Investigate_a_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Project: Movie Dataset Analysis\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>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "In this analysis we will be investigating The Movie Dataset which has the data of the movies of the last 50+ years and we'll be looking at the trends  and correlation between different data features and how each generation of movies differs from one another. We'll also make our analysis for each decade where required to find better insights.\n",
    "\n",
    "###### We'll be looking at the following things in our analysis:\n",
    "Is the data clean? does it contain any zero,null or dupicate values?,\n",
    "\n",
    "Are all the columns/rows important or are some of them redundant and can be dropped?,\n",
    "\n",
    "How the popularity and vote_count related and what is their trend in different decades?,\n",
    "\n",
    "What are the top cast and directors in our dataset?,\n",
    "\n",
    "Which revenue/budget value to use (revenue/budget-adj or revenue/budget)? Which decade was most profitable?,\n",
    "\n",
    "What are the most popular genres and keywords of each decade?,\n",
    "\n",
    "and are all the titles in the dataset \"movies\"? if no then what other titles exist and how to differentiate them?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Libraries\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "import numpy as np\n",
    "% matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Reading the dataset\n",
    "df=pd.read_csv('tmdb-movies.csv')\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling and Cleaning\n",
    "\n",
    "In this step we will be converting our data into a more usable format, we will be applying operations to clean,modify and transform our data.\n",
    "\n",
    "We'll be looking at :\n",
    "\n",
    "##### Null values : Zero values:  Redundant Columns:  Duplicate Rows/Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data is:  (10866, 21)\n",
      "<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",
      "None\n"
     ]
    }
   ],
   "source": [
    "#Getting info about the data\n",
    "print (\"Shape of data is: \",df.shape)\n",
    "print (df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see the data has 10866 rows using the shape function, they are many values which are null, these values doesn't help us . Lets see whats the percentage of null values in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                       0.000000\n",
       "imdb_id                  0.092030\n",
       "popularity               0.000000\n",
       "budget                   0.000000\n",
       "revenue                  0.000000\n",
       "original_title           0.000000\n",
       "cast                     0.699429\n",
       "homepage                72.979937\n",
       "director                 0.404933\n",
       "tagline                 25.989324\n",
       "keywords                13.740107\n",
       "overview                 0.036812\n",
       "runtime                  0.000000\n",
       "genres                   0.211669\n",
       "production_companies     9.479109\n",
       "release_date             0.000000\n",
       "vote_count               0.000000\n",
       "vote_average             0.000000\n",
       "release_year             0.000000\n",
       "budget_adj               0.000000\n",
       "revenue_adj              0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage of null values of each column\n",
    "df.isnull().sum()/len(df)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see the homepage and tagline columns have high percentage of null values, this means these columns are not that useful and these columns are not important for our questions anyway so its better if we just drop them to make our data more compact.Also i think we dont need any analysis to know that the ID and imdb_id have nothing to do with the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping irrelevant columns\n",
    "df.drop(['homepage'], axis=1, inplace=True)\n",
    "df.drop(['tagline'],axis=1, inplace=True)\n",
    "df.drop(['id'], axis=1, inplace=True)\n",
    "df.drop(['imdb_id'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['popularity', 'budget', 'revenue', 'original_title', 'cast', 'director',\n",
       "       'keywords', 'overview', 'runtime', 'genres', 'production_companies',\n",
       "       'release_date', 'vote_count', 'vote_average', 'release_year',\n",
       "       'budget_adj', 'revenue_adj'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing columns\n",
    "df.columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both the \"tagline\" and \"homepage\" columns have been removed along with the IDs. As we have removed the top 2 null values columns in terms of percentage there are still many null values present in the dataset.The keywords column and production_companies have higher percentage relative to other columns but we are not sure what to do with them yet.As for other columns with low percentage we cant remove the whole column but we can remove the null value rows since the percentage is low."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping rows where columns have null values\n",
    "df.dropna(subset=['cast', 'director','overview','genres'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10730 entries, 0 to 10865\n",
      "Data columns (total 17 columns):\n",
      "popularity              10730 non-null float64\n",
      "budget                  10730 non-null int64\n",
      "revenue                 10730 non-null int64\n",
      "original_title          10730 non-null object\n",
      "cast                    10730 non-null object\n",
      "director                10730 non-null object\n",
      "keywords                9306 non-null object\n",
      "overview                10730 non-null object\n",
      "runtime                 10730 non-null int64\n",
      "genres                  10730 non-null object\n",
      "production_companies    9773 non-null object\n",
      "release_date            10730 non-null object\n",
      "vote_count              10730 non-null int64\n",
      "vote_average            10730 non-null float64\n",
      "release_year            10730 non-null int64\n",
      "budget_adj              10730 non-null float64\n",
      "revenue_adj             10730 non-null float64\n",
      "dtypes: float64(4), int64(5), object(8)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# Checking null values\n",
    "df.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that we have all the non-null values except keywords and production companies.These two don't have very high percentage of null values for their columns to be dropped and they dont have very low percentage of null values for their rows to be dropped.So we wont change them just yet and see if they would affect our data analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we are going to check the zero values in the data. Zero values are sometimes correct because an entity can be zero and sometimes data is wrong. Manual work is required here to check if the value is really zero or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "popularity              False\n",
       "budget                   True\n",
       "revenue                  True\n",
       "original_title          False\n",
       "cast                    False\n",
       "director                False\n",
       "keywords                False\n",
       "overview                False\n",
       "runtime                  True\n",
       "genres                  False\n",
       "production_companies    False\n",
       "release_date            False\n",
       "vote_count              False\n",
       "vote_average            False\n",
       "release_year            False\n",
       "budget_adj               True\n",
       "revenue_adj              True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if columns have any zero values\n",
    "(df==0).any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "budgets and revenues have zero values in them along with runtimes we have to closely look at these values to see if they are right or wrong. Im assuming the revenue_adj and budget_adj are revenues and budgets respectively after adjusting them for inflation so they are more or less the same thing (if we are not interesting in finding inflation rate) and we dont need both of them so lets first check how closely are they related and remove them if they are redundant/duplicate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48    0\n",
      "67    0\n",
      "74    0\n",
      "75    0\n",
      "92    0\n",
      "Name: revenue, dtype: int64\n",
      "30    0\n",
      "36    0\n",
      "72    0\n",
      "74    0\n",
      "75    0\n",
      "Name: budget, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# zero values in respctive columns of budgets/budget_adj and revenue/revenue_adj\n",
    "print (df.revenue[df.revenue_adj==0].head(5))\n",
    "print (df.budget[df.budget_adj==0].head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see that budget is zero where adjusted budget is zero and same for revenue for all values (only few are shown here for better understanding). , lets look at their plots to further prove my assumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44015f9c88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting budget and budget_adj and revenue with revenue_adj\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title('Budget',fontsize=\"20\")\n",
    "plt.xlabel(\"Budget\",fontsize=\"15\")\n",
    "plt.ylabel(\"Budget_adj\",fontsize=\"15\")\n",
    "\n",
    "plt.scatter(x=df.budget,y=df.budget_adj);\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title('Revenue',fontsize=\"20\")\n",
    "plt.xlabel(\"Revenue\",fontsize=\"15\")\n",
    "plt.ylabel(\"Revenue_adj\",fontsize=\"15\")\n",
    "\n",
    "\n",
    "plt.scatter(x=df.revenue,y=df.revenue_adj);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see both are directly proportional with their adjusted values. So as we have deduced that both the \"adj\" columns of budgets and revenue provide same features as their original column so we can only keep one and still be okay with it so lets just keep the adjusted values because i think its better if we consider inflation in our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping redundant columns\n",
    "df.drop(['revenue'], axis=1, inplace=True)\n",
    "df.drop(['budget'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still we have some zero values in runtime, revenue_adj and budget_adj lets find their percentage to see how many they are with respect to their column nonzero values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of zero values in revenue_adj:    54.85554520037279\n",
      "Percentage of zero values in budget_adj:    51.96644920782851\n",
      "Percentage of zero values in runtime:    0.25163094128611374\n"
     ]
    }
   ],
   "source": [
    "#Percentage of zero values\n",
    "print (\"Percentage of zero values in revenue_adj:   \",len(df[df.revenue_adj==0])/float(len(df.revenue_adj))*100)\n",
    "print (\"Percentage of zero values in budget_adj:   \",len(df[df.budget_adj==0])/float(len(df.budget_adj))*100)\n",
    "print (\"Percentage of zero values in runtime:   \",len(df[df.runtime==0])/float(len(df.runtime))*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As revenue and budget have high percentage and by manually checking i can see that these values are not right for the movies, as they have budget and revenues on the internet. so we will replace zero values with mean values because zero values affect the mean/median/mode of data and other statistical operations. An incase of runtime the percentage is small so we are just going to remove the rows.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing rows of runtime with zero values\n",
    "df=df[df.runtime!=0]\n",
    "#replacing zeros with mean\n",
    "df['budget_adj']=df['budget_adj'].replace(0,float(df.budget_adj.mean()))\n",
    "df['revenue_adj']=df['revenue_adj'].replace(0,float(df.revenue_adj.mean()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "popularity              False\n",
       "original_title          False\n",
       "cast                    False\n",
       "director                False\n",
       "keywords                False\n",
       "overview                False\n",
       "runtime                 False\n",
       "genres                  False\n",
       "production_companies    False\n",
       "release_date            False\n",
       "vote_count              False\n",
       "vote_average            False\n",
       "release_year            False\n",
       "budget_adj              False\n",
       "revenue_adj             False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking zero values\n",
    "(df==0).any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see all the columns have non zero values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last we check if there are any duplicates in data and dropping them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>keywords</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>2090</th>\n",
       "      <td>0.59643</td>\n",
       "      <td>TEKKEN</td>\n",
       "      <td>Jon Foo|Kelly Overton|Cary-Hiroyuki Tagawa|Ian...</td>\n",
       "      <td>Dwight H. Little</td>\n",
       "      <td>martial arts|dystopia|based on video game|mart...</td>\n",
       "      <td>In the year of 2039, after World Wars destroy ...</td>\n",
       "      <td>92</td>\n",
       "      <td>Crime|Drama|Action|Thriller|Science Fiction</td>\n",
       "      <td>Namco|Light Song Films</td>\n",
       "      <td>3/20/10</td>\n",
       "      <td>110</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>30000000.0</td>\n",
       "      <td>967000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      popularity original_title  \\\n",
       "2090     0.59643         TEKKEN   \n",
       "\n",
       "                                                   cast          director  \\\n",
       "2090  Jon Foo|Kelly Overton|Cary-Hiroyuki Tagawa|Ian...  Dwight H. Little   \n",
       "\n",
       "                                               keywords  \\\n",
       "2090  martial arts|dystopia|based on video game|mart...   \n",
       "\n",
       "                                               overview  runtime  \\\n",
       "2090  In the year of 2039, after World Wars destroy ...       92   \n",
       "\n",
       "                                           genres    production_companies  \\\n",
       "2090  Crime|Drama|Action|Thriller|Science Fiction  Namco|Light Song Films   \n",
       "\n",
       "     release_date  vote_count  vote_average  release_year  budget_adj  \\\n",
       "2090      3/20/10         110           5.0          2010  30000000.0   \n",
       "\n",
       "      revenue_adj  \n",
       "2090     967000.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding duplicates\n",
    "df[df.duplicated()==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>keywords</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [popularity, original_title, cast, director, keywords, overview, runtime, genres, production_companies, release_date, vote_count, vote_average, release_year, budget_adj, revenue_adj]\n",
       "Index: []"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping duplicates\n",
    "df.drop_duplicates(keep=False, inplace=True)\n",
    "# finding duplicates\n",
    "df[df.duplicated()==True]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Wrangling Conclusion\n",
    "Following steps were taked in data wrangling process:\n",
    "\n",
    "1) Drop Columns with high percentage of null values\n",
    "\n",
    "2) Drop Rows of null values where percentage was low\n",
    "\n",
    "3) Replacing Zero values with mean where values were large\n",
    "\n",
    "4) Dropping Zero values where values were low\n",
    "\n",
    "5) Dropping Duplicates\n",
    "\n",
    "6) Dropping redundant columns "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "##### Here we will explore the dataset and ask questions about the characteristics of the data and their visualizations.\n",
    "\n",
    "Following are the questions which we will ask:\n",
    "\n",
    "Q1) How  has the popularity and vote count increased in the last few decades. \n",
    "\n",
    "Q2) Which decade was the most successful monetarily?\n",
    "\n",
    "Q3) What are the most popular genres and keywords of the last decade\n",
    "\n",
    "Q4) Which director and actor/actress has been the most successful?\n",
    "\n",
    "Q5) What is the ratio of movies and tv-series in the dataset ? How to differentiate.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 1:\n",
    "#### How  has the popularity and vote count increased in the last few decades. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets first find the correlation between popularity and vote_count because they look almost similar and it would be interesting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f440131aa90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation Matrix\n",
    "plt.figure(figsize=(8,6))\n",
    "corr=df.corr()\n",
    "plt.title(\"Correlation Matrix\",fontsize=\"15\")\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the heatmap we can see that the correlation between popularity and vote_count is in 0.8 range which is good for our cause.Lets plot them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44012260b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting all values\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title('All Values',fontsize=\"20\")\n",
    "plt.xlabel(\"Popularity\",fontsize=\"15\")\n",
    "plt.ylabel(\"Vote Count\",fontsize=\"15\")\n",
    "plt.yticks(np.arange(0, 100001, step=1000))\n",
    "plt.xticks(np.arange(0, 35, step=3))\n",
    "\n",
    "plt.scatter(x=df.popularity,y=df.vote_count)\n",
    "#plotting just first 100 values\n",
    "plt.subplot(1,2,2)\n",
    "plt.title('First 500',fontsize=\"20\")\n",
    "plt.xlabel(\"Popularity\",fontsize=\"15\")\n",
    "plt.ylabel(\"Vote Count\",fontsize=\"15\")\n",
    "plt.xticks(np.arange(0, 35, step=3))\n",
    "plt.yticks(np.arange(0, 7001, step=1000))\n",
    "\n",
    "plt.scatter(x=df.popularity.head(500),y=df.vote_count.head(500));\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both plots are showing  even vote_Count and popularity are directly proportional to each other to some extent which makes sense as the number of voters increases popularity generally. We can see that the popularity has some outliers it would be better if we clean this data. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Popularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10701.000000\n",
       "mean         0.653859\n",
       "std          1.005775\n",
       "min          0.000188\n",
       "25%          0.211508\n",
       "50%          0.388036\n",
       "75%          0.722465\n",
       "max         32.985763\n",
       "Name: popularity, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.popularity.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "75% of popularity values are less than 1 but the max value is 32 which makes are plot uneven. So, lets remove the outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "q=df.popularity.quantile(0.99)\n",
    "df=df[df.popularity<q]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10593.000000\n",
       "mean         0.586488\n",
       "std          0.628073\n",
       "min          0.000188\n",
       "25%          0.210293\n",
       "50%          0.384763\n",
       "75%          0.706023\n",
       "max          4.586426\n",
       "Name: popularity, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.popularity.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only removing 1% of outliers made our data cleaner, now lets plot the graphs again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4401141710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting all values\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title('All Values',fontsize=\"20\")\n",
    "plt.xlabel(\"Popularity\",fontsize=\"15\")\n",
    "plt.ylabel(\"Vote Count\",fontsize=\"15\")\n",
    "plt.xticks(np.arange(0, 5, step=0.5))\n",
    "plt.yticks(np.arange(0, 8001, step=1000))\n",
    "\n",
    "plt.scatter(x=df.popularity,y=df.vote_count)\n",
    "#plotting just first 100 values\n",
    "plt.subplot(1,2,2)\n",
    "plt.title('First 500',fontsize=\"20\")\n",
    "plt.xlabel(\"Popularity\",fontsize=\"15\")\n",
    "plt.ylabel(\"Vote Count\",fontsize=\"15\")\n",
    "plt.xticks(np.arange(0, 5, step=0.5))\n",
    "plt.yticks(np.arange(0, 4001, step=500))\n",
    "\n",
    "plt.scatter(x=df.popularity.head(500),y=df.vote_count.head(500));\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we get better distribution of the data but still popularity and vote count have a positive correlation. Now lets find their stats for each decade, first lets divide decade into bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['decades']=pd.cut(df['release_year'],[1960,1970,1980,1990,2000,2010,2020],right=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets find what is the the average popularity throughout the decade for each decade"
   ]
  },
  {
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decades</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[1960, 1970)</th>\n",
       "      <td>0.428118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[1970, 1980)</th>\n",
       "      <td>0.444115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[1980, 1990)</th>\n",
       "      <td>0.508504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[1990, 2000)</th>\n",
       "      <td>0.593464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2000, 2010)</th>\n",
       "      <td>0.601830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2010, 2020)</th>\n",
       "      <td>0.628450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Popularity\n",
       "decades                 \n",
       "[1960, 1970)    0.428118\n",
       "[1970, 1980)    0.444115\n",
       "[1980, 1990)    0.508504\n",
       "[1990, 2000)    0.593464\n",
       "[2000, 2010)    0.601830\n",
       "[2010, 2020)    0.628450"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decade_popularity=df.groupby(['decades']).popularity.mean()\n",
    "decade_popularity=decade_popularity.to_frame('Popularity')\n",
    "decade_popularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4400f8fbe0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "decade_popularity.plot(kind=\"bar\",figsize=(10,5),legend=True)\n",
    "plt.title('Popularity of each decade',fontsize=\"20\")\n",
    "plt.xlabel(\"Decades\",fontsize=\"15\")\n",
    "plt.ylabel(\"Popularity\",fontsize=\"15\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see from the data and the graph that popularity is increasing with time meaning that people of this generation are more interested in movies than previou/s generations.\n",
    "\n",
    "Now lets do the same for vote count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vote_Count:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decades</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[1960, 1970)</th>\n",
       "      <td>71.251397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[1970, 1980)</th>\n",
       "      <td>88.284861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[1980, 1990)</th>\n",
       "      <td>111.298095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[1990, 2000)</th>\n",
       "      <td>160.264269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2000, 2010)</th>\n",
       "      <td>197.083858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2010, 2020)</th>\n",
       "      <td>233.520512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Votes\n",
       "decades                 \n",
       "[1960, 1970)   71.251397\n",
       "[1970, 1980)   88.284861\n",
       "[1980, 1990)  111.298095\n",
       "[1990, 2000)  160.264269\n",
       "[2000, 2010)  197.083858\n",
       "[2010, 2020)  233.520512"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decade_vote=df.groupby(['decades']).vote_count.mean()\n",
    "decade_vote=decade_vote.to_frame(\"Votes\")\n",
    "decade_vote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4400f8f978>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "decade_vote.plot(kind=\"bar\",figsize=(10,5),legend=True)\n",
    "plt.title('Vote Count of each decade',fontsize=\"20\")\n",
    "plt.xlabel(\"Decades\",fontsize=\"15\")\n",
    "plt.yticks(np.arange(0, 300, step=50))\n",
    "\n",
    "plt.ylabel(\"Votes\",fontsize=\"15\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And as proven above the graph and data for vote_count is similar to popularity i.e people of current decade vote more than previous generations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 2  \n",
    "\n",
    "#### Which decade was the most successful monetarily?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we are dividing generations by decades so we will use the same decades(bins) column as above to show revenue_adj(Earned) and budget_adj(cost). Adjusted values are used so that the comparision makes some sense because money value is not the same in each decade."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decades</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[1960, 1970)</th>\n",
       "      <td>34881824860.5288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[1970, 1980)</th>\n",
       "      <td>54665854522.4536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[1980, 1990)</th>\n",
       "      <td>79767389478.3975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[1990, 2000)</th>\n",
       "      <td>151477968510.9974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2000, 2010)</th>\n",
       "      <td>262069838405.4179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2010, 2020)</th>\n",
       "      <td>212051254499.0825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Revenue\n",
       "decades                       \n",
       "[1960, 1970)  34881824860.5288\n",
       "[1970, 1980)  54665854522.4536\n",
       "[1980, 1990)  79767389478.3975\n",
       "[1990, 2000) 151477968510.9974\n",
       "[2000, 2010) 262069838405.4179\n",
       "[2010, 2020) 212051254499.0825"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to show large numbers\n",
    "pd.options.display.float_format = '{:.4f}'.format\n",
    "\n",
    "#groupby each decade and sum of revenues of that group\n",
    "decade_revenue=df.groupby(['decades']).revenue_adj.sum()\n",
    "decade_revenues=decade_revenue.to_frame('Revenue')\n",
    "decade_revenues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44373f9a20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()\n",
    "decade_revenues.plot(kind=\"bar\",figsize=(10,5),legend=True)\n",
    "plt.title('Revenue of each decade',fontsize=\"20\")\n",
    "plt.xlabel(\"Decades\",fontsize=\"15\")\n",
    "plt.ylabel(\"Revenue\",fontsize=\"15\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The revenues of movies get larger as the year increases. The current generation has only 5 year data for that decade but still its in the second place, which tells you the revenue is growing stronger and stronger each year. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Budget</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decades</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[1960, 1970)</th>\n",
       "      <td>8111373406.9956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[1970, 1980)</th>\n",
       "      <td>10007221683.4289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[1980, 1990)</th>\n",
       "      <td>23002704857.3082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[1990, 2000)</th>\n",
       "      <td>55986321105.9445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2000, 2010)</th>\n",
       "      <td>103321643923.2535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2010, 2020)</th>\n",
       "      <td>76916283826.4143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Budget\n",
       "decades                       \n",
       "[1960, 1970)   8111373406.9956\n",
       "[1970, 1980)  10007221683.4289\n",
       "[1980, 1990)  23002704857.3082\n",
       "[1990, 2000)  55986321105.9445\n",
       "[2000, 2010) 103321643923.2535\n",
       "[2010, 2020)  76916283826.4143"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#groupby each decade and sum of budgets of that group\n",
    "\n",
    "decade_budget=df.groupby(['decades']).budget_adj.sum()\n",
    "decade_budgets=decade_budget.to_frame(\"Budget\")\n",
    "decade_budgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7DIDjjz+e7bbbjl133ZUvfelLt9e56qqr2H333dlpp5044IAD2Gqrrbj66qsB+OxnP8suu+zCjjvuyAEHHMBtt93GQQcdxI033siOO+7IC17wghl9LSZpkiRprTaRJG233Xa84hWv4B3veMdK6y9btoxXvvKVfO1rX+OUU07hiiuuuH3bu9/9bh7/+Mdz1lln8YxnPINLL70UgPPPP5+jjz6aH/zgB5x99tnMmzePI444gkMOOeT2nrwjjjhiRl+XpzslSXPCTJ+G69LqnPKbywZPd5566qm8+MUv5pxzzpmy/gUXXMDWW2/NNttsA8ALX/hCDj30UAC+//3v8+UvfxmAPfbY4/ZeuRNPPJEzzzyTnXfeGWgSw80222zWXhOYpEmSpDHy6Ec/mquvvpqrrrqKddZZh+XLl9++bXA5jKlmXlbVlOUveclL+MAHPjCzAa+EpzslSdLYuOCCC7jtttvYeOON2WqrrTjvvPO46aabuOGGGzjxxBMB2G677fjlL3/Jz3/+cwCOPPLI2/ffdddd+fznPw/At771La677joAnvCEJ3DMMcdw5ZVXAnDttdfyq1/9CoD58+dzyy23zPhrsSdNkiSt1SbGpEHT43X44Yczb948tthiC57znOewww47sM022/Dwhz8caC7XdOihh7LXXnuxySabsOuuu95+evRd73oX++67L0cffTSPfexjuf/9788GG2zAJptswt///d/zpCc9ieXLlzN//nw++tGPstVWW7H//vuzww47sNNOO83ouLRM1a03G5J8CngqcGVVPXSS7QH+BXgK8Edgv6o6a1XHXbx4cS1ZsmSmw5UkjRHHpM2O888/nwc/+MGjDmPG3HTTTcybN4911lmHU089lVe96lW3j3dbE5O1T5Izq2rxqvbtuiftMODfgc9MsX1PYJv29kjgP9p7SZKkWXfppZfynOc8h+XLl7Puuuvyn//5nyOLpdMkraq+l2TRSqrsA3ymmu6905JslOT+VfWbTgKUJElz2jbbbMOPf/zjUYcB9G/iwObAZQOPl7ZlkiRJc0rfkrTJ5sNOOmguyf5JliRZctVVV81yWJIkaSpdjm9fm9zVdulbkrYU2GLg8ULg15NVrKpDq2pxVS3edNNNOwlOkiStaMGCBVxzzTUmakOqimuuuYYFCxas8TH6tgTHscCBSY6imTBwg+PRJEnqr4ULF7J06VI8q3VnCxYsYOHChWu8f6dJWpIjgd2ATZIsBd4FzAeoqo8Dx9Esv3ExzRIcL+0yPkmSND3z589n6623HnUYY6nr2Z37rmJ7AX/dUTiSJEm91bcxaZIkScIkTZIkqZdM0iRJknrIJE2SJKmHTNIkSZJ6yCRNkiSph0zSJEmSesgkTZIkqYdM0iRJknrIJE2SJKmHTNIkSZJ6yCRNkiSph0zSJEmSesgkTZIkqYdM0iRJknrIJE2SJKmHTNIkSZJ6yCRNkiSph0zSJEmSesgkTZIkqYdM0iRJknrIJE2SJKmHTNIkSZJ6yCRNkiSph0zSJEmSesgkTZIkqYdM0iRJknrIJE2SJKmHTNIkSZJ6yCRNkiSph0zSJEmSesgkTZIkqYdM0iRJknrIJE2SJKmHTNIkSZJ6yCRNkiSph0zSJEmSesgkTZIkqYdM0iRJknrIJE2SJKmHTNIkSZJ6yCRNkiSphzpP0pLskeTCJBcnOWiS7Vsm+W6SHyf5aZKndB2jJEnSqHWapCWZB3wU2BPYHtg3yfZD1d4OfL6qHg48D/hYlzFKkiT1Qdc9absAF1fVL6rqZuAoYJ+hOgXcq/15Q+DXHcYnSZLUC10naZsDlw08XtqWDToYeGGSpcBxwN9MdqAk+ydZkmTJVVddNRuxSpIkjUzXSVomKauhx/sCh1XVQuApwH8nuVOcVXVoVS2uqsWbbrrpLIQqSZI0Ol0naUuBLQYeL+TOpzNfDnweoKpOBRYAm3QSnSRJUk90naSdAWyTZOsk69JMDDh2qM6lwBMAkjyYJknzfKYkSZpTOk3SqupW4EDgBOB8mlmc5yZ5T5K922pvAF6Z5CfAkcB+VTV8SlSSJGmsrdP1E1bVcTQTAgbL3jnw83nAn3cdlyRJUp94xQFJkqQeMkmTJEnqoc5Pd0qSpLlh0UHfGHUIa+ySQ/YadQj2pEmSJPWRSZokSVIPmaRJkiT1kEmaJElSD5mkSZIk9ZBJmiRJUg+ZpEmSJPWQSZokSVIPmaRJkiT1kEmaJElSD5mkSZIk9ZBJmiRJUg+ZpEmSJPWQSZokSVIPmaRJkiT1kEmaJElSD5mkSZIk9ZBJmiRJUg+ZpEmSJPWQSZokSVIPmaRJkiT1kEmaJElSD5mkSZIk9ZBJmiRJUg+ZpEmSJPWQSZokSVIPmaRJkiT1kEmaJElSD5mkSZIk9ZBJmiRJUg+ZpEmSJPWQSZokSVIPTStJS/KLJA+bYttDk/xiZsKSJEma26bbk7YIuPsU29YDFt6laCRJkgTAOquqkORewEYDRfdLsuVQtQXA84DLZzA2SZKkOWuVSRrwt8C7gGpvX56iXoA3zFBckiRJc9rqJGmfA5bQJGHHAn8HXDhU52bgwqq6dGbDkyRJmptWmaRV1UXARQBJHgecVVX/N9uBSZIkzWXTmjhQVSdX1f8l2TPJO5IcOjE+LcljkjxgVcdIskeSC5NcnOSgKeo8J8l5Sc5N8rnpxChJkjQOVud05+2S3JfmlOcjgEuArYGPA5cCLwWWAa9ayf7zgI8CuwNLgTOSHFtV5w3U2QZ4C/DnVXVdks2mE6MkSdI4mO4SHP8GrA9s194ysO1/gCesYv9dgIur6hdVdTNwFLDPUJ1XAh+tqusAqurKacYoSZK01ptukrYH8PaquphmpuegpcDmq9h/c+CyVeyzLbBtkh8kOS3JHtOMUZIkaa03rdOdrdumKN8EuHEV+2aSsuFkbx1gG2A3msVxT0ny0Kq6foUDJfsD+wNsueXwsm2SJElrt+n2pJ0C/E07tmzCRJL1MuA7q9h/KbDFwOOFwK8nqfPVqrqlqn5Js9zHNsMHqqpDq2pxVS3edNNNp/MaJEmSem+6SdqbgZ2Bc4D30iRor0zyPeDRwNtXsf8ZwDZJtk6yLs1VCo4dqvMV4HEASTahOf3pNUElSdKcMt0lOM6hmdm5BNiP5tTnM2nGmT2yqn62iv1vBQ4ETgDOBz5fVecmeU+SvdtqJwDXJDkP+C7wxqq6ZjpxSpIkre2mPSatqn4OvGhNn7CqjgOOGyp758DPBby+vUmSJM1J0z3dKUmSpA5MdzHblU0MWA78Djgb+HRVXbaSupIkSVqJ6fakXQM8CNgVuAfw+/Z+V5oB/vcEXgOcm2TnGYxTkiRpTplukvZ14CpgUVU9uqr2rqpH01we6mrgC8CfAOcBH5jRSCVJkuaQ6SZp7wTeU1UrrG1WVZcD7wHeVlW/Az4EPHJmQpQkSZp7pju78/7A3afYtgC4b/vzlUx+dQFJErDooG+MOoQ1dskhe406BGlOmG5P2snAIUkeMViYZDHN6c2T2qJtgEvvcnSSJElz1HSTtP1pZnCenuTyJGcnuRz4EXA9cMDAcf9h5sKUJEmaW6Z1urNdVmPHJHsBi4H7AVcAZ7SL1E7U+8SMRilJkjTHTPuKAwBV9Q1g7R1QIUmS1HOrTNKSbDmdA1aVY9EkSZLuotXpSbsEqGkcc96ahSJJkqQJq5OkPW3g53vRTAg4H/gSzVIbmwF/CWwHvHGmA5QkSZqLVpmktePPAEhyGPD1qnrVULWPJ/k4sBdw1IxGKEmSNAdNdwmOZ9L0oE3mi8Dedy0cSZIkwfSTtBtpLqY+mb8Alt21cCRJkgTTX4LjP4B3JNkYOJY7xqTtQ7OQ7ftmNjxJkqS5abqL2R6c5DrgTcCraWZ9hmZB27+rqo/MfIiSJElzz7QXs62qf0nyb8AW3HHFgcuqavlMBydJkjRXrekVB5YDv2pvkiRJmmHTStKSrPKi6VX1pjUPR5IkSTD9nrRnT1J2b5pFbm8AJsarSZIk6S6Y7sSBrScrT/JI4FDgr2YiKEmSpLluuuukTaqqfgT8I/DvM3E8SZKkuW5GkrTWNcCfzuDxJEmS5qzpThxYb5LidYEHA+8Bzp2JoCRJkua66U4c+D3NArbDAlwOPP0uRyRJkqRpJ2kv485J2jJgKXB6Vd0yI1FJkiTNcdOd3XnY4OMkGwEPpLnigAmaJEnSDFmtiQNJnpfkqCRfTPKCtuwdwG+A04HftNvuOYuxSpIkzRmrTNKSvBL4HLA1sCHw6SQfBl4PvA3YCzgIeEL7WJIkSXfR6pzu/BvgI1X1eoAkLwQOB15bVRProh2f5FaaxWzfOiuRSpIkzSGrc7rzgcDXBh5/lWY255lD9ZYAW81QXJIkSXPa6iRp9wD+MPD4j+39TUP1bgbmz0RQkiRJc93qXnFgsrXRJiuTJEnSDFjdJThOaMecDTpxqGy6a65JkiRpCquTWL171qOQJEnSClaZpFWVSZokSVLHVndMmiRJkjpkkiZJktRDJmmSJEk91HmSlmSPJBcmuTjJQSup96wklWRxl/FJkiT1QadJWpJ5wEeBPYHtgX2TbD9JvQ2A1wA/6jI+SZKkvui6J20X4OKq+kVV3QwcBewzSb33Av8ALOsyOEmSpL7oOknbHLhs4PHStux2SR4ObFFVX+8yMEmSpD7pOknLJGW3X14qyd2ADwNvWOWBkv2TLEmy5KqrrprBECVJkkav6yRtKbDFwOOFwK8HHm8APBQ4KcklwKOAYyebPFBVh1bV4qpavOmmm85iyJIkSd3rOkk7A9gmydZJ1gWeBxw7sbGqbqiqTapqUVUtAk4D9q6qJR3HKUmSNFKdJmlVdStwIHACcD7w+ao6N8l7kuzdZSySJEl9tjoXWJ9RVXUccNxQ2TunqLtbFzFJkiT1jVcckCRJ6iGTNEmSpB4ySZMkSeohkzRJkqQeMkmTJEnqIZM0SZKkHjJJkyRJ6iGTNEmSpB4ySZMkSeohkzRJkqQeMkmTJEnqIZM0SZKkHjJJkyRJ6iGTNEmSpB4ySZMkSeohkzRJkqQeMkmTJEnqIZM0SZKkHjJJkyRJ6iGTNEmSpB4ySZMkSeohkzRJkqQeMkmTJEnqIZM0SZKkHjJJkyRJ6iGTNEmSpB4ySZMkSeohkzRJkqQeMkmTJEnqIZM0SZKkHjJJkyRJ6iGTNEmSpB4ySZMkSeohkzRJkqQeMkmTJEnqIZM0SZKkHjJJkyRJ6qF1Rh2ApNFbdNA3Rh3CGrvkkL1GHYIkzQp70iRJknrIJE2SJKmHTNIkSZJ6qPMkLckeSS5McnGSgybZ/vok5yX5aZITk2zVdYySJEmj1mmSlmQe8FFgT2B7YN8k2w9V+zGwuKp2AI4B/qHLGCVJkvqg6560XYCLq+oXVXUzcBSwz2CFqvpuVf2xfXgasLDjGCVJkkau6yRtc+CygcdL27KpvBz45qxGJEmS1ENdr5OWScpq0orJC4HFwGOn2L4/sD/AlltuOVPxSZIk9ULXPWlLgS0GHi8Efj1cKckTgbcBe1fVTZMdqKoOrarFVbV40003nZVgJUmSRqXrJO0MYJskWydZF3gecOxghSQPBz5Bk6Bd2XF8kiRJvdBpklZVtwIHAicA5wOfr6pzk7wnyd5ttX8E1ge+kOTsJMdOcThJkqSx1fm1O6vqOOC4obJ3Dvz8xK5jkiRJ6huvOCBJktRDJmmSJEk9ZJImSZLUQyZpkiRJPWSSJkmS1EMmaZIkST1kkiZJktRDJmmSJEk9ZJImSZLUQyZpkiRJPWSSJkmS1EMmaZIkST1kkiZJktRDJmmSJEk9ZJImSZLUQyZpkiRJPWSSJkmS1EMmaZIkST1kkiZJktRDJmmSJEk9tM6oA5CGLTroG6MOYY1dcsheow5BkjQm7EmTJEnqIZM0SZKkHjJJkyRJ6iGTNEmSpB5y4sAqOIhdkiSNgj1pkiRJPWSSJkmS1EMmaZIkST1kkiZJktRDJmmSJEk9ZJImSZLUQyZpkiRJPWSSJkmS1EMmaZIkST1kkiZJktRDJmmSJEk9ZJImSZLUQyZpkiRJPWSSJkmS1EMmaZIkST3UeZKWZI8kFya5OMlBk2y/e5J/IyPmAAAWe0lEQVSj2+0/SrKo6xglSZJGrdMkLck84KPAnsD2wL5Jth+q9nLguqp6EPBh4INdxihJktQHXfek7QJcXFW/qKqbgaOAfYbq7AMc3v58DPCEJOkwRkmSpJHrOknbHLhs4PHStmzSOlV1K3ADsHEn0UmSJPXEOh0/32Q9YrUGdUiyP7B/+/D3SS68i7GNyibA1bNx4HiieCq2efds8+7Z5t2zzbu3trb5VqtTqeskbSmwxcDjhcCvp6izNMk6wIbAtcMHqqpDgUNnKc7OJFlSVYtHHcdcYpt3zzbvnm3ePdu8e+Pe5l2f7jwD2CbJ1knWBZ4HHDtU51jgJe3PzwK+U1V36kmTJEkaZ532pFXVrUkOBE4A5gGfqqpzk7wHWFJVxwKfBP47ycU0PWjP6zJGSZKkPuj6dCdVdRxw3FDZOwd+XgY8u+u4RmitP2W7FrLNu2ebd882755t3r2xbvN4JlGSJKl/vCyUJElSD5mkSZIk9ZBJ2ogkuWd7mSxJkqQ7cUxaR5LcjWam6guAnYGbgLsDV9FMpDi0qi4aXYTjKcmjgRcCfwHcH7gROAf4BvDZqrphhOGNJdu8e0kW0ny//AXwAFZs829W1fIRhjeWkiwAnsokbV5V544ytnE1Fz/nJmkdSXIy8D/AV4FzJj5MSe4DPA54PvDlqvrs6KIcL0m+SbNY8leBJcCVwAJgW5o2fxrwoXbpF80A27x7ST5Nczm9rzN5mz8COKiqvjeyIMdMkoNpPssnAWdy5zZfALyhqn46ohDHzlz9nJukdSTJ/Kq65a7W0epLsklVrfRyIatTR6vPNu9ekodW1Tkr2b4usGVVXdxhWGMtyV5V9Y2VbN+Mps2XdBjWWJurn3OTtA4lCbALzX8DRdPjcLpXVJhdSe7LQJtX1W9HHNLYs81Ho+2Zr6q6btSxSLNlLn3OTdI6kuRJwMeAi4DL2+KFwIOAV1fVt0YV27hKsiPwcZrrvw62+fU0bX7WqGIbV7Z595JsCfwD8ASadg5wL+A7NKd/LhlddOMpyYbAW4CnA5vR/DNyJc1p/kOq6voRhjeW5urn3CStI0nOB/Yc/iAl2Ro4rqoePJLAxliSs4EDqupHQ+WPAj5RVQ8bTWTjyzbvXpJTgY8Ax1TVbW3ZPJort7yuqh41yvjGUZITaJKDw6vqirbsfjTXnX5iVe0+yvjG0Vz9nJukdSTJRcCDq+rWofJ1gfOq6kGjiWx8JbmoqraZYtvFtvnMs827t4o2n3Kb1lySC6vqT6e7TWturn7OO7925xz2KeCMJEcBl7VlW9BMJ/7kyKIab99M8g3gM6zY5i8Gjh9ZVOPNNu/emUk+BhzOim3+EuDHI4tqvP0qyZtoetJ+C7ePw9yPO94Dzaw5+Tm3J61DSR4M7EMzoDrAUuDYqjpvpIGNsSRPAfbmzm1+3EgDG2NJ9mTyz7ltPgva3viXM0mbA5+sqptGGN5YSnJv4CCaNt+sLf4tTZt/sKquHVVs42qKz/llwNcY48+5SZokSVIPeVmojiQ5K8nbk/zJqGOZK5LsnOS7ST6bZIsk305yfZIzkjx81PGNoyR3S/LSJF9P8pMkZyY5Ksluo45tXCVZJ8kBSb6Z5Kdtu38zyV8lmT/q+OaaJC8ddQzjKsmTk7w8yVZD5S8bVUyzzZ60jiT5JfBF4DnAFcCRwNFV9euRBjbGkpwOvAvYiGbq9t9W1TFJngD8fVU9eqQBjqF2VfBf0Vxd41nA74BTgDcDX62qfxtheGMpyZE0SxIcTnOaE5plT14C3Keqnjuq2OaiJJdW1ZajjmPcJHk/sCtwFs3VHj4y8X2S5Kyq2mmU8c0Wk7SODH6IkvwFsC/wTOB84MiqOnSU8Y2jJD+uqoe3P6/wxTm4TTMnyU+raoeBx6dV1aOS3B0426VmZt4qZhr+rKq27TqmcZdkqss9Bdi2qu7eZTxzQZL/BR5eVbcm2Qj4HHBhVf3tOH+fO7tzBKrqFOCUJH8D7A48FzBJm3nL2kWENwQqydOr6itJHgvcNuLYxtUtSR5YVT9PshNwM0BV3ZTE/whnx3VJng18ceCawHejWT9q7FdkH5H7Ak/mzu0b4IfdhzMnrDOxhFVVXZ/kacChSb4ArDva0GaPSVp3fjZc0C7IdzwuTTBb/ormNOdymi/UVyU5jGYl/FeOMK5x9kbgu0mWAfNplpghyaY0F0bWzHse8EHgY0muo0kUNqJZbPV5owxsjH0dWL+qzh7ekOSk7sOZE36e5LFVdTLc/vfz5Un+HvjL0YY2ezzdKWlGtdeo3diLqHcvycY03+u2vcZKknsAVNWNk2zbvKouv/Neaz+TtA4leSDwDJoF+G6luY7nkVV1w0gDG2NTtPnnqup3Iw1sjCVZH9iDFdv8WxOn4jTzkmzHHetHFfBrmokaF4w0sDHW/jOyCyu2+enlH9VZ014zdQ9WbPMTxvlaqS7B0ZEkr6W58PQCYGfgHjR/xE51eYLZkeQ1TN7mp9nmsyPJc4Dv0nyRHkjzR+xFwNlJdljZvlozSd4MHEVzmvN04Iz256OSHDTK2MZVO9b1IuBg4CnAXsC7gYvabZphSV5MM7NzN2A94J7A42iuRPDiEYY2q+xJ60g7M2XHqrotyXo0F1XfLcmWNP/xjuXMlFGyzbvXznp7VFX9MckmwBFV9eQ2Qft4Vf2/EYc4dpL8DHhIVd0yVL4ucO64XtNwlJKcD+xZVZcMlW9N8z3jLOYZluRC4JHDvWbt1R9+NK6zmO1J69bERI27AxsAVNWlNAOsNTts824FmBgz8gfaS+ZU1U+Be40qqDG3HHjAJOX3b7dp5q3DHWvSDbocv1tmS2hOcQ5b3m4bS87u7M5/0Vxg/TTgMTSzsSZmvXmdt9lhm3fvOOD4JCcDewJfAEhyH8b4i3TEXgecmOQi7rjw9JbAg2hOOWvmfYrmu+UoVrzY9/OAT44sqvH2PuCsJN9ixc/57sB7RxbVLPN0Z4eSPAR4MHCOA3q7YZt3r72o/fbAT6rq223Z3YD543oR5FFr23diEPvEBdbPaJcp0CxIsj2wN0MXta+q80Ya2BhrT20+mRXb/ISqGtv1AE3SeiDJ+lX1+1HHMZfY5pKkvnNMWj/4n1f3bPOOtRM5NMOS7JDktCSXJTm07W2Y2Hb6KGMbV0nuleQDSf47yb5D2z42qrjGWZItkhyV5JQkb00yf2DbV0YZ22xyTFpHkrx+qk3A+l3GMlfY5t1L8sypNgH36zKWOeRjNEtBnAa8Avh+kr2r6uc4iH22fJpmCY4vAi9L8izg+e3p/EeNNLLx9Sma9j4NeDlwcpKnVdU1wFYjjWwWmaR15/3AP9Is7jnMHs3ZYZt372jgCCafhbWg41jmivWrauLScv+U5EyayRsvYvL3QXfdA6tq4lJEX0nyNuA7SfYeZVBjbtOq+nj7898keSHwvbbNx/ZzbpLWnbOAr1TVmcMbkrxiBPHMBbZ5934K/FNVnTO8IckTRxDPXJAkG05cuaSqvpvkL2l6He4z2tDG1t2T3G3iKhpV9b4kS4HvYS/9bJmfZEFVLQOoqs8muQI4gWZh27Fkb0J3Xgr8aopti7sMZA6xzbv3OmCqS249o8tA5pAP0sxgvl27Lt0TgC+NJKLx9zXg8YMFVXU48Abg5pFENP7+C3jkYEFV/Q/wbOBO/xSOC2d3SpIk9ZA9aZIkST1kkiZJktRDJmmSJEk9ZJI2YkleneS5SZxp2xHbvHtJ9knyyFXX1Ezxc969JIuTbD7qOOaScf9uMUkbvQC74iysLtnm3Xsk8PYk3xx1IHOIn/Pu/Q3w9SRHjzqQOWSsv1uc3SlJ0gxKskFV/d+o49DazyStQ0meDDwd2JxmheRfA18dWC1cM8w2716S7YB9WLHNj62q80ca2Bjzc969JBsCe7Bim59QVdePNLAxNhe/W0zSOpLkI8C2wGeApW3xQuDFwEVV9dpRxTaubPPuJXkzsC9wFCu2+fOAo6rqkFHFNq78nHcvyYuBdwHfAi5vixcCuwPvrqrPjCq2cTVXv1tM0jqS5GdVte0k5QF+VlXbjCCssWabdy/Jz4CHVNUtQ+XrAufa5jPPz3n3klwIPHK41yzJvYEfTfZ+6K6Zq98tThzozrIku0xSvjOwrOtg5gjbvHvLgQdMUn7/dptmnp/z7oXJL+q9vN2mmTcnv1ucmt2d/YD/SLIBd3TVbkFzncP9RhTTuHsp8DHbvFOvA05MchFwWVu2JfAg4MCRRTXe9sPvlq69DzgrybdY8XO+O/DekUU13ubkd4unOzuW5H40gx4DLK2qK0Yc0tizzbuV5G7ALgy0OXBGVd020sDGnJ/zbrWnNp/Mip/zE6rqupEGNsbm4neLSVpH2vPmt1Tb4EkeB+xEcy7dGVizIMkOVfXTUccx1yTZEvhdVV2fZBGwGDi/qs4daWBjLslimh60W2kmDFww4pDGXpL7MjDTsKp+O+KQ5qQk61fV70cdx2wwSetIkp8Au1XVdUneCDwDOA54LLCkqt4y0gDHUJLbgF8CRwJHVtV5Iw5p7CU5CDgAuAn4J+DvgB8AjwI+WVUfGmF4YynJY4F/Bq4HHkHT3vcGbgFeVFWXrWR3rYEkOwIfBzak6c0JzUzD64FXV9VZIwxvzklyaVVtOeo4ZoNJWkeSnFNVD21/XgL8RVXd2F6y5ayq2mG0EY6fJD8GXkQzbfu5wB9oErajquqSEYY2tpKcS9Nzth5wCfAnVXVVknvSzHp76CjjG0ft5/xJbTtvDXyoqp6RZHfgjVX1pBGHOHaSnA0cUFU/Gip/FPCJqnrYaCIbX0leP9Um4G1VdZ8u4+mKszu787skE3+grgYWtD+vg+/DbKmqOqeq3lZVDwJeCWwGnJLkhyOObVzdVlU30vQo3AhcA1BVfxhpVONtXlVd1f58KbAVQFV9m+ZUnGbePYcTNICqOg245wjimQveT9NDvMHQbX3G+G+oszu781fAEe1pzyuBJUlOBnag+fBp5q0wFb6qTgdOT/IG4DGjCWnsnZXkczR/qE4EDk9yPPB4wNPNs2NJkk/StPc+wEkASdYD5o0wrnH2zSTfoFlAeOJ08hY0Cwg7xnh2nAV8parOHN6Q5BUjiKcTnu7sUJJ5wJNoVgdfhztmA3kZkVmQ5PlV9blRxzGXtKfvn00zkPoYmosf70vTw/NRe9RmXpL5NL3E2wM/AT5VVbcluQewWVX9aqQBjqkke3LHJYomZhoeW1XHjTSwMZXkT4FrB3qNB7fdd1wnbZikSZIk9dDYnsftmyTrJ3lPknOT3JDkqiSnJdlv1LGNK9u8eytp85eMOrZxNdDm5/g570aSDZMckuT8JNe0t/Pbso1GHd84GmjzC+ZSm5ukdecI4Bc0ix++G/hXmpmHj0vimLTZYZt3b6o2f7xtPmsm2nwP/Jx35fPAdcDjqmrjqtoYeBzNhJkvjDSy8TXR5rsNtfl1jHGbe7qzI0l+MjgtO8kZVbVzu4LyeVW13QjDG0u2efds8+7Z5t1LcmFV/el0t2nNzdU2tyetO39IsitAkqcB1wJUlRfknT22efds8+7Z5t37VZI3tVccAJrB60nezB2zPTWz5mSbuwRHd/4K+K8k2wLnAC8DSLIp8NFRBjbGbPPu2ebds82791zgIODkJJu1Zb8FjgWeM7KoxtucbHNPd0qSJPWQpzt7IMlLRx3DXGObd882755tPnuSbJfkCWkueTZYvseoYhp3c7HN7UnrgYzxxWH7yjbvnm3ePdt8diR5DfDXwPnAjsBrq+qr7bazqmqnUcY3juZqmzsmrSNJfjrVJuC+U2zTXWCbd882755tPhKvBB5RVb9Psgg4JsmiqvoXnKwxW+Zkm5ukdee+NGtHXTdUHsCLfc8O27x7tnn3bPPuzauq3wNU1SVJdqNJGrZijBOGEZuTbW6S1p2vA+tX1dnDG5Kc1H04c4Jt3j3bvHu2efeuSLLjRJu3vTtPBT4F/NloQxtbc7LNHZMmSdI0JFkI3FpVV0yy7c+r6gcjCGuszdU2N0mTJEnqIZfg6EiSs2aijlafbd4927x7tnn3bPPuzdU2tyetI0luBC5aWRVgQ6fLzxzbvHu2efds8+7Z5t2bq23uxIHurM5Fjm+b9SjmFtu8e7Z592zz7tnm3ZuTbW5PmiRJUg85Jk2SJKmHTNIkSZJ6yCRNUq8lOThJtbflSa5LckaS9yW536jjG5Tk6iQHjzoOSePBiQOS1gY3AHu0P28I7AS8Ctg/yR5VdebIIpOkWWKSJmltcGtVnTbw+IQk/wF8Dzg6yZ9W1djN7JI0t3m6U9JaqaquB94EPBDYHSDJgiT/kOSyJDcl+UmSpwzvm+SVSf43ybIkv01yTJIN222PTnJskl8n+UOSs5O8YJJjPKY9/rIkZyb5f5PFmWSfJEvaele08c0f2L4wyeeTXJnkxiQ/T/LeGWomSWsxe9Ikrc2+C9wKPAo4HjgG2AV4F/Bz4DnAsUkWT1yYOcnbgfcAHwPeCKwH7AWsT3NadSvgB8DHgWXAnwOfTrK8qo5sj/EA4JvA6cCzgAcAR7THul2S5wBHAp8A3kqTUH6A5h/kv2urfQa4B7A/cD3wJ6zemlCSxpxJmqS1VlXdlORq4L5JnkCTbO1WVSe3Vb6VZFvgbcCzk2xEkyx9pKpeP3CoLw0c86iJn5OE5pTqQuCVNAkXwOtoEri9quqPbd0/AJ8d2vcfgc9U1asHym8CPprkA1V1DU1SuW9Vfa2tctJdaRNJ48PTnZLWdmnvnwhcAfwgyToTN+BEYHFb59E0vVafnvJgyb2T/GuSXwG3tLf9gW0Hqu0CfHsiQWt9iRVtC2wJfH4onu8AC4CHtvXOBj6QZL8kY3VJG0l3jT1pktZaSRYAGwO/BTYH7keTVA2bmFSwcXv/m5Uc9jCa06fvBc4Dfkczk3SfgTr3A346uFNV3Zjk9wNFm7T3x03xPFu0988F3gd8GNgoyU+AN1TViSuJUdIcYJImaW32OJrvsVOBxwOXA09fSf1r2vv7A1cPb2yTvr2AA6vq4wPlw2cdrgA2G9r3HjTj2iZc297vD/x4klh+CVBVlwP7tc+xC3AwzTi6LdvToZLmKJM0SWuldnzZB4GLgf8BCngD8PuqumCK3U4FbgRewh0D9wfdHZgH3DTwPBsAe7fHn3AG8LIk6w2c8nzm0LEupEkaF1XVf67q9VTVcuC0JO8GfkgzgcEkTZrDTNIkrQ3WSfKo9ucNgEfQnIJcD9ijqm5L8m3gBODbST4InAvcC9gRWFBVb6mq69vlLd6XZF2aU5F3p+k9e3dVXZ7kDOCdSX4HLAcOopn1ea+BeD4C/DXw9SQfopnd+RaaBBBokq4kbwD+O8m9aGaD3kwze/PpNLNC57cxfwb4WRvLG2h66s6fobaTtJYySZO0NtiQphesaMaIXUwzk/LfquoKgKqqJM+kmb35OppB+9fSDMz/t4kDVdUHklwLvBY4ALiOZgbn/7VVng8cSpM4XQP8O00yeODAMS5v11/7V+CLNAnVC4GvDgZdVUe3yd5bgZfRjI37BfB1moTtNuB/21i2AP4InAY8qapuRNKclqpadS1JkiR1yiU4JEmSesgkTZIkqYdM0iRJknrIJE2SJKmHTNIkSZJ6yCRNkiSph0zSJEmSesgkTZIkqYdM0iRJknro/wNTl0yHs8nExQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4401a7b9e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.show()\n",
    "decade_budgets.plot(kind=\"bar\",figsize=(10,5),legend=True)\n",
    "plt.title('Budget of each decade',fontsize=\"20\")\n",
    "plt.xlabel(\"Decades\",fontsize=\"15\")\n",
    "plt.ylabel(\"Budget\",fontsize=\"15\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same as revenue the Budget increases with years, the highest is 2000-2010 which was the last decade, the current decade is second because we only have values till year 2015 in our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both revenue and budget show a trend that with increasing year the values will grow. It will cost more to make a movie but you will also earn more making a movie compared to previous year. To gain better knowledge if movie buissness is moving towards more profit or less profit we can take ratio of revenue and budget sums.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Profit Ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decades</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[1960, 1970)</th>\n",
       "      <td>4.3004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[1970, 1980)</th>\n",
       "      <td>5.4626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[1980, 1990)</th>\n",
       "      <td>3.4677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[1990, 2000)</th>\n",
       "      <td>2.7056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2000, 2010)</th>\n",
       "      <td>2.5364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2010, 2020)</th>\n",
       "      <td>2.7569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Profit Ratio\n",
       "decades                   \n",
       "[1960, 1970)        4.3004\n",
       "[1970, 1980)        5.4626\n",
       "[1980, 1990)        3.4677\n",
       "[1990, 2000)        2.7056\n",
       "[2000, 2010)        2.5364\n",
       "[2010, 2020)        2.7569"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Profit\n",
    "profit=(decade_revenue/decade_budget)\n",
    "profit=profit.to_frame('Profit Ratio')\n",
    "profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ud/bZZ3P66aczbtw4AMaOHQukxHDZZZfl4x//eNXt9t57b0477TRmz57NOuusw5577slqq63WaZyV+zv00ENrHltvcaLZIh5/fk6v/yMxs4Gpnv8AzaxvbbPNNjz22GPMmDGDXXbZhXHjxnHfffdx5ZVXcu+99/LDH/6QiGD8+PGcfPLJfPe73+Wee+7h73//O7vtthvjx4/nJz/5CaeddhpnnHEGgwYN4ne/+x2nnnoq22yzTdU+hw8fzlprrcXMmTNZbbXVuOaaa/j+97/P/PnzGT58OBdeeCFvvPHGu/Z36623suyyy/Ktb32LyZMnc+ihhzJ37lw+/OEPc84557Diiiv22nnxpXMzMzOzHliwYAE33HADG264IQBTp07lgAMO4JFHHmHIkCEcffTR3HbbbUyePJkHH3yQK6+8kmOPPZaxY8dy4YUX8uMf/3jRvtra2jj00EP5xje+weTJkztNMgGeffZZ3nzzTTbaaCMAtt56a+6//34eeeQR9tlnH370ox/V3N8BBxzAySefzGOPPcaGG27ICSec0KvnxiOaZmZmZt3wxhtvMGbMGCCNaB500EG88MILrLHGGmy55ZYAPPjgg2y33XasvPLKQLrEftddd7H77rt3u9+LL76Y22+/nalTp/LrX/+aoUOHAumWT3vvvTczZ85k/vz5NW9JNGfOHF599VU+8YlPADBx4kT22muvbsdVjUc0zczMzLqhY47m5MmTOfXUU1lyySUBeN/73reoTUT0er977703TzzxBHfffTff/OY3+ec//wnAV7/6VQ4//HAef/xxzjzzzJa4mb0TTTMzM7OSbLHFFtx555289NJLLFy4kIsuumjRCGJnlltuOV5//fWa+/7Yxz7G/vvvzy9+8QsgjVCOHDkSgPPPP7/m/oYNG8aKK67I3XffDcBvf/vbmrE1yommmZmZWUlGjBjBiSeeyLhx49h4443ZZJNNmDBhQpfbfOYzn+GKK65gzJgxi5LAzhx99NGce+65vP766xx//PHstddebLPNNqy00kp17e/888/nqKOOYqONNmLy5Mkce+yx3T/YKlTGkK41bqkRo2PExJ83Owwz6we86twMnnzySdZbb71mh/GeUO1cS3ooIsbW2tYjmmZmZmZWCieaZmZmZlaKPks0JbVJekPS5Px+NUm3S3pS0hOSvl5o+35Jt0ialp9XzOXrSrpP0jxJ36rY/86SpkqaLumYOuI5UtJfJT0m6VZJaxTqJua+p0mamMuWkXSdpKdyvCcV2i8l6eLc9wOS2nL5hpLO69GJMzMzM+un+npE8+mIGJNfLwC+GRHrAVsCh0laP9cdA9waEaOBW/N7gJeBrwE/Ke5U0iDgl8AuwPrAvoV9deYRYGxEbARcCvwo7+v9wHHAFsDmwHEdiS7wk4hYF/gosJWkXXL5QcArEbEW8DPgZICIeBwYJWn1us6OmZmZ1c3rTMrX03PctEvnETEzIh7Or18HngRG5uoJQMe6/POB3XO7WRHxIPBWxe42B6ZHxDMRMR/4Q95HV/3fHhFz89v7gVH59U7ALRHxckS8AtwC7BwRcyPi9rztfODhwjbFeC8FdpCk/P4aYJ+aJ8TMzMzqNnToUGbPnu1ks0QRwezZsxfdEL47WuKbgfKl5o8CD+SiVSNiJqSEVNIqNXYxEniu8L6dNCJZr4OAG7rY18hiY0krAJ8BflG5TUQskDQHGA68BEwijcj+qLJTSYcAhwAMWn7lBsI1MzN7bxs1ahTt7e28+OKLzQ5lQBs6dCijRo2q3bATTU80JS0LXAYcERGvdXc3Vcrq+hNH0heAsUDHHUq73JekwcBFwCkR8Uwd28wCPlit74g4CzgL0u2N6onXzMzMYMiQITW/YtGar6mrziUNISWZF0bE5YWqf0kakduMICVrXWkHViu8HwW8UEf/nwT+C9gtIubVua+zgGkRUbzp5aJtciI6jDSfFGAo8EatWMzMzMwGmqYlmnkO49nAkxHx04rqq4GJ+fVE4Koau3sQGC1pTUlLkuZEXp37OVHSZ6v0/1HgTFKSWUxkbwJ2lLRiXgS0Yy5D0vdJSeQRXcS7J3BbLJ40sjYwpUb8ZmZmZgNOMy+dbwXsDzzeccsj4D8j4nrgJOASSQcBzwJ7AUj6AGnO4/LA25KOANaPiNckHU5KCAcB50TEE3mfG5KTzgo/BpYF/pjX7TwbEbtFxMuSvkdKXgG+m8tGkUY/nwIeztucFhG/ISXMv5U0nTSSWVz8Mw64rvunyczMzKx/alqiGRH3UH1uIxExG9ihSvk/WbzSu7LueuD6KlVDIuK+Ku0/2UVs5wDnVJS1dxHvm+RkuEjSUqT5n5UjoGZmZmYDXl9eOl8IDCuMXvaJiNipL/ursDpwTEQsaGIMZmZmZk3RZyOaEfEc71xkM+BFxDRgWrPjMDMzM2sGf9e5mZmZmZXCiaaZmZmZlcKJppmZmZmVounfDGTJhiOHMemk8c0Ow8zMzKzXeETTzMzMzErhRNPMzMzMSuFE08zMzMxK4UTTzMzMzErhRNPMzMzMSuFE08zMzMxK4UTTzMzMzErhRNPMzMzMSuFE08zMzMxK4UTTzMzMzErhRNPMzMzMSuFE08zMzMxK4UTTzMzMzErhRNPMzMzMSuFE08zMzMxK4UTTzMzMzErhRNPMzMzMSuFE08zMzMxK4UTTzMzMzErhRNPMzMzMSuFE08zMzMxK4UTTzMzMzEoxuNkBWPL483NoO+a6ZodhZr1sxknjmx2CmVnTeETTzMzMzErhRNPMzMzMSuFE08zMzMxK4UTTzMzMzErhRNPMzMzMSuFE08zMzMxK4UTTzMzMzErRp4mmpDZJb0ianN+vJul2SU9KekLS1wtt3y/pFknT8vOKuVySTpE0XdJjkjYpbDMxt58maWId8fxY0lN5P1dIWqFQ9+3cx1RJOxXKz5E0S9KUin11Fu+ukk7oyXkzMzMz64+aMaL5dESMya8XAN+MiPWALYHDJK2f644Bbo2I0cCt+T3ALsDo/DgE+BWkRA84DtgC2Bw4riPZ68ItwAYRsRHwN+DbeV/rA/sAHwF2Bk6XNChvc14uq9RZvNcBu0lapkYsZmZmZgNKUy+dR8TMiHg4v34deBIYmasnAOfn1+cDuxfKL4jkfmAFSSOAnYBbIuLliHiFlERWSwiL/d8cEQvy2/uBUYU+/hAR8yLi78B0UvJKRNwFvFxld1XjjYgA7gB2rXE6zMzMzAaUlpmjKakN+CjwQC5aNSJmQkpIgVVy+UjgucKm7bmss/J6fRG4oUYfXeksXoBJwDaVG0g6RNIkSZMWzp3TQKhmZmZmra8lEk1JywKXAUdExGu1mlcpiy7K6+n/v0iX8S+s0Ud3zQI++K4dRpwVEWMjYuygZYb1YPdmZmZmrafpiaakIaQk88KIuLxQ9a98SZz8PCuXtwOrFdqNAl7oorxW/xNJl7X3y5e5u+qjK53FCzAUeKNWLGZmZmYDSVMTTUkCzgaejIifVlRfDXSsHJ8IXFUoPyCvPt8SmJMvVd8E7ChpxbwIaMdchqQLJG1epf+dgaOB3SJibkXf+0haStKapIVHf6lxOJ3FC7A2MOVdW5iZmZkNYM0e0dwK2B/YXtLk/Ph0rjsJ+JSkacCn8nuA64FnSAt0fg18BSAiXga+BzyYH9/NZQAbATOr9H8asBxwS+77jLyvJ4BLgL8CNwKHRcRCAEkXAfcB60hql3RQjXgBxpFWn5uZmZm9ZwxuZucRcQ/V50MSEbOBHaqUB3BYJ9ucA5xTLJO0PDAtIp6r0n6tLmL7AfCDKuX7NhKvpFWBpSPi8c76MjMzMxuI+npEcyEwrOOG7X0hIl6LiL36qr8qVge+2cT+zczMzJqiT0c086jiajUbDiAR8WCzYzAzMzNrhmbP0TQzMzOzAcqJppmZmZmVwommmZmZmZWiqavObbENRw5j0knjmx2GmZmZWa/xiKaZmZmZlcKJppmZmZmVwommmZmZmZXCiaaZmZmZlcKJppmZmZmVwommmZmZmZXCiaaZmZmZlcKJppmZmZmVwommmZmZmZXCiaaZmZmZlcKJppmZmZmVwommmZmZmZXCiaaZmZmZlcKJppmZmZmVwommmZmZmZXCiaaZmZmZlcKJppmZmZmVwommmZmZmZXCiaaZmZmZlcKJppmZmZmVwommmZmZmZXCiaaZmZmZlWJwswOw5PHn59B2zHXNDsPMWsyMk8Y3OwQzs27ziKaZmZmZlcKJppmZmZmVwommmZmZmZXCiaaZmZmZlaLHiaakFSWNkbRUbwRkZmZmZgNDQ4mmpBMknVR4vz3wLPAQ8LSkj/RyfGZmZmbWTzU6orkf8FTh/f8A9wBbAVOBE3spLjMzMzPr5xpNND8IPAMgaTVgY+C4iLgf+CmwZWcbSmqT9IakyYWycyTNkjSlou3Gku6T9LikayQtn8uXlHRuLn9U0naFbTbN5dMlnSJJXR2IpP0kPZYf90rauFC3s6SpeV/HFMrXlPSApGmSLpa0ZC5fKr+fnuvbcvmGks6rcU7NzMzMBqRGE83XgWH59fbAKxHxl/z+TWCZGts/HRFjCu/PA3au0u43wDERsSFwBXBULj8YIJd/CvgfSR3H8CvgEGB0flTbb9HfgU9ExEbA94CzACQNAn4J7AKsD+wraf28zcnAzyJiNPAKcFAuP4h0LtYCfpbbERGPA6MkrV4jFjMzM7MBp9FE807gGEnjgW8BVxXq1gaea2RnEXEX8HKVqnWAu/LrW4DP5dfrA7fmbWcBrwJjJY0Alo+I+yIigAuA3Wv0fW9EvJLf3g+Myq83B6ZHxDMRMR/4AzAhj5BuD1ya251f6GNCfk+u36EwonoNsE9XsZiZmZkNRI0mmt8A5pGSr1eB/yrUHcDi5LCnpgC75dd7Aavl14+Skr7BktYENs11I4H2wvbtuaxeBwE35NcjeWfC3LGv4cCrEbGgSh+Ltsn1c3J7gEnANtU6lXSIpEmSJi2cO6eBcM3MzMxaX0PfdR4Rz5NG9arZiXT5vDd8EThF0rHA1cD8XH4OsB4pefsHcC+wAKg2HzPq6UjSOFKiuXVHUSf76qqPrupmkea2vrtBxFnkS/ZLjRhdV7xmZmZm/UVDiWYHSSsCG5BGE2/Il6Dnk5K+HouIp4Adc19rA+Nz+QLSqGpHHPcC00jzJUcVdjEKeKGO49iINB90l4iYnYvbWTyCWtzXS8AKkgbnOIp9dGzTLmkwaR5rx5SAocAbdR24mZmZ2QDS6H00B0n6ESmxuhP4LbBmrr4MOK43gpK0Sn5eAvgOcEZ+v4yk9+XXnwIWRMRfI2Im8LqkLfPcyAPI80clHS7p8Cp9rA5cDuwfEX8rVD0IjM4rzJckza+8Os/9vB3YM7ebyOI5qlfn9+T623J7SHNX37Gq3szMzOy9oNE5mj8krfw+HPgQ77xkfBXwmUZ2Juki4D5gHUntkjpWce8r6W+ke3a+AJyby1cBHpb0JHA0sH9hd18mjU5OB55m8ZzLdYHZvNuxpHmUp0uaLGkSLBo1PRy4CXgSuCQinsjbHA0cKWl63vbsXH42MDyXHwkcs7gbxgHX1X9WzMzMzAYGLR54q6OxNBM4PiLOzLcBegsYGxEPS9oBuCwiVuhk2zbg2ojYoOdh10/StcAeeQV5n8pfy3knsHVhEVFVS40YHSMm/rxvAjOzfmPGSeObHYKZ2btIeigixtZq1+iI5gqk0cJqlgQGdbHtQmBY8YbtfSEidm1GkpmtTrofaK/MXTUzMzPrTxpdDDSFdM/IP1Wp2wV4uLMNI+I53rnIZsCLiGmkxUpmZmZm7zmNJprfBy6TtDTwR9ItfMZI+izwJRbf+9LMzMzM3uMaunQeEVcBnwc+SVpsI9ICnANJq7dv6u0AzczMzKx/avg+mhFxCXBJvr/lSqT7RU6NRlYVmZmZmdmA160btgPke0/+rWZDMzMzM3tPqplo5q+BrFtEfLf74bx3bThyGJN8GxMzMzMbQOoZ0fxqxfulgWXy6/8Fls2v5+aHE00zMzMzq70YKCJW7niQVpXPAr4ALBMRy5OSzv1z+YQygzUzMzOz/qPROZqnAD+MiN93FETEm8CF+TvIfwkJAvmEAAAaxklEQVRs0ovxmZmZmVk/1eg3A21A+u7xap4H1utZOGZmZmY2UDSaaP4NODJ/h/cikoYCRwJTeyswMzMzM+vfGr10/lXgeqBd0i2keZmrAJ8izdXcpXfDMzMzM7P+qtFvBroLGA2cC4wAdsrP5wKjc72ZmZmZWbe+GWgm8B8lxGJmZmZmA0ijczTNzMzMzOrS8IimpL2Bg4G1gaGV9RGxSi/EZWZmZmb9XEMjmpI+D5wPTAdGAVcD1+b9vAac1tsBmpmZmVn/1Oil86OA7wGH5fenR8QXgTWBl0hfQWlmZmZm1nCiORr4c0QsBBYCywNExOvAycDhvRuemZmZmfVXjSaac4COm7VXfhOQgOG9EZSZmZmZ9X+NLgaaBGwE3ESan3mspAXAfOBY4IHeDc/MzMzM+qtGE80TgTXy62Pz69OBQcCDwCG9F5qZmZmZ9WcNJZoRcT9wf379KjAhf+/5UhHxWgnxmZmZmVk/1ejtjZaTNKJYFhHzIuI1SSMkLdu74ZmZmZlZf9XopfOzSQuCDq5SdzwwDNinhzGZmZmZ2QDQ6KrzbYHrOqm7PtebmZmZmTWcaA6j85uyvwms2LNwzMzMzGygaPTS+TRgPHBzlbpPA0/3OKL3qMefn0PbMZ0NFpuZWVdmnDS+2SGYWRWNJpqnAmdImg+cB8wERgATSV9L+eVejc7MzMzM+q1Gb2/0a0mrAt8GjixUvQl8JyJ+3ZvBmZmZmVn/1eiIJhHxfUmnAh8H3g/MBu6LiDm9HZyZmZmZ9V8NJ5oAOam8oZdjMTMzM7MBpNFV50jaSNLFkp6WNE/SJrn8B5J26f0QzczMzKw/avSbgXYBHgI+AFwADClUzwO+2nuhmZmZmVl/1uiI5onAeRHxCeAHFXWTgTG9EpWZmZmZ9XuNJprrAhfn11FR9xppcZCZmZmZWcOJ5izgQ53UfQR4trMNJbVJekPS5ELZOZJmSZpS0XZjSfdJelzSNZKWz+VDJJ2fy5+U9O3CNjtLmippuqRjah2IpG0lPSxpgaQ9K+pOljQlP/YulG+ft5mS4xicyyXplNz3Y4V5qytLurFWLGZmZmYDUaOJ5h+A70raulAWktYGjgYurLH90xFRvLx+HrBzlXa/AY6JiA2BK4CjcvlewFK5fFPgSzmBHQT8EtgFWB/YV9L6NWJ5FjgQ+H2xUNJ4YBPSNIAtgKMkLS9pCeB8YJ+I2AD4B+lG9eR+R+fHIcCvACLiRWCmpK1qxGJmZmY24DSaaP43MAm4i8Wjl1cBU4DHgB82srOIuAt4uUrVOrkPgFuAz3VsArwvjyQuDcwnXbLfHJgeEc9ExHxSQjyhRt8zIuIx4O2KqvWBOyNiQUT8H/AoKRkeDsyLiL9ViWsCcEEk9wMrSBqR664E9usqFjMzM7OBqKFEMyLmRcSuwCdJo3u/IY0Ijo+IXSPirV6KawqwW369F7Bafn0p8H+kr758FvhJRLwMjASeK2zfnsu641FgF0nLSFoJGJf7fwkYImlsbrdnIa6u+p8EbFOtI0mHSJokadLCub7fvZmZmQ0sdd+wXZKATwFbAqvm4nbgPuBPvRzXF4FTJB0LXE0auYQ0crkQ+CCwInC3pD8BqrKPysVKdYmImyVtBtwLvEg6vgUREZL2AX4maSngZmBB3qyr/mfleKv1dRZwFsBSI0Z3K14zMzOzVlVXoinpo6TV5h8mJXovkZKr4cAgYJqkfSJicud7qV9EPAXsmPteGxifqz4P3JhHTmdJ+jMwljSauFphF6OAF3rQ/w/It2+S9HtgWi6/jzw6KWlHYO28SXsX/Q8F3uhuLGZmZmb9Vc1L55JWBW4iJUufBpaNiA9GxAhgOWBX0ojjTZJW6Y2gOvaTF+B8BzgjVz0LbJ9Xeb+PNLr6FPAgMFrSmpKWBPYhjYQi6URJn22g70GShufXGwEbkUYvi3EtRVr81BHX1cABOa4tgTkRMTPXrU2aCmBmZmb2nlLPHM2vkpLMbSLiprzYBlg0Z/MGYNvc5vBGOpd0EenS9DqS2iUdlKv2lfQ3UhL5AnBuLv8lsCwpcXsQODciHouIBbnvm4AngUsi4om8zYbAP6v0vZmkdtIc0DMldbQfQrok/1fSZe0v5P1DWoH+JGnh0zURcVsuvx54BpgO/Br4SqGrccB1jZwXMzMzs4FAEV1PDZT0F+CyiDi5Rrujgc9FxOad1LcB1+ZbA/UZSTdFxE592WdF/3cBEyLila7aLTVidIyY+PM+isrMbGCZcdL42o3MrNdIeigixtZqV8+I5lrAw3W0eyi37cxCYFjxhu19oclJ5srAT2slmWZmZmYDUT2LgYYB9dx753Vg+c4qI6Jywc6Al2/YfmWz4zAzMzNrhnpGNEX9twqqdpsfMzMzM3sPqvc+mjdJWlCjTd335DQzMzOzga+e5PCE0qMwMzMzswGn5qpz6xtjx46NSZMmNTsMMzMzs5p6c9W5mZmZmVnDnGiamZmZWSmcaJqZmZlZKZxompmZmVkpnGiamZmZWSmcaJqZmZlZKZxompmZmVkpnGiamZmZWSmcaJqZmZlZKZxompmZmVkpnGiamZmZWSmcaJqZmZlZKZxompmZmVkpnGiamZmZWSmcaJqZmZlZKZxompmZmVkpnGiamZmZWSmcaJqZmZlZKZxompmZmVkpnGiamZmZWSmcaJqZmZlZKZxompmZmVkpBjc7AEsef34Obcdc1+wwzMzMrB+bcdL4ZofwDh7RNDMzM7NSONE0MzMzs1I40TQzMzOzUjjRNDMzM7NSONE0MzMzs1I40TQzMzOzUjjRNDMzM7NS9FmiKalN0huSJhfKzpE0S9KUirYbS7pP0uOSrpG0fC7fT9LkwuNtSWNy3aa5/XRJp0hSjXjWzX3Mk/StirqvS5oi6QlJRxTKx0i6P/c9SdLmuVy5z+mSHpO0SS5fWdKNPT13ZmZmZv1RX49oPh0RYwrvzwN2rtLuN8AxEbEhcAVwFEBEXBgRY/I+9gdmRERH4vor4BBgdH5U22/Ry8DXgJ8UCyVtABwMbA5sDOwqaXSu/hFwQu7/2PweYJdCv4fkWIiIF4GZkraqEYuZmZnZgNPUS+cRcRcp4au0DnBXfn0L8LkqbfYFLgKQNAJYPiLui4gALgB2r9H3rIh4EHiromo94P6ImBsRC4A7gc92bAYsn18PA17IrycAF0RyP7BCjgngSmC/rmIxMzMzG4hadY7mFGC3/HovYLUqbfYmJ5rASKC9UNeey7rb97aShktaBvh0of8jgB9Leo40EvrtQv/PddL/JGCbah1JOiRfgp+0cO6cboZrZmZm1ppaNdH8InCYpIeA5YD5xUpJWwBzI6Jjbme1+ZjRnY4j4kngZNJI6o3Ao8CCXP1l4BsRsRrwDeDsOvqfBXywk77OioixETF20DLDuhOumZmZWctqyUQzIp6KiB0jYlPSqOXTFU32YfFoJqQRxFGF96NYfFm7O/2fHRGbRMS2pEv703LVRODy/PqPpHmcHf0XR12L/Q8F3uhuLGZmZmb9VUsmmpJWyc9LAN8BzijULUG6nP6HjrKImAm8LmnLvNr8AOCq3P5wSYd3s//VgT1YnNS+AHwiv96exQno1cABefX5lsCcHBPA2qTL8WZmZmbvKYOb2bmki4DtgJUktQPHRcTZwL6SDsvNLgfOLWy2LdAeEc9U7O7LpFXsSwM35AfAusCfq/T9AdL8yeWBt/NtjNaPiNeAyyQNJy0UOiwiXsmbHQz8QtJg4E3SCnOA60lzOacDc4F/L3Q1DriurhNiZmZmNoAoLdLug46kNuDaiNigTzpc3O+1wB4RMb9m43L6vwuYUEhWq1pqxOgYMfHnfRSVmZmZDUQzThrfJ/1IeigixtZq15eXzhcCw4o3bO8LEbFrE5PMlYGf1koyzczMzAaiPrt0HhHPUf02RQNWvmH7lc2Ow8zMzKwZWnIxkJmZmZn1f040zczMzKwUTjTNzMzMrBRNvb2RLbbhyGFM6qOVYmZmZmZ9wSOaZmZmZlYKJ5pmZmZmVgonmmZmZmZWCieaZmZmZlYKJ5pmZmZmVgonmmZmZmZWCieaZmZmZlYKJ5pmZmZmVgonmmZmZmZWCieaZmZmZlYKJ5pmZmZmVgonmmZmZmZWCieaZmZmZlYKJ5pmZmZmVgonmmZmZmZWCieaZmZmZlYKJ5pmZmZmVgonmmZmZmZWCieaZmZmZlYKJ5pmZmZmVgonmmZmZmZWCieaZmZmZlaKwc0OwJLHn59D2zHXNTsMMzN7j5px0vhmh2ADkEc0zczMzKwUTjTNzMzMrBRONM3MzMysFE40zczMzKwUTjTNzMzMrBRONM3MzMysFE40zczMzKwUfZZoSmqT9IakyYWycyTNkjSlou3Gku6T9LikayQtX6jbKNc9keuH5vJN8/vpkk6RpBrxrJv3M0/Styrqvi5pSu7jiEL5xZIm58eMimP5du57qqSdctmSku6S5PuVmpmZ2XtOX49oPh0RYwrvzwN2rtLuN8AxEbEhcAVwFEBO2H4HHBoRHwG2A97K2/wKOAQYnR/V9lv0MvA14CfFQkkbAAcDmwMbA7tKGg0QEXtHxJh8DJcBl+dt1gf2AT6S+z1d0qCImA/cCuxdIxYzMzOzAaepl84j4i5SwldpHeCu/PoW4HP59Y7AYxHxaN5+dkQslDQCWD4i7ouIAC4Adq/R96yIeJDFiWqH9YD7I2JuRCwA7gQ+W2yQR0v/DbgoF00A/hAR8yLi78B0UqIKcCWwX1exmJmZmQ1ErTpHcwqwW369F7Bafr02EJJukvSwpP/I5SOB9sL27bmsu31vK2m4pGWATxf677AN8K+ImFbo/7lO+p8CbFatI0mHSJokadLCuXO6Ga6ZmZlZa2rVRPOLwGGSHgKWA+bn8sHA1qQRwq2Bz0raAag2HzO603FEPAmcTBpJvRF4FFhQ0WxfFo9m0lX/EbEQmC9puSp9nRURYyNi7KBlhnUnXDMzM7OW1ZKJZkQ8FRE7RsSmpITu6VzVDtwZES9FxFzgemCTXD6qsItRwAs96P/siNgkIrYlXdrvGLnsmCe6B3BxYZN23jnqWdn/UsCb3Y3HzMzMrD9qyURT0ir5eQngO8AZueomYCNJy+SE7xPAXyNiJvC6pC3z/MkDgKvyPg6XdHg3+1+dlFQWRy8/CTwVEcVL9VcD+0haStKapMVIf8n7GA68GBGVc0HNzMzMBrSm3nZH0kWkleMrSWoHjouIs4F9JR2Wm10OnAsQEa9I+inwIOnS9PURcV1u92XSKvalgRvyA2Bd4M9V+v4AMAlYHng738Zo/Yh4DbgsJ4hvAYdFxCuFTffhnYknEfGEpEuAv5Iusx+WL5kDjCONvJqZmZm9pygt0u6DjqQ24NqI2KBPOlzc77XAHvlWQ31O0uXAtyNialftlhoxOkZM/HkfRWVmZvZOM04a3+wQrB+R9FBEjK3Vri8vnS8EhhVvct4XImLXJiaZSwJX1koyzczMzAaiPrt0HhHP8e7bBA1oOcG9oNlxmJmZmTVDSy4GMjMzM7P+z4mmmZmZmZXCiaaZmZmZlaKptzeyxTYcOYxJXvFnZmZmA4hHNM3MzMysFE40zczMzKwUTjTNzMzMrBRONM3MzMysFE40zczMzKwUTjTNzMzMrBRONM3MzMysFE40zczMzKwUTjTNzMzMrBRONM3MzMysFE40zczMzKwUiohmx2CApNeBqc2Oo4dWAl5qdhA90N/jBx9DK+jv8YOPoRX09/ih/x9Df48fyj2GNSJi5VqNBpfUuTVuakSMbXYQPSFpUn8+hv4eP/gYWkF/jx98DK2gv8cP/f8Y+nv80BrH4EvnZmZmZlYKJ5pmZmZmVgonmq3jrGYH0Av6+zH09/jBx9AK+nv84GNoBf09fuj/x9Df44cWOAYvBjIzMzOzUnhE08zMzMxK4USzj0naWdJUSdMlHVOlfilJF+f6ByS19X2Unasj/gMlvShpcn78v2bE2RVJ50iaJWlKJ/WSdEo+xsckbdLXMXaljvi3kzSn8Bkc29cxdkXSapJul/SkpCckfb1Km1b/DOo5hlb/HIZK+oukR/MxnFClTcv+Pqoz/pb/fQQgaZCkRyRdW6WuZT+DDjXib/nPQNIMSY/n+CZVqW/p30dQ1zE07/dRRPjRRw9gEPA08CFgSeBRYP2KNl8Bzsiv9wEubnbcDcZ/IHBas2OtcRzbApsAUzqp/zRwAyBgS+CBZsfcYPzbAdc2O84u4h8BbJJfLwf8rcrPUat/BvUcQ6t/DgKWza+HAA8AW1a0aeXfR/XE3/K/j3KcRwK/r/bz0sqfQZ3xt/xnAMwAVuqivqV/H9V5DE37feQRzb61OTA9Ip6JiPnAH4AJFW0mAOfn15cCO0hSH8bYlXrib3kRcRfwchdNJgAXRHI/sIKkEX0TXW11xN/SImJmRDycX78OPAmMrGjW6p9BPcfQ0vK5/d/8dkh+VE7ab9nfR3XG3/IkjQLGA7/ppEnLfgZQV/wDQUv/Pmp1TjT71kjgucL7dt79n9OiNhGxAJgDDO+T6GqrJ36Az+XLC5dKWq1vQutV9R5nK/tYvqR4g6SPNDuYzuTLgB8ljUYV9ZvPoItjgBb/HPIlz8nALOCWiOj0c2jB30f1xA+t//vo58B/AG93Ut/SnwG144fW/wwCuFnSQ5IOqVLfH34f1ToGaNLvIyeafavaX6GVf4HX06ZZ6ontGqAtIjYC/sTiv8T7k1b+DOrxMOmrwTYGTgWubHI8VUlaFrgMOCIiXqusrrJJy30GNY6h5T+HiFgYEWOAUcDmkjaoaNLSn0Md8bf07yNJuwKzIuKhrppVKWuJz6DO+Fv6M8i2iohNgF2AwyRtW1Hfsp9BQa1jaNrvIyeafasdKP41Nwp4obM2kgYDw2idy6Q144+I2RExL7/9NbBpH8XWm+r5nFpWRLzWcUkxIq4HhkhaqclhvYOkIaQE7cKIuLxKk5b/DGodQ3/4HDpExKvAHcDOFVWt/Ptokc7i7we/j7YCdpM0gzQVaXtJv6to08qfQc34+8FnQES8kJ9nAVeQpokVtfzvo1rH0MzfR040+9aDwGhJa0pakjSx++qKNlcDE/PrPYHbIs/kbQE146+Yt7Ibae5af3M1cEBeabglMCciZjY7qHpJ+kDHHC5Jm5P+nc9ublSL5djOBp6MiJ920qylP4N6jqEffA4rS1ohv14a+CTwVEWzlv19VE/8rf77KCK+HRGjIqKN9Pv0toj4QkWzlv0M6om/1T8DSe+TtFzHa2BHoPKOHq3++6jmMTTz99HgvujEkohYIOlw4CbSCu5zIuIJSd8FJkXE1aT/vH4raTrpr9Z9mhfxO9UZ/9ck7QYsIMV/YNMC7oSki0gr8FaS1A4cR1pIQEScAVxPWmU4HZgL/HtzIq2ujvj3BL4saQHwBrBPq/zHlG0F7A88nufXAfwnsDr0j8+A+o6h1T+HEcD5kgaR/tO5JCKu7S+/j6gv/pb/fVRNP/oMqupnn8GqwBU5BxsM/D4ibpR0KPSb30f1HEPTfh/5m4HMzMzMrBS+dG5mZmZmpXCiaWZmZmalcKJpZmZmZqVwomlmZmZmpXCiaWZmZmalcKJpZtZNko6XFIXHC5Iuk/ThXuxjPUl3S/q/3EebpDskXVpos6OkI+rc3x2FeBdImiHpTEkrdyO2tfM5WKGi/MC8/2Ub3aeZDSxONM3MemYO8LH8+BYwBrg13zi5N/wYWIF0s+uPATOBrwDfLrTZEagr0cxuz/vaDvgpsC9wUTdiW5t0H9cVKsqvy/uf2419mtkA4hu2m5n1zIKIuD+/vl/Ss8DdpBs8/7Gycb7B+KCImF/n/tcFro6IWwtlf+1JwMDLhZjvkbQMcKKkD3Z8lV1PRMSLwIs93Y+Z9X8e0TQz610P5ec2AEnnSZokaXdJTwBvAlvkujGSbpU0V9Irki6UtGqua5MUwIeBb+RL0XfkukWXziUdD3wTWKNwSfy8BmN+ND8v+j5nSetK+oOk53J8T0g6QtISuX474Jrc/O+53xm57l2XziWtJOl8SbPz/u6QNLbBOM2sn/GIpplZ72rLz/+sKPsR8F3gX6TEbGXgDtJ3P38eWBY4CbglJ2AzSZefrwBuA04FXqvS32+A0cD2wGdzWaOjiasDbwP/KJSNBKYCFwKvk6YEnAAsDZwIPEyaKvATYI8c77wu+rgSWCtv8xJwFHC7pI9GxPQG4zWzfsKJpplZD0nq+F36IeB0UmL2p0KT4cAnI2JyYZuT8sudIuK1XPY34AHgcxFxEelS/DxgZuFS9ztERLukmcC8ztpUD1mDgUHApqT5nmdFxKLkOF+qv7WjMXAPsAxwMHBiRLwmaWpu/khEzOiis51J3w+/XUTcmctuA2aQEs4v1Rm3mfUzTjTNzHpmOPBW4f2zwN4RMbNQ9nwxycw2B27uSDIBIuIv+fLz1nRvcU699uCdMf8F+FqxgaShpAR0P9KI55BC3eCIWNBAf5sDL3YkmQAR8X+SriUdq5kNUJ6jaWbWM3OAzYCxwCigLSJuqGjzryrbjeik/F/A+3s1wne7jRTz1sDJpETw+xVtTiZd5j6LtLBps0KboQ3218xjNbMm8oimmVnPLIiISTXaRJWymcAqVcpXZfGCorK8Uoj5z3m+6BGSTouI53L5XsCpEfGjjo0kje9mf10d68vd3KeZ9QMe0TQza44HgJ0kLddRIGkz0sKhexrc13waH2UsOi4/f6NQtjSFxT35tkz7VOmXOvp+AFhF0raF/S0DjKfxYzWzfsSJpplZc/w0P98kaYKk/YDLgceByxrc11PAqvm2QmMltTWycUS0A+cDBxe+5ecW4DBJ++eRzGuApSo27VgM9CVJW0jasJP93wT8GbhY0kRJuwLXk5LZHzcSq5n1L040zcyaIN/UfBzpvpoXAb8k3ej9Uw3czL3DJcB5pFsoPQgc342QTiKNTH45v/9qjueXwDnAFNJtjYrH8A/SPM49SInkNXTus6Tk9eekG9kL2N63NjIb2BRRbeqQmZmZmVnPeETTzMzMzErhRNPMzMzMSuFE08zMzMxK4UTTzMzMzErhRNPMzMzMSuFE08zMzMxK4UTTzMzMzErhRNPMzMzMSuFE08zMzMxK8f8B3znF3Cpk2T0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44010bc748>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "profit.plot(kind=\"barh\",figsize=(10,5),legend=True)\n",
    "plt.title('Profit ratio of each decade',fontsize=\"20\")\n",
    "plt.xlabel(\"Profit Ratio\",fontsize=\"15\")\n",
    "plt.ylabel(\"Decades\",fontsize=\"15\")\n",
    "plt.xticks(np.arange(0, 6, step=0.5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tells us that even with high revenue you cannot say that the profit is high coz budget is also high. The decade 1970-80 has one of the lowest revenues but it has the best profit rate out of them all.Profit rate is slightly decreasing with years so it means the return on investment on movies is not as much as it was in the past."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 3 \n",
    "\n",
    "#### What are the most popular genres and keywords of the last decade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets find the most popular genres\n",
    "first."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25                             Action\n",
       "26                             Comedy\n",
       "27      Crime|Comedy|Action|Adventure\n",
       "28             Drama|Thriller|History\n",
       "29    Action|Science Fiction|Thriller\n",
       "Name: genres, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.genres.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see a single row of genres column has multiple values so we have to seperate it to make it useful to us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting genres on |\n",
    "g=df[['genres','release_year']].apply(lambda x:[(n,x[1]) for n in x[0].split('|')],axis=1)\n",
    "all_genres=[]\n",
    "# appending all genres into a list\n",
    "g.apply(lambda x:[all_genres.append(n) for n in x])\n",
    "#making a new dataframe\n",
    "genres_df=pd.DataFrame(all_genres,columns=['genres','year'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that all genre values are independent we can use them.To find most popular genres of each decade we will use the same decade bins we used for popularity and vote count but due to multiple values in each row the quantity of bins will be increased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [2010, 2020)\n",
       "1    [2010, 2020)\n",
       "2    [2010, 2020)\n",
       "3    [2010, 2020)\n",
       "4    [2010, 2020)\n",
       "Name: genre_bins, dtype: category\n",
       "Categories (6, interval[int64]): [[1960, 1970) < [1970, 1980) < [1980, 1990) < [1990, 2000) < [2000, 2010) < [2010, 2020)]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genres_df['genre_bins']=pd.cut(genres_df.year,bins=[1960,1970,1980,1990,2000,2010,2020],right=False)\n",
    "genres_df.genre_bins.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First i will have to groupby genres by their decade bins after that i have to count the number of occurences of each genre in each decade,then sorting the values on the number of count would give me better understanding of the data.For simplicity i am going to use the top 3 values of each decade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genre_bins</th>\n",
       "      <th>genres</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">[1960, 1970)</th>\n",
       "      <th>Drama</th>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">[1970, 1980)</th>\n",
       "      <th>Drama</th>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">[1980, 1990)</th>\n",
       "      <th>Comedy</th>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">[1990, 2000)</th>\n",
       "      <th>Drama</th>\n",
       "      <td>858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">[2000, 2010)</th>\n",
       "      <th>Drama</th>\n",
       "      <td>1602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>1345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">[2010, 2020)</th>\n",
       "      <th>Drama</th>\n",
       "      <td>1421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>1010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count\n",
       "genre_bins   genres         \n",
       "[1960, 1970) Drama       167\n",
       "             Comedy      112\n",
       "             Action       78\n",
       "[1970, 1980) Drama       237\n",
       "             Thriller    147\n",
       "             Comedy      121\n",
       "[1980, 1990) Comedy      427\n",
       "             Drama       421\n",
       "             Action      268\n",
       "[1990, 2000) Drama       858\n",
       "             Comedy      734\n",
       "             Thriller    493\n",
       "[2000, 2010) Drama      1602\n",
       "             Comedy     1345\n",
       "             Thriller    967\n",
       "[2010, 2020) Drama      1421\n",
       "             Comedy     1010\n",
       "             Thriller    936"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function to find the occurences of genres and then using those occurences to sort \n",
    "def func(df):\n",
    "    return df.groupby('genres').size().to_frame('count').sort_values(by='count',ascending=False).iloc[:3]\n",
    "\n",
    "# groupby each decade and using the count function to find top 3 occurences of each decade\n",
    "genre_graph=genres_df.groupby(['genre_bins']).apply(func)\n",
    "genre_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44010667b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "genre_graph.plot(kind='bar',figsize=(8,8),legend=True);\n",
    "plt.title('Top 3 genres of each decade',fontsize=\"20\")\n",
    "plt.xlabel(\"Genres\",fontsize=\"15\")\n",
    "plt.ylabel(\"Counts\",fontsize=\"15\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the drama is most the popular in each decade, people of every decade have been interested in drama movie as most the movies have the genre as drama. The second most popular genre is comedy and like drama it is loved by all the generations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Keywords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can find most popular keywords like the genre method but keywords have null values, so lets remove the null values that we have and compute because we cant do it any other way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10593 entries, 25 to 10865\n",
      "Data columns (total 16 columns):\n",
      "popularity              10593 non-null float64\n",
      "original_title          10593 non-null object\n",
      "cast                    10593 non-null object\n",
      "director                10593 non-null object\n",
      "keywords                9184 non-null object\n",
      "overview                10593 non-null object\n",
      "runtime                 10593 non-null int64\n",
      "genres                  10593 non-null object\n",
      "production_companies    9652 non-null object\n",
      "release_date            10593 non-null object\n",
      "vote_count              10593 non-null int64\n",
      "vote_average            10593 non-null float64\n",
      "release_year            10593 non-null int64\n",
      "budget_adj              10593 non-null float64\n",
      "revenue_adj             10593 non-null float64\n",
      "decades                 10593 non-null category\n",
      "dtypes: category(1), float64(4), int64(3), object(8)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9184 entries, 25 to 10865\n",
      "Data columns (total 16 columns):\n",
      "popularity              9184 non-null float64\n",
      "original_title          9184 non-null object\n",
      "cast                    9184 non-null object\n",
      "director                9184 non-null object\n",
      "keywords                9184 non-null object\n",
      "overview                9184 non-null object\n",
      "runtime                 9184 non-null int64\n",
      "genres                  9184 non-null object\n",
      "production_companies    8552 non-null object\n",
      "release_date            9184 non-null object\n",
      "vote_count              9184 non-null int64\n",
      "vote_average            9184 non-null float64\n",
      "release_year            9184 non-null int64\n",
      "budget_adj              9184 non-null float64\n",
      "revenue_adj             9184 non-null float64\n",
      "decades                 9184 non-null category\n",
      "dtypes: category(1), float64(4), int64(3), object(8)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df_keyword=df.dropna(subset=['keywords'])\n",
    "df_keyword.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now as all the keywords null values have been removed,we can use the genre method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keyword_bins</th>\n",
       "      <th>keywords</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">[1960, 1970)</th>\n",
       "      <th>based on novel</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>england</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>london</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">[1970, 1980)</th>\n",
       "      <th>based on novel</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female nudity</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suspense</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">[1980, 1990)</th>\n",
       "      <th>nudity</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female nudity</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">[1990, 2000)</th>\n",
       "      <th>independent film</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sport</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">[2000, 2010)</th>\n",
       "      <th>independent film</th>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>woman director</th>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">[2010, 2020)</th>\n",
       "      <th>woman director</th>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>biography</th>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>based on novel</th>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               count\n",
       "keyword_bins keywords               \n",
       "[1960, 1970) based on novel       16\n",
       "             england              15\n",
       "             london               13\n",
       "[1970, 1980) based on novel       23\n",
       "             female nudity        18\n",
       "             suspense             18\n",
       "[1980, 1990) nudity               39\n",
       "             sex                  36\n",
       "             female nudity        34\n",
       "[1990, 2000) independent film     80\n",
       "             sex                  51\n",
       "             sport                49\n",
       "[2000, 2010) independent film    214\n",
       "             sex                 121\n",
       "             woman director      119\n",
       "[2010, 2020) woman director      226\n",
       "             biography            95\n",
       "             based on novel       93"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# splitting keywords on |\n",
    "k=df_keyword[['keywords','release_year']].apply(lambda x:[(n,x[1]) for n in x[0].split('|')],axis=1)\n",
    "all_keywords=[]\n",
    "# appending all genres into a list\n",
    "k.apply(lambda x:[all_keywords.append(n) for n in x])\n",
    "#making a new dataframe\n",
    "keywords_df=pd.DataFrame(all_keywords,columns=['keywords','year'])\n",
    "\n",
    "keywords_df['keyword_bins']=pd.cut(keywords_df.year,bins=[1960,1970,1980,1990,2000,2010,2020],right=False)\n",
    "# function to find the occurences of keywords and then using those occurences to sort \n",
    "def func(df):\n",
    "    return df.groupby('keywords').size().to_frame('count').sort_values(by='count',ascending=False).iloc[:3]\n",
    "\n",
    "# groupby each decade and using the count function to find top 3 occurences of each decade\n",
    "keywords_graph=keywords_df.groupby(['keyword_bins']).apply(func)\n",
    "keywords_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44006967f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "keywords_graph.plot(kind='barh',figsize=(10,6),legend=True,title='Top 3 keyowrds of each decade')\n",
    "plt.title('Top 3 keywords of each decade',fontsize=\"20\")\n",
    "plt.xlabel(\"Counts\",fontsize=\"15\")\n",
    "plt.ylabel(\"Keywords\",fontsize=\"15\")\n",
    "plt.xticks(np.arange(0, 300, step=50))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a very interesting insight as it provides the keywords of the movies of diffferent generations and all are telling different things. The earlier decade movies were based on novels then the main focus was on nudity and fornication in the middle decades and then with women empowerment, the women directors emerge as the clear leaders in the recent decades. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 4 \n",
    "\n",
    "#### Which director and actor/actress has been the most successful?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Director"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find the most successful director lets find the directors with most number of movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "director\n",
       "Woody Allen          45\n",
       "Clint Eastwood       34\n",
       "Steven Spielberg     29\n",
       "Martin Scorsese      27\n",
       "Steven Soderbergh    22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top directors with most movies\n",
    "df.groupby(['director']).size().sort_values(ascending =False).iloc[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the top 5 directors with most movies but it doesn't mean that they are successful , because a number of their movies can be flops.So,lets see which director has earned the most revenues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "director\n",
       "Steven Spielberg   15306731532.0330\n",
       "Michael Bay         5234544105.2757\n",
       "Ron Howard          4735664972.6137\n",
       "James Cameron       4387735037.2228\n",
       "Roland Emmerich     4181265738.8914\n",
       "Name: revenue_adj, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top directors with most revenue\n",
    "df.groupby(['director']).revenue_adj.sum().sort_values(ascending =False).iloc[:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even this could be misleading as one bad director could make a lot of movies and increase its total sum, so we should take the mean of revenues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "director\n",
       "James Cameron      626819291.0318\n",
       "Steven Spielberg   527818328.6908\n",
       "Michael Bay        523454410.5276\n",
       "Roland Emmerich    380115067.1719\n",
       "Justin Lin         326134365.1195\n",
       "Name: revenue_adj, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding directors with highest mean with atleast 5 movies\n",
    "direct=df.groupby(['director']).revenue_adj.mean()[df.groupby(['director']).size()>5]\n",
    "direct.sort_values(ascending =False).iloc[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even this doesn't tell the whole picture as the directors with 1 good movie would have the higher than a director with 6 good movies and 1 bad movie, so we should take both the counts of movies and means to generate our final output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "director\n",
       "Clyde Geronimi|Hamilton Luske|Wolfgang Reitherman   1574814739.7048\n",
       "Lee Unkrich                                         1063171911.0000\n",
       "Andrew Adamson|Kelly Asbury|Conrad Vernon           1061903622.7420\n",
       "Andrew Stanton|Lee Unkrich                          1024886689.7700\n",
       "Chris Miller|Raman Hui                               840239200.9675\n",
       "Name: revenue_adj, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['director']).revenue_adj.mean().sort_values(ascending =False).iloc[:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a sound insight as it accounts both mean of revenue and counts of movies. James Cameron and Steven Spielberg are the most successful directors of all time as expected as it follows the general perception."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25    Tom Cruise|Jeremy Renner|Simon Pegg|Rebecca Fe...\n",
       "26    Mark Wahlberg|Seth MacFarlane|Amanda Seyfried|...\n",
       "27    Taron Egerton|Colin Firth|Samuel L. Jackson|Mi...\n",
       "28    Mark Ruffalo|Michael Keaton|Rachel McAdams|Lie...\n",
       "29    Dylan O'Brien|Kaya Scodelario|Thomas Brodie-Sa...\n",
       "Name: cast, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cast.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that a movie has multiple casts like genres so we perform the same operation for it as well.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>cast</th>\n",
       "      <th>year</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tom Cruise</td>\n",
       "      <td>2015</td>\n",
       "      <td>627743451.6729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jeremy Renner</td>\n",
       "      <td>2015</td>\n",
       "      <td>627743451.6729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Simon Pegg</td>\n",
       "      <td>2015</td>\n",
       "      <td>627743451.6729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rebecca Ferguson</td>\n",
       "      <td>2015</td>\n",
       "      <td>627743451.6729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ving Rhames</td>\n",
       "      <td>2015</td>\n",
       "      <td>627743451.6729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mark Wahlberg</td>\n",
       "      <td>2015</td>\n",
       "      <td>198594430.1384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Seth MacFarlane</td>\n",
       "      <td>2015</td>\n",
       "      <td>198594430.1384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Amanda Seyfried</td>\n",
       "      <td>2015</td>\n",
       "      <td>198594430.1384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jessica Barth</td>\n",
       "      <td>2015</td>\n",
       "      <td>198594430.1384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Giovanni Ribisi</td>\n",
       "      <td>2015</td>\n",
       "      <td>198594430.1384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Taron Egerton</td>\n",
       "      <td>2015</td>\n",
       "      <td>371497801.6610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Colin Firth</td>\n",
       "      <td>2015</td>\n",
       "      <td>371497801.6610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Samuel L. Jackson</td>\n",
       "      <td>2015</td>\n",
       "      <td>371497801.6610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Michael Caine</td>\n",
       "      <td>2015</td>\n",
       "      <td>371497801.6610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Mark Strong</td>\n",
       "      <td>2015</td>\n",
       "      <td>371497801.6610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Mark Ruffalo</td>\n",
       "      <td>2015</td>\n",
       "      <td>81278719.3974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Michael Keaton</td>\n",
       "      <td>2015</td>\n",
       "      <td>81278719.3974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Rachel McAdams</td>\n",
       "      <td>2015</td>\n",
       "      <td>81278719.3974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Liev Schreiber</td>\n",
       "      <td>2015</td>\n",
       "      <td>81278719.3974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>John Slattery</td>\n",
       "      <td>2015</td>\n",
       "      <td>81278719.3974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 cast  year        revenue\n",
       "0          Tom Cruise  2015 627743451.6729\n",
       "1       Jeremy Renner  2015 627743451.6729\n",
       "2          Simon Pegg  2015 627743451.6729\n",
       "3    Rebecca Ferguson  2015 627743451.6729\n",
       "4         Ving Rhames  2015 627743451.6729\n",
       "5       Mark Wahlberg  2015 198594430.1384\n",
       "6     Seth MacFarlane  2015 198594430.1384\n",
       "7     Amanda Seyfried  2015 198594430.1384\n",
       "8       Jessica Barth  2015 198594430.1384\n",
       "9     Giovanni Ribisi  2015 198594430.1384\n",
       "10      Taron Egerton  2015 371497801.6610\n",
       "11        Colin Firth  2015 371497801.6610\n",
       "12  Samuel L. Jackson  2015 371497801.6610\n",
       "13      Michael Caine  2015 371497801.6610\n",
       "14        Mark Strong  2015 371497801.6610\n",
       "15       Mark Ruffalo  2015  81278719.3974\n",
       "16     Michael Keaton  2015  81278719.3974\n",
       "17     Rachel McAdams  2015  81278719.3974\n",
       "18     Liev Schreiber  2015  81278719.3974\n",
       "19      John Slattery  2015  81278719.3974"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# splitting cast on |\n",
    "c=df[['cast','release_year','revenue_adj']].apply(lambda x:[(n,x[1],x[2]) for n in x[0].split('|')],axis=1)\n",
    "all_cast=[]\n",
    "# appending all cast into a list\n",
    "c.apply(lambda x:[all_cast.append(n) for n in x])\n",
    "#making a new dataframe\n",
    "cast_df=pd.DataFrame(all_cast,columns=['cast','year','revenue'])\n",
    "cast_df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've seperate the cast into each independent row like genre we can use the same operations we used for director to find most successful actor/actress.As many cast make more than 20 movies so we change our threshold to 20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cast\n",
       "Will Smith          358178517.7555\n",
       "Tom Cruise          307276415.1716\n",
       "Richard Dreyfuss    237855148.9457\n",
       "Tom Hanks           225429626.5552\n",
       "Cameron Diaz        222450346.5196\n",
       "Leonardo DiCaprio   215983968.2786\n",
       "Eddie Murphy        214036394.9093\n",
       "Ben Stiller         208266944.7656\n",
       "Sean Connery        204904526.0697\n",
       "Harrison Ford       200669916.8680\n",
       "Name: revenue, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "direct=cast_df.groupby(['cast']).revenue.mean()[cast_df.groupby(['cast']).size()>20]\n",
    "direct.sort_values(ascending =False).iloc[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to our analysis , will smith has been the most successful actor revenue-wise ,with tom cruise in second. As almsot all the actors/actress we have at the top are popular,this looks a good insight."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 5:\n",
    "\n",
    "#### What is the ratio of movies and tv-series in the dataset ? How to differentiate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By first looking at the data i came across a value of runtime which was too high to be a movie,I searched it online and it was a tv-series which proved that the dataset also contained tv-series. And tv-shows are becoming more popular and popular , i thought it would be a good insight if i compare them with the movies.\n",
    "\n",
    "As the data has no labels for tv-series and movies, we have to differentiate first. The easiest way to do it is by looking at the runtimes of movies and shows as both have very dissimilar runtime in general. So, im gonna use that knowledge to my advantage here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   10593.0000\n",
       "mean      102.4813\n",
       "std        30.0117\n",
       "min         3.0000\n",
       "25%        90.0000\n",
       "50%        99.0000\n",
       "75%       111.0000\n",
       "max       900.0000\n",
       "Name: runtime, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.runtime.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assuming the runtimes are in minutes, the mean is 102mins which is ideal for a movie but we can clearly see that max value is 900mins which cant be a movie because movies are not that long, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>keywords</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",
       "      <th>decades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3894</th>\n",
       "      <td>0.0069</td>\n",
       "      <td>The Story of Film: An Odyssey</td>\n",
       "      <td>Mark Cousins|Jean-Michel Frodon|Cari Beauchamp...</td>\n",
       "      <td>Mark Cousins</td>\n",
       "      <td>cinema|nouvelle vague|hindi cinema|cinema novo...</td>\n",
       "      <td>The Story of Film: An Odyssey, written and dir...</td>\n",
       "      <td>900</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9/3/11</td>\n",
       "      <td>14</td>\n",
       "      <td>9.2000</td>\n",
       "      <td>2011</td>\n",
       "      <td>17813413.9196</td>\n",
       "      <td>52142372.7073</td>\n",
       "      <td>[2010, 2020)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      popularity                 original_title  \\\n",
       "3894      0.0069  The Story of Film: An Odyssey   \n",
       "\n",
       "                                                   cast      director  \\\n",
       "3894  Mark Cousins|Jean-Michel Frodon|Cari Beauchamp...  Mark Cousins   \n",
       "\n",
       "                                               keywords  \\\n",
       "3894  cinema|nouvelle vague|hindi cinema|cinema novo...   \n",
       "\n",
       "                                               overview  runtime       genres  \\\n",
       "3894  The Story of Film: An Odyssey, written and dir...      900  Documentary   \n",
       "\n",
       "     production_companies release_date  vote_count  vote_average  \\\n",
       "3894                  NaN       9/3/11          14        9.2000   \n",
       "\n",
       "      release_year    budget_adj   revenue_adj       decades  \n",
       "3894          2011 17813413.9196 52142372.7073  [2010, 2020)  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#row where runtime is 900mins\n",
    "df[df.runtime==900]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By researching online it is confirmed that the following title is not a movie but rather a 15 episode long tv-series, this proves the hypothesis. Next step is to find a threshold value where movies and shows seperate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f440018abe0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.scatter(y=df.index,x=df.runtime)\n",
    "plt.title(\"Runtimes Scatter Plot\",fontsize=\"20\")\n",
    "plt.xlabel(\"Runtimes\",fontsize=\"15\")\n",
    "plt.ylabel(\"Index\",fontsize=\"15\")\n",
    "plt.yticks(np.arange(0, 12000, step=1000))\n",
    "plt.xticks(np.arange(0, 1100, step=100))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see how data is scattered and all of it is in the 100-200 and rest can be considered outliers but in our case these outliers are actually tv-series which looks like 1% of the entire data so if we can seperate that 1% from the other 99% we can assign new labels and that would help us in understanding why some titles have e.g 500mins on them. We create new columns with these labels which are \"movie\" and \"tv_Shows\" which might be useful later on.\n",
    "We can see that most of the outliers are around 200mins so lets see if quantile of 99% is in that range to prove our assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "225.0\n"
     ]
    }
   ],
   "source": [
    "# finding the threshold value\n",
    "q=df.runtime.quantile(0.995)\n",
    "print (q)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>keywords</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",
       "      <th>decades</th>\n",
       "      <th>tv_show</th>\n",
       "      <th>movie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4.5667</td>\n",
       "      <td>Mission: Impossible - Rogue Nation</td>\n",
       "      <td>Tom Cruise|Jeremy Renner|Simon Pegg|Rebecca Fe...</td>\n",
       "      <td>Christopher McQuarrie</td>\n",
       "      <td>spy|sequel|mission</td>\n",
       "      <td>Ethan and team take on their most impossible m...</td>\n",
       "      <td>131</td>\n",
       "      <td>Action</td>\n",
       "      <td>Paramount Pictures|Skydance Productions|China ...</td>\n",
       "      <td>7/23/15</td>\n",
       "      <td>2349</td>\n",
       "      <td>7.1000</td>\n",
       "      <td>2015</td>\n",
       "      <td>137999939.2800</td>\n",
       "      <td>627743451.6729</td>\n",
       "      <td>[2010, 2020)</td>\n",
       "      <td>nan</td>\n",
       "      <td>131.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.5645</td>\n",
       "      <td>Ted 2</td>\n",
       "      <td>Mark Wahlberg|Seth MacFarlane|Amanda Seyfried|...</td>\n",
       "      <td>Seth MacFarlane</td>\n",
       "      <td>sperm bank|sequel|buddy|courthouse|teddy bear</td>\n",
       "      <td>Newlywed couple Ted and Tami-Lynn want to have...</td>\n",
       "      <td>115</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Universal Pictures|Media Rights Capital|Fuzzy ...</td>\n",
       "      <td>6/25/15</td>\n",
       "      <td>1666</td>\n",
       "      <td>6.3000</td>\n",
       "      <td>2015</td>\n",
       "      <td>62559972.4736</td>\n",
       "      <td>198594430.1384</td>\n",
       "      <td>[2010, 2020)</td>\n",
       "      <td>nan</td>\n",
       "      <td>115.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4.5038</td>\n",
       "      <td>Kingsman: The Secret Service</td>\n",
       "      <td>Taron Egerton|Colin Firth|Samuel L. Jackson|Mi...</td>\n",
       "      <td>Matthew Vaughn</td>\n",
       "      <td>spy|great britain|secret organization|secret a...</td>\n",
       "      <td>The story of a super-secret spy organization t...</td>\n",
       "      <td>130</td>\n",
       "      <td>Crime|Comedy|Action|Adventure</td>\n",
       "      <td>Twentieth Century Fox Film Corporation|Marv Fi...</td>\n",
       "      <td>1/24/15</td>\n",
       "      <td>3833</td>\n",
       "      <td>7.6000</td>\n",
       "      <td>2015</td>\n",
       "      <td>74519967.2112</td>\n",
       "      <td>371497801.6610</td>\n",
       "      <td>[2010, 2020)</td>\n",
       "      <td>nan</td>\n",
       "      <td>130.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4.0623</td>\n",
       "      <td>Spotlight</td>\n",
       "      <td>Mark Ruffalo|Michael Keaton|Rachel McAdams|Lie...</td>\n",
       "      <td>Tom McCarthy</td>\n",
       "      <td>child abuse|journalism|judge|florida|boston</td>\n",
       "      <td>The true story of how The Boston Globe uncover...</td>\n",
       "      <td>128</td>\n",
       "      <td>Drama|Thriller|History</td>\n",
       "      <td>Participant Media|Open Road Films|Anonymous Co...</td>\n",
       "      <td>11/6/15</td>\n",
       "      <td>1559</td>\n",
       "      <td>7.8000</td>\n",
       "      <td>2015</td>\n",
       "      <td>18399991.9040</td>\n",
       "      <td>81278719.3974</td>\n",
       "      <td>[2010, 2020)</td>\n",
       "      <td>nan</td>\n",
       "      <td>128.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3.9689</td>\n",
       "      <td>Maze Runner: The Scorch Trials</td>\n",
       "      <td>Dylan O'Brien|Kaya Scodelario|Thomas Brodie-Sa...</td>\n",
       "      <td>Wes Ball</td>\n",
       "      <td>based on novel|resistance|maze|post-apocalypti...</td>\n",
       "      <td>Thomas and his fellow Gladers face their great...</td>\n",
       "      <td>132</td>\n",
       "      <td>Action|Science Fiction|Thriller</td>\n",
       "      <td>Gotham Group|Temple Hill Entertainment|TSG Ent...</td>\n",
       "      <td>9/9/15</td>\n",
       "      <td>1849</td>\n",
       "      <td>6.4000</td>\n",
       "      <td>2015</td>\n",
       "      <td>56119975.3072</td>\n",
       "      <td>286356245.9233</td>\n",
       "      <td>[2010, 2020)</td>\n",
       "      <td>nan</td>\n",
       "      <td>132.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    popularity                      original_title  \\\n",
       "25      4.5667  Mission: Impossible - Rogue Nation   \n",
       "26      4.5645                               Ted 2   \n",
       "27      4.5038        Kingsman: The Secret Service   \n",
       "28      4.0623                           Spotlight   \n",
       "29      3.9689      Maze Runner: The Scorch Trials   \n",
       "\n",
       "                                                 cast               director  \\\n",
       "25  Tom Cruise|Jeremy Renner|Simon Pegg|Rebecca Fe...  Christopher McQuarrie   \n",
       "26  Mark Wahlberg|Seth MacFarlane|Amanda Seyfried|...        Seth MacFarlane   \n",
       "27  Taron Egerton|Colin Firth|Samuel L. Jackson|Mi...         Matthew Vaughn   \n",
       "28  Mark Ruffalo|Michael Keaton|Rachel McAdams|Lie...           Tom McCarthy   \n",
       "29  Dylan O'Brien|Kaya Scodelario|Thomas Brodie-Sa...               Wes Ball   \n",
       "\n",
       "                                             keywords  \\\n",
       "25                                 spy|sequel|mission   \n",
       "26      sperm bank|sequel|buddy|courthouse|teddy bear   \n",
       "27  spy|great britain|secret organization|secret a...   \n",
       "28        child abuse|journalism|judge|florida|boston   \n",
       "29  based on novel|resistance|maze|post-apocalypti...   \n",
       "\n",
       "                                             overview  runtime  \\\n",
       "25  Ethan and team take on their most impossible m...      131   \n",
       "26  Newlywed couple Ted and Tami-Lynn want to have...      115   \n",
       "27  The story of a super-secret spy organization t...      130   \n",
       "28  The true story of how The Boston Globe uncover...      128   \n",
       "29  Thomas and his fellow Gladers face their great...      132   \n",
       "\n",
       "                             genres  \\\n",
       "25                           Action   \n",
       "26                           Comedy   \n",
       "27    Crime|Comedy|Action|Adventure   \n",
       "28           Drama|Thriller|History   \n",
       "29  Action|Science Fiction|Thriller   \n",
       "\n",
       "                                 production_companies release_date  \\\n",
       "25  Paramount Pictures|Skydance Productions|China ...      7/23/15   \n",
       "26  Universal Pictures|Media Rights Capital|Fuzzy ...      6/25/15   \n",
       "27  Twentieth Century Fox Film Corporation|Marv Fi...      1/24/15   \n",
       "28  Participant Media|Open Road Films|Anonymous Co...      11/6/15   \n",
       "29  Gotham Group|Temple Hill Entertainment|TSG Ent...       9/9/15   \n",
       "\n",
       "    vote_count  vote_average  release_year     budget_adj    revenue_adj  \\\n",
       "25        2349        7.1000          2015 137999939.2800 627743451.6729   \n",
       "26        1666        6.3000          2015  62559972.4736 198594430.1384   \n",
       "27        3833        7.6000          2015  74519967.2112 371497801.6610   \n",
       "28        1559        7.8000          2015  18399991.9040  81278719.3974   \n",
       "29        1849        6.4000          2015  56119975.3072 286356245.9233   \n",
       "\n",
       "         decades  tv_show    movie  \n",
       "25  [2010, 2020)      nan 131.0000  \n",
       "26  [2010, 2020)      nan 115.0000  \n",
       "27  [2010, 2020)      nan 130.0000  \n",
       "28  [2010, 2020)      nan 128.0000  \n",
       "29  [2010, 2020)      nan 132.0000  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating new columns\n",
    "df['tv_show']=df.runtime[df[\"runtime\"] >   q]\n",
    "df['movie']=df.runtime[df[\"runtime\"] <   q]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#amount of time movie was null so it is a show\n",
    "show_count=df.movie.isna().sum()\n",
    "\n",
    "#amount of time show was null so it is a movie\n",
    "movie_count=df.tv_show.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "195.2037037037037"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ratio \n",
    "movie_count/show_count\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which means for every tv-show there are 195 movies , thats how much movies dominate this dataset. As it is our assumption that runtime differentiates movies and shows so it might not be a very good insight but its still worth to look at."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Tv shows in each decade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df6=df.copy()\n",
    "df6.tv_show=df6.tv_show[~df6.tv_show.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "decades\n",
       "[1960, 1970)     1\n",
       "[1970, 1980)     0\n",
       "[1980, 1990)     4\n",
       "[1990, 2000)     8\n",
       "[2000, 2010)    22\n",
       "[2010, 2020)    17\n",
       "Name: tv_show, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tv_show_graph=df6.groupby(['decades']).tv_show.count()\n",
    "tv_show_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f440118c160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# selecting colors for each division\n",
    "\n",
    "cmap = plt.cm.prism\n",
    "colors = cmap(np.linspace(0., 1., 60))\n",
    "\n",
    "# plotting the results\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.pie(tv_show_graph, autopct='%1.0f%%',shadow=False,colors=colors, startangle=0, pctdistance=0.5,explode=(0.05, 0.05, 0.05, 0.05,0.05,0.05));\n",
    "plt.title(\"Tv shows in each decade\", bbox={'facecolor':'0.8', 'pad':5})\n",
    "plt.legend(tv_show_graph.index,loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see that the count of shows in recent decades have been increasing and is on a rise.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "\n",
    "\n",
    "Following were the questions and our analysis of those questions:\n",
    "\n",
    "##### Q1) How  has the popularity and vote count increased in the last few decades. \n",
    "The modernization of showbiz industry is causing an increase of popularity and vote_Count.The popularity and vote_Count show similar characteristics, movies are popular now than they were few decades ago and hence the vote_Count also increases.\n",
    "\n",
    "The popularity outliers were removed based on quantile, had they been used, results might have been different. Also relating popularity and vote_count is questionable if you consider that people had almost no mediums to cast votes on movies in the earlier decades but movies were still popular, other than that this insight is pretty sound.\n",
    "\n",
    "##### Q2) Which decade was the most successful monetarily?\n",
    "Considering just revenues, the later movies made way more money as compared to the classic movies and the same goes for budget. But the classics had better revenue-budget ratio. Hence the trend shows as the time progresses budget and revenue increases but the profit ratio decreases with few exceptions.\n",
    "\n",
    "One basic shortcoming of this insight was that we had to fill in mean values where there were zero values, as there were alot of zeros values(more than 50%).\n",
    "\n",
    "We chose mean because  it doesn't affect relationship between the variables and it is very fast. We could have used other imputations but chose mean for the sake of simplicity. These replacements are never perfect because not all missing values are equal to mean and it reduces variance in the dataset.s\n",
    "\n",
    "\n",
    "##### Q3) What are the most popular genres and keywords of different decades.\n",
    "Most popular genres were \"Drama\" and \"Comedy\".\n",
    "\n",
    "Most popular keywords were \"Based on novels\" for earlier decades and \"Women Directors\" for the recent decades.\n",
    "\n",
    "The limitation with this result is that we have based the popularity of genre and keywords purely on the number of occurences of each. There can be a lot of drama movies in a decade which have not been well received by the audience hence the popularity will be low even tho count is large.\n",
    "\n",
    "Also keywords had many null values and they were to be removed to make keywords data usable, results would have been better if all values were non-null from the start.\n",
    "\n",
    "#### Q4) Which director and actor/actress has been the most successful?\n",
    "The most successful directors(movies more than 5 and highest mean) are \"James Cameron\" and \"Steven Spielsberg\".\n",
    "\n",
    "The most successful Cast(movies more than 20 and highest mean) are \"Will Smith\" and \"Tom Cruise\".\n",
    "\n",
    "Although we have collected means and counts of movies and have provided sound arguments, the threshold value can be questionable as you get different results with different threshold values and it can never be perfect.\n",
    "\n",
    "##### Q5) What is the ratio of movies and tv-series in the dataset ? How to differentiate.\n",
    "We assumed that the runtime of movies cannot exceed from a certain threshold hence why there were many outliers in the runtime, we also assumed these outliers are tv-shows (verified through manual check).We then seperated these tv-shows from movies using the threshold and found out that for 1 tv-show there are 195 movies in the dataset.\n",
    "And analysis of each decade showed that number of tv-shows in the later generations are increasing.\n",
    "\n",
    "The limitation of this insight is that  even though we have manually checked that shows do exist in the dataset but the threshold value is an assumption that no movie can be greater than the threshold or no show can be smaller than the threshold. It is very much possible that there are shows that have runtime less than the movies and vice versa which makes the threshold technique wrong. Also our differentiation solely depends on one value (runtime) which is not a good practice when collecting insights.\n",
    "\n",
    "###### Note:\n",
    "Although we used almost all the columns for our advantage but their were few which were untouched i.e titles, release_date, production companies etc.Had we predicted that these were not to be used in our analysis , we would have dropped them from the start. This would have made our data more compact and easier to look at. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submitting your Project \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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