IMDb+Movie+Assignment_stub_pynb_file.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtering out the warnings\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = blue> IMDb Movie Assignment </font>\n",
    "\n",
    "You have the data for the 100 top-rated movies from the past decade along with various pieces of information about the movie, its actors, and the voters who have rated these movies online. In this assignment, you will try to find some interesting insights into these movies and their voters, using Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Task 1: Reading the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Subtask 1.1: Read the Movies Data.\n",
    "\n",
    "Read the movies data file provided and store it in a dataframe `movies`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>budget</th>\n",
       "      <th>Gross</th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_3_name</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>...</th>\n",
       "      <th>Votes3044M</th>\n",
       "      <th>Votes3044F</th>\n",
       "      <th>Votes45A</th>\n",
       "      <th>Votes45AM</th>\n",
       "      <th>Votes45AF</th>\n",
       "      <th>Votes1000</th>\n",
       "      <th>VotesUS</th>\n",
       "      <th>VotesnUS</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>La La Land</td>\n",
       "      <td>2016</td>\n",
       "      <td>30000000</td>\n",
       "      <td>151101803</td>\n",
       "      <td>Ryan Gosling</td>\n",
       "      <td>Emma Stone</td>\n",
       "      <td>Amiée Conn</td>\n",
       "      <td>14000</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zootopia</td>\n",
       "      <td>2016</td>\n",
       "      <td>150000000</td>\n",
       "      <td>341268248</td>\n",
       "      <td>Ginnifer Goodwin</td>\n",
       "      <td>Jason Bateman</td>\n",
       "      <td>Idris Elba</td>\n",
       "      <td>2800</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lion</td>\n",
       "      <td>2016</td>\n",
       "      <td>12000000</td>\n",
       "      <td>51738905</td>\n",
       "      <td>Dev Patel</td>\n",
       "      <td>Nicole Kidman</td>\n",
       "      <td>Rooney Mara</td>\n",
       "      <td>33000</td>\n",
       "      <td>96000.0</td>\n",
       "      <td>9800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arrival</td>\n",
       "      <td>2016</td>\n",
       "      <td>47000000</td>\n",
       "      <td>100546139</td>\n",
       "      <td>Amy Adams</td>\n",
       "      <td>Jeremy Renner</td>\n",
       "      <td>Forest Whitaker</td>\n",
       "      <td>35000</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Manchester by the Sea</td>\n",
       "      <td>2016</td>\n",
       "      <td>9000000</td>\n",
       "      <td>47695371</td>\n",
       "      <td>Casey Affleck</td>\n",
       "      <td>Michelle Williams</td>\n",
       "      <td>Kyle Chandler</td>\n",
       "      <td>518</td>\n",
       "      <td>71000.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Title  title_year     budget      Gross      actor_1_name  \\\n",
       "0             La La Land        2016   30000000  151101803      Ryan Gosling   \n",
       "1               Zootopia        2016  150000000  341268248  Ginnifer Goodwin   \n",
       "2                   Lion        2016   12000000   51738905         Dev Patel   \n",
       "3                Arrival        2016   47000000  100546139         Amy Adams   \n",
       "4  Manchester by the Sea        2016    9000000   47695371     Casey Affleck   \n",
       "\n",
       "         actor_2_name     actor_3_name  actor_1_facebook_likes  \\\n",
       "0          Emma Stone       Amiée Conn                   14000   \n",
       "1       Jason Bateman       Idris Elba                    2800   \n",
       "2       Nicole Kidman      Rooney Mara                   33000   \n",
       "3       Jeremy Renner  Forest Whitaker                   35000   \n",
       "4  Michelle Williams     Kyle Chandler                     518   \n",
       "\n",
       "   actor_2_facebook_likes  actor_3_facebook_likes  ...  Votes3044M Votes3044F  \\\n",
       "0                 19000.0                     NaN  ...         7.9        7.8   \n",
       "1                 28000.0                 27000.0  ...         7.8        8.1   \n",
       "2                 96000.0                  9800.0  ...         7.9        8.2   \n",
       "3                  5300.0                     NaN  ...         7.8        7.8   \n",
       "4                 71000.0                  3300.0  ...         7.7        7.7   \n",
       "\n",
       "  Votes45A Votes45AM  Votes45AF  Votes1000  VotesUS  VotesnUS  content_rating  \\\n",
       "0      7.6       7.6        7.5        7.1      8.3       8.1           PG-13   \n",
       "1      7.8       7.8        8.1        7.6      8.0       8.0              PG   \n",
       "2      8.0       7.9        8.4        7.1      8.1       8.0           PG-13   \n",
       "3      7.6       7.6        7.7        7.3      8.0       7.9           PG-13   \n",
       "4      7.6       7.6        7.6        7.1      7.9       7.8               R   \n",
       "\n",
       "     Country  \n",
       "0        USA  \n",
       "1        USA  \n",
       "2  Australia  \n",
       "3        USA  \n",
       "4        USA  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the csv file using 'read_csv'. Please write your dataset location here.\n",
    "movies = pd.read_csv(\"Movie+Assignment+Data.csv\")\n",
    "\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ###  Subtask 1.2: Inspect the Dataframe\n",
    "\n",
    "Inspect the dataframe for dimensions, null-values, and summary of different numeric columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of rows and columns of the dataframe 'movies': (100, 62)\n"
     ]
    }
   ],
   "source": [
    "# Check the number of rows and columns in the dataframe\n",
    "\n",
    "print(\"number of rows and columns of the dataframe 'movies':\",movies.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column-wise info of the dataframe 'movies':\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 62 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Title                   100 non-null    object \n",
      " 1   title_year              100 non-null    int64  \n",
      " 2   budget                  100 non-null    int64  \n",
      " 3   Gross                   100 non-null    int64  \n",
      " 4   actor_1_name            100 non-null    object \n",
      " 5   actor_2_name            100 non-null    object \n",
      " 6   actor_3_name            100 non-null    object \n",
      " 7   actor_1_facebook_likes  100 non-null    int64  \n",
      " 8   actor_2_facebook_likes  99 non-null     float64\n",
      " 9   actor_3_facebook_likes  98 non-null     float64\n",
      " 10  IMDb_rating             100 non-null    float64\n",
      " 11  genre_1                 100 non-null    object \n",
      " 12  genre_2                 97 non-null     object \n",
      " 13  genre_3                 74 non-null     object \n",
      " 14  MetaCritic              95 non-null     float64\n",
      " 15  Runtime                 100 non-null    int64  \n",
      " 16  CVotes10                100 non-null    int64  \n",
      " 17  CVotes09                100 non-null    int64  \n",
      " 18  CVotes08                100 non-null    int64  \n",
      " 19  CVotes07                100 non-null    int64  \n",
      " 20  CVotes06                100 non-null    int64  \n",
      " 21  CVotes05                100 non-null    int64  \n",
      " 22  CVotes04                100 non-null    int64  \n",
      " 23  CVotes03                100 non-null    int64  \n",
      " 24  CVotes02                100 non-null    int64  \n",
      " 25  CVotes01                100 non-null    int64  \n",
      " 26  CVotesMale              100 non-null    int64  \n",
      " 27  CVotesFemale            100 non-null    int64  \n",
      " 28  CVotesU18               100 non-null    int64  \n",
      " 29  CVotesU18M              100 non-null    int64  \n",
      " 30  CVotesU18F              100 non-null    int64  \n",
      " 31  CVotes1829              100 non-null    int64  \n",
      " 32  CVotes1829M             100 non-null    int64  \n",
      " 33  CVotes1829F             100 non-null    int64  \n",
      " 34  CVotes3044              100 non-null    int64  \n",
      " 35  CVotes3044M             100 non-null    int64  \n",
      " 36  CVotes3044F             100 non-null    int64  \n",
      " 37  CVotes45A               100 non-null    int64  \n",
      " 38  CVotes45AM              100 non-null    int64  \n",
      " 39  CVotes45AF              100 non-null    int64  \n",
      " 40  CVotes1000              100 non-null    int64  \n",
      " 41  CVotesUS                100 non-null    int64  \n",
      " 42  CVotesnUS               100 non-null    int64  \n",
      " 43  VotesM                  100 non-null    float64\n",
      " 44  VotesF                  100 non-null    float64\n",
      " 45  VotesU18                100 non-null    float64\n",
      " 46  VotesU18M               100 non-null    float64\n",
      " 47  VotesU18F               100 non-null    float64\n",
      " 48  Votes1829               100 non-null    float64\n",
      " 49  Votes1829M              100 non-null    float64\n",
      " 50  Votes1829F              100 non-null    float64\n",
      " 51  Votes3044               100 non-null    float64\n",
      " 52  Votes3044M              100 non-null    float64\n",
      " 53  Votes3044F              100 non-null    float64\n",
      " 54  Votes45A                100 non-null    float64\n",
      " 55  Votes45AM               100 non-null    float64\n",
      " 56  Votes45AF               100 non-null    float64\n",
      " 57  Votes1000               100 non-null    float64\n",
      " 58  VotesUS                 100 non-null    float64\n",
      " 59  VotesnUS                100 non-null    float64\n",
      " 60  content_rating          100 non-null    object \n",
      " 61  Country                 100 non-null    object \n",
      "dtypes: float64(21), int64(32), object(9)\n",
      "memory usage: 48.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check the column-wise info of the dataframe\n",
    "\n",
    "print(\"column-wise info of the dataframe 'movies':\\n\")\n",
    "\n",
    "movies.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title_year</th>\n",
       "      <th>budget</th>\n",
       "      <th>Gross</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>IMDb_rating</th>\n",
       "      <th>MetaCritic</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>CVotes10</th>\n",
       "      <th>...</th>\n",
       "      <th>Votes1829F</th>\n",
       "      <th>Votes3044</th>\n",
       "      <th>Votes3044M</th>\n",
       "      <th>Votes3044F</th>\n",
       "      <th>Votes45A</th>\n",
       "      <th>Votes45AM</th>\n",
       "      <th>Votes45AF</th>\n",
       "      <th>Votes1000</th>\n",
       "      <th>VotesUS</th>\n",
       "      <th>VotesnUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2012.820000</td>\n",
       "      <td>7.838400e+07</td>\n",
       "      <td>1.468679e+08</td>\n",
       "      <td>13407.270000</td>\n",
       "      <td>7377.303030</td>\n",
       "      <td>3002.153061</td>\n",
       "      <td>7.883000</td>\n",
       "      <td>78.252632</td>\n",
       "      <td>126.420000</td>\n",
       "      <td>73212.160000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.982000</td>\n",
       "      <td>7.732000</td>\n",
       "      <td>7.723000</td>\n",
       "      <td>7.780000</td>\n",
       "      <td>7.65100</td>\n",
       "      <td>7.624000</td>\n",
       "      <td>7.770000</td>\n",
       "      <td>7.274000</td>\n",
       "      <td>7.958000</td>\n",
       "      <td>7.793000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.919491</td>\n",
       "      <td>7.445295e+07</td>\n",
       "      <td>1.454004e+08</td>\n",
       "      <td>10649.037862</td>\n",
       "      <td>13471.568216</td>\n",
       "      <td>6940.301133</td>\n",
       "      <td>0.247433</td>\n",
       "      <td>9.122066</td>\n",
       "      <td>19.050799</td>\n",
       "      <td>82669.594746</td>\n",
       "      <td>...</td>\n",
       "      <td>0.321417</td>\n",
       "      <td>0.251814</td>\n",
       "      <td>0.260479</td>\n",
       "      <td>0.282128</td>\n",
       "      <td>0.21485</td>\n",
       "      <td>0.213258</td>\n",
       "      <td>0.301344</td>\n",
       "      <td>0.361987</td>\n",
       "      <td>0.232327</td>\n",
       "      <td>0.264099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>3.000000e+06</td>\n",
       "      <td>2.238380e+05</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>6420.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.10000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2011.000000</td>\n",
       "      <td>1.575000e+07</td>\n",
       "      <td>4.199752e+07</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>580.000000</td>\n",
       "      <td>319.750000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>114.750000</td>\n",
       "      <td>30587.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>7.50000</td>\n",
       "      <td>7.475000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>7.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2013.000000</td>\n",
       "      <td>4.225000e+07</td>\n",
       "      <td>1.070266e+08</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>626.500000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>54900.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>7.65000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>7.950000</td>\n",
       "      <td>7.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1.500000e+08</td>\n",
       "      <td>2.107548e+08</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>11000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>83.500000</td>\n",
       "      <td>136.250000</td>\n",
       "      <td>80639.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.80000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>7.925000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>7.925000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2.600000e+08</td>\n",
       "      <td>9.366622e+08</td>\n",
       "      <td>35000.000000</td>\n",
       "      <td>96000.000000</td>\n",
       "      <td>46000.000000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>584839.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>8.10000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>8.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        title_year        budget         Gross  actor_1_facebook_likes  \\\n",
       "count   100.000000  1.000000e+02  1.000000e+02              100.000000   \n",
       "mean   2012.820000  7.838400e+07  1.468679e+08            13407.270000   \n",
       "std       1.919491  7.445295e+07  1.454004e+08            10649.037862   \n",
       "min    2010.000000  3.000000e+06  2.238380e+05               39.000000   \n",
       "25%    2011.000000  1.575000e+07  4.199752e+07             1000.000000   \n",
       "50%    2013.000000  4.225000e+07  1.070266e+08            13000.000000   \n",
       "75%    2014.000000  1.500000e+08  2.107548e+08            20000.000000   \n",
       "max    2016.000000  2.600000e+08  9.366622e+08            35000.000000   \n",
       "\n",
       "       actor_2_facebook_likes  actor_3_facebook_likes  IMDb_rating  \\\n",
       "count               99.000000               98.000000   100.000000   \n",
       "mean              7377.303030             3002.153061     7.883000   \n",
       "std              13471.568216             6940.301133     0.247433   \n",
       "min                 12.000000                0.000000     7.500000   \n",
       "25%                580.000000              319.750000     7.700000   \n",
       "50%               1000.000000              626.500000     7.800000   \n",
       "75%              11000.000000             1000.000000     8.100000   \n",
       "max              96000.000000            46000.000000     8.800000   \n",
       "\n",
       "       MetaCritic     Runtime       CVotes10  ...  Votes1829F   Votes3044  \\\n",
       "count   95.000000  100.000000     100.000000  ...  100.000000  100.000000   \n",
       "mean    78.252632  126.420000   73212.160000  ...    7.982000    7.732000   \n",
       "std      9.122066   19.050799   82669.594746  ...    0.321417    0.251814   \n",
       "min     62.000000   91.000000    6420.000000  ...    7.300000    7.300000   \n",
       "25%     72.000000  114.750000   30587.000000  ...    7.700000    7.600000   \n",
       "50%     78.000000  124.000000   54900.500000  ...    8.000000    7.700000   \n",
       "75%     83.500000  136.250000   80639.000000  ...    8.200000    7.900000   \n",
       "max    100.000000  180.000000  584839.000000  ...    8.800000    8.700000   \n",
       "\n",
       "       Votes3044M  Votes3044F   Votes45A   Votes45AM   Votes45AF   Votes1000  \\\n",
       "count  100.000000  100.000000  100.00000  100.000000  100.000000  100.000000   \n",
       "mean     7.723000    7.780000    7.65100    7.624000    7.770000    7.274000   \n",
       "std      0.260479    0.282128    0.21485    0.213258    0.301344    0.361987   \n",
       "min      7.200000    7.200000    7.10000    7.100000    7.000000    6.400000   \n",
       "25%      7.500000    7.600000    7.50000    7.475000    7.500000    7.100000   \n",
       "50%      7.700000    7.800000    7.65000    7.600000    7.800000    7.300000   \n",
       "75%      7.900000    8.000000    7.80000    7.800000    7.925000    7.500000   \n",
       "max      8.700000    8.500000    8.10000    8.100000    8.500000    8.200000   \n",
       "\n",
       "          VotesUS    VotesnUS  \n",
       "count  100.000000  100.000000  \n",
       "mean     7.958000    7.793000  \n",
       "std      0.232327    0.264099  \n",
       "min      7.500000    7.300000  \n",
       "25%      7.800000    7.600000  \n",
       "50%      7.950000    7.750000  \n",
       "75%      8.100000    7.925000  \n",
       "max      8.700000    8.800000  \n",
       "\n",
       "[8 rows x 53 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the summary for the numeric columns \n",
    "\n",
    "movies.describe(exclude='O')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2: Data Analysis\n",
    "\n",
    "Now that we have loaded the dataset and inspected it, we see that most of the data is in place. As of now, no data cleaning is required, so let's start with some data manipulation, analysis, and visualisation to get various insights about the data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  ###  Subtask 2.1: Reduce those Digits!\n",
    "\n",
    "These numbers in the `budget` and `gross` are too big, compromising its readability. Let's convert the unit of the `budget` and `gross` columns from `$` to `million $` first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Divide the 'gross' and 'budget' columns by 1000000 to convert '$' to 'million $'\n",
    "movies['Gross'] = (movies['Gross']/1000000)\n",
    "movies['budget'] = (movies['budget']/1000000)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  ###  Subtask 2.2: Let's Talk Profit!\n",
    "\n",
    "    1. Create a new column called `profit` which contains the difference of the two columns: `gross` and `budget`.\n",
    "    2. Sort the dataframe using the `profit` column as reference.\n",
    "    3. Extract the top ten profiting movies in descending order and store them in a new dataframe - `top10`.\n",
    "    4. Plot a scatter or a joint plot between the columns `budget` and `profit` and write a few words on what you observed.\n",
    "    5. Extract the movies with a negative profit and store them in a new dataframe - `neg_profit`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>budget</th>\n",
       "      <th>Gross</th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_3_name</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>...</th>\n",
       "      <th>Votes3044F</th>\n",
       "      <th>Votes45A</th>\n",
       "      <th>Votes45AM</th>\n",
       "      <th>Votes45AF</th>\n",
       "      <th>Votes1000</th>\n",
       "      <th>VotesUS</th>\n",
       "      <th>VotesnUS</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>Country</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>La La Land</td>\n",
       "      <td>2016</td>\n",
       "      <td>30.0</td>\n",
       "      <td>151.101803</td>\n",
       "      <td>Ryan Gosling</td>\n",
       "      <td>Emma Stone</td>\n",
       "      <td>Amiée Conn</td>\n",
       "      <td>14000</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>121.101803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zootopia</td>\n",
       "      <td>2016</td>\n",
       "      <td>150.0</td>\n",
       "      <td>341.268248</td>\n",
       "      <td>Ginnifer Goodwin</td>\n",
       "      <td>Jason Bateman</td>\n",
       "      <td>Idris Elba</td>\n",
       "      <td>2800</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>USA</td>\n",
       "      <td>191.268248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lion</td>\n",
       "      <td>2016</td>\n",
       "      <td>12.0</td>\n",
       "      <td>51.738905</td>\n",
       "      <td>Dev Patel</td>\n",
       "      <td>Nicole Kidman</td>\n",
       "      <td>Rooney Mara</td>\n",
       "      <td>33000</td>\n",
       "      <td>96000.0</td>\n",
       "      <td>9800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Australia</td>\n",
       "      <td>39.738905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arrival</td>\n",
       "      <td>2016</td>\n",
       "      <td>47.0</td>\n",
       "      <td>100.546139</td>\n",
       "      <td>Amy Adams</td>\n",
       "      <td>Jeremy Renner</td>\n",
       "      <td>Forest Whitaker</td>\n",
       "      <td>35000</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>53.546139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Manchester by the Sea</td>\n",
       "      <td>2016</td>\n",
       "      <td>9.0</td>\n",
       "      <td>47.695371</td>\n",
       "      <td>Casey Affleck</td>\n",
       "      <td>Michelle Williams</td>\n",
       "      <td>Kyle Chandler</td>\n",
       "      <td>518</td>\n",
       "      <td>71000.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>38.695371</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Title  title_year  budget       Gross      actor_1_name  \\\n",
       "0             La La Land        2016    30.0  151.101803      Ryan Gosling   \n",
       "1               Zootopia        2016   150.0  341.268248  Ginnifer Goodwin   \n",
       "2                   Lion        2016    12.0   51.738905         Dev Patel   \n",
       "3                Arrival        2016    47.0  100.546139         Amy Adams   \n",
       "4  Manchester by the Sea        2016     9.0   47.695371     Casey Affleck   \n",
       "\n",
       "         actor_2_name     actor_3_name  actor_1_facebook_likes  \\\n",
       "0          Emma Stone       Amiée Conn                   14000   \n",
       "1       Jason Bateman       Idris Elba                    2800   \n",
       "2       Nicole Kidman      Rooney Mara                   33000   \n",
       "3       Jeremy Renner  Forest Whitaker                   35000   \n",
       "4  Michelle Williams     Kyle Chandler                     518   \n",
       "\n",
       "   actor_2_facebook_likes  actor_3_facebook_likes  ...  Votes3044F Votes45A  \\\n",
       "0                 19000.0                     NaN  ...         7.8      7.6   \n",
       "1                 28000.0                 27000.0  ...         8.1      7.8   \n",
       "2                 96000.0                  9800.0  ...         8.2      8.0   \n",
       "3                  5300.0                     NaN  ...         7.8      7.6   \n",
       "4                 71000.0                  3300.0  ...         7.7      7.6   \n",
       "\n",
       "  Votes45AM Votes45AF  Votes1000  VotesUS  VotesnUS  content_rating  \\\n",
       "0       7.6       7.5        7.1      8.3       8.1           PG-13   \n",
       "1       7.8       8.1        7.6      8.0       8.0              PG   \n",
       "2       7.9       8.4        7.1      8.1       8.0           PG-13   \n",
       "3       7.6       7.7        7.3      8.0       7.9           PG-13   \n",
       "4       7.6       7.6        7.1      7.9       7.8               R   \n",
       "\n",
       "     Country      profit  \n",
       "0        USA  121.101803  \n",
       "1        USA  191.268248  \n",
       "2  Australia   39.738905  \n",
       "3        USA   53.546139  \n",
       "4        USA   38.695371  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the new column named 'profit' by subtracting the 'budget' column from the 'gross' column\n",
    "\n",
    "movies['profit'] = movies['Gross'] - movies['budget']\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>budget</th>\n",
       "      <th>Gross</th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>actor_2_name</th>\n",
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       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>...</th>\n",
       "      <th>Votes3044F</th>\n",
       "      <th>Votes45A</th>\n",
       "      <th>Votes45AM</th>\n",
       "      <th>Votes45AF</th>\n",
       "      <th>Votes1000</th>\n",
       "      <th>VotesUS</th>\n",
       "      <th>VotesnUS</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>Country</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
       "      <td>2015</td>\n",
       "      <td>245.0</td>\n",
       "      <td>936.662225</td>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>Rob Walker</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>691.662225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>The Avengers</td>\n",
       "      <td>2012</td>\n",
       "      <td>220.0</td>\n",
       "      <td>623.279547</td>\n",
       "      <td>Chris Hemsworth</td>\n",
       "      <td>Robert Downey Jr.</td>\n",
       "      <td>Scarlett Johansson</td>\n",
       "      <td>26000</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>403.279547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Deadpool</td>\n",
       "      <td>2016</td>\n",
       "      <td>58.0</td>\n",
       "      <td>363.024263</td>\n",
       "      <td>Ryan Reynolds</td>\n",
       "      <td>Ed Skrein</td>\n",
       "      <td>Stefan Kapicic</td>\n",
       "      <td>16000</td>\n",
       "      <td>805.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>305.024263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>The Hunger Games: Catching Fire</td>\n",
       "      <td>2013</td>\n",
       "      <td>130.0</td>\n",
       "      <td>424.645577</td>\n",
       "      <td>Jennifer Lawrence</td>\n",
       "      <td>Josh Hutcherson</td>\n",
       "      <td>Sandra Ellis Lafferty</td>\n",
       "      <td>34000</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>523.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.4</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>294.645577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>2010</td>\n",
       "      <td>200.0</td>\n",
       "      <td>414.984497</td>\n",
       "      <td>Tom Hanks</td>\n",
       "      <td>John Ratzenberger</td>\n",
       "      <td>Don Rickles</td>\n",
       "      <td>15000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>721.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>G</td>\n",
       "      <td>USA</td>\n",
       "      <td>214.984497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Title  title_year  budget  \\\n",
       "97  Star Wars: Episode VII - The Force Awakens        2015   245.0   \n",
       "11                                The Avengers        2012   220.0   \n",
       "47                                    Deadpool        2016    58.0   \n",
       "32             The Hunger Games: Catching Fire        2013   130.0   \n",
       "12                                 Toy Story 3        2010   200.0   \n",
       "\n",
       "         Gross       actor_1_name       actor_2_name           actor_3_name  \\\n",
       "97  936.662225        Doug Walker         Rob Walker                      0   \n",
       "11  623.279547    Chris Hemsworth  Robert Downey Jr.     Scarlett Johansson   \n",
       "47  363.024263      Ryan Reynolds          Ed Skrein         Stefan Kapicic   \n",
       "32  424.645577  Jennifer Lawrence    Josh Hutcherson  Sandra Ellis Lafferty   \n",
       "12  414.984497          Tom Hanks  John Ratzenberger            Don Rickles   \n",
       "\n",
       "    actor_1_facebook_likes  actor_2_facebook_likes  actor_3_facebook_likes  \\\n",
       "97                     131                    12.0                     0.0   \n",
       "11                   26000                 21000.0                 19000.0   \n",
       "47                   16000                   805.0                   361.0   \n",
       "32                   34000                 14000.0                   523.0   \n",
       "12                   15000                  1000.0                   721.0   \n",
       "\n",
       "    ...  Votes3044F Votes45A Votes45AM Votes45AF  Votes1000  VotesUS  \\\n",
       "97  ...         8.2      7.9       7.8       8.2        7.7      8.2   \n",
       "11  ...         8.1      7.9       7.9       8.1        7.4      8.3   \n",
       "47  ...         7.9      7.8       7.8       7.9        7.3      8.1   \n",
       "32  ...         7.9      7.3       7.2       7.9        6.7      7.7   \n",
       "12  ...         8.3      8.1       8.1       8.1        8.1      8.5   \n",
       "\n",
       "    VotesnUS  content_rating  Country      profit  \n",
       "97       7.9           PG-13      USA  691.662225  \n",
       "11       7.9           PG-13      USA  403.279547  \n",
       "47       7.9               R      USA  305.024263  \n",
       "32       7.4           PG-13      USA  294.645577  \n",
       "12       8.3               G      USA  214.984497  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataframe with the 'profit' column as reference using the 'sort_values' function. Make sure to set the argument\n",
    "#'ascending' to 'False'\n",
    "\n",
    "movies.sort_values('profit', ascending = False, inplace=True) \n",
    "movies.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>budget</th>\n",
       "      <th>Gross</th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_3_name</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>...</th>\n",
       "      <th>Votes3044F</th>\n",
       "      <th>Votes45A</th>\n",
       "      <th>Votes45AM</th>\n",
       "      <th>Votes45AF</th>\n",
       "      <th>Votes1000</th>\n",
       "      <th>VotesUS</th>\n",
       "      <th>VotesnUS</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>Country</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
       "      <td>2015</td>\n",
       "      <td>245.0</td>\n",
       "      <td>936.662225</td>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>Rob Walker</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>691.662225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>The Avengers</td>\n",
       "      <td>2012</td>\n",
       "      <td>220.0</td>\n",
       "      <td>623.279547</td>\n",
       "      <td>Chris Hemsworth</td>\n",
       "      <td>Robert Downey Jr.</td>\n",
       "      <td>Scarlett Johansson</td>\n",
       "      <td>26000</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>403.279547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Deadpool</td>\n",
       "      <td>2016</td>\n",
       "      <td>58.0</td>\n",
       "      <td>363.024263</td>\n",
       "      <td>Ryan Reynolds</td>\n",
       "      <td>Ed Skrein</td>\n",
       "      <td>Stefan Kapicic</td>\n",
       "      <td>16000</td>\n",
       "      <td>805.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>305.024263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>The Hunger Games: Catching Fire</td>\n",
       "      <td>2013</td>\n",
       "      <td>130.0</td>\n",
       "      <td>424.645577</td>\n",
       "      <td>Jennifer Lawrence</td>\n",
       "      <td>Josh Hutcherson</td>\n",
       "      <td>Sandra Ellis Lafferty</td>\n",
       "      <td>34000</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>523.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.4</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>294.645577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>2010</td>\n",
       "      <td>200.0</td>\n",
       "      <td>414.984497</td>\n",
       "      <td>Tom Hanks</td>\n",
       "      <td>John Ratzenberger</td>\n",
       "      <td>Don Rickles</td>\n",
       "      <td>15000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>721.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>G</td>\n",
       "      <td>USA</td>\n",
       "      <td>214.984497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>2012</td>\n",
       "      <td>250.0</td>\n",
       "      <td>448.130642</td>\n",
       "      <td>Tom Hardy</td>\n",
       "      <td>Christian Bale</td>\n",
       "      <td>Joseph Gordon-Levitt</td>\n",
       "      <td>27000</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>198.130642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>The Lego Movie</td>\n",
       "      <td>2014</td>\n",
       "      <td>60.0</td>\n",
       "      <td>257.756197</td>\n",
       "      <td>Morgan Freeman</td>\n",
       "      <td>Will Ferrell</td>\n",
       "      <td>Alison Brie</td>\n",
       "      <td>11000</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>PG</td>\n",
       "      <td>Australia</td>\n",
       "      <td>197.756197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zootopia</td>\n",
       "      <td>2016</td>\n",
       "      <td>150.0</td>\n",
       "      <td>341.268248</td>\n",
       "      <td>Ginnifer Goodwin</td>\n",
       "      <td>Jason Bateman</td>\n",
       "      <td>Idris Elba</td>\n",
       "      <td>2800</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>USA</td>\n",
       "      <td>191.268248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Despicable Me</td>\n",
       "      <td>2010</td>\n",
       "      <td>69.0</td>\n",
       "      <td>251.501645</td>\n",
       "      <td>Steve Carell</td>\n",
       "      <td>Miranda Cosgrove</td>\n",
       "      <td>Jack McBrayer</td>\n",
       "      <td>7000</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>975.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>PG</td>\n",
       "      <td>USA</td>\n",
       "      <td>182.501645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Inside Out</td>\n",
       "      <td>2015</td>\n",
       "      <td>175.0</td>\n",
       "      <td>356.454367</td>\n",
       "      <td>Amy Poehler</td>\n",
       "      <td>Mindy Kaling</td>\n",
       "      <td>Phyllis Smith</td>\n",
       "      <td>1000</td>\n",
       "      <td>767.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>PG</td>\n",
       "      <td>USA</td>\n",
       "      <td>181.454367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Title  title_year  budget  \\\n",
       "97  Star Wars: Episode VII - The Force Awakens        2015   245.0   \n",
       "11                                The Avengers        2012   220.0   \n",
       "47                                    Deadpool        2016    58.0   \n",
       "32             The Hunger Games: Catching Fire        2013   130.0   \n",
       "12                                 Toy Story 3        2010   200.0   \n",
       "8                        The Dark Knight Rises        2012   250.0   \n",
       "45                              The Lego Movie        2014    60.0   \n",
       "1                                     Zootopia        2016   150.0   \n",
       "41                               Despicable Me        2010    69.0   \n",
       "18                                  Inside Out        2015   175.0   \n",
       "\n",
       "         Gross       actor_1_name       actor_2_name           actor_3_name  \\\n",
       "97  936.662225        Doug Walker         Rob Walker                      0   \n",
       "11  623.279547    Chris Hemsworth  Robert Downey Jr.     Scarlett Johansson   \n",
       "47  363.024263      Ryan Reynolds          Ed Skrein         Stefan Kapicic   \n",
       "32  424.645577  Jennifer Lawrence    Josh Hutcherson  Sandra Ellis Lafferty   \n",
       "12  414.984497          Tom Hanks  John Ratzenberger            Don Rickles   \n",
       "8   448.130642          Tom Hardy     Christian Bale   Joseph Gordon-Levitt   \n",
       "45  257.756197     Morgan Freeman       Will Ferrell            Alison Brie   \n",
       "1   341.268248   Ginnifer Goodwin      Jason Bateman             Idris Elba   \n",
       "41  251.501645       Steve Carell   Miranda Cosgrove          Jack McBrayer   \n",
       "18  356.454367        Amy Poehler       Mindy Kaling          Phyllis Smith   \n",
       "\n",
       "    actor_1_facebook_likes  actor_2_facebook_likes  actor_3_facebook_likes  \\\n",
       "97                     131                    12.0                     0.0   \n",
       "11                   26000                 21000.0                 19000.0   \n",
       "47                   16000                   805.0                   361.0   \n",
       "32                   34000                 14000.0                   523.0   \n",
       "12                   15000                  1000.0                   721.0   \n",
       "8                    27000                 23000.0                 23000.0   \n",
       "45                   11000                  8000.0                  2000.0   \n",
       "1                     2800                 28000.0                 27000.0   \n",
       "41                    7000                  2000.0                   975.0   \n",
       "18                    1000                   767.0                   384.0   \n",
       "\n",
       "    ...  Votes3044F Votes45A Votes45AM Votes45AF  Votes1000  VotesUS  \\\n",
       "97  ...         8.2      7.9       7.8       8.2        7.7      8.2   \n",
       "11  ...         8.1      7.9       7.9       8.1        7.4      8.3   \n",
       "47  ...         7.9      7.8       7.8       7.9        7.3      8.1   \n",
       "32  ...         7.9      7.3       7.2       7.9        6.7      7.7   \n",
       "12  ...         8.3      8.1       8.1       8.1        8.1      8.5   \n",
       "8   ...         8.2      7.9       7.9       7.9        7.8      8.4   \n",
       "45  ...         7.5      7.4       7.4       7.4        7.2      8.0   \n",
       "1   ...         8.1      7.8       7.8       8.1        7.6      8.0   \n",
       "41  ...         7.9      7.6       7.5       7.9        7.0      7.6   \n",
       "18  ...         8.1      7.9       7.9       7.9        7.6      8.2   \n",
       "\n",
       "    VotesnUS  content_rating    Country      profit  \n",
       "97       7.9           PG-13        USA  691.662225  \n",
       "11       7.9           PG-13        USA  403.279547  \n",
       "47       7.9               R        USA  305.024263  \n",
       "32       7.4           PG-13        USA  294.645577  \n",
       "12       8.3               G        USA  214.984497  \n",
       "8        8.4           PG-13        USA  198.130642  \n",
       "45       7.6              PG  Australia  197.756197  \n",
       "1        8.0              PG        USA  191.268248  \n",
       "41       7.6              PG        USA  182.501645  \n",
       "18       8.1              PG        USA  181.454367  \n",
       "\n",
       "[10 rows x 63 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the top 10 profitable movies by using position based indexing. Specify the rows till 10 (0-9)\n",
    "top10=movies.iloc[:10,]\n",
    "top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot profit vs budget\n",
    "plt.scatter(movies['profit'], movies['budget'])\n",
    "plt.xlabel('Profit')\n",
    "plt.ylabel('Budget')\n",
    "plt.title('profit vs budget', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As per the plot drawn for profit vs budget, the movie title 'Star Wars: Episode VII - The Force Awakens' has earned the highest profit. Plot reflects that profit is not at all dependent on Budget, there are many movies which have high budget but have failed to make profit out of it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains the 100 best performing movies from the year 2010 to 2016. However, the scatter plot tells a different story. You can notice that there are some movies with negative profit. Although good movies do incur losses, but there appear to be quite a few movie with losses. What can be the reason behind this? Lets have a closer look at this by finding the movies with negative profit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>budget</th>\n",
       "      <th>Gross</th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_3_name</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>...</th>\n",
       "      <th>Votes3044F</th>\n",
       "      <th>Votes45A</th>\n",
       "      <th>Votes45AM</th>\n",
       "      <th>Votes45AF</th>\n",
       "      <th>Votes1000</th>\n",
       "      <th>VotesUS</th>\n",
       "      <th>VotesnUS</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>Country</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Tucker and Dale vs Evil</td>\n",
       "      <td>2010</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.223838</td>\n",
       "      <td>Katrina Bowden</td>\n",
       "      <td>Tyler Labine</td>\n",
       "      <td>Chelan Simmons</td>\n",
       "      <td>948</td>\n",
       "      <td>779.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>R</td>\n",
       "      <td>Canada</td>\n",
       "      <td>-4.776162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Amour</td>\n",
       "      <td>2012</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.225377</td>\n",
       "      <td>Isabelle Huppert</td>\n",
       "      <td>Emmanuelle Riva</td>\n",
       "      <td>Jean-Louis Trintignant</td>\n",
       "      <td>678</td>\n",
       "      <td>432.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>France</td>\n",
       "      <td>-8.674623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Rush</td>\n",
       "      <td>2013</td>\n",
       "      <td>38.0</td>\n",
       "      <td>26.903709</td>\n",
       "      <td>Chris Hemsworth</td>\n",
       "      <td>Olivia Wilde</td>\n",
       "      <td>Alexandra Maria Lara</td>\n",
       "      <td>26000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>R</td>\n",
       "      <td>UK</td>\n",
       "      <td>-11.096291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Warrior</td>\n",
       "      <td>2011</td>\n",
       "      <td>25.0</td>\n",
       "      <td>13.651662</td>\n",
       "      <td>Tom Hardy</td>\n",
       "      <td>Frank Grillo</td>\n",
       "      <td>Kevin Dunn</td>\n",
       "      <td>27000</td>\n",
       "      <td>798.0</td>\n",
       "      <td>581.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>-11.348338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Flipped</td>\n",
       "      <td>2010</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.752214</td>\n",
       "      <td>Madeline Carroll</td>\n",
       "      <td>Rebecca De Mornay</td>\n",
       "      <td>Aidan Quinn</td>\n",
       "      <td>1000</td>\n",
       "      <td>872.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.6</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.7</td>\n",
       "      <td>PG</td>\n",
       "      <td>USA</td>\n",
       "      <td>-12.247786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>X-Men: First Class</td>\n",
       "      <td>2011</td>\n",
       "      <td>160.0</td>\n",
       "      <td>146.405371</td>\n",
       "      <td>Jennifer Lawrence</td>\n",
       "      <td>Michael Fassbender</td>\n",
       "      <td>Oliver Platt</td>\n",
       "      <td>34000</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>-13.594629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Scott Pilgrim vs. the World</td>\n",
       "      <td>2010</td>\n",
       "      <td>60.0</td>\n",
       "      <td>31.494270</td>\n",
       "      <td>Anna Kendrick</td>\n",
       "      <td>Kieran Culkin</td>\n",
       "      <td>Ellen Wong</td>\n",
       "      <td>10000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>719.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>-28.505730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tangled</td>\n",
       "      <td>2010</td>\n",
       "      <td>260.0</td>\n",
       "      <td>200.807262</td>\n",
       "      <td>Brad Garrett</td>\n",
       "      <td>Donna Murphy</td>\n",
       "      <td>M.C. Gainey</td>\n",
       "      <td>799</td>\n",
       "      <td>553.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>PG</td>\n",
       "      <td>USA</td>\n",
       "      <td>-59.192738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Edge of Tomorrow</td>\n",
       "      <td>2014</td>\n",
       "      <td>178.0</td>\n",
       "      <td>100.189501</td>\n",
       "      <td>Tom Cruise</td>\n",
       "      <td>Lara Pulver</td>\n",
       "      <td>Noah Taylor</td>\n",
       "      <td>10000</td>\n",
       "      <td>854.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>-77.810499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>The Little Prince</td>\n",
       "      <td>2015</td>\n",
       "      <td>81.2</td>\n",
       "      <td>1.339152</td>\n",
       "      <td>Jeff Bridges</td>\n",
       "      <td>James Franco</td>\n",
       "      <td>Mackenzie Foy</td>\n",
       "      <td>12000</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>PG</td>\n",
       "      <td>France</td>\n",
       "      <td>-79.860848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Hugo</td>\n",
       "      <td>2011</td>\n",
       "      <td>170.0</td>\n",
       "      <td>73.820094</td>\n",
       "      <td>ChloÃ« Grace Moretz</td>\n",
       "      <td>Christopher Lee</td>\n",
       "      <td>Ray Winstone</td>\n",
       "      <td>17000</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>PG</td>\n",
       "      <td>USA</td>\n",
       "      <td>-96.179906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Title  title_year  budget       Gross  \\\n",
       "99      Tucker and Dale vs Evil        2010     5.0    0.223838   \n",
       "89                        Amour        2012     8.9    0.225377   \n",
       "56                         Rush        2013    38.0   26.903709   \n",
       "66                      Warrior        2011    25.0   13.651662   \n",
       "82                      Flipped        2010    14.0    1.752214   \n",
       "28           X-Men: First Class        2011   160.0  146.405371   \n",
       "46  Scott Pilgrim vs. the World        2010    60.0   31.494270   \n",
       "7                       Tangled        2010   260.0  200.807262   \n",
       "17             Edge of Tomorrow        2014   178.0  100.189501   \n",
       "39            The Little Prince        2015    81.2    1.339152   \n",
       "22                         Hugo        2011   170.0   73.820094   \n",
       "\n",
       "           actor_1_name        actor_2_name            actor_3_name  \\\n",
       "99       Katrina Bowden        Tyler Labine          Chelan Simmons   \n",
       "89     Isabelle Huppert     Emmanuelle Riva  Jean-Louis Trintignant   \n",
       "56      Chris Hemsworth        Olivia Wilde    Alexandra Maria Lara   \n",
       "66            Tom Hardy        Frank Grillo              Kevin Dunn   \n",
       "82     Madeline Carroll   Rebecca De Mornay             Aidan Quinn   \n",
       "28    Jennifer Lawrence  Michael Fassbender            Oliver Platt   \n",
       "46        Anna Kendrick       Kieran Culkin              Ellen Wong   \n",
       "7          Brad Garrett        Donna Murphy             M.C. Gainey   \n",
       "17           Tom Cruise         Lara Pulver             Noah Taylor   \n",
       "39         Jeff Bridges        James Franco           Mackenzie Foy   \n",
       "22  ChloÃ« Grace Moretz     Christopher Lee            Ray Winstone   \n",
       "\n",
       "    actor_1_facebook_likes  actor_2_facebook_likes  actor_3_facebook_likes  \\\n",
       "99                     948                   779.0                   440.0   \n",
       "89                     678                   432.0                   319.0   \n",
       "56                   26000                 10000.0                   471.0   \n",
       "66                   27000                   798.0                   581.0   \n",
       "82                    1000                   872.0                   767.0   \n",
       "28                   34000                 13000.0                  1000.0   \n",
       "46                   10000                  1000.0                   719.0   \n",
       "7                      799                   553.0                   284.0   \n",
       "17                   10000                   854.0                   509.0   \n",
       "39                   12000                 11000.0                  6000.0   \n",
       "22                   17000                 16000.0                  1000.0   \n",
       "\n",
       "    ...  Votes3044F Votes45A Votes45AM Votes45AF  Votes1000  VotesUS  \\\n",
       "99  ...         7.7      7.5       7.4       7.7        7.1      7.7   \n",
       "89  ...         7.9      7.9       7.8       8.1        7.2      7.9   \n",
       "56  ...         7.9      7.8       7.8       7.8        7.1      7.9   \n",
       "66  ...         8.0      7.7       7.7       7.5        7.1      8.2   \n",
       "82  ...         7.7      7.4       7.3       7.6        6.4      7.5   \n",
       "28  ...         7.8      7.6       7.5       7.7        7.3      7.8   \n",
       "46  ...         7.2      7.1       7.1       7.0        6.6      7.8   \n",
       "7   ...         8.0      7.7       7.6       7.9        6.9      7.9   \n",
       "17  ...         7.7      7.8       7.8       7.8        7.5      8.0   \n",
       "39  ...         7.9      7.5       7.4       7.9        6.6      7.7   \n",
       "22  ...         7.4      7.5       7.5       7.6        7.4      7.7   \n",
       "\n",
       "    VotesnUS  content_rating  Country     profit  \n",
       "99       7.5               R   Canada  -4.776162  \n",
       "89       7.8           PG-13   France  -8.674623  \n",
       "56       8.1               R       UK -11.096291  \n",
       "66       8.1           PG-13      USA -11.348338  \n",
       "82       7.7              PG      USA -12.247786  \n",
       "28       7.7           PG-13      USA -13.594629  \n",
       "46       7.4           PG-13      USA -28.505730  \n",
       "7        7.7              PG      USA -59.192738  \n",
       "17       7.8           PG-13      USA -77.810499  \n",
       "39       7.7              PG   France -79.860848  \n",
       "22       7.5              PG      USA -96.179906  \n",
       "\n",
       "[11 rows x 63 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the movies with negative profit\n",
    "neg_profit= movies[movies['profit'] < 0]\n",
    "neg_profit\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`Checkpoint 1:`** Can you spot the movie `Tangled` in the dataset? You may be aware of the movie 'Tangled'. Although its one of the highest grossing movies of all time, it has negative profit as per this result. If you cross check the gross values of this movie (link: https://www.imdb.com/title/tt0398286/), you can see that the gross in the dataset accounts only for the domestic gross and not the worldwide gross. This is true for may other movies also in the list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Subtask 2.3: The General Audience and the Critics\n",
    "\n",
    "You might have noticed the column `MetaCritic` in this dataset. This is a very popular website where an average score is determined through the scores given by the top-rated critics. Second, you also have another column `IMDb_rating` which tells you the IMDb rating of a movie. This rating is determined by taking the average of hundred-thousands of ratings from the general audience. \n",
    "\n",
    "As a part of this subtask, you are required to find out the highest rated movies which have been liked by critics and audiences alike.\n",
    "1. Firstly you will notice that the `MetaCritic` score is on a scale of `100` whereas the `IMDb_rating` is on a scale of 10. First convert the `MetaCritic` column to a scale of 10.\n",
    "2. Now, to find out the movies which have been liked by both critics and audiences alike and also have a high rating overall, you need to -\n",
    "    - Create a new column `Avg_rating` which will have the average of the `MetaCritic` and `Rating` columns\n",
    "    - Retain only the movies in which the absolute difference(using abs() function) between the `IMDb_rating` and `Metacritic` columns is less than 0.5. Refer to this link to know how abs() funtion works - https://www.geeksforgeeks.org/abs-in-python/ .\n",
    "    - Sort these values in a descending order of `Avg_rating` and retain only the movies with a rating equal to or greater than `8` and store these movies in a new dataframe `UniversalAcclaim`.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97    8.1\n",
       "11    6.9\n",
       "47    6.5\n",
       "32    7.6\n",
       "12    9.2\n",
       "     ... \n",
       "46    6.9\n",
       "7     7.1\n",
       "17    7.1\n",
       "39    7.0\n",
       "22    8.3\n",
       "Name: MetaCritic, Length: 100, dtype: float64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change the scale of MetaCritic\n",
    "\n",
    "movies['MetaCritic']=movies['MetaCritic']/10\n",
    "movies['MetaCritic']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>budget</th>\n",
       "      <th>Gross</th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_3_name</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>...</th>\n",
       "      <th>Votes45A</th>\n",
       "      <th>Votes45AM</th>\n",
       "      <th>Votes45AF</th>\n",
       "      <th>Votes1000</th>\n",
       "      <th>VotesUS</th>\n",
       "      <th>VotesnUS</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>Country</th>\n",
       "      <th>profit</th>\n",
       "      <th>Avg_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
       "      <td>2015</td>\n",
       "      <td>245.0</td>\n",
       "      <td>936.662225</td>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>Rob Walker</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>691.662225</td>\n",
       "      <td>8.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>The Avengers</td>\n",
       "      <td>2012</td>\n",
       "      <td>220.0</td>\n",
       "      <td>623.279547</td>\n",
       "      <td>Chris Hemsworth</td>\n",
       "      <td>Robert Downey Jr.</td>\n",
       "      <td>Scarlett Johansson</td>\n",
       "      <td>26000</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>403.279547</td>\n",
       "      <td>7.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Deadpool</td>\n",
       "      <td>2016</td>\n",
       "      <td>58.0</td>\n",
       "      <td>363.024263</td>\n",
       "      <td>Ryan Reynolds</td>\n",
       "      <td>Ed Skrein</td>\n",
       "      <td>Stefan Kapicic</td>\n",
       "      <td>16000</td>\n",
       "      <td>805.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>305.024263</td>\n",
       "      <td>7.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>The Hunger Games: Catching Fire</td>\n",
       "      <td>2013</td>\n",
       "      <td>130.0</td>\n",
       "      <td>424.645577</td>\n",
       "      <td>Jennifer Lawrence</td>\n",
       "      <td>Josh Hutcherson</td>\n",
       "      <td>Sandra Ellis Lafferty</td>\n",
       "      <td>34000</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>523.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.4</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>294.645577</td>\n",
       "      <td>7.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>2010</td>\n",
       "      <td>200.0</td>\n",
       "      <td>414.984497</td>\n",
       "      <td>Tom Hanks</td>\n",
       "      <td>John Ratzenberger</td>\n",
       "      <td>Don Rickles</td>\n",
       "      <td>15000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>721.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>G</td>\n",
       "      <td>USA</td>\n",
       "      <td>214.984497</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Title  title_year  budget  \\\n",
       "97  Star Wars: Episode VII - The Force Awakens        2015   245.0   \n",
       "11                                The Avengers        2012   220.0   \n",
       "47                                    Deadpool        2016    58.0   \n",
       "32             The Hunger Games: Catching Fire        2013   130.0   \n",
       "12                                 Toy Story 3        2010   200.0   \n",
       "\n",
       "         Gross       actor_1_name       actor_2_name           actor_3_name  \\\n",
       "97  936.662225        Doug Walker         Rob Walker                      0   \n",
       "11  623.279547    Chris Hemsworth  Robert Downey Jr.     Scarlett Johansson   \n",
       "47  363.024263      Ryan Reynolds          Ed Skrein         Stefan Kapicic   \n",
       "32  424.645577  Jennifer Lawrence    Josh Hutcherson  Sandra Ellis Lafferty   \n",
       "12  414.984497          Tom Hanks  John Ratzenberger            Don Rickles   \n",
       "\n",
       "    actor_1_facebook_likes  actor_2_facebook_likes  actor_3_facebook_likes  \\\n",
       "97                     131                    12.0                     0.0   \n",
       "11                   26000                 21000.0                 19000.0   \n",
       "47                   16000                   805.0                   361.0   \n",
       "32                   34000                 14000.0                   523.0   \n",
       "12                   15000                  1000.0                   721.0   \n",
       "\n",
       "    ...  Votes45A Votes45AM Votes45AF Votes1000  VotesUS  VotesnUS  \\\n",
       "97  ...       7.9       7.8       8.2       7.7      8.2       7.9   \n",
       "11  ...       7.9       7.9       8.1       7.4      8.3       7.9   \n",
       "47  ...       7.8       7.8       7.9       7.3      8.1       7.9   \n",
       "32  ...       7.3       7.2       7.9       6.7      7.7       7.4   \n",
       "12  ...       8.1       8.1       8.1       8.1      8.5       8.3   \n",
       "\n",
       "    content_rating  Country      profit  Avg_rating  \n",
       "97           PG-13      USA  691.662225        8.10  \n",
       "11           PG-13      USA  403.279547        7.50  \n",
       "47               R      USA  305.024263        7.25  \n",
       "32           PG-13      USA  294.645577        7.60  \n",
       "12               G      USA  214.984497        8.75  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the average ratings\n",
    "\n",
    "movies['Avg_rating']= (movies['MetaCritic'] + movies['IMDb_rating'])/2\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>...</th>\n",
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       "      <th>Votes45AF</th>\n",
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       "      <th>94</th>\n",
       "      <td>Boyhood</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.359200</td>\n",
       "      <td>Ellar Coltrane</td>\n",
       "      <td>Lorelei Linklater</td>\n",
       "      <td>Libby Villari</td>\n",
       "      <td>230</td>\n",
       "      <td>193.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>21.359200</td>\n",
       "      <td>8.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>12 Years a Slave</td>\n",
       "      <td>2013</td>\n",
       "      <td>20.0</td>\n",
       "      <td>56.667870</td>\n",
       "      <td>QuvenzhanÃ© Wallis</td>\n",
       "      <td>Scoot McNairy</td>\n",
       "      <td>Taran Killam</td>\n",
       "      <td>2000</td>\n",
       "      <td>660.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>36.667870</td>\n",
       "      <td>8.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Inside Out</td>\n",
       "      <td>2015</td>\n",
       "      <td>175.0</td>\n",
       "      <td>356.454367</td>\n",
       "      <td>Amy Poehler</td>\n",
       "      <td>Mindy Kaling</td>\n",
       "      <td>Phyllis Smith</td>\n",
       "      <td>1000</td>\n",
       "      <td>767.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>PG</td>\n",
       "      <td>USA</td>\n",
       "      <td>181.454367</td>\n",
       "      <td>8.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>2010</td>\n",
       "      <td>200.0</td>\n",
       "      <td>414.984497</td>\n",
       "      <td>Tom Hanks</td>\n",
       "      <td>John Ratzenberger</td>\n",
       "      <td>Don Rickles</td>\n",
       "      <td>15000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>721.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>G</td>\n",
       "      <td>USA</td>\n",
       "      <td>214.984497</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Manchester by the Sea</td>\n",
       "      <td>2016</td>\n",
       "      <td>9.0</td>\n",
       "      <td>47.695371</td>\n",
       "      <td>Casey Affleck</td>\n",
       "      <td>Michelle Williams</td>\n",
       "      <td>Kyle Chandler</td>\n",
       "      <td>518</td>\n",
       "      <td>71000.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>38.695371</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Title  title_year  budget       Gross        actor_1_name  \\\n",
       "94                Boyhood        2014     4.0   25.359200      Ellar Coltrane   \n",
       "69       12 Years a Slave        2013    20.0   56.667870  QuvenzhanÃ© Wallis   \n",
       "18             Inside Out        2015   175.0  356.454367         Amy Poehler   \n",
       "12            Toy Story 3        2010   200.0  414.984497           Tom Hanks   \n",
       "4   Manchester by the Sea        2016     9.0   47.695371       Casey Affleck   \n",
       "\n",
       "          actor_2_name   actor_3_name  actor_1_facebook_likes  \\\n",
       "94   Lorelei Linklater  Libby Villari                     230   \n",
       "69       Scoot McNairy   Taran Killam                    2000   \n",
       "18        Mindy Kaling  Phyllis Smith                    1000   \n",
       "12   John Ratzenberger    Don Rickles                   15000   \n",
       "4   Michelle Williams   Kyle Chandler                     518   \n",
       "\n",
       "    actor_2_facebook_likes  actor_3_facebook_likes  ...  Votes45A Votes45AM  \\\n",
       "94                   193.0                   127.0  ...       7.7       7.7   \n",
       "69                   660.0                   500.0  ...       7.8       7.8   \n",
       "18                   767.0                   384.0  ...       7.9       7.9   \n",
       "12                  1000.0                   721.0  ...       8.1       8.1   \n",
       "4                  71000.0                  3300.0  ...       7.6       7.6   \n",
       "\n",
       "   Votes45AF Votes1000  VotesUS  VotesnUS  content_rating  Country  \\\n",
       "94       7.7       7.2      8.0       7.9               R      USA   \n",
       "69       8.1       7.7      8.3       8.0               R      USA   \n",
       "18       7.9       7.6      8.2       8.1              PG      USA   \n",
       "12       8.1       8.1      8.5       8.3               G      USA   \n",
       "4        7.6       7.1      7.9       7.8               R      USA   \n",
       "\n",
       "        profit  Avg_rating  \n",
       "94   21.359200        8.95  \n",
       "69   36.667870        8.85  \n",
       "18  181.454367        8.80  \n",
       "12  214.984497        8.75  \n",
       "4    38.695371        8.75  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort in descending order of average rating \n",
    "\n",
    "\n",
    "movies.sort_values('Avg_rating', ascending = False, inplace=True) \n",
    "movies.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>...</th>\n",
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       "      <th>Votes45AM</th>\n",
       "      <th>Votes45AF</th>\n",
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       "      <th>VotesUS</th>\n",
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       "      <th>content_rating</th>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Whiplash</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.3</td>\n",
       "      <td>13.092000</td>\n",
       "      <td>J.K. Simmons</td>\n",
       "      <td>Melissa Benoist</td>\n",
       "      <td>Chris Mulkey</td>\n",
       "      <td>24000</td>\n",
       "      <td>970.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>9.792000</td>\n",
       "      <td>8.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Django Unchained</td>\n",
       "      <td>2012</td>\n",
       "      <td>100.0</td>\n",
       "      <td>162.804648</td>\n",
       "      <td>Leonardo DiCaprio</td>\n",
       "      <td>Christoph Waltz</td>\n",
       "      <td>Ato Essandoh</td>\n",
       "      <td>29000</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>62.804648</td>\n",
       "      <td>8.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Dallas Buyers Club</td>\n",
       "      <td>2013</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.296514</td>\n",
       "      <td>Matthew McConaughey</td>\n",
       "      <td>Jennifer Garner</td>\n",
       "      <td>Denis O'Hare</td>\n",
       "      <td>11000</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>896.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>22.296514</td>\n",
       "      <td>8.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
       "      <td>2015</td>\n",
       "      <td>245.0</td>\n",
       "      <td>936.662225</td>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>Rob Walker</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>691.662225</td>\n",
       "      <td>8.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arrival</td>\n",
       "      <td>2016</td>\n",
       "      <td>47.0</td>\n",
       "      <td>100.546139</td>\n",
       "      <td>Amy Adams</td>\n",
       "      <td>Jeremy Renner</td>\n",
       "      <td>Forest Whitaker</td>\n",
       "      <td>35000</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>USA</td>\n",
       "      <td>53.546139</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Title  title_year  budget  \\\n",
       "95                                    Whiplash        2014     3.3   \n",
       "35                            Django Unchained        2012   100.0   \n",
       "93                          Dallas Buyers Club        2013     5.0   \n",
       "97  Star Wars: Episode VII - The Force Awakens        2015   245.0   \n",
       "3                                      Arrival        2016    47.0   \n",
       "\n",
       "         Gross         actor_1_name     actor_2_name     actor_3_name  \\\n",
       "95   13.092000         J.K. Simmons  Melissa Benoist     Chris Mulkey   \n",
       "35  162.804648    Leonardo DiCaprio  Christoph Waltz     Ato Essandoh   \n",
       "93   27.296514  Matthew McConaughey  Jennifer Garner     Denis O'Hare   \n",
       "97  936.662225          Doug Walker       Rob Walker                0   \n",
       "3   100.546139            Amy Adams    Jeremy Renner  Forest Whitaker   \n",
       "\n",
       "    actor_1_facebook_likes  actor_2_facebook_likes  actor_3_facebook_likes  \\\n",
       "95                   24000                   970.0                   535.0   \n",
       "35                   29000                 11000.0                   265.0   \n",
       "93                   11000                  3000.0                   896.0   \n",
       "97                     131                    12.0                     0.0   \n",
       "3                    35000                  5300.0                     NaN   \n",
       "\n",
       "    ...  Votes45A Votes45AM Votes45AF Votes1000  VotesUS  VotesnUS  \\\n",
       "95  ...       8.1       8.1       8.2       8.0      8.6       8.4   \n",
       "35  ...       8.0       8.0       8.1       7.8      8.4       8.4   \n",
       "93  ...       7.8       7.8       8.0       7.2      8.0       7.9   \n",
       "97  ...       7.9       7.8       8.2       7.7      8.2       7.9   \n",
       "3   ...       7.6       7.6       7.7       7.3      8.0       7.9   \n",
       "\n",
       "    content_rating  Country      profit  Avg_rating  \n",
       "95               R      USA    9.792000        8.65  \n",
       "35               R      USA   62.804648        8.25  \n",
       "93               R      USA   22.296514        8.20  \n",
       "97           PG-13      USA  691.662225        8.10  \n",
       "3            PG-13      USA   53.546139        8.05  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the movies with metacritic-Imdb rating < 0.5 and also with an average rating of >= 8 (sorted in descending order)\n",
    "\n",
    "Diff_critic_rating = movies[(abs(movies['MetaCritic']-movies['IMDb_rating'])<0.5) & (movies['Avg_rating'] >= 8)]\n",
    "\n",
    "Diff_critic_rating.sort_values(by='Avg_rating', ascending= False, inplace = True)\n",
    "\n",
    "UniversalAcclaim= Diff_critic_rating[Diff_critic_rating['Avg_rating'] >= 8]\n",
    "\n",
    "UniversalAcclaim.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`Checkpoint 2:`** Can you spot a `Star Wars` movie in your final dataset?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Subtask 2.4: Find the Most Popular Trios - I\n",
    "\n",
    "You're a producer looking to make a blockbuster movie. There will primarily be three lead roles in your movie and you wish to cast the most popular actors for it. Now, since you don't want to take a risk, you will cast a trio which has already acted in together in a movie before. The metric that you've chosen to check the popularity is the Facebook likes of each of these actors.\n",
    "\n",
    "The dataframe has three columns to help you out for the same, viz. `actor_1_facebook_likes`, `actor_2_facebook_likes`, and `actor_3_facebook_likes`. Your objective is to find the trios which has the most number of Facebook likes combined. That is, the sum of `actor_1_facebook_likes`, `actor_2_facebook_likes` and `actor_3_facebook_likes` should be maximum.\n",
    "Find out the top 5 popular trios, and output their names in a list.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Dev Patel', 'Nicole Kidman', 'Rooney Mara'],\n",
       " ['Leonardo DiCaprio', 'Tom Hardy', 'Joseph Gordon-Levitt'],\n",
       " ['Jennifer Lawrence', 'Peter Dinklage', 'Hugh Jackman'],\n",
       " ['Casey Affleck', 'Michelle Williams ', 'Kyle Chandler'],\n",
       " ['Tom Hardy', 'Christian Bale', 'Joseph Gordon-Levitt']]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code here\n",
    "\n",
    "movies['fb_sum_likes'] = movies['actor_1_facebook_likes'] + movies['actor_2_facebook_likes']  + movies['actor_3_facebook_likes']\n",
    "movies.sort_values('fb_sum_likes', ascending = False)\n",
    "top5 = movies.head()\n",
    "(top5[['actor_1_name','actor_2_name','actor_3_name']].values).tolist()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Subtask 2.5: Find the Most Popular Trios - II\n",
    "\n",
    "In the previous subtask you found the popular trio based on the total number of facebook likes. Let's add a small condition to it and make sure that all three actors are popular. The condition is **none of the three actors' Facebook likes should be less than half of the other two**. For example, the following is a valid combo:\n",
    "- actor_1_facebook_likes: 70000\n",
    "- actor_2_facebook_likes: 40000\n",
    "- actor_3_facebook_likes: 50000\n",
    "\n",
    "But the below one is not:\n",
    "- actor_1_facebook_likes: 70000\n",
    "- actor_2_facebook_likes: 40000\n",
    "- actor_3_facebook_likes: 30000\n",
    "\n",
    "since in this case, `actor_3_facebook_likes` is 30000, which is less than half of `actor_1_facebook_likes`.\n",
    "\n",
    "Having this condition ensures that you aren't getting any unpopular actor in your trio (since the total likes calculated in the previous question doesn't tell anything about the individual popularities of each actor in the trio.).\n",
    "\n",
    "You can do a manual inspection of the top 5 popular trios you have found in the previous subtask and check how many of those trios satisfy this condition. Also, which is the most popular trio after applying the condition above? Write your answers in the markdown cell provided below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Write your answers below.**\n",
    "\n",
    "- **`No. of trios that satisfy the above condition:`** 3\n",
    "\n",
    "- **`Most popular trio after applying the condition:`** ['Leonardo DiCaprio', 'Tom Hardy', 'Joseph Gordon-Levitt']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`Optional:`** Even though you are finding this out by a natural inspection of the dataframe, can you also achieve this through some *if-else* statements to incorporate this. You can try this out on your own time after you are done with the assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your answer here (optional and not graded)\n",
    "\n",
    "popular_trio = movies[~((((movies['actor_1_facebook_likes'] < (movies['actor_2_facebook_likes']/2)) |\n",
    "                       movies['actor_1_facebook_likes']< (movies['actor_3_facebook_likes']/2)))==True) |\n",
    "                   ((((movies['actor_2_facebook_likes']< (movies['actor_1_facebook_likes']/2)) |\n",
    "                       movies['actor_2_facebook_likes']<(movies['actor_3_facebook_likes']/2)))==True) |\n",
    "                   ((((movies['actor_3_facebook_likes']< (movies['actor_1_facebook_likes']/2)) |\n",
    "                       movies['actor_2_facebook_likes']< (movies['actor_3_facebook_likes']/2)))==True)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Subtask 2.6: Runtime Analysis\n",
    "\n",
    "There is a column named `Runtime` in the dataframe which primarily shows the length of the movie. It might be intersting to see how this variable this distributed. Plot a `histogram` or `distplot` of seaborn to find the `Runtime` range most of the movies fall into."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Runtime histogram/density plot\n",
    "\n",
    "plt.figure(figsize= [6,3])\n",
    "runtime=sns.distplot(movies['Runtime'])\n",
    "runtime.set_xlabel('Runtime')\n",
    "runtime.axes.set_title(\"Runtime distribution\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`Checkpoint 3:`** Most of the movies appear to be sharply 2 hour-long."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Subtask 2.7: R-Rated Movies\n",
    "\n",
    "Although R rated movies are restricted movies for the under 18 age group, still there are vote counts from that age group. Among all the R rated movies that have been voted by the under-18 age group, find the top 10 movies that have the highest number of votes i.e.`CVotesU18` from the `movies` dataframe. Store these in a dataframe named `PopularR`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>budget</th>\n",
       "      <th>Gross</th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_3_name</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>...</th>\n",
       "      <th>Votes45AM</th>\n",
       "      <th>Votes45AF</th>\n",
       "      <th>Votes1000</th>\n",
       "      <th>VotesUS</th>\n",
       "      <th>VotesnUS</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>Country</th>\n",
       "      <th>profit</th>\n",
       "      <th>Avg_rating</th>\n",
       "      <th>fb_sum_likes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Deadpool</td>\n",
       "      <td>2016</td>\n",
       "      <td>58.0</td>\n",
       "      <td>363.024263</td>\n",
       "      <td>Ryan Reynolds</td>\n",
       "      <td>Ed Skrein</td>\n",
       "      <td>Stefan Kapicic</td>\n",
       "      <td>16000</td>\n",
       "      <td>805.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>305.024263</td>\n",
       "      <td>7.25</td>\n",
       "      <td>17166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>The Wolf of Wall Street</td>\n",
       "      <td>2013</td>\n",
       "      <td>100.0</td>\n",
       "      <td>116.866727</td>\n",
       "      <td>Leonardo DiCaprio</td>\n",
       "      <td>Matthew McConaughey</td>\n",
       "      <td>Jon Favreau</td>\n",
       "      <td>29000</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>16.866727</td>\n",
       "      <td>7.85</td>\n",
       "      <td>44000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Django Unchained</td>\n",
       "      <td>2012</td>\n",
       "      <td>100.0</td>\n",
       "      <td>162.804648</td>\n",
       "      <td>Leonardo DiCaprio</td>\n",
       "      <td>Christoph Waltz</td>\n",
       "      <td>Ato Essandoh</td>\n",
       "      <td>29000</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>62.804648</td>\n",
       "      <td>8.25</td>\n",
       "      <td>40265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>2015</td>\n",
       "      <td>150.0</td>\n",
       "      <td>153.629485</td>\n",
       "      <td>Tom Hardy</td>\n",
       "      <td>Charlize Theron</td>\n",
       "      <td>ZoÃ« Kravitz</td>\n",
       "      <td>27000</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>943.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>R</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3.629485</td>\n",
       "      <td>8.55</td>\n",
       "      <td>36943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Whiplash</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.3</td>\n",
       "      <td>13.092000</td>\n",
       "      <td>J.K. Simmons</td>\n",
       "      <td>Melissa Benoist</td>\n",
       "      <td>Chris Mulkey</td>\n",
       "      <td>24000</td>\n",
       "      <td>970.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>9.792000</td>\n",
       "      <td>8.65</td>\n",
       "      <td>25505.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>The Revenant</td>\n",
       "      <td>2015</td>\n",
       "      <td>135.0</td>\n",
       "      <td>183.635922</td>\n",
       "      <td>Leonardo DiCaprio</td>\n",
       "      <td>Tom Hardy</td>\n",
       "      <td>Lukas Haas</td>\n",
       "      <td>29000</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>733.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>48.635922</td>\n",
       "      <td>7.80</td>\n",
       "      <td>56733.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Shutter Island</td>\n",
       "      <td>2010</td>\n",
       "      <td>80.0</td>\n",
       "      <td>127.968405</td>\n",
       "      <td>Leonardo DiCaprio</td>\n",
       "      <td>Joseph Sikora</td>\n",
       "      <td>Nellie Sciutto</td>\n",
       "      <td>29000</td>\n",
       "      <td>223.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>47.968405</td>\n",
       "      <td>7.20</td>\n",
       "      <td>29386.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Gone Girl</td>\n",
       "      <td>2014</td>\n",
       "      <td>61.0</td>\n",
       "      <td>167.735396</td>\n",
       "      <td>Patrick Fugit</td>\n",
       "      <td>Sela Ward</td>\n",
       "      <td>Emily Ratajkowski</td>\n",
       "      <td>835</td>\n",
       "      <td>812.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>106.735396</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2272.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>The Grand Budapest Hotel</td>\n",
       "      <td>2014</td>\n",
       "      <td>25.0</td>\n",
       "      <td>59.073773</td>\n",
       "      <td>Bill Murray</td>\n",
       "      <td>Tom Wilkinson</td>\n",
       "      <td>F. Murray Abraham</td>\n",
       "      <td>13000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>34.073773</td>\n",
       "      <td>8.45</td>\n",
       "      <td>14670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Birdman or (The Unexpected Virtue of Ignorance)</td>\n",
       "      <td>2014</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.335698</td>\n",
       "      <td>Emma Stone</td>\n",
       "      <td>Naomi Watts</td>\n",
       "      <td>Merritt Wever</td>\n",
       "      <td>15000</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>529.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>R</td>\n",
       "      <td>USA</td>\n",
       "      <td>24.335698</td>\n",
       "      <td>8.30</td>\n",
       "      <td>21529.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Title  title_year  budget  \\\n",
       "47                                         Deadpool        2016    58.0   \n",
       "36                          The Wolf of Wall Street        2013   100.0   \n",
       "35                                 Django Unchained        2012   100.0   \n",
       "29                               Mad Max: Fury Road        2015   150.0   \n",
       "95                                         Whiplash        2014     3.3   \n",
       "31                                     The Revenant        2015   135.0   \n",
       "40                                   Shutter Island        2010    80.0   \n",
       "43                                        Gone Girl        2014    61.0   \n",
       "65                         The Grand Budapest Hotel        2014    25.0   \n",
       "72  Birdman or (The Unexpected Virtue of Ignorance)        2014    18.0   \n",
       "\n",
       "         Gross       actor_1_name         actor_2_name       actor_3_name  \\\n",
       "47  363.024263      Ryan Reynolds            Ed Skrein     Stefan Kapicic   \n",
       "36  116.866727  Leonardo DiCaprio  Matthew McConaughey        Jon Favreau   \n",
       "35  162.804648  Leonardo DiCaprio      Christoph Waltz       Ato Essandoh   \n",
       "29  153.629485          Tom Hardy      Charlize Theron       ZoÃ« Kravitz   \n",
       "95   13.092000       J.K. Simmons      Melissa Benoist       Chris Mulkey   \n",
       "31  183.635922  Leonardo DiCaprio            Tom Hardy         Lukas Haas   \n",
       "40  127.968405  Leonardo DiCaprio        Joseph Sikora     Nellie Sciutto   \n",
       "43  167.735396      Patrick Fugit            Sela Ward  Emily Ratajkowski   \n",
       "65   59.073773        Bill Murray        Tom Wilkinson  F. Murray Abraham   \n",
       "72   42.335698         Emma Stone          Naomi Watts      Merritt Wever   \n",
       "\n",
       "    actor_1_facebook_likes  actor_2_facebook_likes  actor_3_facebook_likes  \\\n",
       "47                   16000                   805.0                   361.0   \n",
       "36                   29000                 11000.0                  4000.0   \n",
       "35                   29000                 11000.0                   265.0   \n",
       "29                   27000                  9000.0                   943.0   \n",
       "95                   24000                   970.0                   535.0   \n",
       "31                   29000                 27000.0                   733.0   \n",
       "40                   29000                   223.0                   163.0   \n",
       "43                     835                   812.0                   625.0   \n",
       "65                   13000                  1000.0                   670.0   \n",
       "72                   15000                  6000.0                   529.0   \n",
       "\n",
       "    ...  Votes45AM Votes45AF Votes1000 VotesUS  VotesnUS  content_rating  \\\n",
       "47  ...        7.8       7.9       7.3     8.1       7.9               R   \n",
       "36  ...        7.6       7.5       7.8     8.1       8.1               R   \n",
       "35  ...        8.0       8.1       7.8     8.4       8.4               R   \n",
       "29  ...        7.5       7.2       8.0     8.2       8.0               R   \n",
       "95  ...        8.1       8.2       8.0     8.6       8.4               R   \n",
       "31  ...        7.8       7.8       7.6     8.1       7.9               R   \n",
       "40  ...        7.4       7.6       7.6     7.8       8.1               R   \n",
       "43  ...        7.7       7.7       7.6     8.1       8.1               R   \n",
       "65  ...        7.8       7.9       7.7     8.1       8.0               R   \n",
       "72  ...        7.3       7.0       7.1     7.9       7.7               R   \n",
       "\n",
       "      Country      profit  Avg_rating  fb_sum_likes  \n",
       "47        USA  305.024263        7.25       17166.0  \n",
       "36        USA   16.866727        7.85       44000.0  \n",
       "35        USA   62.804648        8.25       40265.0  \n",
       "29  Australia    3.629485        8.55       36943.0  \n",
       "95        USA    9.792000        8.65       25505.0  \n",
       "31        USA   48.635922        7.80       56733.0  \n",
       "40        USA   47.968405        7.20       29386.0  \n",
       "43        USA  106.735396        8.00        2272.0  \n",
       "65        USA   34.073773        8.45       14670.0  \n",
       "72        USA   24.335698        8.30       21529.0  \n",
       "\n",
       "[10 rows x 65 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code here\n",
    "\n",
    "R_rated_movies=movies[movies['content_rating']=='R']\n",
    "\n",
    "PopularR = R_rated_movies.sort_values('CVotesU18', ascending=False)[0:10]\n",
    "\n",
    "PopularR.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`Checkpoint 4:`** Are these kids watching `Deadpool` a lot?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3 : Demographic analysis\n",
    "\n",
    "If you take a look at the last columns in the dataframe, most of these are related to demographics of the voters (in the last subtask, i.e., 2.8, you made use one of these columns - CVotesU18). We also have three genre columns indicating the genres of a particular movie. We will extensively use these columns for the third and the final stage of our assignment wherein we will analyse the voters across all demographics and also see how these vary across various genres. So without further ado, let's get started with `demographic analysis`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  ###  Subtask 3.1 Combine the Dataframe by Genres\n",
    "\n",
    "There are 3 columns in the dataframe - `genre_1`, `genre_2`, and `genre_3`. As a part of this subtask, you need to aggregate a few values over these 3 columns. \n",
    "1. First create a new dataframe `df_by_genre` that contains `genre_1`, `genre_2`, and `genre_3` and all the columns related to **CVotes/Votes** from the `movies` data frame. There are 47 columns to be extracted in total.\n",
    "2. Now, Add a column called `cnt` to the dataframe `df_by_genre` and initialize it to one. You will realise the use of this column by the end of this subtask.\n",
    "3. First group the dataframe `df_by_genre` by `genre_1` and find the sum of all the numeric columns such as `cnt`, columns related to CVotes and Votes columns and store it in a dataframe `df_by_g1`.\n",
    "4. Perform the same operation for `genre_2` and `genre_3` and store it dataframes `df_by_g2` and `df_by_g3` respectively. \n",
    "5. Now that you have 3 dataframes performed by grouping over `genre_1`, `genre_2`, and `genre_3` separately, it's time to combine them. For this, add the three dataframes and store it in a new dataframe `df_add`, so that the corresponding values of Votes/CVotes get added for each genre.There is a function called `add()` in pandas which lets you do this. You can refer to this link to see how this function works. https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.DataFrame.add.html\n",
    "6. The column `cnt` on aggregation has basically kept the track of the number of occurences of each genre.Subset the genres that have atleast 10 movies into a new dataframe `genre_top10` based on the `cnt` column value.\n",
    "7. Now, take the mean of all the numeric columns by dividing them with the column value `cnt` and store it back to the same dataframe. We will be using this dataframe for further analysis in this task unless it is explicitly mentioned to use the dataframe `movies`.\n",
    "8. Since the number of votes can't be a fraction, type cast all the CVotes related columns to integers. Also, round off all the Votes related columns upto two digits after the decimal point.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre_1</th>\n",
       "      <th>genre_2</th>\n",
       "      <th>genre_3</th>\n",
       "      <th>CVotes10</th>\n",
       "      <th>CVotes09</th>\n",
       "      <th>CVotes08</th>\n",
       "      <th>CVotes07</th>\n",
       "      <th>CVotes06</th>\n",
       "      <th>CVotes05</th>\n",
       "      <th>CVotes04</th>\n",
       "      <th>...</th>\n",
       "      <th>Votes1829F</th>\n",
       "      <th>Votes3044</th>\n",
       "      <th>Votes3044M</th>\n",
       "      <th>Votes3044F</th>\n",
       "      <th>Votes45A</th>\n",
       "      <th>Votes45AM</th>\n",
       "      <th>Votes45AF</th>\n",
       "      <th>Votes1000</th>\n",
       "      <th>VotesUS</th>\n",
       "      <th>VotesnUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Biography</td>\n",
       "      <td>Drama</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23325</td>\n",
       "      <td>29830</td>\n",
       "      <td>40564</td>\n",
       "      <td>20296</td>\n",
       "      <td>5842</td>\n",
       "      <td>1669</td>\n",
       "      <td>558</td>\n",
       "      <td>...</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>584839</td>\n",
       "      <td>485218</td>\n",
       "      <td>304457</td>\n",
       "      <td>130972</td>\n",
       "      <td>46393</td>\n",
       "      <td>20595</td>\n",
       "      <td>10050</td>\n",
       "      <td>...</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>91765</td>\n",
       "      <td>127521</td>\n",
       "      <td>183578</td>\n",
       "      <td>104658</td>\n",
       "      <td>33027</td>\n",
       "      <td>10059</td>\n",
       "      <td>3710</td>\n",
       "      <td>...</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.4</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Drama</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18191</td>\n",
       "      <td>33532</td>\n",
       "      <td>46596</td>\n",
       "      <td>29626</td>\n",
       "      <td>11879</td>\n",
       "      <td>4539</td>\n",
       "      <td>1976</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Action</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>380589</td>\n",
       "      <td>341965</td>\n",
       "      <td>281426</td>\n",
       "      <td>134959</td>\n",
       "      <td>50406</td>\n",
       "      <td>20106</td>\n",
       "      <td>9589</td>\n",
       "      <td>...</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      genre_1    genre_2 genre_3  CVotes10  CVotes09  CVotes08  CVotes07  \\\n",
       "2   Biography      Drama     NaN     23325     29830     40564     20296   \n",
       "27     Action  Adventure  Sci-Fi    584839    485218    304457    130972   \n",
       "14     Action  Adventure  Sci-Fi     91765    127521    183578    104658   \n",
       "4       Drama        NaN     NaN     18191     33532     46596     29626   \n",
       "8      Action   Thriller     NaN    380589    341965    281426    134959   \n",
       "\n",
       "    CVotes06  CVotes05  CVotes04  ...  Votes1829F  Votes3044  Votes3044M  \\\n",
       "2       5842      1669       558  ...         8.4        8.0         7.9   \n",
       "27     46393     20595     10050  ...         8.8        8.7         8.7   \n",
       "14     33027     10059      3710  ...         8.2        7.8         7.8   \n",
       "4      11879      4539      1976  ...         7.8        7.7         7.7   \n",
       "8      50406     20106      9589  ...         8.6        8.3         8.3   \n",
       "\n",
       "    Votes3044F  Votes45A  Votes45AM  Votes45AF  Votes1000  VotesUS  VotesnUS  \n",
       "2          8.2       8.0        7.9        8.4        7.1      8.1       8.0  \n",
       "27         8.5       8.1        8.1        8.0        8.2      8.7       8.8  \n",
       "14         8.0       7.7        7.7        7.9        7.4      8.1       7.9  \n",
       "4          7.7       7.6        7.6        7.6        7.1      7.9       7.8  \n",
       "8          8.2       7.9        7.9        7.9        7.8      8.4       8.4  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the dataframe df_by_genre\n",
    "col_list=[i for i in movies.columns if i.startswith('ge')|i.startswith('CV')| i.startswith('V')]\n",
    "\n",
    "df_by_genre= movies.loc[:,col_list]\n",
    "\n",
    "df_by_genre.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a column cnt and initialize it to 1\n",
    "\n",
    "df_by_genre['cnt']= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CVotes10</th>\n",
       "      <th>CVotes09</th>\n",
       "      <th>CVotes08</th>\n",
       "      <th>CVotes07</th>\n",
       "      <th>CVotes06</th>\n",
       "      <th>CVotes05</th>\n",
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       "      <th>CVotes03</th>\n",
       "      <th>CVotes02</th>\n",
       "      <th>CVotes01</th>\n",
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       "      <th>Votes3044</th>\n",
       "      <th>Votes3044M</th>\n",
       "      <th>Votes3044F</th>\n",
       "      <th>Votes45A</th>\n",
       "      <th>Votes45AM</th>\n",
       "      <th>Votes45AF</th>\n",
       "      <th>Votes1000</th>\n",
       "      <th>VotesUS</th>\n",
       "      <th>VotesnUS</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genre_1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>2928407</td>\n",
       "      <td>3261919</td>\n",
       "      <td>4247693</td>\n",
       "      <td>2662020</td>\n",
       "      <td>986774</td>\n",
       "      <td>364234</td>\n",
       "      <td>156150</td>\n",
       "      <td>89483</td>\n",
       "      <td>61975</td>\n",
       "      <td>162426</td>\n",
       "      <td>...</td>\n",
       "      <td>209.1</td>\n",
       "      <td>208.8</td>\n",
       "      <td>210.0</td>\n",
       "      <td>206.5</td>\n",
       "      <td>206.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>197.2</td>\n",
       "      <td>215.8</td>\n",
       "      <td>209.5</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>1058779</td>\n",
       "      <td>1179818</td>\n",
       "      <td>1560541</td>\n",
       "      <td>966275</td>\n",
       "      <td>365486</td>\n",
       "      <td>136985</td>\n",
       "      <td>58559</td>\n",
       "      <td>33174</td>\n",
       "      <td>22018</td>\n",
       "      <td>48100</td>\n",
       "      <td>...</td>\n",
       "      <td>92.7</td>\n",
       "      <td>92.6</td>\n",
       "      <td>93.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>91.6</td>\n",
       "      <td>93.8</td>\n",
       "      <td>88.9</td>\n",
       "      <td>95.3</td>\n",
       "      <td>93.5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>681562</td>\n",
       "      <td>798227</td>\n",
       "      <td>1153214</td>\n",
       "      <td>722782</td>\n",
       "      <td>251076</td>\n",
       "      <td>83069</td>\n",
       "      <td>30718</td>\n",
       "      <td>15733</td>\n",
       "      <td>10026</td>\n",
       "      <td>25193</td>\n",
       "      <td>...</td>\n",
       "      <td>85.4</td>\n",
       "      <td>84.9</td>\n",
       "      <td>87.8</td>\n",
       "      <td>84.5</td>\n",
       "      <td>84.1</td>\n",
       "      <td>86.7</td>\n",
       "      <td>80.0</td>\n",
       "      <td>87.6</td>\n",
       "      <td>86.1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biography</th>\n",
       "      <td>666831</td>\n",
       "      <td>1088430</td>\n",
       "      <td>1654704</td>\n",
       "      <td>962977</td>\n",
       "      <td>306247</td>\n",
       "      <td>100005</td>\n",
       "      <td>38874</td>\n",
       "      <td>21536</td>\n",
       "      <td>15365</td>\n",
       "      <td>37469</td>\n",
       "      <td>...</td>\n",
       "      <td>100.8</td>\n",
       "      <td>100.7</td>\n",
       "      <td>101.3</td>\n",
       "      <td>100.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>102.9</td>\n",
       "      <td>94.7</td>\n",
       "      <td>103.3</td>\n",
       "      <td>101.5</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>371217</td>\n",
       "      <td>496905</td>\n",
       "      <td>770395</td>\n",
       "      <td>518566</td>\n",
       "      <td>205434</td>\n",
       "      <td>81933</td>\n",
       "      <td>35788</td>\n",
       "      <td>20965</td>\n",
       "      <td>15286</td>\n",
       "      <td>33241</td>\n",
       "      <td>...</td>\n",
       "      <td>68.6</td>\n",
       "      <td>68.7</td>\n",
       "      <td>68.9</td>\n",
       "      <td>67.7</td>\n",
       "      <td>67.5</td>\n",
       "      <td>68.7</td>\n",
       "      <td>62.7</td>\n",
       "      <td>70.9</td>\n",
       "      <td>69.4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>383290</td>\n",
       "      <td>690221</td>\n",
       "      <td>1083469</td>\n",
       "      <td>627593</td>\n",
       "      <td>206756</td>\n",
       "      <td>71460</td>\n",
       "      <td>30336</td>\n",
       "      <td>17190</td>\n",
       "      <td>11757</td>\n",
       "      <td>25839</td>\n",
       "      <td>...</td>\n",
       "      <td>69.4</td>\n",
       "      <td>69.7</td>\n",
       "      <td>68.8</td>\n",
       "      <td>68.7</td>\n",
       "      <td>68.6</td>\n",
       "      <td>69.6</td>\n",
       "      <td>66.3</td>\n",
       "      <td>71.9</td>\n",
       "      <td>70.1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>1080725</td>\n",
       "      <td>1494053</td>\n",
       "      <td>1827363</td>\n",
       "      <td>1078966</td>\n",
       "      <td>417205</td>\n",
       "      <td>163874</td>\n",
       "      <td>75525</td>\n",
       "      <td>45846</td>\n",
       "      <td>32068</td>\n",
       "      <td>71464</td>\n",
       "      <td>...</td>\n",
       "      <td>139.3</td>\n",
       "      <td>139.0</td>\n",
       "      <td>139.7</td>\n",
       "      <td>137.7</td>\n",
       "      <td>137.2</td>\n",
       "      <td>138.7</td>\n",
       "      <td>130.0</td>\n",
       "      <td>143.2</td>\n",
       "      <td>141.1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>150405</td>\n",
       "      <td>230844</td>\n",
       "      <td>278844</td>\n",
       "      <td>132349</td>\n",
       "      <td>45167</td>\n",
       "      <td>15615</td>\n",
       "      <td>7061</td>\n",
       "      <td>3780</td>\n",
       "      <td>2662</td>\n",
       "      <td>4703</td>\n",
       "      <td>...</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           CVotes10  CVotes09  CVotes08  CVotes07  CVotes06  CVotes05  \\\n",
       "genre_1                                                                 \n",
       "Action      2928407   3261919   4247693   2662020    986774    364234   \n",
       "Adventure   1058779   1179818   1560541    966275    365486    136985   \n",
       "Animation    681562    798227   1153214    722782    251076     83069   \n",
       "Biography    666831   1088430   1654704    962977    306247    100005   \n",
       "Comedy       371217    496905    770395    518566    205434     81933   \n",
       "Crime        383290    690221   1083469    627593    206756     71460   \n",
       "Drama       1080725   1494053   1827363   1078966    417205    163874   \n",
       "Mystery      150405    230844    278844    132349     45167     15615   \n",
       "\n",
       "           CVotes04  CVotes03  CVotes02  CVotes01  ...  Votes3044  Votes3044M  \\\n",
       "genre_1                                            ...                          \n",
       "Action       156150     89483     61975    162426  ...      209.1       208.8   \n",
       "Adventure     58559     33174     22018     48100  ...       92.7        92.6   \n",
       "Animation     30718     15733     10026     25193  ...       85.4        84.9   \n",
       "Biography     38874     21536     15365     37469  ...      100.8       100.7   \n",
       "Comedy        35788     20965     15286     33241  ...       68.6        68.7   \n",
       "Crime         30336     17190     11757     25839  ...       69.4        69.7   \n",
       "Drama         75525     45846     32068     71464  ...      139.3       139.0   \n",
       "Mystery        7061      3780      2662      4703  ...        7.9         7.9   \n",
       "\n",
       "           Votes3044F  Votes45A  Votes45AM  Votes45AF  Votes1000  VotesUS  \\\n",
       "genre_1                                                                     \n",
       "Action          210.0     206.5      206.0      209.0      197.2    215.8   \n",
       "Adventure        93.5      92.0       91.6       93.8       88.9     95.3   \n",
       "Animation        87.8      84.5       84.1       86.7       80.0     87.6   \n",
       "Biography       101.3     100.5      100.0      102.9       94.7    103.3   \n",
       "Comedy           68.9      67.7       67.5       68.7       62.7     70.9   \n",
       "Crime            68.8      68.7       68.6       69.6       66.3     71.9   \n",
       "Drama           139.7     137.7      137.2      138.7      130.0    143.2   \n",
       "Mystery           8.0       7.5        7.4        7.6        7.6      7.8   \n",
       "\n",
       "           VotesnUS  cnt  \n",
       "genre_1                   \n",
       "Action        209.5   27  \n",
       "Adventure      93.5   12  \n",
       "Animation      86.1   11  \n",
       "Biography     101.5   13  \n",
       "Comedy         69.4    9  \n",
       "Crime          70.1    9  \n",
       "Drama         141.1   18  \n",
       "Mystery         8.1    1  \n",
       "\n",
       "[8 rows x 45 columns]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the movies by individual genres\n",
    "\n",
    "# grouping the genres\n",
    "\n",
    "df_by_g1 = df_by_genre.groupby('genre_1')\n",
    "df_by_g2 = df_by_genre.groupby('genre_2')\n",
    "df_by_g3 = df_by_genre.groupby('genre_3')\n",
    "\n",
    "# Calculating the sum\n",
    "\n",
    "df_by_g1 = pd.DataFrame(df_by_g1.sum())\n",
    "df_by_g2 = pd.DataFrame(df_by_g2.sum())\n",
    "df_by_g3 = pd.DataFrame(df_by_g3.sum())\n",
    "\n",
    "df_by_g1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Action</th>\n",
       "      <td>3166467.0</td>\n",
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       "      <td>2922126.0</td>\n",
       "      <td>1075354.0</td>\n",
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       "      <td>171247.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>239.5</td>\n",
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       "      <td>247.6</td>\n",
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       "      <th>Adventure</th>\n",
       "      <td>3594659.0</td>\n",
       "      <td>4014192.0</td>\n",
       "      <td>5262328.0</td>\n",
       "      <td>3281981.0</td>\n",
       "      <td>1212075.0</td>\n",
       "      <td>438970.0</td>\n",
       "      <td>183070.0</td>\n",
       "      <td>103318.0</td>\n",
       "      <td>69737.0</td>\n",
       "      <td>173858.0</td>\n",
       "      <td>...</td>\n",
       "      <td>294.6</td>\n",
       "      <td>293.7</td>\n",
       "      <td>299.2</td>\n",
       "      <td>291.7</td>\n",
       "      <td>290.4</td>\n",
       "      <td>298.0</td>\n",
       "      <td>280.6</td>\n",
       "      <td>303.5</td>\n",
       "      <td>296.2</td>\n",
       "      <td>38.0</td>\n",
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       "      <th>Animation</th>\n",
       "      <td>681562.0</td>\n",
       "      <td>798227.0</td>\n",
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       "      <td>722782.0</td>\n",
       "      <td>251076.0</td>\n",
       "      <td>83069.0</td>\n",
       "      <td>30718.0</td>\n",
       "      <td>15733.0</td>\n",
       "      <td>10026.0</td>\n",
       "      <td>25193.0</td>\n",
       "      <td>...</td>\n",
       "      <td>85.4</td>\n",
       "      <td>84.9</td>\n",
       "      <td>87.8</td>\n",
       "      <td>84.5</td>\n",
       "      <td>84.1</td>\n",
       "      <td>86.7</td>\n",
       "      <td>80.0</td>\n",
       "      <td>87.6</td>\n",
       "      <td>86.1</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biography</th>\n",
       "      <td>852003.0</td>\n",
       "      <td>1401608.0</td>\n",
       "      <td>2231078.0</td>\n",
       "      <td>1332980.0</td>\n",
       "      <td>425595.0</td>\n",
       "      <td>138648.0</td>\n",
       "      <td>53718.0</td>\n",
       "      <td>29510.0</td>\n",
       "      <td>20613.0</td>\n",
       "      <td>51297.0</td>\n",
       "      <td>...</td>\n",
       "      <td>139.1</td>\n",
       "      <td>138.9</td>\n",
       "      <td>139.8</td>\n",
       "      <td>138.5</td>\n",
       "      <td>137.9</td>\n",
       "      <td>141.7</td>\n",
       "      <td>130.1</td>\n",
       "      <td>142.7</td>\n",
       "      <td>139.9</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>1383616.0</td>\n",
       "      <td>1774987.0</td>\n",
       "      <td>2506851.0</td>\n",
       "      <td>1591069.0</td>\n",
       "      <td>600287.0</td>\n",
       "      <td>226852.0</td>\n",
       "      <td>97469.0</td>\n",
       "      <td>56218.0</td>\n",
       "      <td>39391.0</td>\n",
       "      <td>88367.0</td>\n",
       "      <td>...</td>\n",
       "      <td>177.4</td>\n",
       "      <td>177.4</td>\n",
       "      <td>178.3</td>\n",
       "      <td>175.0</td>\n",
       "      <td>174.7</td>\n",
       "      <td>177.1</td>\n",
       "      <td>165.4</td>\n",
       "      <td>182.6</td>\n",
       "      <td>178.9</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>574526.0</td>\n",
       "      <td>967118.0</td>\n",
       "      <td>1419495.0</td>\n",
       "      <td>821390.0</td>\n",
       "      <td>278391.0</td>\n",
       "      <td>98690.0</td>\n",
       "      <td>42271.0</td>\n",
       "      <td>24713.0</td>\n",
       "      <td>16985.0</td>\n",
       "      <td>37217.0</td>\n",
       "      <td>...</td>\n",
       "      <td>84.9</td>\n",
       "      <td>85.4</td>\n",
       "      <td>83.7</td>\n",
       "      <td>83.9</td>\n",
       "      <td>83.8</td>\n",
       "      <td>84.5</td>\n",
       "      <td>81.3</td>\n",
       "      <td>87.8</td>\n",
       "      <td>85.8</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>3404438.0</td>\n",
       "      <td>4935375.0</td>\n",
       "      <td>7107053.0</td>\n",
       "      <td>4319700.0</td>\n",
       "      <td>1529356.0</td>\n",
       "      <td>552312.0</td>\n",
       "      <td>235475.0</td>\n",
       "      <td>135126.0</td>\n",
       "      <td>94185.0</td>\n",
       "      <td>211308.0</td>\n",
       "      <td>...</td>\n",
       "      <td>501.3</td>\n",
       "      <td>501.1</td>\n",
       "      <td>501.8</td>\n",
       "      <td>496.8</td>\n",
       "      <td>495.3</td>\n",
       "      <td>503.2</td>\n",
       "      <td>469.5</td>\n",
       "      <td>515.9</td>\n",
       "      <td>506.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>98165.0</td>\n",
       "      <td>95675.0</td>\n",
       "      <td>180381.0</td>\n",
       "      <td>143401.0</td>\n",
       "      <td>59137.0</td>\n",
       "      <td>22971.0</td>\n",
       "      <td>9472.0</td>\n",
       "      <td>5128.0</td>\n",
       "      <td>3317.0</td>\n",
       "      <td>7545.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.8</td>\n",
       "      <td>14.7</td>\n",
       "      <td>15.5</td>\n",
       "      <td>14.9</td>\n",
       "      <td>14.8</td>\n",
       "      <td>15.6</td>\n",
       "      <td>14.1</td>\n",
       "      <td>15.6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>572452.0</td>\n",
       "      <td>602223.0</td>\n",
       "      <td>889767.0</td>\n",
       "      <td>599747.0</td>\n",
       "      <td>241831.0</td>\n",
       "      <td>93484.0</td>\n",
       "      <td>39403.0</td>\n",
       "      <td>22233.0</td>\n",
       "      <td>14693.0</td>\n",
       "      <td>38841.0</td>\n",
       "      <td>...</td>\n",
       "      <td>53.3</td>\n",
       "      <td>53.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>53.4</td>\n",
       "      <td>52.8</td>\n",
       "      <td>55.5</td>\n",
       "      <td>50.5</td>\n",
       "      <td>54.9</td>\n",
       "      <td>53.7</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>History</th>\n",
       "      <td>151261.0</td>\n",
       "      <td>260387.0</td>\n",
       "      <td>394531.0</td>\n",
       "      <td>223062.0</td>\n",
       "      <td>67861.0</td>\n",
       "      <td>21233.0</td>\n",
       "      <td>7964.0</td>\n",
       "      <td>4384.0</td>\n",
       "      <td>3148.0</td>\n",
       "      <td>9291.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.2</td>\n",
       "      <td>31.1</td>\n",
       "      <td>31.3</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.7</td>\n",
       "      <td>32.1</td>\n",
       "      <td>29.4</td>\n",
       "      <td>32.2</td>\n",
       "      <td>31.2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror</th>\n",
       "      <td>16572.0</td>\n",
       "      <td>19818.0</td>\n",
       "      <td>44460.0</td>\n",
       "      <td>35863.0</td>\n",
       "      <td>13456.0</td>\n",
       "      <td>4588.0</td>\n",
       "      <td>1684.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>848.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Music</th>\n",
       "      <td>184649.0</td>\n",
       "      <td>233055.0</td>\n",
       "      <td>197296.0</td>\n",
       "      <td>94838.0</td>\n",
       "      <td>33954.0</td>\n",
       "      <td>14075.0</td>\n",
       "      <td>6935.0</td>\n",
       "      <td>4698.0</td>\n",
       "      <td>3670.0</td>\n",
       "      <td>9525.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.2</td>\n",
       "      <td>16.2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.7</td>\n",
       "      <td>15.7</td>\n",
       "      <td>15.7</td>\n",
       "      <td>15.1</td>\n",
       "      <td>16.9</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Musical</th>\n",
       "      <td>54268.0</td>\n",
       "      <td>47750.0</td>\n",
       "      <td>63323.0</td>\n",
       "      <td>45160.0</td>\n",
       "      <td>22393.0</td>\n",
       "      <td>10744.0</td>\n",
       "      <td>5551.0</td>\n",
       "      <td>3484.0</td>\n",
       "      <td>2490.0</td>\n",
       "      <td>5020.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>510164.0</td>\n",
       "      <td>827124.0</td>\n",
       "      <td>1166485.0</td>\n",
       "      <td>655612.0</td>\n",
       "      <td>228873.0</td>\n",
       "      <td>80568.0</td>\n",
       "      <td>34679.0</td>\n",
       "      <td>19392.0</td>\n",
       "      <td>13170.0</td>\n",
       "      <td>29444.0</td>\n",
       "      <td>...</td>\n",
       "      <td>54.1</td>\n",
       "      <td>54.1</td>\n",
       "      <td>54.7</td>\n",
       "      <td>53.1</td>\n",
       "      <td>52.9</td>\n",
       "      <td>54.0</td>\n",
       "      <td>51.7</td>\n",
       "      <td>55.6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romance</th>\n",
       "      <td>549959.0</td>\n",
       "      <td>689492.0</td>\n",
       "      <td>1069280.0</td>\n",
       "      <td>712841.0</td>\n",
       "      <td>281289.0</td>\n",
       "      <td>110901.0</td>\n",
       "      <td>48913.0</td>\n",
       "      <td>27698.0</td>\n",
       "      <td>19200.0</td>\n",
       "      <td>40075.0</td>\n",
       "      <td>...</td>\n",
       "      <td>98.9</td>\n",
       "      <td>98.9</td>\n",
       "      <td>99.6</td>\n",
       "      <td>97.8</td>\n",
       "      <td>97.5</td>\n",
       "      <td>98.9</td>\n",
       "      <td>89.9</td>\n",
       "      <td>101.8</td>\n",
       "      <td>100.1</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sci-Fi</th>\n",
       "      <td>2325284.0</td>\n",
       "      <td>2530855.0</td>\n",
       "      <td>3002994.0</td>\n",
       "      <td>1802098.0</td>\n",
       "      <td>671811.0</td>\n",
       "      <td>254175.0</td>\n",
       "      <td>111925.0</td>\n",
       "      <td>65904.0</td>\n",
       "      <td>46171.0</td>\n",
       "      <td>114435.0</td>\n",
       "      <td>...</td>\n",
       "      <td>133.6</td>\n",
       "      <td>133.5</td>\n",
       "      <td>133.2</td>\n",
       "      <td>131.1</td>\n",
       "      <td>130.8</td>\n",
       "      <td>131.5</td>\n",
       "      <td>127.9</td>\n",
       "      <td>137.5</td>\n",
       "      <td>134.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sport</th>\n",
       "      <td>117081.0</td>\n",
       "      <td>169246.0</td>\n",
       "      <td>273043.0</td>\n",
       "      <td>184163.0</td>\n",
       "      <td>60902.0</td>\n",
       "      <td>18939.0</td>\n",
       "      <td>6810.0</td>\n",
       "      <td>3357.0</td>\n",
       "      <td>2292.0</td>\n",
       "      <td>5234.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.9</td>\n",
       "      <td>22.9</td>\n",
       "      <td>22.6</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.5</td>\n",
       "      <td>21.3</td>\n",
       "      <td>23.9</td>\n",
       "      <td>23.1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>1081701.0</td>\n",
       "      <td>1465491.0</td>\n",
       "      <td>1993378.0</td>\n",
       "      <td>1175799.0</td>\n",
       "      <td>416046.0</td>\n",
       "      <td>149953.0</td>\n",
       "      <td>65281.0</td>\n",
       "      <td>37940.0</td>\n",
       "      <td>25767.0</td>\n",
       "      <td>57630.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.6</td>\n",
       "      <td>100.7</td>\n",
       "      <td>100.1</td>\n",
       "      <td>99.6</td>\n",
       "      <td>99.3</td>\n",
       "      <td>100.7</td>\n",
       "      <td>96.2</td>\n",
       "      <td>103.1</td>\n",
       "      <td>101.5</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>War</th>\n",
       "      <td>52664.0</td>\n",
       "      <td>72310.0</td>\n",
       "      <td>143841.0</td>\n",
       "      <td>106966.0</td>\n",
       "      <td>40505.0</td>\n",
       "      <td>14401.0</td>\n",
       "      <td>5690.0</td>\n",
       "      <td>3114.0</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>4001.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>15.1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>15.4</td>\n",
       "      <td>13.4</td>\n",
       "      <td>15.2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>255918.0</td>\n",
       "      <td>380230.0</td>\n",
       "      <td>378736.0</td>\n",
       "      <td>188620.0</td>\n",
       "      <td>61306.0</td>\n",
       "      <td>21418.0</td>\n",
       "      <td>9147.0</td>\n",
       "      <td>5454.0</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>9737.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.9</td>\n",
       "      <td>15.9</td>\n",
       "      <td>15.8</td>\n",
       "      <td>15.7</td>\n",
       "      <td>15.7</td>\n",
       "      <td>15.8</td>\n",
       "      <td>15.1</td>\n",
       "      <td>16.3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            CVotes10   CVotes09   CVotes08   CVotes07   CVotes06  CVotes05  \\\n",
       "Action     3166467.0  3547429.0  4677755.0  2922126.0  1075354.0  393484.0   \n",
       "Adventure  3594659.0  4014192.0  5262328.0  3281981.0  1212075.0  438970.0   \n",
       "Animation   681562.0   798227.0  1153214.0   722782.0   251076.0   83069.0   \n",
       "Biography   852003.0  1401608.0  2231078.0  1332980.0   425595.0  138648.0   \n",
       "Comedy     1383616.0  1774987.0  2506851.0  1591069.0   600287.0  226852.0   \n",
       "Crime       574526.0   967118.0  1419495.0   821390.0   278391.0   98690.0   \n",
       "Drama      3404438.0  4935375.0  7107053.0  4319700.0  1529356.0  552312.0   \n",
       "Family       98165.0    95675.0   180381.0   143401.0    59137.0   22971.0   \n",
       "Fantasy     572452.0   602223.0   889767.0   599747.0   241831.0   93484.0   \n",
       "History     151261.0   260387.0   394531.0   223062.0    67861.0   21233.0   \n",
       "Horror       16572.0    19818.0    44460.0    35863.0    13456.0    4588.0   \n",
       "Music       184649.0   233055.0   197296.0    94838.0    33954.0   14075.0   \n",
       "Musical      54268.0    47750.0    63323.0    45160.0    22393.0   10744.0   \n",
       "Mystery     510164.0   827124.0  1166485.0   655612.0   228873.0   80568.0   \n",
       "Romance     549959.0   689492.0  1069280.0   712841.0   281289.0  110901.0   \n",
       "Sci-Fi     2325284.0  2530855.0  3002994.0  1802098.0   671811.0  254175.0   \n",
       "Sport       117081.0   169246.0   273043.0   184163.0    60902.0   18939.0   \n",
       "Thriller   1081701.0  1465491.0  1993378.0  1175799.0   416046.0  149953.0   \n",
       "War          52664.0    72310.0   143841.0   106966.0    40505.0   14401.0   \n",
       "Western     255918.0   380230.0   378736.0   188620.0    61306.0   21418.0   \n",
       "\n",
       "           CVotes04  CVotes03  CVotes02  CVotes01  ...  Votes3044  Votes3044M  \\\n",
       "Action     166970.0   95004.0   65573.0  171247.0  ...      240.0       239.5   \n",
       "Adventure  183070.0  103318.0   69737.0  173858.0  ...      294.6       293.7   \n",
       "Animation   30718.0   15733.0   10026.0   25193.0  ...       85.4        84.9   \n",
       "Biography   53718.0   29510.0   20613.0   51297.0  ...      139.1       138.9   \n",
       "Comedy      97469.0   56218.0   39391.0   88367.0  ...      177.4       177.4   \n",
       "Crime       42271.0   24713.0   16985.0   37217.0  ...       84.9        85.4   \n",
       "Drama      235475.0  135126.0   94185.0  211308.0  ...      501.3       501.1   \n",
       "Family       9472.0    5128.0    3317.0    7545.0  ...       14.8        14.7   \n",
       "Fantasy     39403.0   22233.0   14693.0   38841.0  ...       53.3        53.1   \n",
       "History      7964.0    4384.0    3148.0    9291.0  ...       31.2        31.1   \n",
       "Horror       1684.0     855.0     479.0     848.0  ...        7.5         7.5   \n",
       "Music        6935.0    4698.0    3670.0    9525.0  ...       16.2        16.2   \n",
       "Musical      5551.0    3484.0    2490.0    5020.0  ...        7.3         7.2   \n",
       "Mystery     34679.0   19392.0   13170.0   29444.0  ...       54.1        54.1   \n",
       "Romance     48913.0   27698.0   19200.0   40075.0  ...       98.9        98.9   \n",
       "Sci-Fi     111925.0   65904.0   46171.0  114435.0  ...      133.6       133.5   \n",
       "Sport        6810.0    3357.0    2292.0    5234.0  ...       22.9        22.9   \n",
       "Thriller    65281.0   37940.0   25767.0   57630.0  ...      100.6       100.7   \n",
       "War          5690.0    3114.0    1971.0    4001.0  ...       14.7        14.6   \n",
       "Western      9147.0    5454.0    3960.0    9737.0  ...       15.9        15.9   \n",
       "\n",
       "           Votes3044F  Votes45A  Votes45AM  Votes45AF  Votes1000  VotesUS  \\\n",
       "Action          241.8     237.0      236.4      240.4      226.2    247.6   \n",
       "Adventure       299.2     291.7      290.4      298.0      280.6    303.5   \n",
       "Animation        87.8      84.5       84.1       86.7       80.0     87.6   \n",
       "Biography       139.8     138.5      137.9      141.7      130.1    142.7   \n",
       "Comedy          178.3     175.0      174.7      177.1      165.4    182.6   \n",
       "Crime            83.7      83.9       83.8       84.5       81.3     87.8   \n",
       "Drama           501.8     496.8      495.3      503.2      469.5    515.9   \n",
       "Family           15.5      14.9       14.8       15.6       14.1     15.6   \n",
       "Fantasy          55.0      53.4       52.8       55.5       50.5     54.9   \n",
       "History          31.3      31.0       30.7       32.1       29.4     32.2   \n",
       "Horror            7.7       7.5        7.4        7.7        7.1      7.7   \n",
       "Music            16.0      15.7       15.7       15.7       15.1     16.9   \n",
       "Musical           7.6       7.4        7.3        7.7        6.6      7.6   \n",
       "Mystery          54.7      53.1       52.9       54.0       51.7     55.6   \n",
       "Romance          99.6      97.8       97.5       98.9       89.9    101.8   \n",
       "Sci-Fi          133.2     131.1      130.8      131.5      127.9    137.5   \n",
       "Sport            22.6      22.5       22.5       22.5       21.3     23.9   \n",
       "Thriller        100.1      99.6       99.3      100.7       96.2    103.1   \n",
       "War              15.1      15.0       14.9       15.4       13.4     15.2   \n",
       "Western          15.8      15.7       15.7       15.8       15.1     16.3   \n",
       "\n",
       "           VotesnUS   cnt  \n",
       "Action        240.6  31.0  \n",
       "Adventure     296.2  38.0  \n",
       "Animation      86.1  11.0  \n",
       "Biography     139.9  18.0  \n",
       "Comedy        178.9  23.0  \n",
       "Crime          85.8  11.0  \n",
       "Drama         506.0  65.0  \n",
       "Family         15.0   2.0  \n",
       "Fantasy        53.7   7.0  \n",
       "History        31.2   4.0  \n",
       "Horror          7.5   1.0  \n",
       "Music          16.5   2.0  \n",
       "Musical         7.5   1.0  \n",
       "Mystery        55.0   7.0  \n",
       "Romance       100.1  13.0  \n",
       "Sci-Fi        134.0  17.0  \n",
       "Sport          23.1   3.0  \n",
       "Thriller      101.5  13.0  \n",
       "War            15.0   2.0  \n",
       "Western        16.0   2.0  \n",
       "\n",
       "[20 rows x 45 columns]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add the grouped data frames and store it in a new data frame\n",
    "\n",
    "df_add_1_2 = df_by_g1.add(df_by_g2, fill_value=0)\n",
    "df_add = df_add_1_2.add(df_by_g3, fill_value=0)\n",
    "\n",
    "df_add\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
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       "      <th>CVotes10</th>\n",
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       "      <td>296.2</td>\n",
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       "      <td>...</td>\n",
       "      <td>85.4</td>\n",
       "      <td>84.9</td>\n",
       "      <td>87.8</td>\n",
       "      <td>84.5</td>\n",
       "      <td>84.1</td>\n",
       "      <td>86.7</td>\n",
       "      <td>80.0</td>\n",
       "      <td>87.6</td>\n",
       "      <td>86.1</td>\n",
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       "    <tr>\n",
       "      <th>Biography</th>\n",
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       "      <td>2231078.0</td>\n",
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       "      <td>51297.0</td>\n",
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       "      <td>137.9</td>\n",
       "      <td>141.7</td>\n",
       "      <td>130.1</td>\n",
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       "      <td>139.9</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
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       "      <td>1774987.0</td>\n",
       "      <td>2506851.0</td>\n",
       "      <td>1591069.0</td>\n",
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       "      <td>39391.0</td>\n",
       "      <td>88367.0</td>\n",
       "      <td>...</td>\n",
       "      <td>177.4</td>\n",
       "      <td>177.4</td>\n",
       "      <td>178.3</td>\n",
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       "      <td>177.1</td>\n",
       "      <td>165.4</td>\n",
       "      <td>182.6</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
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       "            CVotes10   CVotes09   CVotes08   CVotes07   CVotes06  CVotes05  \\\n",
       "Action     3166467.0  3547429.0  4677755.0  2922126.0  1075354.0  393484.0   \n",
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       "\n",
       "           CVotes04  CVotes03  CVotes02  CVotes01  ...  Votes3044  Votes3044M  \\\n",
       "Action     166970.0   95004.0   65573.0  171247.0  ...      240.0       239.5   \n",
       "Adventure  183070.0  103318.0   69737.0  173858.0  ...      294.6       293.7   \n",
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       "Comedy      97469.0   56218.0   39391.0   88367.0  ...      177.4       177.4   \n",
       "\n",
       "           Votes3044F  Votes45A  Votes45AM  Votes45AF  Votes1000  VotesUS  \\\n",
       "Action          241.8     237.0      236.4      240.4      226.2    247.6   \n",
       "Adventure       299.2     291.7      290.4      298.0      280.6    303.5   \n",
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       "Biography       139.8     138.5      137.9      141.7      130.1    142.7   \n",
       "Comedy          178.3     175.0      174.7      177.1      165.4    182.6   \n",
       "\n",
       "           VotesnUS   cnt  \n",
       "Action        240.6  31.0  \n",
       "Adventure     296.2  38.0  \n",
       "Animation      86.1  11.0  \n",
       "Biography     139.9  18.0  \n",
       "Comedy        178.9  23.0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract genres with atleast 10 occurences\n",
    "\n",
    "genre_top10 = df_add[df_add['cnt']>=10]\n",
    "\n",
    "col_cnt= genre_top10['cnt']\n",
    "\n",
    "genre_top10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
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       "      <td>77867.111111</td>\n",
       "      <td>123948.777778</td>\n",
       "      <td>74054.444444</td>\n",
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       "    <tr>\n",
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      "text/plain": [
       "                CVotes10       CVotes09       CVotes08      CVotes07  \\\n",
       "Action     102144.096774  114433.193548  150895.322581  94262.129032   \n",
       "Adventure   94596.289474  105636.631579  138482.315789  86367.921053   \n",
       "Animation   61960.181818   72566.090909  104837.636364  65707.454545   \n",
       "Biography   47333.500000   77867.111111  123948.777778  74054.444444   \n",
       "Comedy      60157.217391   77173.347826  108993.521739  69176.913043   \n",
       "\n",
       "               CVotes06      CVotes05     CVotes04     CVotes03     CVotes02  \\\n",
       "Action     34688.838710  12693.032258  5386.129032  3064.645161  2115.258065   \n",
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       "Comedy     26099.434783   9863.130435  4237.782609  2444.260870  1712.652174   \n",
       "\n",
       "              CVotes01  ...  Votes3044  Votes3044M  Votes3044F  Votes45A  \\\n",
       "Action     5524.096774  ...   7.741935    7.725806    7.800000  7.645161   \n",
       "Adventure  4575.210526  ...   7.752632    7.728947    7.873684  7.676316   \n",
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       "Biography  2849.833333  ...   7.727778    7.716667    7.766667  7.694444   \n",
       "Comedy     3842.043478  ...   7.713043    7.713043    7.752174  7.608696   \n",
       "\n",
       "           Votes45AM  Votes45AF  Votes1000   VotesUS  VotesnUS  cnt  \n",
       "Action      7.625806   7.754839   7.296774  7.987097  7.761290  1.0  \n",
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       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take the mean for every column by dividing with cnt \n",
    "\n",
    "\n",
    "genre_top10 = genre_top10.div(genre_top10['cnt'], axis = 'index')\n",
    "\n",
    "genre_top10.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CVotes10</th>\n",
       "      <th>CVotes09</th>\n",
       "      <th>CVotes08</th>\n",
       "      <th>CVotes07</th>\n",
       "      <th>CVotes06</th>\n",
       "      <th>CVotes05</th>\n",
       "      <th>CVotes04</th>\n",
       "      <th>CVotes03</th>\n",
       "      <th>CVotes02</th>\n",
       "      <th>CVotes01</th>\n",
       "      <th>...</th>\n",
       "      <th>Votes3044</th>\n",
       "      <th>Votes3044M</th>\n",
       "      <th>Votes3044F</th>\n",
       "      <th>Votes45A</th>\n",
       "      <th>Votes45AM</th>\n",
       "      <th>Votes45AF</th>\n",
       "      <th>Votes1000</th>\n",
       "      <th>VotesUS</th>\n",
       "      <th>VotesnUS</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>102144.10</td>\n",
       "      <td>114433.19</td>\n",
       "      <td>150895.32</td>\n",
       "      <td>94262.13</td>\n",
       "      <td>34688.84</td>\n",
       "      <td>12693.03</td>\n",
       "      <td>5386.13</td>\n",
       "      <td>3064.65</td>\n",
       "      <td>2115.26</td>\n",
       "      <td>5524.10</td>\n",
       "      <td>...</td>\n",
       "      <td>7.74</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.80</td>\n",
       "      <td>7.65</td>\n",
       "      <td>7.63</td>\n",
       "      <td>7.75</td>\n",
       "      <td>7.30</td>\n",
       "      <td>7.99</td>\n",
       "      <td>7.76</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>94596.29</td>\n",
       "      <td>105636.63</td>\n",
       "      <td>138482.32</td>\n",
       "      <td>86367.92</td>\n",
       "      <td>31896.71</td>\n",
       "      <td>11551.84</td>\n",
       "      <td>4817.63</td>\n",
       "      <td>2718.89</td>\n",
       "      <td>1835.18</td>\n",
       "      <td>4575.21</td>\n",
       "      <td>...</td>\n",
       "      <td>7.75</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.87</td>\n",
       "      <td>7.68</td>\n",
       "      <td>7.64</td>\n",
       "      <td>7.84</td>\n",
       "      <td>7.38</td>\n",
       "      <td>7.99</td>\n",
       "      <td>7.79</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>61960.18</td>\n",
       "      <td>72566.09</td>\n",
       "      <td>104837.64</td>\n",
       "      <td>65707.45</td>\n",
       "      <td>22825.09</td>\n",
       "      <td>7551.73</td>\n",
       "      <td>2792.55</td>\n",
       "      <td>1430.27</td>\n",
       "      <td>911.45</td>\n",
       "      <td>2290.27</td>\n",
       "      <td>...</td>\n",
       "      <td>7.76</td>\n",
       "      <td>7.72</td>\n",
       "      <td>7.98</td>\n",
       "      <td>7.68</td>\n",
       "      <td>7.65</td>\n",
       "      <td>7.88</td>\n",
       "      <td>7.27</td>\n",
       "      <td>7.96</td>\n",
       "      <td>7.83</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biography</th>\n",
       "      <td>47333.50</td>\n",
       "      <td>77867.11</td>\n",
       "      <td>123948.78</td>\n",
       "      <td>74054.44</td>\n",
       "      <td>23644.17</td>\n",
       "      <td>7702.67</td>\n",
       "      <td>2984.33</td>\n",
       "      <td>1639.44</td>\n",
       "      <td>1145.17</td>\n",
       "      <td>2849.83</td>\n",
       "      <td>...</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.72</td>\n",
       "      <td>7.77</td>\n",
       "      <td>7.69</td>\n",
       "      <td>7.66</td>\n",
       "      <td>7.87</td>\n",
       "      <td>7.23</td>\n",
       "      <td>7.93</td>\n",
       "      <td>7.77</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>60157.22</td>\n",
       "      <td>77173.35</td>\n",
       "      <td>108993.52</td>\n",
       "      <td>69176.91</td>\n",
       "      <td>26099.43</td>\n",
       "      <td>9863.13</td>\n",
       "      <td>4237.78</td>\n",
       "      <td>2444.26</td>\n",
       "      <td>1712.65</td>\n",
       "      <td>3842.04</td>\n",
       "      <td>...</td>\n",
       "      <td>7.71</td>\n",
       "      <td>7.71</td>\n",
       "      <td>7.75</td>\n",
       "      <td>7.61</td>\n",
       "      <td>7.60</td>\n",
       "      <td>7.70</td>\n",
       "      <td>7.19</td>\n",
       "      <td>7.94</td>\n",
       "      <td>7.78</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            CVotes10   CVotes09   CVotes08  CVotes07  CVotes06  CVotes05  \\\n",
       "Action     102144.10  114433.19  150895.32  94262.13  34688.84  12693.03   \n",
       "Adventure   94596.29  105636.63  138482.32  86367.92  31896.71  11551.84   \n",
       "Animation   61960.18   72566.09  104837.64  65707.45  22825.09   7551.73   \n",
       "Biography   47333.50   77867.11  123948.78  74054.44  23644.17   7702.67   \n",
       "Comedy      60157.22   77173.35  108993.52  69176.91  26099.43   9863.13   \n",
       "\n",
       "           CVotes04  CVotes03  CVotes02  CVotes01  ...  Votes3044  Votes3044M  \\\n",
       "Action      5386.13   3064.65   2115.26   5524.10  ...       7.74        7.73   \n",
       "Adventure   4817.63   2718.89   1835.18   4575.21  ...       7.75        7.73   \n",
       "Animation   2792.55   1430.27    911.45   2290.27  ...       7.76        7.72   \n",
       "Biography   2984.33   1639.44   1145.17   2849.83  ...       7.73        7.72   \n",
       "Comedy      4237.78   2444.26   1712.65   3842.04  ...       7.71        7.71   \n",
       "\n",
       "           Votes3044F  Votes45A  Votes45AM  Votes45AF  Votes1000  VotesUS  \\\n",
       "Action           7.80      7.65       7.63       7.75       7.30     7.99   \n",
       "Adventure        7.87      7.68       7.64       7.84       7.38     7.99   \n",
       "Animation        7.98      7.68       7.65       7.88       7.27     7.96   \n",
       "Biography        7.77      7.69       7.66       7.87       7.23     7.93   \n",
       "Comedy           7.75      7.61       7.60       7.70       7.19     7.94   \n",
       "\n",
       "           VotesnUS  cnt  \n",
       "Action         7.76  1.0  \n",
       "Adventure      7.79  1.0  \n",
       "Animation      7.83  1.0  \n",
       "Biography      7.77  1.0  \n",
       "Comedy         7.78  1.0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rounding off the columns of Votes to two decimals\n",
    "\n",
    "genre_top10 = genre_top10.apply(lambda x: round(x,2))\n",
    "\n",
    "genre_top10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CVotes10</th>\n",
       "      <th>CVotes09</th>\n",
       "      <th>CVotes08</th>\n",
       "      <th>CVotes07</th>\n",
       "      <th>CVotes06</th>\n",
       "      <th>CVotes05</th>\n",
       "      <th>CVotes04</th>\n",
       "      <th>CVotes03</th>\n",
       "      <th>CVotes02</th>\n",
       "      <th>CVotes01</th>\n",
       "      <th>...</th>\n",
       "      <th>CVotes1829F</th>\n",
       "      <th>CVotes3044</th>\n",
       "      <th>CVotes3044M</th>\n",
       "      <th>CVotes3044F</th>\n",
       "      <th>CVotes45A</th>\n",
       "      <th>CVotes45AM</th>\n",
       "      <th>CVotes45AF</th>\n",
       "      <th>CVotes1000</th>\n",
       "      <th>CVotesUS</th>\n",
       "      <th>CVotesnUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>102144</td>\n",
       "      <td>114433</td>\n",
       "      <td>150895</td>\n",
       "      <td>94262</td>\n",
       "      <td>34688</td>\n",
       "      <td>12693</td>\n",
       "      <td>5386</td>\n",
       "      <td>3064</td>\n",
       "      <td>2115</td>\n",
       "      <td>5524</td>\n",
       "      <td>...</td>\n",
       "      <td>36996</td>\n",
       "      <td>155623</td>\n",
       "      <td>132836</td>\n",
       "      <td>20627</td>\n",
       "      <td>28862</td>\n",
       "      <td>24092</td>\n",
       "      <td>4269</td>\n",
       "      <td>681</td>\n",
       "      <td>69190</td>\n",
       "      <td>225998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>94596</td>\n",
       "      <td>105636</td>\n",
       "      <td>138482</td>\n",
       "      <td>86367</td>\n",
       "      <td>31896</td>\n",
       "      <td>11551</td>\n",
       "      <td>4817</td>\n",
       "      <td>2718</td>\n",
       "      <td>1835</td>\n",
       "      <td>4575</td>\n",
       "      <td>...</td>\n",
       "      <td>39896</td>\n",
       "      <td>138490</td>\n",
       "      <td>115795</td>\n",
       "      <td>20750</td>\n",
       "      <td>26641</td>\n",
       "      <td>21910</td>\n",
       "      <td>4267</td>\n",
       "      <td>662</td>\n",
       "      <td>61734</td>\n",
       "      <td>208034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>61960</td>\n",
       "      <td>72566</td>\n",
       "      <td>104837</td>\n",
       "      <td>65707</td>\n",
       "      <td>22825</td>\n",
       "      <td>7551</td>\n",
       "      <td>2792</td>\n",
       "      <td>1430</td>\n",
       "      <td>911</td>\n",
       "      <td>2290</td>\n",
       "      <td>...</td>\n",
       "      <td>39314</td>\n",
       "      <td>94853</td>\n",
       "      <td>75824</td>\n",
       "      <td>17751</td>\n",
       "      <td>16305</td>\n",
       "      <td>12966</td>\n",
       "      <td>3063</td>\n",
       "      <td>578</td>\n",
       "      <td>47673</td>\n",
       "      <td>150424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biography</th>\n",
       "      <td>47333</td>\n",
       "      <td>77867</td>\n",
       "      <td>123948</td>\n",
       "      <td>74054</td>\n",
       "      <td>23644</td>\n",
       "      <td>7702</td>\n",
       "      <td>2984</td>\n",
       "      <td>1639</td>\n",
       "      <td>1145</td>\n",
       "      <td>2849</td>\n",
       "      <td>...</td>\n",
       "      <td>29157</td>\n",
       "      <td>111073</td>\n",
       "      <td>92158</td>\n",
       "      <td>17354</td>\n",
       "      <td>22550</td>\n",
       "      <td>18039</td>\n",
       "      <td>4126</td>\n",
       "      <td>600</td>\n",
       "      <td>45389</td>\n",
       "      <td>171004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>60157</td>\n",
       "      <td>77173</td>\n",
       "      <td>108993</td>\n",
       "      <td>69176</td>\n",
       "      <td>26099</td>\n",
       "      <td>9863</td>\n",
       "      <td>4237</td>\n",
       "      <td>2444</td>\n",
       "      <td>1712</td>\n",
       "      <td>3842</td>\n",
       "      <td>...</td>\n",
       "      <td>37509</td>\n",
       "      <td>106680</td>\n",
       "      <td>86346</td>\n",
       "      <td>18841</td>\n",
       "      <td>19954</td>\n",
       "      <td>15979</td>\n",
       "      <td>3621</td>\n",
       "      <td>605</td>\n",
       "      <td>49220</td>\n",
       "      <td>165869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           CVotes10  CVotes09  CVotes08  CVotes07  CVotes06  CVotes05  \\\n",
       "Action       102144    114433    150895     94262     34688     12693   \n",
       "Adventure     94596    105636    138482     86367     31896     11551   \n",
       "Animation     61960     72566    104837     65707     22825      7551   \n",
       "Biography     47333     77867    123948     74054     23644      7702   \n",
       "Comedy        60157     77173    108993     69176     26099      9863   \n",
       "\n",
       "           CVotes04  CVotes03  CVotes02  CVotes01  ...  CVotes1829F  \\\n",
       "Action         5386      3064      2115      5524  ...        36996   \n",
       "Adventure      4817      2718      1835      4575  ...        39896   \n",
       "Animation      2792      1430       911      2290  ...        39314   \n",
       "Biography      2984      1639      1145      2849  ...        29157   \n",
       "Comedy         4237      2444      1712      3842  ...        37509   \n",
       "\n",
       "           CVotes3044  CVotes3044M  CVotes3044F  CVotes45A  CVotes45AM  \\\n",
       "Action         155623       132836        20627      28862       24092   \n",
       "Adventure      138490       115795        20750      26641       21910   \n",
       "Animation       94853        75824        17751      16305       12966   \n",
       "Biography      111073        92158        17354      22550       18039   \n",
       "Comedy         106680        86346        18841      19954       15979   \n",
       "\n",
       "           CVotes45AF  CVotes1000  CVotesUS  CVotesnUS  \n",
       "Action           4269         681     69190     225998  \n",
       "Adventure        4267         662     61734     208034  \n",
       "Animation        3063         578     47673     150424  \n",
       "Biography        4126         600     45389     171004  \n",
       "Comedy           3621         605     49220     165869  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting CVotes to int type\n",
    "\n",
    "convert_cvotes_list = [i for i in genre_top10.columns if i.startswith('CVotes')]\n",
    "\n",
    "genre_top10= genre_top10[convert_cvotes_list].astype('int')\n",
    "\n",
    "genre_top10.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you take a look at the final dataframe that you have gotten, you will see that you now have the complete information about all the demographic (Votes- and CVotes-related) columns across the top 10 genres. We can use this dataset to extract exciting insights about the voters!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  ###  Subtask 3.2: Genre Counts!\n",
    "\n",
    "Now let's derive some insights from this data frame. Make a bar chart plotting different genres vs cnt using seaborn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Countplot for genres\n",
    "\n",
    "plt.figure(figsize=[10,5])\n",
    "sns.barplot(genre_top10.index, col_cnt)\n",
    "plt.title('Genres vs cnt', fontsize=25)\n",
    "plt.xlabel('Genres', fontsize=15)\n",
    "plt.ylabel('Count', fontsize = 15)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`Checkpoint 5:`** Is the bar for `Drama` the tallest?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  ###  Subtask 3.3: Gender and Genre\n",
    "\n",
    "If you have closely looked at the Votes- and CVotes-related columns, you might have noticed the suffixes `F` and `M` indicating Female and Male. Since we have the vote counts for both males and females, across various age groups, let's now see how the popularity of genres vary between the two genders in the dataframe. \n",
    "\n",
    "1. Make the first heatmap to see how the average number of votes of males is varying across the genres. Use seaborn heatmap for this analysis. The X-axis should contain the four age-groups for males, i.e., `CVotesU18M`,`CVotes1829M`, `CVotes3044M`, and `CVotes45AM`. The Y-axis will have the genres and the annotation in the heatmap tell the average number of votes for that age-male group. \n",
    "\n",
    "2. Make the second heatmap to see how the average number of votes of females is varying across the genres. Use seaborn heatmap for this analysis. The X-axis should contain the four age-groups for females, i.e., `CVotesU18F`,`CVotes1829F`, `CVotes3044F`, and `CVotes45AF`. The Y-axis will have the genres and the annotation in the heatmap tell the average number of votes for that age-female group. \n",
    "\n",
    "3. Make sure that you plot these heatmaps side by side using `subplots` so that you can easily compare the two genders and derive insights.\n",
    "\n",
    "4. Write your any three inferences from this plot. You can make use of the previous bar plot also here for better insights.\n",
    "Refer to this link- https://seaborn.pydata.org/generated/seaborn.heatmap.html. You might have to plot something similar to the fifth chart in this page (You have to plot two such heatmaps side by side).\n",
    "\n",
    "5. Repeat subtasks 1 to 4, but now instead of taking the CVotes-related columns, you need to do the same process for the Votes-related columns. These heatmaps will show you how the two genders have rated movies across various genres.\n",
    "\n",
    "You might need the below link for formatting your heatmap.\n",
    "https://stackoverflow.com/questions/56942670/matplotlib-seaborn-first-and-last-row-cut-in-half-of-heatmap-plot\n",
    "\n",
    "-  Note : Use `genre_top10` dataframe for this subtask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1st set of heat maps for CVotes-related columns\n",
    "\n",
    "male_cvotes = genre_top10.groupby(genre_top10.index)['CVotesU18M','CVotes1829M', 'CVotes3044M', 'CVotes45AM'].mean()\n",
    "\n",
    "female_cvotes = genre_top10.groupby(genre_top10.index)['CVotesU18F','CVotes1829F', 'CVotes3044F', 'CVotes45AF'].mean()\n",
    "\n",
    "\n",
    "f,(ax1,ax2)=plt.subplots(1,2)\n",
    "ax1.get_shared_y_axes().join(ax1,ax2)\n",
    "\n",
    "Male_plot=sns.heatmap(male_cvotes, cmap=\"Reds\", annot=True,cbar=False,  ax=ax1, annot_kws={\"size\": 7})\n",
    "Male_plot.set_ylabel('Genres', fontsize=15)\n",
    "Male_plot.set_xlabel('Age group - Male', fontsize=15)\n",
    "\n",
    "Female_plot = sns.heatmap(female_cvotes, cmap=\"Reds\", annot=True,cbar=True , ax=ax2, annot_kws={\"size\": 6})\n",
    "Female_plot.set_ylabel(' ')\n",
    "Female_plot.set_yticks([])\n",
    "Female_plot.set_xlabel('Age group - Female', fontsize=15)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.show()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`Inferences:`** A few inferences that can be seen from the heatmap above is that males have voted more than females, and Sci-Fi appears to be most popular among the 18-29 age group irrespective of their gender. What more can you infer from the two heatmaps that you have plotted? Write your three inferences/observations below:\n",
    "- Inference 1: Females from age 18 to 44 have voted the most.\n",
    "- Inference 2: Males of age 18 have voted more as compared to Females of age 18.\n",
    "- Inference 3: Sci-fi movies are most popular among both Male & Females."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2nd set of heat maps for Votes-related columns\n",
    "\n",
    "male_cvotes = genre_top10.groupby(genre_top10.index)['VotesU18M','Votes1829M', 'Votes3044M', 'Votes45AM'].mean()\n",
    "\n",
    "female_cvotes = genre_top10.groupby(genre_top10.index)['VotesU18F','Votes1829F', 'Votes3044F', 'Votes45AF'].mean()\n",
    "\n",
    "\n",
    "f,(ax1,ax2)=plt.subplots(1,2)\n",
    "ax1.get_shared_y_axes().join(ax1,ax2)\n",
    "\n",
    "Male_plot=sns.heatmap(male_cvotes, cmap=\"Blues\", annot=True,cbar=False,  ax=ax1, annot_kws={\"size\": 7})\n",
    "Male_plot.set_ylabel('Genres', fontsize=15)\n",
    "Male_plot.set_xlabel('Age group - Male', fontsize=15)\n",
    "\n",
    "\n",
    "Female_plot = sns.heatmap(female_cvotes, cmap=\"Blues\", annot=True,cbar=True , ax=ax2, annot_kws={\"size\": 7})\n",
    "Female_plot.set_ylabel(' ')\n",
    "Female_plot.set_yticks([])\n",
    "Female_plot.set_xlabel('Age group - Female', fontsize=15)\n",
    "\n",
    "\n",
    "\n",
    "plt.show()\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`Inferences:`** Sci-Fi appears to be the highest rated genre in the age group of U18 for both males and females. Also, females in this age group have rated it a bit higher than the males in the same age group. What more can you infer from the two heatmaps that you have plotted? Write your three inferences/observations below:\n",
    "- Inference 1: In Male & Felame both age group of 18, have more voted to sci-fi movies.\n",
    "- Inference 2: Crime movies are voted more by males of age group 18 to 44 as compared to females of same age group.\n",
    "- Inference 3: Both Males & Females of age group more than 44, have voted less."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  ###  Subtask 3.4: US vs non-US Cross Analysis\n",
    "\n",
    "The dataset contains both the US and non-US movies. Let's analyse how both the US and the non-US voters have responded to the US and the non-US movies.\n",
    "\n",
    "1. Create a column `IFUS` in the dataframe `movies`. The column `IFUS` should contain the value \"USA\" if the `Country` of the movie is \"USA\". For all other countries other than the USA, `IFUS` should contain the value `non-USA`.\n",
    "\n",
    "\n",
    "2. Now make a boxplot that shows how the number of votes from the US people i.e. `CVotesUS` is varying for the US and non-US movies. Make use of the column `IFUS` to make this plot. Similarly, make another subplot that shows how non US voters have voted for the US and non-US movies by plotting `CVotesnUS` for both the US and non-US movies. Write any of your two inferences/observations from these plots.\n",
    "\n",
    "\n",
    "3. Again do a similar analysis but with the ratings. Make a boxplot that shows how the ratings from the US people i.e. `VotesUS` is varying for the US and non-US movies. Similarly, make another subplot that shows how `VotesnUS` is varying for the US and non-US movies. Write any of your two inferences/observations from these plots.\n",
    "\n",
    "Note : Use `movies` dataframe for this subtask. Make use of this documention to format your boxplot - https://seaborn.pydata.org/generated/seaborn.boxplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     non-USA\n",
       "27        USA\n",
       "14        USA\n",
       "4         USA\n",
       "8         USA\n",
       "Name: IFUS, dtype: object"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating IFUS column\n",
    "\n",
    "movies['IFUS'] = movies['Country'].apply(lambda x: 'USA' if x=='USA' else 'non-USA')\n",
    "\n",
    "movies['IFUS'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box plot - 1: CVotesUS(y) vs IFUS(x)\n",
    "\n",
    "plt.figure(figsize=[10,20])\n",
    "\n",
    "fig, axes= plt.subplots(nrows=1, ncols=2)\n",
    "\n",
    "box1=sns.boxplot(x=movies['IFUS'], y = movies['CVotesUS'], orient='v', ax=axes[0], )\n",
    "\n",
    "box1.set_title('CVotes of USA citizen')\n",
    "\n",
    "box2=sns.boxplot(x=movies['IFUS'], y = movies['CVotesnUS'], orient='v')\n",
    "\n",
    "box2.yaxis.tick_right()\n",
    "\n",
    "box2.set_title('CVotes of Non-USA citizen')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`Inferences:`** Write your two inferences/observations below:\n",
    "- Inference 1: The number of average votes by non-USA people is higher as compared to USA people whether the movie is produced in USA or not.\n",
    "\n",
    "- Inference 2: In both the plots, USA movies are having outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ocJAxM2uCI444gq6udFdIV1cXRx55ZJtzNDQ4yJiZNcG0adPYbLP0kzpq1ChOO63fqVZGBAcZM7MmGDduHFOnTkUSU6dOZdy4ce3O0pDgO/7NzJpk2rRpPProo67FFDjImJk1ybhx4/jyl7/c7mwMKW4uMzOz0pQaZCSdK+k+SfdKuk7Sln1sd6CkdZJOLDM/ZmbWWqUFGUkTgXOA7ojYBxgFnFRju1HAxcBPysqLmZm1R9nNZV3AaEldwBjgyRrbTAe+Azxbcl7MzEq1cOFCDjvsMBYtWtTurAwZpQWZiOgFLgWWAk8BL0TEguI2ubbzQaqm96wm6UxJCyUtXLZsWVlZNjPbJDNnzmT9+vVccEH1DMadoYwujjKby7YHTgB2A3YGtpJ0StVmlwHnRcS6emlFxJyI6I6I7gkTJpSSXzOzTbFw4UJWrlwJwMqVKzuuNlNWF0eZzWVHAI9ExLKIWAPcCLyraptu4HpJjwInAldI+kCJeTIzK8XMmTM3eN2htZmmd3GUeZ/MUuBgSWOA1cDhwMLiBhGxW+W5pLnAjyLieyXmycysFJVaTF+vh4Dxkoq/wXMiYk7lRUT0Sqp0cawGFtTp4jgMOLCRnZYWZCLiDkk3AHcCa4G7gDmSzs7r6/bDmJl1krFjx24QWMaOHdvG3NS0PCK6+1pZ1cXxPPBtSadExDWFzS4jd3FIaminpV5dFhEXRMRbI2KfiDg1Il6JiCtrBZiIOD0ibigzP2ZmZaluLrvwwgvbk5HBK6WLw8PKmJk1QXd392u1mbFjx3LAAQe0O0sDVUoXh4eVMTNrkpkzZ7LZZpt1Yi2GiLgDqHRxLCHFhzmSzq50cwyGazJmZk3S3d3NT3/603ZnY9Ai4gKg+rK4mv3nEXF6I2m6JmNmZqVxkDEzs9I4yJiZWWkcZMzMrDQOMmZmVhoHGTMzK40vYTYzK5g9ezY9PT2Dem9vby8AEydOHNT7p0yZwvTp0wf13qHKQcbMrElWr17d7iwMOQ4yZmYFm1KTmDFjBgCzZs1qVnY6nvtkzMysNA4yZmZWGgcZMzMrjYOMmZmVxkHGzMxK4yBjZmalcZAxM7PSOMiYmVlpHGTMzAwASedKuk/SvZKuk7Rl1fq/knRPfvxS0jv6S9NBxszMkDQROAfojoh9gFHASVWbPQL8WUS8Hfg8MKe/dD2sjJmZVXQBoyWtAcYATxZXRsQvCy9vB97UX4Kl1mTKqHqZDWWS3i9p18Lrf5Z0t6QfSNqtnXmzEW+8pIWFx5nFlRHRC1wKLAWeAl6IiAV10vsY8OP+dlpakCmr6mU2xH0RWAYg6TjgFOCjwA+AK9uYL7PlEdFdeGzweytpe+AEYDdgZ2ArSafUSkjSoaQgc15/O+0zyEgaI2nzwus9c83kQ419HuD1qlcXfVS9IuL3+WVDVS+zIS4iYlV+/iHg6xGxKCKuAia0MV9m/TkCeCQilkXEGuBG4F3VG0l6O3AVcEJErOgv0Xp9MvNJkeohSVOAXwHXAsdJOjAizq+XcET0SqpUvVYDC5pR9SqLJyqyJpGkscAq4HDgisK6LWu/xWxIWAocLGkM6Tf7cGBhcQNJk0jB59SIeLCRROs1l20fEQ/l59OA6yJiOnAMcFx/CTez6iXpzEo74rJly/rbdcutXr3akxVZxWXAYtLBeX9ELASQtB+pndtsSIqIO4AbgDuBJaT4MEfS2ZLOzpv9MzAOuELSYkkLa6f2uno1mSg8Pwy4JGfkVUnrG8jza1UvAEmVqtc1xY0KVa9j+qp65bbDOQDd3d1Ra5tN5YmKrBki4huSfgLsCNxdWPU0cEZ7cmXWmIi4ALigavGVhfUfBz4+kDTrBZl7cnNXLzAFWAAgabsG0y6l6mU2lEnav/ByX0lB6nB9vF15MmunekHmr4EZwGTgqEJn5l6ky9zqiog7JFWqXmuBu8hVr7z+SjasegGsjYjuwX0UsyHh32os20HSFsDJEbG4xfkxa6s+g0xErAb+pcbyXwK/3PgdNdNoetXLbCiLiENrLZfUDXwZeG9rc2TWXn0GGUlL2LBfJoDlwM+ASyPiDyXnzWzYiIiF+aozsxGlXnNZrSvIdiBdaTab1JxmZg2QtBMbnrSZjQj1msseq7H4MeAuSXeVlyWzziVpNhsHkx1IV1bOaH2OzNprsANkevRms9qq7xsIYAXw9xHxbBvy4xuNra3q9cnsX2Px9qSxmH5eWo7MOlhEzGt3HprJNxnbpqpXk6m+FLNyRnYLHsjSrC5J7wZmAruSjjORxjV7c6vz4huNrZ3q9cnUvBTTzBrydeBcYBGwrs15MWubfvtWJM2QtI2SqyTdKemoVmTOrIO9EBE/johnI2JF5dHuTJm1WiMd+B+NiBeBo0jjMZ1BjZs0zWwDP5N0iaR3Stq/8mh3psxarZGry5T/Hgt8MyLuVh4Dxsz6dFD+WxwmKUiDzZqNGI0EmUWSFpCG7D9f0tZAI6Mw2xC3KZe2borKPiudyq3Wistq3adpljQSZD4G7As8HBGrJI3DQ5YPCz09PTx0311MGtvafukt1qRW2lce63cqiqZbunJUS/YjaQbwTeAl4GvA/sCn+pm4z2zYaSTIBGnk5eOAzwFb4Rn+ho1JY9fx6f1fbHc2WuZLd27Tql19NCJmSTqa1/syv0meMsNspGik4/8K4J3Ayfn1S8BXSsuR2fCwUV9mYZnZiNFITeagiNi/Ml5ZRPw+z41hZn1zX6YZjdVk1kgaRR70T9IEfLCY9edjwKeAA/OEf1vgvkwb4iSdK+k+SfdKuk7SllXrJenLknok3dPIZfmNBJkvA98FdpT0ReA24KJBfQKzkaPSl3lOfu2+TBvSJE0kldfuiNgHGAWcVLXZMcAe+XEm8NX+0u23uSwirpW0CDic1Kb8gYi4f2DZNxtxriDV+A8jXTDzEvAd4MB2ZsqsH13AaElrgDHAk1XrTwCujogAbpe0naQ3RsRT9RKsS9J/RsSpwG9rLDOz2tyXaUPNeEnF+wbmRMRrgx1HRK+kS4GlwGpgQY1L7icCjxdeP5GXDT7IAHsXX+T+mQMaeJ/ZSOa+TBtqlkdEd18rJW1PqqnsBjwPfFvSKRFxTXGzGm+tO+Nrn30yks6X9BLwdkkvSnopv34W+H69RM3MfZnWcY4AHomIZRGxBriRNKNr0RPALoXXb2LjJrUN1Bvq/yLgIkkXRcT5g8uz2cjkvkzrQEuBgyWNITWXHc7GM73+APg7SdeTxud7oV5/DDTW8X++pOOB9+ZFt0TEjwaae7ORxH2Z1mki4g5JNwB3AmuBu4A5ks7O668E/pt0g3EPsIoGLstvpOP/IuBPgGvzohmS3u3ajVldTevLbNdAptDewUxbMZCpbSgiLgAuqFp8ZWF9AH87kDQb6fh/H7BvRKwHkDSPFOH6DTKSzgU+TuoYWgKcERF/KKwXMIsUGVcBp0fEnQP5AGZDiaTzgU+TLgN9kdc7Sl9lkNOW9/T0sPje+1k3Zocm5bJxm72a+nQXPfxMS/c7atVzLd2flaeRIAOwHVD5r2/byBsKN/bsFRGrJf0X6caeuYXNijf2HES6secgzDpUWX2Z68bswOq3Htus5Ia80b/973ZnwZqkzyAj6XLgOuBLwJ2SbiGdlb2XBmoxhfSbemOPWSdwX6ZZUq8m8xBwKfBG0vDkjwN3A+dFxNP9JVzWjT31eBKugent7eXll0a1cvj7tnvspVFs1dtb+n7cl2mW1LuEeRYwS9KupGauk4CPAN+SdH1EPFQv4Wbe2CPpTNI4OUyaNKnPfbar7bpd7dbgtushbNB9mWbDSSOXMD8GXAxcLGk/4BvATNLgafW8dmMPgKTKjT3FINPQjT156IM5AN3d3XXvLnXbdeMmTpzIK2ufGnGTlr1h4sRW7W47BtiXaTbcNHIJ8+bAVFJN5nDgVuDCBtIu5cYes6GsSX2ZtolGYtP5UL3ku17H/5Gk2TDfB/wauB44MyJebiThsm7sMRviNqkv05qjp6eHh+67i0lj17V0v1usSSN1vfJY9fl0uZau7K9hqX3q1WQ+DXwL+IeIGFTDfxk39pgNZZval2nNM2nsuhHTFDyUL97pc4DMiDg0Ir422ABjNpJFxGMRcXFE7EcKMh+iMMSM2UjRyMyYZjZAkjaX9H5J1wI/Bh4E/rzN2TJruUbv+DezBmxqX6bZcOMgY9Zcm9yXaTacOMiYNVFEHNruPJgNJe6TMTOz0jjImJlZaRxkzMysNA4yZmaGpD0lLS48XpT0iapttpX0Q0l3S7pP0qZPv2xmZsNfRDwA7AuvTRfeC3y3arO/BX4TEe+XNAF4QNK1EfFqX+kOqyDT29vLqFUvjKhZ9UatWkFv79p2Z8NsSBlpcyWVME/S4cDv8ij8RQFsLUnAWNIo43V/gIZVkDEzsz6Nl1QcuXNOnkallpNIo4lXu5w0ev6TwNbAhytzJvVlWAWZiRMn8vQrXSNuPpmJE3dqdzbMhpSRNldSg/MkLY+I7v42krQFcDy1p6Y4GlgMHAbsDtwk6X8ios8v2h3/ZmZWdAxwZ0TUmur3DODGSHqAR4C31kvMQcbMzIpOpnZTGaTJKA8HkLQTsCfwcL3EhlVzmZmZDV6eyfhI4KzCsuJEk58H5kpaQprx9byIWF4vTQcZMzMDICJWAeOqlhUnmnwSOGogabq5zMzMSuMgY2ZmpXGQMTOz0jjImJlZaRxkzMysNA4yZmZWGl/CbDbEeeBX62Sl1WTKmpvAzMw6R2k1mbLmJjAbaTzwq3WyVjWXNW1ugv6MWvVcy5sVNvtDGoB0/Zatn7ti1KrngMEfjEtXtn7OjWdWpQr0TmPqjhBeiqUrR7FHy/dqNnK1Kshs0twEks4EzgSYNGlSnzuZMmVKM/I6YD09L6X9v7kdZ147Dfpzt+v7erWnB4A37Nr6/e9B+z632UhUepBpxtwEeWKdOQDd3d3R176mT5/epFwPzIwZMwCYNWtWW/Y/WP6+zKxsrbiEualzE5iZWedoRZBp6twEZmbWOUptLitjbgIzM+scpQaZMuYmMDOzzuFhZczMrDQOMmZm1tAoLXm7Q/L6+yTd2l+6HrvMzMwaGqVF0nbAFcDUiFgqacf+0nWQMesA7RjJAto3msWmjmQBI2s0ixJGsuhrlJaPkG47WQoQEc/2l5CDjNkQ184RCto3msXgR7KAkTeaRYMjWYyXtLDwek6+0b2WvkZpeQuwuaRbSKO0zIqIq+vt1EHGbIhr18gM0LmjM3g0i5qWR0R3fxv1M0pLF3AAqaYzGviVpNsj4sG+0nOQMTOzonqjtDxBClYvAy9L+jnwDqDPIOOry8zMrKjeKC3fB94jqSvfbH8QcH+9xFyTMTMzoP9RWiLifknzgXuA9cBVEXFvvTQdZMzMDOh/lJb8+hLgkkbTdHOZmZmVxkHGzMxK4yBjZmalcZAxM7PSOMiYmVlpHGTMzKw0DjJmZlYa3ydjNszNnj2bnjxw40BV3lcZk2ugpkyZ0tax16z9HGTMrE+jR49udxaswznImA1zrklYO7lPxszMSuMgY2ZmpXGQMTOz0jjImJlZaUoLMpL2lLS48HhR0idqbHdIXn+fpFvLyo+ZmbVeaVeXRcQDwL4AkkYBvcB3i9tI2g64ApgaEUsl7VhWfszMrPVa1Vx2OPC7iHisavlHgBsjYilARDzbovyYmVkLtCrInETtOaPfAmwv6RZJiySdVuvNks6UtFDSwmXLlpWaUTMza57Sg4ykLYDjgW/XWN0FHAC8Dzga+CdJb6neKCLmRER3RHRPmDCh1PyamY1Ejfaj520PlLRO0on9pduKO/6PAe6MiGdqrHsCWB4RLwMvS/o58A7gwRbky8zMskb60QvrLgZ+0ki6rWguO5naTWUA3wfeI6lL0hjgIOD+FuTJzMz61lc/OsB04DtAQ33opdZkcuA4EjirsOxsgIi4MiLulzQfuAdYD1wVEfeWmSczsxFqvKSFhddzImJOH9vW7EeXNBH4IHAYcGAjOy01yETEKmBc1bIrq15fAlxSZj7MzIzlEdHd30aFfvTza6y+DDgvItZJaminHoXZzMyK6vWjdwPX5wAzHjhW0tqI+F5fiTnImJlZUZ/96BGxW+W5pLnAj+oFGPDYZWZmlhX60W8sLDu70pc+GK7JmJkZ0Fg/emH56Y2k6ZqMmZmVxkHGzMxK4yBjZmalcZAxM7PSOMiYmVlpHGTMzKw0voQ5mz17Nj09PYN6b+V9M2bMGNT7p0yZwvTp0wf13nZp5/cFnfmdWWfwb0FzOcg0wejRo9udhY7i78uGK5ftjSki2p2HAenu7o6FCxf2v6HZIEha1MgggmVw2bYytatsu0/GzMxK4yBjZmalcZAxM7PSOMiYmVlpHGTMzKw0DjJmZlYaBxkzMyuNg4yZmZWm427GlLQMeKzd+ahhPLC83ZnoIEP1+9o1Iia0Y8cu28PGUP2+2lK2Oy7IDFWSFrbrTvFO5O+rc/h/NTD+vjbk5jIzMyuNg4yZmZXGQaZ55rQ7Ax3G31fn8P9qYPx9FbhPxszMSuOajJmZlcZBxszMSuMg02SSbpHUXXg9WdK9+fkYSddKWiLpXkm3SRpb2HY/SSHp6HbkvVmKn7mwbKakf5B0sKQ7JC2WdL+kmVXbzZLUK8llcwhxuU5ctgfO0y+31gzgmYj4YwBJewJrCutPBm7Lf3/S+uy1xDzgLyPibkmjgD0rK/LB90HgceC9wC1tyaENlMt14rJdw4iKqEX5jOR+SV+TdJ+kBZJGS9pX0u2S7pH0XUnb5+1vkXSxpF9LelDSewax2zcCvZUXEfFARLyS0xdwInA6cJSkLTf9Uw5JOwJPAUTEuoj4TWHdocC9wFdJP0g2QC7XbeWyXcOIDTLZHsBXImJv4Hngz4GrgfMi4u3AEuCCwvZdEfEnwCeqljfqG8B5kn4l6QuS9iisezfwSET8jnSWc+wg0u8E/w48kH/ozqr60TkZuA74LnCcpM3bksPO53LdHi7bNYz0IPNIRCzOzxcBuwPbRcStedk8UtW24sbCtpP7SLPWNeEBkPf1ZuASYAfgfyW9LW9zMnB9fn49nX2209d18RERnwO6gQXAR4D5AJK2IP0AfS8iXgTuAI5qQV6HI5fr8rhsD9BI75N5pfB8HbBdg9uvI393kr4J7Ac8GRHHAiuA7Qvv2YHCYHkRsZJ0UN8oaT1wrKQHSWebx0v6DCBgnKStI+KlQX62dqr+DiB9D48A5LPar0r6GrBM0jjSGe+2wJLUwsIYYBXw/1qV6WHE5bo8LtsDNNJrMtVeAH5faJc+Fbi1zvZExBkRsW8+ECE1CZyS26IBpgE/A5D07kJb+BbAXqRRd48A7o6IXSJickTsCnwH+EDTPlkL5R+cpyQdDiBpB2AqcJuk9xW+mz1IP2zPk85wP54//2RgN1Ib/phW538YcrluEpftgXOQ2dg04BJJ9wD7Ap8b4PvnAC8Bd0u6GxgLXJrX7Q7cKmkJcBewkHTQnUxqqy36DqnK3alOAz4raTHwU+DCfJZ3KqndejHwn8BfAW8AjqZwZhcRL5OuSHp/a7M9bLlcN4/L9gB4WBkzMyuNazJmZlYaBxkzMyuNg4yZmZXGQcbMzErjIGNmZqVxkOlQklbmv5Mlrc4jv1YeWyiPDFv1nkcljc/PP5PHtronv+egdnwOs2ou28PLSL/jf7j4XUTsW1zw+j1hG5P0TuA4YP+IeCUfnFuUmkOzwXHZ7nAOMiPTG4HllZFyI2J5P9ubdQqX7SHGzWXDw+6F5oSvNLD9AmCXPLT7FZL+rOwMmg2Sy3aHc01meNioSYH6o8WulHQA8B7SPBf/V9KnImJuiXk0GwyX7Q7nIDN8rSA1HRRtTRqwj4hYRxr08JY85tQ0YG7rsmc2aC7bHcTNZcPXz0lDrG8NIOlDpBFx10nas2piqX1Jo+aadQKX7Q7imswwFRH3SLqcNAR5AM8CH8+rxwKzJW0HrAV6gDPbklGzAXLZ7iwehdnMzErj5jIzMyuNg4yZmZXGQcbMzErjIGNmZqVxkDEzs9I4yJiZWWkcZMzMrDT/Hy8tZj1q9V99AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box plot - 2: VotesUS(y) vs IFUS(x)\n",
    "\n",
    "plt.figure(figsize=[10,20])\n",
    "\n",
    "fig, axes= plt.subplots(nrows=1, ncols=2)\n",
    "\n",
    "box1=sns.boxplot(x=movies['IFUS'], y = movies['VotesUS'], orient='v', ax=axes[0], )\n",
    "\n",
    "box1.set_title('Votes of USA citizen')\n",
    "\n",
    "box2=sns.boxplot(x=movies['IFUS'], y = movies['VotesnUS'], orient='v')\n",
    "\n",
    "box2.yaxis.tick_right()\n",
    "\n",
    "box2.set_title('Votes of Non-USA citizen')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`Inferences:`** Write your two inferences/observations below:\n",
    "- Inference 1: Both US & Non-US people have voted lesser for Non-USA movies\n",
    "- Inference 2: In both the boxplots, USA movies have some outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Subtask 3.5:  Top 1000 Voters Vs Genres\n",
    "\n",
    "You might have also observed the column `CVotes1000`. This column represents the top 1000 voters on IMDb and gives the count for the number of these voters who have voted for a particular movie. Let's see how these top 1000 voters have voted across the genres. \n",
    "\n",
    "1. Sort the dataframe genre_top10 based on the value of `CVotes1000`in a descending order.\n",
    "\n",
    "2. Make a seaborn barplot for `genre` vs `CVotes1000`.\n",
    "\n",
    "3. Write your inferences. You can also try to relate it with the heatmaps you did in the previous subtasks.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "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>CVotes10</th>\n",
       "      <th>CVotes09</th>\n",
       "      <th>CVotes08</th>\n",
       "      <th>CVotes07</th>\n",
       "      <th>CVotes06</th>\n",
       "      <th>CVotes05</th>\n",
       "      <th>CVotes04</th>\n",
       "      <th>CVotes03</th>\n",
       "      <th>CVotes02</th>\n",
       "      <th>CVotes01</th>\n",
       "      <th>...</th>\n",
       "      <th>Votes3044</th>\n",
       "      <th>Votes3044M</th>\n",
       "      <th>Votes3044F</th>\n",
       "      <th>Votes45A</th>\n",
       "      <th>Votes45AM</th>\n",
       "      <th>Votes45AF</th>\n",
       "      <th>Votes1000</th>\n",
       "      <th>VotesUS</th>\n",
       "      <th>VotesnUS</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sci-Fi</th>\n",
       "      <td>136781.411765</td>\n",
       "      <td>148873.823529</td>\n",
       "      <td>176646.705882</td>\n",
       "      <td>106005.764706</td>\n",
       "      <td>39518.294118</td>\n",
       "      <td>14951.470588</td>\n",
       "      <td>6583.823529</td>\n",
       "      <td>3876.705882</td>\n",
       "      <td>2715.941176</td>\n",
       "      <td>6731.470588</td>\n",
       "      <td>...</td>\n",
       "      <td>7.858824</td>\n",
       "      <td>7.852941</td>\n",
       "      <td>7.835294</td>\n",
       "      <td>7.711765</td>\n",
       "      <td>7.694118</td>\n",
       "      <td>7.735294</td>\n",
       "      <td>7.523529</td>\n",
       "      <td>8.088235</td>\n",
       "      <td>7.882353</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>102144.096774</td>\n",
       "      <td>114433.193548</td>\n",
       "      <td>150895.322581</td>\n",
       "      <td>94262.129032</td>\n",
       "      <td>34688.838710</td>\n",
       "      <td>12693.032258</td>\n",
       "      <td>5386.129032</td>\n",
       "      <td>3064.645161</td>\n",
       "      <td>2115.258065</td>\n",
       "      <td>5524.096774</td>\n",
       "      <td>...</td>\n",
       "      <td>7.741935</td>\n",
       "      <td>7.725806</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>7.645161</td>\n",
       "      <td>7.625806</td>\n",
       "      <td>7.754839</td>\n",
       "      <td>7.296774</td>\n",
       "      <td>7.987097</td>\n",
       "      <td>7.761290</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>83207.769231</td>\n",
       "      <td>112730.076923</td>\n",
       "      <td>153336.769231</td>\n",
       "      <td>90446.076923</td>\n",
       "      <td>32003.538462</td>\n",
       "      <td>11534.846154</td>\n",
       "      <td>5021.615385</td>\n",
       "      <td>2918.461538</td>\n",
       "      <td>1982.076923</td>\n",
       "      <td>4433.076923</td>\n",
       "      <td>...</td>\n",
       "      <td>7.738462</td>\n",
       "      <td>7.746154</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>7.661538</td>\n",
       "      <td>7.638462</td>\n",
       "      <td>7.746154</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>7.930769</td>\n",
       "      <td>7.807692</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>94596.289474</td>\n",
       "      <td>105636.631579</td>\n",
       "      <td>138482.315789</td>\n",
       "      <td>86367.921053</td>\n",
       "      <td>31896.710526</td>\n",
       "      <td>11551.842105</td>\n",
       "      <td>4817.631579</td>\n",
       "      <td>2718.894737</td>\n",
       "      <td>1835.184211</td>\n",
       "      <td>4575.210526</td>\n",
       "      <td>...</td>\n",
       "      <td>7.752632</td>\n",
       "      <td>7.728947</td>\n",
       "      <td>7.873684</td>\n",
       "      <td>7.676316</td>\n",
       "      <td>7.642105</td>\n",
       "      <td>7.842105</td>\n",
       "      <td>7.384211</td>\n",
       "      <td>7.986842</td>\n",
       "      <td>7.794737</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>52229.636364</td>\n",
       "      <td>87919.818182</td>\n",
       "      <td>129045.000000</td>\n",
       "      <td>74671.818182</td>\n",
       "      <td>25308.272727</td>\n",
       "      <td>8971.818182</td>\n",
       "      <td>3842.818182</td>\n",
       "      <td>2246.636364</td>\n",
       "      <td>1544.090909</td>\n",
       "      <td>3383.363636</td>\n",
       "      <td>...</td>\n",
       "      <td>7.718182</td>\n",
       "      <td>7.763636</td>\n",
       "      <td>7.609091</td>\n",
       "      <td>7.627273</td>\n",
       "      <td>7.618182</td>\n",
       "      <td>7.681818</td>\n",
       "      <td>7.390909</td>\n",
       "      <td>7.981818</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                CVotes10       CVotes09       CVotes08       CVotes07  \\\n",
       "Sci-Fi     136781.411765  148873.823529  176646.705882  106005.764706   \n",
       "Action     102144.096774  114433.193548  150895.322581   94262.129032   \n",
       "Thriller    83207.769231  112730.076923  153336.769231   90446.076923   \n",
       "Adventure   94596.289474  105636.631579  138482.315789   86367.921053   \n",
       "Crime       52229.636364   87919.818182  129045.000000   74671.818182   \n",
       "\n",
       "               CVotes06      CVotes05     CVotes04     CVotes03     CVotes02  \\\n",
       "Sci-Fi     39518.294118  14951.470588  6583.823529  3876.705882  2715.941176   \n",
       "Action     34688.838710  12693.032258  5386.129032  3064.645161  2115.258065   \n",
       "Thriller   32003.538462  11534.846154  5021.615385  2918.461538  1982.076923   \n",
       "Adventure  31896.710526  11551.842105  4817.631579  2718.894737  1835.184211   \n",
       "Crime      25308.272727   8971.818182  3842.818182  2246.636364  1544.090909   \n",
       "\n",
       "              CVotes01  ...  Votes3044  Votes3044M  Votes3044F  Votes45A  \\\n",
       "Sci-Fi     6731.470588  ...   7.858824    7.852941    7.835294  7.711765   \n",
       "Action     5524.096774  ...   7.741935    7.725806    7.800000  7.645161   \n",
       "Thriller   4433.076923  ...   7.738462    7.746154    7.700000  7.661538   \n",
       "Adventure  4575.210526  ...   7.752632    7.728947    7.873684  7.676316   \n",
       "Crime      3383.363636  ...   7.718182    7.763636    7.609091  7.627273   \n",
       "\n",
       "           Votes45AM  Votes45AF  Votes1000   VotesUS  VotesnUS  cnt  \n",
       "Sci-Fi      7.694118   7.735294   7.523529  8.088235  7.882353  1.0  \n",
       "Action      7.625806   7.754839   7.296774  7.987097  7.761290  1.0  \n",
       "Thriller    7.638462   7.746154   7.400000  7.930769  7.807692  1.0  \n",
       "Adventure   7.642105   7.842105   7.384211  7.986842  7.794737  1.0  \n",
       "Crime       7.618182   7.681818   7.390909  7.981818  7.800000  1.0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting by CVotes1000\n",
    "CVotes_genre_top10=genre_top10.sort_values('CVotes1000',ascending=False)\n",
    "\n",
    "CVotes_genre_top10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot\n",
    "\n",
    "plt.figure(figsize=[10,5])\n",
    "\n",
    "boxplt1= sns.barplot(x=CVotes_genre_top10.index, y=CVotes_genre_top10.CVotes1000)\n",
    "\n",
    "plt.xlabel('Genres',fontsize=15)\n",
    "\n",
    "plt.ylabel('CVotes1000',fontsize=15)\n",
    "\n",
    "plt.title('genre vs CVotes1000', fontsize=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`Inferences: Sci-Fi movies are the most popular movie type. All movies are having votes more than 500."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`Checkpoint 6:`** The genre `Romance` seems to be most unpopular among the top 1000 voters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the above subtask, your assignment is over. In your free time, do explore the dataset further on your own and see what kind of other insights you can get across various other columns."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}