2005-10-28                    Saw II            5000000   \n",
       "34  59   2011-12-30   Jodaeiye Nader az Simin             800000   \n",
       "35  67   2013-06-07                 The Purge            3000000   \n",
       "36  55   2016-07-22                Lights Out            5000000   \n",
       "37  79   2003-09-12       Lost in Translation            4000000   \n",
       "38  69   2012-10-12                  Sinister            3000000   \n",
       "39  97   2010-11-26         The King's Speech           15000000   \n",
       "40  59   2012-10-19     Paranormal Activity 4            5000000   \n",
       "41  86   2017-07-07             A Ghost Story             100000   \n",
       "42  74   2008-11-12       Slumdog Millionaire           14000000   \n",
       "43  91   2018-04-13             Truth or Dare            3500000   \n",
       "44  17   2013-08-23               You're Next            1000000   \n",
       "45  94   2005-12-25                Wolf Creek            1100000   \n",
       "46  41   2003-11-14        Tupac Resurrection             300000   \n",
       "47  73   2012-06-15      Your Sister's Sister             120000   \n",
       "48  43   2014-06-06    The Fault in Our Stars           12000000   \n",
       "49  36   2018-10-19                 Halloween           10000000   \n",
       "\n",
       "    domestic_gross  worldwide_gross  profit_margin_percent  \\\n",
       "0        107918810        194183034              99.768260   \n",
       "1         22764410         41656474              99.759941   \n",
       "2        140539099        248300000              99.758357   \n",
       "3         11529368         22233808              99.707652   \n",
       "4          9445857         23323631              99.356875   \n",
       "5         44540956         46122713              99.132748   \n",
       "6         30500882         55518641              99.099402   \n",
       "7         10178331         10243159              99.023739   \n",
       "8         53262945        101759490              99.017291   \n",
       "9            15433         44793168              98.883758   \n",
       "10        55968727        103880027              98.844821   \n",
       "11       241438208        374890034              98.666276   \n",
       "12        33456317         33473297              98.506272   \n",
       "13        54009150         99870886              98.498061   \n",
       "14        32789645         64364198              98.446341   \n",
       "15        84752907        177512032              98.309974   \n",
       "16       138141585        278964806              98.207659   \n",
       "17        60755732         63777092              98.196845   \n",
       "18        24146161         53365925              98.126145   \n",
       "19       176040665        255367951              98.042041   \n",
       "20         2335352          2335352              97.858995   \n",
       "21        13182281        484873045              97.772613   \n",
       "22        27854931         65245512              97.700991   \n",
       "23        18119640         42411721              97.642161   \n",
       "24       104028807        207039844              97.585006   \n",
       "25        11284657         81197047              97.536856   \n",
       "26        84273813        256862920              97.469467   \n",
       "27        77437223        133276359              97.448910   \n",
       "28        41034350         70165900              97.434651   \n",
       "29       119114517        221148959              97.286897   \n",
       "30       143495265        231450102              96.975590   \n",
       "31        83586447        161921515              96.912084   \n",
       "32        12391761        294654618              96.775886   \n",
       "33        87025093        152925093              96.730425   \n",
       "34         7098492         24426169              96.724824   \n",
       "35        64473115         91266581              96.712926   \n",
       "36        67268835        148806510              96.639932   \n",
       "37        44585453        117085297              96.583687   \n",
       "38        48086903         87727807              96.580332   \n",
       "39       138797449        430821168              96.518277   \n",
       "40        53900335        142817992              96.499041   \n",
       "41         1594798          2769782              96.389608   \n",
       "42       141330703        384530440              96.359196   \n",
       "43        41411015         95127344              96.320721   \n",
       "44        18494006         26887177              96.280755   \n",
       "45        16186348         29005064              96.207559   \n",
       "46         7718961          7808524              96.158045   \n",
       "47         1597486          3090593              96.117250   \n",
       "48       124872350        307166834              96.093328   \n",
       "49       159342015        254900667              96.076903   \n",
       "\n",
       "    gross_margin_percent        genre genre_sorted  \n",
       "0           43151.785333       Horror       Horror  \n",
       "1           41656.474000       Horror       Horror  \n",
       "2           41383.333333       Horror       Horror  \n",
       "3           34205.858462  Documentary       Horror  \n",
       "4           15549.087333      Musical       Horror  \n",
       "5           11530.678250       Comedy       Horror  \n",
       "6           11103.728200     Thriller       Horror  \n",
       "7           10243.159000    Christian       Horror  \n",
       "8           10175.949000       Horror       Horror  \n",
       "9            8958.633600  Documentary       Horror  \n",
       "10           8656.668917       Horror       Horror  \n",
       "11           7497.800680       Comedy       Horror  \n",
       "12           6694.659400    Christian       Horror  \n",
       "13           6658.059067       Horror       Horror  \n",
       "14           6436.419800       Horror       Horror  \n",
       "15           5917.067733       Horror       Horror  \n",
       "16           5579.296120     Thriller       Horror  \n",
       "17           5545.834087    Christian       Horror  \n",
       "18           5336.592500  Documentary       Horror  \n",
       "19           5107.359020       Horror       Horror  \n",
       "20           4670.704000      Romance       Horror  \n",
       "21           4489.565231      Foreign       Horror  \n",
       "22           4349.700800        Drama       Horror  \n",
       "23           4241.172100       Horror  Documentary  \n",
       "24           4140.796880       Horror  Documentary  \n",
       "25           4059.852350      Foreign  Documentary  \n",
       "26           3951.737231       Horror  Documentary  \n",
       "27           3919.892912  Documentary  Documentary  \n",
       "28           3898.105556       Horror  Documentary  \n",
       "29           3685.815983  Documentary        Drama  \n",
       "30           3306.430029       Comedy        Drama  \n",
       "31           3238.430300       Horror        Drama  \n",
       "32           3101.627558      Foreign        Drama  \n",
       "33           3058.501860       Horror        Drama  \n",
       "34           3053.271125      Foreign        Drama  \n",
       "35           3042.219367       Horror      Foreign  \n",
       "36           2976.130200       Horror      Foreign  \n",
       "37           2927.132425        Drama      Foreign  \n",
       "38           2924.260233       Horror      Foreign  \n",
       "39           2872.141120        Drama       Comedy  \n",
       "40           2856.359840       Horror       Comedy  \n",
       "41           2769.782000        Drama       Comedy  \n",
       "42           2746.646000        Drama       Comedy  \n",
       "43           2717.924114       Horror    Christian  \n",
       "44           2688.717700       Horror    Christian  \n",
       "45           2636.824000       Horror    Christian  \n",
       "46           2602.841333  Documentary     Thriller  \n",
       "47           2575.494167       Comedy     Thriller  \n",
       "48           2559.723617        Drama      Musical  \n",
       "49           2549.006670       Horror      Romance  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Top 50 list and hardcode genre/sorted genre for each movie\n",
    "pd.options.mode.chained_assignment = None\n",
    "top_50_genres = b_and_p.iloc[:50]\n",
    "top_50_genres['genre'] = ['Horror', 'Horror', 'Horror', 'Documentary', 'Musical', 'Comedy', 'Thriller',\n",
    "                    'Christian', 'Horror', 'Documentary', 'Horror', 'Comedy', 'Christian',\n",
    "                    'Horror', 'Horror', 'Horror', 'Thriller', 'Christian', 'Documentary', 'Horror',\n",
    "                    'Romance', 'Foreign', 'Drama', 'Horror', 'Horror', 'Foreign', 'Horror',\n",
    "                    'Documentary', 'Horror', 'Documentary', 'Comedy', 'Horror', 'Foreign', 'Horror',\n",
    "                    'Foreign', 'Horror', 'Horror', 'Drama', 'Horror', 'Drama', 'Horror', 'Drama',\n",
    "                    'Drama', 'Horror', 'Horror', 'Horror', 'Documentary', 'Comedy', 'Drama',\n",
    "                    'Horror']\n",
    "\n",
    "top_50_genres['genre_sorted'] = ['Horror', 'Horror', 'Horror', 'Horror', 'Horror', 'Horror', 'Horror',\n",
    "                          'Horror', 'Horror', 'Horror', 'Horror', 'Horror', 'Horror', 'Horror',\n",
    "                          'Horror', 'Horror', 'Horror', 'Horror', 'Horror', 'Horror', 'Horror', 'Horror',\n",
    "                          'Horror', 'Documentary', 'Documentary', 'Documentary', 'Documentary',\n",
    "                          'Documentary', 'Documentary', 'Drama', 'Drama', 'Drama', 'Drama', 'Drama',\n",
    "                          'Drama', 'Foreign', 'Foreign', 'Foreign', 'Foreign', 'Comedy', 'Comedy',\n",
    "                          'Comedy', 'Comedy', 'Christian', 'Christian', 'Christian', 'Thriller',\n",
    "                          'Thriller', 'Musical', 'Romance']\n",
    "\n",
    "top_50_genres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Data Analysis and Calculations\n",
    "We merge our previous table with another table that has more movies and corresponding genres to create a larger dataset of movies. Then we filter down to a table of single genres and run averages on the budgets and gross revenues. We run another calculation to get return on investment (ROI) variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg Horror ROI: 275.2021771150655, Avg Horror Budget: 24778529.497854076, Avg: Horror Revenue: 92969582.13304721\n",
      "Avg Drama ROI: 175.83723648853314, Avg Drama Budget: 29679766.655398037, Avg: Drama Revenue: 81867848.13849509\n",
      "Avg Comedy ROI: 222.8918060657137, Avg Comedy Budget: 41848300.76173913, Avg: Comedy Revenue: 135124734.1373913\n",
      "Avg Action ROI: 198.34461443240613, Avg Action Budget: 76802216.18962076, Avg: Action Revenue: 229135275.76646706\n"
     ]
    }
   ],
   "source": [
    "# Filter tables by genre, create averages for budget, revenue & ROI\n",
    "genre_data = pd.read_csv('./data/zippedData/imdb.title.basics.csv.gz')\n",
    "gross_genre_merge = b_and_p.merge(genre_data, left_on=['movie'], right_on=['primary_title'])\n",
    "gross_genre_merge['genres'] = gross_genre_merge['genres'].str.replace(',', '')\n",
    "\n",
    "horror_genre = gross_genre_merge[gross_genre_merge['genres'].str.contains(\"orro\")==True]\n",
    "horror_genre = horror_genre.drop_duplicates(subset=['movie'])\n",
    "horror_budg = horror_genre['production_budget'].mean()\n",
    "horror_rev = horror_genre['worldwide_gross'].mean()\n",
    "\n",
    "drama_genre = gross_genre_merge[gross_genre_merge['genres'].str.contains(\"rama\")==True]\n",
    "drama_genre = drama_genre.drop_duplicates(subset=['movie'])\n",
    "drama_budg = drama_genre['production_budget'].mean()\n",
    "drama_rev = drama_genre['worldwide_gross'].mean()\n",
    "\n",
    "comedy_genre = gross_genre_merge[gross_genre_merge['genres'].str.contains(\"omedy\")==True]\n",
    "comedy_genre = comedy_genre.drop_duplicates(subset=['movie'])\n",
    "comedy_budg = comedy_genre['production_budget'].mean()\n",
    "comedy_rev = comedy_genre['worldwide_gross'].mean()\n",
    "\n",
    "action_genre = gross_genre_merge[gross_genre_merge['genres'].str.contains(\"ction\")==True]\n",
    "action_genre = action_genre.drop_duplicates(subset=['movie'])\n",
    "action_budg = action_genre['production_budget'].mean()\n",
    "action_rev = action_genre['worldwide_gross'].mean()\n",
    "\n",
    "horror_roi = ((horror_rev - horror_budg) / horror_budg) * 100\n",
    "drama_roi = ((drama_rev - drama_budg) / drama_budg) * 100\n",
    "comedy_roi = ((comedy_rev - comedy_budg) / comedy_budg) * 100\n",
    "action_roi = ((action_rev - action_budg) / action_budg) * 100\n",
    "\n",
    "print(f\"Avg Horror ROI: {horror_roi}, Avg Horror Budget: {horror_budg}, Avg: Horror Revenue: {horror_rev}\")\n",
    "print(f\"Avg Drama ROI: {drama_roi}, Avg Drama Budget: {drama_budg}, Avg: Drama Revenue: {drama_rev}\")\n",
    "print(f\"Avg Comedy ROI: {comedy_roi}, Avg Comedy Budget: {comedy_budg}, Avg: Comedy Revenue: {comedy_rev}\")\n",
    "print(f\"Avg Action ROI: {action_roi}, Avg Action Budget: {action_budg}, Avg: Action Revenue: {action_rev}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Data Visualization\n",
    "We plot a bar chart to compare the genre subgroups against each other. A second bar chart compares each genre's cost (budget), revenue and ROI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histogram highlighting Top 50 movies w/ highest GM by genre\n",
    "genre_hist = sns.histplot(data=top_50_genres, y='genre_sorted', hue=\"genre_sorted\", legend=False)\n",
    "sns.set(rc={'figure.figsize':(12,9)})\n",
    "sns.set_style('white')\n",
    "plt.rcParams['xtick.bottom'] = True\n",
    "plt.xlabel('Number of Movies', size=14)\n",
    "plt.ylabel('Genre', size=14)\n",
    "plt.title('Top 50 Movies with Highest Profit Margins\\n(last 20 years, minimum $1M gross revenue)',\n",
    "         size=16, weight=900);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot bar graph with avg. budget, revenue and ROI by genre\n",
    "\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "all_labels = ['Horror', 'Drama', 'Comedy', 'Action']\n",
    "prod_budg = [horror_budg, drama_budg, comedy_budg, action_budg]\n",
    "all_revs = [horror_rev, drama_rev, comedy_rev, action_rev]\n",
    "horror_roi = 'Avg. ROI: 275%'\n",
    "comedy_roi = 'Avg. ROI: 223%'\n",
    "drama_roi = 'Avg. ROI: 176%'\n",
    "action_roi = 'Avg. ROI: 198%'\n",
    "\n",
    "x_paul = np.arange(len(all_labels))\n",
    "width_paul = 0.35\n",
    "y_paul = np.arange(4)\n",
    "def millions(x, pos):\n",
    "    return '$%1.0fM' % (x*1e-6)\n",
    "formatter = FuncFormatter(millions)\n",
    "\n",
    "cost_rev, ax = plt.subplots()\n",
    "rects1_paul = ax.bar(x_paul - width_paul/2, prod_budg, width_paul, label='Avg. Production Budget')\n",
    "rects2_paul = ax.bar(x_paul + width_paul/2, all_revs, width_paul, label='Avg. Worldwide Gross Revenue')\n",
    "\n",
    "ax.set_xlabel('Genre', size=14)\n",
    "ax.set_title('Average Return on Investment by Genre\\n(last 20 years)', size=16, weight=900)\n",
    "ax.set_xticks(x_paul)\n",
    "ax.set_xticklabels(all_labels, size=13)\n",
    "ax.tick_params(left=True, bottom=False)\n",
    "ax.legend()\n",
    "plt.rc('legend', fontsize=13)\n",
    "ax.yaxis.set_major_formatter(formatter)\n",
    "plt.yticks(fontsize=13)\n",
    "\n",
    "plt.figtext(0.174, 0.415, horror_roi, fontsize=14, weight=\"bold\")\n",
    "plt.figtext(0.398, 0.377, drama_roi, fontsize=14, weight=\"bold\")\n",
    "plt.figtext(0.622, 0.563, comedy_roi, fontsize=14, weight=\"bold\")\n",
    "plt.figtext(0.846, 0.894, action_roi, fontsize=14, weight=\"bold\")\n",
    "\n",
    "cost_rev.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 Recommendation\n",
    "\n",
    "**An early focus should be placed on making horror movies.** According to the data, they historically yield the lowest average cost and highest average profit margins. They'll serve as an initial, lower-risk entry point."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic 2: Action Genre Profit Analysis \n",
    "As we saw in the above section, despite not having the best return on investment, action movies bring in undeniably impressive profits. In this section we will take a closer look at the highest profiting action movies and deliver Microsoft Studios suggestions on directors and cast members to hire so that they know they are investing in the most valuable names in Hollywood.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Data Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Data Cleaning and Filtering \n",
    "\n",
    "The data we used to find our conclusion came from several data sets acquired from IMDB that had to be merged together to tell a cohesive story. We also used the data set provided from the website “The Numbers” to draw conclusions based on net profits. \n",
    "\n",
    "The biggest hurdle was cleaning up the money data in the “budget” dataframe so that we could sort it by total profits. After removing all commas and dollar signs from the dataframe we were able to then subtract every movie’s world wide gross figure from their budget to then find out the movie’s profit. From that point it was a painless process to merge that large dataframe with genre information taken from IMDB and find the most profitable action movies of the past twenty years.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "namesdf = pd.read_csv('data/zippedData/imdb.name.basics.csv.gz')\n",
    "titlesdf = pd.read_csv('data/zippedData/imdb.title.basics.csv.gz')\n",
    "crewdf = pd.read_csv('data/zippedData/imdb.title.crew.csv.gz')\n",
    "principalsdf = pd.read_csv('data/zippedData/imdb.title.principals.csv.gz')\n",
    "budgetdf = pd.read_csv('data/zippedData/tn.movie_budgets.csv.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the genres from the title dataframe so that we make explore by action movies only \n",
    "# Removed the commas and dollar signs from the data so that it could be converted\n",
    "# Reassigned the data to a float type instead of object so that it may be sorted properly\n",
    "# Assinged that now to an int so that it is easier to read\n",
    "# Since I'm on a PC for some reason I cannot just convert to int\n",
    "# Only selected the movies from the past 20 years\n",
    "# Established a column for net gross by subtracting the budget from the worldwide gross\n",
    "# Merged in my genres so that I may only select action movies\n",
    "# Selected the top 20 grossing action movies from the past 20 years\n",
    "genres = titlesdf[['primary_title', 'genres']]\n",
    "budgetdf.replace(',','', regex=True, inplace=True)\n",
    "budgetdf.replace('\\$','', regex=True, inplace=True)\n",
    "budgetdf['production_budget'] = budgetdf['production_budget'].astype(float)\n",
    "budgetdf['worldwide_gross'] = budgetdf['worldwide_gross'].astype(float)\n",
    "budgetdf['domestic_gross'] = budgetdf['domestic_gross'].astype(float)\n",
    "budgetdf['production_budget'] = budgetdf['production_budget'].astype(int)\n",
    "budgetdf['worldwide_gross'] = budgetdf['worldwide_gross'].astype(int)\n",
    "budgetdf['domestic_gross'] = budgetdf['domestic_gross'].astype(int)\n",
    "budgetdf['release_date'] = pd.to_datetime(budgetdf['release_date'])\n",
    "budgetdf = budgetdf[budgetdf['release_date'].dt.year >= 2001]\n",
    "budgetdf['total_profit'] = budgetdf['worldwide_gross'] - budgetdf['production_budget']\n",
    "budgetandgenres = budgetdf.merge(genres, left_on=['movie'], right_on=['primary_title'])\n",
    "actiondf = budgetandgenres[budgetandgenres['genres'].str.contains('Action') == True]\n",
    "highestactiondf = actiondf.sort_values(by='total_profit', ascending=False)\n",
    "highest20df = highestactiondf.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>total_profit</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>300000000</td>\n",
       "      <td>678815482</td>\n",
       "      <td>2048134200</td>\n",
       "      <td>1748134200</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>34</td>\n",
       "      <td>2015-06-12</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>215000000</td>\n",
       "      <td>652270625</td>\n",
       "      <td>1648854864</td>\n",
       "      <td>1433854864</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>67</td>\n",
       "      <td>2015-04-03</td>\n",
       "      <td>Furious 7</td>\n",
       "      <td>190000000</td>\n",
       "      <td>353007020</td>\n",
       "      <td>1518722794</td>\n",
       "      <td>1328722794</td>\n",
       "      <td>Furious 7</td>\n",
       "      <td>Action,Crime,Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>27</td>\n",
       "      <td>2012-05-04</td>\n",
       "      <td>The Avengers</td>\n",
       "      <td>225000000</td>\n",
       "      <td>623279547</td>\n",
       "      <td>1517935897</td>\n",
       "      <td>1292935897</td>\n",
       "      <td>The Avengers</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>42</td>\n",
       "      <td>2018-02-16</td>\n",
       "      <td>Black Panther</td>\n",
       "      <td>200000000</td>\n",
       "      <td>700059566</td>\n",
       "      <td>1348258224</td>\n",
       "      <td>1148258224</td>\n",
       "      <td>Black Panther</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>13</td>\n",
       "      <td>2018-06-22</td>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>170000000</td>\n",
       "      <td>417719760</td>\n",
       "      <td>1305772799</td>\n",
       "      <td>1135772799</td>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>330600000</td>\n",
       "      <td>459005868</td>\n",
       "      <td>1403013963</td>\n",
       "      <td>1072413963</td>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>44</td>\n",
       "      <td>2018-06-15</td>\n",
       "      <td>Incredibles 2</td>\n",
       "      <td>200000000</td>\n",
       "      <td>608581744</td>\n",
       "      <td>1242520711</td>\n",
       "      <td>1042520711</td>\n",
       "      <td>Incredibles 2</td>\n",
       "      <td>Action,Adventure,Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>48</td>\n",
       "      <td>2013-05-03</td>\n",
       "      <td>Iron Man 3</td>\n",
       "      <td>200000000</td>\n",
       "      <td>408992272</td>\n",
       "      <td>1215392272</td>\n",
       "      <td>1015392272</td>\n",
       "      <td>Iron Man 3</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>36</td>\n",
       "      <td>2018-12-21</td>\n",
       "      <td>Aquaman</td>\n",
       "      <td>160000000</td>\n",
       "      <td>335061807</td>\n",
       "      <td>1146894640</td>\n",
       "      <td>986894640</td>\n",
       "      <td>Aquaman</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>23</td>\n",
       "      <td>2017-04-14</td>\n",
       "      <td>The Fate of the Furious</td>\n",
       "      <td>250000000</td>\n",
       "      <td>225764765</td>\n",
       "      <td>1234846267</td>\n",
       "      <td>984846267</td>\n",
       "      <td>The Fate of the Furious</td>\n",
       "      <td>Action,Crime,Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>96</td>\n",
       "      <td>2019-03-08</td>\n",
       "      <td>Captain Marvel</td>\n",
       "      <td>175000000</td>\n",
       "      <td>426525952</td>\n",
       "      <td>1123061550</td>\n",
       "      <td>948061550</td>\n",
       "      <td>Captain Marvel</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>64</td>\n",
       "      <td>2011-06-29</td>\n",
       "      <td>Transformers: Dark of the Moon</td>\n",
       "      <td>195000000</td>\n",
       "      <td>352390543</td>\n",
       "      <td>1123790543</td>\n",
       "      <td>928790543</td>\n",
       "      <td>Transformers: Dark of the Moon</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>52</td>\n",
       "      <td>2012-11-08</td>\n",
       "      <td>Skyfall</td>\n",
       "      <td>200000000</td>\n",
       "      <td>304360277</td>\n",
       "      <td>1110526981</td>\n",
       "      <td>910526981</td>\n",
       "      <td>Skyfall</td>\n",
       "      <td>Action,Adventure,Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>37</td>\n",
       "      <td>2014-06-27</td>\n",
       "      <td>Transformers: Age of Extinction</td>\n",
       "      <td>210000000</td>\n",
       "      <td>245439076</td>\n",
       "      <td>1104039076</td>\n",
       "      <td>894039076</td>\n",
       "      <td>Transformers: Age of Extinction</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>17</td>\n",
       "      <td>2016-05-06</td>\n",
       "      <td>Captain America: Civil War</td>\n",
       "      <td>250000000</td>\n",
       "      <td>408084349</td>\n",
       "      <td>1140069413</td>\n",
       "      <td>890069413</td>\n",
       "      <td>Captain America: Civil War</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>38</td>\n",
       "      <td>2017-12-20</td>\n",
       "      <td>Jumanji: Welcome to the Jungle</td>\n",
       "      <td>90000000</td>\n",
       "      <td>404508916</td>\n",
       "      <td>964496193</td>\n",
       "      <td>874496193</td>\n",
       "      <td>Jumanji: Welcome to the Jungle</td>\n",
       "      <td>Action,Adventure,Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>45</td>\n",
       "      <td>2016-12-16</td>\n",
       "      <td>Rogue One: A Star Wars Story</td>\n",
       "      <td>200000000</td>\n",
       "      <td>532177324</td>\n",
       "      <td>1049102856</td>\n",
       "      <td>849102856</td>\n",
       "      <td>Rogue One: A Star Wars Story</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>2012-07-20</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>275000000</td>\n",
       "      <td>448139099</td>\n",
       "      <td>1084439099</td>\n",
       "      <td>809439099</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>Action,Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>56</td>\n",
       "      <td>2016-02-12</td>\n",
       "      <td>Deadpool</td>\n",
       "      <td>58000000</td>\n",
       "      <td>363070709</td>\n",
       "      <td>801025593</td>\n",
       "      <td>743025593</td>\n",
       "      <td>Deadpool</td>\n",
       "      <td>Action,Adventure,Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id release_date                            movie  production_budget  \\\n",
       "4     7   2018-04-27           Avengers: Infinity War          300000000   \n",
       "24   34   2015-06-12                   Jurassic World          215000000   \n",
       "58   67   2015-04-03                        Furious 7          190000000   \n",
       "19   27   2012-05-04                     The Avengers          225000000   \n",
       "38   42   2018-02-16                    Black Panther          200000000   \n",
       "102  13   2018-06-22   Jurassic World: Fallen Kingdom          170000000   \n",
       "3     4   2015-05-01          Avengers: Age of Ultron          330600000   \n",
       "39   44   2018-06-15                    Incredibles 2          200000000   \n",
       "43   48   2013-05-03                       Iron Man 3          200000000   \n",
       "124  36   2018-12-21                          Aquaman          160000000   \n",
       "17   23   2017-04-14          The Fate of the Furious          250000000   \n",
       "83   96   2019-03-08                   Captain Marvel          175000000   \n",
       "55   64   2011-06-29   Transformers: Dark of the Moon          195000000   \n",
       "47   52   2012-11-08                          Skyfall          200000000   \n",
       "26   37   2014-06-27  Transformers: Age of Extinction          210000000   \n",
       "12   17   2016-05-06       Captain America: Civil War          250000000   \n",
       "385  38   2017-12-20   Jumanji: Welcome to the Jungle           90000000   \n",
       "40   45   2016-12-16     Rogue One: A Star Wars Story          200000000   \n",
       "7    11   2012-07-20            The Dark Knight Rises          275000000   \n",
       "616  56   2016-02-12                         Deadpool           58000000   \n",
       "\n",
       "     domestic_gross  worldwide_gross  total_profit  \\\n",
       "4         678815482       2048134200    1748134200   \n",
       "24        652270625       1648854864    1433854864   \n",
       "58        353007020       1518722794    1328722794   \n",
       "19        623279547       1517935897    1292935897   \n",
       "38        700059566       1348258224    1148258224   \n",
       "102       417719760       1305772799    1135772799   \n",
       "3         459005868       1403013963    1072413963   \n",
       "39        608581744       1242520711    1042520711   \n",
       "43        408992272       1215392272    1015392272   \n",
       "124       335061807       1146894640     986894640   \n",
       "17        225764765       1234846267     984846267   \n",
       "83        426525952       1123061550     948061550   \n",
       "55        352390543       1123790543     928790543   \n",
       "47        304360277       1110526981     910526981   \n",
       "26        245439076       1104039076     894039076   \n",
       "12        408084349       1140069413     890069413   \n",
       "385       404508916        964496193     874496193   \n",
       "40        532177324       1049102856     849102856   \n",
       "7         448139099       1084439099     809439099   \n",
       "616       363070709        801025593     743025593   \n",
       "\n",
       "                       primary_title                      genres  \n",
       "4             Avengers: Infinity War     Action,Adventure,Sci-Fi  \n",
       "24                    Jurassic World     Action,Adventure,Sci-Fi  \n",
       "58                         Furious 7       Action,Crime,Thriller  \n",
       "19                      The Avengers     Action,Adventure,Sci-Fi  \n",
       "38                     Black Panther     Action,Adventure,Sci-Fi  \n",
       "102   Jurassic World: Fallen Kingdom     Action,Adventure,Sci-Fi  \n",
       "3            Avengers: Age of Ultron     Action,Adventure,Sci-Fi  \n",
       "39                     Incredibles 2  Action,Adventure,Animation  \n",
       "43                        Iron Man 3     Action,Adventure,Sci-Fi  \n",
       "124                          Aquaman    Action,Adventure,Fantasy  \n",
       "17           The Fate of the Furious       Action,Crime,Thriller  \n",
       "83                    Captain Marvel     Action,Adventure,Sci-Fi  \n",
       "55    Transformers: Dark of the Moon     Action,Adventure,Sci-Fi  \n",
       "47                           Skyfall   Action,Adventure,Thriller  \n",
       "26   Transformers: Age of Extinction     Action,Adventure,Sci-Fi  \n",
       "12        Captain America: Civil War     Action,Adventure,Sci-Fi  \n",
       "385   Jumanji: Welcome to the Jungle     Action,Adventure,Comedy  \n",
       "40      Rogue One: A Star Wars Story     Action,Adventure,Sci-Fi  \n",
       "7              The Dark Knight Rises             Action,Thriller  \n",
       "616                         Deadpool     Action,Adventure,Comedy  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest20df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Data Analysis and Calculations\n",
    "\n",
    "From this point we wanted to see who are the cast and crew responsible for these action blockbusters. \n",
    "\n",
    "To do so we took the IMDB databases and prepared them to be merged together; databases having the names of the crew, titles of the movies they worked on, and job they performed. Once those were created and combined we were able to merge them with the database we made above showcasing all of the action movies, sorted once again by most profitable.\n",
    "\n",
    "We focused on three key players to see who are involved with making these huge films.\n",
    "Firstly we dove into the directors. We did some more minor cleaning so that the Russo Brothers would be treated as a pair instead of individuals since all of their data was identical. We then took my newly formed most profitable directors information and grouped them by the total profits of movies they have made. To make the data a bit less overwhelming we only selected the top 10 to visualize. \n",
    "\n",
    "We then repeated this process for actors and actresses, and now the top 10 most profitable directors, actors, and actresses are ready to be visualized. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grab the relevant data in a new dataframe\n",
    "names = namesdf[['nconst','primary_name']]\n",
    "# Do the same for the rest of the databases that are intended to be merged\n",
    "movietitles = titlesdf[['tconst','primary_title']]\n",
    "crew = principalsdf[['tconst','nconst','category']]\n",
    "# Merge the names dataframe to principals\n",
    "crewnames = names.merge(crew, on=['nconst'])\n",
    "# Merge the titles \n",
    "namesandtitles = crewnames.merge(movietitles, on=['tconst'])\n",
    "# Merge all that to the top profiting action movies dataframe\n",
    "grosscrew = namesandtitles.merge(highestactiondf, left_on=['primary_title'], right_on=['movie'])\n",
    "# Copy the dataframe into a new dataframe sorted by the highest total profit\n",
    "highestgrosscrew = grosscrew.sort_values(by='total_profit', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nconst</th>\n",
       "      <th>primary_name</th>\n",
       "      <th>tconst</th>\n",
       "      <th>category</th>\n",
       "      <th>primary_title_x</th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>total_profit</th>\n",
       "      <th>primary_title_y</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3735</th>\n",
       "      <td>nm1165110</td>\n",
       "      <td>Chris Hemsworth</td>\n",
       "      <td>tt4154756</td>\n",
       "      <td>actor</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>7</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>300000000</td>\n",
       "      <td>678815482</td>\n",
       "      <td>2048134200</td>\n",
       "      <td>1748134200</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3732</th>\n",
       "      <td>nm0751648</td>\n",
       "      <td>Joe Russo</td>\n",
       "      <td>tt4154756</td>\n",
       "      <td>director</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>7</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>300000000</td>\n",
       "      <td>678815482</td>\n",
       "      <td>2048134200</td>\n",
       "      <td>1748134200</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3734</th>\n",
       "      <td>nm0749263</td>\n",
       "      <td>Mark Ruffalo</td>\n",
       "      <td>tt4154756</td>\n",
       "      <td>actor</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>7</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>300000000</td>\n",
       "      <td>678815482</td>\n",
       "      <td>2048134200</td>\n",
       "      <td>1748134200</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3736</th>\n",
       "      <td>nm0751577</td>\n",
       "      <td>Anthony Russo</td>\n",
       "      <td>tt4154756</td>\n",
       "      <td>director</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>7</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>300000000</td>\n",
       "      <td>678815482</td>\n",
       "      <td>2048134200</td>\n",
       "      <td>1748134200</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3737</th>\n",
       "      <td>nm0262635</td>\n",
       "      <td>Chris Evans</td>\n",
       "      <td>tt4154756</td>\n",
       "      <td>actor</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>7</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>300000000</td>\n",
       "      <td>678815482</td>\n",
       "      <td>2048134200</td>\n",
       "      <td>1748134200</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>Action,Adventure,Sci-Fi</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9857</th>\n",
       "      <td>nm0126208</td>\n",
       "      <td>John Byrne</td>\n",
       "      <td>tt6565702</td>\n",
       "      <td>writer</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>3</td>\n",
       "      <td>2019-06-07</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>350000000</td>\n",
       "      <td>42762350</td>\n",
       "      <td>149762350</td>\n",
       "      <td>-200237650</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9858</th>\n",
       "      <td>nm1055413</td>\n",
       "      <td>Michael Fassbender</td>\n",
       "      <td>tt6565702</td>\n",
       "      <td>actor</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>3</td>\n",
       "      <td>2019-06-07</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>350000000</td>\n",
       "      <td>42762350</td>\n",
       "      <td>149762350</td>\n",
       "      <td>-200237650</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9859</th>\n",
       "      <td>nm2225369</td>\n",
       "      <td>Jennifer Lawrence</td>\n",
       "      <td>tt6565702</td>\n",
       "      <td>actress</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>3</td>\n",
       "      <td>2019-06-07</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>350000000</td>\n",
       "      <td>42762350</td>\n",
       "      <td>149762350</td>\n",
       "      <td>-200237650</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9860</th>\n",
       "      <td>nm1334526</td>\n",
       "      <td>Simon Kinberg</td>\n",
       "      <td>tt6565702</td>\n",
       "      <td>director</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>3</td>\n",
       "      <td>2019-06-07</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>350000000</td>\n",
       "      <td>42762350</td>\n",
       "      <td>149762350</td>\n",
       "      <td>-200237650</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9853</th>\n",
       "      <td>nm0564215</td>\n",
       "      <td>James McAvoy</td>\n",
       "      <td>tt6565702</td>\n",
       "      <td>actor</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>3</td>\n",
       "      <td>2019-06-07</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>350000000</td>\n",
       "      <td>42762350</td>\n",
       "      <td>149762350</td>\n",
       "      <td>-200237650</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11212 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         nconst        primary_name     tconst  category  \\\n",
       "3735  nm1165110     Chris Hemsworth  tt4154756     actor   \n",
       "3732  nm0751648           Joe Russo  tt4154756  director   \n",
       "3734  nm0749263        Mark Ruffalo  tt4154756     actor   \n",
       "3736  nm0751577       Anthony Russo  tt4154756  director   \n",
       "3737  nm0262635         Chris Evans  tt4154756     actor   \n",
       "...         ...                 ...        ...       ...   \n",
       "9857  nm0126208          John Byrne  tt6565702    writer   \n",
       "9858  nm1055413  Michael Fassbender  tt6565702     actor   \n",
       "9859  nm2225369   Jennifer Lawrence  tt6565702   actress   \n",
       "9860  nm1334526       Simon Kinberg  tt6565702  director   \n",
       "9853  nm0564215        James McAvoy  tt6565702     actor   \n",
       "\n",
       "             primary_title_x  id release_date                   movie  \\\n",
       "3735  Avengers: Infinity War   7   2018-04-27  Avengers: Infinity War   \n",
       "3732  Avengers: Infinity War   7   2018-04-27  Avengers: Infinity War   \n",
       "3734  Avengers: Infinity War   7   2018-04-27  Avengers: Infinity War   \n",
       "3736  Avengers: Infinity War   7   2018-04-27  Avengers: Infinity War   \n",
       "3737  Avengers: Infinity War   7   2018-04-27  Avengers: Infinity War   \n",
       "...                      ...  ..          ...                     ...   \n",
       "9857            Dark Phoenix   3   2019-06-07            Dark Phoenix   \n",
       "9858            Dark Phoenix   3   2019-06-07            Dark Phoenix   \n",
       "9859            Dark Phoenix   3   2019-06-07            Dark Phoenix   \n",
       "9860            Dark Phoenix   3   2019-06-07            Dark Phoenix   \n",
       "9853            Dark Phoenix   3   2019-06-07            Dark Phoenix   \n",
       "\n",
       "      production_budget  domestic_gross  worldwide_gross  total_profit  \\\n",
       "3735          300000000       678815482       2048134200    1748134200   \n",
       "3732          300000000       678815482       2048134200    1748134200   \n",
       "3734          300000000       678815482       2048134200    1748134200   \n",
       "3736          300000000       678815482       2048134200    1748134200   \n",
       "3737          300000000       678815482       2048134200    1748134200   \n",
       "...                 ...             ...              ...           ...   \n",
       "9857          350000000        42762350        149762350    -200237650   \n",
       "9858          350000000        42762350        149762350    -200237650   \n",
       "9859          350000000        42762350        149762350    -200237650   \n",
       "9860          350000000        42762350        149762350    -200237650   \n",
       "9853          350000000        42762350        149762350    -200237650   \n",
       "\n",
       "             primary_title_y                   genres  \n",
       "3735  Avengers: Infinity War  Action,Adventure,Sci-Fi  \n",
       "3732  Avengers: Infinity War  Action,Adventure,Sci-Fi  \n",
       "3734  Avengers: Infinity War  Action,Adventure,Sci-Fi  \n",
       "3736  Avengers: Infinity War  Action,Adventure,Sci-Fi  \n",
       "3737  Avengers: Infinity War  Action,Adventure,Sci-Fi  \n",
       "...                      ...                      ...  \n",
       "9857            Dark Phoenix  Action,Adventure,Sci-Fi  \n",
       "9858            Dark Phoenix  Action,Adventure,Sci-Fi  \n",
       "9859            Dark Phoenix  Action,Adventure,Sci-Fi  \n",
       "9860            Dark Phoenix  Action,Adventure,Sci-Fi  \n",
       "9853            Dark Phoenix  Action,Adventure,Sci-Fi  \n",
       "\n",
       "[11212 rows x 14 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highestgrosscrew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ceated a dataframe for all of the directors in crew dataframe\n",
    "directors = highestgrosscrew.loc[highestgrosscrew['category'] == 'director']\n",
    "# We want to treat the Russo Brothers as one since their data is the same and they work as a pair\n",
    "# Rename them as The Russo Brothers in dataframe and drop the name and title constant columns so that dup value can be dropped\n",
    "directors.replace('Joe Russo','The Russo Brothers', regex=True, inplace=True)\n",
    "directors.replace('Anthony Russo','The Russo Brothers', regex=True, inplace=True)\n",
    "directors = directors.drop(['nconst', 'tconst'], axis=1)\n",
    "directors = directors.drop_duplicates()\n",
    "# Group the directors by the sum of their movie's total profit\n",
    "directorgb = directors.groupby(['primary_name']).sum().sort_values(['total_profit'], ascending=False)\n",
    "# Select the top 10 profitable directors\n",
    "top10directors = directorgb.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>total_profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_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>The Russo Brothers</th>\n",
       "      <td>40</td>\n",
       "      <td>720000000</td>\n",
       "      <td>1346646789</td>\n",
       "      <td>3902605502</td>\n",
       "      <td>3182605502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Joss Whedon</th>\n",
       "      <td>31</td>\n",
       "      <td>555600000</td>\n",
       "      <td>1082285415</td>\n",
       "      <td>2920949860</td>\n",
       "      <td>2365349860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James Wan</th>\n",
       "      <td>103</td>\n",
       "      <td>350000000</td>\n",
       "      <td>688068827</td>\n",
       "      <td>2665617434</td>\n",
       "      <td>2315617434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michael Bay</th>\n",
       "      <td>167</td>\n",
       "      <td>648000000</td>\n",
       "      <td>777873593</td>\n",
       "      <td>2911998250</td>\n",
       "      <td>2263998250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Francis Lawrence</th>\n",
       "      <td>191</td>\n",
       "      <td>484000000</td>\n",
       "      <td>1090402339</td>\n",
       "      <td>2426381826</td>\n",
       "      <td>1942381826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Christopher Nolan</th>\n",
       "      <td>118</td>\n",
       "      <td>585000000</td>\n",
       "      <td>930783574</td>\n",
       "      <td>2419801109</td>\n",
       "      <td>1834801109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sam Mendes</th>\n",
       "      <td>62</td>\n",
       "      <td>500000000</td>\n",
       "      <td>504434452</td>\n",
       "      <td>1990147904</td>\n",
       "      <td>1490147904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zack Snyder</th>\n",
       "      <td>208</td>\n",
       "      <td>950000000</td>\n",
       "      <td>942497822</td>\n",
       "      <td>2420920114</td>\n",
       "      <td>1470920114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colin Trevorrow</th>\n",
       "      <td>34</td>\n",
       "      <td>215000000</td>\n",
       "      <td>652270625</td>\n",
       "      <td>1648854864</td>\n",
       "      <td>1433854864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gareth Edwards</th>\n",
       "      <td>86</td>\n",
       "      <td>360000000</td>\n",
       "      <td>732853393</td>\n",
       "      <td>1578178925</td>\n",
       "      <td>1218178925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     id  production_budget  domestic_gross  worldwide_gross  \\\n",
       "primary_name                                                                  \n",
       "The Russo Brothers   40          720000000      1346646789       3902605502   \n",
       "Joss Whedon          31          555600000      1082285415       2920949860   \n",
       "James Wan           103          350000000       688068827       2665617434   \n",
       "Michael Bay         167          648000000       777873593       2911998250   \n",
       "Francis Lawrence    191          484000000      1090402339       2426381826   \n",
       "Christopher Nolan   118          585000000       930783574       2419801109   \n",
       "Sam Mendes           62          500000000       504434452       1990147904   \n",
       "Zack Snyder         208          950000000       942497822       2420920114   \n",
       "Colin Trevorrow      34          215000000       652270625       1648854864   \n",
       "Gareth Edwards       86          360000000       732853393       1578178925   \n",
       "\n",
       "                    total_profit  \n",
       "primary_name                      \n",
       "The Russo Brothers    3182605502  \n",
       "Joss Whedon           2365349860  \n",
       "James Wan             2315617434  \n",
       "Michael Bay           2263998250  \n",
       "Francis Lawrence      1942381826  \n",
       "Christopher Nolan     1834801109  \n",
       "Sam Mendes            1490147904  \n",
       "Zack Snyder           1470920114  \n",
       "Colin Trevorrow       1433854864  \n",
       "Gareth Edwards        1218178925  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10directors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Repeat the process for Actors and Actresses\n",
    "actors = highestgrosscrew.loc[highestgrosscrew['category'] == 'actor']\n",
    "actorgb = actors.groupby(['primary_name']).sum().sort_values(['total_profit'], ascending=False)\n",
    "top10actors = actorgb.head(10)\n",
    "actresses = highestgrosscrew.loc[highestgrosscrew['category'] == 'actress']\n",
    "actressesgb = actresses.groupby(['primary_name']).sum().sort_values(['total_profit'], ascending=False)\n",
    "top10actresses = actressesgb.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>total_profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_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>Robert Downey Jr.</th>\n",
       "      <td>286</td>\n",
       "      <td>1775600000</td>\n",
       "      <td>3411660407</td>\n",
       "      <td>9361531927</td>\n",
       "      <td>7585931927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chris Evans</th>\n",
       "      <td>152</td>\n",
       "      <td>1480600000</td>\n",
       "      <td>2633741170</td>\n",
       "      <td>7312058858</td>\n",
       "      <td>5831458858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dwayne Johnson</th>\n",
       "      <td>830</td>\n",
       "      <td>1504850000</td>\n",
       "      <td>2118570005</td>\n",
       "      <td>7041969100</td>\n",
       "      <td>5537119100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chris Hemsworth</th>\n",
       "      <td>545</td>\n",
       "      <td>1736600000</td>\n",
       "      <td>2176057640</td>\n",
       "      <td>6221281586</td>\n",
       "      <td>4484681586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vin Diesel</th>\n",
       "      <td>348</td>\n",
       "      <td>938000000</td>\n",
       "      <td>1236266430</td>\n",
       "      <td>4725216060</td>\n",
       "      <td>3787216060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mark Ruffalo</th>\n",
       "      <td>159</td>\n",
       "      <td>900600000</td>\n",
       "      <td>1517955179</td>\n",
       "      <td>4626453194</td>\n",
       "      <td>3725853194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chris Pratt</th>\n",
       "      <td>160</td>\n",
       "      <td>705000000</td>\n",
       "      <td>1754379870</td>\n",
       "      <td>4345749723</td>\n",
       "      <td>3640749723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jason Statham</th>\n",
       "      <td>715</td>\n",
       "      <td>1095500000</td>\n",
       "      <td>997028581</td>\n",
       "      <td>4171967832</td>\n",
       "      <td>3076467832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liam Hemsworth</th>\n",
       "      <td>291</td>\n",
       "      <td>760000000</td>\n",
       "      <td>1639711004</td>\n",
       "      <td>3654502024</td>\n",
       "      <td>2894502024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Josh Hutcherson</th>\n",
       "      <td>188</td>\n",
       "      <td>639000000</td>\n",
       "      <td>1600205599</td>\n",
       "      <td>3324663656</td>\n",
       "      <td>2685663656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    id  production_budget  domestic_gross  worldwide_gross  \\\n",
       "primary_name                                                                 \n",
       "Robert Downey Jr.  286         1775600000      3411660407       9361531927   \n",
       "Chris Evans        152         1480600000      2633741170       7312058858   \n",
       "Dwayne Johnson     830         1504850000      2118570005       7041969100   \n",
       "Chris Hemsworth    545         1736600000      2176057640       6221281586   \n",
       "Vin Diesel         348          938000000      1236266430       4725216060   \n",
       "Mark Ruffalo       159          900600000      1517955179       4626453194   \n",
       "Chris Pratt        160          705000000      1754379870       4345749723   \n",
       "Jason Statham      715         1095500000       997028581       4171967832   \n",
       "Liam Hemsworth     291          760000000      1639711004       3654502024   \n",
       "Josh Hutcherson    188          639000000      1600205599       3324663656   \n",
       "\n",
       "                   total_profit  \n",
       "primary_name                     \n",
       "Robert Downey Jr.    7585931927  \n",
       "Chris Evans          5831458858  \n",
       "Dwayne Johnson       5537119100  \n",
       "Chris Hemsworth      4484681586  \n",
       "Vin Diesel           3787216060  \n",
       "Mark Ruffalo         3725853194  \n",
       "Chris Pratt          3640749723  \n",
       "Jason Statham        3076467832  \n",
       "Liam Hemsworth       2894502024  \n",
       "Josh Hutcherson      2685663656  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10actors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>id</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>total_profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_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>Scarlett Johansson</th>\n",
       "      <td>183</td>\n",
       "      <td>795000000</td>\n",
       "      <td>1458248371</td>\n",
       "      <td>3997825665</td>\n",
       "      <td>3202825665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jennifer Lawrence</th>\n",
       "      <td>367</td>\n",
       "      <td>1252000000</td>\n",
       "      <td>1843026175</td>\n",
       "      <td>4152013406</td>\n",
       "      <td>2900013406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bryce Dallas Howard</th>\n",
       "      <td>47</td>\n",
       "      <td>385000000</td>\n",
       "      <td>1069990385</td>\n",
       "      <td>2954627663</td>\n",
       "      <td>2569627663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cate Blanchett</th>\n",
       "      <td>443</td>\n",
       "      <td>1720000000</td>\n",
       "      <td>1238359236</td>\n",
       "      <td>3674994059</td>\n",
       "      <td>1954994059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angelina Jolie</th>\n",
       "      <td>304</td>\n",
       "      <td>700000000</td>\n",
       "      <td>736127535</td>\n",
       "      <td>2510355287</td>\n",
       "      <td>1810355287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emma Stone</th>\n",
       "      <td>117</td>\n",
       "      <td>615000000</td>\n",
       "      <td>698053924</td>\n",
       "      <td>2144055931</td>\n",
       "      <td>1529055931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gwyneth Paltrow</th>\n",
       "      <td>182</td>\n",
       "      <td>490000000</td>\n",
       "      <td>804779834</td>\n",
       "      <td>2004496389</td>\n",
       "      <td>1514496389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brie Larson</th>\n",
       "      <td>326</td>\n",
       "      <td>437000000</td>\n",
       "      <td>768506745</td>\n",
       "      <td>1929457808</td>\n",
       "      <td>1492457808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Judy Greer</th>\n",
       "      <td>34</td>\n",
       "      <td>215000000</td>\n",
       "      <td>652270625</td>\n",
       "      <td>1648854864</td>\n",
       "      <td>1433854864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morena Baccarin</th>\n",
       "      <td>71</td>\n",
       "      <td>168000000</td>\n",
       "      <td>687662444</td>\n",
       "      <td>1587706150</td>\n",
       "      <td>1419706150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      id  production_budget  domestic_gross  worldwide_gross  \\\n",
       "primary_name                                                                   \n",
       "Scarlett Johansson   183          795000000      1458248371       3997825665   \n",
       "Jennifer Lawrence    367         1252000000      1843026175       4152013406   \n",
       "Bryce Dallas Howard   47          385000000      1069990385       2954627663   \n",
       "Cate Blanchett       443         1720000000      1238359236       3674994059   \n",
       "Angelina Jolie       304          700000000       736127535       2510355287   \n",
       "Emma Stone           117          615000000       698053924       2144055931   \n",
       "Gwyneth Paltrow      182          490000000       804779834       2004496389   \n",
       "Brie Larson          326          437000000       768506745       1929457808   \n",
       "Judy Greer            34          215000000       652270625       1648854864   \n",
       "Morena Baccarin       71          168000000       687662444       1587706150   \n",
       "\n",
       "                     total_profit  \n",
       "primary_name                       \n",
       "Scarlett Johansson     3202825665  \n",
       "Jennifer Lawrence      2900013406  \n",
       "Bryce Dallas Howard    2569627663  \n",
       "Cate Blanchett         1954994059  \n",
       "Angelina Jolie         1810355287  \n",
       "Emma Stone             1529055931  \n",
       "Gwyneth Paltrow        1514496389  \n",
       "Brie Larson            1492457808  \n",
       "Judy Greer             1433854864  \n",
       "Morena Baccarin        1419706150  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10actresses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Data Visualization\n",
    "For these visuals, we wanted to keep them easy to read, concise and on brand. The visual inspiration came from Microsoft’s logo, taking the hexcode value of the four squares seen in their branding and using them as the colors for the four graphs. We really showcase the large profits these creatives are generating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import FuncFormatter\n",
    "def millions(y, pos):\n",
    "    return '$%1.0fM' % (y*1e-6)\n",
    "sns.set_theme(style=\"white\", palette=None)\n",
    "stevenmoviefig, ax = plt.subplots(figsize=(15,8))\n",
    "sns.barplot(y = highest20df['movie'], x = highest20df['total_profit'], color='#ffbb00')\n",
    "\n",
    "ax.set_title('Highest Profiting Action Movies\\n Over The Past Two Decades', fontsize= 16, weight=900)\n",
    "ax.set_xlabel('Profit', fontsize= 16)\n",
    "ax.set_ylabel('Movie', fontsize= 16)\n",
    "formatter = FuncFormatter(millions)\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stevendirectorfig, ax = plt.subplots(figsize=(15,8))\n",
    "sns.barplot(y = top10directors.index, x = top10directors['total_profit'], color='#7cbb00')\n",
    "\n",
    "ax.set_title('Highest Profiting Action Movie Directors\\n Over The Past Two Decades', fontsize= 14, weight=900)\n",
    "ax.set_xlabel('Total Profit', fontsize= 14)\n",
    "ax.set_ylabel('Director', fontsize= 14)\n",
    "formatter = FuncFormatter(millions)\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stevenactorfig, ax = plt.subplots(figsize=(15,8))\n",
    "sns.barplot(y = top10actors.index, x = top10actors['total_profit'], color='#00a1f1')\n",
    "\n",
    "ax.set_title('Highest Profiting Action Movie Actors\\n Over The Past Two Decades',fontsize= 14,weight=900)\n",
    "ax.set_xlabel('Total Profit',fontsize= 14)\n",
    "ax.set_ylabel('Actor',fontsize= 14)\n",
    "formatter = FuncFormatter(millions)\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stevenactressfig, ax = plt.subplots(figsize=(15,8))\n",
    "sns.barplot(y = top10actresses.index, x = top10actresses['total_profit'], color='#f65314')\n",
    "\n",
    "ax.set_title('Highest Profiting Action Movie Actresses\\n Over The Past Two Decades',fontsize= 14,weight=900)\n",
    "ax.set_xlabel('Total Profit',fontsize= 14)\n",
    "ax.set_ylabel('Actresses',fontsize= 14)\n",
    "formatter = FuncFormatter(millions)\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Recommendation\n",
    "\n",
    "Just from a cursory glance one can tell that the most profitable action movies are dominated by the Marvel franchises. It is our suggestion that if Microsoft wants to be able to compete in this very saturated market, and earn these incredible billion dollar profits, then they will need to focus on contacting some of these large genre defining names who've historically yielded high box office revenues to develop thier IPs so that they can compete with such behemoth studios."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic 3: Keeping Up with the Competition\n",
    "In this section, we analyze numbers from other studios. We learn that the top 2 competing studios with the highest revenues (Buena Vista/Disney, Warner Bros) own Marvel and DC comics, respectively. Perhaps a comparable franchise could be fashioned from high-grossing Microsoft games (Halo, Gears of War, Forza Motorsport, etc.)?\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Data Import\n",
    "Look for dataset that includes studio name, total gross, foreign & domestic gross and import it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import standard packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>studio</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>BV</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652000000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alice in Wonderland (2010)</td>\n",
       "      <td>BV</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry Potter and the Deathly Hallows Part 1</td>\n",
       "      <td>WB</td>\n",
       "      <td>296000000.0</td>\n",
       "      <td>664300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Inception</td>\n",
       "      <td>WB</td>\n",
       "      <td>292600000.0</td>\n",
       "      <td>535700000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shrek Forever After</td>\n",
       "      <td>P/DW</td>\n",
       "      <td>238700000.0</td>\n",
       "      <td>513900000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3382</th>\n",
       "      <td>The Quake</td>\n",
       "      <td>Magn.</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3383</th>\n",
       "      <td>Edward II (2018 re-release)</td>\n",
       "      <td>FM</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3384</th>\n",
       "      <td>El Pacto</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>The Swan</td>\n",
       "      <td>Synergetic</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>An Actor Prepares</td>\n",
       "      <td>Grav.</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3387 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            title      studio  domestic_gross  \\\n",
       "0                                     Toy Story 3          BV     415000000.0   \n",
       "1                      Alice in Wonderland (2010)          BV     334200000.0   \n",
       "2     Harry Potter and the Deathly Hallows Part 1          WB     296000000.0   \n",
       "3                                       Inception          WB     292600000.0   \n",
       "4                             Shrek Forever After        P/DW     238700000.0   \n",
       "...                                           ...         ...             ...   \n",
       "3382                                    The Quake       Magn.          6200.0   \n",
       "3383                  Edward II (2018 re-release)          FM          4800.0   \n",
       "3384                                     El Pacto        Sony          2500.0   \n",
       "3385                                     The Swan  Synergetic          2400.0   \n",
       "3386                            An Actor Prepares       Grav.          1700.0   \n",
       "\n",
       "     foreign_gross  year  \n",
       "0        652000000  2010  \n",
       "1        691300000  2010  \n",
       "2        664300000  2010  \n",
       "3        535700000  2010  \n",
       "4        513900000  2010  \n",
       "...            ...   ...  \n",
       "3382           NaN  2018  \n",
       "3383           NaN  2018  \n",
       "3384           NaN  2018  \n",
       "3385           NaN  2018  \n",
       "3386           NaN  2018  \n",
       "\n",
       "[3387 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grossdf = pd.read_csv('./data/zippedData/bom.movie_gross.csv.gz')\n",
    "grossdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Data Cleaning and Filtering\n",
    "We have to start by cleaning up null values, converting data types if needed to make sorting work. We created a new \"gross_total\" column for the analysis and visualization. Last we created a Top 50 list sorted by studios for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>studio</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>BV</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652000000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alice in Wonderland (2010)</td>\n",
       "      <td>BV</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry Potter and the Deathly Hallows Part 1</td>\n",
       "      <td>WB</td>\n",
       "      <td>296000000.0</td>\n",
       "      <td>664300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Inception</td>\n",
       "      <td>WB</td>\n",
       "      <td>292600000.0</td>\n",
       "      <td>535700000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shrek Forever After</td>\n",
       "      <td>P/DW</td>\n",
       "      <td>238700000.0</td>\n",
       "      <td>513900000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         title studio  domestic_gross  \\\n",
       "0                                  Toy Story 3     BV     415000000.0   \n",
       "1                   Alice in Wonderland (2010)     BV     334200000.0   \n",
       "2  Harry Potter and the Deathly Hallows Part 1     WB     296000000.0   \n",
       "3                                    Inception     WB     292600000.0   \n",
       "4                          Shrek Forever After   P/DW     238700000.0   \n",
       "\n",
       "  foreign_gross  year  \n",
       "0     652000000  2010  \n",
       "1     691300000  2010  \n",
       "2     664300000  2010  \n",
       "3     535700000  2010  \n",
       "4     513900000  2010  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grossdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3387, 5)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grossdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3387 entries, 0 to 3386\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   title           3387 non-null   object \n",
      " 1   studio          3382 non-null   object \n",
      " 2   domestic_gross  3359 non-null   float64\n",
      " 3   foreign_gross   2037 non-null   object \n",
      " 4   year            3387 non-null   int64  \n",
      "dtypes: float64(1), int64(1), object(3)\n",
      "memory usage: 132.4+ KB\n"
     ]
    }
   ],
   "source": [
    "#this shows there are no missing values bc of nonnull\n",
    "#may have to convert some dtypes tho for sorting\n",
    "grossdf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       652000000\n",
       "1       691300000\n",
       "2       664300000\n",
       "3       535700000\n",
       "4       513900000\n",
       "          ...    \n",
       "3382          NaN\n",
       "3383          NaN\n",
       "3384          NaN\n",
       "3385          NaN\n",
       "3386          NaN\n",
       "Name: foreign_gross, Length: 3387, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grossdf.foreign_gross = grossdf.foreign_gross.str.replace(\",\",\"\")\n",
    "grossdf.foreign_gross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       652000000\n",
       "1       691300000\n",
       "2       664300000\n",
       "3       535700000\n",
       "4       513900000\n",
       "          ...    \n",
       "3382            0\n",
       "3383            0\n",
       "3384            0\n",
       "3385            0\n",
       "3386            0\n",
       "Name: foreign_gross, Length: 3387, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get rid of all NaN values\n",
    "grossdf.foreign_gross = grossdf.foreign_gross.fillna(0)\n",
    "grossdf.foreign_gross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       652000000.0\n",
       "1       691300000.0\n",
       "2       664300000.0\n",
       "3       535700000.0\n",
       "4       513900000.0\n",
       "           ...     \n",
       "3382            0.0\n",
       "3383            0.0\n",
       "3384            0.0\n",
       "3385            0.0\n",
       "3386            0.0\n",
       "Name: foreign_gross, Length: 3387, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert object to float\n",
    "grossdf.foreign_gross = grossdf.foreign_gross.astype(float)\n",
    "grossdf.foreign_gross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.067000e+09\n",
       "1       1.025500e+09\n",
       "2       9.603000e+08\n",
       "3       8.283000e+08\n",
       "4       7.526000e+08\n",
       "            ...     \n",
       "3382    6.200000e+03\n",
       "3383    4.800000e+03\n",
       "3384    2.500000e+03\n",
       "3385    2.400000e+03\n",
       "3386    1.700000e+03\n",
       "Length: 3387, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make a new column called gross_total\n",
    "gross_total = grossdf.domestic_gross + grossdf.foreign_gross\n",
    "gross_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Toy Story 3</td>\n",
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       "      <td>415000000.0</td>\n",
       "      <td>652000000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alice in Wonderland (2010)</td>\n",
       "      <td>BV</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry Potter and the Deathly Hallows Part 1</td>\n",
       "      <td>WB</td>\n",
       "      <td>296000000.0</td>\n",
       "      <td>664300000.0</td>\n",
       "      <td>2010</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Inception</td>\n",
       "      <td>WB</td>\n",
       "      <td>292600000.0</td>\n",
       "      <td>535700000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shrek Forever After</td>\n",
       "      <td>P/DW</td>\n",
       "      <td>238700000.0</td>\n",
       "      <td>513900000.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3382</th>\n",
       "      <td>The Quake</td>\n",
       "      <td>Magn.</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3383</th>\n",
       "      <td>Edward II (2018 re-release)</td>\n",
       "      <td>FM</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>3384</th>\n",
       "      <td>El Pacto</td>\n",
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       "      <td>2500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>The Swan</td>\n",
       "      <td>Synergetic</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>An Actor Prepares</td>\n",
       "      <td>Grav.</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3387 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            title      studio  domestic_gross  \\\n",
       "0                                     Toy Story 3          BV     415000000.0   \n",
       "1                      Alice in Wonderland (2010)          BV     334200000.0   \n",
       "2     Harry Potter and the Deathly Hallows Part 1          WB     296000000.0   \n",
       "3                                       Inception          WB     292600000.0   \n",
       "4                             Shrek Forever After        P/DW     238700000.0   \n",
       "...                                           ...         ...             ...   \n",
       "3382                                    The Quake       Magn.          6200.0   \n",
       "3383                  Edward II (2018 re-release)          FM          4800.0   \n",
       "3384                                     El Pacto        Sony          2500.0   \n",
       "3385                                     The Swan  Synergetic          2400.0   \n",
       "3386                            An Actor Prepares       Grav.          1700.0   \n",
       "\n",
       "      foreign_gross  year  \n",
       "0       652000000.0  2010  \n",
       "1       691300000.0  2010  \n",
       "2       664300000.0  2010  \n",
       "3       535700000.0  2010  \n",
       "4       513900000.0  2010  \n",
       "...             ...   ...  \n",
       "3382            0.0  2018  \n",
       "3383            0.0  2018  \n",
       "3384            0.0  2018  \n",
       "3385            0.0  2018  \n",
       "3386            0.0  2018  \n",
       "\n",
       "[3387 rows x 5 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grossdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>studio</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>BV</td>\n",
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       "      <td>652000000.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BV</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000.0</td>\n",
       "      <td>1.025500e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WB</td>\n",
       "      <td>296000000.0</td>\n",
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       "      <td>9.603000e+08</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WB</td>\n",
       "      <td>292600000.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P/DW</td>\n",
       "      <td>238700000.0</td>\n",
       "      <td>513900000.0</td>\n",
       "      <td>7.526000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3382</th>\n",
       "      <td>Magn.</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.200000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3383</th>\n",
       "      <td>FM</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.800000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3384</th>\n",
       "      <td>Sony</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.500000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>Synergetic</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.400000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>Grav.</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.700000e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3387 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          studio  domestic_gross  foreign_gross   gross_total\n",
       "0             BV     415000000.0    652000000.0  1.067000e+09\n",
       "1             BV     334200000.0    691300000.0  1.025500e+09\n",
       "2             WB     296000000.0    664300000.0  9.603000e+08\n",
       "3             WB     292600000.0    535700000.0  8.283000e+08\n",
       "4           P/DW     238700000.0    513900000.0  7.526000e+08\n",
       "...          ...             ...            ...           ...\n",
       "3382       Magn.          6200.0            0.0  6.200000e+03\n",
       "3383          FM          4800.0            0.0  4.800000e+03\n",
       "3384        Sony          2500.0            0.0  2.500000e+03\n",
       "3385  Synergetic          2400.0            0.0  2.400000e+03\n",
       "3386       Grav.          1700.0            0.0  1.700000e+03\n",
       "\n",
       "[3387 rows x 4 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#start cleaning data\n",
    "#take only relevant topics for your analysis, drop title and year\n",
    "#add new column, total gross\n",
    "top_50 = {\"studio\" : grossdf[\"studio\"],\n",
    "                    \"domestic_gross\" : grossdf[\"domestic_gross\"],\n",
    "                    \"foreign_gross\" : grossdf[\"foreign_gross\"],\n",
    "                    \"gross_total\" : gross_total}\n",
    "top_50 = pd.DataFrame(top_50)\n",
    "top_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .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>studio</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>gross_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BV</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652000000.0</td>\n",
       "      <td>1.067000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BV</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000.0</td>\n",
       "      <td>1.025500e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WB</td>\n",
       "      <td>296000000.0</td>\n",
       "      <td>664300000.0</td>\n",
       "      <td>9.603000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WB</td>\n",
       "      <td>292600000.0</td>\n",
       "      <td>535700000.0</td>\n",
       "      <td>8.283000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P/DW</td>\n",
       "      <td>238700000.0</td>\n",
       "      <td>513900000.0</td>\n",
       "      <td>7.526000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3382</th>\n",
       "      <td>Magn.</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.200000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3383</th>\n",
       "      <td>FM</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.800000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3384</th>\n",
       "      <td>Sony</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.500000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>Synergetic</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.400000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>Grav.</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.700000e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3382 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          studio  domestic_gross  foreign_gross   gross_total\n",
       "0             BV     415000000.0    652000000.0  1.067000e+09\n",
       "1             BV     334200000.0    691300000.0  1.025500e+09\n",
       "2             WB     296000000.0    664300000.0  9.603000e+08\n",
       "3             WB     292600000.0    535700000.0  8.283000e+08\n",
       "4           P/DW     238700000.0    513900000.0  7.526000e+08\n",
       "...          ...             ...            ...           ...\n",
       "3382       Magn.          6200.0            0.0  6.200000e+03\n",
       "3383          FM          4800.0            0.0  4.800000e+03\n",
       "3384        Sony          2500.0            0.0  2.500000e+03\n",
       "3385  Synergetic          2400.0            0.0  2.400000e+03\n",
       "3386       Grav.          1700.0            0.0  1.700000e+03\n",
       "\n",
       "[3382 rows x 4 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop na values to clean some rows\n",
    "top_50 = top_50.dropna(axis=0, how='any', thresh=None, subset=[\"studio\"], inplace=False)\n",
    "top_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IFC             166\n",
       "Uni.            147\n",
       "WB              140\n",
       "Magn.           136\n",
       "Fox             136\n",
       "               ... \n",
       "BSC               1\n",
       "IW                1\n",
       "Conglomerate      1\n",
       "Triu              1\n",
       "Spanglish         1\n",
       "Name: studio, Length: 257, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_50['studio'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Data Analysis and Calculations\n",
    "We filter the new list down even more by highest grossing studios out of the top 50. Then we filtered down to two more tables of domestic gross vs studio and foreign gross vs studio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>studio</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>gross_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>BV</td>\n",
       "      <td>623400000.0</td>\n",
       "      <td>895500000.0</td>\n",
       "      <td>1.518900e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>BV</td>\n",
       "      <td>459000000.0</td>\n",
       "      <td>946400000.0</td>\n",
       "      <td>1.405400e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3080</th>\n",
       "      <td>BV</td>\n",
       "      <td>700100000.0</td>\n",
       "      <td>646900000.0</td>\n",
       "      <td>1.347000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>WB</td>\n",
       "      <td>381000000.0</td>\n",
       "      <td>960500000.0</td>\n",
       "      <td>1.341500e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2758</th>\n",
       "      <td>BV</td>\n",
       "      <td>620200000.0</td>\n",
       "      <td>712400000.0</td>\n",
       "      <td>1.332600e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3081</th>\n",
       "      <td>Uni.</td>\n",
       "      <td>417700000.0</td>\n",
       "      <td>891800000.0</td>\n",
       "      <td>1.309500e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>BV</td>\n",
       "      <td>400700000.0</td>\n",
       "      <td>875700000.0</td>\n",
       "      <td>1.276400e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2759</th>\n",
       "      <td>BV</td>\n",
       "      <td>504000000.0</td>\n",
       "      <td>759500000.0</td>\n",
       "      <td>1.263500e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3082</th>\n",
       "      <td>BV</td>\n",
       "      <td>608600000.0</td>\n",
       "      <td>634200000.0</td>\n",
       "      <td>1.242800e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>BV</td>\n",
       "      <td>409000000.0</td>\n",
       "      <td>805800000.0</td>\n",
       "      <td>1.214800e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1876</th>\n",
       "      <td>Uni.</td>\n",
       "      <td>336000000.0</td>\n",
       "      <td>823400000.0</td>\n",
       "      <td>1.159400e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2322</th>\n",
       "      <td>BV</td>\n",
       "      <td>408100000.0</td>\n",
       "      <td>745200000.0</td>\n",
       "      <td>1.153300e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3083</th>\n",
       "      <td>WB</td>\n",
       "      <td>335100000.0</td>\n",
       "      <td>812700000.0</td>\n",
       "      <td>1.147800e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>P/DW</td>\n",
       "      <td>352400000.0</td>\n",
       "      <td>771400000.0</td>\n",
       "      <td>1.123800e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>Sony</td>\n",
       "      <td>304400000.0</td>\n",
       "      <td>804200000.0</td>\n",
       "      <td>1.108600e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1477</th>\n",
       "      <td>Par.</td>\n",
       "      <td>245400000.0</td>\n",
       "      <td>858600000.0</td>\n",
       "      <td>1.104000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>WB</td>\n",
       "      <td>448100000.0</td>\n",
       "      <td>636800000.0</td>\n",
       "      <td>1.084900e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BV</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652000000.0</td>\n",
       "      <td>1.067000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2323</th>\n",
       "      <td>BV</td>\n",
       "      <td>532200000.0</td>\n",
       "      <td>523900000.0</td>\n",
       "      <td>1.056100e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>BV</td>\n",
       "      <td>241100000.0</td>\n",
       "      <td>804600000.0</td>\n",
       "      <td>1.045700e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2761</th>\n",
       "      <td>Uni.</td>\n",
       "      <td>264600000.0</td>\n",
       "      <td>770200000.0</td>\n",
       "      <td>1.034800e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2324</th>\n",
       "      <td>BV</td>\n",
       "      <td>486300000.0</td>\n",
       "      <td>542300000.0</td>\n",
       "      <td>1.028600e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BV</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000.0</td>\n",
       "      <td>1.025500e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2325</th>\n",
       "      <td>BV</td>\n",
       "      <td>341300000.0</td>\n",
       "      <td>682500000.0</td>\n",
       "      <td>1.023800e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>WB (NL)</td>\n",
       "      <td>303000000.0</td>\n",
       "      <td>718100000.0</td>\n",
       "      <td>1.021100e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>Uni.</td>\n",
       "      <td>368100000.0</td>\n",
       "      <td>602700000.0</td>\n",
       "      <td>9.708000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2326</th>\n",
       "      <td>BV</td>\n",
       "      <td>364000000.0</td>\n",
       "      <td>602500000.0</td>\n",
       "      <td>9.665000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2762</th>\n",
       "      <td>Sony</td>\n",
       "      <td>404500000.0</td>\n",
       "      <td>557600000.0</td>\n",
       "      <td>9.621000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WB</td>\n",
       "      <td>296000000.0</td>\n",
       "      <td>664300000.0</td>\n",
       "      <td>9.603000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>WB (NL)</td>\n",
       "      <td>258399999.0</td>\n",
       "      <td>700000000.0</td>\n",
       "      <td>9.584000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1478</th>\n",
       "      <td>WB (NL)</td>\n",
       "      <td>255100000.0</td>\n",
       "      <td>700900000.0</td>\n",
       "      <td>9.560000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>BV</td>\n",
       "      <td>936700000.0</td>\n",
       "      <td>1131.6</td>\n",
       "      <td>9.367011e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3084</th>\n",
       "      <td>Fox</td>\n",
       "      <td>216400000.0</td>\n",
       "      <td>687200000.0</td>\n",
       "      <td>9.036000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>Sony</td>\n",
       "      <td>200100000.0</td>\n",
       "      <td>680600000.0</td>\n",
       "      <td>8.807000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2763</th>\n",
       "      <td>Sony</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>546000000.0</td>\n",
       "      <td>8.802000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>Fox</td>\n",
       "      <td>161300000.0</td>\n",
       "      <td>715900000.0</td>\n",
       "      <td>8.772000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2327</th>\n",
       "      <td>Uni.</td>\n",
       "      <td>368400000.0</td>\n",
       "      <td>507100000.0</td>\n",
       "      <td>8.755000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2328</th>\n",
       "      <td>WB</td>\n",
       "      <td>330400000.0</td>\n",
       "      <td>543300000.0</td>\n",
       "      <td>8.737000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2764</th>\n",
       "      <td>HC</td>\n",
       "      <td>2700000.0</td>\n",
       "      <td>867600000.0</td>\n",
       "      <td>8.703000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131</th>\n",
       "      <td>LGF</td>\n",
       "      <td>424700000.0</td>\n",
       "      <td>440300000.0</td>\n",
       "      <td>8.650000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2765</th>\n",
       "      <td>BV</td>\n",
       "      <td>389800000.0</td>\n",
       "      <td>473900000.0</td>\n",
       "      <td>8.637000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1878</th>\n",
       "      <td>BV</td>\n",
       "      <td>356500000.0</td>\n",
       "      <td>501100000.0</td>\n",
       "      <td>8.576000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3085</th>\n",
       "      <td>Sony</td>\n",
       "      <td>213500000.0</td>\n",
       "      <td>641500000.0</td>\n",
       "      <td>8.550000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2766</th>\n",
       "      <td>BV</td>\n",
       "      <td>315100000.0</td>\n",
       "      <td>538900000.0</td>\n",
       "      <td>8.540000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>LG/S</td>\n",
       "      <td>292300000.0</td>\n",
       "      <td>537400000.0</td>\n",
       "      <td>8.297000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WB</td>\n",
       "      <td>292600000.0</td>\n",
       "      <td>535700000.0</td>\n",
       "      <td>8.283000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2767</th>\n",
       "      <td>WB</td>\n",
       "      <td>412600000.0</td>\n",
       "      <td>409300000.0</td>\n",
       "      <td>8.219000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2329</th>\n",
       "      <td>WB</td>\n",
       "      <td>234000000.0</td>\n",
       "      <td>580000000.0</td>\n",
       "      <td>8.140000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2768</th>\n",
       "      <td>BV</td>\n",
       "      <td>209700000.0</td>\n",
       "      <td>597400000.0</td>\n",
       "      <td>8.071000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2769</th>\n",
       "      <td>BV</td>\n",
       "      <td>172600000.0</td>\n",
       "      <td>622300000.0</td>\n",
       "      <td>7.949000e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       studio  domestic_gross  foreign_gross   gross_total\n",
       "727        BV     623400000.0    895500000.0  1.518900e+09\n",
       "1875       BV     459000000.0    946400000.0  1.405400e+09\n",
       "3080       BV     700100000.0    646900000.0  1.347000e+09\n",
       "328        WB     381000000.0    960500000.0  1.341500e+09\n",
       "2758       BV     620200000.0    712400000.0  1.332600e+09\n",
       "3081     Uni.     417700000.0    891800000.0  1.309500e+09\n",
       "1127       BV     400700000.0    875700000.0  1.276400e+09\n",
       "2759       BV     504000000.0    759500000.0  1.263500e+09\n",
       "3082       BV     608600000.0    634200000.0  1.242800e+09\n",
       "1128       BV     409000000.0    805800000.0  1.214800e+09\n",
       "1876     Uni.     336000000.0    823400000.0  1.159400e+09\n",
       "2322       BV     408100000.0    745200000.0  1.153300e+09\n",
       "3083       WB     335100000.0    812700000.0  1.147800e+09\n",
       "329      P/DW     352400000.0    771400000.0  1.123800e+09\n",
       "728      Sony     304400000.0    804200000.0  1.108600e+09\n",
       "1477     Par.     245400000.0    858600000.0  1.104000e+09\n",
       "729        WB     448100000.0    636800000.0  1.084900e+09\n",
       "0          BV     415000000.0    652000000.0  1.067000e+09\n",
       "2323       BV     532200000.0    523900000.0  1.056100e+09\n",
       "330        BV     241100000.0    804600000.0  1.045700e+09\n",
       "2761     Uni.     264600000.0    770200000.0  1.034800e+09\n",
       "2324       BV     486300000.0    542300000.0  1.028600e+09\n",
       "1          BV     334200000.0    691300000.0  1.025500e+09\n",
       "2325       BV     341300000.0    682500000.0  1.023800e+09\n",
       "730   WB (NL)     303000000.0    718100000.0  1.021100e+09\n",
       "1129     Uni.     368100000.0    602700000.0  9.708000e+08\n",
       "2326       BV     364000000.0    602500000.0  9.665000e+08\n",
       "2762     Sony     404500000.0    557600000.0  9.621000e+08\n",
       "2          WB     296000000.0    664300000.0  9.603000e+08\n",
       "1130  WB (NL)     258399999.0    700000000.0  9.584000e+08\n",
       "1478  WB (NL)     255100000.0    700900000.0  9.560000e+08\n",
       "1872       BV     936700000.0         1131.6  9.367011e+08\n",
       "3084      Fox     216400000.0    687200000.0  9.036000e+08\n",
       "1877     Sony     200100000.0    680600000.0  8.807000e+08\n",
       "2763     Sony     334200000.0    546000000.0  8.802000e+08\n",
       "731       Fox     161300000.0    715900000.0  8.772000e+08\n",
       "2327     Uni.     368400000.0    507100000.0  8.755000e+08\n",
       "2328       WB     330400000.0    543300000.0  8.737000e+08\n",
       "2764       HC       2700000.0    867600000.0  8.703000e+08\n",
       "1131      LGF     424700000.0    440300000.0  8.650000e+08\n",
       "2765       BV     389800000.0    473900000.0  8.637000e+08\n",
       "1878       BV     356500000.0    501100000.0  8.576000e+08\n",
       "3085     Sony     213500000.0    641500000.0  8.550000e+08\n",
       "2766       BV     315100000.0    538900000.0  8.540000e+08\n",
       "732      LG/S     292300000.0    537400000.0  8.297000e+08\n",
       "3          WB     292600000.0    535700000.0  8.283000e+08\n",
       "2767       WB     412600000.0    409300000.0  8.219000e+08\n",
       "2329       WB     234000000.0    580000000.0  8.140000e+08\n",
       "2768       BV     209700000.0    597400000.0  8.071000e+08\n",
       "2769       BV     172600000.0    622300000.0  7.949000e+08"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group domestic + foreign together so you can ascend both at same time\n",
    "t_top_50 = top_50.sort_values(\"gross_total\", ascending=False).head(50)\n",
    "t_top_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>gross_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>5.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.656120e+08</td>\n",
       "      <td>6.643880e+08</td>\n",
       "      <td>1.030000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.528027e+08</td>\n",
       "      <td>1.656287e+08</td>\n",
       "      <td>1.823695e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.700000e+06</td>\n",
       "      <td>1.131600e+03</td>\n",
       "      <td>7.949000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.715250e+08</td>\n",
       "      <td>5.489000e+08</td>\n",
       "      <td>8.741500e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.468500e+08</td>\n",
       "      <td>6.724500e+08</td>\n",
       "      <td>9.959500e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.144000e+08</td>\n",
       "      <td>7.711000e+08</td>\n",
       "      <td>1.141800e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.367000e+08</td>\n",
       "      <td>9.605000e+08</td>\n",
       "      <td>1.518900e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       domestic_gross  foreign_gross   gross_total\n",
       "count    5.000000e+01   5.000000e+01  5.000000e+01\n",
       "mean     3.656120e+08   6.643880e+08  1.030000e+09\n",
       "std      1.528027e+08   1.656287e+08  1.823695e+08\n",
       "min      2.700000e+06   1.131600e+03  7.949000e+08\n",
       "25%      2.715250e+08   5.489000e+08  8.741500e+08\n",
       "50%      3.468500e+08   6.724500e+08  9.959500e+08\n",
       "75%      4.144000e+08   7.711000e+08  1.141800e+09\n",
       "max      9.367000e+08   9.605000e+08  1.518900e+09"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_top_50.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        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>gross_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>studio</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BV</th>\n",
       "      <td>2.408190e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WB</th>\n",
       "      <td>7.872400e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uni.</th>\n",
       "      <td>5.350000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sony</th>\n",
       "      <td>4.686600e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WB (NL)</th>\n",
       "      <td>2.935500e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>1.780800e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P/DW</th>\n",
       "      <td>1.123800e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Par.</th>\n",
       "      <td>1.104000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HC</th>\n",
       "      <td>8.703000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGF</th>\n",
       "      <td>8.650000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LG/S</th>\n",
       "      <td>8.297000e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          gross_total\n",
       "studio               \n",
       "BV       2.408190e+10\n",
       "WB       7.872400e+09\n",
       "Uni.     5.350000e+09\n",
       "Sony     4.686600e+09\n",
       "WB (NL)  2.935500e+09\n",
       "Fox      1.780800e+09\n",
       "P/DW     1.123800e+09\n",
       "Par.     1.104000e+09\n",
       "HC       8.703000e+08\n",
       "LGF      8.650000e+08\n",
       "LG/S     8.297000e+08"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#made a groupby to sort total gross by studios to come up with a specific number for each studio in top 50\n",
    "#also do this twice for the second graph for domestic + foreign gross\n",
    "studiosum = t_top_50.groupby([\"studio\"])[[\"gross_total\"]].sum().sort_values(by=\"gross_total\", ascending = False)\n",
    "studiosum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domestic_gross</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>studio</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BV</th>\n",
       "      <td>9.827600e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>3.777000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HC</th>\n",
       "      <td>2.700000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LG/S</th>\n",
       "      <td>2.923000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGF</th>\n",
       "      <td>4.247000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P/DW</th>\n",
       "      <td>3.524000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Par.</th>\n",
       "      <td>2.454000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sony</th>\n",
       "      <td>1.456700e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uni.</th>\n",
       "      <td>1.754800e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WB</th>\n",
       "      <td>2.729800e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WB (NL)</th>\n",
       "      <td>8.165000e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         domestic_gross\n",
       "studio                 \n",
       "BV         9.827600e+09\n",
       "Fox        3.777000e+08\n",
       "HC         2.700000e+06\n",
       "LG/S       2.923000e+08\n",
       "LGF        4.247000e+08\n",
       "P/DW       3.524000e+08\n",
       "Par.       2.454000e+08\n",
       "Sony       1.456700e+09\n",
       "Uni.       1.754800e+09\n",
       "WB         2.729800e+09\n",
       "WB (NL)    8.165000e+08"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "domesticsum = t_top_50.groupby([\"studio\"])[[\"domestic_gross\"]].sum()\n",
    "domesticsum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>foreign_gross</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>studio</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BV</th>\n",
       "      <td>1.425430e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>1.403100e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HC</th>\n",
       "      <td>8.676000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LG/S</th>\n",
       "      <td>5.374000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGF</th>\n",
       "      <td>4.403000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P/DW</th>\n",
       "      <td>7.714000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Par.</th>\n",
       "      <td>8.586000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sony</th>\n",
       "      <td>3.229900e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uni.</th>\n",
       "      <td>3.595200e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WB</th>\n",
       "      <td>5.142600e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WB (NL)</th>\n",
       "      <td>2.119000e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         foreign_gross\n",
       "studio                \n",
       "BV        1.425430e+10\n",
       "Fox       1.403100e+09\n",
       "HC        8.676000e+08\n",
       "LG/S      5.374000e+08\n",
       "LGF       4.403000e+08\n",
       "P/DW      7.714000e+08\n",
       "Par.      8.586000e+08\n",
       "Sony      3.229900e+09\n",
       "Uni.      3.595200e+09\n",
       "WB        5.142600e+09\n",
       "WB (NL)   2.119000e+09"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foreignsum = t_top_50.groupby([\"studio\"])[[\"foreign_gross\"]].sum()\n",
    "foreignsum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#have made 2 seperate tables of foreign and domestic gross by studio\n",
    "#converted foreign_gross column to float type instead of object for sorting purposes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Data Visualization\n",
    "We plot each studio based on how much total box office they made in the top 50. Another bar graph compared the breakdown between foreign and domestic gross of each studio in the top 50 to see if there was any correlation between them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#changed millions from billions in custom ticker, adjust the le-# to one that matches with my totals.\n",
    "#increase size of graph, titles, labels, and tickers\n",
    "#make it in descending order so you can easily it more clearly\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "studio_labels = ['Buena Vista', 'Warner Bros', 'Universal', 'Sony', 'Warner Bros NL', 'Fox', 'DreamWorks', 'Paramount', 'HC', 'Lionsgate', 'LG/S']\n",
    "x_sume = np.arange(len(studio_labels))\n",
    "def billions(y, pos):\n",
    "    return '$%1.0fB' % (y*1e-9)\n",
    "sumedhstudiofig, ax = plt.subplots(figsize=(14,9))\n",
    "sns.barplot(x = studiosum.index, y = studiosum['gross_total'])\n",
    "sns.color_palette(\"cubehelix\", as_cmap=True)\n",
    "\n",
    "\n",
    "formatter = FuncFormatter(billions)\n",
    "ax.yaxis.set_major_formatter(formatter)\n",
    "\n",
    "ax.set_title('Competitors Earnings of Top 50 Grossing Movies\\n(Box Office Mojo)', fontsize=17, weight=950)\n",
    "ax.set_xlabel('Studio', fontsize=15)\n",
    "ax.set_ylabel('Total Gross', fontsize=15)\n",
    "ax.set_xticks(x_sume)\n",
    "ax.set_xticklabels(studio_labels, size=13)\n",
    "plt.xticks(fontsize=10.5)\n",
    "plt.yticks(fontsize=10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#do a double bar chart for domestic vs foreign total sales for each studio in top 50 movies\n",
    "\n",
    "studio_labels = ['Buena Vista', 'Warner Bros', 'Universal', 'Sony', 'Warner Bros NL', 'Fox', 'DreamWorks', 'Paramount', 'HC', 'Lionsgate', 'LG/S']\n",
    "foreignsum = [1.425430e+10, 5.142600e+09, 3.595200e+09, 3.229900e+09, 2.119000e+09, 1.403100e+09, 7.714000e+08, 8.586000e+08, 8.676000e+08, 4.403000e+08, 4.403000e+08]\n",
    "domesticsum = [9.827600e+09, 2.729800e+09, 1.754800e+09, 1.456700e+09, 8.165000e+08, 3.777000e+08, 3.524000e+08, 2.454000e+08,  2.700000e+06, 4.247000e+08, 2.923000e+08]\n",
    "\n",
    "x_sume = np.arange(len(studio_labels))\n",
    "width_sum = 0.35\n",
    "def billions(y, pos):\n",
    "    return '$%1.0fB' % (y*1e-9)\n",
    "formatter = FuncFormatter(billions)\n",
    "\n",
    "\n",
    "fvsdfig, ax = plt.subplots(figsize=(14,9))\n",
    "rects1_sum = ax.bar(x_sume - width_sum/2, foreignsum, width_sum, label='Foreign Gross')\n",
    "rects2_sum = ax.bar(x_sume + width_sum/2, domesticsum, width_sum, label='Domestic Gross')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.yaxis.set_major_formatter(formatter)\n",
    "ax.set_xlabel('Competition', size=14)\n",
    "ax.set_ylabel('Gross Amount')\n",
    "ax.set_title('Foreign vs Domestic Gross of Competition\\n(Top 50 Movies, Box Office Mojo)')\n",
    "ax.set_xticks(x_sume)\n",
    "ax.set_xticklabels(studio_labels, size=10)\n",
    "ax.legend()\n",
    "plt.rc('legend', fontsize=13)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 Recommendations\n",
    "Based on these results, the top two earners are Buena Vista (Disney) & Warner Bros. These two studios are home to the biggest comic book franchises, Marvel & DC which is a reason why they're so sucessfull internationally and domestically. Our suggestion for Microsoft would be to see these company models and to look into converting their own video game exclusives such as Halo, Gears of War and Forza Motorsport into movies to potentially reach the same succcess as their competitors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic 4: Seasonality\n",
    "In this section, we analyze datasets to determine if releasing movies during a specific month or time of year has an impact on profits.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Data Import\n",
    "Imported a table that contained costs 'production_budget' and revenue 'worldwide gross'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import standard packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</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>1</td>\n",
       "      <td>Dec 18, 2009</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>$425,000,000</td>\n",
       "      <td>$760,507,625</td>\n",
       "      <td>$2,776,345,279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>May 20, 2011</td>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>$410,600,000</td>\n",
       "      <td>$241,063,875</td>\n",
       "      <td>$1,045,663,875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Jun 7, 2019</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>$350,000,000</td>\n",
       "      <td>$42,762,350</td>\n",
       "      <td>$149,762,350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>May 1, 2015</td>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>$330,600,000</td>\n",
       "      <td>$459,005,868</td>\n",
       "      <td>$1,403,013,963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Dec 15, 2017</td>\n",
       "      <td>Star Wars Ep. VIII: The Last Jedi</td>\n",
       "      <td>$317,000,000</td>\n",
       "      <td>$620,181,382</td>\n",
       "      <td>$1,316,721,747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5777</th>\n",
       "      <td>78</td>\n",
       "      <td>Dec 31, 2018</td>\n",
       "      <td>Red 11</td>\n",
       "      <td>$7,000</td>\n",
       "      <td>$0</td>\n",
       "      <td>$0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5778</th>\n",
       "      <td>79</td>\n",
       "      <td>Apr 2, 1999</td>\n",
       "      <td>Following</td>\n",
       "      <td>$6,000</td>\n",
       "      <td>$48,482</td>\n",
       "      <td>$240,495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5779</th>\n",
       "      <td>80</td>\n",
       "      <td>Jul 13, 2005</td>\n",
       "      <td>Return to the Land of Wonders</td>\n",
       "      <td>$5,000</td>\n",
       "      <td>$1,338</td>\n",
       "      <td>$1,338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5780</th>\n",
       "      <td>81</td>\n",
       "      <td>Sep 29, 2015</td>\n",
       "      <td>A Plague So Pleasant</td>\n",
       "      <td>$1,400</td>\n",
       "      <td>$0</td>\n",
       "      <td>$0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5781</th>\n",
       "      <td>82</td>\n",
       "      <td>Aug 5, 2005</td>\n",
       "      <td>My Date With Drew</td>\n",
       "      <td>$1,100</td>\n",
       "      <td>$181,041</td>\n",
       "      <td>$181,041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5782 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  release_date                                        movie  \\\n",
       "0      1  Dec 18, 2009                                       Avatar   \n",
       "1      2  May 20, 2011  Pirates of the Caribbean: On Stranger Tides   \n",
       "2      3   Jun 7, 2019                                 Dark Phoenix   \n",
       "3      4   May 1, 2015                      Avengers: Age of Ultron   \n",
       "4      5  Dec 15, 2017            Star Wars Ep. VIII: The Last Jedi   \n",
       "...   ..           ...                                          ...   \n",
       "5777  78  Dec 31, 2018                                       Red 11   \n",
       "5778  79   Apr 2, 1999                                    Following   \n",
       "5779  80  Jul 13, 2005                Return to the Land of Wonders   \n",
       "5780  81  Sep 29, 2015                         A Plague So Pleasant   \n",
       "5781  82   Aug 5, 2005                            My Date With Drew   \n",
       "\n",
       "     production_budget domestic_gross worldwide_gross  \n",
       "0         $425,000,000   $760,507,625  $2,776,345,279  \n",
       "1         $410,600,000   $241,063,875  $1,045,663,875  \n",
       "2         $350,000,000    $42,762,350    $149,762,350  \n",
       "3         $330,600,000   $459,005,868  $1,403,013,963  \n",
       "4         $317,000,000   $620,181,382  $1,316,721,747  \n",
       "...                ...            ...             ...  \n",
       "5777            $7,000             $0              $0  \n",
       "5778            $6,000        $48,482        $240,495  \n",
       "5779            $5,000         $1,338          $1,338  \n",
       "5780            $1,400             $0              $0  \n",
       "5781            $1,100       $181,041        $181,041  \n",
       "\n",
       "[5782 rows x 6 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create variable & open\n",
    "movie_budgets = pd.read_csv('data/zippedData/tn.movie_budgets.csv.gz')\n",
    "movie_budgets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Data Cleaning and Filtering\n",
    "Once we get the data types from 'movie_budgets' we notice that they are objects instead of ints or floats in order to make calculations. Also, we have to remove commas and dollars signs from the dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5782 entries, 0 to 5781\n",
      "Data columns (total 6 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   id                 5782 non-null   int64 \n",
      " 1   release_date       5782 non-null   object\n",
      " 2   movie              5782 non-null   object\n",
      " 3   production_budget  5782 non-null   object\n",
      " 4   domestic_gross     5782 non-null   object\n",
      " 5   worldwide_gross    5782 non-null   object\n",
      "dtypes: int64(1), object(5)\n",
      "memory usage: 271.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Basic info about the movie_budgets file\n",
    "movie_budgets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       $2776345279\n",
       "1       $1045663875\n",
       "2        $149762350\n",
       "3       $1403013963\n",
       "4       $1316721747\n",
       "           ...     \n",
       "5777             $0\n",
       "5778        $240495\n",
       "5779          $1338\n",
       "5780             $0\n",
       "5781        $181041\n",
       "Name: worldwide_gross, Length: 5782, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change budget, domestic gross & worldwide gross columns data types\n",
    "# Remove comma\n",
    "production_budget = movie_budgets.production_budget.str.replace(\",\", \"\")\n",
    "domestic_gross = movie_budgets.domestic_gross.str.replace(\",\", \"\")\n",
    "worldwide_gross = movie_budgets.worldwide_gross.str.replace(\",\", \"\")\n",
    "production_budget\n",
    "domestic_gross\n",
    "worldwide_gross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2776345279\n",
       "1       1045663875\n",
       "2        149762350\n",
       "3       1403013963\n",
       "4       1316721747\n",
       "           ...    \n",
       "5777             0\n",
       "5778        240495\n",
       "5779          1338\n",
       "5780             0\n",
       "5781        181041\n",
       "Name: worldwide_gross, Length: 5782, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now remove dollar sign\n",
    "production_budget = production_budget.str.replace(\"$\", \"\")\n",
    "domestic_gross = domestic_gross.str.replace(\"$\", \"\")\n",
    "worldwide_gross = worldwide_gross.str.replace(\"$\", \"\")\n",
    "production_budget\n",
    "domestic_gross\n",
    "worldwide_gross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2.776345e+09\n",
       "1       1.045664e+09\n",
       "2       1.497624e+08\n",
       "3       1.403014e+09\n",
       "4       1.316722e+09\n",
       "            ...     \n",
       "5777    0.000000e+00\n",
       "5778    2.404950e+05\n",
       "5779    1.338000e+03\n",
       "5780    0.000000e+00\n",
       "5781    1.810410e+05\n",
       "Name: worldwide_gross, Length: 5782, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change data type from object to float\n",
    "production_budget = production_budget.astype(float)\n",
    "domestic_gross = domestic_gross.astype(float)\n",
    "worldwide_gross = worldwide_gross.astype(float)\n",
    "production_budget\n",
    "domestic_gross\n",
    "worldwide_gross"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Data Analysis and Calculations\n",
    "Calculate foreign and domestic total gross sums. Calculate profits by month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       3.536853e+09\n",
       "1       1.286728e+09\n",
       "2       1.925247e+08\n",
       "3       1.862020e+09\n",
       "4       1.936903e+09\n",
       "            ...     \n",
       "5777    0.000000e+00\n",
       "5778    2.889770e+05\n",
       "5779    2.676000e+03\n",
       "5780    0.000000e+00\n",
       "5781    3.620820e+05\n",
       "Length: 5782, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate total_gross\n",
    "total_gross = domestic_gross + worldwide_gross\n",
    "total_gross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       3.111853e+09\n",
       "1       8.761278e+08\n",
       "2      -1.574753e+08\n",
       "3       1.531420e+09\n",
       "4       1.619903e+09\n",
       "            ...     \n",
       "5777   -7.000000e+03\n",
       "5778    2.829770e+05\n",
       "5779   -2.324000e+03\n",
       "5780   -1.400000e+03\n",
       "5781    3.609820e+05\n",
       "Length: 5782, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate movie_profit\n",
    "movie_profit = total_gross - production_budget\n",
    "movie_profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>original_title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>total_gross</th>\n",
       "      <th>movie_profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>Dec 18, 2009</td>\n",
       "      <td>3.536853e+09</td>\n",
       "      <td>3.111853e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>May 20, 2011</td>\n",
       "      <td>1.286728e+09</td>\n",
       "      <td>8.761278e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>Jun 7, 2019</td>\n",
       "      <td>1.925247e+08</td>\n",
       "      <td>-1.574753e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>May 1, 2015</td>\n",
       "      <td>1.862020e+09</td>\n",
       "      <td>1.531420e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Star Wars Ep. VIII: The Last Jedi</td>\n",
       "      <td>Dec 15, 2017</td>\n",
       "      <td>1.936903e+09</td>\n",
       "      <td>1.619903e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5777</th>\n",
       "      <td>Red 11</td>\n",
       "      <td>Dec 31, 2018</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-7.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5778</th>\n",
       "      <td>Following</td>\n",
       "      <td>Apr 2, 1999</td>\n",
       "      <td>2.889770e+05</td>\n",
       "      <td>2.829770e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5779</th>\n",
       "      <td>Return to the Land of Wonders</td>\n",
       "      <td>Jul 13, 2005</td>\n",
       "      <td>2.676000e+03</td>\n",
       "      <td>-2.324000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5780</th>\n",
       "      <td>A Plague So Pleasant</td>\n",
       "      <td>Sep 29, 2015</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.400000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5781</th>\n",
       "      <td>My Date With Drew</td>\n",
       "      <td>Aug 5, 2005</td>\n",
       "      <td>3.620820e+05</td>\n",
       "      <td>3.609820e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5782 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   original_title  release_date   total_gross  \\\n",
       "0                                          Avatar  Dec 18, 2009  3.536853e+09   \n",
       "1     Pirates of the Caribbean: On Stranger Tides  May 20, 2011  1.286728e+09   \n",
       "2                                    Dark Phoenix   Jun 7, 2019  1.925247e+08   \n",
       "3                         Avengers: Age of Ultron   May 1, 2015  1.862020e+09   \n",
       "4               Star Wars Ep. VIII: The Last Jedi  Dec 15, 2017  1.936903e+09   \n",
       "...                                           ...           ...           ...   \n",
       "5777                                       Red 11  Dec 31, 2018  0.000000e+00   \n",
       "5778                                    Following   Apr 2, 1999  2.889770e+05   \n",
       "5779                Return to the Land of Wonders  Jul 13, 2005  2.676000e+03   \n",
       "5780                         A Plague So Pleasant  Sep 29, 2015  0.000000e+00   \n",
       "5781                            My Date With Drew   Aug 5, 2005  3.620820e+05   \n",
       "\n",
       "      movie_profit  \n",
       "0     3.111853e+09  \n",
       "1     8.761278e+08  \n",
       "2    -1.574753e+08  \n",
       "3     1.531420e+09  \n",
       "4     1.619903e+09  \n",
       "...            ...  \n",
       "5777 -7.000000e+03  \n",
       "5778  2.829770e+05  \n",
       "5779 -2.324000e+03  \n",
       "5780 -1.400000e+03  \n",
       "5781  3.609820e+05  \n",
       "\n",
       "[5782 rows x 4 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create movie_budget dataframe \n",
    "movie_budget = {\"original_title\" : movie_budgets[\"movie\"],\n",
    "                \"release_date\" : movie_budgets['release_date'],\n",
    "                \"total_gross\" : total_gross,\n",
    "                \"movie_profit\" : movie_profit}\n",
    "df_movie_budget = pd.DataFrame(movie_budget)\n",
    "df_movie_budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5782 entries, 0 to 5781\n",
      "Data columns (total 6 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   id                 5782 non-null   int64 \n",
      " 1   release_date       5782 non-null   object\n",
      " 2   movie              5782 non-null   object\n",
      " 3   production_budget  5782 non-null   object\n",
      " 4   domestic_gross     5782 non-null   object\n",
      " 5   worldwide_gross    5782 non-null   object\n",
      "dtypes: int64(1), object(5)\n",
      "memory usage: 271.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# revisit movie_budgets\n",
    "movie_budgets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the release_date field to type datetime object\n",
    "df_movie_budget['release_date'] = pd.to_datetime(df_movie_budget['release_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add new column called release_month with only the month from the release_date\n",
    "month =  [x.strftime('%B') for x in df_movie_budget['release_date']]\n",
    "df_movie_budget['release_month'] = month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>release_month</th>\n",
       "      <th>movie_profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>January</td>\n",
       "      <td>1.723547e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>February</td>\n",
       "      <td>3.093380e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>March</td>\n",
       "      <td>4.155941e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>April</td>\n",
       "      <td>2.880441e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>7.400512e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>June</td>\n",
       "      <td>7.915562e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>July</td>\n",
       "      <td>7.002402e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>August</td>\n",
       "      <td>3.352490e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>September</td>\n",
       "      <td>2.367912e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>October</td>\n",
       "      <td>3.065286e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>November</td>\n",
       "      <td>7.354285e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>December</td>\n",
       "      <td>8.533407e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   release_month  movie_profit\n",
       "0        January  1.723547e+10\n",
       "1       February  3.093380e+10\n",
       "2          March  4.155941e+10\n",
       "3          April  2.880441e+10\n",
       "4            May  7.400512e+10\n",
       "5           June  7.915562e+10\n",
       "6           July  7.002402e+10\n",
       "7         August  3.352490e+10\n",
       "8      September  2.367912e+10\n",
       "9        October  3.065286e+10\n",
       "10      November  7.354285e+10\n",
       "11      December  8.533407e+10"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Caclulate profit by month\n",
    "profit_vs_monthsum = df_movie_budget.groupby('release_month', sort=False, as_index=True)['movie_profit'].sum()\n",
    "new_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n",
    "profit_vs_monthsum = profit_vs_monthsum.reindex(new_order, axis=0).reset_index()\n",
    "profit_vs_monthsum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>release_month</th>\n",
       "      <th>movie_profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>January</td>\n",
       "      <td>4.966995e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>February</td>\n",
       "      <td>7.891276e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>March</td>\n",
       "      <td>8.842428e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>April</td>\n",
       "      <td>6.344583e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>1.818308e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>June</td>\n",
       "      <td>1.652518e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>July</td>\n",
       "      <td>1.591455e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>August</td>\n",
       "      <td>6.759052e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>September</td>\n",
       "      <td>4.803067e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>October</td>\n",
       "      <td>5.349540e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>November</td>\n",
       "      <td>1.513227e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>December</td>\n",
       "      <td>1.145424e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   release_month  movie_profit\n",
       "0        January  4.966995e+07\n",
       "1       February  7.891276e+07\n",
       "2          March  8.842428e+07\n",
       "3          April  6.344583e+07\n",
       "4            May  1.818308e+08\n",
       "5           June  1.652518e+08\n",
       "6           July  1.591455e+08\n",
       "7         August  6.759052e+07\n",
       "8      September  4.803067e+07\n",
       "9        October  5.349540e+07\n",
       "10      November  1.513227e+08\n",
       "11      December  1.145424e+08"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profit_vs_monthmean = df_movie_budget.groupby('release_month', sort=False, as_index=True)['movie_profit'].mean()\n",
    "profit_vs_monthmean = profit_vs_monthmean.reindex(new_order, axis=0).reset_index()\n",
    "profit_vs_monthmean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# profit_vs_month['release_month'] = pd.Categorical(profit_vs_month['release_month'], ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'])\n",
    "# profit_by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</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>1</td>\n",
       "      <td>Dec 18, 2009</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>$425,000,000</td>\n",
       "      <td>$760,507,625</td>\n",
       "      <td>$2,776,345,279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>May 20, 2011</td>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>$410,600,000</td>\n",
       "      <td>$241,063,875</td>\n",
       "      <td>$1,045,663,875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Jun 7, 2019</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>$350,000,000</td>\n",
       "      <td>$42,762,350</td>\n",
       "      <td>$149,762,350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>May 1, 2015</td>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>$330,600,000</td>\n",
       "      <td>$459,005,868</td>\n",
       "      <td>$1,403,013,963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Dec 15, 2017</td>\n",
       "      <td>Star Wars Ep. VIII: The Last Jedi</td>\n",
       "      <td>$317,000,000</td>\n",
       "      <td>$620,181,382</td>\n",
       "      <td>$1,316,721,747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5777</th>\n",
       "      <td>78</td>\n",
       "      <td>Dec 31, 2018</td>\n",
       "      <td>Red 11</td>\n",
       "      <td>$7,000</td>\n",
       "      <td>$0</td>\n",
       "      <td>$0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5778</th>\n",
       "      <td>79</td>\n",
       "      <td>Apr 2, 1999</td>\n",
       "      <td>Following</td>\n",
       "      <td>$6,000</td>\n",
       "      <td>$48,482</td>\n",
       "      <td>$240,495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5779</th>\n",
       "      <td>80</td>\n",
       "      <td>Jul 13, 2005</td>\n",
       "      <td>Return to the Land of Wonders</td>\n",
       "      <td>$5,000</td>\n",
       "      <td>$1,338</td>\n",
       "      <td>$1,338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5780</th>\n",
       "      <td>81</td>\n",
       "      <td>Sep 29, 2015</td>\n",
       "      <td>A Plague So Pleasant</td>\n",
       "      <td>$1,400</td>\n",
       "      <td>$0</td>\n",
       "      <td>$0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5781</th>\n",
       "      <td>82</td>\n",
       "      <td>Aug 5, 2005</td>\n",
       "      <td>My Date With Drew</td>\n",
       "      <td>$1,100</td>\n",
       "      <td>$181,041</td>\n",
       "      <td>$181,041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5782 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  release_date                                        movie  \\\n",
       "0      1  Dec 18, 2009                                       Avatar   \n",
       "1      2  May 20, 2011  Pirates of the Caribbean: On Stranger Tides   \n",
       "2      3   Jun 7, 2019                                 Dark Phoenix   \n",
       "3      4   May 1, 2015                      Avengers: Age of Ultron   \n",
       "4      5  Dec 15, 2017            Star Wars Ep. VIII: The Last Jedi   \n",
       "...   ..           ...                                          ...   \n",
       "5777  78  Dec 31, 2018                                       Red 11   \n",
       "5778  79   Apr 2, 1999                                    Following   \n",
       "5779  80  Jul 13, 2005                Return to the Land of Wonders   \n",
       "5780  81  Sep 29, 2015                         A Plague So Pleasant   \n",
       "5781  82   Aug 5, 2005                            My Date With Drew   \n",
       "\n",
       "     production_budget domestic_gross worldwide_gross  \n",
       "0         $425,000,000   $760,507,625  $2,776,345,279  \n",
       "1         $410,600,000   $241,063,875  $1,045,663,875  \n",
       "2         $350,000,000    $42,762,350    $149,762,350  \n",
       "3         $330,600,000   $459,005,868  $1,403,013,963  \n",
       "4         $317,000,000   $620,181,382  $1,316,721,747  \n",
       "...                ...            ...             ...  \n",
       "5777            $7,000             $0              $0  \n",
       "5778            $6,000        $48,482        $240,495  \n",
       "5779            $5,000         $1,338          $1,338  \n",
       "5780            $1,400             $0              $0  \n",
       "5781            $1,100       $181,041        $181,041  \n",
       "\n",
       "[5782 rows x 6 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Next Analysis \n",
    "foreign_gross = pd.read_csv('data/zippedData/tn.movie_budgets.csv.gz')\n",
    "foreign_gross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5782 entries, 0 to 5781\n",
      "Data columns (total 6 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   id                 5782 non-null   int64 \n",
      " 1   release_date       5782 non-null   object\n",
      " 2   movie              5782 non-null   object\n",
      " 3   production_budget  5782 non-null   object\n",
      " 4   domestic_gross     5782 non-null   object\n",
      " 5   worldwide_gross    5782 non-null   object\n",
      "dtypes: int64(1), object(5)\n",
      "memory usage: 271.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Repeat steps- find out basic info about the foreign_gross file\n",
    "foreign_gross.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       $2776345279\n",
       "1       $1045663875\n",
       "2        $149762350\n",
       "3       $1403013963\n",
       "4       $1316721747\n",
       "           ...     \n",
       "5777             $0\n",
       "5778        $240495\n",
       "5779          $1338\n",
       "5780             $0\n",
       "5781        $181041\n",
       "Name: worldwide_gross, Length: 5782, dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change budget, domestic gross, and worldwide gross columns data types\n",
    "# Remove the comma\n",
    "production_budget = foreign_gross.production_budget.str.replace(\",\", \"\")\n",
    "domestic_gross = foreign_gross.domestic_gross.str.replace(\",\", \"\")\n",
    "worldwide_gross = foreign_gross.worldwide_gross.str.replace(\",\", \"\")\n",
    "production_budget\n",
    "domestic_gross\n",
    "worldwide_gross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2776345279\n",
       "1       1045663875\n",
       "2        149762350\n",
       "3       1403013963\n",
       "4       1316721747\n",
       "           ...    \n",
       "5777             0\n",
       "5778        240495\n",
       "5779          1338\n",
       "5780             0\n",
       "5781        181041\n",
       "Name: worldwide_gross, Length: 5782, dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the dollar sign\n",
    "production_budget = production_budget.str.replace(\"$\", \"\")\n",
    "domestic_gross = domestic_gross.str.replace(\"$\", \"\")\n",
    "worldwide_gross = worldwide_gross.str.replace(\"$\", \"\")\n",
    "production_budget\n",
    "domestic_gross\n",
    "worldwide_gross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2.776345e+09\n",
       "1       1.045664e+09\n",
       "2       1.497624e+08\n",
       "3       1.403014e+09\n",
       "4       1.316722e+09\n",
       "            ...     \n",
       "5777    0.000000e+00\n",
       "5778    2.404950e+05\n",
       "5779    1.338000e+03\n",
       "5780    0.000000e+00\n",
       "5781    1.810410e+05\n",
       "Name: worldwide_gross, Length: 5782, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change data type from object to float\n",
    "production_budget = production_budget.astype(float)\n",
    "domestic_gross = domestic_gross.astype(float)\n",
    "worldwide_gross = worldwide_gross.astype(float)\n",
    "production_budget\n",
    "domestic_gross\n",
    "worldwide_gross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2.015838e+09\n",
       "1       8.046000e+08\n",
       "2       1.070000e+08\n",
       "3       9.440081e+08\n",
       "4       6.965404e+08\n",
       "            ...     \n",
       "5777    0.000000e+00\n",
       "5778    1.920130e+05\n",
       "5779    0.000000e+00\n",
       "5780    0.000000e+00\n",
       "5781    0.000000e+00\n",
       "Length: 5782, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate foreign_gross\n",
    "foreign_gross_calc = worldwide_gross - domestic_gross \n",
    "foreign_gross_calc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>original_title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>domestic_gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>Dec 18, 2009</td>\n",
       "      <td>2.015838e+09</td>\n",
       "      <td>760507625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>May 20, 2011</td>\n",
       "      <td>8.046000e+08</td>\n",
       "      <td>241063875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>Jun 7, 2019</td>\n",
       "      <td>1.070000e+08</td>\n",
       "      <td>42762350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>May 1, 2015</td>\n",
       "      <td>9.440081e+08</td>\n",
       "      <td>459005868.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Star Wars Ep. VIII: The Last Jedi</td>\n",
       "      <td>Dec 15, 2017</td>\n",
       "      <td>6.965404e+08</td>\n",
       "      <td>620181382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5777</th>\n",
       "      <td>Red 11</td>\n",
       "      <td>Dec 31, 2018</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5778</th>\n",
       "      <td>Following</td>\n",
       "      <td>Apr 2, 1999</td>\n",
       "      <td>1.920130e+05</td>\n",
       "      <td>48482.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5779</th>\n",
       "      <td>Return to the Land of Wonders</td>\n",
       "      <td>Jul 13, 2005</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5780</th>\n",
       "      <td>A Plague So Pleasant</td>\n",
       "      <td>Sep 29, 2015</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5781</th>\n",
       "      <td>My Date With Drew</td>\n",
       "      <td>Aug 5, 2005</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>181041.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5782 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   original_title  release_date  \\\n",
       "0                                          Avatar  Dec 18, 2009   \n",
       "1     Pirates of the Caribbean: On Stranger Tides  May 20, 2011   \n",
       "2                                    Dark Phoenix   Jun 7, 2019   \n",
       "3                         Avengers: Age of Ultron   May 1, 2015   \n",
       "4               Star Wars Ep. VIII: The Last Jedi  Dec 15, 2017   \n",
       "...                                           ...           ...   \n",
       "5777                                       Red 11  Dec 31, 2018   \n",
       "5778                                    Following   Apr 2, 1999   \n",
       "5779                Return to the Land of Wonders  Jul 13, 2005   \n",
       "5780                         A Plague So Pleasant  Sep 29, 2015   \n",
       "5781                            My Date With Drew   Aug 5, 2005   \n",
       "\n",
       "      foreign_gross  domestic_gross  \n",
       "0      2.015838e+09     760507625.0  \n",
       "1      8.046000e+08     241063875.0  \n",
       "2      1.070000e+08      42762350.0  \n",
       "3      9.440081e+08     459005868.0  \n",
       "4      6.965404e+08     620181382.0  \n",
       "...             ...             ...  \n",
       "5777   0.000000e+00             0.0  \n",
       "5778   1.920130e+05         48482.0  \n",
       "5779   0.000000e+00          1338.0  \n",
       "5780   0.000000e+00             0.0  \n",
       "5781   0.000000e+00        181041.0  \n",
       "\n",
       "[5782 rows x 4 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create foreign_gross dataframe \n",
    "foreign_gross = {\"original_title\" : foreign_gross[\"movie\"],\n",
    "                \"release_date\" : foreign_gross['release_date'],\n",
    "                \"foreign_gross\" : foreign_gross_calc,\n",
    "                \"domestic_gross\" : domestic_gross}\n",
    "df_foreign_gross = pd.DataFrame(foreign_gross)\n",
    "df_foreign_gross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>original_title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>domestic_gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>2009-12-18</td>\n",
       "      <td>2.015838e+09</td>\n",
       "      <td>760507625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>2011-05-20</td>\n",
       "      <td>8.046000e+08</td>\n",
       "      <td>241063875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>2019-06-07</td>\n",
       "      <td>1.070000e+08</td>\n",
       "      <td>42762350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>9.440081e+08</td>\n",
       "      <td>459005868.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Star Wars Ep. VIII: The Last Jedi</td>\n",
       "      <td>2017-12-15</td>\n",
       "      <td>6.965404e+08</td>\n",
       "      <td>620181382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5777</th>\n",
       "      <td>Red 11</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5778</th>\n",
       "      <td>Following</td>\n",
       "      <td>1999-04-02</td>\n",
       "      <td>1.920130e+05</td>\n",
       "      <td>48482.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5779</th>\n",
       "      <td>Return to the Land of Wonders</td>\n",
       "      <td>2005-07-13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5780</th>\n",
       "      <td>A Plague So Pleasant</td>\n",
       "      <td>2015-09-29</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5781</th>\n",
       "      <td>My Date With Drew</td>\n",
       "      <td>2005-08-05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>181041.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5782 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   original_title release_date  foreign_gross  \\\n",
       "0                                          Avatar   2009-12-18   2.015838e+09   \n",
       "1     Pirates of the Caribbean: On Stranger Tides   2011-05-20   8.046000e+08   \n",
       "2                                    Dark Phoenix   2019-06-07   1.070000e+08   \n",
       "3                         Avengers: Age of Ultron   2015-05-01   9.440081e+08   \n",
       "4               Star Wars Ep. VIII: The Last Jedi   2017-12-15   6.965404e+08   \n",
       "...                                           ...          ...            ...   \n",
       "5777                                       Red 11   2018-12-31   0.000000e+00   \n",
       "5778                                    Following   1999-04-02   1.920130e+05   \n",
       "5779                Return to the Land of Wonders   2005-07-13   0.000000e+00   \n",
       "5780                         A Plague So Pleasant   2015-09-29   0.000000e+00   \n",
       "5781                            My Date With Drew   2005-08-05   0.000000e+00   \n",
       "\n",
       "      domestic_gross  \n",
       "0        760507625.0  \n",
       "1        241063875.0  \n",
       "2         42762350.0  \n",
       "3        459005868.0  \n",
       "4        620181382.0  \n",
       "...              ...  \n",
       "5777             0.0  \n",
       "5778         48482.0  \n",
       "5779          1338.0  \n",
       "5780             0.0  \n",
       "5781        181041.0  \n",
       "\n",
       "[5782 rows x 4 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group FG & DG by months\n",
    "# Caclulate profit by month\n",
    "df_foreign_gross['release_date'] = pd.to_datetime(df_foreign_gross['release_date'])\n",
    "df_foreign_gross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "month =  [x.strftime('%B') for x in df_foreign_gross['release_date']]\n",
    "df_foreign_gross['release_month'] = month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>release_month</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>domestic_gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>December</td>\n",
       "      <td>4.141630e+10</td>\n",
       "      <td>3.434511e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>May</td>\n",
       "      <td>3.889701e+10</td>\n",
       "      <td>2.714607e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>November</td>\n",
       "      <td>3.769438e+10</td>\n",
       "      <td>2.827605e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>June</td>\n",
       "      <td>3.673696e+10</td>\n",
       "      <td>3.153157e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>July</td>\n",
       "      <td>3.530365e+10</td>\n",
       "      <td>2.672034e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>March</td>\n",
       "      <td>1.976838e+10</td>\n",
       "      <td>1.812930e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>April</td>\n",
       "      <td>1.479670e+10</td>\n",
       "      <td>1.240709e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>October</td>\n",
       "      <td>1.434853e+10</td>\n",
       "      <td>1.399466e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>August</td>\n",
       "      <td>1.428986e+10</td>\n",
       "      <td>1.595543e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>February</td>\n",
       "      <td>1.416291e+10</td>\n",
       "      <td>1.388254e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>September</td>\n",
       "      <td>1.160709e+10</td>\n",
       "      <td>1.141289e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>January</td>\n",
       "      <td>7.847130e+09</td>\n",
       "      <td>8.310517e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   release_month  foreign_gross  domestic_gross\n",
       "2       December   4.141630e+10    3.434511e+10\n",
       "8            May   3.889701e+10    2.714607e+10\n",
       "9       November   3.769438e+10    2.827605e+10\n",
       "6           June   3.673696e+10    3.153157e+10\n",
       "5           July   3.530365e+10    2.672034e+10\n",
       "7          March   1.976838e+10    1.812930e+10\n",
       "0          April   1.479670e+10    1.240709e+10\n",
       "10       October   1.434853e+10    1.399466e+10\n",
       "1         August   1.428986e+10    1.595543e+10\n",
       "3       February   1.416291e+10    1.388254e+10\n",
       "11     September   1.160709e+10    1.141289e+10\n",
       "4        January   7.847130e+09    8.310517e+09"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fg_vs_month = df_foreign_gross.groupby('release_month', as_index=False)[['foreign_gross','domestic_gross']].sum().sort_values(by='foreign_gross', ascending=False)\n",
    "fg_vs_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>release_month</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>domestic_gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>January</td>\n",
       "      <td>7.847130e+09</td>\n",
       "      <td>8.310517e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>February</td>\n",
       "      <td>1.416291e+10</td>\n",
       "      <td>1.388254e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>March</td>\n",
       "      <td>1.976838e+10</td>\n",
       "      <td>1.812930e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>April</td>\n",
       "      <td>1.479670e+10</td>\n",
       "      <td>1.240709e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>May</td>\n",
       "      <td>3.889701e+10</td>\n",
       "      <td>2.714607e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>June</td>\n",
       "      <td>3.673696e+10</td>\n",
       "      <td>3.153157e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>July</td>\n",
       "      <td>3.530365e+10</td>\n",
       "      <td>2.672034e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>August</td>\n",
       "      <td>1.428986e+10</td>\n",
       "      <td>1.595543e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>September</td>\n",
       "      <td>1.160709e+10</td>\n",
       "      <td>1.141289e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>October</td>\n",
       "      <td>1.434853e+10</td>\n",
       "      <td>1.399466e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>November</td>\n",
       "      <td>3.769438e+10</td>\n",
       "      <td>2.827605e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>December</td>\n",
       "      <td>4.141630e+10</td>\n",
       "      <td>3.434511e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   release_month  foreign_gross  domestic_gross\n",
       "4        January   7.847130e+09    8.310517e+09\n",
       "3       February   1.416291e+10    1.388254e+10\n",
       "7          March   1.976838e+10    1.812930e+10\n",
       "0          April   1.479670e+10    1.240709e+10\n",
       "8            May   3.889701e+10    2.714607e+10\n",
       "6           June   3.673696e+10    3.153157e+10\n",
       "5           July   3.530365e+10    2.672034e+10\n",
       "1         August   1.428986e+10    1.595543e+10\n",
       "11     September   1.160709e+10    1.141289e+10\n",
       "10       October   1.434853e+10    1.399466e+10\n",
       "9       November   3.769438e+10    2.827605e+10\n",
       "2       December   4.141630e+10    3.434511e+10"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fg_vs_month['release_month'] = pd.Categorical(fg_vs_month['release_month'], ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'])\n",
    "fg_by_month = fg_vs_month.sort_values(\"release_month\")\n",
    "fg_by_month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Data Visualization\n",
    "Plot 2 bar charts comparing total profit and average profile by month. Plot a 3rd, stacked bar chart that compares foreign vs. domestic gross by month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot total profit vs month (bar chart) \n",
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "ordered_months = ['January', 'February', 'March', 'April', 'May',\n",
    "                 'June', 'July', 'August', 'September', 'October',\n",
    "                 'November', 'December']\n",
    "x = np.arange(len(ordered_months))\n",
    "def ten_billions(y, pos):\n",
    "    return '$%1.0fB' % (y*1e-9)\n",
    "tot_prof_fig, ax = plt.subplots(figsize=(20,10))\n",
    "sns.barplot(x = profit_vs_monthsum['release_month'],\n",
    "            y = profit_vs_monthsum['movie_profit'],\n",
    "            color = 'green')\n",
    "sns.set_style(\"white\")\n",
    "plt.title(\"Total Movie Profit vs. Release Month\", fontsize=20, weight=900)\n",
    "plt.ylabel(\"Movie Profits\", fontsize= 15)\n",
    "plt.xlabel(\"Months\", fontsize=15)\n",
    "plt.xticks(fontsize=13)\n",
    "plt.yticks(fontsize=13)\n",
    "ax.tick_params(left=True, bottom=False)\n",
    "formatter = FuncFormatter(ten_billions)\n",
    "ax.yaxis.set_major_formatter(formatter);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot average profit vs month (bar chart) \n",
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "x = np.arange(len(ordered_months))\n",
    "def hun_millions(y, pos):\n",
    "    return '$%1.0fM' % (y*1e-6)\n",
    "avg_prof_fig, ax = plt.subplots(figsize=(20,10))\n",
    "sns.barplot(x = profit_vs_monthmean['release_month'],\n",
    "            y = profit_vs_monthmean['movie_profit'],\n",
    "            color = 'green')\n",
    "sns.set_style(\"white\")\n",
    "plt.title(\"Average Movie Profit vs. Release Month\", fontsize=20, weight=900)\n",
    "plt.ylabel(\"Movie Profits\", fontsize=16)\n",
    "plt.xlabel(\"Months\", fontsize=16)\n",
    "plt.xticks(fontsize=13)\n",
    "plt.yticks(fontsize=13)\n",
    "ax.tick_params(left=True, bottom=False)\n",
    "formatter = FuncFormatter(hun_millions)\n",
    "ax.yaxis.set_major_formatter(formatter);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replace XFG & ZDG & make double bar chart bigger \n",
    "# Add units on ylabel\n",
    "\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "X = ['Jan','Feb','Mar','Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec']\n",
    "XFG = [7.847130e+09, 1.416291e+10, 1.976838e+10, 1.479670e+10, 3.889701e+10, 3.673696e+10, \n",
    "       3.530365e+10, 1.428986e+10, 1.160709e+10, 1.434853e+10, 3.769438e+10, 4.141630e+10]\n",
    "ZDG = [8.310517e+09, 1.388254e+10, 1.812930e+10, 1.240709e+10, 2.714607e+10, 3.153157e+10, \n",
    "       2.672034e+10, 1.595543e+10, 1.141289e+10, 1.399466e+10, 2.827605e+10, 3.434511e+10]\n",
    "\n",
    "bar_fig, ax = plt.subplots(figsize=(15,10))\n",
    "X_axis = np.arange(len(X))\n",
    "def ten_billions(y, pos):\n",
    "    return '$%1.0fB' % (y*1e-9)\n",
    "plt.bar(X_axis - 0.2, XFG, 0.4, label = 'Foreign Gross')\n",
    "plt.bar(X_axis + 0.2, ZDG, 0.4, label = 'Domestic Gross')\n",
    "sns.set_style(\"white\")\n",
    "plt.xticks(X_axis, X, fontsize=13)\n",
    "ax.tick_params(left=True, bottom=True)\n",
    "plt.xlabel(\"Months\", fontsize=15)\n",
    "plt.title(\"Foreign Gross vs. Domestic Gross in Each Month\", fontsize=17, weight=900)\n",
    "plt.legend(fontsize=13)\n",
    "formatter = FuncFormatter(ten_billions)\n",
    "ax.yaxis.set_major_formatter(formatter)\n",
    "plt.yticks(fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 Recommendations\n",
    "Historically speaking, May, June and July yield the highest average profits. We recommended releasing big budget movies in these months. Our analysis shows that if Microsoft targets releases during these summer months there's a higher probability to generate more profit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overall Conclusions\n",
    "***\n",
    "Based on our analysis, we make the following suggestions for the business:\n",
    "\n",
    "* **An early focus should be placed on making horror movies.** According to the data, they historically yield the lowest average cost and highest average profit margins. They'll serve as an initial, lower-risk entry point.\n",
    "* **When moving into big budget, blockbuster movies, target specific directors and/or actors who've historically yielded high box office revenues.** Top 3 directors: Russo brothers, Joss Whedon, James Wan. Top 3 actors: Robert Downey Jr., Chris Evans, Dwayne 'The Rock' Johnson. Top 3 actresses: Scarlett Johansson, Jennifer Lawrence, Bryce Dallas Howard.\n",
    "* **Explore creating movie franchises out of Microsoft intellectual property, namely video games.** The top 2 competing studios with the highest revenues (Buena Vista/Disney, Warner Bros) own Marvel and DC comics, respectively. A comparable franchise could be fashioned from high-grossing Microsoft games (Halo, Gears of War, Forza Motorsport, etc.)\n",
    "* **Release big budget movies in May, June or July.** These 3 months historically yield the highest average revenues."
   ]
  }
 ],
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