Netflix_shows_movies.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "55ad772b",
   "metadata": {},
   "source": [
    "## NETFLIX MOVIES & TV SHOWS DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e7bed36",
   "metadata": {},
   "source": [
    "### IMPORTING THE LIBRARIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cbf347df-f45e-42c6-ac24-c45653bde084",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.9.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as pg\n",
    "import plotly.offline as po\n",
    "po.init_notebook_mode(connected = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "becb1824",
   "metadata": {},
   "source": [
    "### LOADING THE DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cd7a3fc0-44be-4ff8-b64f-95fa7cdee22a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the dataset \n",
    "df = pd.read_csv(\"netflix_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e1471a1a-4786-473d-9f21-b71ec7abe464",
   "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>show_id</th>\n",
       "      <th>type</th>\n",
       "      <th>title</th>\n",
       "      <th>director</th>\n",
       "      <th>cast</th>\n",
       "      <th>country</th>\n",
       "      <th>date_added</th>\n",
       "      <th>release_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>duration</th>\n",
       "      <th>listed_in</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s1</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Dick Johnson Is Dead</td>\n",
       "      <td>Kirsten Johnson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>September 25, 2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>90 min</td>\n",
       "      <td>Documentaries</td>\n",
       "      <td>As her father nears the end of his life, filmm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s2</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Blood &amp; Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>International TV Shows, TV Dramas, TV Mysteries</td>\n",
       "      <td>After crossing paths at a party, a Cape Town t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s3</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Ganglands</td>\n",
       "      <td>Julien Leclercq</td>\n",
       "      <td>Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Crime TV Shows, International TV Shows, TV Act...</td>\n",
       "      <td>To protect his family from a powerful drug lor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s4</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Jailbirds New Orleans</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Docuseries, Reality TV</td>\n",
       "      <td>Feuds, flirtations and toilet talk go down amo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s5</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Kota Factory</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...</td>\n",
       "      <td>India</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>International TV Shows, Romantic TV Shows, TV ...</td>\n",
       "      <td>In a city of coaching centers known to train I...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  show_id     type                  title         director  \\\n",
       "0      s1    Movie   Dick Johnson Is Dead  Kirsten Johnson   \n",
       "1      s2  TV Show          Blood & Water              NaN   \n",
       "2      s3  TV Show              Ganglands  Julien Leclercq   \n",
       "3      s4  TV Show  Jailbirds New Orleans              NaN   \n",
       "4      s5  TV Show           Kota Factory              NaN   \n",
       "\n",
       "                                                cast        country  \\\n",
       "0                                                NaN  United States   \n",
       "1  Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...   South Africa   \n",
       "2  Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...            NaN   \n",
       "3                                                NaN            NaN   \n",
       "4  Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...          India   \n",
       "\n",
       "           date_added  release_year rating   duration  \\\n",
       "0  September 25, 2021          2020  PG-13     90 min   \n",
       "1  September 24, 2021          2021  TV-MA  2 Seasons   \n",
       "2  September 24, 2021          2021  TV-MA   1 Season   \n",
       "3  September 24, 2021          2021  TV-MA   1 Season   \n",
       "4  September 24, 2021          2021  TV-MA  2 Seasons   \n",
       "\n",
       "                                           listed_in  \\\n",
       "0                                      Documentaries   \n",
       "1    International TV Shows, TV Dramas, TV Mysteries   \n",
       "2  Crime TV Shows, International TV Shows, TV Act...   \n",
       "3                             Docuseries, Reality TV   \n",
       "4  International TV Shows, Romantic TV Shows, TV ...   \n",
       "\n",
       "                                         description  \n",
       "0  As her father nears the end of his life, filmm...  \n",
       "1  After crossing paths at a party, a Cape Town t...  \n",
       "2  To protect his family from a powerful drug lor...  \n",
       "3  Feuds, flirtations and toilet talk go down amo...  \n",
       "4  In a city of coaching centers known to train I...  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6d3b22ea-fafa-4aa0-bd5b-76aed6dee6c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8807, 12)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at the shape of the dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52869207-9924-43d5-a966-bda814a46f8f",
   "metadata": {},
   "source": [
    "##### So here we have `8807` movies and shows with `12` attributes of them"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fe7b46b-0cfd-482d-aece-f1be6c0e5002",
   "metadata": {},
   "source": [
    "#### With printing head(), We can easily see the Nan values, so lets clean them up."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92530c6d",
   "metadata": {},
   "source": [
    "### DATA CLEANING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c17b7936-c6c8-484f-9e6b-f7298a538cd4",
   "metadata": {},
   "source": [
    "#### Check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "db4b2dbe-096f-438f-864f-5bb9b8d4d341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "show_id            0\n",
       "type               0\n",
       "title              0\n",
       "director        2634\n",
       "cast             825\n",
       "country          831\n",
       "date_added        10\n",
       "release_year       0\n",
       "rating             4\n",
       "duration           3\n",
       "listed_in          0\n",
       "description        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7ad1571-276e-484d-b19f-b8fcc6efe079",
   "metadata": {},
   "source": [
    "##### so we can see plenty of null/missing values in director, cast, country columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cf2a0be3-d532-42db-88f6-9f6fa0675ef1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8807 entries, 0 to 8806\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   show_id       8807 non-null   object\n",
      " 1   type          8807 non-null   object\n",
      " 2   title         8807 non-null   object\n",
      " 3   director      6173 non-null   object\n",
      " 4   cast          7982 non-null   object\n",
      " 5   country       7976 non-null   object\n",
      " 6   date_added    8797 non-null   object\n",
      " 7   release_year  8807 non-null   int64 \n",
      " 8   rating        8803 non-null   object\n",
      " 9   duration      8804 non-null   object\n",
      " 10  listed_in     8807 non-null   object\n",
      " 11  description   8807 non-null   object\n",
      "dtypes: int64(1), object(11)\n",
      "memory usage: 825.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd2dfec7-2d83-4745-80b2-484044b3f3cd",
   "metadata": {},
   "source": [
    "##### date_added and duration columns are usually in DataTime object and integer datatype respectively instead of object. Now we will have to carefully analyse them and check for anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f94bd82c-2a7f-4a8e-886e-922ee67cbd2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "show_id         8807\n",
       "type               2\n",
       "title           8807\n",
       "director        4528\n",
       "cast            7692\n",
       "country          748\n",
       "date_added      1767\n",
       "release_year      74\n",
       "rating            17\n",
       "duration         220\n",
       "listed_in        514\n",
       "description     8775\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()  #analysing the number of unique values in every columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f16c1792-10a2-4ce1-8284-032dcd892fff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   date_added :\n",
      "['September 25, 2021' 'September 24, 2021' 'September 23, 2021' ...\n",
      " 'December 6, 2018' 'March 9, 2016' 'January 11, 2020']\n",
      "   duration :\n",
      "['90 min' '2 Seasons' '1 Season' '91 min' '125 min' '9 Seasons' '104 min'\n",
      " '127 min' '4 Seasons' '67 min' '94 min' '5 Seasons' '161 min' '61 min'\n",
      " '166 min' '147 min' '103 min' '97 min' '106 min' '111 min' '3 Seasons'\n",
      " '110 min' '105 min' '96 min' '124 min' '116 min' '98 min' '23 min'\n",
      " '115 min' '122 min' '99 min' '88 min' '100 min' '6 Seasons' '102 min'\n",
      " '93 min' '95 min' '85 min' '83 min' '113 min' '13 min' '182 min' '48 min'\n",
      " '145 min' '87 min' '92 min' '80 min' '117 min' '128 min' '119 min'\n",
      " '143 min' '114 min' '118 min' '108 min' '63 min' '121 min' '142 min'\n",
      " '154 min' '120 min' '82 min' '109 min' '101 min' '86 min' '229 min'\n",
      " '76 min' '89 min' '156 min' '112 min' '107 min' '129 min' '135 min'\n",
      " '136 min' '165 min' '150 min' '133 min' '70 min' '84 min' '140 min'\n",
      " '78 min' '7 Seasons' '64 min' '59 min' '139 min' '69 min' '148 min'\n",
      " '189 min' '141 min' '130 min' '138 min' '81 min' '132 min' '10 Seasons'\n",
      " '123 min' '65 min' '68 min' '66 min' '62 min' '74 min' '131 min' '39 min'\n",
      " '46 min' '38 min' '8 Seasons' '17 Seasons' '126 min' '155 min' '159 min'\n",
      " '137 min' '12 min' '273 min' '36 min' '34 min' '77 min' '60 min' '49 min'\n",
      " '58 min' '72 min' '204 min' '212 min' '25 min' '73 min' '29 min' '47 min'\n",
      " '32 min' '35 min' '71 min' '149 min' '33 min' '15 min' '54 min' '224 min'\n",
      " '162 min' '37 min' '75 min' '79 min' '55 min' '158 min' '164 min'\n",
      " '173 min' '181 min' '185 min' '21 min' '24 min' '51 min' '151 min'\n",
      " '42 min' '22 min' '134 min' '177 min' '13 Seasons' '52 min' '14 min'\n",
      " '53 min' '8 min' '57 min' '28 min' '50 min' '9 min' '26 min' '45 min'\n",
      " '171 min' '27 min' '44 min' '146 min' '20 min' '157 min' '17 min'\n",
      " '203 min' '41 min' '30 min' '194 min' '15 Seasons' '233 min' '237 min'\n",
      " '230 min' '195 min' '253 min' '152 min' '190 min' '160 min' '208 min'\n",
      " '180 min' '144 min' '5 min' '174 min' '170 min' '192 min' '209 min'\n",
      " '187 min' '172 min' '16 min' '186 min' '11 min' '193 min' '176 min'\n",
      " '56 min' '169 min' '40 min' '10 min' '3 min' '168 min' '312 min'\n",
      " '153 min' '214 min' '31 min' '163 min' '19 min' '12 Seasons' nan\n",
      " '179 min' '11 Seasons' '43 min' '200 min' '196 min' '167 min' '178 min'\n",
      " '228 min' '18 min' '205 min' '201 min' '191 min']\n"
     ]
    }
   ],
   "source": [
    "check_col = [\"date_added\", \"duration\"]\n",
    "\n",
    "for col in check_col:\n",
    "    uniques = df[col].unique()\n",
    "    print(\"  \",col,\":\")\n",
    "    print(uniques)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b5a2e43-2154-46fd-bf76-3008c28ef30b",
   "metadata": {},
   "source": [
    "##### Duration column is consistent, so we'll leave as it is, date_added is also consistent. date_added will be useful in further analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d48eab6-209e-41ad-baa0-a39f53b958d9",
   "metadata": {},
   "source": [
    "Datatypes are satisfactory, Hence we're gonna focus on Missing Values. we have already noted that 3 columns have plenty number of missing values. we'll analyse whether to drop the null values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd692df-562f-4f84-8524-13c8ab43c2a3",
   "metadata": {},
   "source": [
    "### Visualizing the Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9089db6d-56dc-4167-836f-73e51a66dc8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1368x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (19,7) )\n",
    "sns.heatmap(df.isna(), cmap=\"YlGnBu\"\n",
    "            ,cbar_kws={'label': 'Missing Data'}\n",
    "           )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49d74ecd-585d-4632-b82a-c0fd97e54633",
   "metadata": {},
   "source": [
    "- `director`, `cast` and `country` have missing values in a random manner. We will thus replace them with \"UNKOWN\" keyword. This will be helpful later on.\n",
    "- for, date_added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "61128638-0ca0-42a9-8b42-687363fa2d7a",
   "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>show_id</th>\n",
       "      <th>type</th>\n",
       "      <th>title</th>\n",
       "      <th>director</th>\n",
       "      <th>cast</th>\n",
       "      <th>country</th>\n",
       "      <th>date_added</th>\n",
       "      <th>release_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>duration</th>\n",
       "      <th>listed_in</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s1</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Dick Johnson Is Dead</td>\n",
       "      <td>Kirsten Johnson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>September 25, 2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>90 min</td>\n",
       "      <td>Documentaries</td>\n",
       "      <td>As her father nears the end of his life, filmm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s2</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Blood &amp; Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>International TV Shows, TV Dramas, TV Mysteries</td>\n",
       "      <td>After crossing paths at a party, a Cape Town t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s3</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Ganglands</td>\n",
       "      <td>Julien Leclercq</td>\n",
       "      <td>Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Crime TV Shows, International TV Shows, TV Act...</td>\n",
       "      <td>To protect his family from a powerful drug lor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s4</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Jailbirds New Orleans</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Docuseries, Reality TV</td>\n",
       "      <td>Feuds, flirtations and toilet talk go down amo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s5</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Kota Factory</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...</td>\n",
       "      <td>India</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>International TV Shows, Romantic TV Shows, TV ...</td>\n",
       "      <td>In a city of coaching centers known to train I...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  show_id     type                  title         director  \\\n",
       "0      s1    Movie   Dick Johnson Is Dead  Kirsten Johnson   \n",
       "1      s2  TV Show          Blood & Water              NaN   \n",
       "2      s3  TV Show              Ganglands  Julien Leclercq   \n",
       "3      s4  TV Show  Jailbirds New Orleans              NaN   \n",
       "4      s5  TV Show           Kota Factory              NaN   \n",
       "\n",
       "                                                cast        country  \\\n",
       "0                                                NaN  United States   \n",
       "1  Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...   South Africa   \n",
       "2  Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...            NaN   \n",
       "3                                                NaN            NaN   \n",
       "4  Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...          India   \n",
       "\n",
       "           date_added  release_year rating   duration  \\\n",
       "0  September 25, 2021          2020  PG-13     90 min   \n",
       "1  September 24, 2021          2021  TV-MA  2 Seasons   \n",
       "2  September 24, 2021          2021  TV-MA   1 Season   \n",
       "3  September 24, 2021          2021  TV-MA   1 Season   \n",
       "4  September 24, 2021          2021  TV-MA  2 Seasons   \n",
       "\n",
       "                                           listed_in  \\\n",
       "0                                      Documentaries   \n",
       "1    International TV Shows, TV Dramas, TV Mysteries   \n",
       "2  Crime TV Shows, International TV Shows, TV Act...   \n",
       "3                             Docuseries, Reality TV   \n",
       "4  International TV Shows, Romantic TV Shows, TV ...   \n",
       "\n",
       "                                         description  \n",
       "0  As her father nears the end of his life, filmm...  \n",
       "1  After crossing paths at a party, a Cape Town t...  \n",
       "2  To protect his family from a powerful drug lor...  \n",
       "3  Feuds, flirtations and toilet talk go down amo...  \n",
       "4  In a city of coaching centers known to train I...  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6680c4b4-477c-4c75-8fa0-6d7c3f13ea35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing the NaN values with UNKNOWN\n",
    "df[ [\"director\", \"cast\", \"country\"] ] = df[ [\"director\", \"cast\", \"country\"] ].replace(np.nan, \"UNKNOWN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4f384803-46c7-43ac-ac56-beeef50273ba",
   "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",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>show_id</th>\n",
       "      <th>type</th>\n",
       "      <th>title</th>\n",
       "      <th>director</th>\n",
       "      <th>cast</th>\n",
       "      <th>country</th>\n",
       "      <th>date_added</th>\n",
       "      <th>release_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>duration</th>\n",
       "      <th>listed_in</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s1</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Dick Johnson Is Dead</td>\n",
       "      <td>Kirsten Johnson</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>United States</td>\n",
       "      <td>September 25, 2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>90 min</td>\n",
       "      <td>Documentaries</td>\n",
       "      <td>As her father nears the end of his life, filmm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s2</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Blood &amp; Water</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>International TV Shows, TV Dramas, TV Mysteries</td>\n",
       "      <td>After crossing paths at a party, a Cape Town t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s3</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Ganglands</td>\n",
       "      <td>Julien Leclercq</td>\n",
       "      <td>Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Crime TV Shows, International TV Shows, TV Act...</td>\n",
       "      <td>To protect his family from a powerful drug lor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s4</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Jailbirds New Orleans</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Docuseries, Reality TV</td>\n",
       "      <td>Feuds, flirtations and toilet talk go down amo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s5</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Kota Factory</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...</td>\n",
       "      <td>India</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>International TV Shows, Romantic TV Shows, TV ...</td>\n",
       "      <td>In a city of coaching centers known to train I...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  show_id     type                  title         director  \\\n",
       "0      s1    Movie   Dick Johnson Is Dead  Kirsten Johnson   \n",
       "1      s2  TV Show          Blood & Water          UNKNOWN   \n",
       "2      s3  TV Show              Ganglands  Julien Leclercq   \n",
       "3      s4  TV Show  Jailbirds New Orleans          UNKNOWN   \n",
       "4      s5  TV Show           Kota Factory          UNKNOWN   \n",
       "\n",
       "                                                cast        country  \\\n",
       "0                                            UNKNOWN  United States   \n",
       "1  Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...   South Africa   \n",
       "2  Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...        UNKNOWN   \n",
       "3                                            UNKNOWN        UNKNOWN   \n",
       "4  Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...          India   \n",
       "\n",
       "           date_added  release_year rating   duration  \\\n",
       "0  September 25, 2021          2020  PG-13     90 min   \n",
       "1  September 24, 2021          2021  TV-MA  2 Seasons   \n",
       "2  September 24, 2021          2021  TV-MA   1 Season   \n",
       "3  September 24, 2021          2021  TV-MA   1 Season   \n",
       "4  September 24, 2021          2021  TV-MA  2 Seasons   \n",
       "\n",
       "                                           listed_in  \\\n",
       "0                                      Documentaries   \n",
       "1    International TV Shows, TV Dramas, TV Mysteries   \n",
       "2  Crime TV Shows, International TV Shows, TV Act...   \n",
       "3                             Docuseries, Reality TV   \n",
       "4  International TV Shows, Romantic TV Shows, TV ...   \n",
       "\n",
       "                                         description  \n",
       "0  As her father nears the end of his life, filmm...  \n",
       "1  After crossing paths at a party, a Cape Town t...  \n",
       "2  To protect his family from a powerful drug lor...  \n",
       "3  Feuds, flirtations and toilet talk go down amo...  \n",
       "4  In a city of coaching centers known to train I...  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ae4c833c-e955-4fac-b6a6-68e7bfdb8913",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>66 min</th>\n",
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       "      <td>3</td>\n",
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       "      <th>NR</th>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
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       "      <td>80</td>\n",
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       "      <th>PG</th>\n",
       "      <td>287</td>\n",
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       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PG-13</th>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
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       "      <td>490</td>\n",
       "      <td>490</td>\n",
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       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV-14</th>\n",
       "      <td>2160</td>\n",
       "      <td>2160</td>\n",
       "      <td>2160</td>\n",
       "      <td>2160</td>\n",
       "      <td>2160</td>\n",
       "      <td>2160</td>\n",
       "      <td>2157</td>\n",
       "      <td>2160</td>\n",
       "      <td>2160</td>\n",
       "      <td>2160</td>\n",
       "      <td>2160</td>\n",
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       "    <tr>\n",
       "      <th>TV-G</th>\n",
       "      <td>220</td>\n",
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       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
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       "    <tr>\n",
       "      <th>TV-MA</th>\n",
       "      <td>3207</td>\n",
       "      <td>3207</td>\n",
       "      <td>3207</td>\n",
       "      <td>3207</td>\n",
       "      <td>3207</td>\n",
       "      <td>3207</td>\n",
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       "      <td>863</td>\n",
       "      <td>861</td>\n",
       "      <td>863</td>\n",
       "      <td>863</td>\n",
       "      <td>863</td>\n",
       "      <td>863</td>\n",
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       "    <tr>\n",
       "      <th>TV-Y</th>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "      <td>306</td>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV-Y7</th>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "      <td>333</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV-Y7-FV</th>\n",
       "      <td>6</td>\n",
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      ],
      "text/plain": [
       "          show_id  type  title  director  cast  country  date_added  \\\n",
       "rating                                                                \n",
       "66 min          1     1      1         1     1        1           1   \n",
       "74 min          1     1      1         1     1        1           1   \n",
       "84 min          1     1      1         1     1        1           1   \n",
       "G              41    41     41        41    41       41          41   \n",
       "NC-17           3     3      3         3     3        3           3   \n",
       "NR             80    80     80        80    80       80          79   \n",
       "PG            287   287    287       287   287      287         287   \n",
       "PG-13         490   490    490       490   490      490         490   \n",
       "R             799   799    799       799   799      799         799   \n",
       "TV-14        2160  2160   2160      2160  2160     2160        2157   \n",
       "TV-G          220   220    220       220   220      220         220   \n",
       "TV-MA        3207  3207   3207      3207  3207     3207        3205   \n",
       "TV-PG         863   863    863       863   863      863         861   \n",
       "TV-Y          307   307    307       307   307      307         306   \n",
       "TV-Y7         334   334    334       334   334      334         333   \n",
       "TV-Y7-FV        6     6      6         6     6        6           6   \n",
       "UR              3     3      3         3     3        3           3   \n",
       "\n",
       "          release_year  duration  listed_in  description  \n",
       "rating                                                    \n",
       "66 min               1         0          1            1  \n",
       "74 min               1         0          1            1  \n",
       "84 min               1         0          1            1  \n",
       "G                   41        41         41           41  \n",
       "NC-17                3         3          3            3  \n",
       "NR                  80        80         80           80  \n",
       "PG                 287       287        287          287  \n",
       "PG-13              490       490        490          490  \n",
       "R                  799       799        799          799  \n",
       "TV-14             2160      2160       2160         2160  \n",
       "TV-G               220       220        220          220  \n",
       "TV-MA             3207      3207       3207         3207  \n",
       "TV-PG              863       863        863          863  \n",
       "TV-Y               307       307        307          307  \n",
       "TV-Y7              334       334        334          334  \n",
       "TV-Y7-FV             6         6          6            6  \n",
       "UR                   3         3          3            3  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by the values of the rating column to check the count of the values in each column of the each rating \n",
    "df.groupby(by=\"rating\").count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b81df53-40c6-4c89-a186-019f29b8ed18",
   "metadata": {},
   "source": [
    "\"66 min\" , \"74 min\", \"84 min\" are inaccurate rating. This record is mistakenly placed into rating instead of duration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "af4053dd-582a-40ba-ab92-11ccf470a2c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing the anamolies \n",
    "def anamoly_detect(frame):\n",
    "    if( \"min\" in str(frame[\"rating\"])):\n",
    "        frame[\"duration\"] = frame[\"rating\"]\n",
    "        frame[\"rating\"] = np.nan\n",
    "    return frame;\n",
    "\n",
    "df = df.apply(anamoly_detect, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "885c8221-dda5-43f4-8819-b1faf0414385",
   "metadata": {},
   "outputs": [
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       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC-17</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR</th>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>79</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PG</th>\n",
       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PG-13</th>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV-14</th>\n",
       "      <td>2160</td>\n",
       "      <td>2160</td>\n",
       "      <td>2160</td>\n",
       "      <td>2160</td>\n",
       "      <td>2160</td>\n",
       "      <td>2160</td>\n",
       "      <td>2157</td>\n",
       "      <td>2160</td>\n",
       "      <td>2160</td>\n",
       "      <td>2160</td>\n",
       "      <td>2160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV-G</th>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV-MA</th>\n",
       "      <td>3207</td>\n",
       "      <td>3207</td>\n",
       "      <td>3207</td>\n",
       "      <td>3207</td>\n",
       "      <td>3207</td>\n",
       "      <td>3207</td>\n",
       "      <td>3205</td>\n",
       "      <td>3207</td>\n",
       "      <td>3207</td>\n",
       "      <td>3207</td>\n",
       "      <td>3207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV-PG</th>\n",
       "      <td>863</td>\n",
       "      <td>863</td>\n",
       "      <td>863</td>\n",
       "      <td>863</td>\n",
       "      <td>863</td>\n",
       "      <td>863</td>\n",
       "      <td>861</td>\n",
       "      <td>863</td>\n",
       "      <td>863</td>\n",
       "      <td>863</td>\n",
       "      <td>863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV-Y</th>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "      <td>306</td>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV-Y7</th>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "      <td>333</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV-Y7-FV</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UR</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          show_id  type  title  director  cast  country  date_added  \\\n",
       "rating                                                                \n",
       "G              41    41     41        41    41       41          41   \n",
       "NC-17           3     3      3         3     3        3           3   \n",
       "NR             80    80     80        80    80       80          79   \n",
       "PG            287   287    287       287   287      287         287   \n",
       "PG-13         490   490    490       490   490      490         490   \n",
       "R             799   799    799       799   799      799         799   \n",
       "TV-14        2160  2160   2160      2160  2160     2160        2157   \n",
       "TV-G          220   220    220       220   220      220         220   \n",
       "TV-MA        3207  3207   3207      3207  3207     3207        3205   \n",
       "TV-PG         863   863    863       863   863      863         861   \n",
       "TV-Y          307   307    307       307   307      307         306   \n",
       "TV-Y7         334   334    334       334   334      334         333   \n",
       "TV-Y7-FV        6     6      6         6     6        6           6   \n",
       "UR              3     3      3         3     3        3           3   \n",
       "\n",
       "          release_year  duration  listed_in  description  \n",
       "rating                                                    \n",
       "G                   41        41         41           41  \n",
       "NC-17                3         3          3            3  \n",
       "NR                  80        80         80           80  \n",
       "PG                 287       287        287          287  \n",
       "PG-13              490       490        490          490  \n",
       "R                  799       799        799          799  \n",
       "TV-14             2160      2160       2160         2160  \n",
       "TV-G               220       220        220          220  \n",
       "TV-MA             3207      3207       3207         3207  \n",
       "TV-PG              863       863        863          863  \n",
       "TV-Y               307       307        307          307  \n",
       "TV-Y7              334       334        334          334  \n",
       "TV-Y7-FV             6         6          6            6  \n",
       "UR                   3         3          3            3  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by=\"rating\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "13a796b5-cd4f-45fe-96d1-50b662fa348e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "show_id          0\n",
       "type             0\n",
       "title            0\n",
       "director         0\n",
       "cast             0\n",
       "country          0\n",
       "date_added      10\n",
       "release_year     0\n",
       "rating           7\n",
       "duration         0\n",
       "listed_in        0\n",
       "description      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ecada64-1bfa-4033-b99b-4853e000f6bd",
   "metadata": {},
   "source": [
    "#### Null values in rating Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9ae951cd-114f-4659-a467-e7070ac955d9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"rating\"].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "608e65a9-bc92-43b1-8330-84ee08e79ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>s5542</td>\n",
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       "      <td>UNKNOWN</td>\n",
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       "      <td>Japan</td>\n",
       "      <td>December 1, 2016</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Anime Series, International TV Shows</td>\n",
       "      <td>After falling through a wormhole, a space-dwel...</td>\n",
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       "      <th>7312</th>\n",
       "      <td>s7313</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Little Lunch</td>\n",
       "      <td>UNKNOWN</td>\n",
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       "      <td>Australia</td>\n",
       "      <td>February 1, 2018</td>\n",
       "      <td>2015</td>\n",
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       "      <th>7537</th>\n",
       "      <td>s7538</td>\n",
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       "      <td>My Honor Was Loyalty</td>\n",
       "      <td>Alessandro Pepe</td>\n",
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       "      <td>March 1, 2017</td>\n",
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       "      <td>115 min</td>\n",
       "      <td>Dramas</td>\n",
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      "text/plain": [
       "     show_id     type                                              title  \\\n",
       "5541   s5542    Movie                                    Louis C.K. 2017   \n",
       "5794   s5795    Movie                              Louis C.K.: Hilarious   \n",
       "5813   s5814    Movie               Louis C.K.: Live at the Comedy Store   \n",
       "5989   s5990    Movie  13TH: A Conversation with Oprah Winfrey & Ava ...   \n",
       "6827   s6828  TV Show                  Gargantia on the Verdurous Planet   \n",
       "7312   s7313  TV Show                                       Little Lunch   \n",
       "7537   s7538    Movie                               My Honor Was Loyalty   \n",
       "\n",
       "             director                                               cast  \\\n",
       "5541       Louis C.K.                                         Louis C.K.   \n",
       "5794       Louis C.K.                                         Louis C.K.   \n",
       "5813       Louis C.K.                                         Louis C.K.   \n",
       "5989          UNKNOWN                        Oprah Winfrey, Ava DuVernay   \n",
       "6827          UNKNOWN  Kaito Ishikawa, Hisako Kanemoto, Ai Kayano, Ka...   \n",
       "7312          UNKNOWN  Flynn Curry, Olivia Deeble, Madison Lu, Oisín ...   \n",
       "7537  Alessandro Pepe  Leone Frisa, Paolo Vaccarino, Francesco Miglio...   \n",
       "\n",
       "            country          date_added  release_year rating  duration  \\\n",
       "5541  United States       April 4, 2017          2017    NaN    74 min   \n",
       "5794  United States  September 16, 2016          2010    NaN    84 min   \n",
       "5813  United States     August 15, 2016          2015    NaN    66 min   \n",
       "5989        UNKNOWN    January 26, 2017          2017    NaN    37 min   \n",
       "6827          Japan    December 1, 2016          2013    NaN  1 Season   \n",
       "7312      Australia    February 1, 2018          2015    NaN  1 Season   \n",
       "7537          Italy       March 1, 2017          2015    NaN   115 min   \n",
       "\n",
       "                                 listed_in  \\\n",
       "5541                                Movies   \n",
       "5794                                Movies   \n",
       "5813                                Movies   \n",
       "5989                                Movies   \n",
       "6827  Anime Series, International TV Shows   \n",
       "7312                 Kids' TV, TV Comedies   \n",
       "7537                                Dramas   \n",
       "\n",
       "                                            description  \n",
       "5541  Louis C.K. muses on religion, eternal love, gi...  \n",
       "5794  Emmy-winning comedy writer Louis C.K. brings h...  \n",
       "5813  The comic puts his trademark hilarious/thought...  \n",
       "5989  Oprah Winfrey sits down with director Ava DuVe...  \n",
       "6827  After falling through a wormhole, a space-dwel...  \n",
       "7312  Adopting a child's perspective, this show take...  \n",
       "7537  Amid the chaos and horror of World War II, a c...  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = df[\"rating\"].isna()\n",
    "df[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c80c01-6ef8-40d3-8f2f-b165945c1e80",
   "metadata": {},
   "source": [
    "As rating contains some NAN values, and we cannot fill these values by any means, So we're replacing it with UNKNOWN value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e6faed6d-320e-4cd4-93a2-c898f687b238",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace NaN values\n",
    "df[\"rating\"] = df[\"rating\"].replace(np.nan, \"UNKNOWN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6cbcc975-5ee1-4ff7-977e-ceb4adb17cb5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>74 min</td>\n",
       "      <td>Movies</td>\n",
       "      <td>Louis C.K. muses on religion, eternal love, gi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5794</th>\n",
       "      <td>s5795</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Louis C.K.: Hilarious</td>\n",
       "      <td>Louis C.K.</td>\n",
       "      <td>Louis C.K.</td>\n",
       "      <td>United States</td>\n",
       "      <td>September 16, 2016</td>\n",
       "      <td>2010</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>84 min</td>\n",
       "      <td>Movies</td>\n",
       "      <td>Emmy-winning comedy writer Louis C.K. brings h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5813</th>\n",
       "      <td>s5814</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Louis C.K.: Live at the Comedy Store</td>\n",
       "      <td>Louis C.K.</td>\n",
       "      <td>Louis C.K.</td>\n",
       "      <td>United States</td>\n",
       "      <td>August 15, 2016</td>\n",
       "      <td>2015</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>66 min</td>\n",
       "      <td>Movies</td>\n",
       "      <td>The comic puts his trademark hilarious/thought...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5989</th>\n",
       "      <td>s5990</td>\n",
       "      <td>Movie</td>\n",
       "      <td>13TH: A Conversation with Oprah Winfrey &amp; Ava ...</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>Oprah Winfrey, Ava DuVernay</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>January 26, 2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>37 min</td>\n",
       "      <td>Movies</td>\n",
       "      <td>Oprah Winfrey sits down with director Ava DuVe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6827</th>\n",
       "      <td>s6828</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Gargantia on the Verdurous Planet</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>Kaito Ishikawa, Hisako Kanemoto, Ai Kayano, Ka...</td>\n",
       "      <td>Japan</td>\n",
       "      <td>December 1, 2016</td>\n",
       "      <td>2013</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Anime Series, International TV Shows</td>\n",
       "      <td>After falling through a wormhole, a space-dwel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7312</th>\n",
       "      <td>s7313</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Little Lunch</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>Flynn Curry, Olivia Deeble, Madison Lu, Oisín ...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>February 1, 2018</td>\n",
       "      <td>2015</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Kids' TV, TV Comedies</td>\n",
       "      <td>Adopting a child's perspective, this show take...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7537</th>\n",
       "      <td>s7538</td>\n",
       "      <td>Movie</td>\n",
       "      <td>My Honor Was Loyalty</td>\n",
       "      <td>Alessandro Pepe</td>\n",
       "      <td>Leone Frisa, Paolo Vaccarino, Francesco Miglio...</td>\n",
       "      <td>Italy</td>\n",
       "      <td>March 1, 2017</td>\n",
       "      <td>2015</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>115 min</td>\n",
       "      <td>Dramas</td>\n",
       "      <td>Amid the chaos and horror of World War II, a c...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     show_id     type                                              title  \\\n",
       "5541   s5542    Movie                                    Louis C.K. 2017   \n",
       "5794   s5795    Movie                              Louis C.K.: Hilarious   \n",
       "5813   s5814    Movie               Louis C.K.: Live at the Comedy Store   \n",
       "5989   s5990    Movie  13TH: A Conversation with Oprah Winfrey & Ava ...   \n",
       "6827   s6828  TV Show                  Gargantia on the Verdurous Planet   \n",
       "7312   s7313  TV Show                                       Little Lunch   \n",
       "7537   s7538    Movie                               My Honor Was Loyalty   \n",
       "\n",
       "             director                                               cast  \\\n",
       "5541       Louis C.K.                                         Louis C.K.   \n",
       "5794       Louis C.K.                                         Louis C.K.   \n",
       "5813       Louis C.K.                                         Louis C.K.   \n",
       "5989          UNKNOWN                        Oprah Winfrey, Ava DuVernay   \n",
       "6827          UNKNOWN  Kaito Ishikawa, Hisako Kanemoto, Ai Kayano, Ka...   \n",
       "7312          UNKNOWN  Flynn Curry, Olivia Deeble, Madison Lu, Oisín ...   \n",
       "7537  Alessandro Pepe  Leone Frisa, Paolo Vaccarino, Francesco Miglio...   \n",
       "\n",
       "            country          date_added  release_year   rating  duration  \\\n",
       "5541  United States       April 4, 2017          2017  UNKNOWN    74 min   \n",
       "5794  United States  September 16, 2016          2010  UNKNOWN    84 min   \n",
       "5813  United States     August 15, 2016          2015  UNKNOWN    66 min   \n",
       "5989        UNKNOWN    January 26, 2017          2017  UNKNOWN    37 min   \n",
       "6827          Japan    December 1, 2016          2013  UNKNOWN  1 Season   \n",
       "7312      Australia    February 1, 2018          2015  UNKNOWN  1 Season   \n",
       "7537          Italy       March 1, 2017          2015  UNKNOWN   115 min   \n",
       "\n",
       "                                 listed_in  \\\n",
       "5541                                Movies   \n",
       "5794                                Movies   \n",
       "5813                                Movies   \n",
       "5989                                Movies   \n",
       "6827  Anime Series, International TV Shows   \n",
       "7312                 Kids' TV, TV Comedies   \n",
       "7537                                Dramas   \n",
       "\n",
       "                                            description  \n",
       "5541  Louis C.K. muses on religion, eternal love, gi...  \n",
       "5794  Emmy-winning comedy writer Louis C.K. brings h...  \n",
       "5813  The comic puts his trademark hilarious/thought...  \n",
       "5989  Oprah Winfrey sits down with director Ava DuVe...  \n",
       "6827  After falling through a wormhole, a space-dwel...  \n",
       "7312  Adopting a child's perspective, this show take...  \n",
       "7537  Amid the chaos and horror of World War II, a c...  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"rating\"] == \"UNKNOWN\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37815e79-9c33-4063-b446-8a8086c64b93",
   "metadata": {},
   "source": [
    "#### Null values in Date_added Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f0ceee75-9063-4871-843e-4686203530af",
   "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>show_id</th>\n",
       "      <th>type</th>\n",
       "      <th>title</th>\n",
       "      <th>director</th>\n",
       "      <th>cast</th>\n",
       "      <th>country</th>\n",
       "      <th>date_added</th>\n",
       "      <th>release_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>duration</th>\n",
       "      <th>listed_in</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s1</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Dick Johnson Is Dead</td>\n",
       "      <td>Kirsten Johnson</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>United States</td>\n",
       "      <td>September 25, 2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>90 min</td>\n",
       "      <td>Documentaries</td>\n",
       "      <td>As her father nears the end of his life, filmm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s2</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Blood &amp; Water</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>International TV Shows, TV Dramas, TV Mysteries</td>\n",
       "      <td>After crossing paths at a party, a Cape Town t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s3</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Ganglands</td>\n",
       "      <td>Julien Leclercq</td>\n",
       "      <td>Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Crime TV Shows, International TV Shows, TV Act...</td>\n",
       "      <td>To protect his family from a powerful drug lor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s4</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Jailbirds New Orleans</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Docuseries, Reality TV</td>\n",
       "      <td>Feuds, flirtations and toilet talk go down amo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s5</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Kota Factory</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...</td>\n",
       "      <td>India</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>International TV Shows, Romantic TV Shows, TV ...</td>\n",
       "      <td>In a city of coaching centers known to train I...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  show_id     type                  title         director  \\\n",
       "0      s1    Movie   Dick Johnson Is Dead  Kirsten Johnson   \n",
       "1      s2  TV Show          Blood & Water          UNKNOWN   \n",
       "2      s3  TV Show              Ganglands  Julien Leclercq   \n",
       "3      s4  TV Show  Jailbirds New Orleans          UNKNOWN   \n",
       "4      s5  TV Show           Kota Factory          UNKNOWN   \n",
       "\n",
       "                                                cast        country  \\\n",
       "0                                            UNKNOWN  United States   \n",
       "1  Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...   South Africa   \n",
       "2  Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...        UNKNOWN   \n",
       "3                                            UNKNOWN        UNKNOWN   \n",
       "4  Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...          India   \n",
       "\n",
       "           date_added  release_year rating   duration  \\\n",
       "0  September 25, 2021          2020  PG-13     90 min   \n",
       "1  September 24, 2021          2021  TV-MA  2 Seasons   \n",
       "2  September 24, 2021          2021  TV-MA   1 Season   \n",
       "3  September 24, 2021          2021  TV-MA   1 Season   \n",
       "4  September 24, 2021          2021  TV-MA  2 Seasons   \n",
       "\n",
       "                                           listed_in  \\\n",
       "0                                      Documentaries   \n",
       "1    International TV Shows, TV Dramas, TV Mysteries   \n",
       "2  Crime TV Shows, International TV Shows, TV Act...   \n",
       "3                             Docuseries, Reality TV   \n",
       "4  International TV Shows, Romantic TV Shows, TV ...   \n",
       "\n",
       "                                         description  \n",
       "0  As her father nears the end of his life, filmm...  \n",
       "1  After crossing paths at a party, a Cape Town t...  \n",
       "2  To protect his family from a powerful drug lor...  \n",
       "3  Feuds, flirtations and toilet talk go down amo...  \n",
       "4  In a city of coaching centers known to train I...  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "010d9a4f-77d3-48c7-86a4-8582ca349df8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"date_added\"] = df[\"date_added\"].replace(np.nan, \"UNKNOWN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5e9df6ee-5fc1-4a8d-bf90-c183ebf2a475",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "show_id         0\n",
       "type            0\n",
       "title           0\n",
       "director        0\n",
       "cast            0\n",
       "country         0\n",
       "date_added      0\n",
       "release_year    0\n",
       "rating          0\n",
       "duration        0\n",
       "listed_in       0\n",
       "description     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b82288d2",
   "metadata": {},
   "source": [
    "Dropping the show_id column as it has no importance in our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f815b6e8-5ce3-4f58-aa4a-bc61d1ebdb67",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"show_id\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d2530c7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['type', 'title', 'director', 'cast', 'country', 'date_added',\n",
       "       'release_year', 'rating', 'duration', 'listed_in', 'description'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a21d79d",
   "metadata": {},
   "source": [
    "#### DONE WITH DATA CLEANING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bac0511",
   "metadata": {},
   "source": [
    "### GETTING INSIGHTS THROUGH VISUALTIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "165bf41f",
   "metadata": {},
   "source": [
    "Visualize the frequencies of movies and tv shows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6b002c34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='type', ylabel='count'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# finding out the number of movies and Tv shows\n",
    "sns.countplot(x=\"type\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89a9dff4",
   "metadata": {},
   "source": [
    "From the plot:\n",
    "* It is clear that Movies data is in majority than then the Tv shows on Netlfix, almost double in Numbers.\n",
    "* Movies just above 6000 and Tv shows are close to 3000."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9380a42",
   "metadata": {},
   "source": [
    "Visualize the distributions of rating feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "186309e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='rating', ylabel='count'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking the distributions of the rating feature\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(x=\"rating\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "998820b3",
   "metadata": {},
   "source": [
    "Insights from above the plot:\n",
    "* \"TV-MA\" and \"TV-14\" are the most frequent rating on the Netflix\n",
    "* \"NC-17\", \"TV-Y7-FV\", \"UR\", and \"G\" are least frequent ratings on the Netflix\n",
    "*  There exist some Movies or TV shows in which rating is not labeled or not known."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0361308b",
   "metadata": {},
   "source": [
    "Visualize the distributions along with type of show i.e movie or Tv show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "3f93bd2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='rating', ylabel='count'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comparing the distributions of rating in terms of movie and tv shows\n",
    "plt.figure(figsize=(18,8))\n",
    "sns.countplot(x=\"rating\", hue = \"type\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "104efb7a",
   "metadata": {},
   "source": [
    "Insights from the above plot:\n",
    "\n",
    "* Most of Movies and TV shows has \"TV-MA\" rating\n",
    "* \"TV-MA\" rating is majorly associtated with the Movies on the Netflix in comparison with the TV shows.\n",
    "* Only \"TV-Y\" and \"TV-Y7\" are the only ratings which are given to the TV shows in majority in comparison with Movies.\n",
    "* \"PG-13\", \"PG\", and \"G\" ratings are not linked with TV shows, specifically given to the Movies."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "375ffd22",
   "metadata": {},
   "source": [
    "Visualize the distribution of the release_date "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "abf93d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='release_year', ylabel='count'>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for the counts of movies and tv shows in each release year\n",
    "plt.figure(figsize=(40,8))\n",
    "sns.countplot(x=\"release_year\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "366ac26e",
   "metadata": {},
   "source": [
    "* Majority of the Movies & TV shows are released in period from 2014 to 2021.\n",
    "* In 2018, most of the Movies & TV shows are released, which is close to 1200."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de3ab4d4",
   "metadata": {},
   "source": [
    "Count of countries where movies or tv shows were released, top 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e6b030ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# top 10 countries with most release of movies and tv shows\n",
    "df[\"country\"].value_counts()[:10].plot.pie(autopct=\"%1.1f%%\", figsize=(10,10))\n",
    "plt.title(\"Top 10 Countries with most release of Movies and Tv shows\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fe3d603",
   "metadata": {},
   "source": [
    "* In Movies & TV shows release, the share of America is almost half in comparison with other countries in the top 10.\n",
    "* India has release percentage of around 16% and just behind America.\n",
    "* Almost 14% Movies & TV shows are not labelled the country Names. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c926aeac",
   "metadata": {},
   "source": [
    "Visualizing the count of countries with most number of movies and Tv shows, top 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6c3e7d99",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = df[\"country\"].value_counts()[:10]\n",
    "# selecting on the records of the top 10 countries with most Movies and Tv shows releases \n",
    "top_10_df = df[df[\"country\"].isin(counts.index.to_list()) == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "41381aa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='country', ylabel='count'>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking the counts of movies and tv shows of countries\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.countplot(x=\"country\", data = top_10_df, hue=\"type\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0daf421",
   "metadata": {},
   "source": [
    "* `United States` has the released most number of Movies and TV shows than any other country in world.\n",
    "* `India` is on the second in terms of most Movies release, but not with respect to TV shows."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06c6ee72",
   "metadata": {},
   "source": [
    "Visualizing the count of Listed_in or Genres of movies, top 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "04bc05a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 10 Genres of Movies')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# selecting top 10 frequenct genres of movies\n",
    "df_movies = df[df[\"type\"] == \"Movie\"]\n",
    "df_top_10 = df_movies[df_movies[\"listed_in\"].isin(df_movies[\"listed_in\"].value_counts()[:10].index.to_list()) == True]\n",
    "\n",
    "# plotting the frequency distribution of the genres of the Movies\n",
    "#plt.figure(figsize=(35,10))\n",
    "sns.countplot(y=\"listed_in\", data= df_top_10)\n",
    "plt.title(\"Top 10 Genres of Movies\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "31b9e57d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 10 Genres of TV Shows')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G6F9tPA+kzN5+knoDq0XE4xU7lIKhURHxQ+Bk4CsV+lich0qZ0yBnwvLXKFKQ2p8UzBARN5Eych8AIyTtW8UYBgGfJQXmv27l/P/ub/4FY24hMzsfWDFnwa4gZaJ2AK5h4fmtNN7FZSzQTVIvYHfyms1ykYyPiAtJwWdbrg+koLGRdH1Gk4Kub5ECUiJiNCl4exm4QW18aCb/snAvcATNB8vvAu9J2rRCE61l5ktjns/C/4bmk+ZAwIWFf+ubR8TvSqdupW0B0wvH7hARB+R9XyS9B/sAEyUtDevGzcw6haXhI4VmA6V0wThgD0mbQ1rDJmnLGtvrSvqBCzC4mfOUe5gF2ahBLLqmsdI5XouIuXktXI9W6jcrZwdHkW7hVVrThtK60OLaxgZSZqslo0ljQdI+pFvk70bEi8D6wBYRMYM01tPJQWUOIGbkpQl3A6X1fQ/kjGZz45hLyhjtKmmb5s7fSp9LSgHkLKU1tf2rOKal6/sA+daspBVydq4qOeC9lZSJuyciPiyvI2nj/EtBSQO1XZ8tgU2Ap/Ot/BdJ2cNxpOtSvD49SO+9a4DfkW7nI+l6SbtUO65sOGl5yUb5XJVcCPy6NGeS1pJ0PGmZSc/Sv1XgaODBGs49AjhOC9ZMd5e0IWlejpC0Wr79Xr6UAOBpYAOlB9iQtJLSWuBPAZ+JiJHA94C1gS419MnMzOqwNASVVwN/kTQy3x4bDAzPt5bHkW6L1eLnwIWSxpJurZaMBLbNDzgMKDvmVNItxamkH47fbuUcw4BGSRNIgcFTNfax3HDS+rjmHizoQlpz90Tu47bAkFbaHJL7OJW0FvSYwr5HSbdpIQUr3VkQSA8ApuXbilsD1+cf1psDb7Z0wpz9+gUpCGrp/C2KiLdJ2ckmUib3sSoOa+n6fpt0O72JlPGrZklEUWvXZyXgYqWH0CaT5rC199AVwAq5T7cAg2PBk9djgH9FejJ7DOnW7pi8bx9gsqRJpGzopbl8R+CfNY7rPtKDYZXW8ZZcSZrbx/KSkgeB93NwfSxpqUkTKQN5VbUnjoj7SJntR/Lxt5PWbz5Omo/JwB9YMO7isR+TftH4maQpue7upH/vN+b2JgGX5PeSmZktAWr+Z4lZIml74LiI+E6rlW2Jy1nE30XEkR3dl2XNGv/x2dj66PNar2id0sSLlthHspotUyRNjIhFPoPZ642sVRExjYWfwrelSF5W4IDSzMw61NJw+9vMzMzMlnEOKs3MzMysbr79bWad1jafXo8JXjdnZtYunKk0MzMzs7o5qDQzMzOzujmoNDMzM7O6eU2lmXVaH/9zOi+cv0NHd8OyTc5t6ugumFkdnKk0MzMzs7o5qDQzMzOzujmoNDMzM7O6Oag0MzMzs7o5qDQzMzOzujmoNFtOSVpP0uT89aqklwvbB5bVPU3SFRXa+A9JN0t6VtITku6RtOUSHMNMSevn1w8vqfOamVntHFSaLaci4o2IaIiIBuAq4JL8+kpgYFn1gcDwYoEkAXcCoyJis4jYFjgL2Ghx972SiNi9I85rZmbVcVBp1vncDhwiaRUAST2BjYGHyur1A+ZGxFWlgoiYHBFjlFwkaZqkJkkDclv7SHpQ0q2SnpH0U0mDJI3P9TbL9TaQ9AdJj+WvPXL5epLukzRJ0m8Alc4taU7h9Rn5uKmSzstla0j6s6QpuV8DFsPcmZlZMxxUmnUyEfEGMB44KBcNBG6JiCiruj0wsZlmvgw0AL2A/YGLJHXL+3oB3wZ2AI4GtoyIXYDfAqfkOpeSMqc7A1/J+wB+CDwUETsBdwOblJ9Y0gHAFsAuuQ99JO2Vx/NKRPSKiO2Beyt1XNLxkiZImvDme/OaGZ6ZmdXKQaVZ5zScBbfAF7n1XYW+wPCImBcR/wIeBHbO+x6LiH9GxEfAs8B9ubwJ6Jlf7w9cLmkyKXhcS9KawF7AjQAR8WfgrQrnPiB/TQIeB7YmBZlNwP6SfiZpz4h4p1LHI+LqiGiMiMZ111ihxmGbmVlz/GcazTqnu4BfSuoNrBYRj1eoMx3o38zxaqYc4KPC6/mF7fks+D/nU8BuEfHBQo1KAOUZ00rnvjAifrPIDqkPcDBwoaT7IuL8VtoyM7N24kylWScUEXOAUcC1NJ+l/BuwiqRvlQok7Sxpb2A0MEDSCpI2IGUYx9fQhfuAkwvtNuSXo4FBuewLwDoVjh0BHCepS67XXdKGkjYG3o+IG4GLgd419MfMzOrkTKVZ5zUcuINFnwQHICJC0hHA/0k6E/gQmAmcRgr+dgOmkDKL34uIVyVtXeW5TwV+LWkq6f+h0cCJwHnAcEmPk26pv1ChX/dJ2gZ4JGc25wBfAzYnre2cD8wF/qvKvpiZWTvQomvzzcw6hx27rxZ/OmHzju6GZZuc29TRXTCzKkiaGBGN5eW+/W1mZmZmdXNQaWZmZmZ1c1BpZmZmZnXzgzpm1mmt3G07Njl3Qkd3w8xsueBMpZmZmZnVzUGlmZmZmdXNQaWZmZmZ1c1rKs2s03rqtafY41d7dHQ3zMyWqLGnjF0s7TpTaWZmZmZ1c1BpZmZmZnVzUGlmZmZmdXNQaWZmZmZ1c1BpZmZmZnVzUGlmdZM0p/D6YEl/l7SJpBMlfb1C/Z6SprXS5syy7fUkTc5fr0p6ubD9sKQDy+qfJumKOodmZmZV8kcKmVm7kbQf8CvggIh4AbiqvdqOiDeAhnyeIcCciLg4b58ADARGFA4ZCJzRXuc3M7OWOVNpZu1C0p7ANcAXI+LZXDZE0un5dR9JUyQ9ApxUOG47SeNzxnGqpC3yrtdrOP3twCGSVslt9gQ2Bh6qe2BmZlYVB5Vm1h5WAf4IHB4RTzVT5/fAqRGxW1n5icClEdEANAIvAUTEztWePGcxxwMH5aKBwC0REeV1JR0vaYKkCXPnzK32FGZm1goHlWbWHuYCDwPfqLRTUldg7Yh4MBfdUNj9CHCWpO8DPSLigzb2YTgpmCR/H16pUkRcHRGNEdG4UpeV2ngqMzMr56DSzNrDfOAoYGdJZ1XYL2CRrCFARNwEfAn4ABghad829uEuYD9JvYHVIuLxNrZjZmZt4KDSzNpFRLwPHAIMkvSNsn1vA+9I6puLBpX2SdoUmBERlwF3Azu28fxzgFHAtTSTpTQzs8XHT3+bWbuJiDclHQSMljSrbPexwLWS3mfhp7QHAF+TNBd4FTi/ji4MB+5gwW1wMzNbQlRhHbuZWafQZZMu0euMXh3dDTOzJWrsKWPrOl7SxIhoLC/37W8zMzMzq5uDSjMzMzOrm9dUmlmntfWGW9d9G8jMzBJnKs3MzMysbg4qzczMzKxuDirNzMzMrG4OKs3MzMysbn5Qx8w6rdlPP82De+3d0d2wZdDeox9svZJZJ+NMpZmZmZnVzUGlmZmZmdXNQaWZmZmZ1c1BpZmZmZnVzUGlWRUkzSm8PljS3yVtIulESV+vUL+npGmttDmzbHs9SZPz16uSXi5sPyzpwLL6p0m6ooX2H83HviDp9UJbQyWdUFb3cEn3VGiji6TfSHpW0nRJoyV9rqVxtSdJoyQ15tf3SFp7SZ3bzMxq46e/zWogaT/gV8ABEfECcFV7tR0RbwAN+TxDgDkRcXHePgEYCIwoHDIQOKOF9j6Xjx0MNEbEyXn7QOBM4DdlbQ2v0MxvgeeALSJivqRNgW1qH139IuLgjjivmZlVx5lKsypJ2hO4BvhiRDyby4ZIOj2/7iNpiqRHgJMKx20naXzOEk6VtEXe9XoNp78dOETSKrnNnsDGwENtGMr9wNaSuuW2Vgf2B+4qVpK0GfA54JyImA8QETMi4s95/3ckTctfp5X6JekpSb/N5cMk7S9pbM7u7pLrrSHpWkmPSZok6bBcvpqkm/M83QKsVujPTEnr59dfK8zpbyStkL+G5vM2SfqfNsyNmZm1kYNKs+qsAvwRODwinmqmzu+BUyNit7LyE4FLI6IBaAReAoiInas9ec5ijgcOykUDgVsiIqoewYK25gF3AEfloi8BIyNidlnV7YDJuf5CJPUBjiUFnbsC35K0U969OXApsCOwNfCfQF/gdOCsXOds4G95DvoBF0laA/gv4P2I2BH4CdCnwrm3AQYAe+Q5nQcMImV5u0fE9hGxA+l6LELS8ZImSJrwzty5zc6TmZnVxkGlWXXmAg8D36i0U1JXYO2IKH0i8g2F3Y8AZ0n6PtAjIj5oYx+Gk4JJaP529ZJqqy9wZ0S8FxFzSEHqnnnfcxHRlLOb04EHcvDbBPTMdQ4AzpQ0GRgFrApsAuwF3AgQEVOBqRXOvR8p2HwsH78fsCkwA9hU0q8kHQS8W6njEXF1RDRGRGPXlVaqcdhmZtYcB5Vm1ZlPyuztLOmsCvsFVMwaRsRNpGzgB8AISfu2sQ93AftJ6g2sFhGPt7EdgLFAN0m9gN2BRR7SIQWEvSRV+n9CLbT9UeH1/ML2fBas4xbwlYhoyF+bRMSTeV9r2VcB1xWO3SoihkTEW0AvUpB6Emk9qJmZLSEOKs2qFBHvA4cAgyR9o2zf28A7kvrmokGlffnhlhkRcRlwN+m2cFvOP4cUMF1LWWZRUnO35JtrK4BbgeuAeyLiwwp1ngUmAOdJUj7PFnn942jgcEmr59vWRwBjaujCCOCUQrulW+ejyXMnaXsqz9UDQH9JG+Z660rqkddbfioi/gD8L9C7hv6YmVmdHFSa1SAi3iStazyn9HBJwbHAr/ODOsVb3AOAaflW7dbA9XV0YTgpG3dzqSAHUy1lDqtuq4JvAv8B/ENSE+lBpVdylnQoaZ3no8BvI2JSDef+EbASMFXpo5d+lMuvBLpImgp8L7e/kIh4AjgHuC/X+yvQDegOjMrzPBT4QQ39MTOzOqkN6/zNbCki6RBg05wJtRpsteaacfVOTmha7fYe/WDrlcyWU5ImRkRjebk/p9JsGRcRf+roPpiZmfn2t5mZmZnVzUGlmZmZmdXNt7/NrNNac6utvDbOzKydOFNpZmZmZnVzUGlmZmZmdXNQaWZmZmZ1c1BpZmZmZnXzgzpm1mm99tI7XP7d/9fR3TCr2cm/OLSju2C2CGcqzczMzKxuVWcqJW0JnAH0KB4XEfsuhn6ZmZmZ2TKkltvftwFXAdcA8xZPd8zMzMxsWVRLUPlJRFy52HpiZmZmZsusWtZU/j9J/y2pm6R1S1+LrWe21JA0p4o6p0lafQn0pUHSwYXtL0k6czGcZ07Z9nqSJuevVyW9XNh+WNKBZfVPk3RFhXbPljRd0tR87Ody+UxJ67f3OKoh6c7cl39IeqcwrvskXVhWt0HSkxXaGCXp6TyupyRdLmntJTYIMzPrcLVkKo/J388olAWwaft1x5ZhpwE3Au9Xe4CkFSKi1qUUDUAjcA9ARNwN3F1jGzWLiDfyuZE0BJgTERfn7ROAgcCIwiEDWfjfCpJ2Aw4BekfERzmIXHlx9701EXEEgKR9gNMj4pC8vRXwF+AHheoDgZuaaWpQREyQtDJwIfBHYO9iBUkCFBHz23MMZmbW8arOVEbEZyt8OaDsRCTtkzNSt+ds1DAlpwIbAyMljcx1D5D0iKTHJd0mqUsunynpXEkPAUfm7fNyvSZJW+d6u+QM4KT8fascrJwPDMiZtAGSBku6PB/TQ9IDOVv2gKRNcvlQSZfldmZI6p/Lu+R6pXMf1sapuR04RNIqud2eeT4eKqvXDZgVER8BRMSsiHilsP+UCvOwrqS78pjGSdoxlzdJWjvP/xuSvp7Lb5C0v6TtJI3P8zRV0ha1DioingbeLmVTs6OAm1s57mPge8AmknpJ6inpyZy5fRz4jKQrJU3IWdvzSsfm98MF+b0zQVJvSSMkPSvpxFyn4nWTtIakP0uaImmapAG1jtnMzNqu1aBS0r75+5crfS3+LtpSZidSVnJbUpZ6j4i4DHgF6BcR/XIG7hxg/4joDUwAvlNo48OI6BsRpeBkVq53JXB6LnsK2CsidgLOBS7Iwcq5wC0R0RARt5T17XLg+ojYERgGXFbY1w3oS8oU/rTUD+CIfO5+wC9yJq0mOYs5HjgoFw3MfYyyqveRAqpnJF0hae+y/ZXm4TxgUh7TWcD1uXwssAewHTAD2DOX7wqMA04ELo2IBlJm96Vax5UNz+NB0q7AGxHx99YOyhnoKcDWuWgr0rXZKSKeB86OiEZgR2DvUrCcvRgRuwFjgKFA/zyu8/P+5q7bQcArEdErIrYH7q3UN0nH54B1wpz336l6IszMrGXVZCpLP/gOrfB1yGLqly29xkfES/n25WSgZ4U6u5KCzrGSJpOWTvQo7C8PBu/I3ycW2usK3CZpGnAJKXhqzW4suDV7AymILLkrIuZHxBPARrlMwAWSpgL3A90L+2r17+Arfx9eXiEi5gB9gOOB14FbJA0uVKk0D33zWIiIvwHrSepKCrj2yl9XAjtI6g68mc/zCHCWpO8DPSLigzaO62agv6RPNTeuFhQD9OcjYlxh+yhJjwOTSNd228K+0nKGJuDRiJgdEa8DHyqt02zuujUB+0v6maQ9I6JixBgRV0dEY0Q0dlm9aw3DMTOzlrS6pjIifpi/H9tSPUnHRMR17dUxW2p9VHg9j8rvIQF/jYivNtPGe820WWzvR8DIiDgi304e1Ya+FjOFxX6Xgp1BwAZAn4iYK2kmsGobzgNwF/BLSb2B1SLi8YodShm8UcAoSU2kgHtoWR+L81ApcxrAaOAkYBPgbOAIUkZvTD7PTZIeBb4IjJD0zRyU1iQiXszzsjfwFVLg3ipJKwA7AKWHet4r7PssKRO7c0S8JWkoC897aR7ms/B1m0+al4rXLSKekdQHOBi4UNJ9EXE+Zma2RLTnX9T5dju2Zcue2cCa+fU4YA9JmwNIWl3pw/Nr0RV4Ob8e3Mx5yj3MgmzhIBZd01jpHK/lwKQfC2dTa5Kzg6OAa2kmm5fXhRbXNjYAz7fS9GjSWEoP0syKiHcj4kVgfWCLiJhBGuvp5KBS0qbAjLw04W7SbWbyWsTuNQ5vOClb/GxEtHobXdJKpAd1XoyIqRWqrEUKMt+RtBHwhRr7U/G6SdoYeD8ibgQuBnrX2K6ZmdWhPYPKmtei2XLlauAvkkbmW5WDgeH5FuU4Fqytq9bPSdmmscAKhfKRwLb5AZTyBzFOBY7N5zya1n/RGQY0SppACtyeqrGP5YYDvWj+QZYuwHWSnsh93BYY0kqbQ3Ifp5LWgh5T2Pco8Ex+PYZ0G7gUSA8ApuXlB1sD1+db2JsDb1Y/JCD94YPtaOUBHWBY7uc0YA2g4oNPETGFdNt7OikIH1tjf5q7bjsA4/OYzwZ+XGO7ZmZWBy36LEEbG5IezwvnzWwpJGl74LiI+E6rlTuJTf5ji/jeoF92dDfManbyLw7t6C5YJyZpYn7YciG1fE5lq+dox7bMrJ1FxDQWfgrfzMys3bTn7e9ab2GZmZmZ2XKi1UylpBYzGxHxy/z95PbqlJmZmZktW6q5/V160nYrYGcWfIbcoaQnU83Mlkkbfrqr16aZmbWTaj6n8jwASfeR/mbx7Lw9hPRUqJmZmZl1crWsqdwE+Liw/TGV/5qKmZmZmXUytTz9fQPpM+DuJP1FjyNY8HeIzczMzKwTq+lzKvOfoNszb46OiEmLpVdmZktA9/XWif/+wn4d3Q1bip194+0d3QWzpU5zn1NZ60cKrQ68GxGXAi/lv+FrZmZmZp1c1UGlpB8C3wd+kItWAm5cHJ0yMzMzs2VLLZnKI4AvAe8BRMQrLPi4ITMzMzPrxGoJKj+OtAAzACStsXi6ZGZmZmbLmlqCylsl/QZYW9K3gPuB3y6ebpmZmZnZsqTqjxSKiIslfR54l/TXdc6NiL8utp6ZWYeTNA9oIv1f8RxwdES83YZ2BgONEXGypBOB9yPi+lx+X15OU007dwKfBboAG+Q+AbwGTIyIHxTqNgDDI2KbWvtrZma1q+VBnZ9FxF8j4oyIOD0i/irpZ4uzc2bW4T6IiIaI2B54Ezip3gYj4qqIKH3G7WBg4xqOPSIiGoBvAmNy3xqAU4ABZdUHAjfV218zM6tOLbe/P1+h7Avt1REzW+o9AnQHkLSZpHslTZQ0RtLWufxQSY9KmiTpfkkblTciaYik0yX1BxqBYZImS/pizkSW6n1e0h3VdCwingbelvS5QvFRwM1tH66ZmdWi1aBS0n9JagK2kjS18PUcMHXxd9HMOpqkFYD9gLtz0dXAKRHRBzgduCKXPwTsGhE7kQK67zXXZkTcDkwABuVs4z3ANpI2yFWOBX5fQzeHk7KTSNoVeCMi/l5hLMdLmiBpwnsfflRD82Zm1pJq1lTeBPwFuBA4s1A+OyLeXCy9MrOlxWqSJgM9gYnAXyV1AXYHbpNUqrdK/v5p4BZJ3YCVWbDmsVUREZJuAL4m6ffAbsDXa+jrzcDDkr5LCi6HN3Oeq0lBMd3XW6f6PylmZmYtajVTGRHvRMRM4Bzg1Yh4nrRQ/muS1l683TOzDvZBziL2IAWJJ5H+33i7tJ4xf5UehvkVcHlE7ACcAKxa4/l+D3wN+CpwW0R8Uu2BEfEiMBPYG/gKcGuN5zYzszrUsqbyD8A8SZsDvyMFll4Eb9YJRMQ7wKmkW90fAM9JOhJASa9ctSvwcn59TBVNz6bwRxTyU+CvkH6JHdqGrg4HLgGejYiX2nC8mZm1US1B5fycNfgy8H8R8T9At8XTLTNb2kTEJGAK6dbyIOAbkqYA04HDcrUhpNviY4BZVTQ7FLgqP6izWi4bBrwYEU+0oZu3AdvhB3TMzJa4qj+nEpgr6aukNU6H5rKV2r9LZra0iIguZduHFjYPqlD/j8AfK5QPJWceI2JIofwPpLsgRX2Ba1rp1yhgVIXy1/H/S2ZmHaKWTOWxpIXzP4mI5yR9Frhx8XTLzDojSROBHfH/LWZmy5xa/qLOE6Q1VaXt54CfLo5OmVnnlD+iyMzMlkGtBpWSbo2Io/JnVS7y8RsRseNi6ZmZmZmZLTMU0fLHtEnqFhH/lNSj0v78EUNmZsucxsbGmDBhQkd3w8xsmSJpYkQ0lpe3mqmMiH/m7w4ezczMzKyiam5/z6bCbW9ApD+CsVa798rMzMzMlinVZCrXbK2OmZmZmXVutXxOpZnZcuXDf87myZ/8raO7YR1gm7P37egumC13avmcSjMzMzOzihxUmpmZmVndHFSamZmZWd0cVJqZmZlZ3RxUmpmZmVndHFR2cpKOkBSStq6y/m8lbbsY+nG2pOmSpkqaLOlzNR5/oqSvt3Of5lRR5zRJq7fneZs5T4OkgwvbX5J05mI4z5yy7fXy9Zgs6VVJLxe2H5Z0YFn90yRdUaHditdX0kxJ67f3OMzMbMnzRwrZV4GHgIHAkNYqR8Q327sDknYDDgF6R8RHOchYuYbjV4yIq9q7X1U6DbgReL/aAyStEBHzajxPA9AI3AMQEXcDd9fYRs0i4o18biQNAeZExMV5+wTS+2ZE4ZCBwBnFNuq9vmZmtmxwprITk9QF2AP4BikYKJXvI2mUpNslPSVpmCTlfaMkNebXcyT9TNJESfdL2iXvnyHpS7nOCpIukvRYzlKdUKEr3YBZEfERQETMiohX8vF9JD2YzzFCUrdCPy6Q9CDwbUlDJJ2e920m6d58zJhSFlbSkZKmSZoiaXQN81RxPiSdCmwMjJQ0Mtc9QNIjkh6XdFue41JG7lxJDwFH5u3zcr2mQh93yRnASfn7VpJWBs4HBuQs3wBJgyVdno/pIemBPL8PSNoklw+VdFluZ4ak/qXrnuuVzn1YtXNR5nbgEEmr5HZ75vl4qKxes9c3O6XCPKwr6a48pnGSdszlTZLWzvP/hnJ2WtINkvaXtJ2k8Xmepkraoo1jMzOzGjmo7NwOB+6NiGeANyX1LuzbiZSF2xbYlBR8llsDGBURfYDZwI+BzwNHkIIgSAHrOxGxM7Az8C1Jny1r5z7gM5KekXSFpL0BJK0E/Aron89xLfCTwnFrR8TeEfGLsvauBk7Jx5wOlG7HngscGBG9gC+1PDWLWGQ+IuIy4BWgX0T0yxm4c4D9I6I3MAH4TqGNDyOib0TcnLdn5XpX5n4CPAXsFRE75f5eEBEf59e3RERDRNxS1rfLgesjYkdgGHBZYV83oC8pU/jTUj+AI/K5+wG/KP3SUIucxRwPHJSLBuY+lv9Z14rXt6DSPJwHTMpjOgu4PpePJb0XtwNmAHvm8l2BccCJwKUR0UDK7L5U3m9Jx0uaIGnCm++9XeuwzcysGQ4qO7evAqUA5+a8XTI+Il6KiPnAZKBnheM/Bu7Nr5uAByNibn5dqn8A8HVJk4FHgfWAhbJHETEH6AMcD7wO3CJpMLAVsD3w13z8OcCnC4eWB1el7OvuwG35mN+QAitIAclQSd8CVqgwnpZUMx+7koLOsfncxwA9WujvHfn7xEJ7XXPfpwGXkIKn1uwG3JRf30AKIkvuioj5EfEEsFEuE3CBpKnA/UD3wr5aDWdBlntg3l5IC9e3pNI89M1jISL+BqwnqSswBtgrf10J7CCpO/BmPs8jwFmSvg/0iIgPKvTn6ohojIjGdddYu43DNjOzcl5T2UlJWg/YF9heUpCCrJD0vVzlo0L1eVR+r8wtZKXml46JiPmSSvVFyhqOqHD8v+U1hqOAUZKaSAHZRGB6ROzWzGHvVSj7FPB2zlSVn+NEpQdEvghMltSQs23VqGY+BPw1Ir5aYV+l/pbaLLb3I2BkRByRbyePqrJ/RcVMYbHfpWzkIGADoE9EzJU0E1i1DecBuAv4Zc5yrxYRj1fsUOXrO7Ssj8V5qJQ5DWA0cBKwCXA2KSvenxRsEhE3SXqUdI1HSPpmDkrNzGwxc6ay8+pPumXaIyJ6RsRngOdYOMvVHkYA/5VvZSNpS0lrFCvkdYPF7GUD8DzwNLCB0oMeSFpJUouZu4h4F3hO0pH5GEnqlV9vFhGPRsS5wCzSLdnukh6oY3yzgTXz63HAHpI2z+dbXdKWNbbXFXg5vx7czHnKPcyCbOEgFl3TWOkcr+WAsh8LZ1NrkrODo0hLExbJUkKL17clo0ljQdI+pFvk70bEi8D6wBYRMYM01tPJQaWkTYEZeWnC3cCObRmXmZnVzkFl5/VV4M6ysj8A/9nO5/kt8ATweL6l+xsWzfJ1Aa6T9ES+JbstMCSvJewP/EzSFNJt592rOOcg4Bv5mOlA6UGUi/KDHtNIQcsU0q3xT+oY39XAXySNjIjXSYHg8DyOcUBVH9VU8HPgQkljWfgW/Uhg29KDOmXHnAocm895NPDtVs4xDGiUNIE0V0/V2Mdyw4FeLFhKUa7i9W2lzSG5j1NJa0GPKex7FHgmvx5Dun1fCqQHANPy8oOtWbAW08zMFjMtuqberPOQdDLwQv6IHutktu++Vdz231d2dDesA2xz9r4d3QWzZZakiRHRWF7uNZXWqUXE5R3dBzMzs+WBb3+bmZmZWd2cqTSzTmvVbmv6NqiZWTtxptLMzMzM6uag0szMzMzq5qDSzMzMzOrmoNLMzMzM6uYHdcys03rllVcYMmRIR3fDlgBfZ7PFz5lKMzMzM6ubg0ozMzMzq5uDSjMzMzOrm4NKMzMzM6ubg0ozQ9I8SZMlTZc0RdJ3JC1V/z9IukfS2h3dDzMzq8xPf5sZwAcR0QAgaUPgJqAr8MOO7BSAJAGKiIM7ui9mZta8pSoTYWYdLyJeA44HTlayqqTfS2qSNElSPwBJK0i6OJdPlXRKLp8paf38ulHSqPx675wNnZzbWTOXnyHpsdzGebmsp6QnJV0BPA58pqzdr0kan9v6Te7LCpKGSpqW+/Q/S3jqzMw6NWcqzWwRETEj3/7eEPhaLttB0tbAfZK2BI4FPgvsFBGfSFq3lWZPB06KiLGSugAfSjoA2ALYBRBwt6S9gBeArYBjI+K/AVLCEiRtAwwA9oiIuTnwHARMB7pHxPa53tqVOiHpeFLQTNeuXWufHDMzq8iZSjNrjvL3vsANABHxFPA8sCWwP3BVRHyS973ZSntjgV9KOhVYOx93QP6aRMpIbk0KMgGej4hxFdrZD+gDPCZpct7eFJgBbCrpV5IOAt6t1ImIuDoiGiOicfXVV2+ly2ZmVi1nKs1sEZI2BeYBr7EguFykGhAVyj9hwS+sq5YKI+Knkv4MHAyMk7R/buPCiPhN2fl7Au+1cN7rIuIHFfrdCzgQOAk4CjiumTbMzKydOVNpZguRtAFwFXB5RAQwmnR7mXzbexPgaeA+4ERJK+Z9pdvfM0mZRICvFNrdLCKaIuJnwARSVnIEcFy+HY6k7vlBoZY8APQv1ZO0rqQeeb3lpyLiD8D/Ar3rmAYzM6uRM5VmBrBavpW8EinTeAPwy7zvCuAqSU153+CI+EjSb0m3wadKmgtcA1wOnAf8TtJZwKOFc5yWH/KZBzwB/CW3sw3wSF4zOYe0hnNecx2NiCcknUNa2/kpYC4pM/kB8PvCRyEtksk0M7PFRykRYWbW+Wy88cZx/PHHd3Q3bAkYMmRIR3fBbLkhaWJENJaX+/a3mZmZmdXNQaWZmZmZ1c1BpZmZmZnVzWsqzazTamxsjAkTJnR0N8zMlileU2lmZmZmi42DSjMzMzOrm4NKMzMzM6ubP/zczDqtt956kltv26Wju2Gd0FFHju/oLpi1O2cqzczMzKxuDirNzMzMrG4OKs3MzMysbg4qzczMzKxuDirNzMzMrG7LTVAp6T8k3SzpWUlPSLpH0pbN1H24nc99oKTJ+WuOpKfz69skvSGpa1n9uyQdVVa2uqRhkpokTZP0kKQuknpKmtae/a2VpDlV1DlN0upLoC8Nkg4ubH9J0pmL4TxzyrbXK1zjVyW9XNh+WNKBZfVPk3RFhXbPljRd0tR87Ody+UxJ67f3OKoh6c7cl39IeqcwrvskXVhWt0HSkxXaGJXf91MlPSXpcklrL7FBmJlZh1sugkpJAu4ERkXEZhGxLXAWsFFZvRUAImL39jx/RIyIiIaIaAAmAIPy9pHAfcDhhT50BfoCfypr5tvAvyJih4jYHvgGMLc9+7mYnQbUFFSWrkeNGoB/B5URcXdE/LQN7dQkIt4oXOOrgEsK29cBA8sOGQgMLxZI2g04BOgdETsC+wMvLu6+tyYijsjj+CYwpjCuU4ABZdUHAjc109SgPK4dgY+AP5ZXULJc/L9jZmYLW17+c+8HzI2Iq0oFETE5IsZI2kfSSEk3AU2wIAuV9z0o6VZJz0j6qaRBksbnjOFmud4Gkv4g6bH8tUcNfRvOwgHHEcC9EfF+Wb1uwMuF/j8dER/lzRUkXZMzXPdJWi33q0HSuJwdulPSOpI2lDQx7+8lKSRtkrefzRnRI3M2dIqk0dUOJM/XKEm352zUsBwknApsDIyUNDLXPUDSI5IezxnbLrl8pqRzJT0EHJm3z8v1miRtnevtkjOAk/L3rSStDJwPDMiZtAGSBku6PB/TQ9IDeT4eKIx7qKTLcjszJPXP5V1yvdK5D6t2LsrcDhwiaZXcbs88Hw+V1esGzCpd14iYFRGvFPafUmEe1lXKbE/N13rHXN4kae08/29I+nouv0HS/pK2y+/jyfnYLWodVEQ8DbytnE3NjgJubuW4j4HvAZvk92BPSU8qZW4fBz4j6UpJE/J7+rzSsfn9cEF+70yQ1FvSiPzePTHXqXjdJK0h6c/5fT1NUnlAbGZmi9HyElRuD0xsYf8uwNk5g1muFylLuANwNLBlROwC/JaUqQG4lJSZ2hn4St5XrXuBPpLWy9uLZLCya4Hv5x+mPy4LArYAfh0R2wFv5z4AXA98P2eHmoAfRsRrwKqS1gL2JGVO95TUA3gtB7PnAgdGRC/gSzWMBWAnUlZyW2BTYI+IuAx4BegXEf2UbuOeA+wfEb1zH75TaOPDiOgbEaXgZFaudyVwei57CtgrInbK/b0gByvnArfkbNotZX27HLg+z8cw4LLCvm6kDPEhQCmz+SFwRD53P+AXklTjfBARbwDjgYNy0cDcxyireh8poHpG0hWS9i7bX2kezgMm5TGdRbrmAGOBPYDtgBmkaw2wKzAOOBG4NGccG4GXah1X9u9fiiTtCrwREX9v7aCImAdMAbbORVuRrs1OEfE86d9jIymruXcpWM5ejIjdgDHAUKB/Htf5eX9z1+0g4JWI6JWz/fdW6puk43PAOuHddz+peiLMzKxly0tQ2ZrxEfFcM/sei4h/5uzRs6Qf/JCCtJ759f7A5ZImA3cDa0las5oT50DobqB/DrYaCuco1ptMCtIuAtYFHpO0Td79XN4PKXjuqXQbfe2IeDCXXwfslV8/TAo49gIuyN/3JP2QhhSQDJX0LaDWW9DjI+KliJgPTGbBHBXtSgo6x+Y5OwboUdhfHgzeURxbft0VuE1pPeklpOCpNbux4NbsDaQgsuSuiJgfEU+wYFmEgAskTQXuB7pTtmSiBsWMdMVfHCJiDtAHOB54HbhF0uBClUrz0DePhYj4G7BevvZjSNd1L1IQuoOk7sCb+TyPAGdJ+j7QIyI+aOO4bia9dz/V3LhaUAzQn4+IcYXtoyQ9DkwiXdviL3x35+9NwKMRMTsiXgc+VFqn2dx1awL2l/QzSXtGxDuVOhURV0dEY0Q0rrWW/6iYmVl7WV6CyumkH9bNea+FfR8VXs8vbM9nwZ+x/BSwW2mtWUR0j4jZNfSvFHD0B/4YERXXSkbEnIi4IyL+G7iRBWsHi32cR+t/XnMMKYjsQVrX1osUnIzO5zmRlEn8DDC5kEWtRjV9EfDXwnxtGxHfKOwvvx6lNovt/QgYmTNOhwKr1tDHkmKmsNjvUrAzCNgA6JMzev9q43kA7gL2k9QbWC0iHq/YoYh5ETEqIn4InMyCrHOxj8V5qJQ5DdK13DN/jSIFqf3JvzhExE2kLPQHwAhJ+7ZlUBHxIjAT2Dv39dZqjlNaL7sDUHqo573Cvs+SMrH75Qzsn1l43ov/Bsv/fa5IM9ctIp4h/T/QBFwo6dxqx2lmZvVbXoLKvwGr5MwbAJJ2rnB7sa3uIwUApbYbajx+JOkW9kk0k+mRtIekdfLrlUmZm+ebazBnYd6SVLrteTRQylqOBr4G/D1nFN8kBahjc/ubRcSjEXEuMIt0S7a7pAdqHFfRbKCUvR0H7CFp83y+1dXMk/gt6MqCNaaDmzlPuYdZkC0cxKJrGiud47WImCupHwtnU2uSs4OjSMsYmrvGW5Uta2ighWucjSaNBUn7kG6Rv5uDvfWBLSJiBmmsp5ODSkmbAjPy0oS7SbeZyWsRu9c4vOGkbPGzEdHqbXRJKwEXkm5jT61QZS1SkPmOpI2AL9TYn4rXTdLGwPsRcSNwMdC7xnbNzKwOy0VQmdeuHQF8Pi/onw4MIa3zaw+nAo35gYcnSOvVaunffOAPwHrkbGEFmwEPSmoi3RKckI9pyTHARfk2YAN5zVlEzMz7S+d6CHg7It7K2xflBxym5TpTSGsO61lgdjXwF0kj863KwcDw3LdxLFhbV62fk7JNY1n4Fv1IYNv8AEr5gxinAsfmcx5NWivbkmGk6zqBFLg9VWMfyw0nZYWbe5ClC3Cd0kdeTSX94jCklTaH5D5OJa0FPaaw71Hgmfx6DOk2cCmQHgBMy8sPtgauz7ewNyf9klGL20i3qFt8QAcYlvs5DVgDqPjgU0RMIb3Hp5OC8LE19qe567YDMD6P+WzgxzW2a2ZmddCizxJYZyTpZOCFiLi71cq2TJK0PXBcRHyn1cqdxGabrREX/rSa5bpm7euoI8d3dBfM2kzSxPyw5UK8St0AiIjLO7oPtnhFxDQWfgrfzMys3SwXt7/NzMzMrGM5qDQzMzOzuvn2t5l1Wuuss43XtpmZtRNnKs3MzMysbg4qzczMzKxuDirNzMzMrG5eU2lmndYTb71Lr9tHdHQ3rINN6X9gR3fBbLngTKWZmZmZ1c1BpZmZmZnVzUGlmZmZmdXNQaWZmZmZ1c1BpZmZmZnVbakIKiXNqaLOaZJWXwJ9aZB0cGH7S5LOXAznmVO2vZ6kyfnrVUkvF7YflnRgWf3TJF1Rod2zJU2XNDUf+7lcPlPS+u09jmpJGiXpaUlTJD0mqaGD+tHm6yvp0TynL0h6vXB9hko6oazu4ZLuqdDGcZKa8vWZJumwXD5KUmO942sLSb/O43hC0geFcZ0uaXhZ3fXz2FcpKx8q6bl8fZ+RdL2k7kt2JGZm1pGWpY8UOg24EXi/2gMkrRAR82o8TwPQCNwDEBF3A3fX2EbNIuKNfG4kDQHmRMTFefsEYCBQ/OyTgcAZxTYk7QYcAvSOiI9yELny4u57DQZFxARJxwIXAZ/vgD400MbrGxGlAH0w0BgRJ+ftA4Ezgd8Uqg8EygOyTwNnk67PO5K6ABvUMZZ2EREnAUjqCfwpIhry9lrADEmrR0Tp311/4O6I+KhCU2dExO2SRPr3OlLS9hHxcbFSG/9dmpnZUm6pyFSWSNonZ2xul/SUpGFKTgU2Jv2QGpnrHiDpEUmPS7ot/4AuZeTOlfQQcGTePi/Xa5K0da63S84ATsrft5K0MnA+MCBnagZIGizp8nxMD0kP5CzTA5I2yeVDJV2W25khqX8u75Lrlc59WBun5nbgkFJ2KP/w3xh4qKxeN2BW6Qd+RMyKiFcK+0+pMA/rSrorj2mcpB1zeZOktfP8vyHp67n8Bkn7S9pO0vg8T1MlbVHDeB4Burdy/iGSrpN0X76GX5b089yveyWtlOudq5T5nCbp6hzQlDJ/P8t9fEbSnlVc340k3amUbZsiafcqx3M/sLWkbrmd1YH9gbvK6m0IzAbmAETEnIh4rrD/yGJ/c1urSvp9HvckSf1y+T2FuZok6dz8+keSvimpm6TReZzTSu3VIiLeBUYDhxaKFwmWKxwXEXEJ8CrwhdyvOZLOl/QosFsr1+2S3PcnJe0s6Q5Jf5f049I58ntmolJW/vhctkL+tzgtz9f/1DpmMzNru6UqqMx2ImU5tgU2BfaIiMuAV4B+EdFPKQN3DrB/RPQGJgDfKbTxYUT0jYib8/asXO9K4PRc9hSwV0TsBJwLXJAzKucCt0REQ0TcUta3y4HrI2JHYBhwWWFfN6AvKVP401I/gCPyufsBvyj98KxFzmKOBw7KRQNzH6Os6n3AZ3JQcoWkvcv2V5qH84BJeUxnAdfn8rHAHsB2wAygFJTsCowDTgQuzVmtRuClGoZ0EAsCrubOD7AZ8EXgMFKWemRE7AB8kMsBLo+InSNie2A10vyXrBgRu5DeTz+s4vpeBjwYEb2A3sD0agaTs253AEfloi/lvs4uqzoF+BfwXA4UDy3bv1B/c9lJ+Rw7AF8FrpO0KinY21Mpm/gJ6VpBeg+OAf4TGJGvTy9gcjVjqWA46f2GpI2BLYGRVR77OLB1fr0GMC0iPhcRD9Hydfs4IvYCrgL+SJqD7YHBktbLdY6LiD6k996pubwB6B4R2+f5+n2lTkk6XtIESRM+efedKodiZmatWRqDyvER8VJEzCf9IOxZoc6upKBzrKTJwDFAj8L+8mDhjvx9YqG9rsBtkqYBl5CCp9bsBtyUX99A+gFecldEzI+IJ4CNcpmACyRNJWWzuhf21erfP9xpJlsUEXOAPsDxwOvALUq3aksqzUPfPBYi4m/AepK6kgKTvfLXlcAOSmvk3szneQQ4S9L3gR4R8UEVYxgm6SXg+8CvWjk/wF8iYi7QBKwA3JvLmwr976e01rEJ2JeFr2Ol8bZk3zxWImJeRNQScVRzfeaRAur+wDPAJUpLHVrqb3F+ngKeJwV2pevTF/gz0CVnSHtGxNPAY8Cxuf0dKgS41foT0DcHr0cBt9dw67r4C9Q84A+F7ZauW2k5QhMwPSL+mbPvM4DP5H2nSppC+gXnM8AWef+mkn4l6SDg3UqdioirI6IxIhpXXKtrpSpmZtYGS2NQWVyrNY/K6z4F/DVnmxoiYtuI+EZh/3vNtFls70ekbNL2pNt7q7ahr8VMYbHfpR+mg0hr5vrkjNG/2ngeSJm9/ST1BlaLiMcrdigFQ6Mi4ofAycBXKvSxOA+VMqdBzoTlr1GkILU/KZghIm4iZeQ+AEZI2reKMQwCPksKzH/dyvn/3d/8C8bcQmZ2PrBizthdAfTPmalrWHh+K413cRkLdJPUC9idvGazXL41PD4iLiQFn225PpCCxkbS9RkNTAK+RQpIiYjRpKDzZeAG5eULtcq/LNwLHEEVt77L7AQ8mV9/WApGa7hu81n431Xpuu9DWl6wW84qTwJWjYi3SFnZUaTs5m9r6KuZmdVpaQwqmzMbWDO/HgfsIWlzSGvYJG1ZY3tdST9wAQY3c55yD7MgGzWIRdc0VjrHaxExN6+F69FK/Wbl7OAo4Fqa+cGutC60uLaxgZTZaslo0ljIP6xnRcS7EfEisD6wRUTMII31dHJQKWlTYEZemnA3UFrf94BaeOo3Zx7PAXaVtE1z52+lzyWlQGSW0pra/lUc09L1fQD4r9yXFXJ2rio54L0VuA64JyI+LK8jaeP8S0FJA7Vdny2BTYCn8638F0nZw3Gk61K8Pj1I771rgN+Rbuej9FT2LtWOKxtOWl6yUT5Xi5ScSloScm+FKm25bkVdgbci4n2ltcG75vOuD3wqIv4A/C95zGZmtmQsS0Hl1cBfJI2MiNdJgeDwfGt5HAvWblXr58CFksaSbq2WjAS2zQ84DCg75lTSLcWpwNHAt1s5xzCgUdIEUmDwVI19LDeclIm5uZn9XUhr7p7IfdwWGNJKm0NyH6eS1oIeU9j3KOk2LaRgpTsLAukBwLS8/GBr4HpJnwI2B95s6YQ5+/ULUhDU0vlbFBFvk7JcTaRM7mNVHNbS9f026bZsEynjV82SiKLWrs9KwMVKD6FNJs1ha++hK4AVcp9uAQbHgievxwD/ivRk9hjg0/k7wD7AZEmTSNnQS3P5jsA/axzXfaQHwyqt4y26KN+SfgbYmbQG+uPySm28bkX3kjKWU0l3HEqBbndgVJ7bocAPamzXzMzqoJZ/RphVT9L2pAcovtNqZVvicub1dxFxZEf3ZWmx+mZbxhY/+1XrFW25NqX/ga1XMrN/kzQxIhb5bOVl6XMqbSkXEdNY+Cl8W4rkZQUOKM3MbLFYlm5/m5mZmdlSykGlmZmZmdXNt7/NrNPadp21mOD1dGZm7cKZSjMzMzOrm5/+NrNOS9Js4OmO7sdSbH1gVkd3YinnOWqZ56d1y+Ic9YiIDcoLffvbzDqzpyt9LIYlkiZ4flrmOWqZ56d1y9Mc+fa3mZmZmdXNQaWZmZmZ1c1BpZl1Zld3dAeWcp6f1nmOWub5ad1yM0d+UMfMzMzM6uZMpZmZmZnVzUGlmZmZmdXNQaWZdTqSDpL0tKR/SDqzo/uztJA0U1KTpMmSJuSydSX9VdLf8/d1OrqfS4qkayW9JmlaoazZ+ZD0g/yeelpSp/hTTc3M0RBJL+f30WRJBxf2dao5kvQZSSMlPSlpuqRv5/Ll8n3koNLMOhVJKwC/Br4AbAt8VdK2HdurpUq/iGgofG7emcADEbEF8EDe7iyGAgeVlVWcj/weGghsl4+5Ir/XlndDWXSOAC7J76OGiLgHOu0cfQJ8NyK2AXYFTsrzsFy+jxxUmllnswvwj4iYEREfAzcDh3Vwn5ZmhwHX5dfXAYd3XFeWrIgYDbxZVtzcfBwG3BwRH0XEc8A/SO+15Vozc9ScTjdHEfHPiHg8v54NPAl0Zzl9HzmoNLPOpjvwYmH7pVxmEMB9kiZKOj6XbRQR/4T0AxLYsMN6t3Robj78vlrYyZKm5tvjpVu7nXqOJPUEdgIeZTl9HzmoNLPORhXK/NlqyR4R0Zu0NOAkSXt1dIeWIX5fLXAlsBnQAPwT+EUu77RzJKkL8AfgtIh4t6WqFcqWmTlyUGlmnc1LwGcK258GXumgvixVIuKV/P014E7Sbbd/SeoGkL+/1nE9XCo0Nx9+X2UR8a+ImBcR84FrWHD7tlPOkaSVSAHlsIi4Ixcvl+8jB5Vm1tk8Bmwh6bOSViYtir+7g/vU4SStIWnN0mvgAGAaaW6OydWOAf7YMT1cajQ3H3cDAyWtIumzwBbA+A7oX4crBUvZEaT3EXTCOZIk4HfAkxHxy8Ku5fJ9tGJHd8DMbEmKiE8knQyMAFYAro2I6R3craXBRsCd6WcgKwI3RcS9kh4DbpX0DeAF4MgO7OMSJWk4sA+wvqSXgB8CP6XCfETEdEm3Ak+Qnvg9KSLmdUjHl6Bm5mgfSQ2k27YzgROg087RHsDRQJOkybnsLJbT95H/TKOZmZmZ1c23v83MzMysbg4qzczMzKxuDirNzMzMrG4OKs3MzMysbg4qzczMzKxuDirNzMyWYZJOk7R6R/fDzB8pZGZmtgyTNBNojIhZHd0X69ycqTQzM1vMJH1d0lRJUyTdIKmHpAdy2QOSNsn1hkrqXzhuTv6+j6RRkm6X9JSkYUpOBTYGRkoa2TGjM0v8F3XMzMwWI0nbAWcDe0TELEnrAtcB10fEdZKOAy4DDm+lqZ2A7Uh/C3psbu8ySd8B+jlTaR3NmUozM7PFa1/g9lLQFxFvArsBN+X9NwB9q2hnfES8FBHzgclAz/bvqlnbOag0MzNbvET6O9gtKe3/hPyzWekPsa9cqPNR4fU8fLfRljIOKs3MzBavB4CjJK0HkG9/PwwMzPsHAQ/l1zOBPvn1YcBKVbQ/G1izvTpr1lb+LcfMzGwxiojpkn4CPChpHjAJOBW4VtIZwOvAsbn6NcAfJY0nBaPvVXGKq4G/SPpnRPRr/xGYVccfKWRmZmZmdfPtbzMzMzOrm4NKMzMzM6ubg0ozMzMzq5uDSjMzMzOrm4NKMzMzM6ubg0ozMzMzq5uDSjMzMzOr2/8HaPMVf7vCp2MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# selecting top 10 frequenct genres of tv shows\n",
    "df_tv = df[df[\"type\"] == \"TV Show\"]\n",
    "top_10 = df_tv[df_tv[\"listed_in\"].isin(df_tv[\"listed_in\"].value_counts()[:10].index.to_list()) == True]\n",
    "\n",
    "# plotting the frequency distribution of the genres of the tv shows\n",
    "#plt.figure(figsize=(35,10))\n",
    "sns.countplot(y=\"listed_in\", data= top_10)\n",
    "plt.title(\"Top 10 Genres of TV Shows\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14926c37",
   "metadata": {},
   "source": [
    "### MAP REPRESENTATION OF THE COUNTRY WISE RELEASE OF MOVIES AND TV SHOWS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4ef9bbf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preparing the data to present it into a map\n",
    "def countryWiseMoviesAndTvCounts(df):\n",
    "    clean_df = df[df[\"country\"] != \"UNKNOWN\"]\n",
    "    movie = []\n",
    "    tv = []\n",
    "    map_df = pd.DataFrame()\n",
    "\n",
    "    unique_countries = clean_df[\"country\"].value_counts()\n",
    "\n",
    "    # counting the country wise movies and tv shows\n",
    "    for country in unique_countries.index.to_list():\n",
    "        MovieAndTv = clean_df[clean_df[\"country\"] == country][\"type\"].value_counts()\n",
    "        if \"Movie\" in MovieAndTv.index.to_list():\n",
    "            movie.append(MovieAndTv[\"Movie\"])\n",
    "        else:\n",
    "            movie.append(0)\n",
    "        if \"TV Show\" in MovieAndTv.index.to_list():\n",
    "            tv.append(MovieAndTv[\"TV Show\"])\n",
    "        else:\n",
    "            tv.append(0)\n",
    "    # updating the results into the dataframe\n",
    "    map_df[\"Movie\"] = movie\n",
    "    map_df[\"TV Show\"] = tv\n",
    "    map_df[\"country\"] = unique_countries.index.to_list()\n",
    "    \n",
    "    return map_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calling the function\n",
    "map_df = countryWiseMoviesAndTvCounts(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "45ed31b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# droping those which have been released in multiple countries\n",
    "map_df = map_df[map_df[\"country\"].apply(lambda x: \",\" not in x)] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to create the interactive map representation \n",
    "def create_map(type, color):\n",
    "    data = dict(type = \"choropleth\", \n",
    "    locations = map_df[\"country\"], \n",
    "    locationmode = 'country names', \n",
    "    z = map_df[type],\n",
    "    colorscale=color,\n",
    "    colorbar = {'title' : 'colorbar'}\n",
    "    )\n",
    "\n",
    "    layout = dict(geo = {'scope':'world','showlakes':True})\n",
    "    # creating the map figure\n",
    "    x = pg.Figure(data = [data] , layout = layout)\n",
    "    x.update_layout(\n",
    "            title_text = f\"Number of {type} by Country\",     \n",
    "            font=dict(\n",
    "                family=\"Courier New, monospace\",\n",
    "                size=18,\n",
    "                color=\"RebeccaPurple\"\n",
    "            )\n",
    "    )\n",
    "    po.iplot(x)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deb89dce",
   "metadata": {},
   "source": [
    "#### Number of Movies by Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "0ce98999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "colorbar": {
          "title": {
           "text": "colorbar"
          }
         },
         "colorscale": [
          [
           0,
           "rgb(255,245,235)"
          ],
          [
           0.125,
           "rgb(254,230,206)"
          ],
          [
           0.25,
           "rgb(253,208,162)"
          ],
          [
           0.375,
           "rgb(253,174,107)"
          ],
          [
           0.5,
           "rgb(253,141,60)"
          ],
          [
           0.625,
           "rgb(241,105,19)"
          ],
          [
           0.75,
           "rgb(217,72,1)"
          ],
          [
           0.875,
           "rgb(166,54,3)"
          ],
          [
           1,
           "rgb(127,39,4)"
          ]
         ],
         "locationmode": "country names",
         "locations": [
          "United States",
          "India",
          "United Kingdom",
          "Japan",
          "South Korea",
          "Canada",
          "Spain",
          "France",
          "Mexico",
          "Egypt",
          "Turkey",
          "Nigeria",
          "Australia",
          "Taiwan",
          "Indonesia",
          "Brazil",
          "Philippines",
          "Germany",
          "China",
          "Thailand",
          "Argentina",
          "Hong Kong",
          "Italy",
          "Colombia",
          "South Africa",
          "Poland",
          "Singapore",
          "Malaysia",
          "Netherlands",
          "Pakistan",
          "Lebanon",
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          "New Zealand",
          "Norway",
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          "Belgium",
          "Saudi Arabia",
          "Vietnam",
          "Romania",
          "Kuwait",
          "Austria",
          "Iceland",
          "Peru",
          "Hungary",
          "Uruguay",
          "Ghana",
          "Kenya",
          "Bangladesh",
          "Ukraine",
          "Switzerland",
          "Jordan",
          "Venezuela",
          "West Germany",
          "Guatemala",
          "Senegal",
          "Belarus",
          "Mozambique",
          "Namibia",
          "Luxembourg",
          "Finland",
          "Cameroon",
          "Mauritius",
          "Zimbabwe",
          "Croatia",
          "Bulgaria",
          "Georgia",
          "Cambodia",
          "Cyprus"
         ],
         "type": "choropleth",
         "z": [
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       ],
       "layout": {
        "font": {
         "color": "RebeccaPurple",
         "family": "Courier New, monospace",
         "size": 18
        },
        "geo": {
         "scope": "world",
         "showlakes": true
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
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              "#7201a8"
             ],
             [
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              "#9c179e"
             ],
             [
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              "#bd3786"
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             [
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              "#d8576b"
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             [
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              "#ed7953"
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   "source": [
    "#### Number of TV Shows by Country"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_map(\"TV Show\",\"Reds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b33eb6ea",
   "metadata": {},
   "source": [
    "##### `THANKYOU FOR LOOKING AT MY NOTEBOOK, I HOPE YOU LEARNT A LOT FROM MY WORK`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "944764df",
   "metadata": {},
   "source": [
    "##### `HAPPY LEARNING`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf231719",
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