Investigate_a_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Investigate a Dataset - [tmdb-movies.csv]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Introduction\n",
    "\n",
    "### Dataset Description \n",
    "\n",
    "TMDb movie dataset is a collection of over 10000 movies from The movie database(TMDb). The dataset has 21 columns starting with the,  \n",
    "1. Id: this is basically is a unique idenfier of the movie in the table data\n",
    "2. imdb_id: a unique identifer of the movie in the database.\n",
    "3. popularity:  movie rating\n",
    "4. budget: money spent on the production of the movie.\n",
    "5. revenue: The amount of money a movie fetched.\n",
    "6. original_title: The title of the movie\n",
    "7. cast: Members who took part in making the movie.\n",
    "8. homepage: a url of the website of a movie.\n",
    "9. director: The person involved in creating the movie\n",
    "10. tagline:                  \n",
    "11. keywords                \n",
    "12. overview: General idea of what the movie is about.                \n",
    "13. runtime: how long a movie runs                 \n",
    "14. genres: Classification of a movie.                 \n",
    "15. production_companies: The companies that produced the movie.    \n",
    "16. release_date: The date, month and year movie was released.           \n",
    "17. vote_count : The number of vote a movie garnered.            \n",
    "18. vote_average: The average vote a movie got.            \n",
    "19. release_year: The year a movie was released            \n",
    "20. budget_adj: Budget adjustment accounting for inflation over time.       \n",
    "21. revenue_adj: Budget adjustment accounting for inflation over time.          \n",
    "\n",
    "\n",
    "### Questions for Analysis\n",
    " 1. Which genres are most popular from year to year? \n",
    " 2. What kinds of properties are associated with movies that have high revenues?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import pandas and other libraries that will be used in the data analysis process\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "plt.style.use('seaborn')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.set_option('display.max_rows', 22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>imdb_id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>homepage</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>...</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>http://www.jurassicworld.com/</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>The park is open.</td>\n",
       "      <td>...</td>\n",
       "      <td>Twenty-two years after the events of Jurassic ...</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Universal Studios|Amblin Entertainment|Legenda...</td>\n",
       "      <td>6/9/15</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>1.392446e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76341</td>\n",
       "      <td>tt1392190</td>\n",
       "      <td>28.419936</td>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...</td>\n",
       "      <td>http://www.madmaxmovie.com/</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>What a Lovely Day.</td>\n",
       "      <td>...</td>\n",
       "      <td>An apocalyptic story set in the furthest reach...</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Village Roadshow Pictures|Kennedy Miller Produ...</td>\n",
       "      <td>5/13/15</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>3.481613e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>262500</td>\n",
       "      <td>tt2908446</td>\n",
       "      <td>13.112507</td>\n",
       "      <td>110000000</td>\n",
       "      <td>295238201</td>\n",
       "      <td>Insurgent</td>\n",
       "      <td>Shailene Woodley|Theo James|Kate Winslet|Ansel...</td>\n",
       "      <td>http://www.thedivergentseries.movie/#insurgent</td>\n",
       "      <td>Robert Schwentke</td>\n",
       "      <td>One Choice Can Destroy You</td>\n",
       "      <td>...</td>\n",
       "      <td>Beatrice Prior must confront her inner demons ...</td>\n",
       "      <td>119</td>\n",
       "      <td>Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Summit Entertainment|Mandeville Films|Red Wago...</td>\n",
       "      <td>3/18/15</td>\n",
       "      <td>2480</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.012000e+08</td>\n",
       "      <td>2.716190e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>140607</td>\n",
       "      <td>tt2488496</td>\n",
       "      <td>11.173104</td>\n",
       "      <td>200000000</td>\n",
       "      <td>2068178225</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...</td>\n",
       "      <td>http://www.starwars.com/films/star-wars-episod...</td>\n",
       "      <td>J.J. Abrams</td>\n",
       "      <td>Every generation has a story.</td>\n",
       "      <td>...</td>\n",
       "      <td>Thirty years after defeating the Galactic Empi...</td>\n",
       "      <td>136</td>\n",
       "      <td>Action|Adventure|Science Fiction|Fantasy</td>\n",
       "      <td>Lucasfilm|Truenorth Productions|Bad Robot</td>\n",
       "      <td>12/15/15</td>\n",
       "      <td>5292</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.839999e+08</td>\n",
       "      <td>1.902723e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168259</td>\n",
       "      <td>tt2820852</td>\n",
       "      <td>9.335014</td>\n",
       "      <td>190000000</td>\n",
       "      <td>1506249360</td>\n",
       "      <td>Furious 7</td>\n",
       "      <td>Vin Diesel|Paul Walker|Jason Statham|Michelle ...</td>\n",
       "      <td>http://www.furious7.com/</td>\n",
       "      <td>James Wan</td>\n",
       "      <td>Vengeance Hits Home</td>\n",
       "      <td>...</td>\n",
       "      <td>Deckard Shaw seeks revenge against Dominic Tor...</td>\n",
       "      <td>137</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "      <td>Universal Pictures|Original Film|Media Rights ...</td>\n",
       "      <td>4/1/15</td>\n",
       "      <td>2947</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.747999e+08</td>\n",
       "      <td>1.385749e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id    imdb_id  popularity     budget     revenue  \\\n",
       "0  135397  tt0369610   32.985763  150000000  1513528810   \n",
       "1   76341  tt1392190   28.419936  150000000   378436354   \n",
       "2  262500  tt2908446   13.112507  110000000   295238201   \n",
       "3  140607  tt2488496   11.173104  200000000  2068178225   \n",
       "4  168259  tt2820852    9.335014  190000000  1506249360   \n",
       "\n",
       "                 original_title  \\\n",
       "0                Jurassic World   \n",
       "1            Mad Max: Fury Road   \n",
       "2                     Insurgent   \n",
       "3  Star Wars: The Force Awakens   \n",
       "4                     Furious 7   \n",
       "\n",
       "                                                cast  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "1  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...   \n",
       "2  Shailene Woodley|Theo James|Kate Winslet|Ansel...   \n",
       "3  Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...   \n",
       "4  Vin Diesel|Paul Walker|Jason Statham|Michelle ...   \n",
       "\n",
       "                                            homepage          director  \\\n",
       "0                      http://www.jurassicworld.com/   Colin Trevorrow   \n",
       "1                        http://www.madmaxmovie.com/     George Miller   \n",
       "2     http://www.thedivergentseries.movie/#insurgent  Robert Schwentke   \n",
       "3  http://www.starwars.com/films/star-wars-episod...       J.J. Abrams   \n",
       "4                           http://www.furious7.com/         James Wan   \n",
       "\n",
       "                         tagline      ...       \\\n",
       "0              The park is open.      ...        \n",
       "1             What a Lovely Day.      ...        \n",
       "2     One Choice Can Destroy You      ...        \n",
       "3  Every generation has a story.      ...        \n",
       "4            Vengeance Hits Home      ...        \n",
       "\n",
       "                                            overview runtime  \\\n",
       "0  Twenty-two years after the events of Jurassic ...     124   \n",
       "1  An apocalyptic story set in the furthest reach...     120   \n",
       "2  Beatrice Prior must confront her inner demons ...     119   \n",
       "3  Thirty years after defeating the Galactic Empi...     136   \n",
       "4  Deckard Shaw seeks revenge against Dominic Tor...     137   \n",
       "\n",
       "                                      genres  \\\n",
       "0  Action|Adventure|Science Fiction|Thriller   \n",
       "1  Action|Adventure|Science Fiction|Thriller   \n",
       "2         Adventure|Science Fiction|Thriller   \n",
       "3   Action|Adventure|Science Fiction|Fantasy   \n",
       "4                      Action|Crime|Thriller   \n",
       "\n",
       "                                production_companies release_date vote_count  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...       6/9/15       5562   \n",
       "1  Village Roadshow Pictures|Kennedy Miller Produ...      5/13/15       6185   \n",
       "2  Summit Entertainment|Mandeville Films|Red Wago...      3/18/15       2480   \n",
       "3          Lucasfilm|Truenorth Productions|Bad Robot     12/15/15       5292   \n",
       "4  Universal Pictures|Original Film|Media Rights ...       4/1/15       2947   \n",
       "\n",
       "   vote_average  release_year    budget_adj   revenue_adj  \n",
       "0           6.5          2015  1.379999e+08  1.392446e+09  \n",
       "1           7.1          2015  1.379999e+08  3.481613e+08  \n",
       "2           6.3          2015  1.012000e+08  2.716190e+08  \n",
       "3           7.5          2015  1.839999e+08  1.902723e+09  \n",
       "4           7.3          2015  1.747999e+08  1.385749e+09  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the data using pandas, and get an overview of the forst five rows using .head()\n",
    "moviesdf = pd.read_csv(\"Database_TMDb_movie_data/tmdb-movies.csv\")\n",
    "moviesdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check presence of duplicates in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviesdf.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is one duplicated row data in the dataframe, which therefore needs to be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop duplicated rows\n",
    "moviesdf.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get an overview of the shape of dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10865, 21)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviesdf.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There dataframe has 10865 entries of movie data and 21 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10865.000000</td>\n",
       "      <td>10865.000000</td>\n",
       "      <td>1.086500e+04</td>\n",
       "      <td>1.086500e+04</td>\n",
       "      <td>10865.000000</td>\n",
       "      <td>10865.000000</td>\n",
       "      <td>10865.000000</td>\n",
       "      <td>10865.000000</td>\n",
       "      <td>1.086500e+04</td>\n",
       "      <td>1.086500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66066.374413</td>\n",
       "      <td>0.646446</td>\n",
       "      <td>1.462429e+07</td>\n",
       "      <td>3.982690e+07</td>\n",
       "      <td>102.071790</td>\n",
       "      <td>217.399632</td>\n",
       "      <td>5.975012</td>\n",
       "      <td>2001.321859</td>\n",
       "      <td>1.754989e+07</td>\n",
       "      <td>5.136900e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>92134.091971</td>\n",
       "      <td>1.000231</td>\n",
       "      <td>3.091428e+07</td>\n",
       "      <td>1.170083e+08</td>\n",
       "      <td>31.382701</td>\n",
       "      <td>575.644627</td>\n",
       "      <td>0.935138</td>\n",
       "      <td>12.813260</td>\n",
       "      <td>3.430753e+07</td>\n",
       "      <td>1.446383e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10596.000000</td>\n",
       "      <td>0.207575</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20662.000000</td>\n",
       "      <td>0.383831</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75612.000000</td>\n",
       "      <td>0.713857</td>\n",
       "      <td>1.500000e+07</td>\n",
       "      <td>2.400000e+07</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2.085325e+07</td>\n",
       "      <td>3.370173e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>417859.000000</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>4.250000e+08</td>\n",
       "      <td>2.781506e+09</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>9767.000000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>4.250000e+08</td>\n",
       "      <td>2.827124e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id    popularity        budget       revenue       runtime  \\\n",
       "count   10865.000000  10865.000000  1.086500e+04  1.086500e+04  10865.000000   \n",
       "mean    66066.374413      0.646446  1.462429e+07  3.982690e+07    102.071790   \n",
       "std     92134.091971      1.000231  3.091428e+07  1.170083e+08     31.382701   \n",
       "min         5.000000      0.000065  0.000000e+00  0.000000e+00      0.000000   \n",
       "25%     10596.000000      0.207575  0.000000e+00  0.000000e+00     90.000000   \n",
       "50%     20662.000000      0.383831  0.000000e+00  0.000000e+00     99.000000   \n",
       "75%     75612.000000      0.713857  1.500000e+07  2.400000e+07    111.000000   \n",
       "max    417859.000000     32.985763  4.250000e+08  2.781506e+09    900.000000   \n",
       "\n",
       "         vote_count  vote_average  release_year    budget_adj   revenue_adj  \n",
       "count  10865.000000  10865.000000  10865.000000  1.086500e+04  1.086500e+04  \n",
       "mean     217.399632      5.975012   2001.321859  1.754989e+07  5.136900e+07  \n",
       "std      575.644627      0.935138     12.813260  3.430753e+07  1.446383e+08  \n",
       "min       10.000000      1.500000   1960.000000  0.000000e+00  0.000000e+00  \n",
       "25%       17.000000      5.400000   1995.000000  0.000000e+00  0.000000e+00  \n",
       "50%       38.000000      6.000000   2006.000000  0.000000e+00  0.000000e+00  \n",
       "75%      146.000000      6.600000   2011.000000  2.085325e+07  3.370173e+07  \n",
       "max     9767.000000      9.200000   2015.000000  4.250000e+08  2.827124e+09  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a summary of statistical overview of the dataframe\n",
    "moviesdf.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the summary statistics, the data has movies released from as early as 1960 to the year 2015. The longest of the movie has a runtime of 900. The average runtime of the movies is at 99. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10865 entries, 0 to 10865\n",
      "Data columns (total 21 columns):\n",
      "id                      10865 non-null int64\n",
      "imdb_id                 10855 non-null object\n",
      "popularity              10865 non-null float64\n",
      "budget                  10865 non-null int64\n",
      "revenue                 10865 non-null int64\n",
      "original_title          10865 non-null object\n",
      "cast                    10789 non-null object\n",
      "homepage                2936 non-null object\n",
      "director                10821 non-null object\n",
      "tagline                 8041 non-null object\n",
      "keywords                9372 non-null object\n",
      "overview                10861 non-null object\n",
      "runtime                 10865 non-null int64\n",
      "genres                  10842 non-null object\n",
      "production_companies    9835 non-null object\n",
      "release_date            10865 non-null object\n",
      "vote_count              10865 non-null int64\n",
      "vote_average            10865 non-null float64\n",
      "release_year            10865 non-null int64\n",
      "budget_adj              10865 non-null float64\n",
      "revenue_adj             10865 non-null float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "moviesdf.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop columns that will not be used in the data analysis process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "moviesdf.drop(['id', 'imdb_id', 'original_title', 'homepage', 'tagline', 'overview', 'keywords', 'release_date'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Universal Studios|Amblin Entertainment|Legenda...</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>1.392446e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.419936</td>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Village Roadshow Pictures|Kennedy Miller Produ...</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>3.481613e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.112507</td>\n",
       "      <td>110000000</td>\n",
       "      <td>295238201</td>\n",
       "      <td>Shailene Woodley|Theo James|Kate Winslet|Ansel...</td>\n",
       "      <td>Robert Schwentke</td>\n",
       "      <td>119</td>\n",
       "      <td>Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Summit Entertainment|Mandeville Films|Red Wago...</td>\n",
       "      <td>2480</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.012000e+08</td>\n",
       "      <td>2.716190e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.173104</td>\n",
       "      <td>200000000</td>\n",
       "      <td>2068178225</td>\n",
       "      <td>Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...</td>\n",
       "      <td>J.J. Abrams</td>\n",
       "      <td>136</td>\n",
       "      <td>Action|Adventure|Science Fiction|Fantasy</td>\n",
       "      <td>Lucasfilm|Truenorth Productions|Bad Robot</td>\n",
       "      <td>5292</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.839999e+08</td>\n",
       "      <td>1.902723e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.335014</td>\n",
       "      <td>190000000</td>\n",
       "      <td>1506249360</td>\n",
       "      <td>Vin Diesel|Paul Walker|Jason Statham|Michelle ...</td>\n",
       "      <td>James Wan</td>\n",
       "      <td>137</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "      <td>Universal Pictures|Original Film|Media Rights ...</td>\n",
       "      <td>2947</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.747999e+08</td>\n",
       "      <td>1.385749e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity     budget     revenue  \\\n",
       "0   32.985763  150000000  1513528810   \n",
       "1   28.419936  150000000   378436354   \n",
       "2   13.112507  110000000   295238201   \n",
       "3   11.173104  200000000  2068178225   \n",
       "4    9.335014  190000000  1506249360   \n",
       "\n",
       "                                                cast          director  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   Colin Trevorrow   \n",
       "1  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...     George Miller   \n",
       "2  Shailene Woodley|Theo James|Kate Winslet|Ansel...  Robert Schwentke   \n",
       "3  Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...       J.J. Abrams   \n",
       "4  Vin Diesel|Paul Walker|Jason Statham|Michelle ...         James Wan   \n",
       "\n",
       "   runtime                                     genres  \\\n",
       "0      124  Action|Adventure|Science Fiction|Thriller   \n",
       "1      120  Action|Adventure|Science Fiction|Thriller   \n",
       "2      119         Adventure|Science Fiction|Thriller   \n",
       "3      136   Action|Adventure|Science Fiction|Fantasy   \n",
       "4      137                      Action|Crime|Thriller   \n",
       "\n",
       "                                production_companies  vote_count  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...        5562   \n",
       "1  Village Roadshow Pictures|Kennedy Miller Produ...        6185   \n",
       "2  Summit Entertainment|Mandeville Films|Red Wago...        2480   \n",
       "3          Lucasfilm|Truenorth Productions|Bad Robot        5292   \n",
       "4  Universal Pictures|Original Film|Media Rights ...        2947   \n",
       "\n",
       "   vote_average  release_year    budget_adj   revenue_adj  \n",
       "0           6.5          2015  1.379999e+08  1.392446e+09  \n",
       "1           7.1          2015  1.379999e+08  3.481613e+08  \n",
       "2           6.3          2015  1.012000e+08  2.716190e+08  \n",
       "3           7.5          2015  1.839999e+08  1.902723e+09  \n",
       "4           7.3          2015  1.747999e+08  1.385749e+09  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviesdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10865, 13)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviesdf.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What genres are most popular from year to year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group the data by year (release_year) and then get the most popular of the grouped data for each year and asssign it into a new dataframe called populargenredf."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the genres and popularity columns and view an overview of how the data looks like using .head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>genres</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>Drama|Horror|Thriller</td>\n",
       "      <td>2.610362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>Adventure|Animation|Comedy|Family</td>\n",
       "      <td>2.631987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>Adventure|Action|Thriller</td>\n",
       "      <td>3.170651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>Action|Thriller|Adventure</td>\n",
       "      <td>2.508235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>Adventure|Action|Thriller</td>\n",
       "      <td>3.153791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         genres  popularity\n",
       "release_year                                               \n",
       "1960                      Drama|Horror|Thriller    2.610362\n",
       "1961          Adventure|Animation|Comedy|Family    2.631987\n",
       "1962                  Adventure|Action|Thriller    3.170651\n",
       "1963                  Action|Thriller|Adventure    2.508235\n",
       "1964                  Adventure|Action|Thriller    3.153791"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the data by year and query for the specific columns required in the analysis process\n",
    "groupedbyyeardf = moviesdf.groupby('release_year')\n",
    "populargenredf = groupedbyyeardf.first()[['genres','popularity']]\n",
    "populargenredf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The genres column features a combination of multiple genres separated by pipe(|). To get a particular genre, iterate over the column and separate on the pipe character."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import Counter() from python's collections module. We import this our imports cell at the very top of this jupyter notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a dictionary key, value pairs with the name of the genres as the keys and the number of times it features as the value.  Then we iterate over the dictionary to create two lists, one with the genres and the other with the corresponding counts of the times it occurs over the years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13, 4, 19, 30, 9, 7, 11, 28, 19, 3, 7, 2, 2, 10]\n",
      "['Drama', 'Horror', 'Thriller', 'Adventure', 'Animation', 'Comedy', 'Family', 'Action', 'Science Fiction', 'Mystery', 'Crime', 'Music', 'Romance', 'Fantasy']\n"
     ]
    }
   ],
   "source": [
    "#  create a counter to count how many times a particular genre appears over the years\n",
    "\n",
    "genres = populargenredf['genres']\n",
    "\n",
    "genrecount = Counter()\n",
    "for genre in genres:\n",
    "    genrecount.update(genre.split('|'))\n",
    "\n",
    "genrelist = []\n",
    "countlist = []\n",
    "\n",
    "for g in genrecount.items():\n",
    "    genrelist.append(g[0])\n",
    "    countlist.append(g[1])\n",
    "\n",
    "print(countlist)\n",
    "print(genrelist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have the genres and their corresponding count, we plot the results using matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAFcCAYAAAAkiW7CAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAA7tElEQVR4nO3dd7wcVfnH8U8aQiBgkICiINWvSpfemxSRIopYaJIfIIoKIk0pAgoICIqAgEAoCggoRUKVFhAJvYXyFREQBST0ElrC/f1xZsnkcsvcnZl772af9+uV192dvfPMyd7dM2fOnPOcIR0dHYQQQmgPQwe6ACGEEPpPVPohhNBGotIPIYQ2EpV+CCG0kaj0QwihjUSlH0IIbWT4QBcg9C9J/wfsAswJzAL8CzjA9m0DUJZ1gCsBAx3AEGAqcIjty2o43sHAPLa/14d9VgD2s72VpBWB/7O9a9Vlq4KkU4GTbd8l6UbgBNt/GuBihUEmKv02IulwYC1ga9tPZtvWA8ZLWt72vwegWI/ZXjZXxmWAWyQtbHvyAJRnBrbvBLbKni4BfGIAi9ObDYBTBroQYXCLSr9NSJoP2ANY1PYzje22r5e0JzB79nsfB04AFgRGAH+0fbikhYDrgCuAlYG5gf1tn5+1oFcFPgbcb3tbSfsDXyF1IT4BfNf2072V0/Z9kqYAn5T0MnAssD4wDbgN+KHt1yQ9AVwMrAl8GDjG9knZ1cMJtpfM/j8zPM+9H5sCPyFd7cwLnGX7wOz3jwPeyN6TfYBjgC8AhwJzSTqDdEUy2fZPsnjbAFvZ3rLTcZbI3s+PkK5mjrF9tqRzgbtt/zL7vV2BdW1/TdJmwAFZ2aYAe9m+tav3OXecw4D5gXMkbZ9t3kLSPsB8wLXAzrbfk7QacGT2/3sPONj2+E7l3gbYzfZq2fMFgYnAQsCi2Xv0EWAY8Bvb4yQNBX4FrAKMIl257WT7Fklnkj4ziwLjgcuyv+2w7H05wvafCbWLPv32sSrwcL7Cb7D9e9sPZ09/D4yzvTywEvB5SVtnry0CXG17JWBf4KhcmE8Cn8sq/O2BpYCVslb8FcBpRQop6cukiughUsU3P7BM9m8ocHTu10cCKwLrAIdKWqrgMYYAPwJ2sL0CqZL6saR5sl9ZEviG7WWAtwFsPwUcBNxse0fgROBbkhoNp28DJ3c6znDgL8DxtpcmnTgOl7QqcCqwQ+7XdwROlbQ4cDiwie3lSF1xF0maPfu999/n/LFs7w88DWyT66obRfq7fyY79uqSRgNnANvZ/hywOXBSVqnnXQgsKumz2fOdgLNIf5s/kbq8lgfWBvaStAqpMTA/sKrtz2a/v18u5kjbS9jeFzgEODaLMRZYj9AvoqXfPoaQWlQASBoF3Jw9nQO4ADiM9CWeW9LPcq8tC9wOvEuqwAHuJrXcGibanpo93pR0wrhTEqTW3MhuyrWopHuzxyOAp4AtbE+R9AXS1cS7WZmPBy7J7Xui7Q7gP5KuAjYE7urtjbDdkbWmN5X0TVKlOITsagd4qtH91UOMeyU9DnxR0j9Ild01nX7tU8Csti/K9nla0p+BjYGDgVmzewZTgDGkK6nvkFry12XvHaSKdrHscf597s35tqcBUyQ9SrqiaVwpXJKL3wEsDbzfvWf7HUmnATtL2gv4Fqlr8FOk1vq43P6zActlV1oHAN+WtCjpZPxarjx/yz2+ADgx+ztcS7rqCv2g10pf0izAp23fn31BliOdoT/QYgyD2m3ApyV9xPYLtl8jVebv3+AkVc5DgNVsT8lemwd4K3v9HdvvZfEaN14bXs89HgYcafukLMaHgNHdlGuGPv1OOl+JDiWdGBqmdnptWhflmqVz0KzVfA+pe+hmYBzwpdx+r3fepxsnklqp/wB+l52Aeir/+/+H7MRzOrA96Wri9GzbMOA621/LlXcBUit+yz6UDdJJuqHxvgwjXfGtnIs/P9DV/ZNTSCf7CcAk209kV1Mvd7oPMx/wiqQvkrp9jgEuBR4B8lck75fd9imSLiOdqDcGDpa0tO1X+vD/C00o0r3zB2ArSSuTLsleJV22hRaS9acfB1yYv5TPHq8OTLP9Kqnfds/stQ8DtwBb9PFwVwM7SZoze34oqduor64GdpU0Iusv3g34a+717XP/hw1JI4EmAwtKmjfrxvlSF3EXJ41eOiAbJbQ28CFShdiTqcx40vkTqRH0FdKJozMD72RdVo3K9Su5/8OZpO6Vr5K6XACuBzaU9Olsn02A+4FZeylbV+XrykRgcUlrZfGXBR4lXanMWPh0Y/9WUj/9Sbn/01uSts32XwCYBCxPupF8WXayv4P03nf5nkr6O+nq4ExSF9aH6b5hECpUpNJf2PZBpA/rabZ/RvxxWlLW73s66WbfPZImAReRuiV+nP3aN4FVJD1Aujo4z/Y5fTzUaaSbdRMlPUjqOvhWE0X+OfAscC/wMKlC2z33+sKS7gKuAn7g5CFSC/VOUgXX1RXp/Vn5HpF0N6nifYjpXSjduZV0tXQxpC4QUsV/q+3nO/9y1i31JWB3SfeTujEOtX1D9vqzpG6y+xs3uW0/SKoE/yjpPuBnwOa23+ilbJC6vs6XtGF3v5CNiPoKcHQW//ek/v3uurPOIFXcV+T+z1uQTur3kz47B9q+hXRPY+1s+63AY6S/UVf1zD6k+zD3ADeQhuk+UeD/GEoa0ltq5eyPsgHwd+DLwPOky88l6i9eCF3LRu9slQ2pHKgyzA7cRBqZ1O/zHOqWVdYnAE/aPnKgyxOqUaSlfxSpxXe57UmkD/mhtZYqhEFO0kakm843zKQV/ijgBdJN2xMGuDihQkVa+j+3fUDu+bBsREAIIYQWU6Slv1l2QwyAqPBDCKF1FRmn/wLTb3i92dhoe2xtpQohhFCLIpV+DM8MIYSZRK99+gCS5ibNVmxM7ljY9vU1l43Jk1+rddX20aNH8tJLU1ombp2xI27EjbitH7dhzJhRQ7p7rciM3MNJk2JGkIZrfpw0BnrlnvZrBcOH9zYXZ3DFrTN2xI24Ebf14xZR5EbuN4AFgPOBdYHP0/WU7RBCCINckUr/mWx6/iRgmWw24Xz1FiuEEEIditzIfUXSdqTshd+X9DSRhiGEEFpSkZb+/wHz2r6RtBjGKaQ85yGEEFpMry39LAf4yZKWBvYGZiuY/CmEEMIgU2T0zvqk1v0wYDXgPknb2u68YETn/YaRVgcSKZf3rqS87GdmzyeRlmN7r7sYIYQQqlWke+dwYA3SwgnPkFbDObrHPZLNAGyvTuoOOoy0JuYBttckjfnva572EEIIJRSp9Idmeb8ByPKV98r2JaS84JDW9XyZtNDChGzblaThnyGEEPpJkdE7/5G0KdCRraS0G7m1NHtie6qks0jLvG0FbJBbUu41YK6e9h89emTtkxjGjBnVUnGbib3Zjy4tfczLjmn+oqzV3uOIG3FbOW5vilT63yYts7cAaSWc65negu+V7R0k7UvKyT9b7qVRpNZ/t+qcpgzpTZ88+bXef3GQxK07dk+aPWarvccRN+K2ctx8/O4UqfTXBrbPln4rLBvb/wnbRwBTgPeAOyWtkw3//AJpmbQQQgj9pEif/heARyWdKGnFPsS+CFhO0k2kBa73IHUNHSLpVmAW0vqiIYQQ+kmRcfpjs7VAtyRV2PMB5wFn236uh/3eALbu4qW1my1sCCGEcoq09BsV+JOkG7hzAksD10n6Xo1lCyGEULEik7MOI2XafBwYB+xh+y1Jc2bbYtHkEEJoEUVu5E4D1rf9eH6j7VclbVxPsUIIIdShSJ/+QT28dke1xQkhhFCnQn36IYQQZg5R6YcQQhsp0qePpG8CS5CSpm1l++xaSxVCCKEWvbb0Jf0C2AT4MukksaOkY+ouWAghhOoV6d7ZCNgOeCtbK3cD0izdEEIILaZI905jkZNGdswP5baF0PbG/uL6SuKM22+9SuKE0JMiLf0LgPOBuSXtAdwEnFtnoUIIIdSjyDj9IyVtRErDsCDwU9vjay9ZCCGEyhUdsvlv4C/AJcCrktaqrUQhhBBqUyT3zu9IN27/mdvcAUQHZAghtJgiN3LXBxa1/U7dhQkhhFCvIt07/2bGZQ5DCCG0qG5b+pLOIHXjDAfuy1bAmtp43fbY+osXQgihSj1179yY/ZzQxWsdXWwLIYQwyHVb6ds+C0DSj7PFzd8n6fC6CxZCCKF6PXXv/AKYF9hc0uKd9lkF+EnNZQshhFCxnrp3/gx8ljR6J9/FMxX4WZ2FCiGEUI+eunfuAO6QdIntV/qxTCGEEGrS65DNqPBDCGHmEStnhRBCGymyiMoZ/VGQEEII9SvS0l9S0hy1lySEEELtii6i8m9JBt5sbLQdCddCCKHFFKn092kmsKQRwDhgIdJqWz8HngLGA49mv3aS7fObiR9CCKHviozemUAam/8ZYCLQkW3rzbbAC7bXBDYGTgCWB461vU72Lyr8EELoR0Vu5O5OaqXvCcwBnCJprwKxLwQOzB4PIZ04lge+KOkmSadLGtVcsUMIITSjSPfOt4CVgdtsvyBpReB24Jc97WT7dYCsYv8TcACpm+c023dJ2h/4KdDtCWT06JEMHz6syP+jaWPG1HPeqStu3bHrOGarvccD8f6WOW6rvQ8Rt964vSlS6U+z/Y6kxvO3gGlFgktaALgY+K3tcyV92PbL2csXA8f3tP9LL00pcpimjRkzismTX2uZuHXH7kmzx2y193ig3l9o7j1utfch4tYbNx+/O0WGbE6Q9EtgdklfIq2Ve11vO0maD7gG2Nf2uGzz1ZJWyh6vD9xV4PghhBAqUqSlvzewM3AfsD1wBXBygf1+AowGDpTU6NvfE/iVpHeBZ4Fd+lziEEIITStS6R8L/MH2KX0JbHt3YPcuXlq9L3FCCCFUp0il/yjwa0lzA+eSTgBP1FqqEEIItSgyTv9E22uQxtq/BVwi6W+1lyyEEELlCmXZlDQX8HlgQ9LVwdV1FiqEEEI9eu3ekXQZsBxpiOWBtm+rvVQhhBBqUaRP/3fAlban1l2YEEII9SpS6d8GnCNp/ez3bwB2tf2/WksWQgihckX69E8G7gAWIWXMvBU4vcYyhRBCqEmRlv4itr+ce36UpO3qKlAIIYT6FGnpd2Q5dACQtCDwbn1FCiGEUJciLf0DgVsl3UZKkbwykT4hhBBaUq+Vvu3xkpYDViJdGexq+7naSxZCCKFyRVr62J4MXF5zWUIIIdSs0IzcEEIIM4eo9EMIoY0UScOwKLAKKcPmKaSUDD+0HUnXQgihxRRp6Z8BvANsAXyKtBBKj+vjhhBCGJyKVPqz2r4Q2BQ4x/bNwIh6ixVCCKEORSr9aZK+Qqr0x2fr5BZaGD2EEMLgUqTS3wX4IrCb7WeArwM71VqqEEIItSgyTv8Y2xs2ntj+eo3lCSGEUKMiLf3Z8rl3QgghtK4iLf15gCckPQe8Scq/02F7kVpLFkIIoXJFKv2Nay9FCCGEflGk0n8W2ASYg9TKHwYsDBxUY7lCCCHUoEilfxEwElgMuBlYi7R6VgghhBZT5EaugPWAi4GjSCmWP15noUIIIdSjSEv/f7Y7JD0CLG37bEkf6m0nSSOAcaR1dT8E/Bx4CDgT6AAmkcb+v9dk2UMIIfRRkZb+g5KOB24EfihpP4qlYdgWeMH2mqSbwScAxwIHZNuGkPL5hBBC6CdFKv3vABfYfgj4KfAx4JsF9ruQtNQipAp+KrA8MCHbdiXw+T6VNoQQQilFlkucJuk9SbuSMm6+bHtSgf1eB5A0CvgTcADwS9sd2a+8BszVU4zRo0cyfPiw3g5Vypgxo1oqbt2x6zhmq73HA/H+ljluq70PEbfeuL0pkk9/d+BLpJu3FwKnSDrddq/plbOZvBcDv7V9rqSjci+PAl7uaf+XXprS2yFKGTNmFJMnv9YyceuO3ZNmj9lq7/FAvb/Q3Hvcau9DxK03bj5+d4p073wL2Ah4w/YLwIrA2N52kjQfcA2wr+1x2eZ7JK2TPf4CaQhoCCGEflJk9M402+9Iajx/i2KplX8CjAYOlNTo298d+I2kWYCHSd0+IYQQ+kmRSn+CpF8Cs2e59HcBruttJ9u7kyr5ztbuUwlDCCFUpkj3zt7Ao8B9wHbAFcBedRYqhBBCPXqt9LPJU7cBE0nDLa+1PbXugoUQQqhekdE7ewHfBi4lJVu7TNLhts+ou3DtZuwvri8dY9x+61VQkjAYVPF5gPhMhBkV6dPfBVje9qsAkg4FbiGN2Q8hhNBCivTpvwi8m3v+OmliVQghhBZTpKX/GHCrpPNIqRS2BF6VdBCA7UNrLF8IIYQKFan0/5H9mzV7/tfs55BaShRCCKE2RXLvHCJpXmANUkv/Ztsv1V6yEEIIleu1T1/SNsD9pMyaOwKTJG1Sd8FCCCFUr0j3zoGk0Tv/BZD0SeAy0iStEEIILaTI6J1XgWcaT2w/CbxTW4lCCCHUpkhL/wHgCklnkPr0twaekbQ9gO2zayxfCCGEChWp9IeSWvobZ8+nZP/WJa11G5V+CCG0iCKjd3bsvE3SbLbfrKdIIYQQ6lIk985XgIOAOUhj84cBI4Ex9RYthBBC1YrcyD0K2IO06Mk2pJw759dYphBCCDUpUum/ZPsGUmrluWwfDKxaa6lCCCHUokil/6akT5Fa+utkSx3OVW+xQggh1KFIpX8A8HNgPLA+8D/g4joLFUIIoR5FRu9MIK2YBbCipNGReyeEEFpTkZb+DKLCDyGE1tXnSj+EEELriko/hBDaSJHJWZ8ETgMWAtYCzgHG2n6i1pKFEEKoXJGW/inA0aR1cZ8FziPy7YQQQksqUunPY/saYIjtDtunAnPWXK4QQgg1KDo56xOkjJpIWgN4u9ZShRBCqEWR1Mo/JE3MWlTSvcDcwFeLBJe0MnCk7XUkLZfFeTR7+STbkcMnhBD6UZFKfz5gReBTpAybj9judeUsSfsA2wFvZJuWB461fUyTZQ0hhFBSkUr/KNuXAw/2MfZjwJeB32fPlwckaQtSa38P26/1MWYIIYQSilT6j0kaB9wGvL9wSm/LJNr+s6SFcptuB06zfZek/YGfAnv1FGP06JEMHz6sQBGbN2bMqJaKO1DHLRN3MJZpIOIO1HGbjTvYyhNxq1Gk0n+BtHjKKrltzSyTeLHtlxuPgeN72+Gll6b08RB9M2bMKCZPrv5io664RdR13Gbjttp7HH+7pNXe34j7wfjdaWq5xCZdLen7tm8nZeu8q6K4IYQQCioyI3cjUmrluUktfgBsL9LHY30HOF7Su6RJXrv0cf8QQgglFeneOR7YE5hENla/qCxVwyrZ47uB1ftYvhBCCBUqUuk/b3t87SUJIYRQuyKV/s2SjgWuAt5qbLR9U22lCiGEUIsilf5K2c/lcts6gPWqL04IIYQ6FRm9s25/FCSEEEL9+ppPf03gXCKffgghtKS+5NN/HfgfkU8/hBBaVl/y6RP59EMIobVFPv0QQmgjRUbv7MkH8+lvXWehQggh1KPI6J07JPU5n/5gMPYX15eOMW6/GJkaQph5FBm9M67Tpg5JbwIPA6e2ygkghBBCsT79acBcwCXZv9mAeUkt/5PrKlgIIYTqFenTX872Co0nki4DbrO9taT76itaCCGEqhVp6c8u6aO55/OSWvtQ7KQRQghhkChSaf8UuEvS30k3clcAdpd0MPDXGssWQgihYkVG71wg6XpSCoZpwC62n5c0wfaLtZcwhBBCZQp1z9h+nrSubX5bVPghhNBiivTphxBCmElEpR9CCG2kyOSseYFtgDlIC6MPAxa2vX3NZQshhFCxIi39i4BlgW2B2YHNgfdqLFMIIYSaFE2tvANwGekEsA6wRJ2FCiGEUI8ilf5L2U8Dy9h+BRhRX5FCCCHUpciQzeslXQjsBVwj6XPAW/UWK4TqVZF1FSLzamhtvbb0be8P7Gf7SeAbpBb/l+suWAghhOoVnZz1WPbzbuDuWksUQgihNrUmTJO0MnCk7XUkLQacSVp2cRKwm+0YBRRCCP2otslZkvYBTgNmzTYdCxxge03SeP8t6jp2CCGErvVa6UuaW9Lns8c/lnShpM8WiP0YM/b9Lw9MyB5fCXy+r4UNIYRQTpGW/nnAp7OK/6vAXyiwYpbtPwPv5jYNsd2RPX6NtBpXCCGEflSkT3+07RMkHQ+cafv3knZv4lj5/vtRwMu9Hnj0SIYPH9bEoaozZsyoft2vrLqOWybuYCzTYDzuYIs72MrTKnE3+9GllRz3smPq6QEvUukPlbQ88CVgbUnLFtyvs3skrWP7RuALwA297fDSS1OaOEy1Jk9+rc/7jBkzqqn9qlDXcZuNW9d7Ee9xvXFb7e/WanGLKHPcnk5URbp39gWOBn5p+1+krp0fNlGOHwGHSLoVmAX4UxMxQgghlFBk5azrJN0GLCJpCLC+7TeKBLf9BLBK9vgfwNolyhpCCKGkIqN31gPuBS4FPgo8LmnDmssVQgihBkW6d44A1gBetv0MKcvm0XUWKoQQQj0K3ci1/awkAGw/1Hgc2lsVCcwieVkI/atIpf8fSZsCHZI+DOwG/LvWUoUQQqhFke6db5OWS1wA+BdpFa1daixTCCGEmhQZvfMcKaVyCCGEFldkYfStgB8Do/PbbS9SV6FCCCHUo0if/jHAdsCTNZclhBBCzYpU+v8E/ha570MIofUVbenfIGkCMLWx0fahtZUqhBBCLYqM3jmMlBt/Gmnxk8a/EEIILaZIS3+E7bG1lySEEELtilT64yV9D7gKeKex0XZM0AohhBZTpNL/WvbzR7ltHUAM2QwhhBZTZHLWwv1RkBBCCPXrttKXdLDtgyWdQWrZzyD6+UMIofX01NK/K/t5Yz+UI4T3VZG9EyKDZ3+Lv1tr6LbSt31Z9nB+20fkX5N0eK2lCiGEUIueund+AcwLbC5p8U77rAL8pOayhRBCqFhP3Tt/Bj4LrA9MyG2fCvyszkKFEEKoR0/dO3cAd0i6xPYr/VimEEIINek1DUNU+CGEMPPottKXNHt/FiSEEEL9emrp3wgg6bf9U5QQQgh16+lG7hyS/gBsLGnWzi/G5KwQQmg9PVX6GwLrAmsy4+idEEIILaqn0TtPAWdLug94CFD2+5NsT+1uvxBCCINXoXz6wKPAC6R7APNJ2tL2bc0cUNLdwKvZ08dt79hMnBBCCH1XpNI/Dvhao5KXtApwPLBSXw+W3RsYYnudvu4bQgihvCLLJc6Rb9Xbngh84MZuQcsAIyVdI+n67AQSQgihnxRp6b8oaQvblwJI+hKpq6cZU4BfAqcBiwNXSlJ39whGjx7J8OHDmjxUNcaMGdWv+5VV13FbLW6dsdslbqt9hgfb+zdYj1uk0t8F+IOk00kLoj8GbNvk8f4B/NN2B/APSS8AHwOe6uqXX3ppSpOHqc7kya/1eZ8xY0Y1tV8V6jpuq8WtM3Y7xG21z3Bd5W2196GhpxNGkZWzHgVWzmboDrVd5h0YCywFfFfS/MCcwDMl4oUQQuiDIi19AGy/UcHxTgfOlPQ30mpcY2P4Zwgh9J/ClX4VbL8DfLM/jxlCCGG6XkfvSNq1PwoSQgihfkWGbH6v9lKEEELoF0W6d56SdD1wG/BmY6PtQ2sr1SBXxQLQsfhzCGEgFKn0J+YeD6mrICGEEOpXZMjmIdlwzUWBScBsFY3kCSGE0M+K3MhdD7gPuBSYD3hC0oZ1FyyEEEL1itzIPQJYA3jZ9jPA2sDRtZYqhBBCLYpU+kNtP9t4YvuhGssTQgihRkVu5P5H0qZAh6QPA7sB/661VCGEEGpRpNL/Nimn/gLAv4DrSEnYQggtqIohxxDDjltVkdE7zwHfkDQn8K7tN3vbJ4QQwuDUa6UvaSngLGDB7PkjwA62H6u5bCGEECpW5EbuycD+tuexPQ9wDDCu3mKFEEKoQ5FKfzbbVzae2L6YlAc/hBBCi+m2e0fSgtnD+yTtR8qFPxXYBri5H8oWQgihYj316U8gLXQyBFiHNIqnoQP4QX3FCiGEUIduK33bC/dnQUIIoSt1DTFt16GrRUbviDQuf3R+u+2xdRUqhBBCPYpMzroY+CNwf81lCSGEULMilf7L7bxgSgghzEyKVPpnSjqMlH5hamOj7ZtqK1UIIYRaFKn01wFWBFbLbesAWuvuRQghhEKV/gq2F6+9JCGEEGpXZEbuA5KWrr0kIYQQalekpb8IcI+kZ4B3SJO1OmwvUmvJQgghVK5Ipf+lugsRQgihfxSp9NfuZvvZfT2YpKHAb4FlgLeBnWz/s69xQgghNKdIpb9u7vEIYE3gJpqo9ElXDbPaXlXSKqQ0zVs0ESeEEEITiqyctWP+uaS5gfObPN4awFVZ3ImSVmgyTgghhCYUGb3T2evAQk0eb07gldzzaZKKXG2EEEKowJCOjo4ef0HSDaTJWJBG7iwCXG77u309mKRjgYm2L8ie/8f2J/oaJ4QQQnOKtLIPzj3uAJ63/VCTx7sF2Ay4IOvTf6DJOCGEEJrQbUs/t3JWl2z/u68Hy43eWZp01bCj7Uf6GieEEEJzeqr0H2f6ylkNHcD8wAjbw+ovXgghhCoVXjlL0hykIZYbATvXXK4QQgg1KDR6R9L6TF9EZSnbf62vSCGEEOrS441cSbMDx5K17qOyDyGE1tZtSz9r3TdG1ywZFX4IIbS+nm7kvge8CzzN9HH6EFk2+5Wk3WyfWFPsT5BGUl0NzG/7qYrirg3MTW4QgO2LSsbcDBhvu+eJJYOEpJHAV/ng+3DsYIzbqiTNYft1SR8C5rL93ECXqTuSlrI94MPUe+reWbiH12YakoYAi9l+tGSc62yvX1Gx8r4DVF7pS/oicBIwjbQq2sOStrF9acm4Z5NWVfsn0xsLHUCpSh/4PnC8pNOA02w/WzLe+yTdTRpKfK7tKRWFPZ800u0BZnwfBmvcut6HRuyVgOWAM4Dlbd9aQcytgcOAxYFPArdIGmv7spJx5wCOBD5NOsEeAfzI9usli/wnSc+TvncX2H6nZLym9Dojd2YjaVfgKGD23ObJtj9aMu5dwFq23ygTp4u4fwZeBG4mpcAAKmk5305KdneF7eUkLQucZXuZknEfJ93sL/sF6Sr2oqSRY98EJgIn276+grirAd8GNgT+DJxk+8GSMR8FPmN7aq+/PAjiZrErfx+yuN8C9gZmBVYFJgH72z61ZNz7gC/a/k/2fAHgUtufKxl3HPAM6fuxEnA6qXfjm2XiZrHXA3YhJa48BzjF9mNl4/ZFM7l3Wt2+wAbA5aSWx0HAxRXEfQN4UtL1kv7S+FdB3LmBxYAdSa3d7wPfqyDuUNvPNJ7YvpdqWoz/rqPCB8i+HPsDPwRWAP4o6QFJa5SM+3fbOwCfAR4C/iLpZklfLRG2kq6yfoxb1/sA8ANSZf9q1v2yPLBHyZgAwxoVPkDWPVlFnbac7f2Bd7Mrnm2AZSuIi+3rbX8d2A7YinSFfbmkT1cRv4h2THb2ou3bJN0LzGf7sKyVXtbpFcT4ANvr9v5bTZmSzbruAJC0JvBWBXFvkfRH4DLgzcbGCq5MFiO18rcjDR/eAxgPrEJqMZXqjpT04Sz2/5GSAl4AbCdpM9vbNxHyAeAGSVcx4/tQtu+9rrhALe8DwDTbr0pqlPUpSVVcqTwn6dtkLXFgB+B/FcSd1un5MOC9skEljSKdQHYGRgK/Bs4CvkC6slqi7DGKaMdK/11Jo4FHSZdufwXmKBvU9lmSPgmsQ1p34MYqFoiR9FHSh3pxUmrq3wM7VNCnvR9wDfAxSbdm8b9SMiakFh3ATrltVfTp3w6cCazd6f7L3yVNKBNY0jnAJqSTyHca/c2STgKavTE4J+m+xmK5bVVcSdUVt673AeDFrPuw0cDYhtRlWdauwHmke14dwF2kSrWsmyQdCcwmaSPS1fUNFcT9L3AtsK/ta3Pb/5idvPpFO/bpjyW1YjYD7iV9mP9ru9RiLtmH4w/A30gtgzWBb1VwY/Qi4EpSl84KwM9JQ2i/WDLufKTVy1bNyjvR9vNlYnaKPxwYYvvdiuLNZvvN3n+zqdg/AU61PbmL1z5j++ESsT9JSltS6QpxdcSt633Iui7+BCwKvES6otyiqpEsWSNuqu3XKoo3gtQo2pT03bgK+LntUlfCkhbIj5CTNKKq70dftF2lD2nSme03JH2cVJFeXcEf9HZSJf9Q9nwJ4A+2lysZ927bn5N0TyOWpAdsL1Uy7iO2K+9HlDQv6ZJ1PdKV5ARgW9tPNxkvn9r7A2yv10zcLPaXe3q9TJdU1h11KWmkzVDgedJNx1IJBuuKm8Wu5TORxR4GfIpUibpMZSdpH9tHSTqeLj4btn/QfEnfP8Zatm9SWjRqLduXVBBzDVJPwFGkgQifJiWdbHZRqqa0XfeOpNmAzbI/ZsNY0lC1MmbJp5y2/WD2QS/rPaXspMD7/YJV3Kx6MhutMdF26f7KnBNIH+hvkL7gPyANUWv2SuqE7OeWwFzAOGAqqd/55TIFJV22d6dsl9QJwFG2zwKQtCPpM9b0SarmuFDTZyL7zm1BGpQAsJYkbDf7nWssxFTZlWmepMNIw5jXJfW97ydpSds/Lxn6aOBA0rKxzwJfJt0ziUq/ZpcBHwYez23roHyl/6akFWzfCaC0FGQVY50vIt2onCvr99uJ9EEp6zOkrqh3Jb3N9El3c5aM+ynbW+ee/1RS08P+bP8ZQNLewGqNykjS5UCpsd413iSHNEjgrNyxzpC05yCOC/V9Jir9ztk+Jft5SGObpIWBBWzf1Hwx37cFaWQftv+jNNnwLlLXahnDbF8r6VTgEttPVNQw7JN2rPQ/DnzW1c/s3AcYn42jBhBpYkcptg+XtD2pdb8B8DvgtLJxSfcc6jBC0qyN7jKlGaRVvNfzkMZ5N06ko5jecmyKpF/b3kPSZXTdTbB5ifDDJc1t+8XsWPN0dYxBFBfq+0zU8p1TmnOzFulq8lbgFUkX2f5xydCd+9rfoYLRO8AwpUlqXwQOk7QkadBHv2rHSv8BYD7S5VWV3gA+C6xMqqAn2n6hisC2zwbOriJWzke62f5kybh/BK6VdEb2fEfSTbyyzgVuy25sDyGdUH9XMuZ12c8qytfZ8cBESY1L968BvxrEcbH9pKStSGPSDyfdbD2vgtB1fed2IlWgXwX+AnyX1LVYttK/JRvJlB8KelvJmJBmD58LnJ618h8Hdq8gbp+03Y3cbKLJqaQP4vtn8zI3BLO4D9v+TMni5eO9RtctuEouubMPXMMswMeAO22vVCZuFntHYGPSye9q0oe89AdN0ubA50nvy1W2rywbs1P8kfnnLpmKQNK65N6HTsP0BmPc/UhXkwuQRnXdApxj+2cl49b1nbvD9opKs9bPt31BfsBDibizAz8D1ifdP7oWOKTs56GL4wyz3XlOQO3asaV/FKkVU/XU5/slfZPUJ5pPl9DseOQlKylVN/zBRXJWIQ1lbYqkObMJOHOTRpfkh6qOpppx2c8CD5LG65eaap8n6YekVtiHsk1DSCeWPve3Svq07UckfY50w/H83Gufs313k2WsJW4nXyddqU60/UL2mbiVVAGWUdd37m1J+wBrAztJ+g7pirsUp1QqVd0neZ+kxUlDr+cgfcaGSVrM9upVH6sn7Vjpv2b7qBribsEH+/Cbqjgyy/fyetlumBnYniipzM3sG0kV8fN0kZWV5t8H4P2rh71I/foXA5dKKp2/JbMnaWZvFZXSL0nju//cxWsdQLPZaeuKm/eu7bc1febsy5KqGEde13duJ9JnYgfbL2VDInfqZZ9eZTduD+aDmUyXLhn6XOBO0sig80hzharIBtAn7Vjpj5e0G2lUzNuNjSVa5A2r267yD1jncEKyFmPDENJ8hdmajefpSa6Gdx7u12l4bLO+T+pymGD7OUnLkybNVFHpP2r7/t5/rXe2N80erulcXhh4f+7GoIrbyVNK2Vc7lFIV70U1jYtavnPZ3ISdcs+rmI0LaYbvOOBuqrtJDjDK9nck/Zo04fI3pIwA/aodK/0fki7jj89tK90SJc3GraxPv+bhhDBji7GDNDP5OxXEvZMPdr38jXSTu4y68rcAnJDdGL2GGfuc+3zzPHeCu1zSOky/0pkFuISU7qLP6orbyfdIaT6WJnWTTCRlNC2rlu+cpHx66fdV0CJ/x/WsT9A4yf2TNKv+jhiy2Q9sN92a7UWlffo1DyeErluMTVfMkq4DVgRGSno199Iw4J5m4+bUlb8FYDfS6JL8jdwOmhsxdR7pZihAfvTWNMpdndUVN2+I7fWzG9rDbL9W5jPRUON3Lp9tdhbSpKemZn53Mkn1LHjyaNbKPws4XSlv/4d63qV6bVfpS5qFNMzr/ZsppEVU9i8Zuuo+/VqGE/bSYryU5luMW5L6QMeRhmk2TCXlJi9rd7L8LZKeJsvfUkFcgAVtV9FSxvZGkHKy2x5bRcw642YxG5+JK/KfCaX8TE1/JiRta/sP3U0eK9uatj1Doj1J1wJ/J92UL2MR4C5JTzJjJtOyVxDfAb5g+55sgtaGpIyb/artKn3SqIdFSEMU7yGNVrixbFDbs5aN0SleY/Wf7V3tily1tBhtvwq8Sqd0AMpWJiNlNW1aNnJlGSrK39LJE5Lmd5P5gbpxkKTf2v6uUp/UkcC3bZdN/VtH3LquIhoni67yRNUxVvwjpJxEZZVtAHbnx7YPBLB9EnCSpONIJ6p+046V/rKkD+NJwLGkVk3ZFAwo5cfZi5QbewSpf/hwl1/h6MPKEsSVLSPU22LM4n6blGNkhpXJgLIrk1WdvyXvTdIl/R3MeKOxTBfamaQJQ5Buht5IWipwkxIxa4lb49XJT7OHz1YwS/YDOvXpDyXNLyg7YQ/bE7Krn9nJ9QaUKOchpGHLX5M0V+6lEcDm9PMErXas9J+2PVXSP0g3Uy7sPCmnSUcAywDHkT6Au5CG2e1RMm5jRa77mfFeQak+fdtjldLzdh6WVna8d2OCz/7AAaRhaZ8oGRPqy5kE6aZ2V0Mhy5jH9m8AspQUv5a0wyCOC/B/khax/S+lDKRLAsfZfqW3HXuxKeVnyb4v1x11MNMr/cnZ44e62qeP8Q9lenmnkro+H6LrK5YibiPd73qPGa+kplLNGhZ90o6V/hvZDdf7gJ0lPUL3KQn6YmNghUaXg1JCsPsqiFvLilySfkHKWfIcMy6wXXa8d10rk9WVMwnnEpjBDF1SZQzPdxll/eNDetlnIOMCnJzFPI6UzfNq0udvq5Jx/yXpGj44yKHZPv38XJDO//cqRuJtDyxI6gnYm5QOedOeduiJ7StI90uutH17ybKV1o6V/m6kmyf7kmagTgB+UkHcofk+5mySS+k+Z6cVuYaRVkyq6ssNKWfLYhX3Y0NNK5NRX/6WurqkjgXuVVrWENKU/r1LxOsqbgcpLUUVcSFNCFyJdLV2lu0fZ11eZTVGWS1MmlxXdlnOs4DVSTeZz3AupXlFnrP9jKSHgWWym9F7VBD3daWcVJ2vrsuOxOuTdqz0x9reJ3v8tQrj3ivpV0zP/74baS3XUrKp5ceSLjGhohmuwFM1VPiQ+lTHk61MJmlLoPQCH8CFwCNZP25l+VsylXdJ2R6XXeGsR7qM/yep7/bcCuLeyfS8MEfbnlQmZs5Q2+9J2oCUNgFmPBH2STZS7lTgYmeLkEgaT7pv0vTMWds7Zl2yXwaOy4Y+/h441/bLzcbNeVfSooCBNSVdTeqTL+ts0k3bCdRzI7uQdqz0K+1fzNmNNPnk70xPNNbTrNqi9gZWtX1vBbHyrpN0FKm1lB+WVqpPP6uUzndamWxVspXJyhUVqC9/C9TXJfVvUsv2u6Srnd80G0gz5t6BVHEAzKLqcu/8U9IVpC6+G5UyTZbpojyUdIWaH52yC+k+zMGkE2xTnJKf/QH4g6RPkBbVuUHSP2yXbcwdQWq8bE7Kob8DcHnJmAAfcgWrepXVjpV+1f2Ljf1fJX04qvZiDRU+wLeyn/m5BaX79FXfymR15W+BiruksqGUPwS2BZ4gpbdYqOQN0WNI80vqzL2zI2m+xd9svyvpZsql9N4UWNG5tY1tP620PsStlKj0OxmT/ZuHcgu4A2B7POlqlWyY8OK2q7g/96ikj9muYt5K09qx0n+R9CVZKHve6C5pSnczZhua7a/LVZoTs/7E85ixW6Ns3pKFe/+tptQ1yqaunEnQdZdUs4uAX0HqGz8fWMf2nZIeLzsCxvYXs591/d0gVfAn234iO9bJJeO94y4Ws3dKp/F2VzsUJWkB0kl1W9KomN8DK1fRZSnpo6RG0dy5bdvkuoWbNRR4MLuKzJ8Io0+/LtmX+dOkoVdTSH3ux7hcXvb8jNlDgYNKxMprjFBo3PDJX4lUkbdkDuAXpHxBXyVd0v7I9us97ti7ukbZ1JUzqbsuqWuaDLcsKVHXJKZPSKtiLYHxzpKuSVraFSWI6+Qi4ABJJ5JOhONKnlSnSRpl+7X8RqV1npteMUrSjaRJeucD29quIs1H3l+A/1B9V+LF2b8B1TaVvqTtSBXyQaR+yg7S2NnjJM1mu6mZh/nhfpL26Dz8r4T7XHIxiF78hpQeYT7SaIo5SV/0sgm2ahll4xryt0j6ne1dsqezAW/Y/i/w3xJhFyR1kXyXNIb+ckpkL835eO7xmVS4nkCD7XOAcyR9mtQld7ukW4Hf2G5mFM95wGmSxjYmFyotUHIa5eZFrEX6zO5EmlvQ2F7Vmr6z2P5yyRgfUNPQ4D5rm0qfNCZ9fdv/zm17WNJEUr6YKpJWVdm6rfvu/nLZBK1NbE9RSmBWxSiQWkbZ1DTjeYXc42uooCLNynMhcKFSsrJdgdmU1k4+pkSXSec1CmqRvc+Lk1rSI0h95CdJus72vn0M92vS2P9nJT1I6t74DHAO6aq4WXV2cUHKu7NkhaOigPpmq/dVO1X6s3Sq8AGw/Y/s5uNgM5uk5ejmC17BaI3Oy7QNo5rFn+saZVPHjOch3TyuRDZ+/AdKyxBuSzoBlO0nh5oaBJIOI/Vl/4t0D+ar2Q3d2UkjkfpU6Tutq7CLpMNJJ9T3gNvL9rvbrnQBoS7cQrq38wwzNlzK3iyva7Z6n7RTpd/TWpRNf+E7jVIZlo0CyU+8aLZPdBHSJXBXZatitMZNko4knVw2IqWpvaFkTKhvlE1dM54baruyyoYX/o5yeWHyjYAPNAjKNAJyn+GPkiqiJ7Lno5TyG70o6RvNxs9uDD/Ry68NJgeTujmrbrjUNTS4T9qp0q9L5xuu+dwaZW40PlRzn/6+pJbHK6RUtFdTfi1UqG+UTR0znofmTtJVnrDrMBszdkHmH5dtBOQ/w9/KtjXehw5Sbv1mb2y3ohdtX1BD3Lpmq/dJO1X6S2vGxT0ahpAm0DTF9tDmizSg1rP9M6qp6PPqGmXTecbz9yg/43kpUoVX9Qm7crYXqjF8I63BJcCZNaQ1aDWXS/ol6Uo733Ap26Va2dDgMoZ0dAzYbOB+pZRRslv90E/YJ5KOs11bytVsVMZHSTexx2WjVgYtSXOSRhx9gdSnfxWw+yBrjbesXFqDHUitzyrTGrQUSY93sbmjgj59lKVJl/RxsqHBXc1lqFPbVPrhgyR9hnQ538g6eqrtS5uMVcsqSd3kb7mcNOphp5Kjd0IXcmkNtgaqSGvQ1vJDgyXNY/v5gSxPO3XvhE5sPwzsK+lPpO6YP9L8mPKeVkkqo6v8LTtTQf6W0K1K0xq0mmy00tHMODx4jyzVSjMqHxpcRlT6bUrSvKRhhDuQPgenk3K7NMXZKkm2d+ztd/uov/K3tIxsiPFipHkVs1bRPVBnWoMW9CvS/Zwts5/fJTWKms2tVevQ4L6KSr99PUoaAfJd27dUFVTS2qQWeOec4c0uKl1b/pZWJGkV0t9tKrAacJ+kzWw3vc5qP6Q1aDUr216m8UTSzsCDFcUe8P70qPTb1wIlLld7ciLp5vDdVPMBryV/Sws7mrRwyjm2/5OlFzmOlFKkWXWnNWg1wyUNzSaXQRo40NM8n94MqqHBUem3GUk3kFXGuS/3+8qmSyC1zEulqe6krvwtrWqk7YcafzvbV2QzacuoO61Bq7kOOF9SY/b0rqQF6Js1qIYGR6Xffhrj3LcE5iK1yqeSRmu8XEH8SZKWsv1ABbGgvvwtraoxwadx4v7gmbuPBttw5UFgT+BAUjqRxoJITc9nGWxzeWLIZpvKEs2t1riEzRJt3Wp75ZJx/0aabfgk03OGd+T7SJuMuxAV5m9pVZI2JVVGHyW1SDcEdrHdjlc9/UbSBrb/OtDlqEK09NvXPKSZyFOy56PILRpRwv65xyOAeSmXFA1oyfwttbA9XtIjpMRdw4BDs6G3oSRJy5NG6bwA7Gj7eUkLku6ZbEw1KbIH3KC67Aj96lzgNkmHSDoUmEi5hGAA2J5ASo+wKnAG6cZumUVqQk42cWpP2yeRcrccka30FMr7Lek+0b9Ii8lsTRq1M5KU4XWmEC39NmX7oCzD3/qkFuPPgN1Jo0OakvUvV702bJjRmaSVnSB1od1Iui+zyQCVZ2Yyl+1jJA0D/kGakbyz7T8OcLkqFS399nYTafWsLUgt8quaDaS0NuxNwDuktWGXJKVZjgq/WvPY/g2A7bds/xr42MAWaaYxBcD2NFLX5yYzW4UP0dJvS120yGelfIt8WWpYGzZ8wHBJ8zduZEuaj0Ewy3MmkX8fJ9u+d6AKUqeo9NtM1iJfnjT7ch3bd0p6vIIWeV1rw4YZHUtKy3sV6aT6eWDvgS3STCM/iWrIQE+iqkt077SfZamhRW57qu0Lba9LSjD1DNnasJJ2LRs/JLbHkUbu3APcCWxk+9yBLdVMozGJ6vns8Qu555MHsFyVinH6bUbScKa3yFcGLgfWsF15v3CWo31bYFfbA5pZcGaSrQ3RObdR2QU+QpuISr+NSfosaYr59qSWzDG2q1i4O9QkW9f4+8D/cpsrWeAjtIeo9EO0yFtItqrT6u06IzmUF5V+CC1E0k221xrocoTWFZV+CC1E0k+B2YFLmZ7bKPr0Q2ExZDOE1vKt7OdXc9s6gOjTD4VESz+EENpItPRDaCGS5gB+QVpT4KvAEcCPbL8+oAULLSMmZ4XQWn4DvALMR1ricE4qyI4a2kdU+iG0luVs7w+8a3sKsA1plnUIhUSlH0Jr6bxA9zDSamIhFBKVfgit5aZsVu5skjYCLgJuGOAyhRYSlX4IrWVf4HVSv/5hpFXKIstmKCyGbIYQQhuJIZshtABJF9jeWtIDdJEK2/bSA1Cs0IKi0g+hNRyZ/fzegJYitLzo0w+hBdi+K3v4GPA12xOAZ0nLXnrAChZaTlT6IbSWM4FHssdPAjcC4waqMKH1RKUfQmuZx/ZvAGy/ZfvXQOWrnoWZV1T6IbSW4ZLmbzyRNB+5ZRND6E3cyA2htRwL3CvpKtIons8T4/RDH8Q4/RBajKSlgfWBqcANticNcJFCC4mWfggtQtIQYJjt+7O1cjcA3h7gYoUWEy39EFqApM8CV5DG6V8H3E3q3hkJ7Gz7rwNYvNBC4kZuCK3haGB/2+OBr5Nu3i4JrAkcPIDlCi0mKv0QWsOCts/JHq8LXGL7PdtPAXMNYLlCi4lKP4TWkM+jvxpwU+75rP1cltDC4kZuCK3hRUnLAKNIk7EmAEhaDfjvQBYstJao9ENoDT8BriV15exj+w1JewH7A18ayIKF1hKjd0JoEZJmAUbafjl7vhow2fajA1qw0FKi0g8hhDYSN3JDCKGNRKUfQghtJG7khpmGpBOB1YFZgMWAh7KXjgM+CWD74AEpXAiDRFT6YaZhezcASQsBN9petvGapIMHplQhDC5R6Yd2spKkvwMfB86wfbCkYaQUB+sAw4Azbf+q846SjgC2Ap4HngH+YvtMSdsDe5C6Su8CdrP9lqRngD8Ba5CyYW5t+3FJTwC3AcuSUihs3Hl/0kSscaQ0CwC/tX1qpe9EaFvRpx/ayXykFAbLA3tLGgXsDGD7c8BKwBaS1szvJGkzUuW9BLAJsFy2fYls/9Wyq4rngL2y3T4KXGd7OdLs2fyC5lfaFjCmm/1XA+bO9v08qcsqhEpESz+0kyttvw28Lel5YG5SpbqspPWy35kDWAq4ObffBsAFtt8B3pF0SbZ9XWBxYKIkSPcS7s7td1X2cxKwVm77bb3sfxIgSVeTMmvuW+L/HMIMotIP7WRq7nEHKVPlMNIM14sAJM0DvNFpv2l0fVU8jHQy+EG27xzkvlO23+p0rIY3e9rf9svZVcQGpCuLuyUt0ZiUFUIZ0b0T2t31wM6SRmSV7t+AlTv9zl+Br0iaRdKcwKakivxGYEtJ82YLnJxE6p8vqsv9JW0O/AG4HPgB8DqwQJP/vxBmEJV+aHcnA48C9wB3km7w3pj/BdtXkPrl7yFVxE8Db9q+DziEdOJ4kPR9+kXRA/ew/5Wkq4EHgduBi2w/0PT/MIScSMMQQi8krQp8yvZZkkYAtwJjbd8/wEULoc+i0g+hF5LmBs4lpTQeCpxl+5cDW6oQmhOVfgghtJHo0w8hhDYSlX4IIbSRqPRDCKGNRKUfQghtJCr9EEJoI1HphxBCG/l/BuXCF1MQK24AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the graph, and rotate the xticks for visibility.\n",
    "plt.bar(genrelist, countlist)\n",
    "plt.title('Genre Popularity over the years')\n",
    "plt.xlabel('The genres')\n",
    "plt.ylabel('Number of times a genre appears over the years')\n",
    "plt.xticks(genrelist, rotation='vertical', size=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56, 2)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Have a look at the popular genre dataframe result set.\n",
    "populargenredf.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most popular genres over the years is Adventure with 30 entries out of the total 56, followed closely by Action with 28 entries, Thriller and Science fiction with 19 entries the Drama at 13 and so forth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What kind of properties are associated with movies that have high revenue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scanning through the data, there are movie entries that have zero budget and zero revenue, let's filter the datframe to see how many entries fetched zero revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6016, 13)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the shape of the movies that didn't generate any revenue\n",
    "moviesdf[(moviesdf['revenue']== 0)].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are over 6000 movies that didn't fecth any revenue, which will be quite significant for our analysis. Let's filter through the dataframe to get movies that fetched some sort of revenue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get this, let's filter out movies with zero revenue and assign it to a new datafarame called highrevenuedf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Universal Studios|Amblin Entertainment|Legenda...</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>1.392446e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.419936</td>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Village Roadshow Pictures|Kennedy Miller Produ...</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>3.481613e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.112507</td>\n",
       "      <td>110000000</td>\n",
       "      <td>295238201</td>\n",
       "      <td>Shailene Woodley|Theo James|Kate Winslet|Ansel...</td>\n",
       "      <td>Robert Schwentke</td>\n",
       "      <td>119</td>\n",
       "      <td>Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Summit Entertainment|Mandeville Films|Red Wago...</td>\n",
       "      <td>2480</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.012000e+08</td>\n",
       "      <td>2.716190e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity     budget     revenue  \\\n",
       "0   32.985763  150000000  1513528810   \n",
       "1   28.419936  150000000   378436354   \n",
       "2   13.112507  110000000   295238201   \n",
       "\n",
       "                                                cast          director  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   Colin Trevorrow   \n",
       "1  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...     George Miller   \n",
       "2  Shailene Woodley|Theo James|Kate Winslet|Ansel...  Robert Schwentke   \n",
       "\n",
       "   runtime                                     genres  \\\n",
       "0      124  Action|Adventure|Science Fiction|Thriller   \n",
       "1      120  Action|Adventure|Science Fiction|Thriller   \n",
       "2      119         Adventure|Science Fiction|Thriller   \n",
       "\n",
       "                                production_companies  vote_count  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...        5562   \n",
       "1  Village Roadshow Pictures|Kennedy Miller Produ...        6185   \n",
       "2  Summit Entertainment|Mandeville Films|Red Wago...        2480   \n",
       "\n",
       "   vote_average  release_year    budget_adj   revenue_adj  \n",
       "0           6.5          2015  1.379999e+08  1.392446e+09  \n",
       "1           7.1          2015  1.379999e+08  3.481613e+08  \n",
       "2           6.3          2015  1.012000e+08  2.716190e+08  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter out movies that fetched some revenue and view the first few entries\n",
    "highrevenuedf = moviesdf[moviesdf['revenue'] != 0]\n",
    "highrevenuedf.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have data entries that fetched revenue, let's have a quick overview of the summary statistics of the new dataframe highrevenuedf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4849.000000</td>\n",
       "      <td>4.849000e+03</td>\n",
       "      <td>4.849000e+03</td>\n",
       "      <td>4849.000000</td>\n",
       "      <td>4849.000000</td>\n",
       "      <td>4849.000000</td>\n",
       "      <td>4849.000000</td>\n",
       "      <td>4.849000e+03</td>\n",
       "      <td>4.849000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.045202</td>\n",
       "      <td>2.956961e+07</td>\n",
       "      <td>8.923886e+07</td>\n",
       "      <td>107.968035</td>\n",
       "      <td>436.283151</td>\n",
       "      <td>6.149000</td>\n",
       "      <td>2000.919777</td>\n",
       "      <td>3.516208e+07</td>\n",
       "      <td>1.151009e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.356752</td>\n",
       "      <td>4.051761e+07</td>\n",
       "      <td>1.620801e+08</td>\n",
       "      <td>21.113779</td>\n",
       "      <td>806.485754</td>\n",
       "      <td>0.798707</td>\n",
       "      <td>11.569650</td>\n",
       "      <td>4.376117e+07</td>\n",
       "      <td>1.988557e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.001117</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.370705e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.387740</td>\n",
       "      <td>1.700000e+06</td>\n",
       "      <td>7.732325e+06</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>2.329409e+06</td>\n",
       "      <td>1.046585e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.680581</td>\n",
       "      <td>1.500000e+07</td>\n",
       "      <td>3.185308e+07</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>2.032801e+07</td>\n",
       "      <td>4.395666e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.210502</td>\n",
       "      <td>4.000000e+07</td>\n",
       "      <td>9.996575e+07</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>4.973516e+07</td>\n",
       "      <td>1.316482e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32.985763</td>\n",
       "      <td>4.250000e+08</td>\n",
       "      <td>2.781506e+09</td>\n",
       "      <td>705.000000</td>\n",
       "      <td>9767.000000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>4.250000e+08</td>\n",
       "      <td>2.827124e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        popularity        budget       revenue      runtime   vote_count  \\\n",
       "count  4849.000000  4.849000e+03  4.849000e+03  4849.000000  4849.000000   \n",
       "mean      1.045202  2.956961e+07  8.923886e+07   107.968035   436.283151   \n",
       "std       1.356752  4.051761e+07  1.620801e+08    21.113779   806.485754   \n",
       "min       0.001117  0.000000e+00  2.000000e+00    15.000000    10.000000   \n",
       "25%       0.387740  1.700000e+06  7.732325e+06    95.000000    46.000000   \n",
       "50%       0.680581  1.500000e+07  3.185308e+07   104.000000   147.000000   \n",
       "75%       1.210502  4.000000e+07  9.996575e+07   117.000000   435.000000   \n",
       "max      32.985763  4.250000e+08  2.781506e+09   705.000000  9767.000000   \n",
       "\n",
       "       vote_average  release_year    budget_adj   revenue_adj  \n",
       "count   4849.000000   4849.000000  4.849000e+03  4.849000e+03  \n",
       "mean       6.149000   2000.919777  3.516208e+07  1.151009e+08  \n",
       "std        0.798707     11.569650  4.376117e+07  1.988557e+08  \n",
       "min        2.100000   1960.000000  0.000000e+00  2.370705e+00  \n",
       "25%        5.600000   1994.000000  2.329409e+06  1.046585e+07  \n",
       "50%        6.200000   2004.000000  2.032801e+07  4.395666e+07  \n",
       "75%        6.700000   2010.000000  4.973516e+07  1.316482e+08  \n",
       "max        8.400000   2015.000000  4.250000e+08  2.827124e+09  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View summary statitics of movies that generated revenue\n",
    "highrevenuedf.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the summary statistics of the highrevenuedf dataframe, the minimum generated revenue is 2, which is too small to be classified as high revenue. Therefore, we need to filter the dataframe further to get high revenue movies. We migh want to classify anything above average as high revenue, however due to disparity of the revenue, the average may not be the correct measure as it is heavily affected by outliers (like the max value, 2781505847 ) and therefore does not paint the real picture of what should be expected in terms of revenue generation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's therefore use the median, and filter high revenues as amounts higher than the median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2425, 13)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter out movies whose revenue generate is higher or equal to the median and get the shape of the new dataframe\n",
    "higherthanmediandf =highrevenuedf[highrevenuedf['revenue'] >= highrevenuedf['revenue'].median()]\n",
    "higherthanmediandf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the new dataframe, higherthanmediandf, let's see if genre was a factor that would make a movie raise high revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Action', 'Adventure', 'Science Fiction', 'Thriller', 'Fantasy', 'Crime', 'Western', 'Drama', 'Family', 'Animation', 'Comedy', 'Mystery', 'Romance', 'War', 'History', 'Music', 'Horror', 'Foreign', 'Documentary', 'TV Movie']\n",
      "[764, 581, 343, 736, 304, 399, 27, 970, 360, 167, 922, 195, 401, 75, 82, 83, 232, 2, 11, 1]\n"
     ]
    }
   ],
   "source": [
    "# count how many times a genre features in the new higherthanmediandf dataframe\n",
    "# We use the Counter() from python's collections module imported at the beginning of this notebook\n",
    "\n",
    "\n",
    "featuregenres = higherthanmediandf['genres']\n",
    "\n",
    "genreappearance = Counter()\n",
    "for gf in featuregenres:\n",
    "    genreappearance.update(gf.split('|'))\n",
    "\n",
    "highgenre = []\n",
    "appearances = []\n",
    "\n",
    "\n",
    "for ga in genreappearance.items():\n",
    "    highgenre.append(ga[0])\n",
    "    appearances.append(ga[1])\n",
    "\n",
    "print(highgenre)\n",
    "print(appearances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x1224 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot on how particular genres featured in the dataset\n",
    "plt.figure(figsize = (9, 17))\n",
    "plt.barh(highgenre, appearances)\n",
    "plt.title('Genre features in high revenue data')\n",
    "plt.ylabel('The genres')\n",
    "plt.xlabel('Number of times a genre features');\n",
    "# plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our high revenue dataset, we see particular genres feature prominently, out of the 2425 entries, Drama and Comedy feature in more than 900 entries which could be a factor that escalates a movie's popularity hence fetching more revenue. Action and Thriller presence is equally significant with over 700 entries, therefore genre is factor that is associated with high revenue movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does the year a movie is released associated with a high revenue. We look at this using a histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a chart on high generating movies over the years.\n",
    "def pltgraph(cl, t,x,y):\n",
    "    plt.figure(figsize = (9, 7))\n",
    "    plt.hist(cl, edgecolor ='black')\n",
    "    plt.title(t)\n",
    "    plt.ylabel(y)\n",
    "    plt.xlabel(x)\n",
    "pltgraph(higherthanmediandf['release_year'],\"Movie distribution over the years\" , 'Years','Count of movies over the years');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the histogram, it shows that, high revenue movies were mostly registered in the most recent releases. From 1990, there was a sigificant increase of movies that generated more revenues as compared to 1970 and earlier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about runtime? Let's see this with a scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Count of movies')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A scatter plot on runtime distribution of high revenue generating movies\n",
    "plt.figure(figsize = (8, 7))\n",
    "plt.scatter(higherthanmediandf['revenue'], higherthanmediandf['runtime']);\n",
    "plt.title(\"Runtime distribution of movies\")\n",
    "plt.ylabel('Movie runtime')\n",
    "plt.xlabel('Count of movies')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average runtime of high revenue movies was around 110. Movies that brought in more revenue had a runtime of between 44 and 200 mostly, although there was one outlier with a runtime of 700."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about budget, is it associated with high revenue data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot distribution of movies' budget\n",
    "pltgraph(higherthanmediandf['budget'],\"Movie budget distribution\", 'The budget distribution','Count of movies');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is skewed to the right, although, there are movies that generated revenue with zero budget, the number was near too small as compared to movies that had a budget. Which goes to show, that movies that were budgeted for ended up fetching high revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While continuing with the analysis, i came accross columns (cast and production_companies) with null values. Since they were not that many, they were filled with a random name from one of the original title column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\wangui\\appdata\\local\\programs\\python\\python36-32\\lib\\site-packages\\pandas\\core\\generic.py:5434: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "# Fill null value with a random name from the original title column\n",
    "higherthanmediandf['cast'].fillna(value = 'Obama', inplace=True)\n",
    "higherthanmediandf['production_companies'].fillna(value = 'Obama', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How did the production companies feature in the high revenue data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Universal Pictures', 'Warner Bros.', 'Paramount Pictures', 'Twentieth Century Fox Film Corporation', 'Columbia Pictures', 'Walt Disney Pictures', 'New Line Cinema', 'Touchstone Pictures', 'Relativity Media', 'Columbia Pictures Corporation', 'Metro-Goldwyn-Mayer (MGM)', 'Village Roadshow Pictures', 'DreamWorks SKG', 'Regency Enterprises', 'Amblin Entertainment', 'TriStar Pictures', 'Dune Entertainment', 'Fox 2000 Pictures', 'Miramax Films', 'Legendary Pictures']\n",
      "\n",
      "[275, 274, 217, 176, 150, 112, 112, 95, 86, 79, 75, 66, 66, 58, 55, 46, 42, 41, 39, 36]\n",
      "2100\n"
     ]
    }
   ],
   "source": [
    "producers = higherthanmediandf['production_companies']\n",
    "\n",
    "prod_comp = Counter()\n",
    "for producer in producers:\n",
    "    prod_comp.update(producer.split('|'))\n",
    "\n",
    "company = []\n",
    "features = []\n",
    "\n",
    "\n",
    "for cf in prod_comp.most_common(20):\n",
    "    company.append(cf[0])\n",
    "    features.append(cf[1])\n",
    "\n",
    "print(company)\n",
    "print()\n",
    "print((features))\n",
    "total = 0\n",
    "for item in features:\n",
    "    total +=item\n",
    "print(total)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot on how production companies featured in the high revenue generating movies.\n",
    "\n",
    "pltgraph(features,\"Distribution of production companies in the dataset\",'Times a production company featured in the movie data', 'Count of production companies in a particular bin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the production companies histogram, it's quite visible that high revenue is associated with production companies, given the number of times a company features in the high revenue data. We have companies with over 200 movies in the dataset, therefore making it a significant factor. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the analysis, it shows that, over the years, there has been a steady increase of movies production. With the increase of movies production, saw a steady rise of movies that generate revenue as compared to earlier years. Also, particular production companies are poised to produce high revenue generating movies. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Limitations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. In the analysis of most popular genres over the years, the data presents a combination of different genre sets, while in the analysis, we considered a genre in its individuality and therefore we may never know the significance of genres that featured less in combination with the popular ones.\n",
    "\n",
    "\n",
    "2. While plotting the distribution of production companies, we have picked the 20 most common of the companies against a total count of over 1900 companies. The figure is a small sample of the whole dataset, and therefore the significance of the companies not plotted may not be well presented.\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Course notes\n",
    "2. N/A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}