investigate-a-dataset-template.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Tip**: Welcome to the Investigate a Dataset project! You will find tips in quoted sections like this to help organize your approach to your investigation. Before submitting your project, it will be a good idea to go back through your report and remove these sections to make the presentation of your work as tidy as possible. First things first, you might want to double-click this Markdown cell and change the title so that it reflects your dataset and investigation.\n",
    "\n",
    "# Project: TMDb movie data\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "> **Tip**: In this section of the report, provide a brief introduction to the dataset you've selected for analysis. At the end of this section, describe the questions that you plan on exploring over the course of the report. Try to build your report around the analysis of at least one dependent variable and three independent variables.\n",
    ">\n",
    "> If you haven't yet selected and downloaded your data, make sure you do that first before coming back here. If you're not sure what questions to ask right now, then make sure you familiarize yourself with the variables and the dataset context for ideas of what to explore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import pytz  # for today's date\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "> **Tip**: In this section of the report, you will load in the data, check for cleanliness, and then trim and clean your dataset for analysis. Make sure that you document your steps carefully and justify your cleaning decisions.\n",
    "\n",
    "### General Properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": [
    "df = pd.read_csv('tmdb-movies.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This dataset has total 10866 rows and 21 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10866, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### It needs to change the datatype of some columns. Budget, revenue's datatype is int64. So, budget_adj, revenue_adj need to change the same datatype.\n",
    "#### release_date : object -> datetime\n",
    "#### budget_adj, revenue_adj : float64 -> int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10866 entries, 0 to 10865\n",
      "Data columns (total 21 columns):\n",
      "id                      10866 non-null int64\n",
      "imdb_id                 10856 non-null object\n",
      "popularity              10866 non-null float64\n",
      "budget                  10866 non-null int64\n",
      "revenue                 10866 non-null int64\n",
      "original_title          10866 non-null object\n",
      "cast                    10790 non-null object\n",
      "homepage                2936 non-null object\n",
      "director                10822 non-null object\n",
      "tagline                 8042 non-null object\n",
      "keywords                9373 non-null object\n",
      "overview                10862 non-null object\n",
      "runtime                 10866 non-null int64\n",
      "genres                  10843 non-null object\n",
      "production_companies    9836 non-null object\n",
      "release_date            10866 non-null object\n",
      "vote_count              10866 non-null int64\n",
      "vote_average            10866 non-null float64\n",
      "release_year            10866 non-null int64\n",
      "budget_adj              10866 non-null float64\n",
      "revenue_adj             10866 non-null float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Look at the descriptive statistics. This dataset has information of movies( popuarity, budget, revenue, runtime etc) from 1960 to 2015. Some data has 0. Particularily, budget and revenue of movies(over 50%) are  0(include budget_adj, revenue_adj). All most data are between 1960 and 2006. Because there is 0 value for more than half of the budget and revenue, we don't analyze the data accurately from 1960 to 2015. Also, if we drop this data with 0 values, we can't analyze the data because of no enough data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>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>10866.000000</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>1.086600e+04</td>\n",
       "      <td>1.086600e+04</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>1.086600e+04</td>\n",
       "      <td>1.086600e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66064.177434</td>\n",
       "      <td>0.646441</td>\n",
       "      <td>1.462570e+07</td>\n",
       "      <td>3.982332e+07</td>\n",
       "      <td>102.070863</td>\n",
       "      <td>217.389748</td>\n",
       "      <td>5.974922</td>\n",
       "      <td>2001.322658</td>\n",
       "      <td>1.755104e+07</td>\n",
       "      <td>5.136436e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>92130.136561</td>\n",
       "      <td>1.000185</td>\n",
       "      <td>3.091321e+07</td>\n",
       "      <td>1.170035e+08</td>\n",
       "      <td>31.381405</td>\n",
       "      <td>575.619058</td>\n",
       "      <td>0.935142</td>\n",
       "      <td>12.812941</td>\n",
       "      <td>3.430616e+07</td>\n",
       "      <td>1.446325e+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>10596.250000</td>\n",
       "      <td>0.207583</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>20669.000000</td>\n",
       "      <td>0.383856</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>75610.000000</td>\n",
       "      <td>0.713817</td>\n",
       "      <td>1.500000e+07</td>\n",
       "      <td>2.400000e+07</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>145.750000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2.085325e+07</td>\n",
       "      <td>3.369710e+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   10866.000000  10866.000000  1.086600e+04  1.086600e+04  10866.000000   \n",
       "mean    66064.177434      0.646441  1.462570e+07  3.982332e+07    102.070863   \n",
       "std     92130.136561      1.000185  3.091321e+07  1.170035e+08     31.381405   \n",
       "min         5.000000      0.000065  0.000000e+00  0.000000e+00      0.000000   \n",
       "25%     10596.250000      0.207583  0.000000e+00  0.000000e+00     90.000000   \n",
       "50%     20669.000000      0.383856  0.000000e+00  0.000000e+00     99.000000   \n",
       "75%     75610.000000      0.713817  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  10866.000000  10866.000000  10866.000000  1.086600e+04  1.086600e+04  \n",
       "mean     217.389748      5.974922   2001.322658  1.755104e+07  5.136436e+07  \n",
       "std      575.619058      0.935142     12.812941  3.430616e+07  1.446325e+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%      145.750000      6.600000   2011.000000  2.085325e+07  3.369710e+07  \n",
       "max     9767.000000      9.200000   2015.000000  4.250000e+08  2.827124e+09  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Tip**: You should _not_ perform too many operations in each cell. Create cells freely to explore your data. One option that you can take with this project is to do a lot of explorations in an initial notebook. These don't have to be organized, but make sure you use enough comments to understand the purpose of each code cell. Then, after you're done with your analysis, create a duplicate notebook where you will trim the excess and organize your steps so that you have a flowing, cohesive report.\n",
    "\n",
    "> **Tip**: Make sure that you keep your reader informed on the steps that you are taking in your investigation. Follow every code cell, or every set of related code cells, with a markdown cell to describe to the reader what was found in the preceding cell(s). Try to make it so that the reader can then understand what they will be seeing in the following cell(s).\n",
    "\n",
    "## Data Cleaning (Replace this with more specific notes!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First, remove the duplicated rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(inplace=True)\n",
    "sum(df.duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As you can see the descritive statistics above, movie's budget and revenue have increased from 1960 to 2015. Particularily, the budget's mean is almost same with its 75% value and the revenue's mean(3.982332e+07) is over its 75%(2.400000e+07). So the budget and revenue with 0 value had better put the mean value of 25%~75% section by 2011. If the values of budget and revenue after 2012 are changed, it will have big influence on the total average value.\n",
    "\n",
    "### So, try to put the mean value of 25%~75% section(mentioned before) into the place that has 0 value the revenue.\n",
    "#### Before putting the mean value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.086500e+04\n",
       "mean     3.982690e+07\n",
       "std      1.170083e+08\n",
       "min      0.000000e+00\n",
       "25%      0.000000e+00\n",
       "50%      0.000000e+00\n",
       "75%      2.400000e+07\n",
       "max      2.781506e+09\n",
       "Name: revenue, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revenue_Series = df.describe().revenue\n",
    "revenue_Series\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### After putting the mean value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.086500e+04\n",
       "mean     4.096226e+07\n",
       "std      1.166256e+08\n",
       "min      2.000000e+00\n",
       "25%      2.050484e+06\n",
       "50%      2.050484e+06\n",
       "75%      2.400000e+07\n",
       "max      2.781506e+09\n",
       "Name: revenue, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revenue_bin_edges = [ revenue_Series[3], revenue_Series[6], revenue_Series[7]] \n",
    "revenue_bin_names = ['revenue_Low', 'revenue_High'] \n",
    "df['revenue_levels'] = pd.cut(df['revenue'], revenue_bin_edges, labels=revenue_bin_names, include_lowest=True)\n",
    "df_revenue_mean = df.groupby('revenue_levels').mean().revenue\n",
    "df_revenue_mean  \n",
    "df['revenue'] = np.where(df['revenue']== 0, df_revenue_mean[0], df['revenue'])\n",
    "df.describe().revenue\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare the above two revenue's descriptive statistics. I put the mean budget value of 25%~75% section by 2011 into the budget with 0 value. As the result, 75% and max value weren't changed  and min,25%,50% have values. Also, the mean value slightly increased from 3.982690e+07 to 3.982690e+07. Considering the increasing revenue over time, it can be assumed that relatively little revenue was spent in 1900's and in the early 2000's in this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the function set_dataFilledMean(data, data_name). \n",
    "#### Because budget, budget_adj, revenue_adj can also be considered the same as revenue, I try to use the function set_dataFilledMean(data, data_name)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before :\n",
      "count    1.086500e+04\n",
      "mean     1.462429e+07\n",
      "std      3.091428e+07\n",
      "min      0.000000e+00\n",
      "25%      0.000000e+00\n",
      "50%      0.000000e+00\n",
      "75%      1.500000e+07\n",
      "max      4.250000e+08\n",
      "Name: budget, dtype: float64\n",
      "[0.0, 15000000.0, 425000000.0]\n",
      "[budget_levels\n",
      "budget_Low     1.900623e+06\n",
      "budget_High    5.300694e+07\n",
      "Name: budget, dtype: float64]\n",
      "After :\n",
      "count    1.086500e+04\n",
      "mean     1.562069e+07\n",
      "std      3.045403e+07\n",
      "min      1.000000e+00\n",
      "25%      1.900623e+06\n",
      "50%      1.900623e+06\n",
      "75%      1.500000e+07\n",
      "max      4.250000e+08\n",
      "Name: budget, dtype: float64\n",
      "Before :\n",
      "count    1.086500e+04\n",
      "mean     5.136900e+07\n",
      "std      1.446383e+08\n",
      "min      0.000000e+00\n",
      "25%      0.000000e+00\n",
      "50%      0.000000e+00\n",
      "75%      3.370173e+07\n",
      "max      2.827124e+09\n",
      "Name: revenue_adj, dtype: float64\n",
      "[0.0, 33701729.0094642, 2827123750.41189]\n",
      "[revenue_adj_levels\n",
      "revenue_adj_Low     2.831740e+06\n",
      "revenue_adj_High    1.969987e+08\n",
      "Name: revenue_adj, dtype: float64]\n",
      "After :\n",
      "count    1.086500e+04\n",
      "mean     5.293695e+07\n",
      "std      1.440872e+08\n",
      "min      2.370705e+00\n",
      "25%      2.831740e+06\n",
      "50%      2.831740e+06\n",
      "75%      3.370173e+07\n",
      "max      2.827124e+09\n",
      "Name: revenue_adj, dtype: float64\n",
      "Before :\n",
      "count    1.086500e+04\n",
      "mean     1.754989e+07\n",
      "std      3.430753e+07\n",
      "min      0.000000e+00\n",
      "25%      0.000000e+00\n",
      "50%      0.000000e+00\n",
      "75%      2.085325e+07\n",
      "max      4.250000e+08\n",
      "Name: budget_adj, dtype: float64\n",
      "[0.0, 20853251.084403798, 425000000.0]\n",
      "[budget_adj_levels\n",
      "budget_adj_Low     2.533402e+06\n",
      "budget_adj_High    6.267132e+07\n",
      "Name: budget_adj, dtype: float64]\n",
      "After :\n",
      "count    1.086500e+04\n",
      "mean     1.887804e+07\n",
      "std      3.364499e+07\n",
      "min      9.210911e-01\n",
      "25%      2.533402e+06\n",
      "50%      2.533402e+06\n",
      "75%      2.085325e+07\n",
      "max      4.250000e+08\n",
      "Name: budget_adj, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "def set_dataFilledMean(data, data_name):\n",
    "    desc_Series = df.describe()[data_name]\n",
    "    print(\"Before :\")\n",
    "    print(desc_Series)\n",
    "    bin_edges = [ desc_Series[3], desc_Series[6], desc_Series[7]] \n",
    "    print(bin_edges)\n",
    "    bin_names = [ data_name + '_Low', data_name + '_High'] \n",
    "    #print(bin_names)\n",
    "    df[data_name+'_levels'] = pd.cut(df[data_name], bin_edges, labels=bin_names, include_lowest=True)\n",
    "    df_mean = df.groupby(data_name+'_levels').mean()[data_name]\n",
    "    print([df_mean])\n",
    "    df[data_name] = np.where(df[data_name]== 0, df_mean[0], df[data_name])\n",
    "    #print(df[data_name])   \n",
    "    print(\"After :\")\n",
    "    print(df.describe()[data_name])\n",
    "    \n",
    "    # clear parameter\n",
    "    data = ''\n",
    "    data_name = ''\n",
    "    bin_edges = ''\n",
    "    bin_names = ''\n",
    "    df_mean = '' \n",
    "    \n",
    "#set_dataFilledMean(df['revenue'], 'revenue')\n",
    "set_dataFilledMean(df['budget'], 'budget') \n",
    "set_dataFilledMean(df['revenue_adj'], 'revenue_adj')\n",
    "set_dataFilledMean(df['budget_adj'], 'budget_adj')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As you can see, budget, budget_adj, revenue_adj seem to have same result. 75% and max value are not changed. Mean value are slightly increased and there is no significant difference in standard deviation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at missing data through the info() of dataframe. \n",
    "#### As I have seen, total count of data is 10866. Particularly the columns, homepage/tagline/keyword, are significantly different with the totla count(10866). Look at the columns closely,  these columns are not associated with the revenue. Now I need to think that which data is associated with the revenue. So, it need to drop the columns not to be associated with the revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10865 entries, 0 to 10865\n",
      "Data columns (total 25 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 float64\n",
      "revenue                 10865 non-null float64\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",
      "revenue_levels          10865 non-null category\n",
      "budget_levels           10865 non-null category\n",
      "revenue_adj_levels      10865 non-null category\n",
      "budget_adj_levels       10865 non-null category\n",
      "dtypes: category(4), float64(6), int64(4), object(11)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Drop the below columns  not to be associated with the revenues. I think genres, casted actor/actress are basically associated with the revenue. So I try to make the accurate genres, cast columns.\n",
    "### Limitation : Which properties are associated with the revenue? genres, cast\n",
    "#### 1. The too detailed datas : id, imdb_id, budget_adj, revenue_adj\n",
    "#### 2. Not associated with revenues but the preference of users : homepage, tagline, keywords, overview \n",
    "#### 3. Inaccurate data(specific rating meaning from level 1 to 9) : vote_count, vote_average\n",
    "#### 4. Not related to the limitation : production_companies\n",
    "#### 5. The category columns no longer needed :  the category columns named with '_level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['id','imdb_id','homepage','tagline','overview','keywords','production_companies','vote_count','vote_average',\n",
    "         'budget_adj','revenue_adj','revenue_levels','budget_levels','revenue_adj_levels','budget_adj_levels'],\n",
    "        axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10865 entries, 0 to 10865\n",
      "Data columns (total 10 columns):\n",
      "popularity        10865 non-null float64\n",
      "budget            10865 non-null float64\n",
      "revenue           10865 non-null float64\n",
      "original_title    10865 non-null object\n",
      "cast              10789 non-null object\n",
      "director          10821 non-null object\n",
      "runtime           10865 non-null int64\n",
      "genres            10842 non-null object\n",
      "release_date      10865 non-null object\n",
      "release_year      10865 non-null int64\n",
      "dtypes: float64(3), int64(2), object(5)\n",
      "memory usage: 933.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.646446</td>\n",
       "      <td>1.562069e+07</td>\n",
       "      <td>4.096226e+07</td>\n",
       "      <td>102.071790</td>\n",
       "      <td>2001.321859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000231</td>\n",
       "      <td>3.045403e+07</td>\n",
       "      <td>1.166256e+08</td>\n",
       "      <td>31.382701</td>\n",
       "      <td>12.813260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000065</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1960.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.207575</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.383831</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.713857</td>\n",
       "      <td>1.500000e+07</td>\n",
       "      <td>2.400000e+07</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>2011.000000</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>900.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         popularity        budget       revenue       runtime  release_year\n",
       "count  10865.000000  1.086500e+04  1.086500e+04  10865.000000  10865.000000\n",
       "mean       0.646446  1.562069e+07  4.096226e+07    102.071790   2001.321859\n",
       "std        1.000231  3.045403e+07  1.166256e+08     31.382701     12.813260\n",
       "min        0.000065  1.000000e+00  2.000000e+00      0.000000   1960.000000\n",
       "25%        0.207575  1.900623e+06  2.050484e+06     90.000000   1995.000000\n",
       "50%        0.383831  1.900623e+06  2.050484e+06     99.000000   2006.000000\n",
       "75%        0.713857  1.500000e+07  2.400000e+07    111.000000   2011.000000\n",
       "max       32.985763  4.250000e+08  2.781506e+09    900.000000   2015.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There are null values in cast, director, genres columns. \n",
    "\n",
    "### Check the missing data in 'cast' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.cast.isnull()].hist(figsize=(8,8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The cast column with null value usually means that their popularities are low and the movie times are short, also they have mean budget(applied with the mean of min~50% section). Also, the revenue is closer to 0. So, it had better drop the missing data of cast."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the missing data in 'director' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.director.isnull()].hist(figsize=(8,8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The director column with null value usually means that the movie times are short and they have mean budget(applied with the mean of min~50% section). Also, the revenue is closer to 0.  So, it had better drop the missing datas.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Check the missing data in 'genres' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.genres.isnull()].hist(figsize=(8,8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The genres column with null values doesn't have constant running time even over 200 minutes. Their budget has the mean budget(applied with the mean of min~50% section). Also, the revenue is closer to the mean value. Some datas of them have much lower popularity than the max value(32). Even the value of 75% of popularity is 0.7. So they will be drop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10731 entries, 0 to 10865\n",
      "Data columns (total 10 columns):\n",
      "popularity        10731 non-null float64\n",
      "budget            10731 non-null float64\n",
      "revenue           10731 non-null float64\n",
      "original_title    10731 non-null object\n",
      "cast              10731 non-null object\n",
      "director          10731 non-null object\n",
      "runtime           10731 non-null int64\n",
      "genres            10731 non-null object\n",
      "release_date      10731 non-null object\n",
      "release_year      10731 non-null int64\n",
      "dtypes: float64(3), int64(2), object(5)\n",
      "memory usage: 922.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10731.000000</td>\n",
       "      <td>1.073100e+04</td>\n",
       "      <td>1.073100e+04</td>\n",
       "      <td>10731.000000</td>\n",
       "      <td>10731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.652615</td>\n",
       "      <td>1.579159e+07</td>\n",
       "      <td>4.144497e+07</td>\n",
       "      <td>102.468829</td>\n",
       "      <td>2001.259622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.004804</td>\n",
       "      <td>3.060482e+07</td>\n",
       "      <td>1.172706e+08</td>\n",
       "      <td>30.493873</td>\n",
       "      <td>12.820151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000188</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1960.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.210765</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.387081</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.720889</td>\n",
       "      <td>1.600000e+07</td>\n",
       "      <td>2.500000e+07</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>2011.000000</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>900.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         popularity        budget       revenue       runtime  release_year\n",
       "count  10731.000000  1.073100e+04  1.073100e+04  10731.000000  10731.000000\n",
       "mean       0.652615  1.579159e+07  4.144497e+07    102.468829   2001.259622\n",
       "std        1.004804  3.060482e+07  1.172706e+08     30.493873     12.820151\n",
       "min        0.000188  1.000000e+00  2.000000e+00      0.000000   1960.000000\n",
       "25%        0.210765  1.900623e+06  2.050484e+06     90.000000   1995.000000\n",
       "50%        0.387081  1.900623e+06  2.050484e+06     99.000000   2006.000000\n",
       "75%        0.720889  1.600000e+07  2.500000e+07    112.000000   2011.000000\n",
       "max       32.985763  4.250000e+08  2.781506e+09    900.000000   2015.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make The popularity the 2 decimal places.\n",
    "\n",
    "##### Reference : https://docs.scipy.org/doc/numpy/reference/generated/numpy.around.html?highlight=around#numpy.around"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    32.985763\n",
       "1    28.419936\n",
       "2    13.112507\n",
       "3    11.173104\n",
       "4     9.335014\n",
       "Name: popularity, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().popularity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Popularity is score for preference of user. For easy understanding, make The popularity the 2 decimal places."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    32.99\n",
       "1    28.42\n",
       "2    13.11\n",
       "3    11.17\n",
       "4     9.34\n",
       "Name: popularity, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['popularity'] = np.around(df['popularity'],2)\n",
    "df.head().popularity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the function: get_todaydate(seg)\n",
    "#### This function returns today's date.\n",
    "#### * parameter :  year, month, day, time\n",
    "#### * return : today's year, month, day, time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_todaydate(seg):\n",
    "    today_date = datetime.datetime.now(pytz.timezone('US/Pacific'))\n",
    "    if seg == 'year':\n",
    "        return today_date.year\n",
    "    elif seg == 'month':\n",
    "        return today_date.month\n",
    "    elif seg == 'day':\n",
    "        return today_date.day\n",
    "    elif seg == 'time':\n",
    "        return today_date.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the function: get_dateformat(time_series)\n",
    "#### This function changes the format of date. also check the 1900s and 2000s.\n",
    "#### * parameter : time data Series, seperator(Y,M,D)\n",
    "#### * return : tiem data Series changed (ex, 2019, 2019-05, 2019-05-15)\n",
    "##### Reference :  https://stackoverflow.com/questions/53403318/make-datetime-series-from-separate-year-month-and-date-columns-in-pandas/53403472#53403472\n",
    "##### Reference : https://stackoverflow.com/questions/47227010/python-to-date-wrong-values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def  get_dateformat(time_data, sep):\n",
    "    df_date = pd.to_datetime(time_data)\n",
    "    \n",
    "    # present year\n",
    "    present_year = get_todaydate('year')\n",
    "\n",
    "    # year\n",
    "    df_year = df_date.dt.year\n",
    "    df_date = df_date.mask(df_year > present_year, df_date - pd.Timedelta(100, unit='Y'))\n",
    "    df_date = df_date.dt.to_period(sep)\n",
    "    return df_date"
   ]
  },
  {
   "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>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>release_date</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10861</th>\n",
       "      <td>0.08</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>The Endless Summer</td>\n",
       "      <td>Michael Hynson|Robert August|Lord 'Tally Ho' B...</td>\n",
       "      <td>Bruce Brown</td>\n",
       "      <td>95</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>1966-06-15</td>\n",
       "      <td>1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10862</th>\n",
       "      <td>0.07</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Grand Prix</td>\n",
       "      <td>James Garner|Eva Marie Saint|Yves Montand|Tosh...</td>\n",
       "      <td>John Frankenheimer</td>\n",
       "      <td>176</td>\n",
       "      <td>Action|Adventure|Drama</td>\n",
       "      <td>1966-12-21</td>\n",
       "      <td>1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10863</th>\n",
       "      <td>0.07</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Beregis Avtomobilya</td>\n",
       "      <td>Innokentiy Smoktunovskiy|Oleg Efremov|Georgi Z...</td>\n",
       "      <td>Eldar Ryazanov</td>\n",
       "      <td>94</td>\n",
       "      <td>Mystery|Comedy</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10864</th>\n",
       "      <td>0.06</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>What's Up, Tiger Lily?</td>\n",
       "      <td>Tatsuya Mihashi|Akiko Wakabayashi|Mie Hama|Joh...</td>\n",
       "      <td>Woody Allen</td>\n",
       "      <td>80</td>\n",
       "      <td>Action|Comedy</td>\n",
       "      <td>1966-11-02</td>\n",
       "      <td>1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10865</th>\n",
       "      <td>0.04</td>\n",
       "      <td>1.900000e+04</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Manos: The Hands of Fate</td>\n",
       "      <td>Harold P. Warren|Tom Neyman|John Reynolds|Dian...</td>\n",
       "      <td>Harold P. Warren</td>\n",
       "      <td>74</td>\n",
       "      <td>Horror</td>\n",
       "      <td>1966-11-15</td>\n",
       "      <td>1966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       popularity        budget       revenue            original_title  \\\n",
       "10861        0.08  1.900623e+06  2.050484e+06        The Endless Summer   \n",
       "10862        0.07  1.900623e+06  2.050484e+06                Grand Prix   \n",
       "10863        0.07  1.900623e+06  2.050484e+06       Beregis Avtomobilya   \n",
       "10864        0.06  1.900623e+06  2.050484e+06    What's Up, Tiger Lily?   \n",
       "10865        0.04  1.900000e+04  2.050484e+06  Manos: The Hands of Fate   \n",
       "\n",
       "                                                    cast            director  \\\n",
       "10861  Michael Hynson|Robert August|Lord 'Tally Ho' B...         Bruce Brown   \n",
       "10862  James Garner|Eva Marie Saint|Yves Montand|Tosh...  John Frankenheimer   \n",
       "10863  Innokentiy Smoktunovskiy|Oleg Efremov|Georgi Z...      Eldar Ryazanov   \n",
       "10864  Tatsuya Mihashi|Akiko Wakabayashi|Mie Hama|Joh...         Woody Allen   \n",
       "10865  Harold P. Warren|Tom Neyman|John Reynolds|Dian...    Harold P. Warren   \n",
       "\n",
       "       runtime                  genres release_date  release_year  \n",
       "10861       95             Documentary   1966-06-15          1966  \n",
       "10862      176  Action|Adventure|Drama   1966-12-21          1966  \n",
       "10863       94          Mystery|Comedy   1966-01-01          1966  \n",
       "10864       80           Action|Comedy   1966-11-02          1966  \n",
       "10865       74                  Horror   1966-11-15          1966  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['release_date'] = get_dateformat(df['release_date'], 'D')\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The columns (cast, genres) consist of multiple values. So, Split the columns' values with segment and create the datasets (df_cast, df_genres)\n",
    "#####  reference : https://stackoverflow.com/questions/52133164/pandas-how-to-plot-the-imdb-movie-total-budget-versus-the-separate-genre-in-pan\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the function : get_seperatedRows(data_series, seperator)\n",
    "#### This function divides the one row whose data has multivalue consisting of seperator(ex: |, ;, :, # etc) into serveral rows.\n",
    "#### * parameter : data series , seperator(ex: |, ;, :, # etc)\n",
    "#### * return : dataframe with seperated values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_seperatedRows(data_series, name, sep):\n",
    "    df_seperated = data_series.str.split(sep, expand=True).stack().to_frame(name = name)\n",
    "    df_seperated.index = df_seperated.index.droplevel(1)\n",
    "    return df_seperated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>original_title</th>\n",
       "      <th>cast</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.99</td>\n",
       "      <td>150000000.0</td>\n",
       "      <td>1.513529e+09</td>\n",
       "      <td>Jurassic World</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>2015-06-09</td>\n",
       "      <td>2015</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.99</td>\n",
       "      <td>150000000.0</td>\n",
       "      <td>1.513529e+09</td>\n",
       "      <td>Jurassic World</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>2015-06-09</td>\n",
       "      <td>2015</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.99</td>\n",
       "      <td>150000000.0</td>\n",
       "      <td>1.513529e+09</td>\n",
       "      <td>Jurassic World</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>2015-06-09</td>\n",
       "      <td>2015</td>\n",
       "      <td>Science Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.99</td>\n",
       "      <td>150000000.0</td>\n",
       "      <td>1.513529e+09</td>\n",
       "      <td>Jurassic World</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>2015-06-09</td>\n",
       "      <td>2015</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.42</td>\n",
       "      <td>150000000.0</td>\n",
       "      <td>3.784364e+08</td>\n",
       "      <td>Mad Max: Fury Road</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>2015-05-13</td>\n",
       "      <td>2015</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity       budget       revenue      original_title  \\\n",
       "0       32.99  150000000.0  1.513529e+09      Jurassic World   \n",
       "0       32.99  150000000.0  1.513529e+09      Jurassic World   \n",
       "0       32.99  150000000.0  1.513529e+09      Jurassic World   \n",
       "0       32.99  150000000.0  1.513529e+09      Jurassic World   \n",
       "1       28.42  150000000.0  3.784364e+08  Mad Max: Fury Road   \n",
       "\n",
       "                                                cast         director  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \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",
       "\n",
       "   runtime                                 genres_all release_date  \\\n",
       "0      124  Action|Adventure|Science Fiction|Thriller   2015-06-09   \n",
       "0      124  Action|Adventure|Science Fiction|Thriller   2015-06-09   \n",
       "0      124  Action|Adventure|Science Fiction|Thriller   2015-06-09   \n",
       "0      124  Action|Adventure|Science Fiction|Thriller   2015-06-09   \n",
       "1      120  Action|Adventure|Science Fiction|Thriller   2015-05-13   \n",
       "\n",
       "   release_year       genres_seg  \n",
       "0          2015           Action  \n",
       "0          2015        Adventure  \n",
       "0          2015  Science Fiction  \n",
       "0          2015         Thriller  \n",
       "1          2015           Action  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_genres_details = get_seperatedRows(df['genres'], 'genres', '|')\n",
    "df_genres = df.join(df_genres_details, lsuffix='_all', rsuffix='_seg')\n",
    "df_genres.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<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>original_title</th>\n",
       "      <th>cast_origin</th>\n",
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       "      <td>32.99</td>\n",
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       "      <td>1.513529e+09</td>\n",
       "      <td>Jurassic World</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",
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       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
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       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>2015-06-09</td>\n",
       "      <td>2015</td>\n",
       "      <td>Bryce Dallas Howard</td>\n",
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       "      <th>0</th>\n",
       "      <td>32.99</td>\n",
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       "      <td>Jurassic World</td>\n",
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       "      <td>Colin Trevorrow</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>2015-06-09</td>\n",
       "      <td>2015</td>\n",
       "      <td>Irrfan Khan</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.99</td>\n",
       "      <td>150000000.0</td>\n",
       "      <td>1.513529e+09</td>\n",
       "      <td>Jurassic World</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>2015-06-09</td>\n",
       "      <td>2015</td>\n",
       "      <td>Vincent D'Onofrio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.99</td>\n",
       "      <td>150000000.0</td>\n",
       "      <td>1.513529e+09</td>\n",
       "      <td>Jurassic World</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>2015-06-09</td>\n",
       "      <td>2015</td>\n",
       "      <td>Nick Robinson</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity       budget       revenue  original_title  \\\n",
       "0       32.99  150000000.0  1.513529e+09  Jurassic World   \n",
       "0       32.99  150000000.0  1.513529e+09  Jurassic World   \n",
       "0       32.99  150000000.0  1.513529e+09  Jurassic World   \n",
       "0       32.99  150000000.0  1.513529e+09  Jurassic World   \n",
       "0       32.99  150000000.0  1.513529e+09  Jurassic World   \n",
       "\n",
       "                                         cast_origin         director  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \n",
       "\n",
       "   runtime                                     genres release_date  \\\n",
       "0      124  Action|Adventure|Science Fiction|Thriller   2015-06-09   \n",
       "0      124  Action|Adventure|Science Fiction|Thriller   2015-06-09   \n",
       "0      124  Action|Adventure|Science Fiction|Thriller   2015-06-09   \n",
       "0      124  Action|Adventure|Science Fiction|Thriller   2015-06-09   \n",
       "0      124  Action|Adventure|Science Fiction|Thriller   2015-06-09   \n",
       "\n",
       "   release_year             cast_seg  \n",
       "0          2015          Chris Pratt  \n",
       "0          2015  Bryce Dallas Howard  \n",
       "0          2015          Irrfan Khan  \n",
       "0          2015    Vincent D'Onofrio  \n",
       "0          2015        Nick Robinson  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_cast\n",
    "df_cast_detail = get_seperatedRows(df['cast'], 'cast', '|')\n",
    "df_cast = df.join(df_cast_detail, lsuffix='_origin', rsuffix='_seg')\n",
    "df_cast.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I completed the dataframes I need. :  df_cast, df_genres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "> **Tip**: Now that you've trimmed and cleaned your data, you're ready to move on to exploration. Compute statistics and create visualizations with the goal of addressing the research questions that you posed in the Introduction section. It is recommended that you be systematic with your approach. Look at one variable at a time, and then follow it up by looking at relationships between variables.\n",
    "\n",
    "### Q1. Which genres are most popular from year to year?\n",
    "#### Create the dataframe for genres grouped by year and then rank for popularity.\n",
    "##### Reference : https://stackoverflow.com/questions/49561140/keyerror-while-trying-to-plot-a-pandas-pivot-table\n",
    "##### Reference : https://stackoverflow.com/questions/42854598/pandas-dataframe-set-index-with-inplace-true-returns-a-nonetype-why"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create id for each genre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
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       "      <th>0</th>\n",
       "      <td>32.99</td>\n",
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       "      <td>1.513529e+09</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
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       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
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       "      <th>0</th>\n",
       "      <td>32.99</td>\n",
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       "      <td>Jurassic World</td>\n",
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       "      <td>2</td>\n",
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       "      <th>0</th>\n",
       "      <td>32.99</td>\n",
       "      <td>1.500000e+08</td>\n",
       "      <td>1.513529e+09</td>\n",
       "      <td>Jurassic World</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>2015-06-09</td>\n",
       "      <td>2015</td>\n",
       "      <td>Science Fiction</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.99</td>\n",
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       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
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       "      <td>2015</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.42</td>\n",
       "      <td>1.500000e+08</td>\n",
       "      <td>3.784364e+08</td>\n",
       "      <td>Mad Max: Fury Road</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>2015-05-13</td>\n",
       "      <td>2015</td>\n",
       "      <td>Action</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.42</td>\n",
       "      <td>1.500000e+08</td>\n",
       "      <td>3.784364e+08</td>\n",
       "      <td>Mad Max: Fury Road</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>2015-05-13</td>\n",
       "      <td>2015</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.42</td>\n",
       "      <td>1.500000e+08</td>\n",
       "      <td>3.784364e+08</td>\n",
       "      <td>Mad Max: Fury Road</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>2015-05-13</td>\n",
       "      <td>2015</td>\n",
       "      <td>Science Fiction</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.42</td>\n",
       "      <td>1.500000e+08</td>\n",
       "      <td>3.784364e+08</td>\n",
       "      <td>Mad Max: Fury Road</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>2015-05-13</td>\n",
       "      <td>2015</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.11</td>\n",
       "      <td>1.100000e+08</td>\n",
       "      <td>2.952382e+08</td>\n",
       "      <td>Insurgent</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>2015-03-18</td>\n",
       "      <td>2015</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.11</td>\n",
       "      <td>1.100000e+08</td>\n",
       "      <td>2.952382e+08</td>\n",
       "      <td>Insurgent</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>2015-03-18</td>\n",
       "      <td>2015</td>\n",
       "      <td>Science Fiction</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.11</td>\n",
       "      <td>1.100000e+08</td>\n",
       "      <td>2.952382e+08</td>\n",
       "      <td>Insurgent</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>2015-03-18</td>\n",
       "      <td>2015</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.17</td>\n",
       "      <td>2.000000e+08</td>\n",
       "      <td>2.068178e+09</td>\n",
       "      <td>Star Wars: The Force Awakens</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>2015-12-15</td>\n",
       "      <td>2015</td>\n",
       "      <td>Action</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.17</td>\n",
       "      <td>2.000000e+08</td>\n",
       "      <td>2.068178e+09</td>\n",
       "      <td>Star Wars: The Force Awakens</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>2015-12-15</td>\n",
       "      <td>2015</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.17</td>\n",
       "      <td>2.000000e+08</td>\n",
       "      <td>2.068178e+09</td>\n",
       "      <td>Star Wars: The Force Awakens</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>2015-12-15</td>\n",
       "      <td>2015</td>\n",
       "      <td>Science Fiction</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.17</td>\n",
       "      <td>2.000000e+08</td>\n",
       "      <td>2.068178e+09</td>\n",
       "      <td>Star Wars: The Force Awakens</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>2015-12-15</td>\n",
       "      <td>2015</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.34</td>\n",
       "      <td>1.900000e+08</td>\n",
       "      <td>1.506249e+09</td>\n",
       "      <td>Furious 7</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>2015-04-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>Action</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.34</td>\n",
       "      <td>1.900000e+08</td>\n",
       "      <td>1.506249e+09</td>\n",
       "      <td>Furious 7</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>2015-04-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>Crime</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.34</td>\n",
       "      <td>1.900000e+08</td>\n",
       "      <td>1.506249e+09</td>\n",
       "      <td>Furious 7</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>2015-04-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.11</td>\n",
       "      <td>1.350000e+08</td>\n",
       "      <td>5.329505e+08</td>\n",
       "      <td>The Revenant</td>\n",
       "      <td>Leonardo DiCaprio|Tom Hardy|Will Poulter|Domhn...</td>\n",
       "      <td>Alejandro GonzÃ¡lez IÃ±Ã¡rritu</td>\n",
       "      <td>156</td>\n",
       "      <td>Western|Drama|Adventure|Thriller</td>\n",
       "      <td>2015-12-25</td>\n",
       "      <td>2015</td>\n",
       "      <td>Western</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.11</td>\n",
       "      <td>1.350000e+08</td>\n",
       "      <td>5.329505e+08</td>\n",
       "      <td>The Revenant</td>\n",
       "      <td>Leonardo DiCaprio|Tom Hardy|Will Poulter|Domhn...</td>\n",
       "      <td>Alejandro GonzÃ¡lez IÃ±Ã¡rritu</td>\n",
       "      <td>156</td>\n",
       "      <td>Western|Drama|Adventure|Thriller</td>\n",
       "      <td>2015-12-25</td>\n",
       "      <td>2015</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.11</td>\n",
       "      <td>1.350000e+08</td>\n",
       "      <td>5.329505e+08</td>\n",
       "      <td>The Revenant</td>\n",
       "      <td>Leonardo DiCaprio|Tom Hardy|Will Poulter|Domhn...</td>\n",
       "      <td>Alejandro GonzÃ¡lez IÃ±Ã¡rritu</td>\n",
       "      <td>156</td>\n",
       "      <td>Western|Drama|Adventure|Thriller</td>\n",
       "      <td>2015-12-25</td>\n",
       "      <td>2015</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.11</td>\n",
       "      <td>1.350000e+08</td>\n",
       "      <td>5.329505e+08</td>\n",
       "      <td>The Revenant</td>\n",
       "      <td>Leonardo DiCaprio|Tom Hardy|Will Poulter|Domhn...</td>\n",
       "      <td>Alejandro GonzÃ¡lez IÃ±Ã¡rritu</td>\n",
       "      <td>156</td>\n",
       "      <td>Western|Drama|Adventure|Thriller</td>\n",
       "      <td>2015-12-25</td>\n",
       "      <td>2015</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.65</td>\n",
       "      <td>1.550000e+08</td>\n",
       "      <td>4.406035e+08</td>\n",
       "      <td>Terminator Genisys</td>\n",
       "      <td>Arnold Schwarzenegger|Jason Clarke|Emilia Clar...</td>\n",
       "      <td>Alan Taylor</td>\n",
       "      <td>125</td>\n",
       "      <td>Science Fiction|Action|Thriller|Adventure</td>\n",
       "      <td>2015-06-23</td>\n",
       "      <td>2015</td>\n",
       "      <td>Science Fiction</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.65</td>\n",
       "      <td>1.550000e+08</td>\n",
       "      <td>4.406035e+08</td>\n",
       "      <td>Terminator Genisys</td>\n",
       "      <td>Arnold Schwarzenegger|Jason Clarke|Emilia Clar...</td>\n",
       "      <td>Alan Taylor</td>\n",
       "      <td>125</td>\n",
       "      <td>Science Fiction|Action|Thriller|Adventure</td>\n",
       "      <td>2015-06-23</td>\n",
       "      <td>2015</td>\n",
       "      <td>Action</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.65</td>\n",
       "      <td>1.550000e+08</td>\n",
       "      <td>4.406035e+08</td>\n",
       "      <td>Terminator Genisys</td>\n",
       "      <td>Arnold Schwarzenegger|Jason Clarke|Emilia Clar...</td>\n",
       "      <td>Alan Taylor</td>\n",
       "      <td>125</td>\n",
       "      <td>Science Fiction|Action|Thriller|Adventure</td>\n",
       "      <td>2015-06-23</td>\n",
       "      <td>2015</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.65</td>\n",
       "      <td>1.550000e+08</td>\n",
       "      <td>4.406035e+08</td>\n",
       "      <td>Terminator Genisys</td>\n",
       "      <td>Arnold Schwarzenegger|Jason Clarke|Emilia Clar...</td>\n",
       "      <td>Alan Taylor</td>\n",
       "      <td>125</td>\n",
       "      <td>Science Fiction|Action|Thriller|Adventure</td>\n",
       "      <td>2015-06-23</td>\n",
       "      <td>2015</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.67</td>\n",
       "      <td>1.080000e+08</td>\n",
       "      <td>5.953803e+08</td>\n",
       "      <td>The Martian</td>\n",
       "      <td>Matt Damon|Jessica Chastain|Kristen Wiig|Jeff ...</td>\n",
       "      <td>Ridley Scott</td>\n",
       "      <td>141</td>\n",
       "      <td>Drama|Adventure|Science Fiction</td>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>2015</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.67</td>\n",
       "      <td>1.080000e+08</td>\n",
       "      <td>5.953803e+08</td>\n",
       "      <td>The Martian</td>\n",
       "      <td>Matt Damon|Jessica Chastain|Kristen Wiig|Jeff ...</td>\n",
       "      <td>Ridley Scott</td>\n",
       "      <td>141</td>\n",
       "      <td>Drama|Adventure|Science Fiction</td>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>2015</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.67</td>\n",
       "      <td>1.080000e+08</td>\n",
       "      <td>5.953803e+08</td>\n",
       "      <td>The Martian</td>\n",
       "      <td>Matt Damon|Jessica Chastain|Kristen Wiig|Jeff ...</td>\n",
       "      <td>Ridley Scott</td>\n",
       "      <td>141</td>\n",
       "      <td>Drama|Adventure|Science Fiction</td>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>2015</td>\n",
       "      <td>Science Fiction</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.40</td>\n",
       "      <td>7.400000e+07</td>\n",
       "      <td>1.156731e+09</td>\n",
       "      <td>Minions</td>\n",
       "      <td>Sandra Bullock|Jon Hamm|Michael Keaton|Allison...</td>\n",
       "      <td>Kyle Balda|Pierre Coffin</td>\n",
       "      <td>91</td>\n",
       "      <td>Family|Animation|Adventure|Comedy</td>\n",
       "      <td>2015-06-17</td>\n",
       "      <td>2015</td>\n",
       "      <td>Family</td>\n",
       "      <td>8</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10853</th>\n",
       "      <td>0.16</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Alfie</td>\n",
       "      <td>Michael Caine|Shelley Winters|Millicent Martin...</td>\n",
       "      <td>Lewis Gilbert</td>\n",
       "      <td>114</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>1966-03-29</td>\n",
       "      <td>1966</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10853</th>\n",
       "      <td>0.16</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Alfie</td>\n",
       "      <td>Michael Caine|Shelley Winters|Millicent Martin...</td>\n",
       "      <td>Lewis Gilbert</td>\n",
       "      <td>114</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>1966-03-29</td>\n",
       "      <td>1966</td>\n",
       "      <td>Romance</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10854</th>\n",
       "      <td>0.15</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>The Chase</td>\n",
       "      <td>Marlon Brando|Jane Fonda|Robert Redford|E.G. M...</td>\n",
       "      <td>Arthur Penn</td>\n",
       "      <td>135</td>\n",
       "      <td>Thriller|Drama|Crime</td>\n",
       "      <td>1966-02-17</td>\n",
       "      <td>1966</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10854</th>\n",
       "      <td>0.15</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>The Chase</td>\n",
       "      <td>Marlon Brando|Jane Fonda|Robert Redford|E.G. M...</td>\n",
       "      <td>Arthur Penn</td>\n",
       "      <td>135</td>\n",
       "      <td>Thriller|Drama|Crime</td>\n",
       "      <td>1966-02-17</td>\n",
       "      <td>1966</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10854</th>\n",
       "      <td>0.15</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>The Chase</td>\n",
       "      <td>Marlon Brando|Jane Fonda|Robert Redford|E.G. M...</td>\n",
       "      <td>Arthur Penn</td>\n",
       "      <td>135</td>\n",
       "      <td>Thriller|Drama|Crime</td>\n",
       "      <td>1966-02-17</td>\n",
       "      <td>1966</td>\n",
       "      <td>Crime</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10855</th>\n",
       "      <td>0.14</td>\n",
       "      <td>7.000000e+05</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>The Ghost &amp; Mr. Chicken</td>\n",
       "      <td>Don Knotts|Joan Staley|Liam Redmond|Dick Sarge...</td>\n",
       "      <td>Alan Rafkin</td>\n",
       "      <td>90</td>\n",
       "      <td>Comedy|Family|Mystery|Romance</td>\n",
       "      <td>1966-01-20</td>\n",
       "      <td>1966</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10855</th>\n",
       "      <td>0.14</td>\n",
       "      <td>7.000000e+05</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>The Ghost &amp; Mr. Chicken</td>\n",
       "      <td>Don Knotts|Joan Staley|Liam Redmond|Dick Sarge...</td>\n",
       "      <td>Alan Rafkin</td>\n",
       "      <td>90</td>\n",
       "      <td>Comedy|Family|Mystery|Romance</td>\n",
       "      <td>1966-01-20</td>\n",
       "      <td>1966</td>\n",
       "      <td>Family</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10855</th>\n",
       "      <td>0.14</td>\n",
       "      <td>7.000000e+05</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>The Ghost &amp; Mr. Chicken</td>\n",
       "      <td>Don Knotts|Joan Staley|Liam Redmond|Dick Sarge...</td>\n",
       "      <td>Alan Rafkin</td>\n",
       "      <td>90</td>\n",
       "      <td>Comedy|Family|Mystery|Romance</td>\n",
       "      <td>1966-01-20</td>\n",
       "      <td>1966</td>\n",
       "      <td>Mystery</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10855</th>\n",
       "      <td>0.14</td>\n",
       "      <td>7.000000e+05</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>The Ghost &amp; Mr. Chicken</td>\n",
       "      <td>Don Knotts|Joan Staley|Liam Redmond|Dick Sarge...</td>\n",
       "      <td>Alan Rafkin</td>\n",
       "      <td>90</td>\n",
       "      <td>Comedy|Family|Mystery|Romance</td>\n",
       "      <td>1966-01-20</td>\n",
       "      <td>1966</td>\n",
       "      <td>Romance</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10856</th>\n",
       "      <td>0.14</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>The Ugly Dachshund</td>\n",
       "      <td>Dean Jones|Suzanne Pleshette|Charles Ruggles|K...</td>\n",
       "      <td>Norman Tokar</td>\n",
       "      <td>93</td>\n",
       "      <td>Comedy|Drama|Family</td>\n",
       "      <td>1966-02-16</td>\n",
       "      <td>1966</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10856</th>\n",
       "      <td>0.14</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>The Ugly Dachshund</td>\n",
       "      <td>Dean Jones|Suzanne Pleshette|Charles Ruggles|K...</td>\n",
       "      <td>Norman Tokar</td>\n",
       "      <td>93</td>\n",
       "      <td>Comedy|Drama|Family</td>\n",
       "      <td>1966-02-16</td>\n",
       "      <td>1966</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10856</th>\n",
       "      <td>0.14</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>The Ugly Dachshund</td>\n",
       "      <td>Dean Jones|Suzanne Pleshette|Charles Ruggles|K...</td>\n",
       "      <td>Norman Tokar</td>\n",
       "      <td>93</td>\n",
       "      <td>Comedy|Drama|Family</td>\n",
       "      <td>1966-02-16</td>\n",
       "      <td>1966</td>\n",
       "      <td>Family</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10857</th>\n",
       "      <td>0.13</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Nevada Smith</td>\n",
       "      <td>Steve McQueen|Karl Malden|Brian Keith|Arthur K...</td>\n",
       "      <td>Henry Hathaway</td>\n",
       "      <td>128</td>\n",
       "      <td>Action|Western</td>\n",
       "      <td>1966-06-10</td>\n",
       "      <td>1966</td>\n",
       "      <td>Action</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10857</th>\n",
       "      <td>0.13</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Nevada Smith</td>\n",
       "      <td>Steve McQueen|Karl Malden|Brian Keith|Arthur K...</td>\n",
       "      <td>Henry Hathaway</td>\n",
       "      <td>128</td>\n",
       "      <td>Action|Western</td>\n",
       "      <td>1966-06-10</td>\n",
       "      <td>1966</td>\n",
       "      <td>Western</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10858</th>\n",
       "      <td>0.32</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>The Russians Are Coming, The Russians Are Coming</td>\n",
       "      <td>Carl Reiner|Eva Marie Saint|Alan Arkin|Brian K...</td>\n",
       "      <td>Norman Jewison</td>\n",
       "      <td>126</td>\n",
       "      <td>Comedy|War</td>\n",
       "      <td>1966-05-25</td>\n",
       "      <td>1966</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10858</th>\n",
       "      <td>0.32</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>The Russians Are Coming, The Russians Are Coming</td>\n",
       "      <td>Carl Reiner|Eva Marie Saint|Alan Arkin|Brian K...</td>\n",
       "      <td>Norman Jewison</td>\n",
       "      <td>126</td>\n",
       "      <td>Comedy|War</td>\n",
       "      <td>1966-05-25</td>\n",
       "      <td>1966</td>\n",
       "      <td>War</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10859</th>\n",
       "      <td>0.09</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Seconds</td>\n",
       "      <td>Rock Hudson|Salome Jens|John Randolph|Will Gee...</td>\n",
       "      <td>John Frankenheimer</td>\n",
       "      <td>100</td>\n",
       "      <td>Mystery|Science Fiction|Thriller|Drama</td>\n",
       "      <td>1966-10-05</td>\n",
       "      <td>1966</td>\n",
       "      <td>Mystery</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10859</th>\n",
       "      <td>0.09</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Seconds</td>\n",
       "      <td>Rock Hudson|Salome Jens|John Randolph|Will Gee...</td>\n",
       "      <td>John Frankenheimer</td>\n",
       "      <td>100</td>\n",
       "      <td>Mystery|Science Fiction|Thriller|Drama</td>\n",
       "      <td>1966-10-05</td>\n",
       "      <td>1966</td>\n",
       "      <td>Science Fiction</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10859</th>\n",
       "      <td>0.09</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Seconds</td>\n",
       "      <td>Rock Hudson|Salome Jens|John Randolph|Will Gee...</td>\n",
       "      <td>John Frankenheimer</td>\n",
       "      <td>100</td>\n",
       "      <td>Mystery|Science Fiction|Thriller|Drama</td>\n",
       "      <td>1966-10-05</td>\n",
       "      <td>1966</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10859</th>\n",
       "      <td>0.09</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Seconds</td>\n",
       "      <td>Rock Hudson|Salome Jens|John Randolph|Will Gee...</td>\n",
       "      <td>John Frankenheimer</td>\n",
       "      <td>100</td>\n",
       "      <td>Mystery|Science Fiction|Thriller|Drama</td>\n",
       "      <td>1966-10-05</td>\n",
       "      <td>1966</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10860</th>\n",
       "      <td>0.09</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Carry On Screaming!</td>\n",
       "      <td>Kenneth Williams|Jim Dale|Harry H. Corbett|Joa...</td>\n",
       "      <td>Gerald Thomas</td>\n",
       "      <td>87</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1966-05-20</td>\n",
       "      <td>1966</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10861</th>\n",
       "      <td>0.08</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>The Endless Summer</td>\n",
       "      <td>Michael Hynson|Robert August|Lord 'Tally Ho' B...</td>\n",
       "      <td>Bruce Brown</td>\n",
       "      <td>95</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>1966-06-15</td>\n",
       "      <td>1966</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10862</th>\n",
       "      <td>0.07</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Grand Prix</td>\n",
       "      <td>James Garner|Eva Marie Saint|Yves Montand|Tosh...</td>\n",
       "      <td>John Frankenheimer</td>\n",
       "      <td>176</td>\n",
       "      <td>Action|Adventure|Drama</td>\n",
       "      <td>1966-12-21</td>\n",
       "      <td>1966</td>\n",
       "      <td>Action</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10862</th>\n",
       "      <td>0.07</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Grand Prix</td>\n",
       "      <td>James Garner|Eva Marie Saint|Yves Montand|Tosh...</td>\n",
       "      <td>John Frankenheimer</td>\n",
       "      <td>176</td>\n",
       "      <td>Action|Adventure|Drama</td>\n",
       "      <td>1966-12-21</td>\n",
       "      <td>1966</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10862</th>\n",
       "      <td>0.07</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Grand Prix</td>\n",
       "      <td>James Garner|Eva Marie Saint|Yves Montand|Tosh...</td>\n",
       "      <td>John Frankenheimer</td>\n",
       "      <td>176</td>\n",
       "      <td>Action|Adventure|Drama</td>\n",
       "      <td>1966-12-21</td>\n",
       "      <td>1966</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10863</th>\n",
       "      <td>0.07</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Beregis Avtomobilya</td>\n",
       "      <td>Innokentiy Smoktunovskiy|Oleg Efremov|Georgi Z...</td>\n",
       "      <td>Eldar Ryazanov</td>\n",
       "      <td>94</td>\n",
       "      <td>Mystery|Comedy</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1966</td>\n",
       "      <td>Mystery</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10863</th>\n",
       "      <td>0.07</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Beregis Avtomobilya</td>\n",
       "      <td>Innokentiy Smoktunovskiy|Oleg Efremov|Georgi Z...</td>\n",
       "      <td>Eldar Ryazanov</td>\n",
       "      <td>94</td>\n",
       "      <td>Mystery|Comedy</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1966</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10864</th>\n",
       "      <td>0.06</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>What's Up, Tiger Lily?</td>\n",
       "      <td>Tatsuya Mihashi|Akiko Wakabayashi|Mie Hama|Joh...</td>\n",
       "      <td>Woody Allen</td>\n",
       "      <td>80</td>\n",
       "      <td>Action|Comedy</td>\n",
       "      <td>1966-11-02</td>\n",
       "      <td>1966</td>\n",
       "      <td>Action</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10864</th>\n",
       "      <td>0.06</td>\n",
       "      <td>1.900623e+06</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>What's Up, Tiger Lily?</td>\n",
       "      <td>Tatsuya Mihashi|Akiko Wakabayashi|Mie Hama|Joh...</td>\n",
       "      <td>Woody Allen</td>\n",
       "      <td>80</td>\n",
       "      <td>Action|Comedy</td>\n",
       "      <td>1966-11-02</td>\n",
       "      <td>1966</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10865</th>\n",
       "      <td>0.04</td>\n",
       "      <td>1.900000e+04</td>\n",
       "      <td>2.050484e+06</td>\n",
       "      <td>Manos: The Hands of Fate</td>\n",
       "      <td>Harold P. Warren|Tom Neyman|John Reynolds|Dian...</td>\n",
       "      <td>Harold P. Warren</td>\n",
       "      <td>74</td>\n",
       "      <td>Horror</td>\n",
       "      <td>1966-11-15</td>\n",
       "      <td>1966</td>\n",
       "      <td>Horror</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26753 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       popularity        budget       revenue  \\\n",
       "0           32.99  1.500000e+08  1.513529e+09   \n",
       "0           32.99  1.500000e+08  1.513529e+09   \n",
       "0           32.99  1.500000e+08  1.513529e+09   \n",
       "0           32.99  1.500000e+08  1.513529e+09   \n",
       "1           28.42  1.500000e+08  3.784364e+08   \n",
       "1           28.42  1.500000e+08  3.784364e+08   \n",
       "1           28.42  1.500000e+08  3.784364e+08   \n",
       "1           28.42  1.500000e+08  3.784364e+08   \n",
       "2           13.11  1.100000e+08  2.952382e+08   \n",
       "2           13.11  1.100000e+08  2.952382e+08   \n",
       "2           13.11  1.100000e+08  2.952382e+08   \n",
       "3           11.17  2.000000e+08  2.068178e+09   \n",
       "3           11.17  2.000000e+08  2.068178e+09   \n",
       "3           11.17  2.000000e+08  2.068178e+09   \n",
       "3           11.17  2.000000e+08  2.068178e+09   \n",
       "4            9.34  1.900000e+08  1.506249e+09   \n",
       "4            9.34  1.900000e+08  1.506249e+09   \n",
       "4            9.34  1.900000e+08  1.506249e+09   \n",
       "5            9.11  1.350000e+08  5.329505e+08   \n",
       "5            9.11  1.350000e+08  5.329505e+08   \n",
       "5            9.11  1.350000e+08  5.329505e+08   \n",
       "5            9.11  1.350000e+08  5.329505e+08   \n",
       "6            8.65  1.550000e+08  4.406035e+08   \n",
       "6            8.65  1.550000e+08  4.406035e+08   \n",
       "6            8.65  1.550000e+08  4.406035e+08   \n",
       "6            8.65  1.550000e+08  4.406035e+08   \n",
       "7            7.67  1.080000e+08  5.953803e+08   \n",
       "7            7.67  1.080000e+08  5.953803e+08   \n",
       "7            7.67  1.080000e+08  5.953803e+08   \n",
       "8            7.40  7.400000e+07  1.156731e+09   \n",
       "...           ...           ...           ...   \n",
       "10853        0.16  1.900623e+06  2.050484e+06   \n",
       "10853        0.16  1.900623e+06  2.050484e+06   \n",
       "10854        0.15  1.900623e+06  2.050484e+06   \n",
       "10854        0.15  1.900623e+06  2.050484e+06   \n",
       "10854        0.15  1.900623e+06  2.050484e+06   \n",
       "10855        0.14  7.000000e+05  2.050484e+06   \n",
       "10855        0.14  7.000000e+05  2.050484e+06   \n",
       "10855        0.14  7.000000e+05  2.050484e+06   \n",
       "10855        0.14  7.000000e+05  2.050484e+06   \n",
       "10856        0.14  1.900623e+06  2.050484e+06   \n",
       "10856        0.14  1.900623e+06  2.050484e+06   \n",
       "10856        0.14  1.900623e+06  2.050484e+06   \n",
       "10857        0.13  1.900623e+06  2.050484e+06   \n",
       "10857        0.13  1.900623e+06  2.050484e+06   \n",
       "10858        0.32  1.900623e+06  2.050484e+06   \n",
       "10858        0.32  1.900623e+06  2.050484e+06   \n",
       "10859        0.09  1.900623e+06  2.050484e+06   \n",
       "10859        0.09  1.900623e+06  2.050484e+06   \n",
       "10859        0.09  1.900623e+06  2.050484e+06   \n",
       "10859        0.09  1.900623e+06  2.050484e+06   \n",
       "10860        0.09  1.900623e+06  2.050484e+06   \n",
       "10861        0.08  1.900623e+06  2.050484e+06   \n",
       "10862        0.07  1.900623e+06  2.050484e+06   \n",
       "10862        0.07  1.900623e+06  2.050484e+06   \n",
       "10862        0.07  1.900623e+06  2.050484e+06   \n",
       "10863        0.07  1.900623e+06  2.050484e+06   \n",
       "10863        0.07  1.900623e+06  2.050484e+06   \n",
       "10864        0.06  1.900623e+06  2.050484e+06   \n",
       "10864        0.06  1.900623e+06  2.050484e+06   \n",
       "10865        0.04  1.900000e+04  2.050484e+06   \n",
       "\n",
       "                                         original_title  \\\n",
       "0                                        Jurassic World   \n",
       "0                                        Jurassic World   \n",
       "0                                        Jurassic World   \n",
       "0                                        Jurassic World   \n",
       "1                                    Mad Max: Fury Road   \n",
       "1                                    Mad Max: Fury Road   \n",
       "1                                    Mad Max: Fury Road   \n",
       "1                                    Mad Max: Fury Road   \n",
       "2                                             Insurgent   \n",
       "2                                             Insurgent   \n",
       "2                                             Insurgent   \n",
       "3                          Star Wars: The Force Awakens   \n",
       "3                          Star Wars: The Force Awakens   \n",
       "3                          Star Wars: The Force Awakens   \n",
       "3                          Star Wars: The Force Awakens   \n",
       "4                                             Furious 7   \n",
       "4                                             Furious 7   \n",
       "4                                             Furious 7   \n",
       "5                                          The Revenant   \n",
       "5                                          The Revenant   \n",
       "5                                          The Revenant   \n",
       "5                                          The Revenant   \n",
       "6                                    Terminator Genisys   \n",
       "6                                    Terminator Genisys   \n",
       "6                                    Terminator Genisys   \n",
       "6                                    Terminator Genisys   \n",
       "7                                           The Martian   \n",
       "7                                           The Martian   \n",
       "7                                           The Martian   \n",
       "8                                               Minions   \n",
       "...                                                 ...   \n",
       "10853                                             Alfie   \n",
       "10853                                             Alfie   \n",
       "10854                                         The Chase   \n",
       "10854                                         The Chase   \n",
       "10854                                         The Chase   \n",
       "10855                           The Ghost & Mr. Chicken   \n",
       "10855                           The Ghost & Mr. Chicken   \n",
       "10855                           The Ghost & Mr. Chicken   \n",
       "10855                           The Ghost & Mr. Chicken   \n",
       "10856                                The Ugly Dachshund   \n",
       "10856                                The Ugly Dachshund   \n",
       "10856                                The Ugly Dachshund   \n",
       "10857                                      Nevada Smith   \n",
       "10857                                      Nevada Smith   \n",
       "10858  The Russians Are Coming, The Russians Are Coming   \n",
       "10858  The Russians Are Coming, The Russians Are Coming   \n",
       "10859                                           Seconds   \n",
       "10859                                           Seconds   \n",
       "10859                                           Seconds   \n",
       "10859                                           Seconds   \n",
       "10860                               Carry On Screaming!   \n",
       "10861                                The Endless Summer   \n",
       "10862                                        Grand Prix   \n",
       "10862                                        Grand Prix   \n",
       "10862                                        Grand Prix   \n",
       "10863                               Beregis Avtomobilya   \n",
       "10863                               Beregis Avtomobilya   \n",
       "10864                            What's Up, Tiger Lily?   \n",
       "10864                            What's Up, Tiger Lily?   \n",
       "10865                          Manos: The Hands of Fate   \n",
       "\n",
       "                                                    cast  \\\n",
       "0      Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "0      Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "0      Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "0      Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "1      Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...   \n",
       "1      Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...   \n",
       "1      Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...   \n",
       "1      Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...   \n",
       "2      Shailene Woodley|Theo James|Kate Winslet|Ansel...   \n",
       "2      Shailene Woodley|Theo James|Kate Winslet|Ansel...   \n",
       "2      Shailene Woodley|Theo James|Kate Winslet|Ansel...   \n",
       "3      Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...   \n",
       "3      Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...   \n",
       "3      Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...   \n",
       "3      Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...   \n",
       "4      Vin Diesel|Paul Walker|Jason Statham|Michelle ...   \n",
       "4      Vin Diesel|Paul Walker|Jason Statham|Michelle ...   \n",
       "4      Vin Diesel|Paul Walker|Jason Statham|Michelle ...   \n",
       "5      Leonardo DiCaprio|Tom Hardy|Will Poulter|Domhn...   \n",
       "5      Leonardo DiCaprio|Tom Hardy|Will Poulter|Domhn...   \n",
       "5      Leonardo DiCaprio|Tom Hardy|Will Poulter|Domhn...   \n",
       "5      Leonardo DiCaprio|Tom Hardy|Will Poulter|Domhn...   \n",
       "6      Arnold Schwarzenegger|Jason Clarke|Emilia Clar...   \n",
       "6      Arnold Schwarzenegger|Jason Clarke|Emilia Clar...   \n",
       "6      Arnold Schwarzenegger|Jason Clarke|Emilia Clar...   \n",
       "6      Arnold Schwarzenegger|Jason Clarke|Emilia Clar...   \n",
       "7      Matt Damon|Jessica Chastain|Kristen Wiig|Jeff ...   \n",
       "7      Matt Damon|Jessica Chastain|Kristen Wiig|Jeff ...   \n",
       "7      Matt Damon|Jessica Chastain|Kristen Wiig|Jeff ...   \n",
       "8      Sandra Bullock|Jon Hamm|Michael Keaton|Allison...   \n",
       "...                                                  ...   \n",
       "10853  Michael Caine|Shelley Winters|Millicent Martin...   \n",
       "10853  Michael Caine|Shelley Winters|Millicent Martin...   \n",
       "10854  Marlon Brando|Jane Fonda|Robert Redford|E.G. M...   \n",
       "10854  Marlon Brando|Jane Fonda|Robert Redford|E.G. M...   \n",
       "10854  Marlon Brando|Jane Fonda|Robert Redford|E.G. M...   \n",
       "10855  Don Knotts|Joan Staley|Liam Redmond|Dick Sarge...   \n",
       "10855  Don Knotts|Joan Staley|Liam Redmond|Dick Sarge...   \n",
       "10855  Don Knotts|Joan Staley|Liam Redmond|Dick Sarge...   \n",
       "10855  Don Knotts|Joan Staley|Liam Redmond|Dick Sarge...   \n",
       "10856  Dean Jones|Suzanne Pleshette|Charles Ruggles|K...   \n",
       "10856  Dean Jones|Suzanne Pleshette|Charles Ruggles|K...   \n",
       "10856  Dean Jones|Suzanne Pleshette|Charles Ruggles|K...   \n",
       "10857  Steve McQueen|Karl Malden|Brian Keith|Arthur K...   \n",
       "10857  Steve McQueen|Karl Malden|Brian Keith|Arthur K...   \n",
       "10858  Carl Reiner|Eva Marie Saint|Alan Arkin|Brian K...   \n",
       "10858  Carl Reiner|Eva Marie Saint|Alan Arkin|Brian K...   \n",
       "10859  Rock Hudson|Salome Jens|John Randolph|Will Gee...   \n",
       "10859  Rock Hudson|Salome Jens|John Randolph|Will Gee...   \n",
       "10859  Rock Hudson|Salome Jens|John Randolph|Will Gee...   \n",
       "10859  Rock Hudson|Salome Jens|John Randolph|Will Gee...   \n",
       "10860  Kenneth Williams|Jim Dale|Harry H. Corbett|Joa...   \n",
       "10861  Michael Hynson|Robert August|Lord 'Tally Ho' B...   \n",
       "10862  James Garner|Eva Marie Saint|Yves Montand|Tosh...   \n",
       "10862  James Garner|Eva Marie Saint|Yves Montand|Tosh...   \n",
       "10862  James Garner|Eva Marie Saint|Yves Montand|Tosh...   \n",
       "10863  Innokentiy Smoktunovskiy|Oleg Efremov|Georgi Z...   \n",
       "10863  Innokentiy Smoktunovskiy|Oleg Efremov|Georgi Z...   \n",
       "10864  Tatsuya Mihashi|Akiko Wakabayashi|Mie Hama|Joh...   \n",
       "10864  Tatsuya Mihashi|Akiko Wakabayashi|Mie Hama|Joh...   \n",
       "10865  Harold P. Warren|Tom Neyman|John Reynolds|Dian...   \n",
       "\n",
       "                             director  runtime  \\\n",
       "0                     Colin Trevorrow      124   \n",
       "0                     Colin Trevorrow      124   \n",
       "0                     Colin Trevorrow      124   \n",
       "0                     Colin Trevorrow      124   \n",
       "1                       George Miller      120   \n",
       "1                       George Miller      120   \n",
       "1                       George Miller      120   \n",
       "1                       George Miller      120   \n",
       "2                    Robert Schwentke      119   \n",
       "2                    Robert Schwentke      119   \n",
       "2                    Robert Schwentke      119   \n",
       "3                         J.J. Abrams      136   \n",
       "3                         J.J. Abrams      136   \n",
       "3                         J.J. Abrams      136   \n",
       "3                         J.J. Abrams      136   \n",
       "4                           James Wan      137   \n",
       "4                           James Wan      137   \n",
       "4                           James Wan      137   \n",
       "5      Alejandro GonzÃ¡lez IÃ±Ã¡rritu      156   \n",
       "5      Alejandro GonzÃ¡lez IÃ±Ã¡rritu      156   \n",
       "5      Alejandro GonzÃ¡lez IÃ±Ã¡rritu      156   \n",
       "5      Alejandro GonzÃ¡lez IÃ±Ã¡rritu      156   \n",
       "6                         Alan Taylor      125   \n",
       "6                         Alan Taylor      125   \n",
       "6                         Alan Taylor      125   \n",
       "6                         Alan Taylor      125   \n",
       "7                        Ridley Scott      141   \n",
       "7                        Ridley Scott      141   \n",
       "7                        Ridley Scott      141   \n",
       "8            Kyle Balda|Pierre Coffin       91   \n",
       "...                               ...      ...   \n",
       "10853                   Lewis Gilbert      114   \n",
       "10853                   Lewis Gilbert      114   \n",
       "10854                     Arthur Penn      135   \n",
       "10854                     Arthur Penn      135   \n",
       "10854                     Arthur Penn      135   \n",
       "10855                     Alan Rafkin       90   \n",
       "10855                     Alan Rafkin       90   \n",
       "10855                     Alan Rafkin       90   \n",
       "10855                     Alan Rafkin       90   \n",
       "10856                    Norman Tokar       93   \n",
       "10856                    Norman Tokar       93   \n",
       "10856                    Norman Tokar       93   \n",
       "10857                  Henry Hathaway      128   \n",
       "10857                  Henry Hathaway      128   \n",
       "10858                  Norman Jewison      126   \n",
       "10858                  Norman Jewison      126   \n",
       "10859              John Frankenheimer      100   \n",
       "10859              John Frankenheimer      100   \n",
       "10859              John Frankenheimer      100   \n",
       "10859              John Frankenheimer      100   \n",
       "10860                   Gerald Thomas       87   \n",
       "10861                     Bruce Brown       95   \n",
       "10862              John Frankenheimer      176   \n",
       "10862              John Frankenheimer      176   \n",
       "10862              John Frankenheimer      176   \n",
       "10863                  Eldar Ryazanov       94   \n",
       "10863                  Eldar Ryazanov       94   \n",
       "10864                     Woody Allen       80   \n",
       "10864                     Woody Allen       80   \n",
       "10865                Harold P. Warren       74   \n",
       "\n",
       "                                      genres_all release_date  release_year  \\\n",
       "0      Action|Adventure|Science Fiction|Thriller   2015-06-09          2015   \n",
       "0      Action|Adventure|Science Fiction|Thriller   2015-06-09          2015   \n",
       "0      Action|Adventure|Science Fiction|Thriller   2015-06-09          2015   \n",
       "0      Action|Adventure|Science Fiction|Thriller   2015-06-09          2015   \n",
       "1      Action|Adventure|Science Fiction|Thriller   2015-05-13          2015   \n",
       "1      Action|Adventure|Science Fiction|Thriller   2015-05-13          2015   \n",
       "1      Action|Adventure|Science Fiction|Thriller   2015-05-13          2015   \n",
       "1      Action|Adventure|Science Fiction|Thriller   2015-05-13          2015   \n",
       "2             Adventure|Science Fiction|Thriller   2015-03-18          2015   \n",
       "2             Adventure|Science Fiction|Thriller   2015-03-18          2015   \n",
       "2             Adventure|Science Fiction|Thriller   2015-03-18          2015   \n",
       "3       Action|Adventure|Science Fiction|Fantasy   2015-12-15          2015   \n",
       "3       Action|Adventure|Science Fiction|Fantasy   2015-12-15          2015   \n",
       "3       Action|Adventure|Science Fiction|Fantasy   2015-12-15          2015   \n",
       "3       Action|Adventure|Science Fiction|Fantasy   2015-12-15          2015   \n",
       "4                          Action|Crime|Thriller   2015-04-01          2015   \n",
       "4                          Action|Crime|Thriller   2015-04-01          2015   \n",
       "4                          Action|Crime|Thriller   2015-04-01          2015   \n",
       "5               Western|Drama|Adventure|Thriller   2015-12-25          2015   \n",
       "5               Western|Drama|Adventure|Thriller   2015-12-25          2015   \n",
       "5               Western|Drama|Adventure|Thriller   2015-12-25          2015   \n",
       "5               Western|Drama|Adventure|Thriller   2015-12-25          2015   \n",
       "6      Science Fiction|Action|Thriller|Adventure   2015-06-23          2015   \n",
       "6      Science Fiction|Action|Thriller|Adventure   2015-06-23          2015   \n",
       "6      Science Fiction|Action|Thriller|Adventure   2015-06-23          2015   \n",
       "6      Science Fiction|Action|Thriller|Adventure   2015-06-23          2015   \n",
       "7                Drama|Adventure|Science Fiction   2015-09-30          2015   \n",
       "7                Drama|Adventure|Science Fiction   2015-09-30          2015   \n",
       "7                Drama|Adventure|Science Fiction   2015-09-30          2015   \n",
       "8              Family|Animation|Adventure|Comedy   2015-06-17          2015   \n",
       "...                                          ...          ...           ...   \n",
       "10853                       Comedy|Drama|Romance   1966-03-29          1966   \n",
       "10853                       Comedy|Drama|Romance   1966-03-29          1966   \n",
       "10854                       Thriller|Drama|Crime   1966-02-17          1966   \n",
       "10854                       Thriller|Drama|Crime   1966-02-17          1966   \n",
       "10854                       Thriller|Drama|Crime   1966-02-17          1966   \n",
       "10855              Comedy|Family|Mystery|Romance   1966-01-20          1966   \n",
       "10855              Comedy|Family|Mystery|Romance   1966-01-20          1966   \n",
       "10855              Comedy|Family|Mystery|Romance   1966-01-20          1966   \n",
       "10855              Comedy|Family|Mystery|Romance   1966-01-20          1966   \n",
       "10856                        Comedy|Drama|Family   1966-02-16          1966   \n",
       "10856                        Comedy|Drama|Family   1966-02-16          1966   \n",
       "10856                        Comedy|Drama|Family   1966-02-16          1966   \n",
       "10857                             Action|Western   1966-06-10          1966   \n",
       "10857                             Action|Western   1966-06-10          1966   \n",
       "10858                                 Comedy|War   1966-05-25          1966   \n",
       "10858                                 Comedy|War   1966-05-25          1966   \n",
       "10859     Mystery|Science Fiction|Thriller|Drama   1966-10-05          1966   \n",
       "10859     Mystery|Science Fiction|Thriller|Drama   1966-10-05          1966   \n",
       "10859     Mystery|Science Fiction|Thriller|Drama   1966-10-05          1966   \n",
       "10859     Mystery|Science Fiction|Thriller|Drama   1966-10-05          1966   \n",
       "10860                                     Comedy   1966-05-20          1966   \n",
       "10861                                Documentary   1966-06-15          1966   \n",
       "10862                     Action|Adventure|Drama   1966-12-21          1966   \n",
       "10862                     Action|Adventure|Drama   1966-12-21          1966   \n",
       "10862                     Action|Adventure|Drama   1966-12-21          1966   \n",
       "10863                             Mystery|Comedy   1966-01-01          1966   \n",
       "10863                             Mystery|Comedy   1966-01-01          1966   \n",
       "10864                              Action|Comedy   1966-11-02          1966   \n",
       "10864                              Action|Comedy   1966-11-02          1966   \n",
       "10865                                     Horror   1966-11-15          1966   \n",
       "\n",
       "            genres_seg genres_id  \n",
       "0               Action         1  \n",
       "0            Adventure         2  \n",
       "0      Science Fiction        16  \n",
       "0             Thriller        18  \n",
       "1               Action         1  \n",
       "1            Adventure         2  \n",
       "1      Science Fiction        16  \n",
       "1             Thriller        18  \n",
       "2            Adventure         2  \n",
       "2      Science Fiction        16  \n",
       "2             Thriller        18  \n",
       "3               Action         1  \n",
       "3            Adventure         2  \n",
       "3      Science Fiction        16  \n",
       "3              Fantasy         9  \n",
       "4               Action         1  \n",
       "4                Crime         5  \n",
       "4             Thriller        18  \n",
       "5              Western        20  \n",
       "5                Drama         7  \n",
       "5            Adventure         2  \n",
       "5             Thriller        18  \n",
       "6      Science Fiction        16  \n",
       "6               Action         1  \n",
       "6             Thriller        18  \n",
       "6            Adventure         2  \n",
       "7                Drama         7  \n",
       "7            Adventure         2  \n",
       "7      Science Fiction        16  \n",
       "8               Family         8  \n",
       "...                ...       ...  \n",
       "10853            Drama         7  \n",
       "10853          Romance        15  \n",
       "10854         Thriller        18  \n",
       "10854            Drama         7  \n",
       "10854            Crime         5  \n",
       "10855           Comedy         4  \n",
       "10855           Family         8  \n",
       "10855          Mystery        14  \n",
       "10855          Romance        15  \n",
       "10856           Comedy         4  \n",
       "10856            Drama         7  \n",
       "10856           Family         8  \n",
       "10857           Action         1  \n",
       "10857          Western        20  \n",
       "10858           Comedy         4  \n",
       "10858              War        19  \n",
       "10859          Mystery        14  \n",
       "10859  Science Fiction        16  \n",
       "10859         Thriller        18  \n",
       "10859            Drama         7  \n",
       "10860           Comedy         4  \n",
       "10861      Documentary         6  \n",
       "10862           Action         1  \n",
       "10862        Adventure         2  \n",
       "10862            Drama         7  \n",
       "10863          Mystery        14  \n",
       "10863           Comedy         4  \n",
       "10864           Action         1  \n",
       "10864           Comedy         4  \n",
       "10865           Horror        12  \n",
       "\n",
       "[26753 rows x 12 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_names = df_genres.groupby('genres_seg').count().index\n",
    "genre_names[0]\n",
    "id_list = []\n",
    "# Index(['Action', 'Adventure', 'Animation', 'Comedy', 'Crime', 'Documentary',\n",
    "#'Drama', 'Family', 'Fantasy', 'Foreign', 'History', 'Horror', 'Music',\n",
    "#'Mystery', 'Romance', 'Science Fiction', 'TV Movie', 'Thriller', 'War', 'Western'],\n",
    "df_genres.head()\n",
    "\n",
    "for i in df_genres['genres_seg']:\n",
    "    if i == genre_names[0]:  #Action\n",
    "        id_list.append('1')\n",
    "    elif i == genre_names[1]: # Adventure\n",
    "        id_list.append('2')\n",
    "    elif i == genre_names[2]:  #Animation\n",
    "        id_list.append('3')\n",
    "    elif i == genre_names[3]:  # Comedy\n",
    "        id_list.append('4')\n",
    "    elif i == genre_names[4]:  # Crime\n",
    "        id_list.append('5')\n",
    "    elif i == genre_names[5]:  # Documentary\n",
    "        id_list.append('6')        \n",
    "    elif i == genre_names[6]:  # Drama\n",
    "        id_list.append('7')\n",
    "    elif i == genre_names[7]:  #Family\n",
    "        id_list.append('8')        \n",
    "    elif i == genre_names[8]:  #Fantasy\n",
    "        id_list.append('9')\n",
    "    elif i == genre_names[9]:  #Foreign\n",
    "        id_list.append('10')\n",
    "    elif i == genre_names[10]:  #History\n",
    "        id_list.append('11')\n",
    "    elif i == genre_names[11]:  #Horror\n",
    "        id_list.append('12')\n",
    "    elif i == genre_names[12]:  #Music\n",
    "        id_list.append('13')\n",
    "    elif i == genre_names[13]:  #Mystery\n",
    "        id_list.append('14')\n",
    "    elif i == genre_names[14]:  #Romance\n",
    "        id_list.append('15')\n",
    "    elif i == genre_names[15]:  #Science Fiction\n",
    "        id_list.append('16')\n",
    "    elif i == genre_names[16]:  #TV Movie\n",
    "        id_list.append('17')\n",
    "    elif i == genre_names[17]:  #Thriller\n",
    "        id_list.append('18')\n",
    "    elif i == genre_names[18]:  #War\n",
    "        id_list.append('19')\n",
    "    elif i == genre_names[19]:  #Western\n",
    "        id_list.append('20')\n",
    "df_genres['genres_id'] = id_list   \n",
    "df_genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <th>genres_seg</th>\n",
       "      <th>genres_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>18</th>\n",
       "      <td>0.810000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>2.630000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>2</th>\n",
       "      <td>0.942857</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>2.180000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <th>War</th>\n",
       "      <th>19</th>\n",
       "      <td>0.933333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <th>Music</th>\n",
       "      <th>13</th>\n",
       "      <td>0.965000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>0.586667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>2.550000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>14</th>\n",
       "      <td>1.517500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <th>Crime</th>\n",
       "      <th>5</th>\n",
       "      <td>0.950000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>1.130000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1971</th>\n",
       "      <th>Family</th>\n",
       "      <th>8</th>\n",
       "      <td>1.530000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <th>9</th>\n",
       "      <td>1.530000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <th>Crime</th>\n",
       "      <th>5</th>\n",
       "      <td>1.072857</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>0.956667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   popularity  rank\n",
       "release_year genres_seg genres_id                  \n",
       "1960         Thriller   18           0.810000     1\n",
       "1961         Animation  3            2.630000     1\n",
       "1962         Adventure  2            0.942857     1\n",
       "1963         Animation  3            2.180000     1\n",
       "1964         War        19           0.933333     1\n",
       "1965         Music      13           0.965000     1\n",
       "1966         Animation  3            0.586667     1\n",
       "1967         Animation  3            2.550000     1\n",
       "1968         Mystery    14           1.517500     1\n",
       "1969         Crime      5            0.950000     1\n",
       "1970         Animation  3            1.130000     1\n",
       "1971         Family     8            1.530000     1\n",
       "             Fantasy    9            1.530000     1\n",
       "1972         Crime      5            1.072857     1\n",
       "1973         Animation  3            0.956667     1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_genres = df_genres.groupby(['release_year','genres_seg','genres_id'])['popularity'].mean().to_frame()\n",
    "#grouped_genres_ordered = grouped_genres.sort_values(['release_year', 'popularity','genres_id'], ascending=False)\n",
    "#pivot = grouped_genres.pivot_table(index=['release_year','genres_seg','genres_id'], aggfunc='max')['popularity'].to_frame()\n",
    "\n",
    "grouped_genres['rank'] = grouped_genres.groupby('release_year')['popularity'].rank(ascending=0)\n",
    "grouped_genres['rank'] = grouped_genres['rank'].astype(int)\n",
    "grouped_genres_rank = grouped_genres[grouped_genres['rank'] == 1]\n",
    "grouped_genres_rank.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Look at the above dataframe. There are duplicated data, 1971.\n",
    "#### Family and Fantasy in 1971 are associated with family genres. So I think it doesn't matter to drop this duplicated data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>9</th>\n",
       "      <td>1.53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   popularity  rank\n",
       "release_year genres_seg genres_id                  \n",
       "1971         Fantasy    9                1.53     1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(grouped_genres_rank.duplicated())\n",
    "grouped_genres_rank[grouped_genres_rank.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\parnx\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: 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",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_genres_rank.drop_duplicates(inplace=True)\n",
    "sum(grouped_genres_rank.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>18</th>\n",
       "      <td>0.810000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>2.630000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>2</th>\n",
       "      <td>0.942857</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>2.180000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <th>War</th>\n",
       "      <th>19</th>\n",
       "      <td>0.933333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <th>Music</th>\n",
       "      <th>13</th>\n",
       "      <td>0.965000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>0.586667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>2.550000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>14</th>\n",
       "      <td>1.517500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <th>Crime</th>\n",
       "      <th>5</th>\n",
       "      <td>0.950000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>1.130000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <th>Family</th>\n",
       "      <th>8</th>\n",
       "      <td>1.530000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <th>Crime</th>\n",
       "      <th>5</th>\n",
       "      <td>1.072857</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>0.956667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>14</th>\n",
       "      <td>0.702000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   popularity  rank\n",
       "release_year genres_seg genres_id                  \n",
       "1960         Thriller   18           0.810000     1\n",
       "1961         Animation  3            2.630000     1\n",
       "1962         Adventure  2            0.942857     1\n",
       "1963         Animation  3            2.180000     1\n",
       "1964         War        19           0.933333     1\n",
       "1965         Music      13           0.965000     1\n",
       "1966         Animation  3            0.586667     1\n",
       "1967         Animation  3            2.550000     1\n",
       "1968         Mystery    14           1.517500     1\n",
       "1969         Crime      5            0.950000     1\n",
       "1970         Animation  3            1.130000     1\n",
       "1971         Family     8            1.530000     1\n",
       "1972         Crime      5            1.072857     1\n",
       "1973         Animation  3            0.956667     1\n",
       "1974         Mystery    14           0.702000     1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_genres_rank.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, visualize the most popular genres from year to year. But this dataframe has multiindex and also the data for x axis, y axis are in the index. So seperate the data of this index into each columns for visualizing. First, Copy the dataframe 'grouped_genres_rank' and then make the new columns consisted of index data(year, genre)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_genres_rank_copy = grouped_genres_rank.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>rank</th>\n",
       "      <th>year</th>\n",
       "      <th>genre</th>\n",
       "      <th>genre_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <th>genres_seg</th>\n",
       "      <th>genres_id</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>1960</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>18</th>\n",
       "      <td>0.810000</td>\n",
       "      <td>1</td>\n",
       "      <td>1960</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>2.630000</td>\n",
       "      <td>1</td>\n",
       "      <td>1961</td>\n",
       "      <td>Animation</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>2</th>\n",
       "      <td>0.942857</td>\n",
       "      <td>1</td>\n",
       "      <td>1962</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>2.180000</td>\n",
       "      <td>1</td>\n",
       "      <td>1963</td>\n",
       "      <td>Animation</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <th>War</th>\n",
       "      <th>19</th>\n",
       "      <td>0.933333</td>\n",
       "      <td>1</td>\n",
       "      <td>1964</td>\n",
       "      <td>War</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <th>Music</th>\n",
       "      <th>13</th>\n",
       "      <td>0.965000</td>\n",
       "      <td>1</td>\n",
       "      <td>1965</td>\n",
       "      <td>Music</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>0.586667</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>Animation</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>2.550000</td>\n",
       "      <td>1</td>\n",
       "      <td>1967</td>\n",
       "      <td>Animation</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>14</th>\n",
       "      <td>1.517500</td>\n",
       "      <td>1</td>\n",
       "      <td>1968</td>\n",
       "      <td>Mystery</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <th>Crime</th>\n",
       "      <th>5</th>\n",
       "      <td>0.950000</td>\n",
       "      <td>1</td>\n",
       "      <td>1969</td>\n",
       "      <td>Crime</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>1.130000</td>\n",
       "      <td>1</td>\n",
       "      <td>1970</td>\n",
       "      <td>Animation</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <th>Family</th>\n",
       "      <th>8</th>\n",
       "      <td>1.530000</td>\n",
       "      <td>1</td>\n",
       "      <td>1971</td>\n",
       "      <td>Family</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <th>Crime</th>\n",
       "      <th>5</th>\n",
       "      <td>1.072857</td>\n",
       "      <td>1</td>\n",
       "      <td>1972</td>\n",
       "      <td>Crime</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>0.956667</td>\n",
       "      <td>1</td>\n",
       "      <td>1973</td>\n",
       "      <td>Animation</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>14</th>\n",
       "      <td>0.702000</td>\n",
       "      <td>1</td>\n",
       "      <td>1974</td>\n",
       "      <td>Mystery</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>2</th>\n",
       "      <td>0.880000</td>\n",
       "      <td>1</td>\n",
       "      <td>1975</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <th>Crime</th>\n",
       "      <th>5</th>\n",
       "      <td>0.706250</td>\n",
       "      <td>1</td>\n",
       "      <td>1976</td>\n",
       "      <td>Crime</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <th>Action</th>\n",
       "      <th>1</th>\n",
       "      <td>1.419286</td>\n",
       "      <td>1</td>\n",
       "      <td>1977</td>\n",
       "      <td>Action</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <th>Music</th>\n",
       "      <th>13</th>\n",
       "      <td>0.680000</td>\n",
       "      <td>1</td>\n",
       "      <td>1978</td>\n",
       "      <td>Music</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <th>Action</th>\n",
       "      <th>1</th>\n",
       "      <td>1.411250</td>\n",
       "      <td>1</td>\n",
       "      <td>1979</td>\n",
       "      <td>Action</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <th>Science Fiction</th>\n",
       "      <th>16</th>\n",
       "      <td>0.896364</td>\n",
       "      <td>1</td>\n",
       "      <td>1980</td>\n",
       "      <td>Science Fiction</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>2</th>\n",
       "      <td>0.875000</td>\n",
       "      <td>1</td>\n",
       "      <td>1981</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <th>War</th>\n",
       "      <th>19</th>\n",
       "      <td>1.145000</td>\n",
       "      <td>1</td>\n",
       "      <td>1982</td>\n",
       "      <td>War</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>2</th>\n",
       "      <td>0.900714</td>\n",
       "      <td>1</td>\n",
       "      <td>1983</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <th>Family</th>\n",
       "      <th>8</th>\n",
       "      <td>0.822222</td>\n",
       "      <td>1</td>\n",
       "      <td>1984</td>\n",
       "      <td>Family</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <th>Family</th>\n",
       "      <th>8</th>\n",
       "      <td>0.925000</td>\n",
       "      <td>1</td>\n",
       "      <td>1985</td>\n",
       "      <td>Family</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>2</th>\n",
       "      <td>0.798824</td>\n",
       "      <td>1</td>\n",
       "      <td>1986</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <th>History</th>\n",
       "      <th>11</th>\n",
       "      <td>0.816667</td>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>History</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>0.748000</td>\n",
       "      <td>1</td>\n",
       "      <td>1988</td>\n",
       "      <td>Animation</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>1.355000</td>\n",
       "      <td>1</td>\n",
       "      <td>1989</td>\n",
       "      <td>Animation</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>2</th>\n",
       "      <td>0.802174</td>\n",
       "      <td>1</td>\n",
       "      <td>1990</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>1.663333</td>\n",
       "      <td>1</td>\n",
       "      <td>1991</td>\n",
       "      <td>Animation</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>1.287500</td>\n",
       "      <td>1</td>\n",
       "      <td>1992</td>\n",
       "      <td>Animation</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>9</th>\n",
       "      <td>0.917692</td>\n",
       "      <td>1</td>\n",
       "      <td>1993</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <th>Crime</th>\n",
       "      <th>5</th>\n",
       "      <td>1.297692</td>\n",
       "      <td>1</td>\n",
       "      <td>1994</td>\n",
       "      <td>Crime</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <th>Animation</th>\n",
       "      <th>3</th>\n",
       "      <td>1.467143</td>\n",
       "      <td>1</td>\n",
       "      <td>1995</td>\n",
       "      <td>Animation</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <th>Crime</th>\n",
       "      <th>5</th>\n",
       "      <td>0.976842</td>\n",
       "      <td>1</td>\n",
       "      <td>1996</td>\n",
       "      <td>Crime</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <th>Science Fiction</th>\n",
       "      <th>16</th>\n",
       "      <td>1.140345</td>\n",
       "      <td>1</td>\n",
       "      <td>1997</td>\n",
       "      <td>Science Fiction</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <th>War</th>\n",
       "      <th>19</th>\n",
       "      <td>1.246667</td>\n",
       "      <td>1</td>\n",
       "      <td>1998</td>\n",
       "      <td>War</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>2</th>\n",
       "      <td>1.012500</td>\n",
       "      <td>1</td>\n",
       "      <td>1999</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>2</th>\n",
       "      <td>0.855000</td>\n",
       "      <td>1</td>\n",
       "      <td>2000</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>9</th>\n",
       "      <td>1.564400</td>\n",
       "      <td>1</td>\n",
       "      <td>2001</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>9</th>\n",
       "      <td>1.430500</td>\n",
       "      <td>1</td>\n",
       "      <td>2002</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>9</th>\n",
       "      <td>1.747407</td>\n",
       "      <td>1</td>\n",
       "      <td>2003</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>9</th>\n",
       "      <td>1.320526</td>\n",
       "      <td>1</td>\n",
       "      <td>2004</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>9</th>\n",
       "      <td>1.146765</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>9</th>\n",
       "      <td>1.023235</td>\n",
       "      <td>1</td>\n",
       "      <td>2006</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>9</th>\n",
       "      <td>0.957021</td>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>2</th>\n",
       "      <td>1.008571</td>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>2</th>\n",
       "      <td>1.153521</td>\n",
       "      <td>1</td>\n",
       "      <td>2009</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>2</th>\n",
       "      <td>1.378793</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <th>Western</th>\n",
       "      <th>20</th>\n",
       "      <td>1.176667</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>Western</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <th>Western</th>\n",
       "      <th>20</th>\n",
       "      <td>1.732500</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>Western</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>2</th>\n",
       "      <td>1.294000</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>2</th>\n",
       "      <td>2.431045</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>2</th>\n",
       "      <td>3.283188</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        popularity  rank  year  \\\n",
       "release_year genres_seg      genres_id                           \n",
       "1960         Thriller        18           0.810000     1  1960   \n",
       "1961         Animation       3            2.630000     1  1961   \n",
       "1962         Adventure       2            0.942857     1  1962   \n",
       "1963         Animation       3            2.180000     1  1963   \n",
       "1964         War             19           0.933333     1  1964   \n",
       "1965         Music           13           0.965000     1  1965   \n",
       "1966         Animation       3            0.586667     1  1966   \n",
       "1967         Animation       3            2.550000     1  1967   \n",
       "1968         Mystery         14           1.517500     1  1968   \n",
       "1969         Crime           5            0.950000     1  1969   \n",
       "1970         Animation       3            1.130000     1  1970   \n",
       "1971         Family          8            1.530000     1  1971   \n",
       "1972         Crime           5            1.072857     1  1972   \n",
       "1973         Animation       3            0.956667     1  1973   \n",
       "1974         Mystery         14           0.702000     1  1974   \n",
       "1975         Adventure       2            0.880000     1  1975   \n",
       "1976         Crime           5            0.706250     1  1976   \n",
       "1977         Action          1            1.419286     1  1977   \n",
       "1978         Music           13           0.680000     1  1978   \n",
       "1979         Action          1            1.411250     1  1979   \n",
       "1980         Science Fiction 16           0.896364     1  1980   \n",
       "1981         Adventure       2            0.875000     1  1981   \n",
       "1982         War             19           1.145000     1  1982   \n",
       "1983         Adventure       2            0.900714     1  1983   \n",
       "1984         Family          8            0.822222     1  1984   \n",
       "1985         Family          8            0.925000     1  1985   \n",
       "1986         Adventure       2            0.798824     1  1986   \n",
       "1987         History         11           0.816667     1  1987   \n",
       "1988         Animation       3            0.748000     1  1988   \n",
       "1989         Animation       3            1.355000     1  1989   \n",
       "1990         Adventure       2            0.802174     1  1990   \n",
       "1991         Animation       3            1.663333     1  1991   \n",
       "1992         Animation       3            1.287500     1  1992   \n",
       "1993         Fantasy         9            0.917692     1  1993   \n",
       "1994         Crime           5            1.297692     1  1994   \n",
       "1995         Animation       3            1.467143     1  1995   \n",
       "1996         Crime           5            0.976842     1  1996   \n",
       "1997         Science Fiction 16           1.140345     1  1997   \n",
       "1998         War             19           1.246667     1  1998   \n",
       "1999         Adventure       2            1.012500     1  1999   \n",
       "2000         Adventure       2            0.855000     1  2000   \n",
       "2001         Fantasy         9            1.564400     1  2001   \n",
       "2002         Fantasy         9            1.430500     1  2002   \n",
       "2003         Fantasy         9            1.747407     1  2003   \n",
       "2004         Fantasy         9            1.320526     1  2004   \n",
       "2005         Fantasy         9            1.146765     1  2005   \n",
       "2006         Fantasy         9            1.023235     1  2006   \n",
       "2007         Fantasy         9            0.957021     1  2007   \n",
       "2008         Adventure       2            1.008571     1  2008   \n",
       "2009         Adventure       2            1.153521     1  2009   \n",
       "2010         Adventure       2            1.378793     1  2010   \n",
       "2011         Western         20           1.176667     1  2011   \n",
       "2012         Western         20           1.732500     1  2012   \n",
       "2013         Adventure       2            1.294000     1  2013   \n",
       "2014         Adventure       2            2.431045     1  2014   \n",
       "2015         Adventure       2            3.283188     1  2015   \n",
       "\n",
       "                                                  genre genre_id  \n",
       "release_year genres_seg      genres_id                            \n",
       "1960         Thriller        18                Thriller       18  \n",
       "1961         Animation       3                Animation        3  \n",
       "1962         Adventure       2                Adventure        2  \n",
       "1963         Animation       3                Animation        3  \n",
       "1964         War             19                     War       19  \n",
       "1965         Music           13                   Music       13  \n",
       "1966         Animation       3                Animation        3  \n",
       "1967         Animation       3                Animation        3  \n",
       "1968         Mystery         14                 Mystery       14  \n",
       "1969         Crime           5                    Crime        5  \n",
       "1970         Animation       3                Animation        3  \n",
       "1971         Family          8                   Family        8  \n",
       "1972         Crime           5                    Crime        5  \n",
       "1973         Animation       3                Animation        3  \n",
       "1974         Mystery         14                 Mystery       14  \n",
       "1975         Adventure       2                Adventure        2  \n",
       "1976         Crime           5                    Crime        5  \n",
       "1977         Action          1                   Action        1  \n",
       "1978         Music           13                   Music       13  \n",
       "1979         Action          1                   Action        1  \n",
       "1980         Science Fiction 16         Science Fiction       16  \n",
       "1981         Adventure       2                Adventure        2  \n",
       "1982         War             19                     War       19  \n",
       "1983         Adventure       2                Adventure        2  \n",
       "1984         Family          8                   Family        8  \n",
       "1985         Family          8                   Family        8  \n",
       "1986         Adventure       2                Adventure        2  \n",
       "1987         History         11                 History       11  \n",
       "1988         Animation       3                Animation        3  \n",
       "1989         Animation       3                Animation        3  \n",
       "1990         Adventure       2                Adventure        2  \n",
       "1991         Animation       3                Animation        3  \n",
       "1992         Animation       3                Animation        3  \n",
       "1993         Fantasy         9                  Fantasy        9  \n",
       "1994         Crime           5                    Crime        5  \n",
       "1995         Animation       3                Animation        3  \n",
       "1996         Crime           5                    Crime        5  \n",
       "1997         Science Fiction 16         Science Fiction       16  \n",
       "1998         War             19                     War       19  \n",
       "1999         Adventure       2                Adventure        2  \n",
       "2000         Adventure       2                Adventure        2  \n",
       "2001         Fantasy         9                  Fantasy        9  \n",
       "2002         Fantasy         9                  Fantasy        9  \n",
       "2003         Fantasy         9                  Fantasy        9  \n",
       "2004         Fantasy         9                  Fantasy        9  \n",
       "2005         Fantasy         9                  Fantasy        9  \n",
       "2006         Fantasy         9                  Fantasy        9  \n",
       "2007         Fantasy         9                  Fantasy        9  \n",
       "2008         Adventure       2                Adventure        2  \n",
       "2009         Adventure       2                Adventure        2  \n",
       "2010         Adventure       2                Adventure        2  \n",
       "2011         Western         20                 Western       20  \n",
       "2012         Western         20                 Western       20  \n",
       "2013         Adventure       2                Adventure        2  \n",
       "2014         Adventure       2                Adventure        2  \n",
       "2015         Adventure       2                Adventure        2  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length = len(grouped_genres_rank.index) # 56\n",
    "level = len(grouped_genres_rank.index.levels) # 3\n",
    "\n",
    "year_list = []\n",
    "genres_list = []\n",
    "id_list = []\n",
    "\n",
    "for i in range(length):\n",
    "    for k in range(level):\n",
    "        if k == 0:\n",
    "            year_list.append(grouped_genres_rank.index[i][k])\n",
    "        elif k == 1:\n",
    "            genres_list.append(grouped_genres_rank.index[i][k])\n",
    "        else:\n",
    "            id_list.append(grouped_genres_rank.index[i][k])\n",
    "\n",
    "grouped_genres_rank_copy['year'] = year_list\n",
    "grouped_genres_rank_copy['genre'] = genres_list\n",
    "grouped_genres_rank_copy['genre_id'] = id_list\n",
    "grouped_genres_rank_copy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Change the genre_id's datatype string to integer for the y-axis of chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_genres_rank_copy.genre_id.dtype\n",
    "grouped_genres_rank_copy['genre_id'] = grouped_genres_rank_copy['genre_id'].astype(int)\n",
    "grouped_genres_rank_copy.genre_id.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the most popular genres from year to year.(using plt.scatter())\n",
    "#### Replace y-axis integer with string\n",
    "##### Reference: https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.xticks#yticks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "year = grouped_genres_rank_copy['year']\n",
    "genre_id = grouped_genres_rank_copy['genre_id']\n",
    "len(genre_id)\n",
    "\n",
    "# plotting\n",
    "sns.set_style('darkgrid')\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x=year, y=genre_id, label='the most', s=80, color='b', marker=\"*\")\n",
    "#plt.legend(['point'], loc='right')\n",
    "plt.suptitle('The Most Popular Genres By Year', fontsize = 20)\n",
    "\n",
    "# replace y-axis integer with string.\n",
    "genre = grouped_genres_rank.index.levels[1] # 20 ea\n",
    "genre_id = grouped_genres_rank.index.levels[2].astype(int) # 20 ea\n",
    "#           location, labels, rotation=45 \n",
    "plt.yticks( genre_id, genre, rotation = 0)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize the most popular genres from year to year.(using sns.stripplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "sns.stripplot(x=\"year\", y=\"genre_id\", data=grouped_genres_rank_copy).set_title(\"The Most Popular Genres By Year\")\n",
    "\n",
    "# replace y-axis integer with string.\n",
    "genre = grouped_genres_rank.index.levels[1] # 20 ea\n",
    "genre_id = grouped_genres_rank.index.levels[2].astype(int) # 20 ea\n",
    "#           location, labels, rotation=45 \n",
    "plt.yticks( genre_id, genre, rotation = 0, fontsize = 12)\n",
    "plt.xticks(rotation = 45, fontsize = 10)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q2. What kinds of properties are associated with movies that have high revenues?\n",
    "#### I guess Movie genres, Casted Actor/Actress are associated with the high revenues.  So,I created df_cast, df_genre."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Q2-1. Cast is associated with the revenue?\n",
    "#### First, I think the famous actor/actress has the high revenues. There are so many actor/actress in here. Total is 18930 person. So I have limitation in order of the highest number of apperances. The Actor/Actress whose the number of movie appearances is high is famous, and then analysis their revenues.\n",
    "#### Limitation : Cast whose the higher number of movie appearances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18930, 1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cast.head()\n",
    "#Number of movie appearances,\n",
    "df_cast_num = df_cast.groupby('cast_seg')['original_title'].count().to_frame()\n",
    "df_cast_num.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the revenues per Actor/Actress. The mean of appearances is 2.764606 and max value is 72.0. It's large difference with mean and max. I think the higher appearances the more famous. So, It needs that famous actor/actress who have the number of times as much as possible. Moreover, there is no mean that compare once a number of appearance(1) with 72 times. (Once a number of appearance is more than 50%.)\n",
    "#### So I divided 75% ~ max section into a quartile over and over until I find higher appearance times . "
   ]
  },
  {
   "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",
       "    }\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18930.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.764606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.747105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       original_title\n",
       "count    18930.000000\n",
       "mean         2.764606\n",
       "std          4.747105\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          1.000000\n",
       "75%          2.000000\n",
       "max         72.000000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cast_num.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the function : get_recursive(df, value)\n",
    "### This is recursive function to get a range of 75% and max over and over,  \n",
    "#### * parameter : dataframe, value\n",
    "#### * return  : get_recursive(df, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Appearance': 'Actors',\n",
       " 2.0: 4344.0,\n",
       " 9.0: 1084.0,\n",
       " 23.0: 247.0,\n",
       " 37.0: 54.0,\n",
       " 46.0: 13.0,\n",
       " 61.0: 3.0}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appearance_list = ['Appearance']\n",
    "count_list = ['Actors']\n",
    "\n",
    "def get_recursive(df, value): \n",
    "    df_call = df[df.original_title > value].describe()\n",
    "    sub_value = df_call['original_title'][6]\n",
    "    sub_count = df_call['original_title'][0]\n",
    "    appearance_list.append(sub_value)\n",
    "    count_list.append(sub_count)\n",
    "    \n",
    "    if sub_value > 70:\n",
    "        return appearance_list, count_list\n",
    "    \n",
    "    return get_recursive(df,sub_value)\n",
    "\n",
    "value = df_cast_num.describe()['original_title'][6] #2\n",
    "appearance_list.append(value)\n",
    "get_recursive(df_cast_num, value)\n",
    "# make the dictionary\n",
    "\n",
    "criteria = dict(zip(appearance_list, count_list))\n",
    "criteria"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Look at the above the dataset.The fifth element(the number of appearance is 46 and the number of actors is 13) is good for analyzing. The number 37  is about twice the maximum value,72. It is better to compare the revenues of actor/actress who do not make a big difference in the number of appearances. Also, the number of actors,13 is enough good number for comparing the revenue each other.\n",
    "### Limitation : The revenue of 13 actor/actress who appeared more than 46.\n",
    "#### After sorting in descending order of appearance times, select top 13 actors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast_seg</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Robert De Niro</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samuel L. Jackson</th>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bruce Willis</th>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicolas Cage</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michael Caine</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robin Williams</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Cusack</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Goodman</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morgan Freeman</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Susan Sarandon</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liam Neeson</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alec Baldwin</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julianne Moore</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   original_title\n",
       "cast_seg                         \n",
       "Robert De Niro                 72\n",
       "Samuel L. Jackson              71\n",
       "Bruce Willis                   62\n",
       "Nicolas Cage                   61\n",
       "Michael Caine                  53\n",
       "Robin Williams                 51\n",
       "John Cusack                    50\n",
       "John Goodman                   49\n",
       "Morgan Freeman                 49\n",
       "Susan Sarandon                 48\n",
       "Liam Neeson                    48\n",
       "Alec Baldwin                   47\n",
       "Julianne Moore                 47"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cast_ordered = df_cast_num.sort_values(by=['original_title'], ascending=False)\n",
    "top_members = criteria.get(46).astype(int) # 13\n",
    "casted_top = df_cast_ordered.head(top_members)\n",
    "casted_top\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the function : get_locations(num)\n",
    "#### This function create the list as a given number.\n",
    "#### parameter : num\n",
    "#### return list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_locations(num):\n",
    "    locations = []\n",
    "    for i in range(num):\n",
    "        locations.append(i+1)\n",
    "        i += 1\n",
    "    return locations\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast_seg</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Robert De Niro</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samuel L. Jackson</th>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bruce Willis</th>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicolas Cage</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michael Caine</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robin Williams</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Cusack</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Goodman</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morgan Freeman</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Susan Sarandon</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liam Neeson</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alec Baldwin</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julianne Moore</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   original_title\n",
       "cast_seg                         \n",
       "Robert De Niro                 72\n",
       "Samuel L. Jackson              71\n",
       "Bruce Willis                   62\n",
       "Nicolas Cage                   61\n",
       "Michael Caine                  53\n",
       "Robin Williams                 51\n",
       "John Cusack                    50\n",
       "John Goodman                   49\n",
       "Morgan Freeman                 49\n",
       "Susan Sarandon                 48\n",
       "Liam Neeson                    48\n",
       "Alec Baldwin                   47\n",
       "Julianne Moore                 47"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "locations = get_locations(top_members)\n",
    "\n",
    "#locations = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]\n",
    "heights = casted_top.original_title\n",
    "labels = casted_top.index\n",
    "\n",
    "plt.setp(plt.xticks()[1], rotation=45)\n",
    "plt.bar(locations, heights, width = 0.5, tick_label = labels)\n",
    "plt.title('Number of movie appearances By Actor/Actress', fontsize = 16)\n",
    "plt.xlabel('Actor/Actres')\n",
    "plt.ylabel('Number of movie appearance');\n",
    "\n",
    "casted_top"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select the revenues of the top 13 actor/actress. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>revenue</th>\n",
       "      <th>original_title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast_seg</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Liam Neeson</th>\n",
       "      <td>115721265</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robin Williams</th>\n",
       "      <td>114113764</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bruce Willis</th>\n",
       "      <td>106368576</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michael Caine</th>\n",
       "      <td>102119797</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samuel L. Jackson</th>\n",
       "      <td>100070273</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morgan Freeman</th>\n",
       "      <td>85440202</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicolas Cage</th>\n",
       "      <td>80281085</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Goodman</th>\n",
       "      <td>80267162</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robert De Niro</th>\n",
       "      <td>71541826</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julianne Moore</th>\n",
       "      <td>51759900</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alec Baldwin</th>\n",
       "      <td>44149952</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Cusack</th>\n",
       "      <td>43762854</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Susan Sarandon</th>\n",
       "      <td>26720397</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     revenue  original_title\n",
       "cast_seg                                    \n",
       "Liam Neeson        115721265              48\n",
       "Robin Williams     114113764              51\n",
       "Bruce Willis       106368576              62\n",
       "Michael Caine      102119797              53\n",
       "Samuel L. Jackson  100070273              71\n",
       "Morgan Freeman      85440202              49\n",
       "Nicolas Cage        80281085              61\n",
       "John Goodman        80267162              49\n",
       "Robert De Niro      71541826              72\n",
       "Julianne Moore      51759900              47\n",
       "Alec Baldwin        44149952              47\n",
       "John Cusack         43762854              50\n",
       "Susan Sarandon      26720397              48"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casted_revenue = df_cast.groupby('cast_seg')['revenue'].mean().to_frame()\n",
    "df_top_revenue = casted_revenue.join(casted_top, lsuffix='', rsuffix='_count', how='right')\n",
    "df_top_revenue['revenue'] = df_top_revenue['revenue'].astype(int)\n",
    "df_top_revenue_ordered = df_top_revenue.sort_values(by='revenue', ascending=False)\n",
    "df_top_revenue_ordered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the mean of the cast. \n",
    "#### Express mean line on the chart to see whether his revenue is higher than the total mean of all cast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42433577.71513429"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_mean_value = df_cast.revenue.mean()\n",
    "total_mean_value  # 42433577.71513429\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The mean value of top 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78639773.3076923"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top13_mean_value = df_top_revenue.revenue.mean()    # 78639773.3076923\n",
    "top13_mean_value   # 78639773.3076923"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Then Visualize to check whether the data is higher than the mean revenue of all cast and the top 13 members. And add mean value on y-axis.\n",
    "##### Reference : https://stackoverflow.com/questions/42877747/add-a-label-to-y-axis-to-show-the-value-of-y-for-a-horizontal-line-in-matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#locations = [1,2,3,4,5,6,7,8,9,10,11,12,13]\n",
    "locations\n",
    "heights = df_top_revenue_ordered.revenue\n",
    "labels = df_top_revenue_ordered.index\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(locations, heights, tick_label = labels)\n",
    "ax.axhline(y=total_mean_value, color=\"green\")\n",
    "ax.axhline(y=top13_mean_value, color=\"red\")\n",
    "ax.text(0,total_mean_value, \"{:.0f}\".format(total_mean_value), color=\"blue\", ha=\"left\", va=\"top\")\n",
    "ax.text(0,top13_mean_value, \"{:.0f}\".format(top13_mean_value), color=\"red\", ha=\"left\", va=\"top\")\n",
    "#plt.axhline(y=total_mean_value, color='#d62728')\n",
    "#plt.axhline(y=top13_mean_value, color='#a99925')\n",
    "plt.setp(plt.xticks()[1], rotation=45)\n",
    "plt.title('The Revenues By Actor/Actress', fontsize = 20)\n",
    "plt.xlabel('Actor/Actres')\n",
    "plt.ylabel('Revenues');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q2-2. Genres are associated with the revenue?\n",
    "####  Secound, I wonder which genres has the most revenue. So, select the genres that has higher revenues than the mean. \n",
    "#### Each genre has different number of screenings and has different revenues. So, divide the total revenues of each genres into the total count of screenings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the number of screenings of the each genres. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1337.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1259.850587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>162.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>381.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1061.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1654.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4746.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       original_title\n",
       "count       20.000000\n",
       "mean      1337.650000\n",
       "std       1259.850587\n",
       "min        162.000000\n",
       "25%        381.750000\n",
       "50%       1061.000000\n",
       "75%       1654.000000\n",
       "max       4746.000000"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_genres_result = df_genres.groupby('genres_seg')['original_title'].count().to_frame()\n",
    "df_genres_result.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The total genres are 20. Each genre has mean number of screenings, 1338(1337.65). I want to analyze the genres related with family, children. So, Adventure, Animation,Comedy,Family, Fantasy seem to be related with family and children.\n",
    "### Limitation :Genres related with family : Adventure, Animation,Comedy, Family, Fantasy \n",
    "#### Check the mean revenue of each genres."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>revenue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres_seg</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>2376</td>\n",
       "      <td>74002938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>1465</td>\n",
       "      <td>114471805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>664</td>\n",
       "      <td>80541482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>3775</td>\n",
       "      <td>38791326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>1353</td>\n",
       "      <td>43278971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Documentary</th>\n",
       "      <td>470</td>\n",
       "      <td>3809987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>4746</td>\n",
       "      <td>30385352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>1214</td>\n",
       "      <td>74631490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>908</td>\n",
       "      <td>98171670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foreign</th>\n",
       "      <td>184</td>\n",
       "      <td>3209897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>History</th>\n",
       "      <td>330</td>\n",
       "      <td>33499620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror</th>\n",
       "      <td>1636</td>\n",
       "      <td>18180445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Music</th>\n",
       "      <td>399</td>\n",
       "      <td>30319632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>808</td>\n",
       "      <td>41327130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romance</th>\n",
       "      <td>1708</td>\n",
       "      <td>36767950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science Fiction</th>\n",
       "      <td>1221</td>\n",
       "      <td>71666505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV Movie</th>\n",
       "      <td>162</td>\n",
       "      <td>2297086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>2902</td>\n",
       "      <td>42846041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>War</th>\n",
       "      <td>268</td>\n",
       "      <td>48962734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>164</td>\n",
       "      <td>29980701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 original_title    revenue\n",
       "genres_seg                                \n",
       "Action                     2376   74002938\n",
       "Adventure                  1465  114471805\n",
       "Animation                   664   80541482\n",
       "Comedy                     3775   38791326\n",
       "Crime                      1353   43278971\n",
       "Documentary                 470    3809987\n",
       "Drama                      4746   30385352\n",
       "Family                     1214   74631490\n",
       "Fantasy                     908   98171670\n",
       "Foreign                     184    3209897\n",
       "History                     330   33499620\n",
       "Horror                     1636   18180445\n",
       "Music                       399   30319632\n",
       "Mystery                     808   41327130\n",
       "Romance                    1708   36767950\n",
       "Science Fiction            1221   71666505\n",
       "TV Movie                    162    2297086\n",
       "Thriller                   2902   42846041\n",
       "War                         268   48962734\n",
       "Western                     164   29980701"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_genres_result['revenue'] = df_genres.groupby('genres_seg')['revenue'].mean()\n",
    "df_genres_result['revenue'] = df_genres_result['revenue'].astype(int)\n",
    "df_genres_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize which genres of family movies has the highest revenues.\n",
    "#### The mean revenue of the all genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48879778.83622551"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_value = df_genres.revenue.mean()\n",
    "mean_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the genres with higher than mean value. So show the line for the mean value and add mean value on y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original_title</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres_seg</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>1465</td>\n",
       "      <td>114471805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>908</td>\n",
       "      <td>98171670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>664</td>\n",
       "      <td>80541482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>1214</td>\n",
       "      <td>74631490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>3775</td>\n",
       "      <td>38791326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            original_title    revenue\n",
       "genres_seg                           \n",
       "Adventure             1465  114471805\n",
       "Fantasy                908   98171670\n",
       "Animation              664   80541482\n",
       "Family                1214   74631490\n",
       "Comedy                3775   38791326"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/PqdOncpzWAgyj+mPPvqIjRs35nrfzK1bt7hx44Y6XdDHQU4CAgK4evUqrq6uWfZx5swZwsPDuX//PmfOnKFevXrqZejly5enZ8+e6tDcs+a56RF4eXkRHh6uXnt+4sQJxo8fD4CbmxuhoaEUL16cIkWKkJqaisFgwMam8KpXoUIFgoKCGDt2LJcvX6Z69epcuXKF8PBw0tLScu1W5qR58+aEhoayaNEiPvvss1zXGzJkCD169KB///4EBQVhY2NDVFQU27ZtY+LEiUDmOO+qVasYP348gYGB3Lp1i+XLl6t3J9+/fz9bd7mg2NnZ8dVXX9GjRw/GjRvHwoULH6udihQpQrNmzVixYgUlS5bMcpdtQbY3QMOGDdmyZQvLli1Dr9cTExOTLRH079+fjh070r17d7p27YqrqyubN28mKiqKgQMH5jp8EhQUxMqVKxk0aBD9+vXD29ubuLg4Fi9eTIcOHfD29n6sWPOjWrVqQOYVNR07duTevXusXbuWnTt3AqjnoMytY8eOjBw5kvLly+frvoPevXtz7do1hgwZQnR0NE2bNsXT05OkpCT27t3Lhg0byMjIoHv37kDBHwc5ad++PStXruTDDz/k448/xsvLiyNHjhAeHo6/vz+enp4oioK7uztjx44lKSmJ0qVLc/ToUWJjY9VYnyXPTSJo2rQpCQkJ6vRXX31FaGgo5cuXZ82aNSxYsIDu3btjbW1N8+bNuXv3LuPGjSvEiDP/6Ly9vfnuu++YPXs2bm5uBAQEMGjQoGzjjnlxd3fnjTfeYO/evbRo0SLX9erUqcOSJUuYNWsWQ4YMATL/MGbOnKmOpbdr146///6btWvXEhUVhYeHBwEBAXTv3p3evXvz66+/0qxZs6ereB7q1atHYGAgW7duZevWrQQGBj5WOz08KdqmTRv1nMVDBdXeACNGjMBoNKrd/7feeovQ0FA+/vhjdZ0KFSqwatUqpk+fzhdffEFaWhplypRhzJgxWe5S/6ciRYoQERHBtGnTmDt3Lnfu3KFMmTIMGzaM995777HizK/atWszduxYFi1aRM+ePXF3d6datWosX76crl27sn//fovckNWgQQMAk72Bh6ysrBg9ejQtWrRgzZo1hIeHc+3aNezt7Slfvjzdu3enQ4cOau8WCvY4yImDgwMRERFMnz6dGTNmkJiYSIkSJQgJCaFv375q3PPmzWPq1KlMmzaNxMRESpYsSZ8+fejVq9dTx1DQrBRL9AkLSEJCAoMHDyYqKooaNWqo12mnpaVRtmxZKlWqxJEjR5g4cSLJycl06dKFhQsX5njCTghheZGRkUyYMIHY2Ngcx/BF4XhuegT/VLZsWSZOnIinpyfx8fFcv36dBw8e4OjoiE6nw8nJCTs7u2wn+IQQlrdhwwZOnz7NypUr6dixoySBZ8xzmwhGjx7NsGHD1Cs7JkyYgJeXF7/99hvBwcFkZGTQunVrs4y3CiEez5kzZ4iIiKBOnTp88sknhR2O+AezDQ3ldqnnpk2bWLp0KTqdDr1ez+jRo7ON9QohhLAcs3wD53ap54MHD5g+fTrLli1j9erVGAwGfv75Z3OEIIQQIp/MMjT0z0s9H7Kzs2P16tXq5Ynp6en5+mEXo9FIRsazfU5bp7N65mM0Fy3XHbRdf6n7s113W1ud6ZUwU4+gadOmOV7Db21trd5aHxERwb179569p/A9sce75f7FouW6g7brL3V/EVj8ZLHRaGTy5Mn89ddfhIeH5+uZJRkZComJlrnh5Um5uTk+8zGai5brDtquv9T92a67h4dLvtazeCIYOXIkdnZ2zJ49W04SCyHEM8AiiWDjxo3cu3cPX19f1q5dS82aNenWrRsA7733nvz4tBBCFKLn4s7itLSMZ74L9jx0E81Fy3UHbddf6v5s1z2/Q0MyNiOEEBoniUAIITROEoEQQmicJAIhhNA4SQRCCKFxz+3TR/PL2dUBB3vLVDO/Z+if1P2UdAxJ9826DyGE9rzwicDB3obXhkcXdhgF4lxYSwyFHYQQ4oUjQ0NCCKFxkgiEEELjJBEIIYTGSSIQQgiNk0QghBAaJ4lACCE0ThKBEEJonCQCIYTQOEkEQgihcZIIhBBC4yQRCCGExkkiEEIIjZNEIIQQGieJQAghNE4SgRBCaJwkAiGE0DhJBEIIoXGSCIQQQuMkEQghhMZJIhBCCI2TRCCEEBpntkRw+PBhQkJCss3fsWMHb7/9Np06dSIqKspcuxdCCJFPNuYodP78+fzwww84ODhkmZ+WlsbXX3/N2rVrcXBwoHPnzjRo0AAPDw9zhCGEECIfzNIj8PLyIjw8PNv8M2fO4OXlRdGiRbGzs6NGjRocOHDAHCEIIYTIJ7P0CJo2bUpCQkK2+QaDARcXF3XayckJg8Fgsjydzgo3N8cCjfF59Sy2g05n/UzGZSlarr/U/cWou1kSQW6cnZ1JTk5Wp5OTk7MkhtxkZCgkJt57on16eJgu/3nypO1gTm5ujs9kXJai5fpL3Z/tuuf3+8+iVw2VK1eO8+fPk5iYSGpqKgcOHKB69eqWDEEIIcQ/WKRHsHHjRu7du0enTp0YPnw43bt3R1EU3n77bUqUKGGJEDTL2dUBB3vzf8yW6HndT0nHkHTf7PsRQmvM9g1RunRp9fLQ1q1bq/MbNmxIw4YNzbVb8Q8O9ja8Njy6sMMoEOfCWmL6jJIQ4nHJDWVCCKFxkgiEEELjJBEIIYTGSSIQQgiNk0QghBAaJ4lACCE0ThKBEEJonCQCIYTQOEkEQgihcZIIhBBC4yQRCCGExkkiEEIIjZNEIIQQGieJQAghNE4SgRBCaJwkAiGE0DhJBEIIoXGSCIQQQuMkEQghhMZJIhBCCI2TRCCEEBoniUAIITROEoEQQmicJAIhhNA4SQRCCKFxkgiEEELjJBEIIYTGSSIQQgiNM0siMBqNjBw5kk6dOhESEsL58+ezLF+4cCHt27fn7bffZtu2beYIQQghRD7ZmKPQmJgYUlNTiYyM5NChQ4SFhTFnzhwAkpKSiIiIYOvWrdy/f5927drRpEkTc4QhhBAiH8ySCOLj46lXrx4Afn5+HD16VF3m4OCAp6cn9+/f5/79+1hZWZkjBCFwdnXAwd4sh3g2Hh4uZi3/fko6hqT7Zt2H0C6z/JUYDAacnZ3VaZ1OR3p6OjY2mbsrVaoULVu2JCMjg169epksT6ezws3N0RyhPne03g6PU39bWx2vDY82YzSWcy6sJTbP4Gev01lr9ph8keqer0RgMBiwsrJi27ZtNGjQgKJFi+a5vrOzM8nJyeq00WhUk0BsbCzXrl1j+/btAHTv3h1/f3+qVq2aa3kZGQqJiffyE2o25v6fmqU9bjtouf5arruluLk5PpNxWcLzUPf8/g2YPFk8dOhQtm/fzuTJk/ntt9/4/PPPTRbq7+9PbGwsAIcOHUKv16vLihYtSpEiRbCzs8Pe3h4XFxeSkpLyFawQQoiCZ7JH8Pfff9O2bVvWrl1LREQE3bp1M1lokyZNiIuLIzg4GEVRCA0NZfHixXh5edGoUSP27NlDx44dsba2xt/fn7p16xZIZYQQQjw+k4kgLS2NzZs3U758eW7dukViYqLJQq2trRk7dmyWeeXKlVPfDxgwgAEDBjxBuEIIIQqayaGhHj16sHXrVnr16kVERASDBg2yRFxCCCEsxGSPIDAwkAoVKnDixAk6depEiRIlLBGXEEIICzGZCJYvX862bdu4c+cOQUFBnD9/npEjR1oiNiGEEBZgcmgoOjqaJUuW4OLiQrdu3Th8+LAl4hJCCGEhJhOBoigA6h3AdnZ25o1ICCGERZkcGmrVqhXvvvsuly5d4qOPPqJx48aWiEsI8ZQs9YgNebzG88/kUdK1a1fefPNNTp48SdmyZalYsaIl4hJCPCUHe5sX4hEb58JaYijsIF5wJhPBt99+q74/c+YMMTEx9OvXz6xBCSGEsByTiaBYsWJA5rmC48ePYzQazR6UEEIIyzGZCIKDg7NM9+jRw2zBCCGEsDyTieCvv/5S31+/fp3Lly+bNSAhhBCWZTIRPHrzmL29PUOHDjVrQEIIISzLZCKIiIiwRBxCCCEKiclEsGHDBubNm0dKSoo67+GPygghhHj+mUwE8+fPZ86cOZQqVcoS8QghhLAwk4ng1VdfpUyZMpaIRQghRCEwmQiKFClCjx49qFSpkvq8ocGDB5s9MCGEEJZhMhH8+9//tkQcQgghConJp4+2bt2a9PR0Ll68iKenpyQGIYR4wZhMBKNGjeLSpUvExcWRnJzMsGHDLBGXEEIICzGZCC5cuMDAgQOxs7OjYcOG3L171xJxCSGEsBCTiSAjI4Nbt25hZWWFwWDA2trkJkIIIZ4jJk8Wf/LJJ3Tu3Jnr16/TqVMnvvjiC0vEJYQQwkJMJgIXFxd++uknbt26xUsvvaReQiqEEOLFYHKcZ/r06QQHBxMTE8O9e/csEZMQQggLMpkI5s6dS3h4OElJSXTv3l2GhoQQ4gWTrzO/6enppKamYjQa0el05o5JCCGEBZk8R9CtWzdSUlJ45513WLJkCY6OjpaISwghhIWYTASff/45Pj4+3LlzR5KAEEK8gEwmgjt37tCqVSsyMjJo1qwZnp6edOjQIc9tjEYjo0eP5sSJE9jZ2TF+/PgsTzDduXMns2bNAuD1119n1KhRcjWSEEIUEpPnCGbMmMHy5cspVqwYH3/8MatWrTJZaExMDKmpqURGRjJkyBDCwsLUZQaDgcmTJzN37lyioqJ45ZVXuH379tPVQgghxBMz2SOwtrbGzc0NKysr7O3tcXJyMllofHw89erVA8DPz4+jR4+qyw4ePIher2fixIlcvHiRDh064O7unmd5Op0Vbm4yLAVovh20XH+p+7NFp7N+JuN6EiYTgZeXF1OnTiUxMZF58+bh6elpslCDwYCzs7M6rdPpSE9Px8bGhtu3b7Nv3z42bNiAo6Mj7777Ln5+fpQtWzbX8jIyFBITn+weBg8Plyfa7ln1uO2g5fprue7wYtX/Sf/+zcnNzfGZjOtR+T0GTA4NjRkzBk9PT2rUqIGDgwPjx483WaizszPJycnqtNFoxMYmM+e4ublRpUoVPDw8cHJyombNmvzxxx/5ClYIIUTByzMRHDt2jEuXLtG5c2dGjRpFhw4dmD59uslC/f39iY2NBeDQoUPo9Xp1ma+vLydPnuTWrVukp6dz+PBhypcv/5TVEEII8aRyHRoaN24cx48fx2Aw0Lt3b8qUKcOAAQOoW7euyUKbNGlCXFwcwcHBKIpCaGgoixcvxsvLi0aNGjFkyBB69OgBQLNmzbIkCiGEEJaVayL47bffWL9+PXfv3qVbt27cv3+f8ePH8+abb5os1NramrFjx2aZV65cOfV9y5Ytadmy5VOELYQQoqDkmggenux1cXEhOTmZhQsXUrp0aYsFJoQQwjJyPUfw6A1exYsXlyQghBAvqFx7BBcvXmTatGkoikJCQgLTpk1Tlw0ePNgiwQkhhDC/XBPBgAEDcnwvhBDixZJrIggKCrJkHEIIIQqJ/BK9EEJonCQCIYTQOJPPGjIYDMTGxpKamqrOa9eunVmDEkIIYTkmE0GfPn0oXrw4pUqVApDfDRBCiBeMyUSgKApTpkyxRCxCCCEKgclzBD4+Phw+fJjU1FT1JYQQ4sVhskfw66+/smPHDnXaysqK7du3mzUoIYQQlmMyEfzwww+WiEMIIUQhsVIURclrhe3bt7Ny5UrS0tJQFIXExEQ2btxoqfgAMN75k/Q9Hz3Rtna2On45e7OAIyocb3i/TGpaxmNto+X6a7nu8OLU/0nqbgk2NtakpxsLO4w82TXfla/1TJ4jmDVrFv369aNUqVIEBQXJbwcIIcQLxuTQ0EsvvUT16tVZvXo17du3Z926dXlvcO0a1KgB27bBgwfw8cdgYwN6PSxYANbWMGUKrFqV+f7zzyEoCMLC4McfM8tITIQrVzJfERFYTZqMlZMzD4Lf5cG77+Ewcxp2O2IAsL5zB+trV7m1Iw7XXh/8r2JHfyf5y9HYdX+P+70CcU65T6KDC8Ob9eemk9uTt1ghOtezJXeu332sbTw8XAgeHm2miCzrceuv5brDi1P/J6lUcnX0AAAWe0lEQVS7Jbi5OXLnWf/N4nyuZ7JHYGtry/79+0lPT2fXrl1cv34995XT0qBXL3BwyJweMwZGjoTduyElBaKjM7/kZ86EvXth61YYNChz3eHD4b//zXyVLg1Ll8KNG/Dll6THbCfx+y3YfxeF9YXz3B8wmDsbNnNnw2YyPD1JCp+LUqKEOi/5i1GkV63Gg5D3ITSU/aUr06HrJJbUaM1nscvy2TRCCKEN+frx+vT0dHr37k1UVFTeTyL99NPMHoCnZ+Z09epw6xYoCty9C7a24OQEZcpAcnLmy/ofIaxbBy+9BE2bwtmz4OcH7u5gbU26nz+28fvVVe02/YBS1I20ho3/t72i4Pz5UAyTvgGdDo4fZ6d3DQAOvFKJWgnH8986QgihASYTQYkSJYDMn67s27cvjRs3znnFJUvAwyPzC/yhChVgwACoVAmuXoW33sqc/+qr8Prr4O+fufxRX38No0b9b/tjxzK3vXcP2107sbr3v66Y48ypJH86PMvmdj9tIcOnIhnlK2TO8POj8al9ADQ5vY8iaSmmqiyEEJpi8hzBtGnTuHLlCmfOnMHW1pZ58+Zl+ZEa1aJFYGUFMTFw6BC8917mvwcPQuXKMGsWDBmSmSguX4a//srcrmlTqFsXateG48fBzQ3Kl89c9tJL8M036Dp1wKV4SdKrVsPo/jIAuhN/ori6YfQulyUM+7WR3P+o9/9mjBhB6Y3tWb76C3aWrcFl12JP1lJCCPGCMtkjiI+PZ9KkSTg6OhIUFERCQkLOK8bGws6dmWP8fn6wbBl4e4Ora+ZyT0+4fTvzy93BAeztoUiRzC/+xMTMdWJioHnz/5WZng6//ELGjv9y99t56E6dJK32GwDYxf5MaqMm2cKwPXyQ9NoBWeJa59uQrsETuOhWggOvVMpXwwghhFaY7BFkZGSQkpKClZUVGRkZWP9zTD8vCxZAcHDmVUN2djB/Prz2WuYX/htvZJ4f+Ne/oMn/f6GfOPG/96BuZxNQGzcbW+716Y/y8v/3CE6fIvXfDbPszurGDRRnl8yeyUM+Pnwa+ykAV1xeZljzgfmPXwghNMBkIujWrRvt27fn1q1bdOjQgffff990qf/97//ex8VlXz5mTObrn2bNyj5v1CjSP/+SxH9cpmWYmH14SilWjNs//2N/5cvzdog8NE8IIXJjMhE0b96cOnXqcP78eUqXLo27u7sl4hJCCGEhuSaCESNG5LrR119/bZZghBBCWF6uieDo0aM8ePCANm3aUL16dUw8kkgIIcRzKtczvxs3bmTWrFmkpKQwb948Dh06hJeXF/Xq1bNkfEIIIcwsz3MEer2eTz/NvOJm//79TJ06lStXrhAVFWWR4IQQQpifyWtBDQYD69evZ+7cudy4cYM2bdqYLNRoNDJy5Eg6depESEgI58+fz3GdHj16sGrVqieLXAghRIHItUewZcsWoqOjuXTpEoGBgYwZM4bSpUvnq9CYmBhSU1OJjIzk0KFDhIWFMWfOnCzrTJ8+nTt37jxd9EIIIZ5arongk08+wdvbm4oVK3Ly5Em++eYbddnUqVPzLDQ+Pl49l+Dn58fRo0ezLP/xxx+xsrKifv36+QpSp7PCzc0xX+u+6LTeDlquv9T92aLTWT+TcT2JXBPBsmVP/rhmg8GAs7OzOq3T6UhPT8fGxoaTJ0+yadMmZs6cyaycbiDLQUaGku2Gsvzy8HB5ou2eVY/bDlquv5brDi9W/Z/079+c3Nwcn8m4HpXfYyDXRFC7du0n3rmzszPJycnqtNFoxMYmc1cbNmzg6tWrdOvWjb///htbW1teeeWVfPcOhBBCFCyTdxY/CX9/f37++WdatGjBoUOHsvy85dChQ9X34eHhFCtWTJKAEKJAObs64GBvlq+3LCzR67qfko4h6b5Z92GWlmrSpAlxcXEEBwejKAqhoaEsXrwYLy8vGjVqZI5dCiGEysHehtdegJ/pBDgX1hKDmfdhlkRgbW3N2LFjs8wrV65ctvX69+9vjt0LIYR4DI/xTGkhhBAvIkkEQgihcZIIhBBC4yQRCCGExkkiEEIIjZNEIIQQGieJQAghNE4SgRBCaJwkAiGE0DhJBEIIoXGSCIQQQuMkEQghhMZJIhBCCI2TRCCEEBoniUAIITROEoEQQmicJAIhhNA4SQRCCKFxkgiEEELjJBEIIYTGSSIQQgiNk0QghBAaJ4lACCE0ThKBEEJonCQCIYTQOEkEQgihcZIIhBBC4yQRCCGExtmYo1Cj0cjo0aM5ceIEdnZ2jB8/njJlyqjLlyxZQnR0NAD//ve/6devnznCEEIIkQ9m6RHExMSQmppKZGQkQ4YMISwsTF128eJFfvjhB1avXk1kZCS7d+/mzz//NEcYQggh8sEsPYL4+Hjq1asHgJ+fH0ePHlWXlSxZkgULFqDT6QBIT0/H3t7eHGEIIYTIB7MkAoPBgLOzszqt0+lIT0/HxsYGW1tb3N3dURSFSZMm8frrr1O2bNk8y9PprHBzczRHqM8drbeDlusvddcuc9ffLInA2dmZ5ORkddpoNGJj879dpaSk8Pnnn+Pk5MSoUaNMlpeRoZCYeO+JYvHwcHmi7Z5Vj9sOWq6/lusOL1b9tVx3ePz6P5TfdjDLOQJ/f39iY2MBOHToEHq9Xl2mKAp9+vTBx8eHsWPHqkNEQgghCodZegRNmjQhLi6O4OBgFEUhNDSUxYsX4+XlhdFo5NdffyU1NZVdu3YBMHjwYKpXr26OUIQQQphglkRgbW3N2LFjs8wrV66c+v733383x26FEEI8AbmhTAghNE4SgRBCaJwkAiGE0DhJBEIIoXGSCIQQQuMkEQghhMZJIhBCCI2TRCCEEBoniUAIITROEoEQQmicJAIhhNA4SQRCCKFxkgiEEELjJBEIIYTGSSIQQgiNk0QghBAaJ4lACCE0ThKBEEJonCQCIYTQOEkEQgihcZIIhBBC4yQRCCGExkkiEEIIjZNEIIQQGieJQAghNE4SgRBCaJwkAiGE0DhJBEIIoXFmSQRGo5GRI0fSqVMnQkJCOH/+fJblUVFRtG/fno4dO/Lzzz+bIwQhhBD5ZGOOQmNiYkhNTSUyMpJDhw4RFhbGnDlzALh+/ToRERF89913pKSk0KVLF+rWrYudnZ05QhFCCGGCWXoE8fHx1KtXDwA/Pz+OHj2qLjty5AjVq1fHzs4OFxcXvLy8+PPPP80RhhBCiHwwS4/AYDDg7OysTut0OtLT07GxscFgMODi4qIuc3JywmAw5Fmera0ODw+XPNfJy7mwlk+87bPmSdpBy/XXct3hxam/lusOT1b/x2GWHoGzszPJycnqtNFoxMbGJsdlycnJWRKDEEIIyzJLIvD39yc2NhaAQ4cOodfr1WVVq1YlPj6elJQU7t69y5kzZ7IsF0IIYVlWiqIoBV2o0Whk9OjRnDx5EkVRCA0NJTY2Fi8vLxo1akRUVBSRkZEoikKvXr1o2rRpQYcghBAin8ySCIQQQjw/5IYyIYTQOEkEQgihcZIIhBBC4zSbCObNm8e//vUvUlJSsi1btWoV4eHhBbKfbdu2cfXq1QIpyxwSEhLw9/cnJCREfX377bePVcazXsd/yuuzf+j69euMHj36qfazf/9+9WbJfv36PVVZ5lYQx8FDf/zxh7pt3bp1CzLMAnPq1Cl69uxJSEgIb7/9NjNnzsScp0sL8jvFHMxyQ9nzYOPGjbRo0YLo6Gjat29vtv0sW7aM0aNHU6JECbPt42mVL1+eiIiIJ97+eajjo/Lz2Xt4eDx1Ivjuu+9o0aIFFStWfOIvVUt62uPgoUqVKlGpUqUCiMg8kpKSGDx4MOHh4bz22mtkZGQwcOBAVq9eTefOnQs7vEKhyUSwb98+vLy8CA4O5rPPPqN9+/YcOHCA0NBQihYtirW1NX5+fixbtoykpCT69etHamoqbdq04YcffiAyMpJNmzZhZWVFixYteO+99xg+fDh2dnb8/fffXLt2jbCwMK5fv84ff/zBsGHDmDx5MsOGDSMqKgqAjh07Mm3aNNavX8/Bgwe5d+8eEyZMYM+ePdnKtrSMjAxGjhzJlStXuH37NvXr12fQoEEm67hy5UrCw8M5evQoycnJlCtXjq+//pr4+HgmTpyIjY0Nrq6uTJkyhVGjRtG6dWveeustzpw5w8SJE5k3b57Z65bTZx8SEkLFihU5deoUBoOBGTNmoCgKgwcPJioqitatW1OzZk1OnjxJ2bJlefnllzlw4AB2dnbMmzePmzdvMnr0aFJSUkhMTKRv376ULFmSXbt2cezYMcqXL0+HDh2Ii4vj+PHjjBs3Dp1Oh729PePGjcNoNDJkyBBKlizJxYsXqVKlCmPGjDF7W5iS13FgY2PDpUuXSE1NpUWLFvz8889cvnyZ2bNnc/nyZVavXs0333wDwN27dwkKCuKnn35Cp9MxefJkfH19ad68eaHUa/v27QQEBPDaa68BmU8+mDhxIra2toSFhREfHw9Aq1at6Natm8n6enl5MXXqVPbv34+iKLz//vs0b948x++UyMhIzp07x7Bhw8jIyKBdu3Z89913hf6sNU0ODa1Zs4YOHTrg7e2NnZ0dhw8f5uuvv2bq1KksXryY0qVLA9C2bVu2bNmCoihs376dBg0acOHCBTZv3szKlStZuXIlMTExnD17FgBPT08WLlxISEgIkZGRvPXWW1SqVEk9yHLj7e3N6tWrURQl17LN6fTp01mGBA4dOoSfnx8LFy5k1apVrFq1Sl03rzqmpqbi6urK4sWLWb16NYcOHeLq1avExMTQpEkTli9fzjvvvENSUhIdOnRg/fr1AKxdu5Z33nnH7PWEnD97yLzRccmSJdStW5fo6Ogs2yQnJ9OqVStWrFjBgQMH8Pf3Z8WKFaSlpXH69GnOnj3LBx98wOLFi/nqq69YsWIFvr6+1KtXj88++wxPT0+1rC+//JKRI0eyfPlyOnfuTFhYGADnzp1jwoQJrFmzhtjYWK5fv26R9njU4xwHr7zyCosWLcLb25uEhATmz59PYGAgO3bsyFaui4sLNWrUYPfu3WRkZBAbG0ujRo0sWbUsrl27xquvvpplnpOTE3FxcSQkJBAVFcXKlSvZtGkTJ06cAPKu786dO0lISGD16tUsW7aMuXPnkpSUlON3SsuWLdm+fTsZGRns2rWLgICAQk8CoMEewZ07d4iNjeXWrVtERERgMBhYvnw5V69epWzZskDmndEXLlygaNGiVKpUifj4eNavX8+wYcM4ceIEly5d4v3331fLu3DhAoDaHS5ZsiS//fZbnnE8Oh75cL8nT57MsWxvb++CbIJs/jkkYDAY+P777/nll19wdnYmNTVVXZZXHe3t7bl16xaDBw/G0dGRe/fukZaWxscff8zcuXPp1q0bJUqUoGrVqgQEBDBhwgRu3rxJXFwcgwcPNmsdIffPHuD1119X63Xjxo1s21auXBkAV1dXypUrp75PSUnBw8ODOXPmsHbtWqysrEhPT881hmvXrqltWKtWLaZOnQqAl5eX+nwuDw+PPM9fmMvjHAcP28vV1VU9Pl1dXbOs86gOHToQERGB0WikTp06hfrl5+npyfHjx7PMu3jxIseOHaNmzZpYWVlha2tLtWrVOHPmDJB3fU+ePMmxY8cICQkBID09nUuXLuX4neLs7EytWrXYvXs369ato0+fPpaqdp401yP44YcfePvtt1m0aBELFy4kKiqKuLg47Ozs1A/9999/V9fv2LEjS5cu5cGDB5QrVw5vb2/Kly/PsmXLiIiIoH379uojMqysrLLtz8rKCkVRsLe35+bNm2RkZJCUlERCQoK6jrV15seQV9mWtG7dOlxcXJg6dSoffvghDx48UBNXXnWMjY3l8uXLTJs2jcGDB6vbbdy4kaCgICIiIqhQoQJRUVFYWVnRunVrJkyYQN26dfPsMRWU3D77W7dumdw2p3o/NGPGDNq2bcvkyZMJCAjI0lb/PAFZvHhx9QTy/v371eGJvMovLI97HOSlZs2aXLx40aK9v9w0aNCAXbt2qf+BS0tLIywsDFdXV3VYKC0tjYMHD1KmTBkg7/p6e3sTEBBAREQES5cupXnz5pQuXRoPD49cv1PWrFnDzZs3qVixormq+Vg01yNYs2YNkyZNUqcdHBwIDAykZMmSDBs2DCcnJ5ycnChatCgAtWvX5quvvqJ3794AVKxYkTfffJPOnTuTmppK1apV8zxJWr16dYYOHcqiRYuoW7cu77zzDl5eXuoB9qjHLdtc3nzzTQYPHkx8fDwODg6UKVOGa9eu5br+wzrOmTOH2bNn07FjR+zs7Hj11Ve5du0aVapUYfjw4Tg6OmJra8vYsWMBaN++PW+99Rbff/+9ReqV22e/du3apyq3WbNmTJgwgf/85z+UKlWK27dvA1CtWjWmTJmiDgsAjB8/nnHjxqEoCjqdjtDQ0Kfatzk97nFgSuvWrfnxxx+pUKFCAUb5+JydnQkLC+PLL79EURSSk5Np0KABISEhXL58mU6dOpGWlkazZs3UnmBeGjZsyK+//kqXLl24d+8ejRs3xtnZWT0v+M/vlGrVqnH+/Hneffddc1c13+QRE6LQXL16laFDh7J06dLCDkVYwPz583nppZcKvUdQ2IxGI507d2bhwoVZHtdfmDQ3NCSeDT/99BM9evRgyJAhhR2KsIDhw4dz4MAB2rRpU9ihFKqLFy8SFBRE27Ztn5kkANIjEEIIzZMegRBCaJwkAiGE0DhJBEIIoXGau3xUvFgSEhJo06ZNlsv8AgICnvghb3/88Qfbt2+nX79+1K1bl7i4OJPbpKenM3fuXHbu3Im9vT2Qealkp06dnigGISxNEoF47hXUw9LgyR6Y9s0332A0Glm9ejU6nY7k5GR69epFzZo11buQhXiWSSIQLyRLPTAtPT2dLVu2sHXrVnQ6HZD53JqIiAj1btScHkiW24PuevfujZubG/Xr16d+/fqMHz8eADc3N0JDQ0lLS2PQoEEoikJaWhpjxozBx8enEFpYvEgkEYjn3sOHpT00ZcoU0tLS8PPzo0OHDqSkpKiJADIfIDZ+/HhGjhypPkBs5syZ7NixI1tv4NEHpv3rX/8iNjaWgQMHqstv375N0aJFsbHJ/FNauXIlW7ZsITk5mTZt2lC2bFn1gWQpKSl07NhRfUZ/1apV+eKLL/jmm2+Ijo6mRYsWXL9+XX0aZceOHQkNDaV8+fKsWbOGBQsWUL16dfWxD6dPn8ZgMJi7eYUGSCIQz72choYMBgO///672R+Y5ubmRmJiIhkZGeh0Orp06UKXLl1YtWoVN27cyPWBZI/G8eiD7kqXLq2Wf+bMGfVx1GlpaZQtW5b69etz7tw5+vTpg42NjfroEyGehlw1JF5Ilnpgmq2tLYGBgUyfPh2j0QhASkoKhw8fxsrKKtcHkuXm4QMIIfOptBMnTiQiIoLPPvuMf//73+zbt4/ixYuzaNEievfuzbRp0x6rLkLkRHoE4oVkyQemffbZZyxYsIB3330XGxsbDAYDjRs35oMPPsDBwSHHB5Llx+jRo9UfMAGYMGECbm5ufPLJJyxduhRra2v69u37xHUS4iF5xIQQ+SAPTBMvMukRCGHC8OHDuX379jP94+NCPA3pEQghhMbJyWIhhNA4SQRCCKFxkgiEEELjJBEIIYTGSSIQQgiN+z+X/h7dQntgMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "family_list = ['Adventure','Animation','Comedy','Family','Fantasy']\n",
    "df_family_genres = df_genres_result[df_genres_result.index.isin(family_list)]\n",
    "df_family_genres_ordered = df_family_genres.sort_values(['revenue'], ascending=False)\n",
    "\n",
    "locations = get_locations(len(family_list))\n",
    "heights = df_family_genres_ordered.revenue\n",
    "labels = df_family_genres_ordered.index\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(locations, heights, tick_label = labels)\n",
    "ax.axhline(y=mean_value, color=\"orange\")\n",
    "ax.text(0,mean_value, \"{:.0f}\".format(mean_value), color=\"red\", ha=\"left\", va=\"top\")\n",
    "#plt.axhline(y=mean_value, color='#c86321')\n",
    "plt.title('The Mean Revenue of Family Genres', fontsize = '17')\n",
    "plt.xlabel('Family Genres')\n",
    "plt.ylabel('Mean Revenues');\n",
    "\n",
    "df_family_genres_ordered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "> **Tip**: Finally, summarize your findings and the results that have been performed. Make sure that you are clear with regards to the limitations of your exploration. If you haven't done any statistical tests, do not imply any statistical conclusions. And make sure you avoid implying causation from correlation!\n",
    "\n",
    "> **Tip**: Once you are satisfied with your work, you should save a copy of the report in HTML or PDF form via the **File** > **Download as** submenu. Before exporting your report, check over it to make sure that the flow of the report is complete. You should probably remove all of the \"Tip\" quotes like this one so that the presentation is as tidy as possible. Congratulations!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q1. Which genres are most popular from year to year?\n",
    "#### C1. In 1900s, various genres were generally popular. History, war, SF, mystery and horror genres. Particularly, mystery and horror were mostly popular. In 2000s, western and horror genres were remarkably popular. If you look at the whole section(from 1960 to 2015), horror genre has been steadily popular.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Q2. What kinds of properties are associated with movies that have high revenues?\n",
    "#### I thought the genres and casted actor/actress are very associated with the revenue first. Because we wonder the actor/actoress of the movies and which genres of the movies when a movie is released. So I found the top 13 actor/actress and then analyzied their revenue.\n",
    "### Q2-1. Cast is associated with the revenue?\n",
    "#### Conclusion 2-1. The Actor/Actress whose the number of movie appearances is high have high revenues.  The mean revenue of the top 13 is 78639773 and the mean revenue of all cast is 42433577. Look at the plot below. The mean of the top 13 is yellow line and the mean of all cast is red line. First, The mean revenue of the top 13 is almost twice revenue of all cast. And the number of members with lower revenue than all cast's mean(red line) is 1. So the actor/actress whose number of appearances is high has the higher revenue. Also, they are all famous. \n",
    "### Q2-2. Genres are associated with the revenue?\n",
    "#### Conclusion 2-2. The number of all genres is 20 and the number of genres associated with family is 5. Those are Adventure, Animation, Comedy, Family, Fantasy. So I limited the range to family genres for comparison between related data. Total mean revenue of all genres is 48879779(brown line). The number of family genres with higher than the mean(brown line) is 4 (Adventure, Animation,Family, Fantasy). Their revenue has much higher revenue. So, most family genres are very associated with the higher revenue. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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