{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "heated-windows",
   "metadata": {},
   "source": [
    "# Analyzing Netflix Dataset With Python\n",
    "\n",
    "(Part-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exclusive-greenhouse",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "encouraging-religion",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style\n",
    "\n",
    "import os\n",
    "import re\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wrong-clearance",
   "metadata": {},
   "source": [
    "### Set default options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "political-belief",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "pd.options.display.max_colwidth = 100\n",
    "plt.rcParams[\"figure.figsize\"] = [15, 5]\n",
    "plt.rcParams[\"axes.titlesize\"] = 20\n",
    "plt.rcParams[\"axes.labelsize\"] = 12\n",
    "plt.rcParams[\"font.size\"] = 14\n",
    "\n",
    "style.use('tableau-colorblind10')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "introductory-salon",
   "metadata": {},
   "source": [
    "### Read and explore Netflix dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fossil-commons",
   "metadata": {},
   "outputs": [],
   "source": [
    "# directory that contains netflix dataset\n",
    "net_dir = os.getcwd() + \"\\\\Netflix_Dataset\\\\\"\n",
    "\n",
    "# zip file containing netflix dataset\n",
    "net_zipfile = \"Netflix_Titles.zip\"\n",
    "\n",
    "# Read the file and save in raw dataframe\n",
    "netflix_raw_data = pd.read_csv(net_dir + net_zipfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "subjective-singapore",
   "metadata": {},
   "source": [
    "#### dataframe.info() and dataframe.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "guilty-alloy",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7787 entries, 0 to 7786\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   show_id       7787 non-null   object\n",
      " 1   type          7787 non-null   object\n",
      " 2   title         7787 non-null   object\n",
      " 3   director      5398 non-null   object\n",
      " 4   cast          7069 non-null   object\n",
      " 5   country       7280 non-null   object\n",
      " 6   date_added    7777 non-null   object\n",
      " 7   release_year  7787 non-null   int64 \n",
      " 8   rating        7780 non-null   object\n",
      " 9   duration      7787 non-null   object\n",
      " 10  listed_in     7787 non-null   object\n",
      " 11  description   7787 non-null   object\n",
      "dtypes: int64(1), object(11)\n",
      "memory usage: 730.2+ KB\n"
     ]
    },
    {
     "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>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       release_year\n",
       "count          7787\n",
       "mean           2014\n",
       "std               9\n",
       "min            1925\n",
       "25%            2013\n",
       "50%            2017\n",
       "75%            2018\n",
       "max            2021"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# info and describe raw dataframe\n",
    "netflix_raw_data.info()\n",
    "\n",
    "pd.options.display.float_format = \"{:.0f}\".format\n",
    "netflix_raw_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "terminal-symbol",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reset float format\n",
    "pd.options.display.float_format = \"{:.3f}\".format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "higher-casino",
   "metadata": {},
   "source": [
    "#### Duplicate rows?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "broadband-african",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of duplicated records (if any): 0\n"
     ]
    }
   ],
   "source": [
    "# check for any duplicate records\n",
    "print(\"No. of duplicated records (if any):\", netflix_raw_data.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "imperial-district",
   "metadata": {},
   "source": [
    "#### Content type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "historical-missouri",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Movie      5377\n",
       "TV Show    2410\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# content category\n",
    "netflix_raw_data[\"type\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "existing-particular",
   "metadata": {},
   "source": [
    "### Sample rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "adolescent-bacon",
   "metadata": {
    "scrolled": false
   },
   "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>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>7748</th>\n",
       "      <td>s7749</td>\n",
       "      <td>Movie</td>\n",
       "      <td>الف مبروك</td>\n",
       "      <td>Ahmed Nader Galal</td>\n",
       "      <td>Ahmed Helmy, Laila Ezz El Arab, Mahmoud El Fis...</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>April 25, 2020</td>\n",
       "      <td>2009</td>\n",
       "      <td>TV-14</td>\n",
       "      <td>115 min</td>\n",
       "      <td>Comedies, Dramas, International Movies</td>\n",
       "      <td>On his wedding day, an arrogant, greedy accoun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>s7750</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Yu-Gi-Oh!</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dan Green, Eric Stuart, Amy Birnbaum, Darren D...</td>\n",
       "      <td>Japan</td>\n",
       "      <td>July 8, 2020</td>\n",
       "      <td>2005</td>\n",
       "      <td>TV-Y7</td>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>Anime Series, Kids' TV</td>\n",
       "      <td>The lives of young Yugi Moto and his friends J...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>s7751</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Yu-Gi-Oh! Arc-V</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mike Liscio, Emily Bauer, Billy Bob Thompson, ...</td>\n",
       "      <td>Japan, Canada</td>\n",
       "      <td>May 1, 2018</td>\n",
       "      <td>2015</td>\n",
       "      <td>TV-Y7</td>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>Anime Series, Kids' TV</td>\n",
       "      <td>Now that he's discovered the Pendulum Summonin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>s7752</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Yucatán</td>\n",
       "      <td>Daniel Monzón</td>\n",
       "      <td>Luis Tosar, Rodrigo de la Serna, Joan Pera, St...</td>\n",
       "      <td>Spain</td>\n",
       "      <td>February 15, 2019</td>\n",
       "      <td>2018</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>130 min</td>\n",
       "      <td>Comedies, International Movies</td>\n",
       "      <td>Competing con artists attempt to creatively an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7752</th>\n",
       "      <td>s7753</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Yummy Mummies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lorinska Merrington, Jane Scandizzo, Rachel Wa...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>July 3, 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>International TV Shows, Reality TV</td>\n",
       "      <td>It's drama Down Under when expectant mothers w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7753</th>\n",
       "      <td>s7754</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Yunus Emre</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gökhan Atalay, Payidar Tüfekçioglu, Baran Akbu...</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>January 17, 2017</td>\n",
       "      <td>2016</td>\n",
       "      <td>TV-PG</td>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>International TV Shows, TV Dramas</td>\n",
       "      <td>During the Mongol invasions, Yunus Emre leaves...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     show_id     type            title           director  \\\n",
       "7748   s7749    Movie        الف مبروك  Ahmed Nader Galal   \n",
       "7749   s7750  TV Show        Yu-Gi-Oh!                NaN   \n",
       "7750   s7751  TV Show  Yu-Gi-Oh! Arc-V                NaN   \n",
       "7751   s7752    Movie          Yucatán      Daniel Monzón   \n",
       "7752   s7753  TV Show    Yummy Mummies                NaN   \n",
       "7753   s7754  TV Show       Yunus Emre                NaN   \n",
       "\n",
       "                                                   cast        country  \\\n",
       "7748  Ahmed Helmy, Laila Ezz El Arab, Mahmoud El Fis...          Egypt   \n",
       "7749  Dan Green, Eric Stuart, Amy Birnbaum, Darren D...          Japan   \n",
       "7750  Mike Liscio, Emily Bauer, Billy Bob Thompson, ...  Japan, Canada   \n",
       "7751  Luis Tosar, Rodrigo de la Serna, Joan Pera, St...          Spain   \n",
       "7752  Lorinska Merrington, Jane Scandizzo, Rachel Wa...      Australia   \n",
       "7753  Gökhan Atalay, Payidar Tüfekçioglu, Baran Akbu...         Turkey   \n",
       "\n",
       "             date_added  release_year rating   duration  \\\n",
       "7748     April 25, 2020          2009  TV-14    115 min   \n",
       "7749       July 8, 2020          2005  TV-Y7  2 Seasons   \n",
       "7750        May 1, 2018          2015  TV-Y7  2 Seasons   \n",
       "7751  February 15, 2019          2018  TV-MA    130 min   \n",
       "7752       July 3, 2019          2019  TV-MA  2 Seasons   \n",
       "7753   January 17, 2017          2016  TV-PG  2 Seasons   \n",
       "\n",
       "                                   listed_in  \\\n",
       "7748  Comedies, Dramas, International Movies   \n",
       "7749                  Anime Series, Kids' TV   \n",
       "7750                  Anime Series, Kids' TV   \n",
       "7751          Comedies, International Movies   \n",
       "7752      International TV Shows, Reality TV   \n",
       "7753       International TV Shows, TV Dramas   \n",
       "\n",
       "                                            description  \n",
       "7748  On his wedding day, an arrogant, greedy accoun...  \n",
       "7749  The lives of young Yugi Moto and his friends J...  \n",
       "7750  Now that he's discovered the Pendulum Summonin...  \n",
       "7751  Competing con artists attempt to creatively an...  \n",
       "7752  It's drama Down Under when expectant mothers w...  \n",
       "7753  During the Mongol invasions, Yunus Emre leaves...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set some display options\n",
    "pd.options.display.max_colwidth = 50\n",
    "pd.options.display.max_columns = 20\n",
    "\n",
    "# sample rows from netflix dataset\n",
    "netflix_raw_data.iloc[7748: 7754, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interesting-bubble",
   "metadata": {},
   "source": [
    "### Table for columns with Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "pregnant-stanley",
   "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>Column Name</th>\n",
       "      <th>No. of rows with missing values</th>\n",
       "      <th>Missing value as % (Netflix Dataset)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>director</td>\n",
       "      <td>2389</td>\n",
       "      <td>30.6793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cast</td>\n",
       "      <td>718</td>\n",
       "      <td>9.2205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>country</td>\n",
       "      <td>507</td>\n",
       "      <td>6.5109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>date_added</td>\n",
       "      <td>10</td>\n",
       "      <td>0.1284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rating</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Column Name  No. of rows with missing values  \\\n",
       "0    director                             2389   \n",
       "1        cast                              718   \n",
       "2     country                              507   \n",
       "3  date_added                               10   \n",
       "4      rating                                7   \n",
       "\n",
       "  Missing value as % (Netflix Dataset)  \n",
       "0                              30.6793  \n",
       "1                               9.2205  \n",
       "2                               6.5109  \n",
       "3                               0.1284  \n",
       "4                               0.0899  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this block of code only displays columns which have missing data and the % of rows with the missing values\n",
    "null_column_list = []\n",
    "\n",
    "# total no. of rows in dataset\n",
    "total_rows = netflix_raw_data.shape[0]\n",
    "\n",
    "for each_col in netflix_raw_data.columns:\n",
    "    if netflix_raw_data[each_col].isnull().sum() > 0:\n",
    "        null_sum = netflix_raw_data[each_col].isnull().sum()\n",
    "        null_perc = \"{:.4f}\".format(null_sum / total_rows * 100)\n",
    "        \n",
    "        null_column_list.append([each_col, null_sum, null_perc])\n",
    "                \n",
    "# display missing row count and % as a dataframe    \n",
    "pd.DataFrame(null_column_list, columns = [\"Column Name\", \"No. of rows with missing values\", \n",
    "                                          \"Missing value as % (Netflix Dataset)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "enormous-sydney",
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete the dummy variables        \n",
    "del total_rows, null_column_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "decent-history",
   "metadata": {},
   "source": [
    "## Explore Netflix Dataset\n",
    "\n",
    "### type Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fabulous-province",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create figure\n",
    "fig, ax = plt.subplots(figsize = (12,3))\n",
    "\n",
    "# plot content type as percentage\n",
    "ax.barh(y = netflix_raw_data[\"type\"].value_counts(normalize = True).index,\n",
    "       width = netflix_raw_data[\"type\"].value_counts(normalize = True).mul(100).values)\n",
    "\n",
    "# set title for the plot \n",
    "ax.set_title(\"Types of content available on Netflix\")\n",
    "\n",
    "# hide the x-axis ticks and labels\n",
    "ax.set_xticklabels(labels = \"\")\n",
    "ax.tick_params(bottom = False)\n",
    "\n",
    "# annotate each bar\n",
    "for p in ax.patches:\n",
    "    ax.annotate(text = \"{:.2f}%\".format(p.get_width()), \n",
    "                xy = [p.get_width()/ 2, p.get_y() + 0.35], \n",
    "                color = \"white\", fontsize = \"large\")\n",
    "    \n",
    "# despine the plot\n",
    "[ax.spines[spine].set_visible(False) for spine in ax.spines if spine != \"left\"]\n",
    "\n",
    "# show the plot\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "excessive-playback",
   "metadata": {},
   "source": [
    "### rating Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "killing-arthritis",
   "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>director</th>\n",
       "      <th>release_year</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>13TH: A Conversation with Oprah Winfrey &amp; Ava ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2359</th>\n",
       "      <td>Gargantia on the Verdurous Planet</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3660</th>\n",
       "      <td>Little Lunch</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3736</th>\n",
       "      <td>Louis C.K. 2017</td>\n",
       "      <td>Louis C.K.</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3737</th>\n",
       "      <td>Louis C.K.: Hilarious</td>\n",
       "      <td>Louis C.K.</td>\n",
       "      <td>2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3738</th>\n",
       "      <td>Louis C.K.: Live at the Comedy Store</td>\n",
       "      <td>Louis C.K.</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4323</th>\n",
       "      <td>My Honor Was Loyalty</td>\n",
       "      <td>Alessandro Pepe</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  title         director  \\\n",
       "67    13TH: A Conversation with Oprah Winfrey & Ava ...              NaN   \n",
       "2359                  Gargantia on the Verdurous Planet              NaN   \n",
       "3660                                       Little Lunch              NaN   \n",
       "3736                                    Louis C.K. 2017       Louis C.K.   \n",
       "3737                              Louis C.K.: Hilarious       Louis C.K.   \n",
       "3738               Louis C.K.: Live at the Comedy Store       Louis C.K.   \n",
       "4323                               My Honor Was Loyalty  Alessandro Pepe   \n",
       "\n",
       "      release_year rating  \n",
       "67            2017    NaN  \n",
       "2359          2013    NaN  \n",
       "3660          2015    NaN  \n",
       "3736          2017    NaN  \n",
       "3737          2010    NaN  \n",
       "3738          2015    NaN  \n",
       "4323          2015    NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# identify the titles with missing-values\n",
    "netflix_raw_data.loc[netflix_raw_data[\"rating\"].isnull(), [\"title\", \"director\", \"release_year\", \"rating\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acceptable-tutorial",
   "metadata": {},
   "source": [
    "#### Mapper for titles with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "statutory-broadway",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'13TH: A Conversation with Oprah Winfrey & Ava DuVernay': 'TV-PG',\n",
       " 'Gargantia on the Verdurous Planet': 'TV-14',\n",
       " 'Little Lunch': 'TV-Y7',\n",
       " 'Louis C.K. 2017': 'TV-MA',\n",
       " 'Louis C.K.: Hilarious': 'TV-MA',\n",
       " 'Louis C.K.: Live at the Comedy Store': 'TV-MA',\n",
       " 'My Honor Was Loyalty': 'PG-13'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dictionary to map title with ratings (Copied from Mike's work)\n",
    "ratings_map = {idx:val for idx,val in zip(netflix_raw_data.loc[netflix_raw_data[\"rating\"].isnull(), \"title\"].values, \n",
    "                                          [\"TV-PG\", \"TV-14\", \"TV-Y7\", \"TV-MA\", \"TV-MA\", \"TV-MA\", \"PG-13\"])}\n",
    "# display ratings dictionary\n",
    "ratings_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "concrete-advantage",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace the missing ratings\n",
    "netflix_raw_data[\"rating\"] = netflix_raw_data[\"title\"].map(ratings_map).fillna(netflix_raw_data[\"rating\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c4ab1334",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pywaffle\n",
    "from pywaffle import Waffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "413b4409",
   "metadata": {},
   "outputs": [],
   "source": [
    "# temp variable for value count of ratings for Netflix Movies\n",
    "data1 = dict(netflix_raw_data[netflix_raw_data[\"type\"] == \"Movie\"]\n",
    "            [\"rating\"].value_counts())\n",
    "\n",
    "# temp variable for value count of ratings for Netflix TV Shows\n",
    "data2 = dict(netflix_raw_data[netflix_raw_data[\"type\"] != \"Movie\"]\n",
    "            [\"rating\"].value_counts()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "91888722",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pywaffle\\waffle.py:394: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  self.ax = self.add_subplot(loc, aspect=\"equal\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Waffle size 1008x468 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make pywaffle chart with 2 subplots for each type of Netflix Content\n",
    "fig = plt.figure(\n",
    "    FigureClass = Waffle,\n",
    "    plots = {\n",
    "        '211': \n",
    "        {'values': data1,\n",
    "         'labels' : [f\"{k} ({int(v / sum(data1.values()) * 100)}%)\" for k, v in data1.items()],\n",
    "         'legend': {\n",
    "             'loc': 'lower left',\n",
    "             'bbox_to_anchor': (0, -0.6),\n",
    "             'fontsize': 12, \n",
    "             'ncol': 5},\n",
    "         'title' : {\"label\" : \"Ratings Wise Distribution of Netflix Movies\"}\n",
    "        },\n",
    "        '212': \n",
    "        {'values': data2,\n",
    "         'labels' : [f\"{k} ({int(v / sum(data1.values()) * 100)}%)\" for k, v in data2.items()],\n",
    "         'legend': {\n",
    "             'loc': 'lower left',\n",
    "             'bbox_to_anchor': (0, -0.45),\n",
    "             'fontsize': 12, \n",
    "             'ncol': 5},\n",
    "         'title' : {\"label\" : \"Ratings Wise Distribution of Netflix Shows\"}\n",
    "        }\n",
    "    },\n",
    "    rows = 3,\n",
    "    columns = 15,\n",
    "    icons = \"user\",\n",
    "    cmap_name = \"tab20\",\n",
    "    figsize = (14, 6.5)\n",
    ")    \n",
    "plt.tight_layout(h_pad = 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "approved-forth",
   "metadata": {},
   "source": [
    "### date_added Column & release Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "guilty-recycling",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7687, 12)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert date_added column to a datetime format\n",
    "netflix_raw_data[\"date_added\"] = pd.to_datetime(netflix_raw_data[\"date_added\"])\n",
    "\n",
    "# select only those records where date_added column has values\n",
    "netflix_raw_data = netflix_raw_data[netflix_raw_data[\"date_added\"].notna()]\n",
    "\n",
    "# shape of resultant netflix_raw_data\n",
    "netflix_raw_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "407fc3e4",
   "metadata": {},
   "source": [
    "#### Additional Analysis\n",
    "\n",
    "This is a small analysis of:\n",
    "- distribution of titles based on the decade they were released\n",
    "- distribution of titles hosted by Netflix since it's inception\n",
    "\n",
    "For ease of plotting:\n",
    "- the titles that were released before the year 1980, have been grouped under `Before 1980`. Why this year? because I was born in this decade!\n",
    "- since Netflix established itself in 190 countries by 2016, all the titles added (on Netflix platform) before 2016 have been grouped under `Added before 2016`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "sharing-objective",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the decade for each year of release\n",
    "decade_df = (netflix_raw_data[\"release_year\"] // 10 * 10).value_counts().reset_index()\n",
    "\n",
    "# rename the dataframe columns\n",
    "decade_df.rename(columns = {\"index\": \"Decade\", \"release_year\" : \"No. of Titles\"}, inplace = True)\n",
    "\n",
    "# identify titles released before the year 1980 \n",
    "decade_df[\"Decade\"] = decade_df[\"Decade\"].apply(lambda d: d if d > 1970 else \"Before 1980\")\n",
    "\n",
    "# label each decade as YYYY's\n",
    "decade_df[\"Decade\"] = decade_df[\"Decade\"].astype(str).apply(lambda s: s + \"'s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "proprietary-thickness",
   "metadata": {},
   "outputs": [],
   "source": [
    "# group the no. of titles by decade in which they were released\n",
    "data1 = decade_df.groupby(\"Decade\")[\"No. of Titles\"].sum()\n",
    "\n",
    "# group the no. of titles by decade in which they were added to Netflix\n",
    "data2 = netflix_raw_data[\"date_added\"].dt.year.apply(lambda d: \"In \" + str(d) if d > 2015 else \"Before 2016\").\\\n",
    "        value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "conservative-penny",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create figure object\n",
    "fig, ax = plt.subplots(ncols = 2, figsize = (13.5, 6))\n",
    "\n",
    "# plot the years when title was released\n",
    "ax[0].pie(x = data1.values, \n",
    "          labels = data1.index,\n",
    "          labeldistance = 1.03,\n",
    "          autopct = \"%.1f%%\",\n",
    "          explode = [0.1, 0.05, 0, 0, 0, 0.15],\n",
    "          pctdistance = 0.85,\n",
    "          startangle = 0, \n",
    "          wedgeprops = {\"width\": 0.4}) \n",
    "\n",
    "# annotation in the center\n",
    "ax[0].annotate(text = \"Decade of Release\", xy = (-0.5,0), fontsize = 15)\n",
    "\n",
    "# plot the years when title was added\n",
    "ax[1].pie(x = data2.values,\n",
    "          labels = data2.index,\n",
    "          labeldistance = 1.03,\n",
    "          autopct = \"%.1f%%\",\n",
    "          explode = [0.1, 0.05, 0, 0, 0, 0, 0.15],\n",
    "          pctdistance = 0.85,\n",
    "          startangle = 0,\n",
    "          rotatelabels = False, \n",
    "          wedgeprops = {\"width\": 0.4})\n",
    "\n",
    "# annotation in the center\n",
    "ax[1].annotate(text = \"Year of Addition\", xy = (-0.3,0), fontsize = 15)\n",
    "\n",
    "# extras \n",
    "# plt.subplots_adjust(wspace = 0.2, hspace = 0)\n",
    "plt.tight_layout(pad = 1)\n",
    "plt.suptitle(t = \"Distribution of Titles on Netflix by the\", fontsize = 20)\n",
    "\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "experimental-worst",
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete non-essential variables\n",
    "del decade_df, data1, data2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suitable-finland",
   "metadata": {},
   "source": [
    "### duration Column\n",
    "\n",
    "This column specifies the duration of a title in No. of seasons or runtime in minutes. However, if we analyze the values, the TV Shows are specified with seasons and Movies have runtime minutes. No series has runtime in minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "solved-victory",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TV Show    2330\n",
      "Name: type, dtype: int64 \n",
      "\n",
      "Movie    5357\n",
      "Name: type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# title type with duration in seasons\n",
    "print(netflix_raw_data.loc[netflix_raw_data[\"duration\"].str.contains(\"Season\"), \"type\"].value_counts(), \"\\n\")\n",
    "\n",
    "# title type with duration in minutes\n",
    "print(netflix_raw_data.loc[netflix_raw_data[\"duration\"].str.contains(\"min\"), \"type\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "physical-smooth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1 Season      1596\n",
       "2 Seasons      357\n",
       "3 Seasons      165\n",
       "4 Seasons       83\n",
       "5 Seasons       54\n",
       "6 Seasons       28\n",
       "7 Seasons       16\n",
       "8 Seasons       13\n",
       "9 Seasons        7\n",
       "10 Seasons       4\n",
       "12 Seasons       2\n",
       "15 Seasons       2\n",
       "13 Seasons       1\n",
       "16 Seasons       1\n",
       "11 Seasons       1\n",
       "Name: duration, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# duration for title type TV Show \n",
    "netflix_raw_data.loc[netflix_raw_data[\"duration\"].str.contains(\"Season\"), \"duration\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "innovative-prison",
   "metadata": {},
   "source": [
    "### Visualize distribution of seasons for titles on netflix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "related-unknown",
   "metadata": {},
   "outputs": [],
   "source": [
    "# temporary variable\n",
    "data = netflix_raw_data.loc[netflix_raw_data[\"duration\"].str.contains(\"Season\")][\"duration\"].value_counts()\n",
    "\n",
    "# group seasons to accommodate in a pie-chart\n",
    "for each in data.index:\n",
    "    if int(each.split()[0]) < 6:\n",
    "        pass\n",
    "    elif int(each.split()[0]) <= 8:\n",
    "        data.rename({each : \"As long as GOT\"}, inplace = True)\n",
    "    elif int(each.split()[0]) <= 10:\n",
    "        data.rename({each : \"As long as F.R.I.E.N.D.S\"}, inplace = True)\n",
    "    else:\n",
    "        data.rename({each : \"Too long\"}, inplace = True)\n",
    "\n",
    "# re-group and total\n",
    "data = data.groupby(data.index).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "annoying-penguin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a figure object\n",
    "fig, ax = plt.subplots(figsize = (12, 8))\n",
    "\n",
    "# draw a pie-chart\n",
    "ax.pie(x = data,\n",
    "       labels = data.index,\n",
    "       autopct = \"%.1f%%\",\n",
    "       pctdistance = 0.89,\n",
    "       explode = [0, 0, 0, 0, 0.1, 0.2, 0.3, 0.4],\n",
    "       labeldistance = 1.03,\n",
    "       startangle = 30, \n",
    "       radius = 1.2,\n",
    "       wedgeprops = {\"width\": 0.5})\n",
    "\n",
    "# set title for the plot\n",
    "ax.set_title(\"Netflix and Chill...\", pad = 20)\n",
    "ax.annotate(text = \"But for How long?\", xy = (-0.5, 0), fontsize = \"18\")\n",
    "\n",
    "## Custom legend for grouped items\n",
    "# handler map\n",
    "handler_map = {\"As long as F.R.I.E.N.D.S\" : \"Upto 10 Seasons\", \n",
    "               \"As long as GOT (Game Of Thrones)\" : \"Upto 8 Seasons\", \n",
    "               \"Too Long\" : \"More than 10 Seasons\"}\n",
    "\n",
    "# get default the handles and labels\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "\n",
    "# place only the last three handles which have grouped seasons \n",
    "ax.legend(handles = handles[-3:], \n",
    "          labels = (\" : \".join([k,v]) for k,v in handler_map.items()), \n",
    "          bbox_to_anchor = (1, 1.05))\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "killing-latter",
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete temporary/intermediate variables\n",
    "del data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "instrumental-masters",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create figure object\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# distribution of runtime in minutes\n",
    "ax.hist(x = netflix_raw_data.loc[netflix_raw_data[\"duration\"].str.contains(\"min\"), \"duration\"].\\\n",
    "        str.strip(\"min\").astype(int), \n",
    "        bins = 50)\n",
    "\n",
    "# draw vertical line - mean runtime for movies\n",
    "ax.axvline(x = netflix_raw_data.loc[netflix_raw_data[\"duration\"].str.contains(\"min\"), \"duration\"].\\\n",
    "           str.strip(\"min\").astype(int).mean(),\n",
    "           lw = 3,\n",
    "           dashes = [5, 2, 1, 2],\n",
    "           color = \"orange\", \n",
    "           label = \"Mean\")\n",
    "\n",
    "# annotation\n",
    "ax.annotate(text = \"{:.2f}\".format(netflix_raw_data.loc[netflix_raw_data[\"duration\"].str.contains(\"min\"), \"duration\"].\\\n",
    "           str.strip(\"min\").astype(int).mean()), \n",
    "            xy = [100, 700])\n",
    "\n",
    "# extras \n",
    "ax.legend()\n",
    "ax.set_title(\"Distribution of Runtime Minutes for Movies on Netflix\")\n",
    "ax.set_ylabel(\"Count\", fontsize = \"large\")\n",
    "ax.set_xlabel(\"Minutes\", fontsize = \"large\")\n",
    "\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interstate-chapter",
   "metadata": {},
   "source": [
    "## Tackle the Missing Values\n",
    "\n",
    "The big bad boy: Missing Values in 3 columns - Director, Cast and Country of origin of a title. \n",
    "The idea is to try out the best approach possible and fill-in as many missing values as we can.\n",
    "\n",
    "Why IMDB? [IMDB.com](https://www.imdb.com/) provides separate datasets with different information. The common link between these datasets are alpha-numeric columns like \"tconst\", \"nconst\" etc. that provide unique identifiers for a title/ cast/ crew member."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dress-mechanics",
   "metadata": {},
   "source": [
    "### Split the Netflix dataset into 2 separate dataframes\n",
    "\n",
    "- <b>netflix_data :</b> dataframe with non-null values for all the columns\n",
    "- <b>missing_data :</b> dataframe with rows where values for any of the 3 columns is missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cardiac-english",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"split the dataframe with missing and non-missing values\"\"\"\n",
    "\n",
    "# non-null data\n",
    "netflix_data = netflix_raw_data[(netflix_raw_data[\"director\"].notna()) &\n",
    "                                (netflix_raw_data[\"cast\"].notna()) &\n",
    "                                (netflix_raw_data[\"country\"].notna())].reset_index(drop = True)\n",
    "\n",
    "# where data for either of the 3 columns - director, cast and country is missing\n",
    "missing_data = netflix_raw_data[netflix_raw_data[\"director\"].isnull() | \n",
    "                                netflix_raw_data[\"cast\"].isnull() |\n",
    "                                netflix_raw_data[\"country\"].isnull()].reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "medical-container",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframe Name: (Rows, Columns)\n",
      "------------------------------\n",
      "Data with Nulls: (2897, 12)\n",
      "Non-null data: (4790, 12)\n"
     ]
    }
   ],
   "source": [
    "# shape of resultant datasets\n",
    "print(\"Dataframe Name: (Rows, Columns)\", \n",
    "      \"-\".rjust(30, \"-\"),\n",
    "      \"Data with Nulls: \" + str(missing_data.shape),\n",
    "      \"Non-null data: \" + str(netflix_data.shape), sep = \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6bba69e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "director    2314\n",
       "cast         709\n",
       "country      503\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data[[\"director\", \"cast\", \"country\"]].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suitable-cabin",
   "metadata": {},
   "source": [
    "### Concatenate the title and release_year columns in missing_data dataframes\n",
    "\n",
    "In order to compare the Netflix dataset and datasets provided by IMDB, \n",
    "\n",
    "- special or non-alphanumeric characters have been removed from title names\n",
    "- lower case has been applied to all the title names\n",
    "- underscore has been added between title and year values while concatenation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "colored-given",
   "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>release_year</th>\n",
       "      <th>combo</th>\n",
       "      <th>director</th>\n",
       "      <th>cast</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3%</td>\n",
       "      <td>2020</td>\n",
       "      <td>3_2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>João Miguel, Bianca Comparato, Michel Gomes, R...</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1983</td>\n",
       "      <td>2018</td>\n",
       "      <td>1983_2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Robert Więckiewicz, Maciej Musiał, Michalina O...</td>\n",
       "      <td>Poland, United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1994</td>\n",
       "      <td>2019</td>\n",
       "      <td>1994_2019</td>\n",
       "      <td>Diego Enrique Osorno</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mexico</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feb-09</td>\n",
       "      <td>2018</td>\n",
       "      <td>feb09_2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shahd El Yaseen, Shaila Sabt, Hala, Hanadi Al-...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>'89</td>\n",
       "      <td>2017</td>\n",
       "      <td>89_2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lee Dixon, Ian Wright, Paul Merson</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>​SAINT SEIYA: Knights of the Zodiac</td>\n",
       "      <td>2020</td>\n",
       "      <td>saint seiya knights of the zodiac_2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bryson Baugus, Emily Neves, Blake Shepard, Pat...</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(T)ERROR</td>\n",
       "      <td>2015</td>\n",
       "      <td>terror_2015</td>\n",
       "      <td>Lyric R. Cabral, David Felix Sutcliffe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(Un)Well</td>\n",
       "      <td>2020</td>\n",
       "      <td>unwell_2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>#blackAF</td>\n",
       "      <td>2020</td>\n",
       "      <td>blackaf_2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kenya Barris, Rashida Jones, Iman Benson, Genn...</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>#cats_the_mewvie</td>\n",
       "      <td>2020</td>\n",
       "      <td>cats_the_mewvie_2020</td>\n",
       "      <td>Michael Margolis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 title  release_year  \\\n",
       "0                                   3%          2020   \n",
       "1                                 1983          2018   \n",
       "2                                 1994          2019   \n",
       "3                               Feb-09          2018   \n",
       "4                                  '89          2017   \n",
       "5  ​SAINT SEIYA: Knights of the Zodiac          2020   \n",
       "6                             (T)ERROR          2015   \n",
       "7                             (Un)Well          2020   \n",
       "8                             #blackAF          2020   \n",
       "9                     #cats_the_mewvie          2020   \n",
       "\n",
       "                                    combo  \\\n",
       "0                                  3_2020   \n",
       "1                               1983_2018   \n",
       "2                               1994_2019   \n",
       "3                              feb09_2018   \n",
       "4                                 89_2017   \n",
       "5  saint seiya knights of the zodiac_2020   \n",
       "6                             terror_2015   \n",
       "7                             unwell_2020   \n",
       "8                            blackaf_2020   \n",
       "9                    cats_the_mewvie_2020   \n",
       "\n",
       "                                 director  \\\n",
       "0                                     NaN   \n",
       "1                                     NaN   \n",
       "2                    Diego Enrique Osorno   \n",
       "3                                     NaN   \n",
       "4                                     NaN   \n",
       "5                                     NaN   \n",
       "6  Lyric R. Cabral, David Felix Sutcliffe   \n",
       "7                                     NaN   \n",
       "8                                     NaN   \n",
       "9                        Michael Margolis   \n",
       "\n",
       "                                                cast                country  \n",
       "0  João Miguel, Bianca Comparato, Michel Gomes, R...                 Brazil  \n",
       "1  Robert Więckiewicz, Maciej Musiał, Michalina O...  Poland, United States  \n",
       "2                                                NaN                 Mexico  \n",
       "3  Shahd El Yaseen, Shaila Sabt, Hala, Hanadi Al-...                    NaN  \n",
       "4                 Lee Dixon, Ian Wright, Paul Merson         United Kingdom  \n",
       "5  Bryson Baugus, Emily Neves, Blake Shepard, Pat...                  Japan  \n",
       "6                                                NaN          United States  \n",
       "7                                                NaN          United States  \n",
       "8  Kenya Barris, Rashida Jones, Iman Benson, Genn...          United States  \n",
       "9                                                NaN                 Canada  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combination of title and release_year columns\n",
    "missing_data[\"combo\"] = missing_data[\"title\"].str.replace(r\"[^\\w\\s]+\", \"\", regex = True).str.lower().\\\n",
    "                             str.cat(missing_data[\"release_year\"].astype(str), sep = \"_\")\n",
    "\n",
    "# sample rows with selected columns only \n",
    "missing_data[[\"title\", \"release_year\", \"combo\", \"director\", \"cast\", \"country\"]].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sunrise-artist",
   "metadata": {},
   "source": [
    "### Read IMDB datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "metallic-restaurant",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set-up directory and Files\n",
    "imdb_dir = os.getcwd() + \"\\\\IMDB_Dataset\\\\\"\n",
    "imdb_file_names = [\"title_akas.tsv\", \"title_basics.tsv\", \"title_crew.tsv\", \"title_principals.tsv\", \"name_basics.tsv\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "improved-joint",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# import the IMDB_work.py module aliased as \"iw\" for now\n",
    "import IMDB_work as iw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "outer-stadium",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Docstring for the module and function \n",
      "\n",
      "python module to read and store IMDB Data Files in pandas dataframe \n",
      "\n",
      "Function to read the \".tsv\" files downloaded from IMDB interface\n",
      "----------------------------------------------------------------\n",
      "Name: read_imdb_files\n",
      "\n",
      "Parameters: \n",
      "- file_name: name of the IMDB dataset file. \n",
      "- file_directory: name of the directory that contains the IMDB dataset files. Parameter has been set to default option.\n",
      "\n",
      "Returns: a dataframe containing all the records of the IMDB dataset file with every column stored as \"object\" dtype\n",
      "\n",
      "Additional Info: \n",
      "- all the fields are stored as object dtype to avoid any errors \n",
      "- prints the shape and missing value % for the datafile\n",
      "\n",
      "- \"\\N\" represents missing or null values in IMDB datasets. These have been replaced with np.nan. \n",
      "    \n"
     ]
    }
   ],
   "source": [
    "print(\"Docstring for the module and function\", \"\\n\")\n",
    "print(iw.__doc__)\n",
    "print(iw.read_imdb_files.__doc__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "moral-xerox",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data file title_basics.tsv contains 7918519 rows and 9 columns\n",
      "============================================================\n",
      "       ColumnName  MissingValue%\n",
      "0          tconst          0.000\n",
      "1       titleType          0.000\n",
      "2    primaryTitle          0.000\n",
      "3   originalTitle          0.000\n",
      "4         isAdult          0.000\n",
      "5       startYear         10.950\n",
      "6         endYear         99.030\n",
      "7  runtimeMinutes         72.080\n",
      "8          genres          7.810\n"
     ]
    }
   ],
   "source": [
    "\"\"\" Read the title_basics.tsv file \"\"\"\n",
    "title_basics = iw.read_imdb_files(file_name = imdb_file_names[1], file_directory = imdb_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "irish-yemen",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data file name_basics.tsv contains 10942043 rows and 6 columns\n",
      "============================================================\n",
      "          ColumnName  MissingValue%\n",
      "0             nconst          0.000\n",
      "1        primaryName          0.000\n",
      "2          birthYear         95.190\n",
      "3          deathYear         98.270\n",
      "4  primaryProfession         21.230\n",
      "5     knownForTitles         18.520\n"
     ]
    }
   ],
   "source": [
    "\"\"\" Read the name_basics.tsv file \"\"\"\n",
    "name_basics = iw.read_imdb_files(file_name = imdb_file_names[-1], file_directory = imdb_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "damaged-cholesterol",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data file title_crew.tsv contains 7918519 rows and 3 columns\n",
      "============================================================\n",
      "  ColumnName  MissingValue%\n",
      "0     tconst          0.000\n",
      "1  directors         42.420\n",
      "2    writers         49.180\n"
     ]
    }
   ],
   "source": [
    "\"\"\" Read the title_crew.tsv file \"\"\"\n",
    "title_crew = iw.read_imdb_files(file_name = imdb_file_names[2], file_directory = imdb_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "given-bhutan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data file title_principals.tsv contains 44887359 rows and 6 columns\n",
      "============================================================\n",
      "   ColumnName  MissingValue%\n",
      "0      tconst          0.000\n",
      "1    ordering          0.000\n",
      "2      nconst          0.000\n",
      "3    category          0.000\n",
      "4         job         83.900\n",
      "5  characters         49.970\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Read title_principals.tsv file\"\"\"\n",
    "title_principals = iw.read_imdb_files(file_name = imdb_file_names[3], file_directory = imdb_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "treated-graphics",
   "metadata": {},
   "source": [
    "### Finding Common Titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "chief-server",
   "metadata": {},
   "outputs": [],
   "source": [
    "# combination of primaryTitle and startYear columns >> title_basics dataset\n",
    "title_basics[\"combo\"] = title_basics[\"primaryTitle\"].str.replace(r\"[^\\w\\s]+\", \"\", regex = True).str.lower().\\\n",
    "                        str.cat(title_basics[\"startYear\"].astype(str), sep = \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "tamil-admission",
   "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>tconst</th>\n",
       "      <th>titleType</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>originalTitle</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>startYear</th>\n",
       "      <th>endYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>combo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000001</td>\n",
       "      <td>short</td>\n",
       "      <td>Carmencita</td>\n",
       "      <td>Carmencita</td>\n",
       "      <td>0</td>\n",
       "      <td>1894</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Documentary,Short</td>\n",
       "      <td>carmencita_1894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000002</td>\n",
       "      <td>short</td>\n",
       "      <td>Le clown et ses chiens</td>\n",
       "      <td>Le clown et ses chiens</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>Animation,Short</td>\n",
       "      <td>le clown et ses chiens_1892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0000003</td>\n",
       "      <td>short</td>\n",
       "      <td>Pauvre Pierrot</td>\n",
       "      <td>Pauvre Pierrot</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Animation,Comedy,Romance</td>\n",
       "      <td>pauvre pierrot_1892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0000004</td>\n",
       "      <td>short</td>\n",
       "      <td>Un bon bock</td>\n",
       "      <td>Un bon bock</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>Animation,Short</td>\n",
       "      <td>un bon bock_1892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0000005</td>\n",
       "      <td>short</td>\n",
       "      <td>Blacksmith Scene</td>\n",
       "      <td>Blacksmith Scene</td>\n",
       "      <td>0</td>\n",
       "      <td>1893</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Comedy,Short</td>\n",
       "      <td>blacksmith scene_1893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tconst titleType            primaryTitle           originalTitle  \\\n",
       "0  tt0000001     short              Carmencita              Carmencita   \n",
       "1  tt0000002     short  Le clown et ses chiens  Le clown et ses chiens   \n",
       "2  tt0000003     short          Pauvre Pierrot          Pauvre Pierrot   \n",
       "3  tt0000004     short             Un bon bock             Un bon bock   \n",
       "4  tt0000005     short        Blacksmith Scene        Blacksmith Scene   \n",
       "\n",
       "  isAdult startYear endYear runtimeMinutes                    genres  \\\n",
       "0       0      1894     NaN              1         Documentary,Short   \n",
       "1       0      1892     NaN              5           Animation,Short   \n",
       "2       0      1892     NaN              4  Animation,Comedy,Romance   \n",
       "3       0      1892     NaN             12           Animation,Short   \n",
       "4       0      1893     NaN              1              Comedy,Short   \n",
       "\n",
       "                         combo  \n",
       "0              carmencita_1894  \n",
       "1  le clown et ses chiens_1892  \n",
       "2          pauvre pierrot_1892  \n",
       "3             un bon bock_1892  \n",
       "4        blacksmith scene_1893  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample rows from title_basics dataframe\n",
    "title_basics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "pressing-realtor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (1750, 2)\n"
     ]
    },
    {
     "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>combo</th>\n",
       "      <th>tconst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100 days my prince_2018</td>\n",
       "      <td>[tt8199972]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100 things to do before high school_2014</td>\n",
       "      <td>[tt3904078, tt3914888]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12 years promise_2014</td>\n",
       "      <td>[tt5476252]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13 reasons why_2020</td>\n",
       "      <td>[tt12301534]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13th a conversation with oprah winfrey  ava du...</td>\n",
       "      <td>[tt6491068]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               combo                  tconst\n",
       "0                            100 days my prince_2018             [tt8199972]\n",
       "1           100 things to do before high school_2014  [tt3904078, tt3914888]\n",
       "2                              12 years promise_2014             [tt5476252]\n",
       "3                                13 reasons why_2020            [tt12301534]\n",
       "4  13th a conversation with oprah winfrey  ava du...             [tt6491068]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "- match the combination in title_basics and missing_data dataframes\n",
    "- collect and store the matched records in a temporary dataframe - match_df\n",
    "\"\"\"\n",
    "\n",
    "all_matches_df = title_basics.loc[title_basics[\"combo\"].isin(missing_data[\"combo\"]), \n",
    "                                  [\"tconst\", \"combo\"]].reset_index(drop = True).\\\n",
    "                                  groupby(\"combo\")[\"tconst\"].unique().reset_index()\n",
    "\n",
    "# sample rows from resultant match_df dataframe\n",
    "print(\"Shape:\", all_matches_df.shape)\n",
    "all_matches_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "effective-tiger",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1386, 2)\n"
     ]
    }
   ],
   "source": [
    "# filter out unique title names i.e. no other title of the same name was released at any other time\n",
    "all_matches_df = all_matches_df[all_matches_df[\"tconst\"].str.len() == 1].\\\n",
    "                 set_index(\"combo\").explode(\"tconst\").reset_index()\n",
    "\n",
    "# shape of resultant match_df\n",
    "print(all_matches_df.shape)\n",
    "\n",
    "# keep all_macthes_df as backup and create a dataframe for intermediate steps\n",
    "match_df = all_matches_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27750877",
   "metadata": {},
   "source": [
    "### Finding Missing Director"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "starting-emphasis",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1178, 3)\n"
     ]
    },
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       "      <th>tconst</th>\n",
       "      <th>director_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>your excellency_2019</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1169</th>\n",
       "      <td>your lie in april_2014</td>\n",
       "      <td>tt3895150</td>\n",
       "      <td>nm4733902,nm3440248,nm4796122,nm1841039,nm4303...</td>\n",
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       "    <tr>\n",
       "      <th>1170</th>\n",
       "      <td>yours fatefully_2012</td>\n",
       "      <td>tt7131690</td>\n",
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       "      <th>1171</th>\n",
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       "      <td>z4_2018</td>\n",
       "      <td>tt8618456</td>\n",
       "      <td>nm0872780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1173</th>\n",
       "      <td>zak storm_2016</td>\n",
       "      <td>tt4209752</td>\n",
       "      <td>nm3569481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1174</th>\n",
       "      <td>zindagi gulzar hai_2012</td>\n",
       "      <td>tt2828240</td>\n",
       "      <td>nm5616300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1175</th>\n",
       "      <td>zoids wild_2018</td>\n",
       "      <td>tt12317724</td>\n",
       "      <td>nm0837219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1176</th>\n",
       "      <td>zona rosa_2019</td>\n",
       "      <td>tt11194518</td>\n",
       "      <td>nm10743407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1177</th>\n",
       "      <td>zz top that little ol band from texas_2019</td>\n",
       "      <td>tt9015306</td>\n",
       "      <td>nm0242757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           combo      tconst  \\\n",
       "1168                        your excellency_2019  tt11489288   \n",
       "1169                      your lie in april_2014   tt3895150   \n",
       "1170                        yours fatefully_2012   tt7131690   \n",
       "1171                               z nation_2018   tt8395332   \n",
       "1172                                     z4_2018   tt8618456   \n",
       "1173                              zak storm_2016   tt4209752   \n",
       "1174                     zindagi gulzar hai_2012   tt2828240   \n",
       "1175                             zoids wild_2018  tt12317724   \n",
       "1176                              zona rosa_2019  tt11194518   \n",
       "1177  zz top that little ol band from texas_2019   tt9015306   \n",
       "\n",
       "                                            director_id  \n",
       "1168                                          nm2481000  \n",
       "1169  nm4733902,nm3440248,nm4796122,nm1841039,nm4303...  \n",
       "1170                    nm11522613,nm11522614,nm6562612  \n",
       "1171                                          nm7914806  \n",
       "1172                                          nm0872780  \n",
       "1173                                          nm3569481  \n",
       "1174                                          nm5616300  \n",
       "1175                                          nm0837219  \n",
       "1176                                         nm10743407  \n",
       "1177                                          nm0242757  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "- match the tconst ID between the match_df and title_crew dataset\n",
    "- extract director ID's associated with a title from title_crew dataframe\n",
    "\"\"\"\n",
    "# match the title ID and map the director ID in match_df dataframe\n",
    "match_df[\"director_id\"] = match_df[\"tconst\"].map(dict(title_crew.loc[title_crew[\"tconst\"].isin(match_df[\"tconst\"]), \n",
    "                                                                     [\"tconst\", \"directors\"]].dropna().values))\n",
    "\n",
    "\"\"\"\n",
    "- the title_crew dataset itself has null values. matches with these records won't help us\n",
    "- so we drop the records where director ID column still contains null\n",
    "\"\"\"\n",
    "\n",
    "# drop null values\n",
    "match_df.dropna( inplace = True) #subset = [\"director_id\"],\n",
    "\n",
    "# reset the index for match_df to linear >> 0,...,n-1\n",
    "match_df.reset_index(drop = True, inplace = True)\n",
    "\n",
    "# sample rows from resultant match_df\n",
    "print(match_df.shape) \n",
    "\n",
    "match_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "balanced-destiny",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>combo</th>\n",
       "      <th>tconst</th>\n",
       "      <th>director_id</th>\n",
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       "      <th>2576</th>\n",
       "      <td>zindagi gulzar hai_2012</td>\n",
       "      <td>tt2828240</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2577</th>\n",
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       "      <td>tt12317724</td>\n",
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       "    </tr>\n",
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       "      <th>2578</th>\n",
       "      <td>zona rosa_2019</td>\n",
       "      <td>tt11194518</td>\n",
       "      <td>nm10743407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2579</th>\n",
       "      <td>zz top that little ol band from texas_2019</td>\n",
       "      <td>tt9015306</td>\n",
       "      <td>nm0242757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           combo      tconst director_id\n",
       "2570                        yours fatefully_2012   tt7131690  nm11522613\n",
       "2571                        yours fatefully_2012   tt7131690  nm11522614\n",
       "2572                        yours fatefully_2012   tt7131690   nm6562612\n",
       "2573                               z nation_2018   tt8395332   nm7914806\n",
       "2574                                     z4_2018   tt8618456   nm0872780\n",
       "2575                              zak storm_2016   tt4209752   nm3569481\n",
       "2576                     zindagi gulzar hai_2012   tt2828240   nm5616300\n",
       "2577                             zoids wild_2018  tt12317724   nm0837219\n",
       "2578                              zona rosa_2019  tt11194518  nm10743407\n",
       "2579  zz top that little ol band from texas_2019   tt9015306   nm0242757"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Since we collected multiple directors as comma separated values \n",
    "we will explode these ID's so that we get one director ID per row.\"\"\"\n",
    "\n",
    "# split the director ID by \",\"\n",
    "match_df[\"director_id\"] = match_df[\"director_id\"].str.split(\",\")\n",
    "\n",
    "# explode on director ID column to get dataframe with each in separate row\n",
    "match_df = match_df.set_index([\"combo\", \"tconst\"]).explode(\"director_id\").reset_index()\n",
    "\n",
    "# sample rows from resultant match_df \n",
    "match_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "excess-cinema",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>combo</th>\n",
       "      <th>tconst</th>\n",
       "      <th>director_id</th>\n",
       "      <th>director_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2575</th>\n",
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       "      <td>Philippe Guyenne</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2576</th>\n",
       "      <td>zindagi gulzar hai_2012</td>\n",
       "      <td>tt2828240</td>\n",
       "      <td>nm5616300</td>\n",
       "      <td>Sultana Siddiqui</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2577</th>\n",
       "      <td>zoids wild_2018</td>\n",
       "      <td>tt12317724</td>\n",
       "      <td>nm0837219</td>\n",
       "      <td>Norihiko Sutô</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2578</th>\n",
       "      <td>zona rosa_2019</td>\n",
       "      <td>tt11194518</td>\n",
       "      <td>nm10743407</td>\n",
       "      <td>Alex Díaz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2579</th>\n",
       "      <td>zz top that little ol band from texas_2019</td>\n",
       "      <td>tt9015306</td>\n",
       "      <td>nm0242757</td>\n",
       "      <td>Sam Dunn</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           combo      tconst director_id  \\\n",
       "2575                              zak storm_2016   tt4209752   nm3569481   \n",
       "2576                     zindagi gulzar hai_2012   tt2828240   nm5616300   \n",
       "2577                             zoids wild_2018  tt12317724   nm0837219   \n",
       "2578                              zona rosa_2019  tt11194518  nm10743407   \n",
       "2579  zz top that little ol band from texas_2019   tt9015306   nm0242757   \n",
       "\n",
       "         director_name  \n",
       "2575  Philippe Guyenne  \n",
       "2576  Sultana Siddiqui  \n",
       "2577     Norihiko Sutô  \n",
       "2578         Alex Díaz  \n",
       "2579          Sam Dunn  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Match the director ID with nconst column in Name Basics dataset and extract the name of the director\"\"\"\n",
    "\n",
    "# match the director ID and nconst columns\n",
    "matched_names = name_basics.loc[name_basics[\"nconst\"].isin(match_df[\"director_id\"]), [\"nconst\", \"primaryName\"]].values\n",
    "\n",
    "# map the names of directors\n",
    "match_df[\"director_name\"] = match_df[\"director_id\"].map(dict(matched_names))\n",
    "\n",
    "# sample rows from resultant match_df \n",
    "match_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "impaired-induction",
   "metadata": {},
   "source": [
    "This is a reverse process to short our long dataframe into a wide one by grouping (multiple if present) director(s) based on a title."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ready-boxing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1178, 2)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group the directors by title ID\n",
    "match_df = match_df.groupby(\"combo\")[\"director_name\"].apply(lambda s: \",\".join(s)).reset_index()\n",
    "\n",
    "# How many missing directors have we been able to identify?\n",
    "match_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "wrong-attachment",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace the missing_directors; this will be later appended to netflix non null dataset\n",
    "missing_data.loc[missing_data[\"director\"].isnull(), \n",
    "                 \"director\"] = missing_data.loc[missing_data[\"director\"].isnull(), \n",
    "                                                \"combo\"].map(dict(match_df.values)).fillna(\"Unknown\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cross-double",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "director      0\n",
       "cast        709\n",
       "country     503\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# have we been able to reduce the missing value count for \"director\" column\n",
    "missing_data[[\"director\", \"cast\", \"country\"]].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "closing-causing",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop match_df dataframe to be re-used for next step\n",
    "del match_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compliant-operator",
   "metadata": {},
   "source": [
    "### Finding Missing Cast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "326c920f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# utilize the backup of dataframe with matched title_id's with title_basics dataset\n",
    "match_df = all_matches_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "simple-white",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['actor',\n",
       " 'actress',\n",
       " 'archive_footage',\n",
       " 'archive_sound',\n",
       " 'cinematographer',\n",
       " 'composer',\n",
       " 'director',\n",
       " 'editor',\n",
       " 'producer',\n",
       " 'production_designer',\n",
       " 'self',\n",
       " 'writer']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Value present in category column \n",
    "sorted(title_principals[\"category\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "beneficial-eight",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "- match the tconst ID between the match_df and title_principals dataset\n",
    "- extract director ID's associated with a title from title_principals dataframe \n",
    "- select only those principals where category is Actor or Actress\n",
    "\"\"\"\n",
    "\n",
    "match_df[\"cast_id\"] = (\n",
    "    match_df[\"tconst\"].map(dict(title_principals.loc[(title_principals[\"tconst\"].isin(match_df[\"tconst\"])) &\n",
    "                                                     (title_principals[\"category\"].isin([\"actress\", \"actor\"])), \n",
    "                                                     [\"tconst\", \"nconst\"]].reset_index(drop = True).\\\n",
    "                                groupby(\"tconst\")[\"nconst\"].unique().reset_index().values))\n",
    ")\n",
    "\n",
    "# drop null values\n",
    "match_df.dropna(inplace = True)\n",
    "\n",
    "# reset index of match_df dataframe\n",
    "match_df.reset_index(drop = True, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "precise-dealer",
   "metadata": {},
   "outputs": [],
   "source": [
    "# explode match_df on cast_id column to get one principal ID per row\n",
    "match_df = match_df.set_index([\"combo\", \"tconst\"]).explode(\"cast_id\").reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "religious-messenger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6154, 4)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# match the cast ID with nconst column\n",
    "matched_names = name_basics.loc[name_basics[\"nconst\"].isin(match_df[\"cast_id\"]), [\"nconst\", \"primaryName\"]].values\n",
    "\n",
    "# map the Names of cast members to match_df dataframe\n",
    "match_df[\"cast_name\"] = match_df[\"cast_id\"].map(dict(matched_names))\n",
    "\n",
    "# resultant match_df shape\n",
    "match_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ce5ed45e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove any null values from match_df\n",
    "match_df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c221de99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "show_id           0\n",
       "type              0\n",
       "title             0\n",
       "director          0\n",
       "cast            709\n",
       "country         503\n",
       "date_added        0\n",
       "release_year      0\n",
       "rating            0\n",
       "duration          0\n",
       "listed_in         0\n",
       "description       0\n",
       "combo             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concatenate multiple cast members as comma separated values\n",
    "match_df.groupby(\"combo\")[\"cast_name\"].apply(lambda s: \",\".join(s)).reset_index()\n",
    "\n",
    "# what's the tally?\n",
    "missing_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c46748aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# group the  by title ID\n",
    "match_df = match_df.groupby(\"combo\")[\"cast_name\"].apply(lambda s: \",\".join(s)).reset_index()\n",
    "\n",
    "# replace the missing_directors; this will be later appended to netflix non null dataset\n",
    "missing_data.loc[missing_data[\"cast\"].isnull(), \n",
    "                 \"cast\"] = missing_data.loc[missing_data[\"cast\"].isnull(), \n",
    "                                            \"combo\"].map(dict(match_df.values)).fillna(\"Unknown\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "331a6275",
   "metadata": {},
   "outputs": [],
   "source": [
    "# recycling the variables\n",
    "del match_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "whole-original",
   "metadata": {},
   "source": [
    "### Finding Missing Country - Approach 1\n",
    "\n",
    "Here we have a change of strategy. Since none of the IMDB datasets contain direct information about the origin country of a title content, we will have to capture the information indirectly or perhaps employ multiple ways to capture the information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "amber-blowing",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Import BeautifulSoup module\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "# import requests module\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bf8bef5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# only select the rows where country column is missing values\n",
    "match_df = all_matches_df[all_matches_df[\"combo\"].isin(missing_data.loc[missing_data[\"country\"].isnull(), \"combo\"])]\n",
    "\n",
    "# reset the index of match_df dataframe\n",
    "match_df = match_df.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "precise-surrey",
   "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>combo</th>\n",
       "      <th>tconst</th>\n",
       "      <th>imdb_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13th a conversation with oprah winfrey  ava du...</td>\n",
       "      <td>tt6491068</td>\n",
       "      <td>https://www.imdb.com/title/tt6491068/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3 deewarein_2003</td>\n",
       "      <td>tt0338490</td>\n",
       "      <td>https://www.imdb.com/title/tt0338490/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a go go cory carson halloween_2020</td>\n",
       "      <td>tt13058374</td>\n",
       "      <td>https://www.imdb.com/title/tt13058374/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a trash truck christmas_2020</td>\n",
       "      <td>tt13458584</td>\n",
       "      <td>https://www.imdb.com/title/tt13458584/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>al acecho_2019</td>\n",
       "      <td>tt11287390</td>\n",
       "      <td>https://www.imdb.com/title/tt11287390/</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               combo      tconst  \\\n",
       "0  13th a conversation with oprah winfrey  ava du...   tt6491068   \n",
       "1                                   3 deewarein_2003   tt0338490   \n",
       "2                 a go go cory carson halloween_2020  tt13058374   \n",
       "3                       a trash truck christmas_2020  tt13458584   \n",
       "4                                     al acecho_2019  tt11287390   \n",
       "\n",
       "                                 imdb_url  \n",
       "0   https://www.imdb.com/title/tt6491068/  \n",
       "1   https://www.imdb.com/title/tt0338490/  \n",
       "2  https://www.imdb.com/title/tt13058374/  \n",
       "3  https://www.imdb.com/title/tt13458584/  \n",
       "4  https://www.imdb.com/title/tt11287390/  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a column and generate url values with each title_id \n",
    "# Each title on IMDB can be accessed using \"https://www.imdb.com/title/<title_id>/\"\n",
    "\n",
    "match_df[\"imdb_url\"] = match_df[\"tconst\"].apply(lambda str_id: \"https://www.imdb.com/title/\" + str_id + \"/\")\n",
    "\n",
    "# sample rows\n",
    "match_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "decent-pavilion",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Function to read the web-page for a title on IMDB.com\n",
    "-----------------------------------------------------\n",
    "Name: get_country_of_origin\n",
    "\n",
    "Parameters: \n",
    "- imdb_url: dedicated web-page of a title on IMDB.com\n",
    "\n",
    "Returns: a string value containing name of the country/countries of origin of a title\n",
    "\n",
    "Additional Info: \n",
    "- To avoid session time-outs/ multiple requests/ Nonetype errors, WITH requests.Session() statement has been used. \n",
    "\n",
    "\"\"\"\n",
    "# function definition\n",
    "def get_country_of_origin(imdb_url):\n",
    "    \n",
    "    # request a session\n",
    "    with requests.Session() as s:\n",
    "        # access the web-page using the passed url string\n",
    "        title_page = s.get(imdb_url)\n",
    "        # parse the web-page contents with BeautifulSoup\n",
    "        soup = BeautifulSoup(title_page.content, 'html.parser')   \n",
    "        \n",
    "        # search for a list item with specific attributes\n",
    "        item = soup.find(\"li\", {\"data-testid\" : \"title-details-origin\"}, \n",
    "                         class_ = \"ipc-metadata-list__item\",)    \n",
    "        \n",
    "    # if the search is successful, return the content string\n",
    "    if item: \n",
    "        return item.a.string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "lonely-copper",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(154, 4)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply the \"get_country_of_origin\" function to the imdb_url column \n",
    "match_df[\"country_name\"] = match_df[\"imdb_url\"].apply(get_country_of_origin, )\n",
    "\n",
    "# how many results have we got successfully\n",
    "match_df[match_df[\"country_name\"] != None].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "98b10dae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace the found country names back to missing_data dataframe\n",
    "missing_data.loc[missing_data[\"country\"].isnull(), \n",
    "                 \"country\"] = missing_data.loc[missing_data[\"country\"].isnull(), \n",
    "                                               \"combo\"].map(dict(match_df[[\"combo\", \n",
    "                                                                           \"country_name\"]].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3a098dd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "show_id           0\n",
       "type              0\n",
       "title             0\n",
       "director          0\n",
       "cast              0\n",
       "country         422\n",
       "date_added        0\n",
       "release_year      0\n",
       "rating            0\n",
       "duration          0\n",
       "listed_in         0\n",
       "description       0\n",
       "combo             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# missing value count\n",
    "missing_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dec9d60",
   "metadata": {},
   "source": [
    "### Finding Missing Country - Approach 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "based-mileage",
   "metadata": {
    "code_folding": []
   },
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Angu Vaikuntapurathu (Malayalam)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>ChuChuTV Bedtime Stories &amp; Moral Stories for Kids (English)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>ChuChuTV Bedtime Stories &amp; Moral Stories for Kids (Hindi)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>ChuChuTV Surprise Eggs Learning Videos (English)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>ChuChuTV Surprise Eggs Learning Videos (Hindi)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1676</th>\n",
       "      <td>Oh! Baby (Malayalam)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>Oh! Baby (Tamil)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>Seven (Telugu)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2228</th>\n",
       "      <td>Thackeray (Marathi)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            title\n",
       "158                              Angu Vaikuntapurathu (Malayalam)\n",
       "468   ChuChuTV Bedtime Stories & Moral Stories for Kids (English)\n",
       "469     ChuChuTV Bedtime Stories & Moral Stories for Kids (Hindi)\n",
       "470              ChuChuTV Surprise Eggs Learning Videos (English)\n",
       "471                ChuChuTV Surprise Eggs Learning Videos (Hindi)\n",
       "1676                                         Oh! Baby (Malayalam)\n",
       "1677                                             Oh! Baby (Tamil)\n",
       "2017                                               Seven (Telugu)\n",
       "2228                                          Thackeray (Marathi)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_colwidth = 200\n",
    "\n",
    "# pattern work to match the Step2B in Mike's Presentation\n",
    "# pattern: title containing \"some words (language)\" \n",
    "missing_data.loc[(missing_data[\"country\"].isnull()) & \n",
    "                 (missing_data[\"title\"].str.contains(r\"\\(\\w+\\)\")), [\"title\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b8186038",
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# select the indexes where the titles matched the pattern\n",
    "select_idx = missing_data.loc[(missing_data[\"country\"].isnull()) & \n",
    "                 (missing_data[\"title\"].str.contains(r\"\\(\\w+\\)\")), [\"title\"]].index\n",
    "\n",
    "# substitute India for country value for the rows at selected indices\n",
    "missing_data.loc[select_idx, \"country\"] = \"India\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "94426717",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# select the indexes where the title or listed_in columns contains the pattern \"korean\"\n",
    "select_idx = missing_data.loc[(missing_data[\"country\"].isnull()) & \n",
    "                 ((missing_data[\"title\"].str.contains(r\"[Kk]orean\", regex = True)) |\n",
    "                  (missing_data[\"listed_in\"].str.contains(r\"[Kk]orean\", regex = True))), [\"title\"]].index\n",
    "\n",
    "# substitute \"South Korea\" for country value for the rows at selected indices\n",
    "missing_data.iloc[select_idx, 5] = \"South Korea\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d4790d36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select the indexes where the listed_in column contains the pattern \"british\"\n",
    "select_idx = missing_data.loc[(missing_data[\"country\"].isnull()) & \n",
    "                  (missing_data[\"listed_in\"].str.contains(r\"[Bb]ritish\", regex = True)), [\"title\"]].index\n",
    "\n",
    "# substitute \"United Kingdom\" for country value for the rows at selected indices\n",
    "missing_data.iloc[select_idx, 5] = \"United Kingdom\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c3ae3016",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "352        BREAK IT ALL: The History of Rock in Latin America\n",
       "390                                        Camarón Revolution\n",
       "729                        Especial 20 años Fútbol de Primera\n",
       "1354                           Los 10 años de Peter Capusotto\n",
       "1732                                        Palazuelos mi rey\n",
       "2147                               Street Food: Latin America\n",
       "2184                             Surviving Escobar - Alias JJ\n",
       "2423    The Least Expected Day: Inside the Movistar Team 2019\n",
       "2700                              Ultimate Beastmaster México\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# identify the indexes where the listed_in column contains the pattern \"spanish\"\n",
    "missing_data.loc[(missing_data[\"listed_in\"].str.lower().str.contains(\"spanish\")) & \n",
    "                    (missing_data[\"country\"].isnull())][\"title\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "29a6c94e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# spanish title dictionary with countries\n",
    "spanish_titles = {\"Camarón Revolution\" : \"Spain\", \n",
    "                  \"Especial 20 años Fútbol de Primera\" : \"Argentina\", \n",
    "                  \"Los 10 años de Peter Capusotto\" : \"Argentina\", \n",
    "                  \"Surviving Escobar - Alias JJ\" : \"Columbia\", \n",
    "                  \"The Least Expected Day: Inside the Movistar Team 2019\" : \"Spain\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "79bece3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select the indexes where the listed_in column contains the pattern \"spanish\"\n",
    "selected_idx = missing_data[missing_data[\"title\"].isin(list(spanish_titles.keys()))].index\n",
    "\n",
    "# replace the None values in country column with dictionary values\n",
    "missing_data.loc[selected_idx, \"country\"] = missing_data.loc[selected_idx, \"title\"].map(spanish_titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "89a6a966",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace the still missing values in missing_data dataframe with \"Unknown\"\n",
    "missing_data[\"country\"].fillna(\"Unknown\", inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10177577",
   "metadata": {},
   "source": [
    "### Combining the Found Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "4cce0a8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "- merge the non-null dataset with the substituted missing values dataset\n",
    "- the resultant dataset has been named \"netflix_clean_data\"\n",
    "\"\"\"\n",
    "# merge two datasets and reset index\n",
    "netflix_clean_data = netflix_data.append(missing_data.iloc[:, :-1]).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "712883cf",
   "metadata": {},
   "source": [
    "## Apriori Analysis - Learn Associations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c30c8e2a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# import mlxtend modules to find out assciation_rules\n",
    "\n",
    "from mlxtend.frequent_patterns import association_rules, apriori\n",
    "from mlxtend.preprocessing import TransactionEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "620c3345",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1181, 12)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subset with only International TV Shows\n",
    "inter_tv = netflix_clean_data.loc[(netflix_clean_data[\"type\"] == \"TV Show\") &\n",
    "                                  (netflix_clean_data[\"listed_in\"].str.contains(r\"International\", regex = True))]\n",
    "inter_tv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "40f05596",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2430, 12)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subset with only International Movies\n",
    "inter_mv = netflix_clean_data.loc[(netflix_clean_data[\"type\"] == \"Movie\") &\n",
    "                                  (netflix_clean_data[\"listed_in\"].str.contains(r\"International\", regex = True))]\n",
    "inter_mv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "55c6da77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1149, 12)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subset with only non-International TV Shows\n",
    "domestic_tv = netflix_clean_data.loc[(netflix_clean_data[\"type\"] == \"TV Show\") &\n",
    "                                     ~(netflix_clean_data[\"listed_in\"].str.contains(r\"International\", regex = True))]\n",
    "domestic_tv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "0c9c52e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2927, 12)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subset with only non-International Movies\n",
    "domestic_mv = netflix_clean_data.loc[(netflix_clean_data[\"type\"] == \"Movie\") &\n",
    "                                     ~(netflix_clean_data[\"listed_in\"].str.contains(r\"International\", regex = True))]\n",
    "domestic_mv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "cfc5f956",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Function to identify similarities/associations between genres, cast members, directors\n",
    "-----------------------------------------------------\n",
    "Name: calculate_association\n",
    "\n",
    "Parameters: \n",
    "- df: subset of netflix_clean_dataset\n",
    "- column_name: column containing attributes for which association needs to be calculated\n",
    "- split_char: string characetrs to split the attributes - default value \", \"\n",
    "- min_support: minimum support value to be passed to apriori module\n",
    "- max_length: - maximum length of the set value to be passed to apriori module \n",
    "              - helps to decide if paired association is required\n",
    "- index_col: column to form index for apriori analysis              \n",
    "\n",
    "Returns: a dataframe comprising of support values and itemsets as its two columns\n",
    "\n",
    "Additional Info: \n",
    "- mlxtends's TransactionEncoder module has been used to transform the columns values \n",
    "\n",
    "\"\"\"\n",
    "\n",
    "# function definition\n",
    "def calculate_association(df, column_name, min_support, max_length, index_col = \"show_id\", split_char = \", \"):\n",
    "    \n",
    "    # obtain list of lists by splitting the values in the column using the split character \n",
    "    col_val_list = list(df[column_name].str.split(split_char))\n",
    "    \n",
    "    # initialize TransactionEncoder\n",
    "    txn_enc = TransactionEncoder()\n",
    "    # fit the list of lists\n",
    "    txn_enc.fit(col_val_list)\n",
    "    # obtain the transformed values using the encoder\n",
    "    transformed_data = txn_enc.transform(col_val_list)\n",
    "    \n",
    "    # create an encoded df with unique show_id column acting as index\n",
    "    encoded_df = pd.DataFrame(transformed_data, columns = txn_enc.columns_, index = df[index_col])\n",
    "    \n",
    "    # apply the apriori algorithm to the transformed dataframe and select \n",
    "    apriori_results = apriori(df = encoded_df[encoded_df.sum(axis = 1) >= 1],\n",
    "                              use_colnames = True,\n",
    "                              min_support = min_support, \n",
    "                              max_len = max_length).sort_values(by = \"support\", ascending = False)\n",
    "\n",
    "    return apriori_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "16291393",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000</td>\n",
       "      <td>(International TV Shows)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.392</td>\n",
       "      <td>(International TV Shows, TV Dramas)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.392</td>\n",
       "      <td>(TV Dramas)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.238</td>\n",
       "      <td>(Romantic TV Shows)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.238</td>\n",
       "      <td>(International TV Shows, Romantic TV Shows)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.224</td>\n",
       "      <td>(Crime TV Shows)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.224</td>\n",
       "      <td>(International TV Shows, Crime TV Shows)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.172</td>\n",
       "      <td>(International TV Shows, TV Comedies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.172</td>\n",
       "      <td>(TV Comedies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.107</td>\n",
       "      <td>(International TV Shows, Korean TV Shows)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.107</td>\n",
       "      <td>(Korean TV Shows)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.107</td>\n",
       "      <td>(Spanish-Language TV Shows)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.107</td>\n",
       "      <td>(International TV Shows, Spanish-Language TV Shows)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.090</td>\n",
       "      <td>(British TV Shows, International TV Shows)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.090</td>\n",
       "      <td>(British TV Shows)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.088</td>\n",
       "      <td>(Anime Series)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.088</td>\n",
       "      <td>(International TV Shows, Anime Series)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.086</td>\n",
       "      <td>(International TV Shows, Docuseries)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.086</td>\n",
       "      <td>(Crime TV Shows, TV Dramas)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.086</td>\n",
       "      <td>(Docuseries)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.072</td>\n",
       "      <td>(Romantic TV Shows, TV Dramas)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.071</td>\n",
       "      <td>(International TV Shows, Reality TV)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.071</td>\n",
       "      <td>(Reality TV)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.067</td>\n",
       "      <td>(Romantic TV Shows, TV Comedies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.054</td>\n",
       "      <td>(Korean TV Shows, Romantic TV Shows)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support                                             itemsets\n",
       "4     1.000                             (International TV Shows)\n",
       "21    0.392                  (International TV Shows, TV Dramas)\n",
       "10    0.392                                          (TV Dramas)\n",
       "7     0.238                                  (Romantic TV Shows)\n",
       "18    0.238          (International TV Shows, Romantic TV Shows)\n",
       "2     0.224                                     (Crime TV Shows)\n",
       "13    0.224             (International TV Shows, Crime TV Shows)\n",
       "20    0.172                (International TV Shows, TV Comedies)\n",
       "9     0.172                                        (TV Comedies)\n",
       "16    0.107            (International TV Shows, Korean TV Shows)\n",
       "5     0.107                                    (Korean TV Shows)\n",
       "8     0.107                          (Spanish-Language TV Shows)\n",
       "19    0.107  (International TV Shows, Spanish-Language TV Shows)\n",
       "12    0.090           (British TV Shows, International TV Shows)\n",
       "1     0.090                                   (British TV Shows)\n",
       "0     0.088                                       (Anime Series)\n",
       "11    0.088               (International TV Shows, Anime Series)\n",
       "15    0.086                 (International TV Shows, Docuseries)\n",
       "14    0.086                          (Crime TV Shows, TV Dramas)\n",
       "3     0.086                                         (Docuseries)\n",
       "24    0.072                       (Romantic TV Shows, TV Dramas)\n",
       "17    0.071                 (International TV Shows, Reality TV)\n",
       "6     0.071                                         (Reality TV)\n",
       "23    0.067                     (Romantic TV Shows, TV Comedies)\n",
       "22    0.054                 (Korean TV Shows, Romantic TV Shows)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate support values for international TV content\n",
    "apriori_results = calculate_association(inter_tv, \"listed_in\", 0.05, 2)\n",
    "apriori_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ac32a33f",
   "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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(International TV Shows)</td>\n",
       "      <td>(TV Dramas)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.392</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(International TV Shows)</td>\n",
       "      <td>(Romantic TV Shows)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.238</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(International TV Shows)</td>\n",
       "      <td>(Crime TV Shows)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.224</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(International TV Shows)</td>\n",
       "      <td>(TV Comedies)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.172</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(International TV Shows)</td>\n",
       "      <td>(Korean TV Shows)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.107</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(International TV Shows)</td>\n",
       "      <td>(Spanish-Language TV Shows)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.107</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(International TV Shows)</td>\n",
       "      <td>(British TV Shows)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.090</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(International TV Shows)</td>\n",
       "      <td>(Anime Series)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.088</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(International TV Shows)</td>\n",
       "      <td>(Docuseries)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.086</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(International TV Shows)</td>\n",
       "      <td>(Reality TV)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.071</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 antecedents                  consequents  antecedent support  \\\n",
       "0   (International TV Shows)                  (TV Dramas)               1.000   \n",
       "2   (International TV Shows)          (Romantic TV Shows)               1.000   \n",
       "4   (International TV Shows)             (Crime TV Shows)               1.000   \n",
       "6   (International TV Shows)                (TV Comedies)               1.000   \n",
       "8   (International TV Shows)            (Korean TV Shows)               1.000   \n",
       "10  (International TV Shows)  (Spanish-Language TV Shows)               1.000   \n",
       "13  (International TV Shows)           (British TV Shows)               1.000   \n",
       "14  (International TV Shows)               (Anime Series)               1.000   \n",
       "16  (International TV Shows)                 (Docuseries)               1.000   \n",
       "20  (International TV Shows)                 (Reality TV)               1.000   \n",
       "\n",
       "    consequent support  support  confidence  lift  leverage  conviction  \n",
       "0                0.392    0.392       0.392 1.000     0.000       1.000  \n",
       "2                0.238    0.238       0.238 1.000     0.000       1.000  \n",
       "4                0.224    0.224       0.224 1.000     0.000       1.000  \n",
       "6                0.172    0.172       0.172 1.000     0.000       1.000  \n",
       "8                0.107    0.107       0.107 1.000     0.000       1.000  \n",
       "10               0.107    0.107       0.107 1.000     0.000       1.000  \n",
       "13               0.090    0.090       0.090 1.000     0.000       1.000  \n",
       "14               0.088    0.088       0.088 1.000     0.000       1.000  \n",
       "16               0.086    0.086       0.086 1.000     0.000       1.000  \n",
       "20               0.071    0.071       0.071 1.000     0.000       1.000  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display association rules and values for international TV content\n",
    "rules_df = association_rules(apriori_results, metric=\"lift\").sort_values(by = [\"antecedent support\", \"lift\", \"support\"],\n",
    "                                                                         ascending = False)[:10]\n",
    "rules_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e0e8867c",
   "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>Genres</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TV Dramas</td>\n",
       "      <td>0.392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Romantic TV Shows</td>\n",
       "      <td>0.238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Crime TV Shows</td>\n",
       "      <td>0.224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>TV Comedies</td>\n",
       "      <td>0.172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Korean TV Shows</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Spanish-Language TV Shows</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>British TV Shows</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Anime Series</td>\n",
       "      <td>0.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Docuseries</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Reality TV</td>\n",
       "      <td>0.071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Genres  support\n",
       "0                   TV Dramas    0.392\n",
       "2           Romantic TV Shows    0.238\n",
       "4              Crime TV Shows    0.224\n",
       "6                 TV Comedies    0.172\n",
       "8             Korean TV Shows    0.107\n",
       "10  Spanish-Language TV Shows    0.107\n",
       "13           British TV Shows    0.090\n",
       "14               Anime Series    0.088\n",
       "16                 Docuseries    0.086\n",
       "20                 Reality TV    0.071"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# result dataframe\n",
    "final_results = pd.DataFrame({\"Genres\" : rules_df[\"consequents\"].apply(lambda s: list(s)[0]), \n",
    "                              \"support\" : rules_df[\"support\"]})\n",
    "final_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b90dc818",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate support values for domestic TV content\n",
    "apriori_results = calculate_association(domestic_tv, \"listed_in\", 0.05, 1)[:10]\n",
    "apriori_results.rename({\"itemsets\" : \"Genres\"}, axis = 1, inplace = True)\n",
    "\n",
    "# to unfreeze the frozensets in apriori_results dataframe\n",
    "apriori_results[\"Genres\"] = apriori_results[\"Genres\"].apply(lambda s: list(s)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f0759ee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Genres</th>\n",
       "      <th>support_x</th>\n",
       "      <th>support_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TV Dramas</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Romantic TV Shows</td>\n",
       "      <td>0.238</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Crime TV Shows</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TV Comedies</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Korean TV Shows</td>\n",
       "      <td>0.107</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Spanish-Language TV Shows</td>\n",
       "      <td>0.107</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>British TV Shows</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Anime Series</td>\n",
       "      <td>0.088</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Docuseries</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Reality TV</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Kids' TV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>TV Action &amp; Adventure</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Science &amp; Nature TV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>TV Sci-Fi &amp; Fantasy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Genres  support_x  support_y\n",
       "0                   TV Dramas      0.392      0.193\n",
       "1           Romantic TV Shows      0.238        NaN\n",
       "2              Crime TV Shows      0.224      0.131\n",
       "3                 TV Comedies      0.172      0.259\n",
       "4             Korean TV Shows      0.107        NaN\n",
       "5   Spanish-Language TV Shows      0.107        NaN\n",
       "6            British TV Shows      0.090      0.094\n",
       "7                Anime Series      0.088        NaN\n",
       "8                  Docuseries      0.086      0.204\n",
       "9                  Reality TV      0.071      0.114\n",
       "10                   Kids' TV        NaN      0.349\n",
       "11      TV Action & Adventure        NaN      0.077\n",
       "12        Science & Nature TV        NaN      0.057\n",
       "13        TV Sci-Fi & Fantasy        NaN      0.050"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge the final results of both the domestic and international TV content\n",
    "final_results = final_results.merge(apriori_results[[\"Genres\", \"support\"]], \n",
    "                                    how = \"outer\", \n",
    "                                    left_on = \"Genres\", \n",
    "                                    right_on = \"Genres\")\n",
    "final_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "5cf810ee",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create figure with shared y-axis\n",
    "fig, ax = plt.subplots(ncols = 2, sharey = True)\n",
    "\n",
    "# Create a lolipop chart because why not!\n",
    "\n",
    "# stem of the sub-plot 1\n",
    "ax[0].hlines(final_results[\"Genres\"], \n",
    "             xmin = 0,\n",
    "             xmax = -1 * final_results[\"support_x\"],\n",
    "             lw = 3)\n",
    "# dot of the sub-plot 1\n",
    "ax[0].scatter(-1 * final_results[\"support_x\"], \n",
    "              final_results[\"Genres\"],\n",
    "              marker = \"o\", \n",
    "              s = 100)\n",
    "\n",
    "# shift the y-axis to right for sub-plot 1 to make the yticklabels appear in center\n",
    "ax[0].yaxis.tick_right() \n",
    "# enable yticks\n",
    "ax[0].set_yticks(final_results[\"Genres\"].index)\n",
    "# enable yticklabels\n",
    "ax[0].set_yticklabels(list(final_results[\"Genres\"].values)) #, fontdict = {'horizontalalignment': \"center\"})\n",
    "# hide ytickmarks\n",
    "ax[0].tick_params(axis = u'y', which = u'both', length=0)\n",
    "\n",
    "# title for sub-plot 1\n",
    "ax[0].set_title(\"International\")\n",
    "# hide the x-axis\n",
    "ax[0].get_xaxis().set_visible(False)\n",
    "\n",
    "\n",
    "# stem of the sub-plot 2\n",
    "ax[1].hlines(final_results[\"Genres\"], \n",
    "             xmin = 0,\n",
    "             xmax = final_results[\"support_y\"], \n",
    "             lw = 3)\n",
    "# dot of the sub-plot 2\n",
    "ax[1].scatter(final_results[\"support_y\"], \n",
    "              final_results[\"Genres\"],\n",
    "              marker = \"o\", \n",
    "              s = 100)\n",
    "\n",
    "# title for sub-plot 2\n",
    "ax[1].set_title(\"Domestic\")\n",
    "# hide tick marks for y-axis\n",
    "ax[1].tick_params(axis = 'y', which = u'both', length=0)\n",
    "# hide the x-axis for sub-plot 2\n",
    "ax[1].get_xaxis().set_visible(False)\n",
    "\n",
    "\n",
    "# despine the sub-plot \n",
    "for i in ax[0].spines:\n",
    "    ax[0].spines[i].set_visible(False)\n",
    "for i in ax[1].spines:\n",
    "    ax[1].spines[i].set_visible(False)\n",
    "\n",
    "# extras     \n",
    "plt.subplots_adjust(wspace = 0.7)\n",
    "plt.suptitle(\"Breakdown of Netflix Content by 10 Most Common Genres (for TV Shows)\", y = 1.02)\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "d363e4a0",
   "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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000</td>\n",
       "      <td>(International Movies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.532</td>\n",
       "      <td>(Dramas)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.532</td>\n",
       "      <td>(Dramas, International Movies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.300</td>\n",
       "      <td>(Comedies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.300</td>\n",
       "      <td>(Comedies, International Movies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.145</td>\n",
       "      <td>(Action &amp; Adventure)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.145</td>\n",
       "      <td>(Action &amp; Adventure, International Movies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.135</td>\n",
       "      <td>(Romantic Movies, International Movies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.135</td>\n",
       "      <td>(Romantic Movies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.107</td>\n",
       "      <td>(Documentaries, International Movies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.107</td>\n",
       "      <td>(Documentaries)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.102</td>\n",
       "      <td>(Independent Movies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.102</td>\n",
       "      <td>(Independent Movies, International Movies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.100</td>\n",
       "      <td>(Comedies, Dramas)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.094</td>\n",
       "      <td>(Thrillers)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.094</td>\n",
       "      <td>(Thrillers, International Movies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.088</td>\n",
       "      <td>(Dramas, Independent Movies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.067</td>\n",
       "      <td>(Music &amp; Musicals)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.067</td>\n",
       "      <td>(Music &amp; Musicals, International Movies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.063</td>\n",
       "      <td>(Dramas, Romantic Movies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.057</td>\n",
       "      <td>(Comedies, Romantic Movies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.050</td>\n",
       "      <td>(Horror Movies, International Movies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.050</td>\n",
       "      <td>(Horror Movies)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support                                    itemsets\n",
       "6     1.000                      (International Movies)\n",
       "3     0.532                                    (Dramas)\n",
       "16    0.532              (Dramas, International Movies)\n",
       "1     0.300                                  (Comedies)\n",
       "12    0.300            (Comedies, International Movies)\n",
       "0     0.145                        (Action & Adventure)\n",
       "10    0.145  (Action & Adventure, International Movies)\n",
       "21    0.135     (Romantic Movies, International Movies)\n",
       "8     0.135                           (Romantic Movies)\n",
       "14    0.107       (Documentaries, International Movies)\n",
       "2     0.107                             (Documentaries)\n",
       "5     0.102                        (Independent Movies)\n",
       "19    0.102  (Independent Movies, International Movies)\n",
       "11    0.100                          (Comedies, Dramas)\n",
       "9     0.094                                 (Thrillers)\n",
       "22    0.094           (Thrillers, International Movies)\n",
       "15    0.088                (Dramas, Independent Movies)\n",
       "7     0.067                          (Music & Musicals)\n",
       "20    0.067    (Music & Musicals, International Movies)\n",
       "17    0.063                   (Dramas, Romantic Movies)\n",
       "13    0.057                 (Comedies, Romantic Movies)\n",
       "18    0.050       (Horror Movies, International Movies)\n",
       "4     0.050                             (Horror Movies)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate support values for international Movie content\n",
    "apriori_results = calculate_association(inter_mv, \"listed_in\", 0.05, 2)\n",
    "apriori_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "05f3a961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(International Movies)</td>\n",
       "      <td>(Dramas)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.532</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(International Movies)</td>\n",
       "      <td>(Comedies)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.300</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(International Movies)</td>\n",
       "      <td>(Action &amp; Adventure)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.145</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(International Movies)</td>\n",
       "      <td>(Romantic Movies)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.135</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(International Movies)</td>\n",
       "      <td>(Documentaries)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.107</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(International Movies)</td>\n",
       "      <td>(Independent Movies)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.102</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(International Movies)</td>\n",
       "      <td>(Thrillers)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.094</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(International Movies)</td>\n",
       "      <td>(Music &amp; Musicals)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.067</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(International Movies)</td>\n",
       "      <td>(Horror Movies)</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.050</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               antecedents           consequents  antecedent support  \\\n",
       "1   (International Movies)              (Dramas)               1.000   \n",
       "3   (International Movies)            (Comedies)               1.000   \n",
       "5   (International Movies)  (Action & Adventure)               1.000   \n",
       "7   (International Movies)     (Romantic Movies)               1.000   \n",
       "9   (International Movies)       (Documentaries)               1.000   \n",
       "11  (International Movies)  (Independent Movies)               1.000   \n",
       "13  (International Movies)           (Thrillers)               1.000   \n",
       "17  (International Movies)    (Music & Musicals)               1.000   \n",
       "23  (International Movies)       (Horror Movies)               1.000   \n",
       "\n",
       "    consequent support  support  confidence  lift  leverage  conviction  \n",
       "1                0.532    0.532       0.532 1.000     0.000       1.000  \n",
       "3                0.300    0.300       0.300 1.000     0.000       1.000  \n",
       "5                0.145    0.145       0.145 1.000     0.000       1.000  \n",
       "7                0.135    0.135       0.135 1.000     0.000       1.000  \n",
       "9                0.107    0.107       0.107 1.000     0.000       1.000  \n",
       "11               0.102    0.102       0.102 1.000     0.000       1.000  \n",
       "13               0.094    0.094       0.094 1.000     0.000       1.000  \n",
       "17               0.067    0.067       0.067 1.000     0.000       1.000  \n",
       "23               0.050    0.050       0.050 1.000     0.000       1.000  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display association rules and values for international TV content\n",
    "rules_df = association_rules(apriori_results, metric=\"lift\").sort_values(by = [\"antecedent support\", \"lift\", \"support\"],\n",
    "                                                                         ascending = False)[:9]\n",
    "rules_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a4c6c370",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Genres</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dramas</td>\n",
       "      <td>0.532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Comedies</td>\n",
       "      <td>0.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Action &amp; Adventure</td>\n",
       "      <td>0.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Romantic Movies</td>\n",
       "      <td>0.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Documentaries</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Independent Movies</td>\n",
       "      <td>0.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Thrillers</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Music &amp; Musicals</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Horror Movies</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Genres  support\n",
       "1               Dramas    0.532\n",
       "3             Comedies    0.300\n",
       "5   Action & Adventure    0.145\n",
       "7      Romantic Movies    0.135\n",
       "9        Documentaries    0.107\n",
       "11  Independent Movies    0.102\n",
       "13           Thrillers    0.094\n",
       "17    Music & Musicals    0.067\n",
       "23       Horror Movies    0.050"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# result dataframe\n",
    "final_results = pd.DataFrame({\"Genres\" : rules_df[\"consequents\"].apply(lambda s: list(s)[0]), \n",
    "                              \"support\" : rules_df[\"support\"]})\n",
    "final_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f4ae85c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate support values for domestic Movie content\n",
    "apriori_results = calculate_association(domestic_mv, \"listed_in\", 0.05, 1)[:10]\n",
    "\n",
    "apriori_results.rename({\"itemsets\" : \"Genres\"}, axis = 1, inplace = True)\n",
    "\n",
    "apriori_results[\"Genres\"] = apriori_results[\"Genres\"].apply(lambda s: list(s)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3384d008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Genres</th>\n",
       "      <th>support_x</th>\n",
       "      <th>support_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dramas</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Comedies</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Action &amp; Adventure</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Romantic Movies</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Documentaries</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Independent Movies</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thrillers</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Music &amp; Musicals</td>\n",
       "      <td>0.067</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Horror Movies</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Children &amp; Family Movies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Stand-Up Comedy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Genres  support_x  support_y\n",
       "0                     Dramas      0.532      0.276\n",
       "1                   Comedies      0.300      0.252\n",
       "2         Action & Adventure      0.145      0.125\n",
       "3            Romantic Movies      0.135      0.069\n",
       "4              Documentaries      0.107      0.178\n",
       "5         Independent Movies      0.102      0.144\n",
       "6                  Thrillers      0.094      0.089\n",
       "7           Music & Musicals      0.067        NaN\n",
       "8              Horror Movies      0.050      0.065\n",
       "9   Children & Family Movies        NaN      0.175\n",
       "10           Stand-Up Comedy        NaN      0.112"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge the final results of both the domestic and international TV content\n",
    "final_results = final_results.merge(apriori_results[[\"Genres\", \"support\"]], \n",
    "                                    how = \"outer\", \n",
    "                                    left_on = \"Genres\", \n",
    "                                    right_on = \"Genres\")\n",
    "final_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "66820525",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create figure with shared y-axis\n",
    "fig, ax = plt.subplots(ncols = 2, sharey = True)\n",
    "\n",
    "# Create a lolipop chart because why not!\n",
    "\n",
    "# stem of the sub-plot 1\n",
    "ax[0].hlines(final_results[\"Genres\"], \n",
    "             xmin = 0,\n",
    "             xmax = -1 * final_results[\"support_x\"],\n",
    "             lw = 3)\n",
    "# dot of the sub-plot 1\n",
    "ax[0].scatter(-1 * final_results[\"support_x\"], \n",
    "              final_results[\"Genres\"],\n",
    "              marker = \"o\", \n",
    "              s = 100)\n",
    "\n",
    "# shift the y-axis to right for sub-plot 1 to make the yticklabels appear in center\n",
    "ax[0].yaxis.tick_right() \n",
    "# enable yticks\n",
    "ax[0].set_yticks(final_results[\"Genres\"].index)\n",
    "# enable yticklabels\n",
    "ax[0].set_yticklabels(list(final_results[\"Genres\"].values)) #, fontdict = {'horizontalalignment': \"center\"})\n",
    "# hide ytickmarks\n",
    "ax[0].tick_params(axis = u'y', which = u'both', length=0)\n",
    "\n",
    "# title for sub-plot 1\n",
    "ax[0].set_title(\"International\")\n",
    "# hide the x-axis\n",
    "ax[0].get_xaxis().set_visible(False)\n",
    "\n",
    "\n",
    "# stem of the sub-plot 2\n",
    "ax[1].hlines(final_results[\"Genres\"], \n",
    "             xmin = 0,\n",
    "             xmax = final_results[\"support_y\"], \n",
    "             lw = 3)\n",
    "# dot of the sub-plot 2\n",
    "ax[1].scatter(final_results[\"support_y\"], \n",
    "              final_results[\"Genres\"],\n",
    "              marker = \"o\", \n",
    "              s = 100)\n",
    "\n",
    "# title for sub-plot 2\n",
    "ax[1].set_title(\"Domestic\")\n",
    "# hide tick marks for y-axis\n",
    "ax[1].tick_params(axis = 'y', which = u'both', length=0)\n",
    "# hide the x-axis for sub-plot 2\n",
    "ax[1].get_xaxis().set_visible(False)\n",
    "\n",
    "\n",
    "# despine the sub-plot \n",
    "for i in ax[0].spines:\n",
    "    ax[0].spines[i].set_visible(False)\n",
    "for i in ax[1].spines:\n",
    "    ax[1].spines[i].set_visible(False)\n",
    "\n",
    "# extras     \n",
    "plt.subplots_adjust(wspace = 0.7)\n",
    "plt.suptitle(\"Breakdown of Netflix Content by 10 Most Common Genres (for Movies)\", y = 1.02)\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "649acaa0",
   "metadata": {},
   "source": [
    "## Compare English Language vs. Non-English Language Content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "2d6651b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the source wikipedia page\n",
    "response = requests.get(\"https://en.wikipedia.org/wiki/List_of_countries_and_territories_where_English_is_an_official_language\")\n",
    "\n",
    "# parse the response content as html\n",
    "soup = BeautifulSoup(response.content, \"html.parser\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "aad57230",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# read the first table with primarily English Speaking Countires\n",
    "df = pd.read_html(str(soup.find_all(\"table\")[0]))[0]\n",
    "\n",
    "# tranform the dataframe to a list\n",
    "english_countries = list(df[\"Country\"].str.extract(\"(\\w+\\s*\\w+)\")[0].values)\n",
    "\n",
    "# print the extracted countries\n",
    "# print(english_countries)\n",
    "\n",
    "# read the second table with Multi-Language Speaking Countires\n",
    "df = pd.read_html(str(soup.find_all(\"table\")[1]))[0]\n",
    "\n",
    "# Filter the countries where English is a primary language and add to the list from above step\n",
    "english_countries = english_countries + list(df.loc[df[\"Primary language?\"].str.contains(\"Yes\"), \n",
    "                                                    \"Country\"].str.replace(\"\\[\\d+\\]\", \"\", regex = True).values)\n",
    "# print the final list\n",
    "# english_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "90fc9ae0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy of cleaned dataframe to avoid warnings\n",
    "netflix_clean_data = netflix_clean_data.copy() \n",
    "\n",
    "# Based on the english speaking countries, segregate the titles into English/Non-English categories\n",
    "netflix_clean_data[\"prim_lang\"] = netflix_clean_data[\"country\"].apply(lambda s: \"E\" if s.split(\",\")[0]\n",
    "                                                                      in english_countries\n",
    "                                                                      else (\"U\" if s.split(\",\")[0] == \"Unknown\" \n",
    "                                                                             else \"NE\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c1ab4d64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the distribution\n",
    "fig, ax = plt.subplots(figsize = (15, 6))\n",
    "\n",
    "# donut chart for breakdown of content by primary language\n",
    "ax.pie(x = netflix_clean_data[\"prim_lang\"].value_counts(), \n",
    "       labels = [\"English\", \"Non-English\", \"Unknown\"], \n",
    "       wedgeprops={'width':0.35}, \n",
    "       autopct = \"%.0f%%\", \n",
    "       pctdistance = 0.85, \n",
    "       startangle = 180)\n",
    "\n",
    "# extras\n",
    "ax.text(-0.3, 0, \"Language!\")\n",
    "ax.set_title(\"Breakdown of Netflix Content: English vs. Non-English Content\") #, fontsize = \"small\")\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ea5dbdd5",
   "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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>0.006</td>\n",
       "      <td>(John Cleese)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.005</td>\n",
       "      <td>(Adam Sandler)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1322</th>\n",
       "      <td>0.005</td>\n",
       "      <td>(Tara Strong)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>0.005</td>\n",
       "      <td>(Nicolas Cage)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>0.005</td>\n",
       "      <td>(Ashleigh Ball)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>0.005</td>\n",
       "      <td>(David Attenborough)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>0.005</td>\n",
       "      <td>(Fred Tatasciore)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>0.005</td>\n",
       "      <td>(Samuel L. Jackson)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1409</th>\n",
       "      <td>0.004</td>\n",
       "      <td>(Vincent Tong)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0.004</td>\n",
       "      <td>(Andrea Libman)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      support              itemsets\n",
       "694     0.006         (John Cleese)\n",
       "11      0.005        (Adam Sandler)\n",
       "1322    0.005         (Tara Strong)\n",
       "1058    0.005        (Nicolas Cage)\n",
       "105     0.005       (Ashleigh Ball)\n",
       "343     0.005  (David Attenborough)\n",
       "481     0.005     (Fred Tatasciore)\n",
       "1236    0.005   (Samuel L. Jackson)\n",
       "1409    0.004        (Vincent Tong)\n",
       "68      0.004       (Andrea Libman)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the most credited cast members for English Content\n",
    "apriori_results_eng = calculate_association(netflix_clean_data[(netflix_clean_data[\"cast\"] != \"Unknown\") & \n",
    "                                                           (netflix_clean_data[\"prim_lang\"] == \"E\")],\n",
    "                                        \"cast\", 0.001, 1)\n",
    "\n",
    "# top 10 most credited cast members for English Content\n",
    "apriori_results_eng[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "ad9a1d5e",
   "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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0.012</td>\n",
       "      <td>(Anupam Kher)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>0.010</td>\n",
       "      <td>(Shah Rukh Khan)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>0.009</td>\n",
       "      <td>(Naseeruddin Shah)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.009</td>\n",
       "      <td>(Akshay Kumar)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.008</td>\n",
       "      <td>(Amitabh Bachchan)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>0.008</td>\n",
       "      <td>(Paresh Rawal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>0.008</td>\n",
       "      <td>(Om Puri)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>0.008</td>\n",
       "      <td>(Takahiro Sakurai)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912</th>\n",
       "      <td>0.008</td>\n",
       "      <td>(Yuki Kaji)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>0.008</td>\n",
       "      <td>(Kareena Kapoor)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     support            itemsets\n",
       "81     0.012       (Anupam Kher)\n",
       "743    0.010    (Shah Rukh Khan)\n",
       "562    0.009  (Naseeruddin Shah)\n",
       "32     0.009      (Akshay Kumar)\n",
       "55     0.008  (Amitabh Bachchan)\n",
       "598    0.008      (Paresh Rawal)\n",
       "590    0.008           (Om Puri)\n",
       "820    0.008  (Takahiro Sakurai)\n",
       "912    0.008         (Yuki Kaji)\n",
       "394    0.008    (Kareena Kapoor)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the most credited cast members for Non-English Content\n",
    "apriori_results_neng = calculate_association(netflix_clean_data[(netflix_clean_data[\"cast\"] != \"Unknown\") & \n",
    "                                                           (netflix_clean_data[\"prim_lang\"] == \"NE\")],\n",
    "                                        \"cast\", 0.001, 1)\n",
    "\n",
    "# top 10 most credited cast members for Non-English Content\n",
    "apriori_results_neng[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "58eb806a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the Top 5 Most credited cast members for English & Non-English Content\n",
    "fig, ax = plt.subplots(figsize = (12, 5)) \n",
    "\n",
    "# stems\n",
    "ax.hlines(y = apriori_results_eng[:5][\"itemsets\"].apply(lambda s: list(s)[0]),\n",
    "          xmin = 0,\n",
    "          xmax = -1 * apriori_results_eng[:5][\"support\"],\n",
    "          lw = 3, \n",
    "          color = \"#FF800E\", \n",
    "          label = \"English\")\n",
    "# dots\n",
    "ax.scatter(y = apriori_results_eng[:5][\"itemsets\"].apply(lambda s: list(s)[0]),\n",
    "           x = -1 * apriori_results_eng[:5][\"support\"],\n",
    "           color = \"#FF800E\",\n",
    "           s = 100)\n",
    "# stems\n",
    "ax.hlines(apriori_results_neng[:5][\"itemsets\"].apply(lambda s: list(s)[0]),\n",
    "          xmin = 0,\n",
    "          xmax = -1 * apriori_results_neng[:5][\"support\"],\n",
    "          lw = 3,\n",
    "          label = \"Non-English\")\n",
    "# dots\n",
    "ax.scatter(y = apriori_results_neng[:5][\"itemsets\"].apply(lambda s: list(s)[0]),\n",
    "           x = -1 * apriori_results_neng[:5][\"support\"], \n",
    "           s = 100)\n",
    "\n",
    "# annotations\n",
    "for i in range(5):\n",
    "    ax.annotate(text = apriori_results_eng[:5][\"itemsets\"].apply(lambda s: list(s)[0]).values[i],\n",
    "                   xy = (0.0001, i - 0.2))\n",
    "for i in range(5):\n",
    "    ax.annotate(text = apriori_results_neng[:5][\"itemsets\"].apply(lambda s: list(s)[0]).values[i],\n",
    "                   xy = (0.0001, (i + 5) - 0.2))\n",
    "\n",
    "# extras    \n",
    "ax.set_title(\"Top 5 Headlining Cast Member Based on No. of Netflix Titles Credited\", fontsize = \"large\")\n",
    "ax.axis(\"off\")\n",
    "ax.legend(bbox_to_anchor = (0.3, 0.4))\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "6b648776",
   "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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0.007</td>\n",
       "      <td>(Jan Suter)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>0.006</td>\n",
       "      <td>(Raúl Campos)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.005</td>\n",
       "      <td>(Cathy Garcia-Molina)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>0.004</td>\n",
       "      <td>(Youssef Chahine)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.003</td>\n",
       "      <td>(David Dhawan)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.003</td>\n",
       "      <td>(Anurag Kashyap)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0.003</td>\n",
       "      <td>(Yılmaz Erdoğan)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.003</td>\n",
       "      <td>(Johnnie To)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>0.003</td>\n",
       "      <td>(Umesh Mehra)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.003</td>\n",
       "      <td>(Hakan Algül)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     support               itemsets\n",
       "61     0.007            (Jan Suter)\n",
       "119    0.006          (Raúl Campos)\n",
       "25     0.005  (Cathy Garcia-Molina)\n",
       "154    0.004      (Youssef Chahine)\n",
       "34     0.003         (David Dhawan)\n",
       "18     0.003       (Anurag Kashyap)\n",
       "155    0.003       (Yılmaz Erdoğan)\n",
       "62     0.003           (Johnnie To)\n",
       "142    0.003          (Umesh Mehra)\n",
       "50     0.003          (Hakan Algül)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# identify the most credited directors for Non-English Content\n",
    "apriori_results_neng = calculate_association(netflix_clean_data[(netflix_clean_data[\"director\"] != \"Unknown\") & \n",
    "                                                           (netflix_clean_data[\"prim_lang\"] == \"NE\")],\n",
    "                                        \"director\", 0.001, 1)\n",
    "\n",
    "# top 10 most credited directors for Non-English Content\n",
    "apriori_results_neng[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "c214bcea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.005</td>\n",
       "      <td>(Marcus Raboy)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.005</td>\n",
       "      <td>(Jay Karas)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.004</td>\n",
       "      <td>(Martin Scorsese)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.004</td>\n",
       "      <td>(Jay Chapman)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.003</td>\n",
       "      <td>(Steven Spielberg)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.003</td>\n",
       "      <td>(Justin G. Dyck)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.003</td>\n",
       "      <td>(Shannon Hartman)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.002</td>\n",
       "      <td>(Robert Rodriguez)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.002</td>\n",
       "      <td>(Leslie Small)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.002</td>\n",
       "      <td>(Kunle Afolayan)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support            itemsets\n",
       "33    0.005      (Marcus Raboy)\n",
       "15    0.005         (Jay Karas)\n",
       "35    0.004   (Martin Scorsese)\n",
       "14    0.004       (Jay Chapman)\n",
       "64    0.003  (Steven Spielberg)\n",
       "22    0.003    (Justin G. Dyck)\n",
       "56    0.003   (Shannon Hartman)\n",
       "50    0.002  (Robert Rodriguez)\n",
       "29    0.002      (Leslie Small)\n",
       "25    0.002    (Kunle Afolayan)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the most credited directors for English Content\n",
    "apriori_results_eng = calculate_association(netflix_clean_data[(netflix_clean_data[\"director\"] != \"Unknown\") & \n",
    "                                                           (netflix_clean_data[\"prim_lang\"] == \"E\")],\n",
    "                                        \"director\", 0.001, 1)\n",
    "# top 10 most credited directors for English Content\n",
    "apriori_results_eng[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "851b916b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the Top 5 Most credited Directors for English & Non-English Content\n",
    "fig, ax = plt.subplots(figsize = (12, 5)) \n",
    "\n",
    "# stems\n",
    "ax.hlines(y = apriori_results_eng[:5][\"itemsets\"].apply(lambda s: list(s)[0]),\n",
    "          xmin = 0,\n",
    "          xmax = -1 * apriori_results_eng[:5][\"support\"],\n",
    "          lw = 3, \n",
    "          color = \"#FF800E\", \n",
    "          label = \"English\")\n",
    "# dots\n",
    "ax.scatter(y = apriori_results_eng[:5][\"itemsets\"].apply(lambda s: list(s)[0]),\n",
    "           x = -1 * apriori_results_eng[:5][\"support\"],\n",
    "           color = \"#FF800E\",\n",
    "           s = 100)\n",
    "\n",
    "# stems\n",
    "ax.hlines(apriori_results_neng[:5][\"itemsets\"].apply(lambda s: list(s)[0]),\n",
    "          xmin = 0,\n",
    "          xmax = -1 * apriori_results_neng[:5][\"support\"],\n",
    "          lw = 3, \n",
    "          label = \"Non-English\")\n",
    "# dots\n",
    "ax.scatter(y = apriori_results_neng[:5][\"itemsets\"].apply(lambda s: list(s)[0]),\n",
    "           x = -1 * apriori_results_neng[:5][\"support\"], \n",
    "           s = 100)\n",
    "\n",
    "# annotations\n",
    "for i in range(5):\n",
    "    ax.annotate(text = apriori_results_eng[:5][\"itemsets\"].apply(lambda s: list(s)[0]).values[i],\n",
    "                   xy = (0.0001, i - 0.2))\n",
    "for i in range(5):\n",
    "    ax.annotate(text = apriori_results_neng[:5][\"itemsets\"].apply(lambda s: list(s)[0]).values[i],\n",
    "                   xy = (0.0001, (i + 5) - 0.2))\n",
    "\n",
    "# extras    \n",
    "ax.set_title(\"Top 5 Headlining Direcors Based on No. of Netflix Titles Credited\", fontsize = \"large\")\n",
    "ax.axis(\"off\")\n",
    "ax.legend(bbox_to_anchor = (0.3, 0.4))\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "36fcf1bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import wordcloud, stopwords and Image modules\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "d907073d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to collect all the STOPWORDS\n",
    "stopwords = set(STOPWORDS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "21333f3a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# to collect all the words from description column of Non-English Content \n",
    "title_words_ne = \"\"\n",
    "\n",
    "\"\"\"\n",
    "- select the descriptions from the rows classified as Non-English Content\n",
    "- remove any non-alpha-numeric characters from the description of a title\n",
    "- collect each word from each row of description column\n",
    "- join all the words to obtain a string collection\n",
    "\"\"\"\n",
    "# format the description column for Non-English Content\n",
    "for row in netflix_clean_data[netflix_clean_data[\"prim_lang\"] == \"NE\"][\"description\"].str.replace(pat = r\"[^a-zA-Z0-9 ]\", \n",
    "                                                                                       repl = \"\", \n",
    "                                                                                       regex = True):\n",
    "    # split the words by space for each row\n",
    "    for each_word in row.split():\n",
    "        # collect all the words in string object\n",
    "        title_words_ne += \"\".join(each_word.lower())+\" \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "eddfc63c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to collect all the words from description column of English Content \n",
    "title_words_e = \"\"\n",
    "\n",
    "\"\"\"\n",
    "- select the descriptions from the rows classified as Non-English Content\n",
    "- remove any non-alpha-numeric characters from the description of a title\n",
    "- collect each word from each row of description column\n",
    "- join all the words to obtain a string collection\n",
    "\"\"\"\n",
    "# format the description column for English Content\n",
    "for row in netflix_clean_data[netflix_clean_data[\"prim_lang\"] == \"E\"][\"description\"].str.replace(pat = r\"[^a-zA-Z0-9 ]\", \n",
    "                                                                                       repl = \"\", \n",
    "                                                                                       regex = True):\n",
    "    # split the words by space for each row\n",
    "    for each_word in row.split():\n",
    "        # collect all the words in string object\n",
    "        title_words_e += \"\".join(each_word.lower())+\" \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "82ee01fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the wordclouds\n",
    "fig, ax = plt.subplots(ncols = 2, figsize = (10, 5))\n",
    "\n",
    "# to obtain the wordcloud in the shape of N\n",
    "mask = np.array(Image.open(\"Netflix_icon2.png\"))\n",
    "\n",
    "# set the properties for and generate the word cloud for Non-English Content\n",
    "wrd = WordCloud(mask = mask, \n",
    "                stopwords = stopwords, \n",
    "                background_color = \"white\", \n",
    "                max_words = 100, \n",
    "                colormap = \"tab10_r\", \n",
    "                random_state = 1).generate(title_words_ne)\n",
    "\n",
    "# plot the word cloud\n",
    "ax[0].imshow(wrd)\n",
    "# extras\n",
    "ax[0].axis(\"off\")\n",
    "ax[0].set_title(\"For Non-English Content\")\n",
    "\n",
    "# set the properties for and generate the word cloud for English Content\n",
    "wrd = WordCloud(mask = mask, \n",
    "                stopwords = stopwords, \n",
    "                background_color = \"white\", \n",
    "                max_words = 100, \n",
    "                colormap = \"tab10_r\", \n",
    "                random_state = 1).generate(title_words_e)\n",
    "\n",
    "# plot the word cloud\n",
    "ax[1].imshow(wrd)\n",
    "# extras\n",
    "ax[1].axis(\"off\")\n",
    "ax[1].set_title(\"For English Content\")\n",
    "plt.tight_layout(pad = 0)\n",
    "plt.suptitle(\"#Spoiler! What's The Plot About?\", y = 1.1, fontsize = 20)\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  }
 ],
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