Netflix - Data Exploration and Visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## importing libraries and reading file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "netflix_df=pd.read_csv(\"netflix.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>show_id</th>\n",
       "      <th>type</th>\n",
       "      <th>title</th>\n",
       "      <th>director</th>\n",
       "      <th>cast</th>\n",
       "      <th>country</th>\n",
       "      <th>date_added</th>\n",
       "      <th>release_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>duration</th>\n",
       "      <th>listed_in</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s1</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Dick Johnson Is Dead</td>\n",
       "      <td>Kirsten Johnson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>September 25, 2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>90 min</td>\n",
       "      <td>Documentaries</td>\n",
       "      <td>As her father nears the end of his life, filmm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s2</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Blood &amp; Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>International TV Shows, TV Dramas, TV Mysteries</td>\n",
       "      <td>After crossing paths at a party, a Cape Town t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s3</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Ganglands</td>\n",
       "      <td>Julien Leclercq</td>\n",
       "      <td>Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Crime TV Shows, International TV Shows, TV Act...</td>\n",
       "      <td>To protect his family from a powerful drug lor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s4</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Jailbirds New Orleans</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Docuseries, Reality TV</td>\n",
       "      <td>Feuds, flirtations and toilet talk go down amo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s5</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Kota Factory</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...</td>\n",
       "      <td>India</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>International TV Shows, Romantic TV Shows, TV ...</td>\n",
       "      <td>In a city of coaching centers known to train I...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  show_id     type                  title         director  \\\n",
       "0      s1    Movie   Dick Johnson Is Dead  Kirsten Johnson   \n",
       "1      s2  TV Show          Blood & Water              NaN   \n",
       "2      s3  TV Show              Ganglands  Julien Leclercq   \n",
       "3      s4  TV Show  Jailbirds New Orleans              NaN   \n",
       "4      s5  TV Show           Kota Factory              NaN   \n",
       "\n",
       "                                                cast        country  \\\n",
       "0                                                NaN  United States   \n",
       "1  Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...   South Africa   \n",
       "2  Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...            NaN   \n",
       "3                                                NaN            NaN   \n",
       "4  Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...          India   \n",
       "\n",
       "           date_added  release_year rating   duration  \\\n",
       "0  September 25, 2021          2020  PG-13     90 min   \n",
       "1  September 24, 2021          2021  TV-MA  2 Seasons   \n",
       "2  September 24, 2021          2021  TV-MA   1 Season   \n",
       "3  September 24, 2021          2021  TV-MA   1 Season   \n",
       "4  September 24, 2021          2021  TV-MA  2 Seasons   \n",
       "\n",
       "                                           listed_in  \\\n",
       "0                                      Documentaries   \n",
       "1    International TV Shows, TV Dramas, TV Mysteries   \n",
       "2  Crime TV Shows, International TV Shows, TV Act...   \n",
       "3                             Docuseries, Reality TV   \n",
       "4  International TV Shows, Romantic TV Shows, TV ...   \n",
       "\n",
       "                                         description  \n",
       "0  As her father nears the end of his life, filmm...  \n",
       "1  After crossing paths at a party, a Cape Town t...  \n",
       "2  To protect his family from a powerful drug lor...  \n",
       "3  Feuds, flirtations and toilet talk go down amo...  \n",
       "4  In a city of coaching centers known to train I...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>show_id</th>\n",
       "      <th>type</th>\n",
       "      <th>title</th>\n",
       "      <th>director</th>\n",
       "      <th>cast</th>\n",
       "      <th>country</th>\n",
       "      <th>date_added</th>\n",
       "      <th>release_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>duration</th>\n",
       "      <th>listed_in</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8807</td>\n",
       "      <td>8807</td>\n",
       "      <td>8807</td>\n",
       "      <td>6173</td>\n",
       "      <td>7982</td>\n",
       "      <td>7976</td>\n",
       "      <td>8797</td>\n",
       "      <td>8807.000000</td>\n",
       "      <td>8803</td>\n",
       "      <td>8804</td>\n",
       "      <td>8807</td>\n",
       "      <td>8807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>8807</td>\n",
       "      <td>2</td>\n",
       "      <td>8807</td>\n",
       "      <td>4528</td>\n",
       "      <td>7692</td>\n",
       "      <td>748</td>\n",
       "      <td>1767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>220</td>\n",
       "      <td>514</td>\n",
       "      <td>8775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>s7281</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Secret Obsession</td>\n",
       "      <td>Rajiv Chilaka</td>\n",
       "      <td>David Attenborough</td>\n",
       "      <td>United States</td>\n",
       "      <td>January 1, 2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Dramas, International Movies</td>\n",
       "      <td>Paranormal activity at a lush, abandoned prope...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>6131</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>2818</td>\n",
       "      <td>109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3207</td>\n",
       "      <td>1793</td>\n",
       "      <td>362</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.180198</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.819312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1925.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       show_id   type             title       director                cast  \\\n",
       "count     8807   8807              8807           6173                7982   \n",
       "unique    8807      2              8807           4528                7692   \n",
       "top      s7281  Movie  Secret Obsession  Rajiv Chilaka  David Attenborough   \n",
       "freq         1   6131                 1             19                  19   \n",
       "mean       NaN    NaN               NaN            NaN                 NaN   \n",
       "std        NaN    NaN               NaN            NaN                 NaN   \n",
       "min        NaN    NaN               NaN            NaN                 NaN   \n",
       "25%        NaN    NaN               NaN            NaN                 NaN   \n",
       "50%        NaN    NaN               NaN            NaN                 NaN   \n",
       "75%        NaN    NaN               NaN            NaN                 NaN   \n",
       "max        NaN    NaN               NaN            NaN                 NaN   \n",
       "\n",
       "              country       date_added  release_year rating  duration  \\\n",
       "count            7976             8797   8807.000000   8803      8804   \n",
       "unique            748             1767           NaN     17       220   \n",
       "top     United States  January 1, 2020           NaN  TV-MA  1 Season   \n",
       "freq             2818              109           NaN   3207      1793   \n",
       "mean              NaN              NaN   2014.180198    NaN       NaN   \n",
       "std               NaN              NaN      8.819312    NaN       NaN   \n",
       "min               NaN              NaN   1925.000000    NaN       NaN   \n",
       "25%               NaN              NaN   2013.000000    NaN       NaN   \n",
       "50%               NaN              NaN   2017.000000    NaN       NaN   \n",
       "75%               NaN              NaN   2019.000000    NaN       NaN   \n",
       "max               NaN              NaN   2021.000000    NaN       NaN   \n",
       "\n",
       "                           listed_in  \\\n",
       "count                           8807   \n",
       "unique                           514   \n",
       "top     Dramas, International Movies   \n",
       "freq                             362   \n",
       "mean                             NaN   \n",
       "std                              NaN   \n",
       "min                              NaN   \n",
       "25%                              NaN   \n",
       "50%                              NaN   \n",
       "75%                              NaN   \n",
       "max                              NaN   \n",
       "\n",
       "                                              description  \n",
       "count                                                8807  \n",
       "unique                                               8775  \n",
       "top     Paranormal activity at a lush, abandoned prope...  \n",
       "freq                                                    4  \n",
       "mean                                                  NaN  \n",
       "std                                                   NaN  \n",
       "min                                                   NaN  \n",
       "25%                                                   NaN  \n",
       "50%                                                   NaN  \n",
       "75%                                                   NaN  \n",
       "max                                                   NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix_df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## count NaN in each columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "show_id 0\n",
      "type 0\n",
      "title 0\n",
      "director 2634\n",
      "cast 825\n",
      "country 831\n",
      "date_added 10\n",
      "release_year 0\n",
      "rating 4\n",
      "duration 3\n",
      "listed_in 0\n",
      "description 0\n"
     ]
    }
   ],
   "source": [
    "for col in list(netflix_df.columns):\n",
    "    print(col, netflix_df[col].isnull().sum())\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding 1\n",
    "\n",
    "When rows with NaN value for rating column was checked, it was observed that, duration values were misplaced in the rating column. Rating values were the ones to be NaN. Hence columns were swapped for these 3 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PG-13', 'TV-MA', 'PG', 'TV-14', 'TV-PG', 'TV-Y', 'TV-Y7', 'R',\n",
       "       'TV-G', 'G', 'NC-17', '74 min', '84 min', '66 min', 'NR', nan,\n",
       "       'TV-Y7-FV', 'UR'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix_df['rating'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>rating</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5541</th>\n",
       "      <td>NaN</td>\n",
       "      <td>74 min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5794</th>\n",
       "      <td>NaN</td>\n",
       "      <td>84 min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5813</th>\n",
       "      <td>NaN</td>\n",
       "      <td>66 min</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     rating duration\n",
       "5541    NaN   74 min\n",
       "5794    NaN   84 min\n",
       "5813    NaN   66 min"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix_df.loc[netflix_df['duration'].isna(), 'duration'] = netflix_df.loc[netflix_df['duration'].isna(), 'rating']\n",
    "\n",
    "# Now, update teh rating field as nan\n",
    "netflix_df.loc[[5541, 5794, 5813], 'rating'] = np.nan\n",
    "\n",
    "# check the data\n",
    "netflix_df.loc[[5541, 5794, 5813], ['rating', 'duration']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PG-13', 'TV-MA', 'PG', 'TV-14', 'TV-PG', 'TV-Y', 'TV-Y7', 'R',\n",
       "       'TV-G', 'G', 'NC-17', nan, 'NR', 'TV-Y7-FV', 'UR'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix_df['rating'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['September 25, 2021', 'September 24, 2021', 'September 23, 2021',\n",
       "       ..., 'December 6, 2018', 'March 9, 2016', 'January 11, 2020'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix_df['date_added'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filling or Removing NaN values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Entries where there are no director and country name would be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "netflix_df.dropna(subset = ['director', 'country'], how='all', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ratings with NaN are only a few compared to size of dataset. Hence those rows are dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "netflix_df.dropna(subset = ['rating'], how='all', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Date added with NaN are also only a few compared to the size of the dataset. Hence those rows are dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "netflix_df.dropna(subset = ['date_added'], how='all', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Directors and Cast with NaN values are labelled as \"No Director\" and \"No Cast\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "netflix_df.director.fillna(\"No Director\", inplace=True)\n",
    "netflix_df.cast.fillna(\"No Cast\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a new dataframe to split and explode cast data. Splitting and exploding cast coloumn gives a huge number of rows.\n",
    "#Since I don't want to disturb the original dataframe where I split ande explode with directors, a copying is done.\n",
    "#Hence, to show visualisations based on cast, cast_df is used\n",
    " \n",
    "cast_df=netflix_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cast_df['cast']=cast_df['cast'].str.split(',')\n",
    "\n",
    "cast_df=cast_df.explode('cast')\n",
    "cast_df['cast']=cast_df['cast'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "director_df=netflix_df.copy()\n",
    "director_df['director']=director_df['director'].str.split(',')\n",
    "director_df=director_df.explode('director')\n",
    "director_df['director']=director_df['director'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No Director              2214\n",
       "Rajiv Chilaka              22\n",
       "Jan Suter                  21\n",
       "Raúl Campos                19\n",
       "Marcus Raboy               16\n",
       "                         ... \n",
       "Raj Amit Kumar              1\n",
       "Charles A. Nichols          1\n",
       "Brandon Dickerson           1\n",
       "John Michael McDonagh       1\n",
       "Adam Randall                1\n",
       "Name: director, Length: 4992, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "director_df['director'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No Cast              729\n",
       "Anupam Kher           43\n",
       "Shah Rukh Khan        35\n",
       "Naseeruddin Shah      32\n",
       "Julie Tejwani         30\n",
       "                    ... \n",
       "Dominic Deutscher      1\n",
       "Deborah Grall          1\n",
       "Derek Klena            1\n",
       "Alex Harries           1\n",
       "Rod Alexander          1\n",
       "Name: cast, Length: 35401, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast_df['cast'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m7rxiPL96Ywu3nDuShz91Ft+4rO+uCmGL75RWVg9xuohkJ6HrkMgQsVu8484vdqVnOnbl2ewJhWzY28KhtsCHj82bMZyPTSzkjmdX9/qcc0rymXNGIUu+fykPffIsLhw7mF/dMv24fSYPM5fv2t7Yzg0zhvONP61mQlE2pQVey96L6Fc28AOni0h2ErrOGQNMyRh33plOFjF32jBe7ta1cMn4IXzlkrF86Q8r6QwavT7n/tdqueDet5h53yJu//Nqlm47xLefW3PcPv/v8vH88vUtpLgV7khftaE1GakylYTDvlxaWZ3ndBHJTELXOdekl0wvcHtzhzpVQHqKi5njBvOPDQc+fOwncyeTmebhj188j1e+OZO7PzEFgMLsNJ78/LlRve4VZxSxbk8LB4/6ae0M8f6uZv7xrVloDZv2H7XkvYioZQFfcbqIZCbjdB3gLSsfAfx00BVfn+3JLih1uh6RdPYDpfVVFYF+9xQDTlq6zrgqdej4PAlc4ZChwKedLiJZSejazFtWXghc4B1/4WinaxFJ7c7SympZ5NQBErr2u1Sled0pg4ad4XQhIqlNBmY6XUQyktC1kbesPA2YnTlh5hDlkpV9heM+53QByUhC116TgfS04ZMcm0msvqqCX9587PBul2LVj+bw28+d2lJsny4fxQ0zhg9UecJeN5VWVmc4XUSy8ThdQJKZk1I4Js3tzXVsYul2f4gJxeZENP6Qwaxxg2lo7Tzl13umZtcAVidslgNcD/zJ6UKSibR0bRI5gTbJW1bu+Am0t2sbuWyiOU/6tdOPn3chNyOFxz9zNq/eMYsXv3YhE4uzUQqWfP9SctKP/Y1++zuzGZyVyrfmlPHlWWMAGDXIy++/cC4vf2Mmz992AWOHyPwqcUC6GGwmoWufcsBIGTTS8RNoL6/dx7XThpHmcTGpOJs1u5s/3Pbty8ezcV8rVz2wmPtfq+WXN09Da3jjgwY+PrkYgOkj89jT1HHcpcMA994wlR8v3Mi1Dy/hnlc28dPIhRUips2RJX3sJd0LNvCWlbuAy9KGT/K4UtNznK5n84GjjMjPYO60YSzqMYvYuaX5fOWPqwBYtu0wed5UstM8vLx2P3d8bBwvrNrDtdOG8vd1x89K5k11c3ZJPo98esaHj6V65JLfOOAC5gKPOl1IspDQtcdIIDd91NQxThfS5c1NDfzw6knc+vhy8jOPDaTobeCmBt7f1URJQSaDMlO54oxiHnpryXH7uJSitSPI1Q8u6eUVRIyrQELXNtK9YI8zAe0ZNHy804V0eX7FHh58q47ahuPnQqjZcYRPTDdHI5w/ZhBNvgBtfnOVl9c2HuBHFZPYerCNZl/wuOe1+UPsburg6qnFHz42aWi2xe9CDJDLSiure5+tXgw4CV2LRaZwvNCTVxx0p2fHzCq/B1o7e12z7Ndv1nHmiFxevWMW379y4ofrnAH8fd1+bpgx4iNdC13ueHY1t5wzklfvmMUb376Yy88osqp8MbC8wGyni0gWMuGNxbxl5UOA+7KmXTnEO+68a5yuR4g+PFxfVXG700UkA2npWm8CQEr+sBKnCxHiBK5yuoBkIaFrvRlAuzu7YJTThQhxAmNLK6uL+99NnC4JXQtFhopN8uQWGa7UjFyn6xGiH+VOF5AMJHStVQikpQ2bKIPPRTw43+kCkoGErrVGAXgKRji2vLoQJ0FaujaQ0LXWGUDAkz1YQlfEg3NLK6slEywm32BrTQZaXOmZQ5wuRIgoZGE2FISFJHQt4i0rzwIKPHlDU2TCchFHJjldQKKT0LXOYMBIKRgprVwRT8qcLiDRSehaZzCgPDlDBjtdiBAnQULXYhK61hkK4M4aJKEr4omErsUkdK0zGvC5MnIKnC5EiJMgoWsxCV3rjAR8rpR0md9QxJPC0spq+Zm1kISuBbxl5W6gAOhUnlRZKEzEG5mDwUISutYwg9blVrg9ssS1iDfSJWYhCV1rZAKGO6vAq1RvC+AIEdMGOV1AIpPQtUYmgDszX7oWRDyS0LWQhK41MgHlzsj2Ol2IEKdAQtdCErrW8AJKeVLk8l8Rj6RP10ISutbIAhTKLd9fEY/kE5qFJBSs4QEMXC75/op4JD+3FpJvrjXcgFYydEHEJ7fTBSQyj9MFJCg3oKV7wTr+A1uXt639x2Kn60gk2ggPTRk04pHc8nnrnK4lkUnoWsNsKSgloWuR4OFd/nDbETnLPrC039fSXL/od+1OF5LIJHSt4QE0RijkdCGJyu3NfRDY5HQdCUYDW5wuItFJ6FpDAdoIdPqdLiQRaa2NjNFnv9rw3H93OF2LECdLPv5aoxNw66CErkV21ldVSOCKuCSha402wG342yV0LaCU2uh0DUKcKglda3QC2vD7JHSt8YHTBQhxqiR0rWGGbmebhK41pKUr4paErjX8AEZHa6c2wjKCYeBJS1fELQlda/gxh9+gA50tDteSULTWGhkqJuKYDBmzxtGuL4yAr8mVnimzNg2cnfVVFf0P3p+fmwU0Yg7fEwNLM79FVkQ5RRK61mgmclWa0dnWTM4QZ6tJIP2NXJg7ISUL+NTHRrsn3nF+WrpNZSUb6TI7DdK9YAFfXU0n5rCxlHBHa7PD5SSa/vpzS4CZxVmuKXYUk6QkdE+DhK51DgLp4famJqcLSTD9jVwoBEJj8l2yVJJ1gk4XEM8kdK2zH8gINR847HQhCaa/lu5ooLMwU0mfjnUkdE+DhK519gLpgYZtjTJsbGBEOXJhNOAblCGha6FGpwuIZxK61jkAgBE2jI6jDQ7Xkih21VdVtPW1ce6EFDcwNCeNYFYqefaVlXT2OF1APJPQtc4+ImN1w21H9jlcS0JQSvXXtTAIUGcWuQtk0Q5LSeieBgld6zQCYcAdammQ0B0Y/Z1EGwLocYNc0rVgLQnd0yChaxFfXY0BbAeyA4d27ne6ngTRX0u3EHCNyJH+XItJ6J4GCV1rbQayAge2HtThkEx+c/r6a+mOAfyFma5CO4pJYnudLiCeSehaqx5woQ0dbju80+liEkB/Ld1SoL1ARi5YTVq6p0FC11q7u74IHtm73clC4p3WOqqRC9mpBGTkguUkdE+DhK61jkRuXv+eD7Y5XUw8i2LkQj7gkpELlvMzv0XG6Z4GCV0L+epqNLASyAsc3H7ICHQ0O1xSPIvm8l/GysgFq8lqwadJQtd6G4nM5hZqPrDV4VriWVQjF0bmKDmJZq3lThcQ7yR0rbcN8yIJl39f7Wani4lj/bV0S4HOoixp6VpMQvc0SehazFdX4wNqgbyO7Su3G0F//xNwi95ENXJB5lyw3DKnC4h3Err2WApkoQ0dPLJng9PFxBut9Z76qoqjfW2fOyHFBQzPTJGRC1bSWjdjjj0Xp0FC1x7rIv+6OuvXrHe0kjjU32oRmHMuuKcVuwtcMnTBMkqpGua3aKfriHcSujbw1dW0AuuBQf49G/caft8Rp2uKM/11LcicC/aQ/twBIKFrn3eATIBAY720dk9ONBPdyJwL1pP+3AEgC1Pa5wPMtaU8vrrla9KGT7pYyUfhaEW1WkRRpk0t3Yx8+NxC8+usIjDC4Dtk3n9jPmz757F9z/8qFIyD6juPf41Z34GpN4IOgzbg5W/B3lXwrXXw+GyIsQ9DWmutlKpxuo5EIKFrE19dTYe3rLwGODd0ZM/+UPOBzSn5Qyc5XVeciCZ0fQVem1q6HU3w6Czz69mVEGiHpQ/B2V+AKTccH7pT5sHr/3X880ecC+M/Do9dDOEAeAeBO9WW0k/DOua3NDtdRCKQ7gV7vQ2kAXRsWyH9Y1HQWu+tr6po6Wt7ZOTCMG8K/qxU8m0s7aM++BuMv/JYgOaNguxi2NXjU3l2MfgOm4ELZqv26IFj28+7DW57B766FAaXmY9l5MOtz8BX34UvvQlFk83Hv7oU0nPNr7+3A6bdan59/WMwZvaAvTWl1AsD9mJJTkLXXtuAXUBe5841u8K+Fplntx9RjFzIB9xnFrkHOT5yoaPJ7CIYN8e8P2UebHjxo/ttewtyh8Ptq6DiF1By0fHbfYfNVvDK38GF3zQfm/0D2L8OfnMR/PN/4PpHzcd318DIciicBE31MOpC8/ER58KeFQP57p4fyBdLZhK6NorMxfAykAvQuXuDtHb7F9XIhbJYGbmwYYEZtmB2NWxY8NF9Au3w2CXw8h3QfghuehKmf+rY9k0vm//uW222lgFGXQDrnjO/3vEOZAyCtBzYudQM7ZKLYOVvoegMyB5q/gEIDMx1OCFDb2B+S92AvJiQ0HXAWqANSPdtemejEfT3OV2hAKKb6CZ2Ri5sroYxl8DQaeDJgP1re99PG1C/BN6+F175Lkyae2xb2H9sH5fb/LrXNryOhO4FZijXLzFD/IzrPtqlcRrcimcG7MWEhK7dfHU1AeAVYIgOB8P+3esXO11TjIvmJFqgOCtGVosItJvhd93DvbdywRzNMGjMsfvFU6Fld+/7dtm5FKbeZH5dOtPsB/Yfhda94C2AgrFm98Ku5XDh7eb+A0QpJV0LA0hC1xlLAQNIaVv3xiqZ8vGE4m/OhfULoPhM2PCX3renZpl9sl+vMU+MDZlotnhP5O0qGHaWuf+c+fC3rxzbtmclHI5MYLdrKeQMG7CWbsjQa5nfIhPwDyCltVzV5wRvWfk84Gpgd9bUOdO94y+8zumaYo3Wet/O+64Z3tf2yMiFR70pHPrTvIxKx0+kJSCt9ffUT1p/5nQdiURaus55A/NiidS2DW+tNTrbDjldUKyJYrWIPMA9tdCdL4FrDelaGHgSug6JzMewEChGG9q3bcVbTtcUg6JaLULmXLBGMKyXMb9FFlQdYBK6zloEdAAZvs2LN4XbjuxyuqAYE81wMdeoXFktwgopbiXdChaQ0HVQZILzvwBFAEfX/qNaa8NwtqqYEs1qEX5ZLWLg+UN6H/CS03UkIgld570LHATyAge2Hgzsr5OZnI6JauRCQSyNXEgcv2R+izQALCAT3jjMV1fj95aVPwlUAi2tqxb+q2DwN6a4UjNyna7NSVrr/Tvvu6apr+2RkQsjMjwczk5jkOUF5Qw3h3llFZkXLax6CmoehRufhMHjzH3Sc6Gz5dhkON2l58Lch8zLdbWGl75uXqY75ydQNgcOrIcXI8PAzrzFnGuh5lHL31ZvQoZuT/Ooxx05eBKQ0I0Nm4ElwHk60LHXV7vklaypl3/S6aKcFOXIhZSpRTaNXDBC8PqPzCvMUrPgtn/B9kWw4AvH9rniLvC39v78K6tg65vw/GfBnQIpXvMy3pHnmfMp3PB/UHgGHNkO0z8Nf7zB8rfUl84Qv8m6p6XP5ZHE6ZHuhRgQmZPhBcwhZOm+Lcu2BJv29defmeiimbjcvjkX2hqOXdIbaIPGWsgedvw+k683L4zoKS3bnBvh/T+Y98NBs0WsjWMzkqVkgBGEi75ptnCNkHXv5QRChvZnpaoqRw6eJCR0Y4SvrqYZ+DMwFKBl+Qt/NwKdfTSbkkI0IxfUyFwH+nPzRsHQM2HvymOPlVwI7Y1mS7Wn/FJzkvNPPAK3LTa7GVK8ZnhvWghfWWxewtvZCsNmQO0rdr2Tj2gP8Dvmtxx2rIAkIKEbW97F7GooMnwtnW3rXv+LTt5LBqM5iRawbbWILqmZcPPT8I8fmHMfdJlyY++tXACXx5wAZ8Vv4bFZ5vwMM79tbnv3AbMP+PUfwWU/gkX3wIzPwk1PwcXfsfztdBc2dDA3Xf3E1oMmIQndGOKrqwkDT2DOKeXt3Llml3/vpmSdEKe/7gV7V4sAMzxvfhrWP39s+kUwZwKbdC1s/Gvvz2vda972rjLvf/CSGcLdFZ9p/nt4qzkR+QufN/t4u0+MY7GmTv0Q81sabDtgkpLQjTG+uppGzOAtBlyt7/317XB7Uz9TUCUWrXVDfVVFn4uEzZ2QooARaW46cuwYudDluofhUC0s+9/jHx8zGw5tgdZ9vT+v7SC07DVnFwNz6sfG2uP3uew/YdHd5kk2FZnOURtmN4QN2gL60GCv64e2HCzJSejGplWYV6sNRxu65b0X/6pDgQ6ni7JLFKtF5AGeqUWufJdS9vwMjzofpn0SRl9s9sF+ZTGUXW5umzLvozOKZRfDp7utcPPq92DeE+YsYcVTYfEvjm2bWAF73zeX7OlsMYeSfXWpObSsYYP17w040KbvYH6L35aDJTmZZSxGecvKM4D5QAZwJL30rJLsGRWfVcqVDH8oH66vqri9r41zJ6SMB773qakpubdOSbnRxroSUkObUVP086PnO11HskiGX+C45Kur6QAeATKBjM761Ts7tr73d4fLsks0E924R8bKahFxLBjWoVa//qzTdSQTCd0Y5qur2Qk8ijmMzNO27vXV/gN1ybCuWn8jF0qIpdUi4ti+o/rRsofatjhdRzKR0I1xvrqaFZgXTowCVMvSZ18PtRxM9EUCoxm50G7ryIUE1OrXjalu7nS6jmQjoRsfqjHH8I5Ea9285I8Lwr7mvU4XZQWt9cH6qoo+B+d3jVxIddNp68iFBHSwXd8+9BdHA07XkWwkdOOAr67GAH4P1ANDjc62QNO/fv/HcEfrAWcrG3hRjlxImVroyrVt5EIC2tFkVI978OhzTteRjOSHNk746mo6gYeAZqDQ8LV0Nr/zh6fDnW2NzlY24KK5/JeyAplD91Q1tBkH3tweSuoJlZwkoRtHfHU1TcDPAB8wJNx2xNe8+Ok/GP72Pi8kiEPRTHSjRuXKSbRT0RnSgbd2hG/98ssdMouYQyR044yvruYQZvAGgcHh1sa25iV/+r3h9/U592yciWbkQrA4S06inSytNe/sDN/1yb/4/uV0LclMQjcO+epqGoD7AQ0MCjXvb2165/e/C3ccTYTr5vsLXXPkQoZ0L5ystQ3Gmw+/F7jb6TqSnYRunPLV1ezDDF4XkRZv06LfPhXPi1tqrRvrqyr67KPuMXKhwMbS4t7eVmPPCxuDNy6sDcoSPA6T0I1jvrqa3cA9mF0NhUZHa+eRt/7v6WDT/k0Ol3ZKolgtIhdIm1LoynW7ZORCtNoDuvOtHaEb7l7sb3G6FiGhG/ciLd67gRZguA76Q02LnnjBH58LXEZzEs2wbbWIBBAytLGoPlT5mRc7VjhdizBJ6CaAyMm1e4DtQAla07L0z6+3b17yojbCQYfLOxnRDBdzleRJ6EYjbGj94qbQbx5fFXzQ6VrEMRK6CcJXV3MU+CVQg7mqQkr7xrfWtSx/4beG3xcvQ8r6a+mWAMGiTBm50B9Da73gg9CLT68LfndhbVCmEowhEroJxFdX4wceB54DRgDZgf1bGo7887HHg80NtSd+dkyIZuSCr8ArLd3+LKwN/eOZ9cF/X1gbTJp5mOOFhG6C8dXVGL66mleAKiAdKDY6jvqb/vnYs5071/1TayMmz15rrQ/VV1Uc7Gt7ZOTCyBQXvlwZuXBCr9YFF/1udfDfFtYG5cRZDJLQTVC+uppNwI+BPZgfy12tK/+2pGXZC4+HO1pjbjxvFCMXcoDUyebIBbcdNcWjt3aElv9mZfDWhbXBeOlSSjoSugkscoLtfuANzODNCeyvbTj82sOPd+754F8x1uqNZuJyPUHmXOjTu7tCq3+9PDBvYW2wz08MwnkSugnOV1cT8NXV/AkzfBUwknCI1poFb7cuX/B/MXQVW1QjF0bmSuj2Zsmu0Nr73g1cv7A22MfqmCJWSOgmCV9dzUbgR8A7mK3ebP++zQcOv/bQ4x073n9Nh4OdzlbYb+iOAkJDs5RMdNONobX+66bg0vvfDcxbWBvc6XQ9on8SuknEV1fTDvwBc8IcNzCKcMh19P2/Lz/yxqMPBg5uX6GdW6k0ytUipKXbJRjWoSfeD77+1JrgFxbWBrc5XY+IjqwGnKS8ZeWZwNXAVZiXER8AdGrRuCFZZ15xpSdn8Bi7atFaH9553zWD+9oeGbnwvx4XTS/clPF9OZEGbQHte6gm8NKyPeHvLawN7nG6HhE9j9MFCGdEWr0veMvKFwO3ADOApkDD1sYjb2x9OmPMOaMzxpVf7MkuKLW6lihHLqRPHuLKkcCF/UeNQ1VL/M/saNb/I6MU4o+EbpLz1dUc8JaVPwhMBD6DeTXb4Y7tK3d0bF+5I33U1BHe8Rdd7MktLLOwjGhOounxMnKBtQfC2+971/9oW4BHFtYG252uR5w8CV2Br65GA5u8ZeX/DZwL3IAZvk2du9bv6dy1/k9pwyYWeyfOnOXJK56olGugzwVEtVpESV7yrhYRMnTo71tCa363Ong/8OLC2mDI6ZrEqZHQFR/y1dWEgGXesvIVwDRgHmb4tvj3bT7g37f5BXdOYVbmhIvOSi0eN8OVmpE3QIeOauRCsq4WcaDNaPjVssCSTYeMXwDLZS6F+CahKz4iEr6rvGXlq4EpwPWY4RsMtx482LrixcXA4oyx545JL5l29gC0fqMauTDYm1yhGzZ0+PVtoTWPrwq+Hdb878La4A6naxKnT0YviH55y8oVZujOitw8mKsStwC4vHkZGWPOGZ9aNGaSJ2fIWOVyR/3HXGt9ZOd91/Q5l0LXyAW3onnBzRnfS5YTaQfbjYMPLA8sX3/QeA6zO0EmrkkQ0tIV/Yr0+e4AdnjLyhcA04ErMT/2a8PXfKR9w5tr2zewVqV6UzLGnD0urbhsoie3aLzypKSf6LWjGLmQDaRPLnRlJ0PgGlobb+0Ir310ZWBxIMyjC2uDcbkKiOibhK44Kb66Gh+w1FtWvgwYCZyJ2fodBaADvhbf5sWbfJsXbwJIHTq+KK14XIknf9god1ZBiSslLavHS0YzcsFIhpELB9uNg4+sCLz3/n5jAbBARickJgldcUoird9dwC5vWXk1UAycwbEA1oAR2L+lObB/y3vAewApg0sGpRaPG+n25o1LHVLa6UrPXN7PoQoBV0kCz7nQ0qkP/2VTcO1Lm0MbNDwBbJCTZYlLQlectkgA74/c/uktK8/HnN9hAmZXRFcI6+Chne3BQzs3AUeAX/jqavpr6Y4kQUcutAf00VfqQqv+vCG4K2SwGHhuYW3wqNN1CWtJ6IoB56uraQKagDXAc96y8mzM4B0LlGGelOvEvPS4P6MBXyKNXOgM6Y5FO0LvP7kmuKMzxDrMrgQZmZAkJHSF5SLrt22M3LpGQ6T46moCJ3peZOTCKJeiOTdd9Tk3Q7wIhnVg6e7w6ifeD2xr8bMFc1mlWulKSC4SusJ2ke6IEwZuRBaQMWmwK+xxqbj9WW3q0I3L9oQ2vLAxdOBwh94JPAusX1gbjKVJ5IVN4vYHWSSFQsCYODj+TqKFDB2qO2xs/PuWUO3iXeEOzO6W54CVcglvcpPQFbFsCOAqyYuf0D3SYTQs2x1e+/zGYENTJ2HM8c3/ANYsrA1G07oXCU5CV8SykcTBahFtAd267Yix5dWtoe1Ld4d9mCcJ3wbeBfZKn63oTkJXxLLRgK8gxkYuGFrrxna9d9MhY8vinaH6FfsMjbkSx1bgNcz+Wr+zVYpYJaErYlK3kQsteQ6PXNBa09SpG3Y06R3rGsL1i+pDh5s7ycBc6LMdc925pbIopIiGhK6IVVmAtyRX+cMGIY/L+p9VrTVtAZqPdOhDB9t1476jRuO2JqNxfYPRfrhDZ3Ds96URWAZswuw+kFEIImoyy5iISXMnpIwB/hPYDTAsW3knDnYVlOS6CoqyVL43RWWkuUlL96i0NA9pqW7SUt0qPdVNWoqLtK7JcUKGDgXD+IMGgUBY+4Nh/P4w/kBYBzpD+NsD2renVTduazIOrW8IHz4aIAXIBDIAA3Px1kPA+5jzRNQvrA22OPE9EYlBWroiVqVhfnwfCah9R3V439FwO4SbgVDkFsYMxo/I8OAOGeigcdx2d+TmidwygPTI63gwh6jtB2qBeqAB2As0yckwMVCkpStiUqRPd1DkVgAMwwzgXMDLsdaom8i8DpFbT6rbzQ90RG7twB7McG2M3I4srA2GrXpPQoCErohzcyekeDBbxV23dMwuAX/kFuj6V/peRSyQ0BVCCBsN9KquQgghTkBCVwghbCShK4QQNpLQFUIIG0noCiGEjSR0hRDCRhK6QghhIwldIYSwkYSuEELYSEJXCCFsJKErhBA2ktAVQggbSegKIYSNJHSFEMJGErpCCGGj/w+unGb/zHw3TQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_type = netflix_df['type'].value_counts()\n",
    "show_type_label = ['Movie','TV Show']\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.pie(show_type, labels = show_type_label, autopct = '%.1f%%', shadow=True, \n",
    "         textprops={'color':'white'},explode=[0, 0.15], labeldistance = 0.4)\n",
    "plt.title('Movie vs TV Show', fontdict={'fontsize':15, 'fontweight':5})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_df=netflix_df.copy()\n",
    "country_df['country']=country_df['country'].str.split(',')\n",
    "\n",
    "country_df=country_df.explode('country')\n",
    "country_df['country']=country_df['country'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States     3681\n",
       "India             1046\n",
       "United Kingdom     805\n",
       "Canada             445\n",
       "France             393\n",
       "                  ... \n",
       "Panama               1\n",
       "Azerbaijan           1\n",
       "Mozambique           1\n",
       "Lithuania            1\n",
       "Liechtenstein        1\n",
       "Name: country, Length: 123, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_df['country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>country</th>\n",
       "      <th>unique_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>3681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>1046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Canada</td>\n",
       "      <td>445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Japan</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Spain</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Germany</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>China</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Australia</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           country  unique_values\n",
       "0    United States           3681\n",
       "1            India           1046\n",
       "2   United Kingdom            805\n",
       "3           Canada            445\n",
       "4           France            393\n",
       "5            Japan            316\n",
       "6            Spain            232\n",
       "7      South Korea            231\n",
       "8          Germany            226\n",
       "9           Mexico            169\n",
       "10           China            162\n",
       "11       Australia            158\n",
       "12           Egypt            117\n",
       "13          Turkey            113\n",
       "14       Hong Kong            105"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_country = country_df['country'].value_counts().rename_axis('country').reset_index(name='unique_values').head(15)\n",
    "top_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y='country',x='unique_values',data=top_country,orient='h')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>director</th>\n",
       "      <th>unique_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rajiv Chilaka</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jan Suter</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Raúl Campos</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Marcus Raboy</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Suhas Kadav</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jay Karas</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cathy Garcia-Molina</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jay Chapman</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Youssef Chahine</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Martin Scorsese</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Steven Spielberg</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Don Michael Paul</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Shannon Hartman</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Yılmaz Erdoğan</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>David Dhawan</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               director  unique_values\n",
       "1         Rajiv Chilaka             22\n",
       "2             Jan Suter             21\n",
       "3           Raúl Campos             19\n",
       "4          Marcus Raboy             16\n",
       "5           Suhas Kadav             16\n",
       "6             Jay Karas             15\n",
       "7   Cathy Garcia-Molina             13\n",
       "8           Jay Chapman             12\n",
       "9       Youssef Chahine             12\n",
       "10      Martin Scorsese             12\n",
       "11     Steven Spielberg             11\n",
       "12     Don Michael Paul             10\n",
       "13      Shannon Hartman              9\n",
       "14       Yılmaz Erdoğan              9\n",
       "15         David Dhawan              9"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_directors = director_df['director'].value_counts().rename_axis('director').reset_index(name='unique_values').head(16)\n",
    "top_directors.drop(index=0,axis=0,inplace=True)\n",
    "top_directors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y='director',x='unique_values',data=top_directors,orient='h')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TVDCLiIiIiDRBBbOIiIiISBNUMIuIiIiINEEFs4iIiIhIE1Qwi4iIiIg0odmC2cxGmtnbZrbAzOaZ2dXB/P5mNtnMCoLbfnHb3GRmhWa2yMyOj5t/oJnNCZbdl3A5ZxERERGRjNPshUvMbCgw1Dk3w8x64y8lfBr+Cm2bnHO/M7MbgX7OuRvMbG/8pZMPwl8S+A1gT+cvBfwxcDXwIfAycJ9z7pWm9j9w4EA3atSodjxFEREREZGmTZ8+faNzblCyZXnNbeycWwusDe6XmtkCYDhwKv5ytAD/xl8C94Zg/pPOuWpgmZkVAgeZ2XKgj3PuAwAzewxfeDdZMI8aNYpp06Y1F1NEREREpM3MbEVjy1o1htnMRgH7Ax8BQ4JiuqGoHhysNhxYFbdZUTBveHA/cb6IiIiISMZqccFsZr2AicA1zrltTa2aZJ5rYn6yfV1mZtPMbFpxcXFLI4qIiIiIhK5FBbOZdcEXy487554JZq8Pxjc3jHPeEMwvAkbGbT4CWBPMH5Fk/uc45x50zo12zo0eNCjpUBIRERERkbRoyVkyDHgYWOCc+0PcoknA2OD+WOD5uPljzCzfzHYD9gA+DoZtlJrZwUGbF8VtIyIiIiKSkZo96A84DLgQmGNmM4N5vwB+B0wws0uBlcDZAM65eWY2AZgP1AE/cs7VB9tdATwKdMcf7NfkAX8iIiIiIlFr9rRyURs9erTTWTJEREREJJXMbLpzbnSyZbrSn4iIiIhIE1Qwi4iIiIg0QQWziIiIiEgTVDCLiIiIiDRBBbOIiIiISBNUMIuIiIiINEEFs4iIiIhIE1Qwi4iIiCTavByeOBc+eQhK10edRiLWkiv9iYiIiHQe1WUw7nzYMA8WvwovXw8jvw5fPgW+/B3YaWTUCSXNVDCLiIiINHAOnrvcF8ufzYvByg/89NpNMOwA2PsUX0AP+EJ0WSVtVDCLiIiINHjnLljwQtPrrJnhpzdugyH7+sJ571Ng8JfTElHSTwWziIiICMD8STDld63bZv1cP035Xxiwhx+ysfcpMGz/1GSUSKhgFhEREVk/D567AnBtb6OkAN77g5922iUY83wKjDwIzEKLKumngllEREQ6t4pNMO48qCkLr80tK+GDv/qp91D40sm+53nXwyAnN7z9SFqoYBYREZHOq74OnhoLW1akbh+la+GTf/qpx0D40onw5VNh9yMgt0vq9iuhUcEsIiIinddrv4BlU9O3v4qNMOMxP3XrC3t+2/c8f+Fo6NItfTmkVVQwi4iISOc04zH4+B/R7b9qK8x+0k9de8Eex/oxz3scB/m9ossln6OCWURERDqflR/BSz+NOsV2NWUw71k/5XXzPc57nwJ7ngDdd4o6XaengllEREQ6l62rYfx3ob4m6iTJ1VXBopf8lNPFj3X+8in+wMGeA6JO1ympYBYREZHOo7YSnjwfyjdEnaRlYrVQ+IafXrwWdj0U9j7VF899hkadrtNQwSwiIiKdx6Qfw9qZUadoG1cPy9/108vX+/M7f/kUf7GUfrtGna5DU8EsIiIincN7f4I5T0WdIiQOVn3kp9dvhqH7BVcZPBUG7hF1uA5HBbOIiIh0fAWT4c1fR50iddbO9NNbt8OgL8M5j8GgPaNO1WHkRB1AREREJKU2FsLTl4KLRZ0kPYoXwAtXg2vHZb5lByqYRUREpOOq2grjxkD11qiTpNfK/8L0R6NO0WGoYBYREZGOKRbzPcslBVEnicYbt0Lp+qhTdAgqmEVERKRjevM2KJwcdYroVG2FV2+IOkWHoIJZREREOp7ZT8H7f446RfTmPQuLX4s6RdZTwSwiIiIdy5pP/fmWxXvpp1BTHnWKrKaCWURERDqOsg3w5AVQVxl1ksyxdRW8dWfUKbKaCmYRERHpGOpqYPyFsG111Ekyz0d/9z3v0ibNFsxm9oiZbTCzuXHzxpvZzGBabmYzg/mjzKwybtnf47Y50MzmmFmhmd1nZpaSZyQiIiKd08s/hVUfRp0iM7l6mPQTiNVHnSQrtaSH+VHghPgZzrlznXP7Oef2AyYCz8QtXtKwzDl3edz8B4DLgD2CaYc2RURERNrsowdhxmNRp8hs62bDB/dHnSIrNVswO+emApuSLQt6ic8BxjXVhpkNBfo45z5wzjngMeC0VqcVERERSbRsKrx2U9QpssOU38LmFVGnyDrtHcP8TWC9cy7+jOC7mdmnZvaOmX0zmDccKIpbpyiYJyIiItJ2m5fDhLEQq4s6SXaorfBnzZBWaW/BfB479i6vBXZxzu0PXAc8YWZ9gGTjlRu9wLmZXWZm08xsWnFxcTsjioiISIdUUw7jzofKpD+ES2MKJ8Ocp6NOkVXaXDCbWR5wBjC+YZ5zrto5VxLcnw4sAfbE9yiPiNt8BLCmsbadcw8650Y750YPGjSorRFFRESko3IOnv0hbJgXdZLs9OqNULk56hRZoz09zMcAC51znw21MLNBZpYb3N8df3DfUufcWqDUzA4Oxj1fBDzfjn2LiIhIZ/bOXbDghahTZK/yYnj9V1GnyBotOa3cOOADYC8zKzKzS4NFY/j8wX6HA7PNbBbwNHC5c67hd5IrgIeAQnzP8ysh5BcREZHOZsELMOV3UafIfp/+B5a/F3WKrGD+pBWZa/To0W7atGlRxxAREZFMsH4+PHws1JRFnaRjGLAHXPE+5OVHnSRyZjbdOTc62TJd6U9ERESyQ8UmGDdGxXKYSgpg6j1Rp8h4KphFREQk89XXwVNjYYvOIRy69/8ExYuiTpHRVDCLiIhI5nvtF/4CJRK++hp44Wp/5hFJSgWziIiIZLYZ/wcf/yPqFB3byg9g+r+iTpGxVDCLiIhI5lr1Mbx0XdQpOofJt0HpuqhTZCQVzCIiIpKZtq6G8d/1QwYk9aq3wis3RJ0iI6lgFhERkcxTWwXjL4Cy9VEn6VzmPweLXo06RcZRwSwiIiKZZ9KPYc2nUafonF7+GVTr1H3xVDCLiIhIZnn/zzBnQtQpOq+tq+CtO6JOkVFUMIuIiEjmKJgMb9wWdQr5+B+wekbUKTKGCmYRERHJDBsLYeKl4GJRJxEXgxd+4i8YIyqYRUREJANUbfWXva7aGnUSabBuDnx4f9QpMoIKZhEREYlWLAYTvw8lBVEnkURTfgebl0edInIqmEVERCRab/4aCl6POoUkU1sBL+rCMSqYRUREJDpznob3/xR1CmnKkjdh9lNRp4iUCmYRERGJxppP4fmrok4hLfHaTVCxKeoUkVHBLCIiIulXtgGe/C7UVUadRFqivBhe/1XUKSKjgllERETSq64Gxl8I24qiTiKtMfM/sOzdqFNEQgWziIiIpNfLP4VVH0adQtrixWugrjrqFGmngllERETS5+N/wozHok4hbVVSCFN/H3WKtFPBLCIiIumxbCq8emPUKaS93vsTbFgQdYq0UsEsIiIiqbd5BUwYCzFdajnrxWrhhavBuaiTpI0KZhEREUmtmnJ48nyo7LynJetwVn0E0x6JOkXaqGAWERGR1HEOnv0hrJ8bdRIJ2xu/htJ1UadICxXMIiIikjpT74EFL0SdQlKheiu8fH3UKdJCBbOIiIikRtE0mPLbqFNIKi2YBAtfjjpFyqlgFhERkfDVVsKzl4OrjzqJpNrL10N1WdQpUkoFs4iIiIRv8q1QUhB1CkmHbUXw1u1Rp0gpFcwiIiISrqXvwMcPRp1C0unjB2H19KhTpIwKZhEREQlP1VZ47kqg85yjVwAXg0lXQ33HPM+2CmYREREJzys3+p/opfNZPwc++GvUKVJCBbOIiIiEY8GLMOuJqFNIlN65CzYtizpF6JotmM3sETPbYGZz4+bdZmarzWxmMJ0Yt+wmMys0s0Vmdnzc/APNbE6w7D4zs/CfjoiIiESifKO/XLJ0brUV8NJ1UacIXUt6mB8FTkgy/4/Ouf2C6WUAM9sbGAPsE2zzNzPLDdZ/ALgM2COYkrUpIiIi2eiFq6FiY9QpJBMseQtmjY86RaiaLZidc1OBll78/VTgSedctXNuGVAIHGRmQ4E+zrkPnHMOeAw4rY2ZRUREJJPMHAcLX4w6hWSS134BFS0tHzNfe8YwX2Vms4MhG/2CecOBVXHrFAXzhgf3E+eLiIhINttaBK/cEHUKyTQVG+H1X0adIjRtLZgfAL4A7AesBe4N5icbl+yamJ+UmV1mZtPMbFpxcXEbI4qIiEhKOedPIVe9NeokkolmPg7LpkadIhRtKpidc+udc/XOuRjwT+CgYFERMDJu1RHAmmD+iCTzG2v/QefcaOfc6EGDBrUlooiIiKTaxw/CsneiTiGZ7IVroLYq6hTt1qaCORiT3OB0oOEMGpOAMWaWb2a74Q/u+9g5txYoNbODg7NjXAQ8347cIiIiEqWNBf7y1yJN2bQEpv4+6hTtltfcCmY2DjgSGGhmRcCtwJFmth9+WMVy4IcAzrl5ZjYBmA/UAT9yztUHTV2BP+NGd+CVYBIREZFsE6uHZy+Husqok0g2eP/P8JWzYPCXo07SZuZPWpG5Ro8e7aZNmxZ1DBEREWnwzu/h7TuiTiHZZMRBcOnrkMGX4TCz6c650cmW6Up/IiIi0nJrZ/mruYm0RtHH8MlDUadoMxXMIiIi0jJ11X4oRqw26iSSjd78DWxbG3WKNlHBLCIiIi3z1h2wYX7UKSRbVW+DV66POkWbqGAWERGR5q34AD74a9QpJNsteAEWvhR1ilZTwSwiIiJNqy6D5y4HF4s6iXQEL18P1aVRp2gVFcwiIiLStNdvhs3Lo04hHcW21fDm7VGnaBUVzCIiItK4gskw/dGoU0hH88k/oWh61ClaTAWziIiIJFexCSb9OOoU0hG5GLzwE6ivizpJi6hgFhERkeRe/hmUZudpwNqrvudg1g0/Dme5UUfpuNbPhf/eF3WKFlHBLCIiIp83d6KfOqElI87g8PK7OHjJxYzJv5/FI8/G5eZHHatjeudu2LQs6hTNUsEsIiIiOypdBy/9NOoUaVfbdzduH3AXRxeexeoqXyB/tKUPxxWczrftfmaMvBiX3zvilB1MXSW8eG3UKZqlgllERER2NOnHULk56hRp43LymDFyLAeU/JqHV49Mus7Csh6cUXAc36i+j6kjryDWfWCaU3ZgS9+GWU9GnaJJKphFRERku+mPQsHrUadIm8qB+/KTXvdyRsHxlNblNbv+6qp8Lir4JvuX/YGXRlxLXe8RaUjZCbz2C3+QaYZSwSwiIiLe5uXw2s1Rp0gLl9edN0dexVfX3MQLGwa1evuttXn8qPBr7FPyO54YdhPV/fZMQcpOpKIko997KphFREQEYjF49gqoKYs6Scpt2flQxuT9kUsLDqU2Zu1qqzqWwy+WfoUvrbuVvw35NeWD9gsnZGc06wlY+k7UKZJSwSwiIiLwwV9h5X+jTpFSsW47MX7Yjey3/Co+2tIn1LadM+5esQf7rPo5dwy8i807HxZq+53G5FuiTpCUCmYREZHObsMCeOuOqFOk1JrhJ3BC7b3csPSrKd/XQ0Uj2X/5j7iu75+Cczmr3Gqxqi1RJ0hKf0EREZHOrL4Wnv0h1FdHnSQl6nsN4y+Db+fQJRexuLx7Wvf9zPrBHLzkYi7p/heWjTwNl9MlrfuX8DR/OKiIiIh0XO/cBWtnRZ0idA5j8cizGbvyRNZt7Bpplimb+jFl0zns1+dE7hwyhb3XPYfVVkSaSVpHBbOIiEhnVTQd3v1D1ClCV7PTF/m1Xc7jBcOijrKDmdt6cdK2k9m9xzH8duR/+Vrx0+Rk6BAE2ZEKZhERkc6ottIPxXD1UScJjcvpwsfDx/K9ZUdQXpcbdZxGLa3oxrkF32Jw/jf57S7TOHLTBHLL10cdS5qggllERKQzeuM2KCmIOkVoygbtz3WVl/J6Qf+oo7TYhuouXFpwCD3zDuL2XWfznbKn6bJ1WdSxJAkVzCIiIp3N0nfgo39EnSIUrmtPXh38A65aMpp6l53nMiivy+W6JftzQ85+3LTrQs6vmUi3kvlRx5I42fnOEhERkbap2gbP/whwUSdpt5Khh3Om/YErCg/K2mI5Xm3M+M2yL/Ol1b/k3sF3sm3w16KOJIHsf3eJiIhIy71yA2xdFXWKdol1H8BjQ2/mwGWXM2Nr76jjpMRfVu7GV1dey8397mHjsCOjjtPpqWAWERHpLBa+5C8/nMVWjjiZY6p/zy3L9ok6Slo8vnYYo5dexhW976NoxEk4y9yDGTsyFcwiIiKdQflGeOHqqFO0WV3vEdwz6H85vPB8llZ0izpO2r1SPJBvFF7A+d3+SsHIs3G5+VFH6lR00J+IiEhn8MLVUF4cdYpWc5bDvBFjGLvieEqKdaW8Dzb35djNp/OlXsfz213eZb91E7GasqhjdXjqYRYREenoZo6DhS9GnaLVqvp/iev73svJBSdTUqNiOd7Csh6cvvh4vlHzF94deTmx7gOjjtShqYdZRESkI9ta5A/0yyIuN5/3h13MD5Z+g8p6jdltyuqqfC4sOJx+XQ7lzl0/5bitE8grXR11rA5HBbOIiEhH5Zw/hVz11qiTtFjp4NH8uOwSphT0izpKVtlcm8eVhV+je+4B3LLrfM7d/CA5lSVRx+owmh2SYWaPmNkGM5sbN+/3ZrbQzGab2bNmtlMwf5SZVZrZzGD6e9w2B5rZHDMrNLP7zMxS8oxERETE+/ifsHRK1ClaxOX35vnhP+Wrq65lyiYVy21VWZ/LTUu/wtVdbsHl94k6TofRkjHMjwInJMybDOzrnPsqsBi4KW7ZEufcfsF0edz8B4DLgD2CKbFNERERCcvGQnjj1qhTtMiGYUdzSuwPXL3kQJxTf1oYXtgwiJt63Irr2jPqKB1CswWzc24qsClh3uvOubrg4YfAiKbaMLOhQB/n3AfOOQc8BpzWpsQiIiLStFg9PPtDqK2IOkmTYj0G8dDOt3LQ0kuZU6rCLmxPrh3KHb1vweV1vtPwhS2Ms2R8D3gl7vFuZvapmb1jZt8M5g0HiuLWKQrmiYiISNje+wOsnhZ1iiYtG3Eah1fczR3L94o6Sof28OqR/Kn/L3E5OstIe7SrYDazm4E64PFg1lpgF+fc/sB1wBNm1gdI9vtKoxexN7PLzGyamU0rLs6+c0aKiIhEZu1smHJX1CkaVdt3FHcMvIujCs+hqEoX30iHP6/cnYcG/0JXCWyHNhfMZjYWOBm4IBhmgXOu2jlXEtyfDiwB9sT3KMcP2xgBrGmsbefcg8650c650YMGDWprRBERkc6lrhqevRxitVEn+RxnucwceRGjN/2Gh4pGRh2n07lz+V6MG/pzXNI+TGlOmwpmMzsBuAE4xTlXETd/kJn/+mJmu+MP7lvqnFsLlJrZwcHZMS4Cnm93ehEREdnu7Tthw7yoU3xO5YB9uabPvZxWcAJba3VG26j8YulXeGHEdVHHyEotOa3cOOADYC8zKzKzS4G/Ar2ByQmnjzscmG1ms4Cngcudcw0HDF4BPAQU4nue48c9i4iISHus/BD++5eoU3zOJyO/x35rb+T59YOjjiLATwoP5I2RP446RtZp9muec+68JLMfbmTdicDERpZNA/ZtVToRERFpXnWZPyuGi0WdZAfrhh/L2QXHRB1DEny/4BDG7VHFIav+GXWUrBHGWTJEREQkSq//EjYvjzrFDup6D+es1edHHUMacV7BUcza5cKoY2QNFcwiIiLZrOANmP6vqFPswFkut+Zdq7NgZLhTF3+bRSPPiTpGVlDBLCIikq0qN8Okq6JO8Tnvj/g+j68dFnUMaYETCk9lxYhToo6R8VQwi4iIZKuXfgqla6NOsYOtQw5mbOE3m19RMoJzxjFLz2Xt8OOjjpLRVDCLiIhko7nPwNykx9lHJtZ9IGNKvk+9U3mRTWpjxreWf5eSoUdEHSVj6R0tIiKSbUrX+d7lDOIw/tz7GhaU9Yg6irRBZX0uR666lK1DDo46SkZSwSwiIpJtJv0YKjc1v14azR15Pn9euXvUMaQdSuvyOGbtFZQNOiDqKBlHBbOIiEg2mf4oFLwedYodVAz8CmOWfjvqGBKC4pouHFv8EyoH6NIZ8VQwi4iIZIvNy+G1m6NOsQPXtRc/KL+S8nqVFB3F2qqunLT5Oqr77Rl1lIyhd7eIiEg2iMXguSuhpizqJDv4z4CreX9z36hjSMiWVnTjtNKfU9t3VNRRMoIKZhERkWzw4f2w4v2oU+xg+YhT+dWyfaKOISmyoKwH51bdRF3v4VFHiZwKZhERkUy3YSG8eXvUKXZQs9MXOHPlGVHHkBSbsbU3F9f/kvqeg6OOEikVzCIiIpnunbugvjrqFJ9xuflcV381JTVdoo4iafDepr5cYbcQ694/6iiRUcEsIiKSycpLYOGLUafYwStDr+TF4oFRx5A0en1jf67regsuv3fUUSKhgllERCSTzRoH9TVRp/jMhmFHc2Xh16KOIRF4bv1gftnjVlyXnlFHSTsVzCIiIplsxr+jTvCZ+l7DOGvtBVHHkAg9vnYYv+37K1xuftRR0koFs4iISKZa8QFsXBx1CgCc5fKbrteysrJb1FEkYg8W7cJ9A36Fy+k8Y9hVMIuIiGSqDOpd/nDEpfx7jU4vJt4fV+7OI0NuwlnnKCU7x7MUERHJNpVbYN5zUacAYNuQr3PRkiOijiEZ5vZlX2LC0J/jsKijpJwKZhERkUw05ymoq4w6BbHu/blg0/epjXX8okha74alX+WlEddGHSPlVDCLiIhkogwZjnF/n2uZU9r5zoogLXdV4WjeGvmjqGOklApmERGRTLN6BqybE3UK5o88j3tXfCHqGJIFvldwGB+PvDTqGCmjgllERCTTZEDvcuWAfTl72YlRx5Asck7B0cwZ2TFPO6iCWUREJJPUlMOciZFGcF178sPKKymvy400h2Sf7xScRMHIs6KOEToVzCIiIplk7kSoKY00wriBP2Hqpp0izSDZ64TC01g54jtRxwiVCmYREZFMMj3a4RgrR3yHXyz9SqQZJLvVuxyOXjqGdcOPjTpKaFQwi4iIZIr182D1tMh2X9t3d85c2fF+Tpf0q40ZRy8fS8nQw6OOEgoVzCIiIpkiwt5ll9uVn/ETims6z+WOJbXK63P4VtH32Tbk61FHaTcVzCIiIpmgtgpmj49s968PvZzn1w+ObP/SMW2tzePotVdSPmi/qKO0iwpmERGRTLBgElRtiWTXG4cdyQ8LD45k39LxFdd04bjiq6kcsE/UUdpMBbOIiEgmiGg4Rn3PnTln3UWR7Fs6j9VV+Zy8+Tpqdvpi1FHaRAWziIhI1DYWwor30r5bZznc2e1allZ0S/u+pfNZUtGd08tvoLbPrlFHabVmC2Yze8TMNpjZ3Lh5/c1sspkVBLf94pbdZGaFZrbIzI6Pm3+gmc0Jlt1nZhb+0xEREclCEV3Z75MRl/DI6pGR7Fs6p3mlPTmv+hfU9xoWdZRWaUkP86PACQnzbgTedM7tAbwZPMbM9gbGAPsE2/zNzBouE/QAcBmwRzAltikiItL51NfCrHFp323p4NFcsORbad+vyLStvbkk9ktiPQZFHaXFmi2YnXNTgU0Js08FGr4O/xs4LW7+k865aufcMqAQOMjMhgJ9nHMfOOcc8FjcNiIiIp3XwpegvDitu4x124kLt1xGbUw/9ko0pm7aiStzbyHWrV/zK2eAto5hHuKcWwsQ3Dach2Y4sCpuvaJg3vDgfuL8pMzsMjObZmbTiovT+5+IiIhIWkUwHOPvfa9l5rZead+vSLxXiwdwff4tuK6Z/14M+6C/ZF9VXRPzk3LOPeicG+2cGz1oUPZ014uIiLTK5hWw5O207nLhyHO5e8Uead2nSGMmrh/Crb1uw3XpEXWUJrW1YF4fDLMguN0QzC8C4o8eGAGsCeaPSDJfRESk8/r0PzTRfxS6qv5f5uxlJ6VtfyIt8diaYdy90y9xuflRR2lUWwvmScDY4P5Y4Pm4+WPMLN/MdsMf3PdxMGyj1MwODs6OcVHcNiIiIp1PrD4omNPDdenB5VVXUVqXl7Z9irTUA6tGcf/Am3E5mXlp9pacVm4c8AGwl5kVmdmlwO+AY82sADg2eIxzbh4wAZgPvAr8yDlXHzR1BfAQ/kDAJcArIT8XERGR7FEwGUrT92PrU4N/zJRN2XGAlXRO96z4In/r/ZOoYyTV7NdM59x5jSw6upH17wTuTDJ/GrBvq9KJiIh0VGk82K9oxEn8vPB/0rY/kbYav2EkP4o6RBK60p+IiEi6bVsLi19Ly65q++zKWavOTsu+RDoqFcwiIiLpNvM/8NmIxdRxOV240a5hXXXXlO9LpCNTwSwiIpJOzsGM/0vLrt4afjkT1w9Jy75EOjIVzCIiIum09G3YsiLluykZejjfLzw45fsR6QxUMIuIiKTTjMdSvov6noMZs2EszunS1yJhUMEsIiKSLuUlsPCllO7CWQ53db+OgvLuKd2PSGeigllERCRdZj0B9TUp3cWMEWN5sGiXlO5DpLNRwSwiIpIuKR6OUTboAM5fkvQyCSLSDiqYRURE0mHFf2Hj4pQ17/L7cvG2H1Id00e7SNj0r0pERCQdpqf2yn4P9buGaVt7p3QfIp2VCmYREZFUq9wC859PWfMFI8/izuV7pax9kc5OBbOIiEiqzZ4AdZUpabq6316ctfyUlLQtIp4KZhERkVRL0cF+Lq87V9X+mK21eSlpX0Q8FcwiIiKptHo6rJ+TkqafHXIVkzf2T0nbIrKdCmYREZFUStHBfmuGn8B1S/ZPSdsisiMVzCIiIqlSXQZzJ4bebF2fkZy5ekzo7YpIciqYRUREUmXuRKgpC7VJl5PHzbnXsraqa6jtikjjVDCLiIikyozwh2O8M/wyxq/dOfR2RaRxKphFRERSYd1cf8BfiDbvfBiXFB4Wapsi0jwVzCIiIqkQcu9yrMdAztt4Cc5ZqO2KSPNUMIuIiISttgpmjw+tOYdxb8/rWFjWI7Q2RaTlVDCLiIiEbf7zULU1tOYWjjyX+1eNCq09EWkdFcwiIiJhC3E4hsvrztVrjg2tPRFpPRXMIiIiYdpYACveD625eUPPYHF599DaE5HWU8EsIiISplB7l7tx/dqjQmtPRNpGBbOIiEhY6mpg5rjQmls49DQW6EA/kcipYBYREQnLopegYmMoTbncfH6+7uhQ2hKR9lHBLCIiEpbp4Q3HWDzsVOaU9gytPRFpOxXMIiIiYdi8ApZOCaUpl9uVG9YfE0pbItJ+KphFRETC8On/AS6UppYM+w4zt/UKpS0RaT8VzCIiIu0Vq4dPHw+lKZeTx40bjgulLREJR5sLZjPby8xmxk3bzOwaM7vNzFbHzT8xbpubzKzQzBaZ2fHhPAUREZGIFbwOpWtCaWrZsO8wbWvvUNoSkXDktXVD59wiYD8AM8sFVgPPApcAf3TO3RO/vpntDYwB9gGGAW+Y2Z7Oufq2ZhAREckIIR3s53LyuGmj+pNEMk1YQzKOBpY451Y0sc6pwJPOuWrn3DKgEDgopP2LiIhEY9sa38McghXDTuSjLX1CaUtEwhNWwTwGiD9T+1VmNtvMHjGzfsG84cCquHWKgnkiIiLZ69PHIYQfS53l8stN3w4hkIiErd0Fs5l1BU4BngpmPQB8AT9cYy1wb8OqSTZPejixmV1mZtPMbFpxcXF7I4qIiKSGc/DpY6E0tWr4t3lvU99Q2hKRcIXRw/xtYIZzbj2Ac269c67eORcD/sn2YRdFwMi47UYASY+QcM496Jwb7ZwbPWjQoBAiioiIpMCSt2DLynY34yyHW9S7LJKxwiiYzyNuOIaZDY1bdjowN7g/CRhjZvlmthuwB/BxCPsXERGJxoxwepfXDDueKZv6Nb+iiESizWfJADCzHsCxwA/jZt9tZvvhh1ssb1jmnJtnZhOA+UAd8COdIUNERLJW+UZY9HK7m3EYt209KYRAIpIq7SqYnXMVwICEeRc2sf6dwJ3t2aeIiEhGmPkE1Ne0u5l1w49j8pL+IQQSkVTRlf5ERETaIoThGA7jN9vUuyyS6VQwi4iItNby96GkoN3NbBh2NK8UDwwhkIikkgpmERGR1poRzpX97ij7TijtiEhqqWAWERFpjcrNMP/5djdTPOxbvLBBp04VyQYqmEVERFpj9gSoq2p3M78tPyWEMCKSDiqYRUREWmN6+4djlAw9gmfWDw4hjIikgwpmERGRliqaDhvmtbuZ31WeGkIYEUkXFcwiIiItNePRdjexaedv8NS6ndufRUTSRgWziIhIS1SXwdxn2t3MPdWnhxBGRNJJBbOIiEhLzH0aasra1cSWnQ/libVDQwokIumigllERKQlQjjY7w81Z4QQRETSTQWziIhIc9bNgTUz2tXE1iFf57E1w0IKJCLppIJZRESkOSH0Lv+lTr3LItlKBbOIiEhTaithzoR2NVE6eDQPrR4ZUiARSTcVzCIiIk2Z/zxUbW1XE/fHzgwpjIhEQQWziIhIU9o5HKNs0AH8vWjXkMKISBRUMIuIiDSmeDGs/G+7mvgH6l0WyXYqmEVERBozo329yxUD/4e/rNotpDAiEhUVzCIiIsnU1cCsce1q4p85Z4cURkSipIJZREQkmYUvQkVJmzevHLgvf1y5e4iBRCQqKphFRESSaedwjIdzzwkpiIhETQWziIhIok3LYOk7bd68csA+3LPiCyEGEpEoqWAWERFJ9On/Aa7Nmz+WdzZgocURkWipYBYREYkXq4eZT7R58+r+e/G7lXuEGEhEoqaCWUREJN7i16B0bZs3/0/+uTin3mWRjkQFs4iISLx2HOxX3W9P/nf5niGGEZFMoIJZRESkwbY1UDC5zZs/2e0c6p0+WkU6Gv2rFhERafDpf8DVt2nTmp2+wG+WfynkQCKSCVQwi4iIAMRiMOP/2rz5Uz3OVe+ySAelf9kiIiIAS9+CrSvbtGlt3924bfk+IQcSkUyhgllERARgetsP9num57nUxnRmDJGOql0Fs5ktN7M5ZjbTzKYF8/qb2WQzKwhu+8Wtf5OZFZrZIjM7vr3hRUREQlFWDIteadOmdX124Zbl+4YcSEQySRg9zEc55/Zzzo0OHt8IvOmc2wN4M3iMme0NjAH2AU4A/mZmuSHsX0REpH1mPQGx2jZt+nzvMVTH9IOtSEeWin/hpwINv2v9Gzgtbv6Tzrlq59wyoBA4KAX7FxERaZ0Zj7Vps7reI/jl8q+EHEZEMk17C2YHvG5m083ssmDeEOfcWoDgdnAwfziwKm7bomCeiIhIdJa/ByWFbdr0xT5jqKzXj6UiHV1eO7c/zDm3xswGA5PNbGET6yY7GsIlXdEX35cB7LLLLu2MKCIi0oQ2HuxX13s4Ny/fL9wsIpKR2tXD7JxbE9xuAJ7FD7FYb2ZDAYLbDcHqRcDIuM1HAGsaafdB59xo59zoQYMGtSeiiIhI4yo3w4JJbdr0lT7nUl6vscsinUGb/6WbWU8z691wHzgOmAtMAsYGq40Fng/uTwLGmFm+me0G7AF83Nb9i4iItNus8VBX1erN6nvuzI0r9k9BIBHJRO0ZkjEEeNbMGtp5wjn3qpl9Akwws0uBlcDZAM65eWY2AZgP1AE/cq6N1x8VEREJQxsP9nu937mUl2jsskhn0eaC2Tm3FPifJPNLgKMb2eZO4M627lNERCQ0RdNgw7xWb1bfczA3LD8wBYFEJFNp8JWIiHRO0x9t02Zv9hvDtrr2HjMvItlEBbOIiHQ+1aUw95lWbxbrMZAbVoxufkUR6VBUMIuISOcz52moLW/1ZlMGnMvmWvUui3Q2KphFRKTzmdH6cy/Hug/g+hW6QK1IZ6SCWUREOpe1s2HNp63e7N2B51BS0yUFgUQk06lgFhGRzqUtvcvd+nH9yoNTEEZEsoEKZhER6TxqKmD2U63e7INB57ChWr3LIp2VCmYREek85j8H1VtbtYnL78vPVh2SmjwikhVUMIuISOfRhiv7fTj4HNZWdU1BGBHJFiqYRUSkc1g/H1Z+0KpNXH5vfrbq0BQFEpFsoYJZREQ6vsrN8NTYVm/2yeBzWF2Vn4JAIpJNVDCLiEjHVlcN486DjYtbtZnr2oufFR2WolAikk1UMIuISMflHDxzWauHYgDMGHIWKyu7pSCUiGQbFcwiItJxvXazPzNGK7kuPbl+9eHh5xGRrKSCWUREOqYP/gYf3t+mTWftfCZLK9S7LCKeCmYREel45j8Pr9/cpk1dlx5cv+aIkAOJSDZTwSwiIh3Lyg/9uGUXa9Pmc3c+nYLy7iGHEpFspoJZREQ6jo0FMG4M1FW1aXOX143r1x4VcigRyXYqmEVEpGMo2wD/OcOfc7mNFgw9nYVlPUIMJSIdgQpmERHJfjXl8PjZsGVlm5twuflcv+5bIYYSkY5CBbOIiGS3+jqYMBbWzmxXM4uGnca80p7hZBKRDkUFs4iIZLeXroXCye1qwuV25cb1R4cUSEQ6GhXMIiKSvd65G2Y81u5mCoedysxtvUIIJCIdkQpmERHJTjOfgLfvbHczLqcLN2w4JoRAItJRqWAWEZHss+QtmPSTUJpaOuw7zNjaO5S2RKRjUsEsIiLZZd0cGH8RxGrb3ZTLyeOmjceFEEpEOjIVzCIikj22rPKnj6spDaW55cNO5uMtfUJpS0Q6LhXMIiKSHSq3wONnQenaUJpzlssvS44PpS0R6dhUMIuISOarq4YnL4DihaE1uWr4iby/uW9o7YlIx6WCWUREMptz8NwVsOK98Jq0HH61+YTQ2hORjk0Fs4iIZLbJt8DciaE2uXr4t3mnpF+obYpIx6WCWUREMtdHD8J/7wu1SWc53Lr526G2KSIdW5sLZjMbaWZvm9kCM5tnZlcH828zs9VmNjOYTozb5iYzKzSzRWamIy1ERKRxC16EV28Ivdm1w47jzZL+obcrIh1XXju2rQN+6pybYWa9gelmNjlY9kfn3D3xK5vZ3sAYYB9gGPCGme3pnKtvRwYREemIVn0ME78PLhZqsw7jtq0nh9qmiHR8be5hds6tdc7NCO6XAguA4U1scirwpHOu2jm3DCgEDmrr/kVEpIMqWQLjxkBdZehNrx92DK9vVO+yiLROKGOYzWwUsD/wUTDrKjObbWaPmFnDURXDgVVxmxXRSIFtZpeZ2TQzm1ZcXBxGRBERyQZlxfCfM6CiJPSmHcZvSr8Tersi0vG1u2A2s17AROAa59w24AHgC8B+wFrg3oZVk2zukrXpnHvQOTfaOTd60KBB7Y0oIiLZoKYCnjgHNi8PvWlnOUwbeQkvFw8MvW0R6fjaM4YZM+uCL5Yfd849A+CcWx+3/J/Ai8HDImBk3OYjgDXt2b+IiHQQsXp4+hJYMyP0pqv77cktXM74gp1Db1tEOoc2F8xmZsDDwALn3B/i5g91zjVct/R0YG5wfxLwhJn9AX/Q3x7Ax23dv4iIdCAv/RQWvxpqky4nj0+Gj+WSpUdRXq+zqIpI27Wnh/kw4EJgjpnNDOb9AjjPzPbDD7dYDvwQwDk3z8wmAPPxZ9j4kc6QISIiTL0Hpv8r1CYrB+7LT6sv4+UCDcEQkfZrc8HsnHuP5OOSX25imzuBO9u6TxER6WBmjYe3bg+tOZfXjXeGfo8fLjmU6ph6lUUkHPrfJEtMmTIFM2PKlClRRxERCcfSKfD8j0JrrnTwaC7Ov5eLC76hYllEQqX/UdJg1Cgwgw5Z686+DZ4w+ODiiIOISFZZNxfGXwix2nY35br25KUR1/LVVdfyTkm/5jcQEWmldp0lI5uNGgUrVvj7770Hhx3m77/7Lhx+uL+/666wfHn79/W978GmTTBiRPvbSur5UVC+ovHlX38IPvo+9BwFpy7bcdlL+8DW+XDgfbDXjz+/7bo3YfYtsGUOEINuO0P/A+Eb40N8AiLSqWxdDY+fDdXb2t3U5p0P49LNFzGjsHcIwUREkuu0BXO8Bx7YXjD//e/ht3/LLeG3uYPdvwc1m/z9ggcgVgMjz4QeQYXefzTkdoPy5VD8AQw6xM/fPMsXy5YHu475fLsVq+GdU7a316UPlC6G1ZNS/IREpMOq2gqPnwWl7TurqMvvy4QBV3DD0q+GFExEpHGdfkhGv37w9NOwcSMUF8PEiX5evOXL4eyzYehQv+yoo+Cj4JqG//qXH25xyinb1//3v/28k0/2jxOHZFRUwI03whe/CD17wgEHwHPPteNJfOUWOPBPfsrt7uftedX2ef3+B4YFYVaMi3tiT/jbocdDtyQXiCn5COorYNiJ8I0n4esPwjFT4LTVn1+3vhI+vBQm9IJJX4R1b2xfVlXse7if2wUm9IHXDoY1r25f9oTBU33BxaB6EzyRA88E50stX+WXTxwMLul1bkQkW9TVwJMXwIb57Wpm3bBjObH+XhXLIpI2nb5gHjsWqqvhkUf8VF0NF1+8fXl5OXzrW76o3nNPf3/KFH+7ZAmcdRb06AGvvQZbtvhtJkzwtxddlHyfl14Kd90FffvCmWfCqlVwxhkpHuO823f97coJ/gIBzsGKJ/28Ud9Nvk23of52zYvw1rEw+1bY8C503enz6658yg8L6bsvlC2BD7/n57uY76Ve8jDkD4QRp8Km6fDOSVD8vi/U++4Ntdtgy1zY+F/AQdV6KC306wAMPtx/6xCR7OScP8Bv+bttbiLWYyAP7XwrBy+9hAVlPUIMJyLStE5fMB9xBOy9Nzz4oJ/23nv7GGaAl16CZctg9919QTtxIpx2mu8lfvhh6N3bP66p8b3EW7bAG2/4Yji+17lBcTE8+STk5MChh0L//rDPPv6zJBXDQT4z7ETIH+AL0Q1v+0K0YiXk9fZFbDKDDoEv/www32M89zfwxuHw6tegZsuO6/bdG741GQ4LerArVkHVRiiZBiUfQl4vOPZdOPT/fO+3i8Hi+/26g4/wtxv/63P1HAU5XaH4Pdj4/o7riEh2evPXMGdCmzdfOeI7fKvybu5YvleIoUREWqbTF8wAl1/ue4uXLoUrrthxWcNBf3vttb2D80tf8rcNBw2OHetvx4+HZ5/1xfO550K3bp/fV0N7sRj89a/w5z/DO+/4eYWFYT2jJHK6wMizgxDjYEUwHGOXMyGve+Pb7f97OGM9HDYevniZb2fzDFjyyI7r9dvPv0Dxvc91ZX7cNECPkZDX09/vE7yADQcqDj7S3xYHBfOQo6DfAf5+8X/9siFHtvopi0iG+OQheO+Pbdq0rvdw/jj4Dg4vPI/llUn+UxURSQMd9IcfOnHjjb7eu/BCePvt7ctGjfK3ixf7XmAzWLTIz9t1V397zDEwbBi8+eb2YRmNDcdoaK9rV1i9GgYGF6GqrYV160J8Usns9l0o/DusegZygj/9qAsbX798pT/lU+8vwK7n+KlqPRQ9D3WlO65rDW+lhGETPUf524pVUFcBeT1gW/AC9gxewIbe4+Kpvv3dx0LX/rBqot8uf4Af6iEi2WfRK/Dyz1u9mcMoGHkW3115MhuKu6QgmIhIy6lgxg+fmDp1+/14J53ki9wlS/zBfgMH+l7k7t396eLAD6+44AL4/e/hww/hC1/YftaNRIMGwTnn+HHOX/86HHsslJT409ldfjncdluqniUw6DDotTuULfWPuw9vuud2y2yYeioMONj3CtdXwOqXAIMhR7dsnwNGw4Cv+wMIJ38T+u4THHhosMeVQY4hvv1tC/3jgYf5gnnhvUFujV8WyUpF0+Hp74Grb9VmtX1347d5l/NIwcgUBRMRaR0NyQgceKCfEvXs6XuOzzwTFi7045OPOMLP++IXt6/XMCwDfC91Ux5+2Pdo5+TAo4/C++/DIYfACSeE8lSaNuqCuPvngzXxFui7D+x2EVRtgJXjYfWLsNNX4ND/wOBvtGx/lgNHTILdL4HqDVD0LPTb38+Lb6Ohlzl/APTZyxf3ictEJHtsWgpPnAO1FS3exFkus0deyOhNv+aR1SqWRSRzmMvwU3WNHj3aTZs2LeoYkZsyZQpHHXUUb7/9NkceeWTUcUREGldeAg8f44vmFqruvxc3xy7n6XVDUhhMRDLdLv17MPXnR0WybzOb7pwbnWyZhmSIiEh4aith3LktLpZdThc+Gn4xly49gvJ6/egpIplJBbOIiIQjFoOnL4WiT1q0esXAr3Jd9Q94tWBAioOJiLSPCuYsceSRR5Lpw2dEpJN75eew6KVmV3N53Xl76KX8cMkh1MZ0QK+IZD4VzCIi0n7v/Qk++Wezq20bchBXll7CewV9m11XRCRTqGAWEZH2mfM0vHFbk6u4rr14YfAPuXrJATinXmURyS4qmEVEpO2WvQvPXQE0PmSsZOjh/GDTd5lR2Ct9uUREQqSCWURE2mbDAhh/AdTXJF0c69aPJ/tfzi+WfiXNwUREwqWCWUREWm/bWvjPWVC1NenidcOP46J157J4afc0BxMRCZ8KZhERaZ3qUnj8bNhW9LlF9T0H81CvK/ntkj0jCCYikhoqmEVEpOWqy2D8hbB+zucWLR9xKt8tOo2ikvwIgomIpI4KZhERSa5iE6yd5ad1s2HtbNi0BFxsh9Xqeg/nT91+xF8LR0WTU0QkxVQwi4gIbFsTFMeztxfIW1c1uYnDWDTyHC5ccSLFxV3SFFREJP1UMIuIdCbOwaalcb3GQZFcsbFVzdT23Z3bc6/ksYJhKQoqIpI5VDCLiHRU9XWwcdH2onjdbFg3B6q3tblJl9eNmcPO5aKlR1Nap48QEekc9L+diEhHUFsF6+fBulnbC+QN86GuqlXNuLzu1PYaTmn3YZTkDWENg1hWO4AFVf2YVdqHxeXdcYt1pT4R6VxUMIuIZJuqbdsPwmsYWrFxMcTqmt3UdelJTa/hlHUbysa8IaxuKIgrd2JWWV8KyrpDWRqeg4hIFlHBLCKSycqKg6I47oC8zctp7FLUrmtPanqNoDTfF8RFbhBL6wawsLIfM0t7s7S0O5Sm9RmIiGQ9FcwiIpliy8ode43XzoLStTus4vJ7U91/L0q7DaM4NyiIa/uzoGonZm7rw4pt3aDtQ5RFRCQJFcwiIukUi0Ftub+09LrZsHbm9gPyKjfj8vtQ1WsE2/KHUrzTUazuO4gltf2ZX7ETn5b2ZvXWbpD8atQiIpIiaS+YzewE4M9ALvCQc+536c4gItKs+jqoKYOacqitCO5XBI/L/W1NxY73a8qCdcs/m1xdNTEHMef8RA61Od2oyNuJ4tzBFLmBLKk5mnl5ZzKTPqzd2lUFsYhIhklrwWxmucD9wLFAEfCJmU1yzs1PZw6RtojFHJW19VTU1FNZUx/cr6Oytp7q2hh5uUa3Lrnk5+XQrUsu3fJy6dYlh/wuwW1ebtRPoWOqrfp8EZukcN1xecPj7fdj9XVBUeuL23rrQl1ON2pyulOd251qulFl3agkn3K6UeHyKXX5lMW6Ulrfl631A9lS15UtdV3ZXJtHSW0XSmq6UF6vv7uISLZLdw/zQUChc24pgJk9CZwKqGCWdquPOV/AflbM+qmqocitraeypi7uvp8qEu5X1dRTUevXq4pbXl0Xaz5EE3IMuiYU09265JK/Q5Ed3DYs++zxjvcbCvCG9XZoLy/FRXp9HdRXQ10w1VdDXU3CbTXU1yTcJqxXV4Wrr8HV1xGL1UF9La6+DtdwP1bn9xWLvx9M9bX+1sWos67U5nb3hW1ON6qtO5XkU0l3yulKuetGaawrZbF8ttYPYlt9FzbXdmVLvS9sN9V0YWNtF2pjOlWaiIgkl+6CeTgQf63VIuDrac7QIkVL5lNbW4PDcBBMwX0HYGCGc4YzglvDueDYdTNizn8Ab982bnnwOBZ3/7P9uGD9z9ozCNZ1Lti/bd/et2tgDdnAzLdrfjZmFtxuX274hUmXx22LJTxuWP+zbXfcFwntNWxLsvYT2q6ui8X14tbt2KObUNw2LKsKenpr6x2ukTMHtEWPrrn06JpL/9BaTK4+FqOiJkZFTfOnBGutXDPy83LJTyywu+QERXYuXfNycLFarM4Xoc754tRitbj6enB1WKwOV1+HuXpiMUc9Rp0z6p1RR46/dTnUO6hzOcEE9a47dfSgLgZ1LjdYbtS6HOpi/t9OuzmgNd9lDOga3O0OA/CTiIhEb2jf7lFHSCrdBXOyj8fPVThmdhlwWfCwzMwWpTRVcgOB1l0rNnMoezSyNXu25gZlj4qyRyNbs2drblD2SDw0NrLsuza2IN0FcxEwMu7xCGBN4krOuQeBB9MVKhkzm+acGx1lhrZS9mhka/ZszQ3KHhVlj0a2Zs/W3KDsUcnE7Dlp3t8nwB5mtpuZdQXGAJPSnEFEREREpMXS2sPsnKszs6uA1/CnlXvEOTcvnRlERERERFoj7edhds69DLyc7v22QaRDQtpJ2aORrdmzNTcoe1SUPRrZmj1bc4OyRyXjsptz4Z1VQERERESko0n3GGYRERERkazSaQpmMxtpZm+b2QIzm2dmVwfz+5vZZDMrCG77xW1zk5kVmtkiMzs+bv55ZjbHzGab2atmNjCLsp8b5J5nZnenMndbspvZgGD9MjP7a0JbBwave6GZ3WdmYZzFN13Z7zSzVWZWlsrMYeY2sx5m9pKZLQzaSfll7EN+zV81s1lBO383f6XRrMge1+YkM5ubytxhZzezKcH/OzODaXAWZe9qZg+a2eLgfX9mNmQ3s95xr/dMM9toZn/K9NzBsoz+PG0me6Z/nh5rZtOD13e6mX0rrq1M/zxtKnvaPk934JzrFBMwFDgguN8bWAzsDdwN3BjMvxG4K7i/NzALyAd2A5bgD1TMAzYAA4P17gZuy5LsA4CVwKBgvX8DR2dY9p7AN4DLgb8mtPUxcAj+fN6vAN/OouwHB+2VZeB7PWluoAdwVHC/K/Bulr3mfYJbAyYCY7Ile7D8DOAJYG62vGeCZVOA0anOnKLsvwbuCO7nEPw/nw3ZE9qdDhye6bnJjs/TxrJnw+fp/sCw4P6+wOq4tjL987Sp7Gn7PN3hOaRzZ5k0Ac8DxwKLgKFxf9BFwf2bgJvi1n8teHN1AYrxJ7c24O/AZVmS/WvAG3HzLwT+lknZ49a7OOE/p6HAwrjH5wH/yIbsCcvS+g88rNzB8j8DP8i27MG/2ReAc7MlO9ALeA//gZLygjnk7FNIY8EccvZVQM9szB63bI/geVim5yYLPk+byJ41n6fBfANK8B1pWfN5mpg9YX5aP087zZCMeGY2Cv/t5SNgiHNuLUBw2/DzYbLLeA93ztUCVwBz8Bdd2Rt4OD3J25cdKAS+ZGajzCwPOI0dLySTUi3M3pjh+OfRoOE5pUU7s0cmrNxmthPwHeDN8FM2us9RtDO7mb2G78EqBZ5OTdKk+x1F+7LfDtwLVKQqY2NCes/8Kxga8KtU/9Qbrz3Zg/c4wO1mNsPMnjKzISmMm7j/UYTzf8x5wHgXVBSp1p7cWfJ52phs+zw9E/jUOVdN9n2exmePTKcrmM2sF/7n2Wucc9uaWjXJPGdmXfD/wPcHhgGz8T26Kdfe7M65zfjs4/E/ry8H6sLOmTRQy7M32kSSeen6QGhv9kiElTv4MBgH3OecWxpWvmb2GUp259zx+F6LfOBbzaweivZmN7P9gC86554NO1sL9h3G636Bc+4rwDeD6cKw8jUlhOx5+KvPvu+cOwD4ALgnxIiNCvn/mDH4f68pF8J7PRs+T5PKps9TM9sHuAv4YcOsJKtl5OdpkuyR6VQFc/CPcyLwuHPumWD2ejMbGiwfiu+NgsYv470fgHNuSfANfgJwaJZkxzn3gnPu6865Q/A/hRRkWPbGFOGfR4Okl1UPW0jZ0y7k3A8CBc65P4UeNImwX3PnXBX+iqKnhp01UUjZDwEONLPl+GEZe5rZlNQk3i6s1905tzq4LcWPwT4oNYm3Cyl7Cb5Hv+GLylPAASmIu4Mw3+9m9j9AnnNuekrC7rivMHLvBxn/edqobPg8NbMR+Pf0Rc65JcHsrPg8bSR7ZDpNwRz8LPgwsMA594e4RZOAscH9sfhxNQ3zx5hZvpnthh8X9jGwGtjbzAYF6x0LLMiS7FhwxHpwJOqVwEMZlj2p4KeaUjM7OGjzoua2aa+wsqdbmLnN7A6gL3BNyDEb218o2c2sV9x/wnnAicDC8BPvsM+w3usPOOeGOedG4Q82WuycOzL8xNuF+LrnWXCWg+DD8WQgpWf5CPF1d/ix7kcGs44G5ocaNkEK/o85jzT0LoeYOxs+T5tqK6M/T4NhRi/hj2l6v2HlbPg8bSx7pNoy8DkbJ/wHj8P/5DMzmE7EH+n6Jv6b4ZtA/7htbsafYWIRcUeQ4o+WXRC09QIwIIuyj8N/CMwnxWcNaEf25cAmoAz/TXjvYP5o/IfvEuCvpPiglpCz3x08jgW3t2V6bnyvgwve6w3tfD8bXnNgCPBJ0M484C/4nreMz57Q5ijSc5aMsF73nvgzNDS87n8GcrMhezB/V2Bq0NabwC7Zkj1YthT4Ura8X4L52fB52lj2jP48BX4JlMetOxMYHCzL6M/TZrKn7fM0ftKV/kREREREmtBphmSIiIiIiLSFCmYRERERkSaoYBYRERERaYIKZhERERGRJqhgFhERERFpggpmEREREZEmqGAWEREREWmCCmYRERERkSb8P3hhXxuwha7bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "netflix_df['date_proper'] = pd.to_datetime(netflix_df['date_added'])\n",
    "\n",
    "netflix_df['added_year'] = netflix_df['date_proper'].dt.year\n",
    "netflix_df['added_year'].fillna(0, inplace = True)\n",
    "netflix_df['added_year'] = netflix_df['added_year'].astype(int)\n",
    "\n",
    "netflix_year_compare = netflix_df.copy()\n",
    "netflix_year_compare.drop(['show_id', 'title', 'director', 'cast', 'country',\n",
    "       'date_added', 'rating', 'duration', 'listed_in',\n",
    "       'description', 'date_proper'], axis = 1, \n",
    "                          inplace = True)\n",
    "\n",
    "add_year = netflix_year_compare.groupby('added_year').type.value_counts()\n",
    "add_year = add_year.unstack().fillna(0)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize = (12, 6))\n",
    "\n",
    "ax.stackplot(add_year.index, add_year['Movie'], add_year['TV Show'])\n",
    "\n",
    "fig.text(0.12, 0.9,'Movies and TV Shows added on Netflix', fontdict={'fontsize':15})\n",
    "\n",
    "fig.text(0.15,0.18,\"Movie\", fontweight=\"bold\", fontsize=12,color='blue')\n",
    "fig.text(0.2,0.18,\"|\", fontweight=\"bold\", fontsize=14, color='black')\n",
    "fig.text(0.21,0.18,\"TV Show\", fontweight=\"bold\", fontsize=12,color='orange')\n",
    "\n",
    "plt.xticks(np.arange(2008, 2022, 1))\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}