movie_services_dataset_analysis-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "afe8d4c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the necessary libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as  plt \n",
    "# import plotly.offline as pyo\n",
    "# pyo.init_notebook_mode()\n",
    "# import plotly.express as px\n",
    "# %matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "b9b483df",
   "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>Unnamed: 0</th>\n",
       "      <th>ID</th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Age</th>\n",
       "      <th>IMDb</th>\n",
       "      <th>Rotten Tomatoes</th>\n",
       "      <th>Netflix</th>\n",
       "      <th>Hulu</th>\n",
       "      <th>Prime Video</th>\n",
       "      <th>Disney+</th>\n",
       "      <th>Type</th>\n",
       "      <th>Directors</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>Runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>13+</td>\n",
       "      <td>8.8</td>\n",
       "      <td>87%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Action,Adventure,Sci-Fi,Thriller</td>\n",
       "      <td>United States,United Kingdom</td>\n",
       "      <td>English,Japanese,French</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>The Matrix</td>\n",
       "      <td>1999</td>\n",
       "      <td>18+</td>\n",
       "      <td>8.7</td>\n",
       "      <td>87%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Lana Wachowski,Lilly Wachowski</td>\n",
       "      <td>Action,Sci-Fi</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>2018</td>\n",
       "      <td>13+</td>\n",
       "      <td>8.5</td>\n",
       "      <td>84%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Anthony Russo,Joe Russo</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Back to the Future</td>\n",
       "      <td>1985</td>\n",
       "      <td>7+</td>\n",
       "      <td>8.5</td>\n",
       "      <td>96%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Robert Zemeckis</td>\n",
       "      <td>Adventure,Comedy,Sci-Fi</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>The Good, the Bad and the Ugly</td>\n",
       "      <td>1966</td>\n",
       "      <td>18+</td>\n",
       "      <td>8.8</td>\n",
       "      <td>97%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sergio Leone</td>\n",
       "      <td>Western</td>\n",
       "      <td>Italy,Spain,West Germany</td>\n",
       "      <td>Italian</td>\n",
       "      <td>161.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  ID                           Title  Year  Age  IMDb  \\\n",
       "0           0   1                       Inception  2010  13+   8.8   \n",
       "1           1   2                      The Matrix  1999  18+   8.7   \n",
       "2           2   3          Avengers: Infinity War  2018  13+   8.5   \n",
       "3           3   4              Back to the Future  1985   7+   8.5   \n",
       "4           4   5  The Good, the Bad and the Ugly  1966  18+   8.8   \n",
       "\n",
       "  Rotten Tomatoes  Netflix  Hulu  Prime Video  Disney+  Type  \\\n",
       "0             87%        1     0            0        0     0   \n",
       "1             87%        1     0            0        0     0   \n",
       "2             84%        1     0            0        0     0   \n",
       "3             96%        1     0            0        0     0   \n",
       "4             97%        1     0            1        0     0   \n",
       "\n",
       "                        Directors                            Genres  \\\n",
       "0               Christopher Nolan  Action,Adventure,Sci-Fi,Thriller   \n",
       "1  Lana Wachowski,Lilly Wachowski                     Action,Sci-Fi   \n",
       "2         Anthony Russo,Joe Russo           Action,Adventure,Sci-Fi   \n",
       "3                 Robert Zemeckis           Adventure,Comedy,Sci-Fi   \n",
       "4                    Sergio Leone                           Western   \n",
       "\n",
       "                        Country                 Language  Runtime  \n",
       "0  United States,United Kingdom  English,Japanese,French    148.0  \n",
       "1                 United States                  English    136.0  \n",
       "2                 United States                  English    149.0  \n",
       "3                 United States                  English    116.0  \n",
       "4      Italy,Spain,West Germany                  Italian    161.0  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import the dataset into a dataframe\n",
    "df = pd.read_csv (r'C:\\Users\\user\\Desktop\\test\\moviestreams.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4e331b6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16744, 17)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the number of rows and  columns of the dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e4dfc917",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show number of dimensions of the df\n",
    "df.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "073a7c91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284648"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count the number of elements in the dataset\n",
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "345dbf0d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Unnamed: 0',\n",
       " 'ID',\n",
       " 'Title',\n",
       " 'Year',\n",
       " 'Age',\n",
       " 'IMDb',\n",
       " 'Rotten Tomatoes',\n",
       " 'Netflix',\n",
       " 'Hulu',\n",
       " 'Prime Video',\n",
       " 'Disney+',\n",
       " 'Type',\n",
       " 'Directors',\n",
       " 'Genres',\n",
       " 'Country',\n",
       " 'Language',\n",
       " 'Runtime']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show columns names\n",
    "cols = df.columns.tolist()\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0d674012",
   "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>Unnamed: 0</th>\n",
       "      <th>ID</th>\n",
       "      <th>Year</th>\n",
       "      <th>IMDb</th>\n",
       "      <th>Netflix</th>\n",
       "      <th>Hulu</th>\n",
       "      <th>Prime Video</th>\n",
       "      <th>Disney+</th>\n",
       "      <th>Type</th>\n",
       "      <th>Runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16744.000000</td>\n",
       "      <td>16744.000000</td>\n",
       "      <td>16744.000000</td>\n",
       "      <td>16173.000000</td>\n",
       "      <td>16744.000000</td>\n",
       "      <td>16744.000000</td>\n",
       "      <td>16744.000000</td>\n",
       "      <td>16744.000000</td>\n",
       "      <td>16744.0</td>\n",
       "      <td>16152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8371.500000</td>\n",
       "      <td>8372.500000</td>\n",
       "      <td>2003.014035</td>\n",
       "      <td>5.902751</td>\n",
       "      <td>0.212613</td>\n",
       "      <td>0.053930</td>\n",
       "      <td>0.737817</td>\n",
       "      <td>0.033684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.413447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4833.720789</td>\n",
       "      <td>4833.720789</td>\n",
       "      <td>20.674321</td>\n",
       "      <td>1.347867</td>\n",
       "      <td>0.409169</td>\n",
       "      <td>0.225886</td>\n",
       "      <td>0.439835</td>\n",
       "      <td>0.180419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.219222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1902.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4185.750000</td>\n",
       "      <td>4186.750000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8371.500000</td>\n",
       "      <td>8372.500000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12557.250000</td>\n",
       "      <td>12558.250000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16743.000000</td>\n",
       "      <td>16744.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1256.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0            ID          Year          IMDb       Netflix  \\\n",
       "count  16744.000000  16744.000000  16744.000000  16173.000000  16744.000000   \n",
       "mean    8371.500000   8372.500000   2003.014035      5.902751      0.212613   \n",
       "std     4833.720789   4833.720789     20.674321      1.347867      0.409169   \n",
       "min        0.000000      1.000000   1902.000000      0.000000      0.000000   \n",
       "25%     4185.750000   4186.750000   2000.000000      5.100000      0.000000   \n",
       "50%     8371.500000   8372.500000   2012.000000      6.100000      0.000000   \n",
       "75%    12557.250000  12558.250000   2016.000000      6.900000      0.000000   \n",
       "max    16743.000000  16744.000000   2020.000000      9.300000      1.000000   \n",
       "\n",
       "               Hulu   Prime Video       Disney+     Type       Runtime  \n",
       "count  16744.000000  16744.000000  16744.000000  16744.0  16152.000000  \n",
       "mean       0.053930      0.737817      0.033684      0.0     93.413447  \n",
       "std        0.225886      0.439835      0.180419      0.0     28.219222  \n",
       "min        0.000000      0.000000      0.000000      0.0      1.000000  \n",
       "25%        0.000000      0.000000      0.000000      0.0     82.000000  \n",
       "50%        0.000000      1.000000      0.000000      0.0     92.000000  \n",
       "75%        0.000000      1.000000      0.000000      0.0    104.000000  \n",
       "max        1.000000      1.000000      1.000000      0.0   1256.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() # statistical description"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59bc3900",
   "metadata": {},
   "source": [
    "# Data manipulation \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "f30434be",
   "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>Year</th>\n",
       "      <th>Age</th>\n",
       "      <th>IMDb</th>\n",
       "      <th>Rotten Tomatoes</th>\n",
       "      <th>Netflix</th>\n",
       "      <th>Hulu</th>\n",
       "      <th>Prime Video</th>\n",
       "      <th>Disney+</th>\n",
       "      <th>Type</th>\n",
       "      <th>Directors</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>Runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>13+</td>\n",
       "      <td>8.8</td>\n",
       "      <td>87%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Action,Adventure,Sci-Fi,Thriller</td>\n",
       "      <td>United States,United Kingdom</td>\n",
       "      <td>English,Japanese,French</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Matrix</td>\n",
       "      <td>1999</td>\n",
       "      <td>18+</td>\n",
       "      <td>8.7</td>\n",
       "      <td>87%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Lana Wachowski,Lilly Wachowski</td>\n",
       "      <td>Action,Sci-Fi</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>2018</td>\n",
       "      <td>13+</td>\n",
       "      <td>8.5</td>\n",
       "      <td>84%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Anthony Russo,Joe Russo</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Back to the Future</td>\n",
       "      <td>1985</td>\n",
       "      <td>7+</td>\n",
       "      <td>8.5</td>\n",
       "      <td>96%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Robert Zemeckis</td>\n",
       "      <td>Adventure,Comedy,Sci-Fi</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The Good, the Bad and the Ugly</td>\n",
       "      <td>1966</td>\n",
       "      <td>18+</td>\n",
       "      <td>8.8</td>\n",
       "      <td>97%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sergio Leone</td>\n",
       "      <td>Western</td>\n",
       "      <td>Italy,Spain,West Germany</td>\n",
       "      <td>Italian</td>\n",
       "      <td>161.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Title  Year  Age  IMDb Rotten Tomatoes  Netflix  \\\n",
       "0                       Inception  2010  13+   8.8             87%        1   \n",
       "1                      The Matrix  1999  18+   8.7             87%        1   \n",
       "2          Avengers: Infinity War  2018  13+   8.5             84%        1   \n",
       "3              Back to the Future  1985   7+   8.5             96%        1   \n",
       "4  The Good, the Bad and the Ugly  1966  18+   8.8             97%        1   \n",
       "\n",
       "   Hulu  Prime Video  Disney+  Type                       Directors  \\\n",
       "0     0            0        0     0               Christopher Nolan   \n",
       "1     0            0        0     0  Lana Wachowski,Lilly Wachowski   \n",
       "2     0            0        0     0         Anthony Russo,Joe Russo   \n",
       "3     0            0        0     0                 Robert Zemeckis   \n",
       "4     0            1        0     0                    Sergio Leone   \n",
       "\n",
       "                             Genres                       Country  \\\n",
       "0  Action,Adventure,Sci-Fi,Thriller  United States,United Kingdom   \n",
       "1                     Action,Sci-Fi                 United States   \n",
       "2           Action,Adventure,Sci-Fi                 United States   \n",
       "3           Adventure,Comedy,Sci-Fi                 United States   \n",
       "4                           Western      Italy,Spain,West Germany   \n",
       "\n",
       "                  Language  Runtime  \n",
       "0  English,Japanese,French    148.0  \n",
       "1                  English    136.0  \n",
       "2                  English    149.0  \n",
       "3                  English    116.0  \n",
       "4                  Italian    161.0  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop no needed columns \n",
    "df.drop(['Unnamed: 0','ID'],axis = 1, inplace=True )\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a7d1b9bd",
   "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>Year</th>\n",
       "      <th>IMDb</th>\n",
       "      <th>Netflix</th>\n",
       "      <th>Hulu</th>\n",
       "      <th>Prime Video</th>\n",
       "      <th>Disney+</th>\n",
       "      <th>Type</th>\n",
       "      <th>Runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16744.000000</td>\n",
       "      <td>16173.000000</td>\n",
       "      <td>16744.000000</td>\n",
       "      <td>16744.000000</td>\n",
       "      <td>16744.000000</td>\n",
       "      <td>16744.000000</td>\n",
       "      <td>16744.0</td>\n",
       "      <td>16152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2003.014035</td>\n",
       "      <td>5.902751</td>\n",
       "      <td>0.212613</td>\n",
       "      <td>0.053930</td>\n",
       "      <td>0.737817</td>\n",
       "      <td>0.033684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.413447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>20.674321</td>\n",
       "      <td>1.347867</td>\n",
       "      <td>0.409169</td>\n",
       "      <td>0.225886</td>\n",
       "      <td>0.439835</td>\n",
       "      <td>0.180419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.219222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1902.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1256.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Year          IMDb       Netflix          Hulu   Prime Video  \\\n",
       "count  16744.000000  16173.000000  16744.000000  16744.000000  16744.000000   \n",
       "mean    2003.014035      5.902751      0.212613      0.053930      0.737817   \n",
       "std       20.674321      1.347867      0.409169      0.225886      0.439835   \n",
       "min     1902.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%     2000.000000      5.100000      0.000000      0.000000      0.000000   \n",
       "50%     2012.000000      6.100000      0.000000      0.000000      1.000000   \n",
       "75%     2016.000000      6.900000      0.000000      0.000000      1.000000   \n",
       "max     2020.000000      9.300000      1.000000      1.000000      1.000000   \n",
       "\n",
       "            Disney+     Type       Runtime  \n",
       "count  16744.000000  16744.0  16152.000000  \n",
       "mean       0.033684      0.0     93.413447  \n",
       "std        0.180419      0.0     28.219222  \n",
       "min        0.000000      0.0      1.000000  \n",
       "25%        0.000000      0.0     82.000000  \n",
       "50%        0.000000      0.0     92.000000  \n",
       "75%        0.000000      0.0    104.000000  \n",
       "max        1.000000      0.0   1256.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() # statistical description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "213cc46c",
   "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>Year</th>\n",
       "      <th>Age</th>\n",
       "      <th>IMDb</th>\n",
       "      <th>Rotten Tomatoes</th>\n",
       "      <th>Netflix</th>\n",
       "      <th>Hulu</th>\n",
       "      <th>Prime Video</th>\n",
       "      <th>Disney+</th>\n",
       "      <th>Type</th>\n",
       "      <th>Directors</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>Runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Title, Year, Age, IMDb, Rotten Tomatoes, Netflix, Hulu, Prime Video, Disney+, Type, Directors, Genres, Country, Language, Runtime]\n",
       "Index: []"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show duplicated rows\n",
    "df[df.duplicated()]\n",
    "\n",
    "#NO duplicated rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea0e55bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16744 entries, 0 to 16743\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Title            16744 non-null  object \n",
      " 1   Year             16744 non-null  int64  \n",
      " 2   Age              7354 non-null   object \n",
      " 3   IMDb             16173 non-null  float64\n",
      " 4   Rotten Tomatoes  5158 non-null   object \n",
      " 5   Netflix          16744 non-null  int64  \n",
      " 6   Hulu             16744 non-null  int64  \n",
      " 7   Prime Video      16744 non-null  int64  \n",
      " 8   Disney+          16744 non-null  int64  \n",
      " 9   Type             16744 non-null  int64  \n",
      " 10  Directors        16018 non-null  object \n",
      " 11  Genres           16469 non-null  object \n",
      " 12  Country          16309 non-null  object \n",
      " 13  Language         16145 non-null  object \n",
      " 14  Runtime          16152 non-null  float64\n",
      "dtypes: float64(2), int64(6), object(7)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "#count non null values and show column type\n",
    "df.info(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "566bf08c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title                  0\n",
       "Year                   0\n",
       "Age                 9390\n",
       "IMDb                 571\n",
       "Rotten Tomatoes    11586\n",
       "Netflix                0\n",
       "Hulu                   0\n",
       "Prime Video            0\n",
       "Disney+                0\n",
       "Type                   0\n",
       "Directors            726\n",
       "Genres               275\n",
       "Country              435\n",
       "Language             599\n",
       "Runtime              592\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count null values per columns\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ec99f60d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.heatmap(df.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8d07384f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    13+\n",
       "1    18+\n",
       "2    13+\n",
       "3     7+\n",
       "4    18+\n",
       "Name: Age, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change data type of 'age' column\n",
    "\n",
    "df['Age'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c8101c8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEDCAYAAADZUdTgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAATSElEQVR4nO3df5Bd9Xnf8fcnAuNMTIIwW1WW1IjGcjK4mQi6ATruNA6MQcAf4JnEhcwYlSFVksLUmUk7kT3tYJvSoW1sps4YUmVQEG0SwuQXGlCNFULieFJ+LI4CCEzZ8qNII8MmwsQe16SQp3/sV9W12NXdlVb3Lvm+XzN39pznfM+5z7lz9LlH5567m6pCktSH7xp3A5Kk0TH0Jakjhr4kdcTQl6SOGPqS1JGTxt3A0Zxxxhm1fv36cbchSW8rjz322F9U1cRcy5Z16K9fv56pqalxtyFJbytJXpxvmZd3JKkjhr4kdcTQl6SOGPqS1JGhoZ/knUkeSfLnSfYm+VSr35Hk+SR72mNjqyfJ55JMJ3k8yTkD29qc5Nn22HzC9kqSNKeF3L3zOnBBVX0zycnAl5P897bsX1fVbx8x/hJgQ3ucB9wGnJfkdOAGYBIo4LEkO6vq1aXYEUnScEPP9GvWN9vsye1xtF/NeTlwZ1vvIeC0JKuBi4HdVXWwBf1uYNPxtS9JWowFXdNPsiLJHuAVZoP74bbopnYJ55Ykp7TaGuClgdX3tdp89SOfa0uSqSRTMzMzi9sbSdJRLSj0q+rNqtoIrAXOTfIPgI8DPwT8KHA68ItL0VBVbauqyaqanJiY8wtlkqRjtKhv5FbV15M8CGyqql9q5deT/Brwr9r8fmDdwGprW20/8MEj6n90DD0vyvqt953op1iQF26+bNwtSNKC7t6ZSHJam/5u4EPAV9t1epIEuAJ4sq2yE7i63cVzPvBaVR0A7gcuSrIyyUrgolaTJI3IQs70VwM7kqxg9k3i7qq6N8kfJpkAAuwBfraN3wVcCkwD3wKuAaiqg0luBB5t4z5dVQeXbE8kSUMNDf2qehw4e476BfOML+C6eZZtB7YvskdJ0hLxG7mS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHRka+knemeSRJH+eZG+ST7X6mUkeTjKd5LeSvKPVT2nz0235+oFtfbzVn0ly8QnbK0nSnBZypv86cEFV/QiwEdiU5HzgPwC3VNV7gVeBa9v4a4FXW/2WNo4kZwFXAu8HNgG3JlmxhPsiSRpiaOjXrG+22ZPbo4ALgN9u9R3AFW368jZPW35hkrT6XVX1elU9D0wD5y7FTkiSFmZB1/STrEiyB3gF2A38L+DrVfVGG7IPWNOm1wAvAbTlrwHvHqzPsc7gc21JMpVkamZmZtE7JEma34JCv6rerKqNwFpmz85/6EQ1VFXbqmqyqiYnJiZO1NNIUpcWdfdOVX0deBD4R8BpSU5qi9YC+9v0fmAdQFv+fcBfDtbnWEeSNAILuXtnIslpbfq7gQ8BTzMb/j/Rhm0G7mnTO9s8bfkfVlW1+pXt7p4zgQ3AI0u0H5KkBThp+BBWAzvanTbfBdxdVfcmeQq4K8m/A/4MuL2Nvx34r0mmgYPM3rFDVe1NcjfwFPAGcF1Vvbm0uyNJOpqhoV9VjwNnz1F/jjnuvqmqbwM/Oc+2bgJuWnybkqSl4DdyJakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjoyNPSTrEvyYJKnkuxN8rFW/2SS/Un2tMelA+t8PMl0kmeSXDxQ39Rq00m2nphdkiTN56QFjHkD+IWq+kqSU4HHkuxuy26pql8aHJzkLOBK4P3Ae4A/SPK+tvjzwIeAfcCjSXZW1VNLsSOSpOGGhn5VHQAOtOlvJHkaWHOUVS4H7qqq14Hnk0wD57Zl01X1HECSu9pYQ1+SRmRR1/STrAfOBh5upeuTPJ5ke5KVrbYGeGlgtX2tNl/9yOfYkmQqydTMzMxi2pMkDbHg0E/yLuB3gJ+vqr8CbgN+ANjI7P8EPrMUDVXVtqqarKrJiYmJpdikJKlZyDV9kpzMbOD/elX9LkBVvTyw/FeBe9vsfmDdwOprW42j1CVJI7CQu3cC3A48XVWfHaivHhj2YeDJNr0TuDLJKUnOBDYAjwCPAhuSnJnkHcx+2LtzaXZDkrQQCznT/wDwUeCJJHta7RPAVUk2AgW8APwMQFXtTXI3sx/QvgFcV1VvAiS5HrgfWAFsr6q9S7YnkqShFnL3zpeBzLFo11HWuQm4aY76rqOtJ0k6sfxGriR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOjI09JOsS/JgkqeS7E3ysVY/PcnuJM+2nytbPUk+l2Q6yeNJzhnY1uY2/tkkm0/cbkmS5rKQM/03gF+oqrOA84HrkpwFbAUeqKoNwANtHuASYEN7bAFug9k3CeAG4DzgXOCGQ28UkqTRGBr6VXWgqr7Spr8BPA2sAS4HdrRhO4Ar2vTlwJ016yHgtCSrgYuB3VV1sKpeBXYDm5ZyZyRJR7eoa/pJ1gNnAw8Dq6rqQFv0NWBVm14DvDSw2r5Wm69+5HNsSTKVZGpmZmYx7UmShlhw6Cd5F/A7wM9X1V8NLquqAmopGqqqbVU1WVWTExMTS7FJSVKzoNBPcjKzgf/rVfW7rfxyu2xD+/lKq+8H1g2svrbV5qtLkkZkIXfvBLgdeLqqPjuwaCdw6A6czcA9A/Wr21085wOvtctA9wMXJVnZPsC9qNUkSSNy0gLGfAD4KPBEkj2t9gngZuDuJNcCLwIfact2AZcC08C3gGsAqupgkhuBR9u4T1fVwaXYCUnSwgwN/ar6MpB5Fl84x/gCrptnW9uB7YtpUJK0dPxGriR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHhoZ+ku1JXkny5EDtk0n2J9nTHpcOLPt4kukkzyS5eKC+qdWmk2xd+l2RJA2zkDP9O4BNc9RvqaqN7bELIMlZwJXA+9s6tyZZkWQF8HngEuAs4Ko2VpI0QicNG1BVX0qyfoHbuxy4q6peB55PMg2c25ZNV9VzAEnuamOfWnzLkqRjdTzX9K9P8ni7/LOy1dYALw2M2ddq89XfIsmWJFNJpmZmZo6jPUnSkY419G8DfgDYCBwAPrNUDVXVtqqarKrJiYmJpdqsJIkFXN6ZS1W9fGg6ya8C97bZ/cC6gaFrW42j1CVJI3JMoZ9kdVUdaLMfBg7d2bMT+I0knwXeA2wAHgECbEhyJrNhfyXwU8fTuBZv/db7xt0CAC/cfNm4W5C6NTT0k/wm8EHgjCT7gBuADybZCBTwAvAzAFW1N8ndzH5A+wZwXVW92bZzPXA/sALYXlV7l3pnJElHt5C7d66ao3z7UcbfBNw0R30XsGtR3UmSlpTfyJWkjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4c0+/ekd7u/D1E6pVn+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkeGhn6S7UleSfLkQO30JLuTPNt+rmz1JPlckukkjyc5Z2CdzW38s0k2n5jdkSQdzULO9O8ANh1R2wo8UFUbgAfaPMAlwIb22ALcBrNvEsANwHnAucANh94oJEmjMzT0q+pLwMEjypcDO9r0DuCKgfqdNesh4LQkq4GLgd1VdbCqXgV289Y3EknSCXas1/RXVdWBNv01YFWbXgO8NDBuX6vNV3+LJFuSTCWZmpmZOcb2JElzOe4PcquqgFqCXg5tb1tVTVbV5MTExFJtVpLEsYf+y+2yDe3nK62+H1g3MG5tq81XlySN0LGG/k7g0B04m4F7BupXt7t4zgdea5eB7gcuSrKyfYB7UatJkkZo6J9LTPKbwAeBM5LsY/YunJuBu5NcC7wIfKQN3wVcCkwD3wKuAaiqg0luBB5t4z5dVUd+OCxJOsGGhn5VXTXPogvnGFvAdfNsZzuwfVHdSZKWlN/IlaSOGPqS1BFDX5I6MvSavqS/3dZvvW/cLQDwws2XjbuFLnimL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR05rtBP8kKSJ5LsSTLVaqcn2Z3k2fZzZasnyeeSTCd5PMk5S7EDkqSFW4oz/R+vqo1VNdnmtwIPVNUG4IE2D3AJsKE9tgC3LcFzS5IW4URc3rkc2NGmdwBXDNTvrFkPAaclWX0Cnl+SNI/jDf0CvpjksSRbWm1VVR1o018DVrXpNcBLA+vua7XvkGRLkqkkUzMzM8fZniRp0EnHuf4/rqr9Sf4OsDvJVwcXVlUlqcVssKq2AdsAJicnF7WuJOnojutMv6r2t5+vAL8HnAu8fOiyTfv5Shu+H1g3sPraVpMkjcgxh36S70ly6qFp4CLgSWAnsLkN2wzc06Z3Ale3u3jOB14buAwkSRqB47m8swr4vSSHtvMbVfWFJI8Cdye5FngR+Egbvwu4FJgGvgVccxzPLUk6Bscc+lX1HPAjc9T/ErhwjnoB1x3r80mSjp/fyJWkjhj6ktQRQ1+SOmLoS1JHDH1J6sjxfiNXkv7WWL/1vnG3AMALN192wrbtmb4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdGXnoJ9mU5Jkk00m2jvr5JalnIw39JCuAzwOXAGcBVyU5a5Q9SFLPRn2mfy4wXVXPVdVfA3cBl4+4B0nqVqpqdE+W/ASwqap+us1/FDivqq4fGLMF2NJmfxB4ZmQNzu8M4C/G3cQy4WtxmK/FYb4Why2H1+L7q2pirgXL7m/kVtU2YNu4+xiUZKqqJsfdx3Lga3GYr8VhvhaHLffXYtSXd/YD6wbm17aaJGkERh36jwIbkpyZ5B3AlcDOEfcgSd0a6eWdqnojyfXA/cAKYHtV7R1lD8doWV1uGjNfi8N8LQ7ztThsWb8WI/0gV5I0Xn4jV5I6YuhLUkcMfUnqiKEvSR0x9LVgSX553D1IOj7L7hu5WtY+MO4GNF5JvgHMdctfgKqq7x1xS8tKkpVV9eq4+zgaz/SHSPI/xt2Dlp9ej4uqOrWqvneOx6m9B37zwLgbGMYz/eHeOe4GxinJ88ye2QVYneQ5Dp/V/f2xNjdeXR4XSU4/2vKqOjiqXpapjLuBYQz9OST5J4cmge8ZmKeqvjSersajqs48NJ3kz6rq7HH2M04eFwA8xuGTgEMOzRfQ3YlAkqsPTQIrB+apqjvH09X8DP25XTMw/W7gn3H4oO7lH7feqvvj4oiTgNOBDXT6v54BZw5MnwKs5/Bxsez4axiGSPKVqjpn3H0sB0k+UVX/ftx9LAe9HxdJfhr4GLO/KXcPcD7wp1V14Tj7Gre3w3HhB7nDLftrdCdKkn+Z5P//KmwD/zt0e1w0HwN+FHixqn4cOBt4bbwtLQvL/rgw9If7xXE3MEY3Ag8n+ZMk/yLJnH+Jp1M9HxcA366qbwMkOaWqvsrsX7rr3UfH3cAwhv4QVfXFcfcwRs8x+9/3G4F/CDyV5AtJNic5dbytjVfnxwXAviSnAb8P7E5yD/DiWDtaBqrqySTvHncfR2PoHyHJZJIHk/y3JOuS7E7yWpJHk/R250pV1d9U1Rer6lrgPcCtwCZm3xC6keTvJrktyeeTvDvJJ5M8keTuJKvH3d+oVdWHq+rrVfVJ4N8CtwNXjLWpMUhyc5Iz2vRku6X54SQvJvmxMbc3J0P/rW4F/iNwH/CnwH+pqu8DtrZlPfmO65NV9X+ramdVXQV8/5h6Gpc7gKeAl4AHgf8DXAr8CfAr42tr/Krqj9tx8dfj7mUMLquqQ38E/T8B/7Sq3gt8CPjM+Nqan3fvHGHwXvQk/7uq/t5cy3qQ5H1V9T/H3cdyMOS42FNVG8fWnMYmydPAD7e/CvhQVZ0/sOyJqvrhMbY3J8/03+rbSS5K8pNAJbkCoP1X7c2xdjZiBv53GPy3cuQXblaMshEtK7cCu5JcAHwhyX9O8mNJPsXsrazLjl/OequfZfbyzt8AFwM/l+QOYD/wz8fYl8brniTvqqpvVtW/OVRM8l7gmTH2pTGqql9O8gTwc8D7mM3UDcx+wH3jGFubl5d3FiHJNVX1a+PuQ8uLx4XmslyPC0N/EY68liuBx4XmtlyPCy/vHCHJ4/MtAlaNshctHx4Xmsvb8bgw9N9qFbPX8o/8Qwhh9hZO9cnjQnN52x0Xhv5b3Qu8q6r2HLkgyR+NvBstFx4Xmsvb7rjwmr4kdcT79CWpI4a+JHXE0Jekjhj6ktSR/weDzpN4lYooRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Age.value_counts().plot.bar() #visualise the number of movies per category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8f07e874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['13+', '18+', '7+', nan, 'all', '16+'], dtype=object)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "41a7fd7d",
   "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>Year</th>\n",
       "      <th>Age</th>\n",
       "      <th>IMDb</th>\n",
       "      <th>Rotten Tomatoes</th>\n",
       "      <th>Netflix</th>\n",
       "      <th>Hulu</th>\n",
       "      <th>Prime Video</th>\n",
       "      <th>Disney+</th>\n",
       "      <th>Type</th>\n",
       "      <th>Directors</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>NewAge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>13+</td>\n",
       "      <td>8.8</td>\n",
       "      <td>87%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Action,Adventure,Sci-Fi,Thriller</td>\n",
       "      <td>United States,United Kingdom</td>\n",
       "      <td>English,Japanese,French</td>\n",
       "      <td>148.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Matrix</td>\n",
       "      <td>1999</td>\n",
       "      <td>18+</td>\n",
       "      <td>8.7</td>\n",
       "      <td>87%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Lana Wachowski,Lilly Wachowski</td>\n",
       "      <td>Action,Sci-Fi</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>136.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>2018</td>\n",
       "      <td>13+</td>\n",
       "      <td>8.5</td>\n",
       "      <td>84%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Anthony Russo,Joe Russo</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>149.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Back to the Future</td>\n",
       "      <td>1985</td>\n",
       "      <td>7+</td>\n",
       "      <td>8.5</td>\n",
       "      <td>96%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Robert Zemeckis</td>\n",
       "      <td>Adventure,Comedy,Sci-Fi</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>116.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The Good, the Bad and the Ugly</td>\n",
       "      <td>1966</td>\n",
       "      <td>18+</td>\n",
       "      <td>8.8</td>\n",
       "      <td>97%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sergio Leone</td>\n",
       "      <td>Western</td>\n",
       "      <td>Italy,Spain,West Germany</td>\n",
       "      <td>Italian</td>\n",
       "      <td>161.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Title  Year  Age  IMDb Rotten Tomatoes  Netflix  \\\n",
       "0                       Inception  2010  13+   8.8             87%        1   \n",
       "1                      The Matrix  1999  18+   8.7             87%        1   \n",
       "2          Avengers: Infinity War  2018  13+   8.5             84%        1   \n",
       "3              Back to the Future  1985   7+   8.5             96%        1   \n",
       "4  The Good, the Bad and the Ugly  1966  18+   8.8             97%        1   \n",
       "\n",
       "   Hulu  Prime Video  Disney+  Type                       Directors  \\\n",
       "0     0            0        0     0               Christopher Nolan   \n",
       "1     0            0        0     0  Lana Wachowski,Lilly Wachowski   \n",
       "2     0            0        0     0         Anthony Russo,Joe Russo   \n",
       "3     0            0        0     0                 Robert Zemeckis   \n",
       "4     0            1        0     0                    Sergio Leone   \n",
       "\n",
       "                             Genres                       Country  \\\n",
       "0  Action,Adventure,Sci-Fi,Thriller  United States,United Kingdom   \n",
       "1                     Action,Sci-Fi                 United States   \n",
       "2           Action,Adventure,Sci-Fi                 United States   \n",
       "3           Adventure,Comedy,Sci-Fi                 United States   \n",
       "4                           Western      Italy,Spain,West Germany   \n",
       "\n",
       "                  Language  Runtime  NewAge  \n",
       "0  English,Japanese,French    148.0    13.0  \n",
       "1                  English    136.0    18.0  \n",
       "2                  English    149.0    13.0  \n",
       "3                  English    116.0     7.0  \n",
       "4                  Italian    161.0    18.0  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age_mapp = {'13+':13,'18+':18,'7+':7,'16+':16,'All':0}\n",
    "\n",
    "df['NewAge']=df['Age'].map(Age_mapp)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f4c5497",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a21a91d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    87%\n",
       "1    87%\n",
       "2    84%\n",
       "3    96%\n",
       "4    97%\n",
       "Name: Rotten Tomatoes, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change data type of 'Rotten Tomatoes' column in onther way\n",
    "\n",
    "df['Rotten Tomatoes'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aab6f3e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['87%', '84%', '96%', '97%', '95%', '89%', '100%', '93%', '88%',\n",
       "       '91%', '86%', '92%', '90%', '94%', '80%', '81%', '77%', '85%',\n",
       "       '79%', '67%', '83%', '75%', '98%', '65%', '71%', '82%', '99%',\n",
       "       '68%', '73%', '66%', '78%', '63%', '76%', '74%', '55%', '70%',\n",
       "       '61%', '69%', '40%', '43%', '72%', '60%', '39%', '62%', '31%',\n",
       "       '64%', '59%', '35%', '52%', '46%', '48%', '49%', '56%', '57%',\n",
       "       '41%', '53%', '23%', '28%', '50%', '36%', nan, '33%', '54%', '58%',\n",
       "       '17%', '27%', '42%', '38%', '30%', '45%', '47%', '26%', '29%',\n",
       "       '24%', '20%', '44%', '51%', '34%', '19%', '37%', '32%', '22%',\n",
       "       '16%', '11%', '25%', '13%', '15%', '12%', '18%', '8%', '9%', '14%',\n",
       "       '6%', '21%', '5%', '10%', '4%', '7%', '3%', '2%'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Rotten Tomatoes'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "5bdbc733",
   "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>Year</th>\n",
       "      <th>Age</th>\n",
       "      <th>IMDb</th>\n",
       "      <th>Rotten Tomatoes</th>\n",
       "      <th>Netflix</th>\n",
       "      <th>Hulu</th>\n",
       "      <th>Prime Video</th>\n",
       "      <th>Disney+</th>\n",
       "      <th>Type</th>\n",
       "      <th>Directors</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>NewAge</th>\n",
       "      <th>Rot_Toma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>13+</td>\n",
       "      <td>8.8</td>\n",
       "      <td>87%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Action,Adventure,Sci-Fi,Thriller</td>\n",
       "      <td>United States,United Kingdom</td>\n",
       "      <td>English,Japanese,French</td>\n",
       "      <td>148.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Matrix</td>\n",
       "      <td>1999</td>\n",
       "      <td>18+</td>\n",
       "      <td>8.7</td>\n",
       "      <td>87%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Lana Wachowski,Lilly Wachowski</td>\n",
       "      <td>Action,Sci-Fi</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>136.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>2018</td>\n",
       "      <td>13+</td>\n",
       "      <td>8.5</td>\n",
       "      <td>84%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Anthony Russo,Joe Russo</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>149.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Back to the Future</td>\n",
       "      <td>1985</td>\n",
       "      <td>7+</td>\n",
       "      <td>8.5</td>\n",
       "      <td>96%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Robert Zemeckis</td>\n",
       "      <td>Adventure,Comedy,Sci-Fi</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>116.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The Good, the Bad and the Ugly</td>\n",
       "      <td>1966</td>\n",
       "      <td>18+</td>\n",
       "      <td>8.8</td>\n",
       "      <td>97%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sergio Leone</td>\n",
       "      <td>Western</td>\n",
       "      <td>Italy,Spain,West Germany</td>\n",
       "      <td>Italian</td>\n",
       "      <td>161.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Title  Year  Age  IMDb Rotten Tomatoes  Netflix  \\\n",
       "0                       Inception  2010  13+   8.8             87%        1   \n",
       "1                      The Matrix  1999  18+   8.7             87%        1   \n",
       "2          Avengers: Infinity War  2018  13+   8.5             84%        1   \n",
       "3              Back to the Future  1985   7+   8.5             96%        1   \n",
       "4  The Good, the Bad and the Ugly  1966  18+   8.8             97%        1   \n",
       "\n",
       "   Hulu  Prime Video  Disney+  Type                       Directors  \\\n",
       "0     0            0        0     0               Christopher Nolan   \n",
       "1     0            0        0     0  Lana Wachowski,Lilly Wachowski   \n",
       "2     0            0        0     0         Anthony Russo,Joe Russo   \n",
       "3     0            0        0     0                 Robert Zemeckis   \n",
       "4     0            1        0     0                    Sergio Leone   \n",
       "\n",
       "                             Genres                       Country  \\\n",
       "0  Action,Adventure,Sci-Fi,Thriller  United States,United Kingdom   \n",
       "1                     Action,Sci-Fi                 United States   \n",
       "2           Action,Adventure,Sci-Fi                 United States   \n",
       "3           Adventure,Comedy,Sci-Fi                 United States   \n",
       "4                           Western      Italy,Spain,West Germany   \n",
       "\n",
       "                  Language  Runtime  NewAge Rot_Toma  \n",
       "0  English,Japanese,French    148.0    13.0       87  \n",
       "1                  English    136.0    18.0       87  \n",
       "2                  English    149.0    13.0       84  \n",
       "3                  English    116.0     7.0       96  \n",
       "4                  Italian    161.0    18.0       97  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['Rot_Toma']=df['Rotten Tomatoes'].str.replace('%','')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "67fe5278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Rot_Toma'].dtype ##Still object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "5e10add9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Rot_Toma'] = df['Rot_Toma'].astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8fbccac7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    87.0\n",
       "1    87.0\n",
       "2    84.0\n",
       "3    96.0\n",
       "4    97.0\n",
       "Name: Rot_Toma, dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Rot_Toma'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "dfc75108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 87.,  84.,  96.,  97.,  95.,  89., 100.,  93.,  88.,  91.,  86.,\n",
       "        92.,  90.,  94.,  80.,  81.,  77.,  85.,  79.,  67.,  83.,  75.,\n",
       "        98.,  65.,  71.,  82.,  99.,  68.,  73.,  66.,  78.,  63.,  76.,\n",
       "        74.,  55.,  70.,  61.,  69.,  40.,  43.,  72.,  60.,  39.,  62.,\n",
       "        31.,  64.,  59.,  35.,  52.,  46.,  48.,  49.,  56.,  57.,  41.,\n",
       "        53.,  23.,  28.,  50.,  36.,  nan,  33.,  54.,  58.,  17.,  27.,\n",
       "        42.,  38.,  30.,  45.,  47.,  26.,  29.,  24.,  20.,  44.,  51.,\n",
       "        34.,  19.,  37.,  32.,  22.,  16.,  11.,  25.,  13.,  15.,  12.,\n",
       "        18.,   8.,   9.,  14.,   6.,  21.,   5.,  10.,   4.,   7.,   3.,\n",
       "         2.])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Rot_Toma'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5ddfb35",
   "metadata": {},
   "source": [
    "# Data Quering for comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ab1d4f6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18+    3474\n",
       "7+     1462\n",
       "13+    1255\n",
       "all     843\n",
       "16+     320\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show the number of movies for each age category\n",
    "df['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1fa64456",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    " #visualise the number of movies per category\n",
    "\n",
    "df['Age'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dd7b5b3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#show the top 10 languages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "5f8d973d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                        English,Japanese,French\n",
       "1                                        English\n",
       "2                                        English\n",
       "3                                        English\n",
       "4                                        Italian\n",
       "5                                English,Spanish\n",
       "6                         English,German,Russian\n",
       "7                  English,German,French,Italian\n",
       "8    English,German,Hebrew,Spanish,Arabic,Nepali\n",
       "9                  English,German,French,Italian\n",
       "Name: Language, dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Language'].head(10)\n",
    "\n",
    "#PB : Some values contain multiple languages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3557d82a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Top 10 Languages in streaming services'}>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Languages =  df['Language'].value_counts().head(10)\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.title('Top 10 Languages in streaming services')\n",
    "sns.barplot(x=Languages.index,y=Languages.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "141ea761",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['English,Japanese,French',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Italian',\n",
       " 'English,Spanish',\n",
       " 'English,German,Russian',\n",
       " 'English,German,French,Italian',\n",
       " 'English,German,Hebrew,Spanish,Arabic,Nepali',\n",
       " 'English,German,French,Italian',\n",
       " 'English,Spanish',\n",
       " 'Hindi,English',\n",
       " 'Spanish',\n",
       " 'English',\n",
       " 'English,French,Latin',\n",
       " 'Italian,English,Spanish',\n",
       " 'English,German,Greek,Arabic',\n",
       " 'English,French,Italian',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,American Sign Language',\n",
       " 'English,French',\n",
       " 'English,Japanese',\n",
       " 'English',\n",
       " 'English,Latin',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Nyanja,Afrikaans,Zulu,Xhosa,Southern Sotho',\n",
       " 'English,Italian,Latin,Spanish,German',\n",
       " 'English,Spanish',\n",
       " 'English,Spanish',\n",
       " 'English,Spanish',\n",
       " 'Korean,Hawaiian',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English,Saami',\n",
       " 'English,Swedish',\n",
       " 'English',\n",
       " 'Hindi,English',\n",
       " 'English,German',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Cantonese,Mandarin,Japanese',\n",
       " 'English,Sinhalese,Hindi',\n",
       " 'English,French',\n",
       " 'English,Cantonese',\n",
       " 'English,Portuguese,French,Japanese',\n",
       " 'English',\n",
       " 'English,German,French',\n",
       " 'English,Spanish,French',\n",
       " 'English,French',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,French,German,Italian',\n",
       " 'English',\n",
       " 'English,Russian,French',\n",
       " 'Japanese,Japanese Sign Language',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'English',\n",
       " 'Spanish',\n",
       " 'English,Irish',\n",
       " 'English,Spanish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Cantonese,Mandarin',\n",
       " 'French,English',\n",
       " 'Spanish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi,English,Awadhi,Urdu',\n",
       " 'English',\n",
       " 'English,French',\n",
       " 'English,Spanish,Italian,Latin,Portuguese,French,German',\n",
       " 'English,Japanese',\n",
       " 'English,Spanish,Portuguese',\n",
       " 'English',\n",
       " 'Telugu,Tamil',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Lingala,French',\n",
       " 'Spanish',\n",
       " 'English,Korean,Spanish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Russian',\n",
       " 'Japanese',\n",
       " 'English',\n",
       " 'English,American Sign Language',\n",
       " 'English',\n",
       " 'English,Russian,Spanish',\n",
       " 'English,Spanish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Cantonese,Mandarin,English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Telugu,Tamil',\n",
       " 'English',\n",
       " 'Hindi,English,Urdu,Persian,Kurdish',\n",
       " 'English,Hindi,Bengali',\n",
       " 'English,French,Greek',\n",
       " 'English',\n",
       " 'Hindi,Japanese,Spanish,English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,French',\n",
       " 'English,Russian',\n",
       " 'Korean,English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Japanese',\n",
       " 'English',\n",
       " 'English,French',\n",
       " 'Thai,English',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'French,English',\n",
       " 'East-Greenlandic,English',\n",
       " 'Hindi',\n",
       " 'French,English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,French',\n",
       " 'English,Arabic,Spanish,Japanese,Berber languages,French,Russian,Japanese Sign Language',\n",
       " 'Swedish,English,Danish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi,English',\n",
       " 'English,Russian,Hungarian,French,Turkish',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'Hindi',\n",
       " 'English,American Sign Language',\n",
       " 'English,French',\n",
       " 'English',\n",
       " 'English',\n",
       " 'French',\n",
       " 'English',\n",
       " 'Japanese,English,French',\n",
       " 'English',\n",
       " 'English,French,Swahili',\n",
       " 'English,Spanish',\n",
       " 'Filipino,English',\n",
       " 'English,Yiddish,Hebrew',\n",
       " 'English,French',\n",
       " 'English',\n",
       " 'Arabic,Nyanja,English',\n",
       " 'Hindi',\n",
       " 'English,French',\n",
       " 'English',\n",
       " 'Hindi,English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Ukrainian,Russian,English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,German',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'English,Ukrainian',\n",
       " 'Hindi',\n",
       " 'Hungarian',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Mandarin',\n",
       " 'English,French,German',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Cheyenne,French',\n",
       " 'Marathi',\n",
       " 'English',\n",
       " 'English,Korean',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'Japanese',\n",
       " 'English,Spanish',\n",
       " 'English,Arabic,German',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Spanish',\n",
       " 'English',\n",
       " 'English,German,Russian',\n",
       " 'English,German,French',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,French',\n",
       " 'English',\n",
       " 'Persian',\n",
       " 'English',\n",
       " 'Norwegian,English,French,Swedish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Indonesian,English,Mandarin,French',\n",
       " 'English,Spanish',\n",
       " 'Hindi',\n",
       " 'English,Mandarin',\n",
       " 'English',\n",
       " 'English,Russian,Korean',\n",
       " 'English,French',\n",
       " 'Spanish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Romanian',\n",
       " 'English,Italian',\n",
       " 'French',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Arabic,English',\n",
       " 'Italian',\n",
       " 'English,French',\n",
       " 'English',\n",
       " 'English,German',\n",
       " 'English',\n",
       " 'English,French',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Cantonese,English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'English',\n",
       " 'English,French',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English,Spanish,Arabic,Latin',\n",
       " 'English,Korean,Estonian',\n",
       " 'English',\n",
       " 'English,French',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Korean',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Portuguese,English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Korean',\n",
       " 'English',\n",
       " 'English,French',\n",
       " 'English,French',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'French',\n",
       " 'English,Spanish',\n",
       " 'English',\n",
       " 'Portuguese',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'Spanish',\n",
       " 'Italian,English',\n",
       " 'English,French',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Vietnamese,English,Japanese',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Vietnamese',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Telugu',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English,Polynesian,Spanish',\n",
       " 'Arabic',\n",
       " 'English',\n",
       " 'English,Spanish,French,Romanian',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Norwegian',\n",
       " 'English',\n",
       " 'English,German',\n",
       " 'English,Spanish,Portuguese',\n",
       " 'English,German',\n",
       " 'English',\n",
       " 'English,Spanish,Russian',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'English,Hindi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'English',\n",
       " 'English,Korean',\n",
       " 'English',\n",
       " 'English,Korean,French',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'English,Aboriginal',\n",
       " 'Spanish,Quechua,English',\n",
       " 'English',\n",
       " 'Japanese',\n",
       " 'English',\n",
       " 'Khmer,English,French',\n",
       " 'Mandarin',\n",
       " 'English,Spanish',\n",
       " 'English,French',\n",
       " 'English',\n",
       " nan,\n",
       " 'English',\n",
       " 'English,French,Scottish Gaelic',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Mandarin',\n",
       " 'English,German,Danish,Mandarin,Cantonese',\n",
       " 'English',\n",
       " 'English,Hebrew,German',\n",
       " 'Spanish,English',\n",
       " 'English,French',\n",
       " 'French,Flemish,English',\n",
       " 'Spanish,English',\n",
       " 'Hindi',\n",
       " 'English,Hindi',\n",
       " 'English,German,French,Latin',\n",
       " 'Hindi,English',\n",
       " 'Hindi,Urdu',\n",
       " 'English,Latin',\n",
       " 'English',\n",
       " 'French,English',\n",
       " 'English,Swedish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Russian,Romanian,Slovenian',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Filipino,Tagalog,English,Spanish',\n",
       " 'English,Portuguese,Japanese',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'French,English',\n",
       " nan,\n",
       " 'English',\n",
       " 'English',\n",
       " 'Spanish,Portuguese,Italian',\n",
       " 'English,Aboriginal',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Ukrainian',\n",
       " 'French,English,Italian,German,Yiddish',\n",
       " 'Georgian',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English,Turkmen,Cantonese,Italian,Spanish,Ukrainian,French',\n",
       " 'English,German',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Irish,French',\n",
       " 'English,Italian',\n",
       " 'English,Spanish,German,Russian',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Spanish,English,Catalan',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Russian,Spanish',\n",
       " 'English,German,French,Latin',\n",
       " 'Hindi,English',\n",
       " 'English',\n",
       " 'English,Mandarin',\n",
       " 'English',\n",
       " 'French',\n",
       " 'English',\n",
       " 'English,French,Hungarian',\n",
       " 'Hindi,Urdu',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English,Turkish,Greek,Russian',\n",
       " 'English',\n",
       " 'English,Japanese,German,French',\n",
       " 'English',\n",
       " 'English,Russian',\n",
       " 'English,Persian,Arabic',\n",
       " 'English,Latin',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Korean,Cantonese,Spanish,German,Icelandic,Italian,Arabic',\n",
       " 'Mandarin,Cantonese,Japanese',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,French,Tibetan,Mandarin,Russian,Hindi,Portuguese,Latin,Italian,Spanish',\n",
       " 'Spanish,Hindi,English,Mandarin,French',\n",
       " 'English,Min Nan,Mandarin',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Cantonese,Mandarin,English',\n",
       " 'Spanish,French,Dutch,Mapudungun',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English,Akan',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Russian',\n",
       " 'English',\n",
       " 'Hindi,English,Marathi,Tamil',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Portuguese,English',\n",
       " 'Spanish,German',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi,English',\n",
       " 'Japanese',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'Hindi,English',\n",
       " 'English,Dutch',\n",
       " 'English,Japanese',\n",
       " 'French,English,Spanish,Arabic',\n",
       " 'English,German,Russian',\n",
       " 'English,German',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English,Russian',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " nan,\n",
       " 'Mandarin,English,Russian,French,Japanese,Korean,Indonesian,Hindi',\n",
       " 'English,French',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Urdu,Pushto',\n",
       " 'English,Russian,Spanish',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Hindi',\n",
       " 'Basque',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'Hindi,English',\n",
       " 'English',\n",
       " nan,\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Tamil',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Spanish,Arabic,Polish,Turkish,Russian,Italian,Persian',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Spanish,French,English',\n",
       " 'English,Mandarin',\n",
       " 'English,French',\n",
       " 'English',\n",
       " nan,\n",
       " 'Japanese',\n",
       " 'English',\n",
       " 'English,Spanish,Chinese,Russian,Portuguese',\n",
       " 'English,Hebrew,Arabic,Italian',\n",
       " 'English',\n",
       " 'Spanish',\n",
       " 'English',\n",
       " 'English,French',\n",
       " 'English',\n",
       " 'Japanese',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Mandarin,Arabic,English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Spanish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Arabic,English,Dutch',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'Danish',\n",
       " 'English,Italian,Polish,French',\n",
       " 'Hindi,English,Urdu',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi,English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Filipino,Tagalog',\n",
       " 'English,French',\n",
       " 'English',\n",
       " 'English,Serbo-Croatian',\n",
       " 'English,French',\n",
       " 'Italian',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English,Chinese',\n",
       " 'Hindi',\n",
       " 'English,German',\n",
       " 'Japanese,English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi,Urdu',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Italian,French',\n",
       " 'Mandarin,English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,French,Italian',\n",
       " 'English,Spanish,Swedish',\n",
       " 'Japanese',\n",
       " 'English',\n",
       " 'English,Mandarin',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi,English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Mandarin',\n",
       " 'English,Catalan,Spanish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Ukrainian',\n",
       " 'English',\n",
       " 'English,Mandarin',\n",
       " 'English',\n",
       " 'Hindi,Urdu,English,Italian',\n",
       " 'English',\n",
       " 'Turkish,English',\n",
       " 'French',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'Danish,German',\n",
       " 'Hindi',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English,Khmer',\n",
       " 'English',\n",
       " 'Telugu,Tamil,Hindi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Spanish,Portuguese',\n",
       " 'Arabic',\n",
       " 'English,French,Mandarin,German',\n",
       " 'Hindi,English',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Mandarin',\n",
       " 'English',\n",
       " 'English,Amharic',\n",
       " 'English',\n",
       " 'Akan,English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Portuguese,Spanish,French',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi,English,Chinese,Spanish',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Japanese',\n",
       " 'Cantonese,English,Mandarin',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " nan,\n",
       " 'English,French',\n",
       " 'Spanish,French',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Italian',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'English,Korean,German',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Spanish,English',\n",
       " 'English,Mandarin,Swiss German,Cantonese,Russian',\n",
       " 'English,Hindi,Manipuri,Xhosa',\n",
       " 'Hindi',\n",
       " 'English,Arabic,Persian',\n",
       " 'German,English',\n",
       " 'English,Cantonese',\n",
       " 'Hindi,English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Russian',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,German,French,Swedish',\n",
       " 'Spanish',\n",
       " 'English,Chinese',\n",
       " 'Cantonese,Mandarin',\n",
       " 'English,Spanish',\n",
       " 'Hindi',\n",
       " 'Arabic',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'Indonesian',\n",
       " 'English,Korean',\n",
       " 'Russian,Spanish,Finnish',\n",
       " 'English,Russian',\n",
       " 'Spanish',\n",
       " 'Thai',\n",
       " 'English,German,Arabic,Russian,French',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Spanish,Kriolu,Aragonese',\n",
       " 'English,Spanish',\n",
       " 'English',\n",
       " 'French',\n",
       " 'English,Italian',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Japanese,Spanish',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English,Brazilian Sign Language',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Arabic,Hebrew,English',\n",
       " 'Hindi,Urdu',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'German',\n",
       " 'English',\n",
       " 'Hindi,English',\n",
       " 'Korean,Japanese',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Dutch',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Japanese',\n",
       " 'Spanish',\n",
       " 'English,French,Italian,Russian,Spanish',\n",
       " 'English,Portuguese',\n",
       " 'Yiddish,English,Spanish',\n",
       " 'English',\n",
       " 'English,Dari',\n",
       " 'German',\n",
       " 'Mandarin',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Japanese',\n",
       " 'Spanish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Mandarin,Spanish,Russian',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Turkish',\n",
       " 'Turkish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi,English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'Italian,Spanish,German,English',\n",
       " 'English',\n",
       " 'English,Greek',\n",
       " 'Japanese,English',\n",
       " 'English,Urdu',\n",
       " 'Mandarin',\n",
       " 'Spanish,Catalan',\n",
       " 'English,Mandarin,Thai',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'Marathi',\n",
       " 'English',\n",
       " nan,\n",
       " 'English',\n",
       " 'Japanese',\n",
       " 'Hindi,Bengali',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English,Arabic',\n",
       " 'English',\n",
       " 'English,Indonesian',\n",
       " 'English',\n",
       " 'English,French,Latin',\n",
       " 'English',\n",
       " 'English,Mandarin,German',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'Japanese,English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English,Spanish,German',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,French',\n",
       " 'Spanish,English,Japanese,Latin',\n",
       " 'Hindi,English',\n",
       " 'English',\n",
       " nan,\n",
       " 'Korean,Mandarin',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Spanish',\n",
       " 'English,Italian',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English,Japanese',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Spanish',\n",
       " 'Wolof,French,English,Arabic',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi,Tamil,English,Marathi',\n",
       " 'Japanese,English',\n",
       " 'English',\n",
       " 'Tamil,Telugu,Hindi,English,Malayalam,Mandarin,French',\n",
       " 'English',\n",
       " 'Marathi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Pushto',\n",
       " 'English',\n",
       " 'English,French',\n",
       " 'English',\n",
       " 'English,Ukrainian',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Cantonese,English',\n",
       " nan,\n",
       " 'English',\n",
       " 'Italian',\n",
       " 'English,Spanish,Russian,Italian',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English,Japanese,Chinese,Spanish',\n",
       " 'English',\n",
       " 'English,Spanish',\n",
       " 'Hindi,English',\n",
       " 'English,Spanish',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English,Italian',\n",
       " 'English',\n",
       " 'Hindi,English',\n",
       " 'Hindi,Marathi,English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'Hindi',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " nan,\n",
       " 'English',\n",
       " 'English,Arabic',\n",
       " 'Filipino,Tagalog',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " 'English',\n",
       " ...]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#solution for multiple  languages\n",
    "\n",
    "L3 = df['Language'].to_list()\n",
    "L3\n",
    "#L30 =  = [char for entry in L3 for char in entry]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "632250f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "33014c4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "flattened = [char for entry in L3 for char in str(entry).split(',')]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "8f00efe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'English': 13233,\n",
       "         'Japanese': 317,\n",
       "         'French': 799,\n",
       "         'Italian': 440,\n",
       "         'Spanish': 872,\n",
       "         'German': 483,\n",
       "         'Russian': 222,\n",
       "         'Hebrew': 86,\n",
       "         'Arabic': 190,\n",
       "         'Nepali': 6,\n",
       "         'Hindi': 731,\n",
       "         'Latin': 63,\n",
       "         'Greek': 33,\n",
       "         'American Sign Language': 17,\n",
       "         'Nyanja': 2,\n",
       "         'Afrikaans': 26,\n",
       "         'Zulu': 6,\n",
       "         'Xhosa': 8,\n",
       "         'Southern Sotho': 2,\n",
       "         'Korean': 176,\n",
       "         'Hawaiian': 7,\n",
       "         'Saami': 3,\n",
       "         'Swedish': 82,\n",
       "         'Cantonese': 175,\n",
       "         'Mandarin': 352,\n",
       "         'Sinhalese': 7,\n",
       "         'Portuguese': 108,\n",
       "         'Japanese Sign Language': 2,\n",
       "         'Irish': 11,\n",
       "         'Awadhi': 4,\n",
       "         'Urdu': 58,\n",
       "         'Telugu': 112,\n",
       "         'Tamil': 150,\n",
       "         'Lingala': 6,\n",
       "         'Persian': 23,\n",
       "         'Kurdish': 4,\n",
       "         'Bengali': 46,\n",
       "         'Thai': 51,\n",
       "         'East-Greenlandic': 1,\n",
       "         'Berber languages': 1,\n",
       "         'Danish': 60,\n",
       "         'Hungarian': 22,\n",
       "         'Turkish': 95,\n",
       "         'Swahili': 14,\n",
       "         'Filipino': 72,\n",
       "         'Yiddish': 25,\n",
       "         'Ukrainian': 30,\n",
       "         'Cheyenne': 1,\n",
       "         'Marathi': 60,\n",
       "         'Norwegian': 36,\n",
       "         'Indonesian': 59,\n",
       "         'Romanian': 26,\n",
       "         'Estonian': 4,\n",
       "         'Vietnamese': 18,\n",
       "         'Polynesian': 1,\n",
       "         'Aboriginal': 11,\n",
       "         'Quechua': 6,\n",
       "         'Khmer': 9,\n",
       "         'nan': 599,\n",
       "         'Scottish Gaelic': 1,\n",
       "         'Flemish': 6,\n",
       "         'Slovenian': 6,\n",
       "         'Tagalog': 69,\n",
       "         'Georgian': 8,\n",
       "         'Turkmen': 2,\n",
       "         'Catalan': 20,\n",
       "         'Icelandic': 19,\n",
       "         'Tibetan': 5,\n",
       "         'Min Nan': 10,\n",
       "         'Dutch': 68,\n",
       "         'Mapudungun': 1,\n",
       "         'Akan': 3,\n",
       "         'Pushto': 7,\n",
       "         'Basque': 11,\n",
       "         'Polish': 57,\n",
       "         'Chinese': 62,\n",
       "         'Serbo-Croatian': 10,\n",
       "         'Amharic': 4,\n",
       "         'Swiss German': 15,\n",
       "         'Manipuri': 2,\n",
       "         'Finnish': 24,\n",
       "         'Kriolu': 1,\n",
       "         'Aragonese': 1,\n",
       "         'Brazilian Sign Language': 5,\n",
       "         'Dari': 7,\n",
       "         'Wolof': 4,\n",
       "         'Malayalam': 61,\n",
       "         'Serbian': 23,\n",
       "         'Bosnian': 12,\n",
       "         'Croatian': 17,\n",
       "         'Aramaic': 3,\n",
       "         'Punjabi': 69,\n",
       "         'Mongolian': 5,\n",
       "         'Armenian': 7,\n",
       "         'Mohawk': 1,\n",
       "         'Kannada': 19,\n",
       "         'Gujarati': 12,\n",
       "         'Malay': 18,\n",
       "         'Assamese': 3,\n",
       "         'Mixtec': 1,\n",
       "         'Maori': 4,\n",
       "         'Minangkabau': 1,\n",
       "         'Chechen': 1,\n",
       "         'Czech': 27,\n",
       "         'Tarahumara': 1,\n",
       "         'Greenlandic': 1,\n",
       "         'Bhojpuri': 5,\n",
       "         'Sanskrit': 5,\n",
       "         'Tajik': 2,\n",
       "         'Yoruba': 8,\n",
       "         'Ibo': 2,\n",
       "         'Hausa': 2,\n",
       "         'Teochew': 2,\n",
       "         'Esperanto': 4,\n",
       "         'Sign Languages': 2,\n",
       "         'Macedonian': 3,\n",
       "         'Klingon': 6,\n",
       "         'Navajo': 1,\n",
       "         'Somali': 2,\n",
       "         'Lithuanian': 2,\n",
       "         'Australian Sign Language': 1,\n",
       "         'Bemba': 1,\n",
       "         'Welsh': 3,\n",
       "         'Spanish Sign Language': 1,\n",
       "         'Latvian': 4,\n",
       "         'Bulgarian': 11,\n",
       "         'Luxembourgish': 2,\n",
       "         'French Sign Language': 2,\n",
       "         'Albanian': 7,\n",
       "         'None': 20,\n",
       "         'Maya': 3,\n",
       "         'Middle English': 3,\n",
       "         'Cornish': 1,\n",
       "         'Papiamento': 1,\n",
       "         'Tupi': 1,\n",
       "         'Haryanvi': 1,\n",
       "         'Inuktitut': 5,\n",
       "         'Guarani': 2,\n",
       "         'Maltese': 1,\n",
       "         'Hakka': 1,\n",
       "         'Gallegan': 3,\n",
       "         'Shanghainese': 3,\n",
       "         'Rajasthani': 2,\n",
       "         'Scots': 1,\n",
       "         'Ewe': 1,\n",
       "         'Assyrian Neo-Aramaic': 2,\n",
       "         'Sioux': 3,\n",
       "         'Sicilian': 1,\n",
       "         'Creek': 1,\n",
       "         'Kinyarwanda': 4,\n",
       "         'Slovak': 6,\n",
       "         'Haitian': 2,\n",
       "         'Belarusian': 1,\n",
       "         'Kabyle': 1,\n",
       "         'Occitan': 1,\n",
       "         'Low German': 1,\n",
       "         'Kirghiz': 1,\n",
       "         'Fulah': 1,\n",
       "         'Athapascan languages': 1,\n",
       "         'Apache languages': 3,\n",
       "         'Micmac': 1,\n",
       "         'Tatar': 1,\n",
       "         'Lao': 1,\n",
       "         'Kazakh': 3,\n",
       "         'North American Indian': 3,\n",
       "         ' Ancient (to 1453)': 1,\n",
       "         'Romany': 1,\n",
       "         'Kudmali': 1,\n",
       "         'Masai': 1,\n",
       "         'Azerbaijani': 1,\n",
       "         'Dyula': 1,\n",
       "         'More': 1,\n",
       "         'Uighur': 1,\n",
       "         'British Sign Language': 1,\n",
       "         'Nushi': 1,\n",
       "         'Acholi': 1,\n",
       "         'Hokkien': 1,\n",
       "         'Nama': 1,\n",
       "         'Algonquin': 1})"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "D3 = Counter(flattened)\n",
    "D3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "c92ddb41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['English',\n",
       " 'Spanish',\n",
       " 'French',\n",
       " 'Hindi',\n",
       " 'nan',\n",
       " 'German',\n",
       " 'Italian',\n",
       " 'Mandarin',\n",
       " 'Japanese',\n",
       " 'Russian']"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#D3.pop(\"nan\")\n",
    "\n",
    "sorted(D3, key=D3.get, reverse=True)[:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "c39913fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'English': 13233,\n",
       " 'Spanish': 872,\n",
       " 'French': 799,\n",
       " 'Hindi': 731,\n",
       " 'nan': 599,\n",
       " 'German': 483,\n",
       " 'Italian': 440,\n",
       " 'Mandarin': 352,\n",
       " 'Japanese': 317,\n",
       " 'Russian': 222}"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import operator\n",
    "dic = dict(sorted(D3.items(), key=operator.itemgetter(1), reverse=True)[:10])\n",
    "dic\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "a983d2a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Top 10 languages in Streaming services'}>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.title('Top 10 languages in Streaming services')\n",
    "sns.barplot(x=list(dic.keys()),y=list(dic.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "36e3520f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Show results in a pie chart\n",
    "fig = plt.figure(figsize =(10,8))\n",
    "plt.pie(list(dic.values()), labels = list(dic.keys()))\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "7ac4ecaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Count the numberr of movies in specific age category in all streamong services \n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.title('The number of movies in specific age category in all streaming services')\n",
    "chart  = sns.barplot(df['Age'].value_counts().index,y=df['Age'].value_counts())\n",
    "chart.bar_label(chart.containers[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "46601f66",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Count the numberr of movies in specific age category in Netflix\n",
    "\n",
    "netflix_df = df[df['Netflix'] == 1 ]\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.title('The number of movies in specific age category in Netflix')\n",
    "chart  = sns.barplot(netflix_df['Age'].value_counts().index,y=netflix_df['Age'].value_counts())\n",
    "chart.bar_label(chart.containers[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c2dc05a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Count the numberr of movies in specific age category in Amazon prime\n",
    "\n",
    "prime_df= df[df['Prime Video'] == 1 ]\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.title('The number of movies in specific age category in Amazon prime')\n",
    "chart  = sns.barplot(prime_df['Age'].value_counts().index,y=prime_df['Age'].value_counts())\n",
    "chart.bar_label(chart.containers[0])\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ffd5d77f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Count the numberr of movies in specific age category in Hulu\n",
    "\n",
    "hulu_df = df[df['Hulu'] == 1 ]\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.title('The number of movies in specific age category in Hulu')\n",
    "chart  = sns.barplot(hulu_df['Age'].value_counts().index,y=hulu_df['Age'].value_counts())\n",
    "chart.bar_label(chart.containers[0])\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "addb79e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Count the numberr of movies in specific age category in Disney+\n",
    "\n",
    "disney_df = df[df['Disney+'] == 1 ]\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.title('The number of movies in specific age category in Disney+')\n",
    "chart  = sns.barplot(disney_df['Age'].value_counts().index,y=disney_df['Age'].value_counts())\n",
    "chart.bar_label(chart.containers[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "dc80184e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2520x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Rotten Tomato rating for all services\n",
    "\n",
    "plt.figure(figsize=(35,15))\n",
    "plt.title('Rotten Tomato rating for all services')\n",
    "chart  = sns.barplot(df['Rotten Tomatoes'].value_counts().index,y=df['Rotten Tomatoes'].value_counts())\n",
    "chart.bar_label(chart.containers[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "763e60eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Number of Rotten Tomato 100% rated movies for each service\n",
    "rt_rates = pd.DataFrame({'Streaming Services' :['Prime Video', 'Netflix', 'Hulu', 'Disney+'],\n",
    " 'Rotten Tomato Score':[prime_df['Rotten Tomatoes'].value_counts()[0],\n",
    "                        netflix_df['Rotten Tomatoes'].value_counts()[0],\n",
    "                        hulu_df['Rotten Tomatoes'].value_counts()[0],\n",
    "                        disney_df['Rotten Tomatoes'].value_counts()[0]]})\n",
    "\n",
    "sorted_rt_rates=rt_rates.sort_values(ascending=False,by='Rotten Tomato Score')\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.title('The number of Rotten Tomato 100% rated movies for each service')\n",
    "chart  = sns.barplot(x=sorted_rt_rates['Streaming Services'],\n",
    "                     y=sorted_rt_rates['Rotten Tomato Score'])\n",
    "chart.bar_label(chart.containers[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6e99befe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2520x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Number of IMDB rated movies for each service\n",
    "plt.figure(figsize=(35,15))\n",
    "plt.title('IMDb rating for all services')\n",
    "chart  = sns.barplot(df['IMDb'].value_counts().index,y=df['IMDb'].value_counts())\n",
    "chart.bar_label(chart.containers[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "64099569",
   "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>Runtime</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90.0</td>\n",
       "      <td>971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95.0</td>\n",
       "      <td>489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>92.0</td>\n",
       "      <td>434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93.0</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85.0</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>19.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>32.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>10.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Runtime  Count\n",
       "0       90.0    971\n",
       "1       95.0    489\n",
       "2       92.0    434\n",
       "3       93.0    422\n",
       "4       85.0    408\n",
       "..       ...    ...\n",
       "152     19.0      8\n",
       "153     32.0      8\n",
       "154      9.0      8\n",
       "155      7.0      8\n",
       "156     10.0      8\n",
       "\n",
       "[157 rows x 2 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count Runtimes of movies\n",
    "Runtime_df =pd.DataFrame( dict(df['Runtime'].value_counts().sort_values(ascending=False)[:10]).items(),columns=['Runtime','Count'])\n",
    "Runtime_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bb4ffcb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 3240x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(45,15))\n",
    "plt.title('Runtime for all services')\n",
    "chart  = sns.barplot(df['Runtime'].value_counts().index,y=df['Runtime'].value_counts())\n",
    "chart.bar_label(chart.containers[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ab52e9d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                     Christopher Nolan\n",
       "1        Lana Wachowski,Lilly Wachowski\n",
       "2               Anthony Russo,Joe Russo\n",
       "3                       Robert Zemeckis\n",
       "4                          Sergio Leone\n",
       "                      ...              \n",
       "16739                      Bruce Bilson\n",
       "16740                       Neal Israel\n",
       "16741                      Neil Gelinas\n",
       "16742              Richard Slater-Jones\n",
       "16743                       James Algar\n",
       "Name: Directors, Length: 16744, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count number of movies for each director\n",
    "\n",
    "df['Directors'] # for some movies more than director\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "96c7a867",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Jay Chapman': 36,\n",
       " 'Joseph Kane': 31,\n",
       " 'Cheh Chang': 29,\n",
       " 'Jim Wynorski': 23,\n",
       " 'William Beaudine': 23}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove messing values\n",
    "from collections import Counter\n",
    "import operator\n",
    "new_df = df[df['Directors']!=np.nan]\n",
    "\n",
    "direc = list(new_df['Directors'])\n",
    "\n",
    "flattened = [char for entry in direc for char in str(entry).split(',')]\n",
    "D3 = dict(Counter(flattened))\n",
    "#sorted(fla, key=D3.get, reverse=True)\n",
    "D3.pop('nan')\n",
    "dic = dict(sorted(D3.items(), key=operator.itemgetter(1), reverse=True)[:5])\n",
    "dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7fd4bc54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Top 5 Directors '}>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.title('Top 5 Directors ')\n",
    "sns.barplot(x=list(dic.keys()),y=list(dic.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "13f9dea5",
   "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>Directors</th>\n",
       "      <th>Title</th>\n",
       "      <th>IMDb</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>Genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Jim Gaffigan: Obsessed</td>\n",
       "      <td>7.6</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Documentary,Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Iliza Shlesinger: War Paint</td>\n",
       "      <td>6.7</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1417</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Tom Segura: Completely Normal</td>\n",
       "      <td>7.8</td>\n",
       "      <td>74.0</td>\n",
       "      <td>Documentary,Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2211</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Todd Barry: Spicy Honey</td>\n",
       "      <td>6.7</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2331</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Craig Ferguson: Tickle Fight</td>\n",
       "      <td>6.6</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2393</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Colin Quinn: Unconstitutional</td>\n",
       "      <td>7.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2447</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Lucas Brothers: On Drugs</td>\n",
       "      <td>5.5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2523</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>D.L. Hughley: Contrarian</td>\n",
       "      <td>6.5</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2636</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>D.L. Hughley: Clear</td>\n",
       "      <td>6.7</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2721</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Moshe Kasher: Live in Oakland</td>\n",
       "      <td>6.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2859</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Todd Glass Stand-Up Special</td>\n",
       "      <td>6.1</td>\n",
       "      <td>46.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Brad Paisley's Comedy Rodeo</td>\n",
       "      <td>5.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7743</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Chris Porter: Ugly and Angry</td>\n",
       "      <td>7.7</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7962</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Gary Gulman: In This Economy?</td>\n",
       "      <td>7.4</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Documentary,Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8041</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Kathleen Madigan: Madigan Again</td>\n",
       "      <td>7.1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8803</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Elon Gold: Chosen and Taken</td>\n",
       "      <td>7.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8824</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Jim Breuer: And Laughter for All</td>\n",
       "      <td>7.2</td>\n",
       "      <td>59.0</td>\n",
       "      <td>Documentary,Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8994</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Arnez J: Racially Motivated</td>\n",
       "      <td>6.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9296</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Morgan Murphy: Irish Goodbye</td>\n",
       "      <td>6.7</td>\n",
       "      <td>51.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9730</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Brian Posehn: The Fartist</td>\n",
       "      <td>6.5</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9866</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Josh Blue: Sticky Change</td>\n",
       "      <td>7.5</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Documentary,Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9885</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Bob Saget: Zero to Sixty</td>\n",
       "      <td>6.1</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10362</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Myq Kaplan: Small, Dork and Handsome</td>\n",
       "      <td>6.7</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10543</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Tammy Pescatelli: Finding the Funny</td>\n",
       "      <td>6.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10771</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Kevin Nealon: Whelmed, But Not Overly</td>\n",
       "      <td>6.3</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11108</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Lisa Lampanelli: Back to the Drawing Board</td>\n",
       "      <td>5.9</td>\n",
       "      <td>59.0</td>\n",
       "      <td>Documentary,Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11342</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Michael Ian Black: Very Famous</td>\n",
       "      <td>6.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11614</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Janeane Garofalo: If I May</td>\n",
       "      <td>4.3</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11955</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Adam Newman: Fuzzies</td>\n",
       "      <td>5.9</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12193</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Dana Gould: I Know It's Wrong</td>\n",
       "      <td>6.4</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Documentary,Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12425</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Steve Byrne: Champion</td>\n",
       "      <td>6.9</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12698</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Nick Di Paolo: Raw Nerve</td>\n",
       "      <td>6.7</td>\n",
       "      <td>46.0</td>\n",
       "      <td>Documentary,Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12967</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Aaron Karo: The Rest Is History</td>\n",
       "      <td>4.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12996</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Tom Rhodes: Light, Sweet, Crude</td>\n",
       "      <td>5.5</td>\n",
       "      <td>56.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13347</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>The Sklar Brothers: What Are We Talking About?</td>\n",
       "      <td>5.8</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14281</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>Jasper Redd: Jazz Talk</td>\n",
       "      <td>5.8</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Directors                                           Title  IMDb  \\\n",
       "1014   Jay Chapman                          Jim Gaffigan: Obsessed   7.6   \n",
       "1234   Jay Chapman                     Iliza Shlesinger: War Paint   6.7   \n",
       "1417   Jay Chapman                   Tom Segura: Completely Normal   7.8   \n",
       "2211   Jay Chapman                         Todd Barry: Spicy Honey   6.7   \n",
       "2331   Jay Chapman                    Craig Ferguson: Tickle Fight   6.6   \n",
       "2393   Jay Chapman                   Colin Quinn: Unconstitutional   7.1   \n",
       "2447   Jay Chapman                        Lucas Brothers: On Drugs   5.5   \n",
       "2523   Jay Chapman                        D.L. Hughley: Contrarian   6.5   \n",
       "2636   Jay Chapman                             D.L. Hughley: Clear   6.7   \n",
       "2721   Jay Chapman                   Moshe Kasher: Live in Oakland   6.8   \n",
       "2859   Jay Chapman                     Todd Glass Stand-Up Special   6.1   \n",
       "3041   Jay Chapman                     Brad Paisley's Comedy Rodeo   5.5   \n",
       "7743   Jay Chapman                    Chris Porter: Ugly and Angry   7.7   \n",
       "7962   Jay Chapman                   Gary Gulman: In This Economy?   7.4   \n",
       "8041   Jay Chapman                 Kathleen Madigan: Madigan Again   7.1   \n",
       "8803   Jay Chapman                     Elon Gold: Chosen and Taken   7.0   \n",
       "8824   Jay Chapman                Jim Breuer: And Laughter for All   7.2   \n",
       "8994   Jay Chapman                     Arnez J: Racially Motivated   6.8   \n",
       "9296   Jay Chapman                    Morgan Murphy: Irish Goodbye   6.7   \n",
       "9730   Jay Chapman                       Brian Posehn: The Fartist   6.5   \n",
       "9866   Jay Chapman                        Josh Blue: Sticky Change   7.5   \n",
       "9885   Jay Chapman                        Bob Saget: Zero to Sixty   6.1   \n",
       "10362  Jay Chapman            Myq Kaplan: Small, Dork and Handsome   6.7   \n",
       "10543  Jay Chapman             Tammy Pescatelli: Finding the Funny   6.3   \n",
       "10771  Jay Chapman           Kevin Nealon: Whelmed, But Not Overly   6.3   \n",
       "11108  Jay Chapman      Lisa Lampanelli: Back to the Drawing Board   5.9   \n",
       "11342  Jay Chapman                  Michael Ian Black: Very Famous   6.0   \n",
       "11614  Jay Chapman                      Janeane Garofalo: If I May   4.3   \n",
       "11955  Jay Chapman                            Adam Newman: Fuzzies   5.9   \n",
       "12193  Jay Chapman                   Dana Gould: I Know It's Wrong   6.4   \n",
       "12425  Jay Chapman                           Steve Byrne: Champion   6.9   \n",
       "12698  Jay Chapman                        Nick Di Paolo: Raw Nerve   6.7   \n",
       "12967  Jay Chapman                 Aaron Karo: The Rest Is History   4.9   \n",
       "12996  Jay Chapman                 Tom Rhodes: Light, Sweet, Crude   5.5   \n",
       "13347  Jay Chapman  The Sklar Brothers: What Are We Talking About?   5.8   \n",
       "14281  Jay Chapman                          Jasper Redd: Jazz Talk   5.8   \n",
       "\n",
       "       Runtime              Genres  \n",
       "1014      60.0  Documentary,Comedy  \n",
       "1234      75.0              Comedy  \n",
       "1417      74.0  Documentary,Comedy  \n",
       "2211      60.0              Comedy  \n",
       "2331      66.0              Comedy  \n",
       "2393      55.0              Comedy  \n",
       "2447      50.0              Comedy  \n",
       "2523      58.0              Comedy  \n",
       "2636      60.0              Comedy  \n",
       "2721      61.0              Comedy  \n",
       "2859      46.0              Comedy  \n",
       "3041      63.0              Comedy  \n",
       "7743      60.0              Comedy  \n",
       "7962      60.0  Documentary,Comedy  \n",
       "8041      65.0              Comedy  \n",
       "8803      61.0              Comedy  \n",
       "8824      59.0  Documentary,Comedy  \n",
       "8994      60.0              Comedy  \n",
       "9296      51.0              Comedy  \n",
       "9730      62.0              Comedy  \n",
       "9866      52.0  Documentary,Comedy  \n",
       "9885      64.0              Comedy  \n",
       "10362     66.0              Comedy  \n",
       "10543     58.0              Comedy  \n",
       "10771     57.0              Comedy  \n",
       "11108     59.0  Documentary,Comedy  \n",
       "11342     53.0              Comedy  \n",
       "11614     64.0              Comedy  \n",
       "11955     66.0              Comedy  \n",
       "12193     55.0  Documentary,Comedy  \n",
       "12425     70.0              Comedy  \n",
       "12698     46.0  Documentary,Comedy  \n",
       "12967     65.0              Comedy  \n",
       "12996     56.0              Comedy  \n",
       "13347     69.0              Comedy  \n",
       "14281     62.0              Comedy  "
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df[df['Directors']=='Jay Chapman'][['Directors','Title','IMDb','Runtime','Genres']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f7693c35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Drama': 7227,\n",
       " 'Comedy': 4637,\n",
       " 'Thriller': 3354,\n",
       " 'Action': 2683,\n",
       " 'Documentary': 2546,\n",
       " 'Romance': 2359,\n",
       " 'Horror': 2201,\n",
       " 'Crime': 1833,\n",
       " 'Adventure': 1778,\n",
       " 'Family': 1514}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Explore Genres of movies\n",
    "new_df = df[df['Genres']!=np.nan]\n",
    "\n",
    "direc = list(new_df['Genres'])\n",
    "\n",
    "flattened = [char for entry in direc for char in str(entry).split(',')]\n",
    "D3 = dict(Counter(flattened))\n",
    "D3.pop('nan')\n",
    "\n",
    "dic = dict(sorted(D3.items(), key=operator.itemgetter(1), reverse=True)[:10])\n",
    "dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "06e198f3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Top 10 Genres'}>"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "plt.title('Top 10 Genres')\n",
    "sns.barplot(x=list(dic.keys()),y=list(dic.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1fc1b940",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Netflix top movies\n",
    "top_netflix = netflix_df[netflix_df['IMDb']>8.5][['Title','IMDb']]\n",
    "top_net = top_netflix.sort_values(ascending=False,by='IMDb').head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6e370d90",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Top 10 movies in Netflix'}, xlabel='Title', ylabel='IMDb'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "plt.title('Top 10 movies in Netflix')\n",
    "sns.barplot(x=top_net['Title'],y=top_net['IMDb'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "966b918c",
   "metadata": {},
   "source": [
    "# Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "2f53a2ad",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title                  0\n",
       "Year                   0\n",
       "Age                 9390\n",
       "IMDb                 571\n",
       "Rotten Tomatoes    11586\n",
       "Netflix                0\n",
       "Hulu                   0\n",
       "Prime Video            0\n",
       "Disney+                0\n",
       "Type                   0\n",
       "Directors            726\n",
       "Genres               275\n",
       "Country              435\n",
       "Language             599\n",
       "Runtime              592\n",
       "NewAge             10233\n",
       "Rot_Toma           11586\n",
       "dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "67fcbc1b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    United States\n",
       "Name: Country, dtype: object"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#most repeated country\n",
    "\n",
    "df['Country'].mode()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "bf6c0c77",
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace rows with missing value for country,rating  with most repeated country\n",
    "df['Country'].fillna(df['Country'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "1cb155d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Language'].fillna(df['Language'].value_counts().index[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "75e23811",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Runtime'].fillna(df['Runtime'].value_counts().index[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "7ed1f8a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 9390 Age values and 11586 Rotten Tomatoes values are missing we can't do anything for missing values so we are dropping it\n",
    "df.drop(columns={'Age','Rotten Tomatoes', 'NewAge', 'Rot_Toma'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "07609fb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking dataframe from those in which values are present in it\n",
    "\n",
    "df=df[df['Genres'].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "4df14f0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['IMDb'].fillna(df['IMDb'].value_counts().index[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "dc41bf90",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns={'Directors'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "f3b6c586",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title          0\n",
       "Year           0\n",
       "IMDb           0\n",
       "Netflix        0\n",
       "Hulu           0\n",
       "Prime Video    0\n",
       "Disney+        0\n",
       "Type           0\n",
       "Genres         0\n",
       "Country        0\n",
       "Language       0\n",
       "Runtime        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "ca8e853d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if there is some wrong values in some columns \n",
    "df['Type'].unique() #==> only 0 : drop it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "33cb51be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns={'Type'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "30cabe48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16469 entries, 0 to 16743\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Title        16469 non-null  object \n",
      " 1   Year         16469 non-null  int64  \n",
      " 2   IMDb         16469 non-null  float64\n",
      " 3   Netflix      16469 non-null  int64  \n",
      " 4   Hulu         16469 non-null  int64  \n",
      " 5   Prime Video  16469 non-null  int64  \n",
      " 6   Disney+      16469 non-null  int64  \n",
      " 7   Genres       16469 non-null  object \n",
      " 8   Country      16469 non-null  object \n",
      " 9   Language     16469 non-null  object \n",
      " 10  Runtime      16469 non-null  float64\n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "02439f61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum() #no duplicated rows\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "6276f3ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Title'].duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cab9d9e8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}