Towards_Democratizing_Rational_Data_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "adb92766",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import bokeh as beh\n",
    "import os\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "87c9ddb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 7789 entries, s1 to s7787\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Category      7789 non-null   object\n",
      " 1   Title         7789 non-null   object\n",
      " 2   Director      5401 non-null   object\n",
      " 3   Cast          7071 non-null   object\n",
      " 4   Country       7282 non-null   object\n",
      " 5   Release_Date  7779 non-null   object\n",
      " 6   Rating        7782 non-null   object\n",
      " 7   Duration      7789 non-null   object\n",
      " 8   Type          7789 non-null   object\n",
      " 9   Description   7789 non-null   object\n",
      "dtypes: object(10)\n",
      "memory usage: 669.4+ KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Category           0\n",
       "Title              0\n",
       "Director        2388\n",
       "Cast             718\n",
       "Country          507\n",
       "Release_Date      10\n",
       "Rating             7\n",
       "Duration           0\n",
       "Type               0\n",
       "Description        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Netflix Dataset.csv', index_col=[0])\n",
    "print(df.info())\n",
    "null = df.isnull().sum()\n",
    "null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9d86409f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77890\n",
      "(7789, 10)\n"
     ]
    }
   ],
   "source": [
    "print(df.size)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "acf2c83f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category        object\n",
       "Title           object\n",
       "Director        object\n",
       "Cast            object\n",
       "Country         object\n",
       "Release_Date    object\n",
       "Rating          object\n",
       "Duration        object\n",
       "Type            object\n",
       "Description     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a8e73c32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Category</th>\n",
       "      <th>Title</th>\n",
       "      <th>Director</th>\n",
       "      <th>Cast</th>\n",
       "      <th>Country</th>\n",
       "      <th>Release_Date</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Type</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Show_Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>s1</th>\n",
       "      <td>TV Show</td>\n",
       "      <td>3%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>João Miguel, Bianca Comparato, Michel Gomes, R...</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>August 14, 2020</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>4 Seasons</td>\n",
       "      <td>International TV Shows, TV Dramas, TV Sci-Fi &amp;...</td>\n",
       "      <td>In a future where the elite inhabit an island ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s2</th>\n",
       "      <td>Movie</td>\n",
       "      <td>07:19</td>\n",
       "      <td>Jorge Michel Grau</td>\n",
       "      <td>Demián Bichir, Héctor Bonilla, Oscar Serrano, ...</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>December 23, 2016</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>93 min</td>\n",
       "      <td>Dramas, International Movies</td>\n",
       "      <td>After a devastating earthquake hits Mexico Cit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s3</th>\n",
       "      <td>Movie</td>\n",
       "      <td>23:59</td>\n",
       "      <td>Gilbert Chan</td>\n",
       "      <td>Tedd Chan, Stella Chung, Henley Hii, Lawrence ...</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>December 20, 2018</td>\n",
       "      <td>R</td>\n",
       "      <td>78 min</td>\n",
       "      <td>Horror Movies, International Movies</td>\n",
       "      <td>When an army recruit is found dead, his fellow...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s4</th>\n",
       "      <td>Movie</td>\n",
       "      <td>9</td>\n",
       "      <td>Shane Acker</td>\n",
       "      <td>Elijah Wood, John C. Reilly, Jennifer Connelly...</td>\n",
       "      <td>United States</td>\n",
       "      <td>November 16, 2017</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>80 min</td>\n",
       "      <td>Action &amp; Adventure, Independent Movies, Sci-Fi...</td>\n",
       "      <td>In a postapocalyptic world, rag-doll robots hi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s5</th>\n",
       "      <td>Movie</td>\n",
       "      <td>21</td>\n",
       "      <td>Robert Luketic</td>\n",
       "      <td>Jim Sturgess, Kevin Spacey, Kate Bosworth, Aar...</td>\n",
       "      <td>United States</td>\n",
       "      <td>January 1, 2020</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>123 min</td>\n",
       "      <td>Dramas</td>\n",
       "      <td>A brilliant group of students become card-coun...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Category  Title           Director  \\\n",
       "Show_Id                                      \n",
       "s1       TV Show     3%                NaN   \n",
       "s2         Movie  07:19  Jorge Michel Grau   \n",
       "s3         Movie  23:59       Gilbert Chan   \n",
       "s4         Movie      9        Shane Acker   \n",
       "s5         Movie     21     Robert Luketic   \n",
       "\n",
       "                                                      Cast        Country  \\\n",
       "Show_Id                                                                     \n",
       "s1       João Miguel, Bianca Comparato, Michel Gomes, R...         Brazil   \n",
       "s2       Demián Bichir, Héctor Bonilla, Oscar Serrano, ...         Mexico   \n",
       "s3       Tedd Chan, Stella Chung, Henley Hii, Lawrence ...      Singapore   \n",
       "s4       Elijah Wood, John C. Reilly, Jennifer Connelly...  United States   \n",
       "s5       Jim Sturgess, Kevin Spacey, Kate Bosworth, Aar...  United States   \n",
       "\n",
       "              Release_Date Rating   Duration  \\\n",
       "Show_Id                                        \n",
       "s1         August 14, 2020  TV-MA  4 Seasons   \n",
       "s2       December 23, 2016  TV-MA     93 min   \n",
       "s3       December 20, 2018      R     78 min   \n",
       "s4       November 16, 2017  PG-13     80 min   \n",
       "s5         January 1, 2020  PG-13    123 min   \n",
       "\n",
       "                                                      Type  \\\n",
       "Show_Id                                                      \n",
       "s1       International TV Shows, TV Dramas, TV Sci-Fi &...   \n",
       "s2                            Dramas, International Movies   \n",
       "s3                     Horror Movies, International Movies   \n",
       "s4       Action & Adventure, Independent Movies, Sci-Fi...   \n",
       "s5                                                  Dramas   \n",
       "\n",
       "                                               Description  \n",
       "Show_Id                                                     \n",
       "s1       In a future where the elite inhabit an island ...  \n",
       "s2       After a devastating earthquake hits Mexico Cit...  \n",
       "s3       When an army recruit is found dead, his fellow...  \n",
       "s4       In a postapocalyptic world, rag-doll robots hi...  \n",
       "s5       A brilliant group of students become card-coun...  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3e82726d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Title'].is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "390f999e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Category</th>\n",
       "      <th>Title</th>\n",
       "      <th>Director</th>\n",
       "      <th>Cast</th>\n",
       "      <th>Country</th>\n",
       "      <th>Release_Date</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Type</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Show_Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>s684</th>\n",
       "      <td>Movie</td>\n",
       "      <td>Backfire</td>\n",
       "      <td>Dave Patten</td>\n",
       "      <td>Black Deniro, Byron \"Squally\" Vinson, Dominic ...</td>\n",
       "      <td>United States</td>\n",
       "      <td>April 5, 2019</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>97 min</td>\n",
       "      <td>Dramas, Independent Movies, Thrillers</td>\n",
       "      <td>When two would-be robbers accidentally kill a ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s6621</th>\n",
       "      <td>Movie</td>\n",
       "      <td>The Lost Okoroshi</td>\n",
       "      <td>Abba T. Makama</td>\n",
       "      <td>Seun Ajayi, Judith Audu, Tope Tedela, Ifu Enna...</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>September 4, 2020</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>94 min</td>\n",
       "      <td>Comedies, Dramas, Independent Movies</td>\n",
       "      <td>A disillusioned security guard transforms into...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Category              Title        Director  \\\n",
       "Show_Id                                               \n",
       "s684       Movie           Backfire     Dave Patten   \n",
       "s6621      Movie  The Lost Okoroshi  Abba T. Makama   \n",
       "\n",
       "                                                      Cast        Country  \\\n",
       "Show_Id                                                                     \n",
       "s684     Black Deniro, Byron \"Squally\" Vinson, Dominic ...  United States   \n",
       "s6621    Seun Ajayi, Judith Audu, Tope Tedela, Ifu Enna...        Nigeria   \n",
       "\n",
       "              Release_Date Rating Duration  \\\n",
       "Show_Id                                      \n",
       "s684         April 5, 2019  TV-MA   97 min   \n",
       "s6621    September 4, 2020  TV-MA   94 min   \n",
       "\n",
       "                                          Type  \\\n",
       "Show_Id                                          \n",
       "s684     Dramas, Independent Movies, Thrillers   \n",
       "s6621     Comedies, Dramas, Independent Movies   \n",
       "\n",
       "                                               Description  \n",
       "Show_Id                                                     \n",
       "s684     When two would-be robbers accidentally kill a ...  \n",
       "s6621    A disillusioned security guard transforms into...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "95f8a545",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47353bf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = open(\"Example.csv\", \"r\") #alternate for duplicate values\n",
    "lines = reader.read().split(\"\\n\")\n",
    "reader.close()\n",
    " \n",
    "writer = open(\"Example_new.csv\", \"w\")\n",
    "for line in set(lines):\n",
    "    writer.write(line + \"\\n\")\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6849044f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7787, 10)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "41404a0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category           0\n",
       "Title              0\n",
       "Director        2388\n",
       "Cast             718\n",
       "Country          507\n",
       "Release_Date      10\n",
       "Rating             7\n",
       "Duration           0\n",
       "Type               0\n",
       "Description        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "31448dfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Show_Id'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wqKQ3AC+RSlXelK/fH7hE0pEkgfbuRpPo94jhIAi6E/eAOqjSnQApUnhzSUdI2th2MctnfdqIW4CVba8DHE0qHYntu4AjgMuAi4HJJP0/pLiDA2yvCBxACiqbg4gYDoKgI/TATqBr0kbYftb28/n7RcC8kpbMx8fbXs/2e4EngVqiuM+SoowBziTZDYIgCLqDmSP4dIhuShvxRlImUEvakCSgnsjnlrb9WE4t8RHgXfm2h4H3AVeS0khHFtGKCSN0ELROL6iDuiltxMdIheink3T/OxaKxp+dbQKvAnvZfiq3fx44KieR+w9Z7z9oxMIcBF3K9O4XApE2IgiCoAmjTRvx1Mc3aXm9WfzMKyNtRBAEQV/R/WECXRUxvDhwArAqSbWzm+0p+Z7FSHUG1ibFBuxm+1pJ65AihhcC7gc+1aDgTN8TNoEg6E7CJtCYZhHD3wIm2d5e0lrAr4D35+uOAi62/TFJ8wEL5PbjSPEEV0naDfga8J02PUfXEAtzEHQpg74TGEmheeAtwGEAtu+WNE7SMiQj8XuBXfK5V0jpIwDWBCbm75cBlzCAQiB2AkHQnfRATZmuihieTHL/vCa7iK5MEhAzgH8DJ2b1z83AfrZfIEUTb0tKPPdxYMVGk+j3iOFYmIOgO/H04a/pNN0UMXw4sHiuHrYPcCspMngeYD3gGNtvB14Avpnv2Q3YS9LNwMLM2iHMRkQMB0HQEUoOFpP0AUl/kzRV0jebXLOJpEmS7pB01XB9VroTsH2PpPVJ+X4Ok3Sp7UMKEcNfKFz7LLArvFZX+L78WQCYZvv6fOlZZCFg+25gy3zPGsAHq3yeIAiCkVCmOkjSWJKtdAtgGnCjpPNt31m4ZjHSC/YHbD8gaenh+q06i+hywIu2TwGOBNYrRAzvVBcxvFg2+kIyFk/MqST+BTwoac187v3AnfmepfPPMcC3SZ5CQRAEXYFntv5pgQ2BqbbvzbbR3wPb1V3zSVIA7gMAth8brtNuihh+M3CypBmkRX73Qj/7AKdmIXEveccA7CRpr/z9HODEip8nCIKgZUayEyjaLjMTbE8oHC8PPFg4nga8o66bNUh5164kqciPsn3yUONWrQ66hOSxM+yYtq8FVm9ybhIptqC+/SiS+2gQBEHX4RmtBwHnBX/CEJc06qw+EGEeUtLO9wPzk162r8vJPBsSEcN9QLiIBkF34pmlZoKYxuwekCswZyGtacDj2XvyBUkTgXWApkKgau+gOZB0fK40dpuksyQtlNu3y22TJN0k6T11942VdKukCwptS0i6TNLf88/F2/08QRAEzSjZJnAjsLqkVbJqfEfg/Lpr/gBsLGkeSQuQ1EV3DdXpsDsBSUsMdd72k8P1UccBtdQOkn4K7E1yD70cOD+nkn4bKchsrcJ9+5EeZpFC2zeBy20fnt2lvgl8Y4Tz6Xni7TwIuhO7vJ2A7emS9iap2McCJ9i+Q9IX8/ljbd8l6WLgNpLj6XG19DvNaEUddDNJ7yRgJeCp/H0x4AFglWY3DhUxnN1A5899Uysok1mQgq5L0gok989DKRSiIVnGN8nff0uqKzBwQgDapxJqp8Dpx2cKBouyI4Zzwa2L6tqOrTv+MfDjVvscVgjYXgVA0rGkN/WL8vFWwObD3N4wYljSiaTYgTuBr9QulrQ9KXXE0szu8/9z4Oska3eRZWw/kuf5SCs+sf1KPy5k/fhMwWBRsk2gEkZiE9igJgAAbP+JVNVrKBpGDNveFViOpN7ZodDnubbXAj4M/ABA0oeAx2zfPIK5zoakPbKd4aaZM1+Y226CIAhGxMwZavnTKUYiBB6X9O2c2G1lSQeRyz82o1mN4XxuBnA68NEG900EVs01hjcCtpV0Pyk4YjNJp+RLH5W0LED+2TAwItJGBEHQCTxTLX86xUiEwE6kVA/nAueRVDY7DXVDg4jh9SWtls8J2Aa4Ox+vltuQtB4wH/CE7QNtr2B7HMka/n+2a6mozycVmyf//MMInicIgqBS7NY/naLlOIHsBbTfsBfOTn3E8F7AbyUtQjIuTyZFEUPaEXxG0quk9NE7FGoMN+Nw4AxJu5OM1B8f4fz6gogTGD3xOwyqoBdsAsPWGJb0R+aMSnsN29uWPakq6ccaw7GABUE1jLbG8D/W/u+W15tVp1zStTWGj6x8FkHQgHARDXqdvigqY3vYfNQAks62PYeRN6iefl3E+vW5gsFhxsy2J2UYMWXO8E2tXDRE2oiv5ZQRkyRNkTSjFq0s6QRJj0maUtfXx3PhhJmS5kgwFwRB0El6wTuozARyreq+GqaNKEa5SdomX1dLSXES8EugPiXqFFJJyt+MbupBNxK2jqDX6aTXT6t0rNB8fdqIOnYCTqsd2J4oaVz9Rbbvyn2VPvde4qWHr+7LRawfnykYLHrBO6hMIdDoaUeUNiKfWyDft3dpE+vzQvMQRtQg6EZmlphAripaFgKSNgOus/1ik0saJW67HThS0hHABbWi8rZ3zfUyjyaljShWBNsG+MtcZCdtSrFYQz+6iPYrIdiCXmdmn+0EdgGOlfQEcHX+XGP7KQDbl9bf0KzQfD43Q9LpwNeYXQjsSEEVFHQXoacPgtbpq52A7c/Aa6kgPkaqer/cUH3ka5+0fYqk54FdJa1me2p92oh8/aKkpHQ7N+4xaEQslkHQnZRZT6AqRqIO2hnYmJQK4nGSt85wr4UjSRsBsD1waS6NVhz7NFLdgCUlTQO+a/v4nHr6aFJOowslTbL9360+UxAEQZX0gnfQsGkjXrtQehz4B3AscIXt+yucV2WETSAIglYZbdqIm1b4cMvrzfhp53Vt2ggAbC8p6b+A9wKHSlod+JvtT1c2u6DrCJtAELROv6mDFiGVl1wZGAcsSqphOVdIOhrY1XYtYljAUSQj8ovALrZvyecOAD5Hiim4Pd/3H0k/JtkVXiHtUna1/fTczikYnliYg6B1ZvSTEACuKXx+aXva3A6aUzwsVte8FbB6/rwDOAZ4h6TlgX2Bt9h+SdIZJA+ik4DLgANzAeYjgAMZ0BrD7SJ2AkHQOv3mHfQ2AEkL02KKiEYRw8BZpPQQnyQZgmtsB5ycawhcJ2mxWtWwPM/5c62BBYCH85yKbqnXkbyWBo5YmIOgO+k3ddDawP8CS6RD/Rv4rO0pQ9zWKGJ4b1LB+kfq0j0sDzxYOJ4GLG/7JklHkorGvETyHpojJgHYjVSustHc+zpiOBbmIOhOeiCT9IjUQROAL9u+AkDSJrnt3UPcM1vEMElv/3GSu2c9jUSmJS1O2iWsAjwNnClp51yykjyXg4DpwKmNJtHvEcOxEwiC7sQNl7XuYiRCYMGaAACwfWVW9zSlPmKYpMNfDZiadwELSJpqezXSm/+KhdtXIKl9Ngfus/1vAEnnkATPKfn4s8CHgPe3UI6yL4mFOQi6k+n9pA4C7pX0HZJKCFJU731D3dAgYngX228snH8+CwBIReP3lvR7kmH4mawyegB4Z04s9xLwfuCmfP8HSIbg9w2R06jviZ1AEHQn/bYT2A34PnAOSXUzEdh1mHvqI4b3HOLai0g7hqkkF9FdAWxfL+ks4BaSyudWsmqHFLX8OuCyvLO4zvYXR/BMfUEszEHQnfSCTaDliOF+oR9tAkEQVMNoI4YvXWbHltebLR/9fXdHDEtaA/gqKVDstftsb1b+tIIgCHqfXtgJjEQddCYpb9BxwIxqphMEQdA/9JsQmG77mLIGrk8bUWjfgBT4tYPts3Lb/cBzJOEz3fb43P4DkvvoTOAxkuH54bLm2CuEYTgIupMZPVD6dlghIGmJ/PWPkr4EnAu8XDs/NxXAmqSNIFcbOwK4pMFtm9p+vK7tx7a/k+/dFzgYCMNwEARdwcw+8Q66mZQmovY0X607/6ZmN44wbQTAPsDZwAYtzAvbzxYOF6TFdBb9SJRiDILuoxcWpFaEwA7Ag7YfgdeCsz4K3A98b5h7W04bkRPFbQ9sxpxCwMClkgz8JkcA1+47FPgM8AywaaNJ9HvaCIjFOQi6kX6xCRxLitpF0ntJkb/7AOuS/PWHSto2krQRPwe+kWsP15/byPbDkpYmxQTcbXsigO2DgIMkHUgSMN+tv7nf00a0k7A/BEHrzOwHmwAwtqD33wGYYPts4GxJk4a6cYRpI8YDv8/tSwJbS5pu+7yasdf2Y5LOBTYkBasV+R1wIQ2EQL8TC3MQdCe98MbZkhCQNI/t6aSUDXu0ev9I0kbYXqXQfhJwge3zsl1hjO3n8vctgUPydavb/nu+bVsKResHiViYg6A7mV7yRiCnyjmKZGM9zvbhTa6bw8uyGa0IgdOAq3KN4ZfIxeUlrUbSww/FSNJGNGMZ4Ny8Q5gH+J3ti/O5wyWtSVK9/ZMB9AwKgqB7KdM7KHtP/grYgpRw80ZJ59u+s8F1zbws52BYIWD7UEmXA8uScvnXdjhjSLaBoe69ZKiJ1McIFNp3KXy/F1inyXUfHXLyQRAEHaRkddCGwNS8JpKTbW4H3Fl33Yi8LFsKFrN9XYO2e1q5NwiCYFCZOYKNQNGLMTOh6AlJ48Jb76jrYygvy4aMJGK4VBoUml+UVCNgpTyvI22fKGlF4GTgjSS1zwTbR9X19VVS7MFSDQLK+p5+NQxH7EPQ64zERbToxdiEhoW36o5/TnMvy4Z0RAg0iRjeC7jT9jaSlgL+JulUUvror9i+Jdc3vlnSZTU9WBYSW5DKTw4k/bqI9etzBYPDjHINw80KbxVp6mXZrNMxpU6xDkkLSrpQ0mRJUyTtkI0WPwa+Xne5gYWVZr8Q8CQpT9Ajtm8BsP0ccBdpW1TjZ7mvXvDGCoJggJg5gk8L3AisLmkVSfMBO5KKcb2G7VVsj7M9jpSd4UtDCQCoficwkkLzvyQ90MPAwiTXptl+N5LGAW8Hrs/H2wIP2Z481NZnECKG20k71DSxCwj6gTIjhm1Pl7Q3ydlmLHCC7TskfTGfP3Zu+q20qEyuQXAJKX9QLWL4DGCT/EDPF2wCHwM2Ar4MrEoKLFunlh9I0kLAVcChts/J5SavALa0/UzONDp+OJtAP0YM96tNIAg6zWiLyhy74s4trzdffPCUjoQXV6oOyh5E65PSRxwGfJ5ZEcP3kyOG8+W7Auc4MZVUv3gtAEnzklyeTrV9Tr5+VWAVYHLuawXgFkmvBaMFQRB0kpLVQZVQqTpohIXmHyBFJF8taRlgTVJxewHHA3fZ/mntXtu3A0sX+rqfFnYCQRAE7aJfEsiNhpFEDP8AOEnS7SRXqG/YflzSe4BPA7cXchV9y/ZFFc476AJCzRX0OiV7B1VCpUJgJBHDOUnclg2uuYbG/rH1142bu1n2PvMvt3FbF8x2EQtz0OvETiBoC/0oAIKgH+gFIVCpYbgRkk6SdJ+kSfmzbm5fS9K1kl7OEcDFe/bLcQZ3SNq/QZ9flWRJS7bnKYIgCIbHI/h0ik7tBL7WIL3pk8C+wIeLjZLWJnkVbQi8Alws6cJaCumIGA61SRB0KyPJHdQp2h4x3Oxa24/ZvpFkQC7yZuA62y/mmgZXMXtt4ogYDoKgKxl4F1EaRwxvBRwq6WDgcuCbtl8eoo8p+fo3kOoZbA3clPtrKWJ4EIhka0HQfczogXfTqoXAbDWGbV+dawH/C5iPlDHvG+RKYY2wfVe+/zLgeWAyMD1HDB9EA4+iegYhbUQszkHQfQy8Ybg+YljSwTkhnPPb/4kkXf9w/Rxvez3b7yXZDv7OCCKGbU+wPd72+H4UAEEQdCcDbxhuFDEsadmcPE4kI/CUFvpZOheZXwn4CPAu208REcNABFUFQbfSCzuBTkQMn5rrBQiYRK4LnN/gbwIWAWZmV9C35ARyZ2ebwKvAXlkABJlYmIOgO+kF76BORAxv1uTaf5FUOo3ODbvKDXLEcBAE3UkvGIbbHiwWlE9EDAdBdxIuokFbCHVQEHQnM2MnMCdDpI3YRNIzhfaDC/csJuksSXdLukvSu3L79yQ9VLhn63Y/TxAEQTMG3jtoCBqljQC42vaHGrQfBVxs+2O5tuYChXM/s31kJbMMgiAYBQPvHSRpQVI5yRVINTF/MBd9LAK8F9gFwPYrpBxCQRAEXU2og2aljVjH9trAxbn9UEm3SfqZpNcVrn9XzjP0J0n/ldveBPwbOFHSrZKOy8Klxt65rxMkLd5oEpL2kHSTpJtmznyh7GfsOGEYDoLuZMYIPp2iaiFwO7C5pCMkbWz7GeBAUu3gDYAlSGkjAG4BVra9DnA0cF5unwdYDzjG9tuBF4Bv5nPHkCKH1wUeAX7SaBL9HjEchuEg6E48gv86RdekjbD9rO3n8/eLgHlzfYBpwDTb1+duzyIJBWw/anuG7ZnA/9BCCoogCIJ2MfAuoiNJG5Ejhh+1bUkbkgTUE/n4QUlr2v4bqRj9nfmeZW0/kofbnhZSUPQjkTYiCLqTXrAJdE3aCOBjwJ6SppNSRu9ou/Yb3CffNx9wL7Brbv9RdjE1cD/whYqfpyuJhTkIupPuFwGgWevsYDDPfMv33QO/9PDVIQiCoAKmv/LQqLL/fGHcx1teb35z/5kdyTQUEcN9QhSVCYLuI3IHNWCIiOHFJZ2b3T1vyLWFkfT6fDw5F5r/fl1/+0j6Wz73o3Y/TxAEQTMG3jA8BI0ihr8FTLK9vaS1gF+RjMAvA5vZfl7SvMA1kv5k+zpJmwLbAW+z/bKkpRlA4u08CLqTTrp+tko3RQy/BTgMwPbdksZJWsb2o6SykgDz5k/tN7sncHitRrHtx8p/iu4nvIOCoDsZ+LQRjKzQ/GRS1bBrsovoyiTh8aikscDNwGrArwoxA2sAG0s6FPgP8FXbN9ZPot9rDMfCHATdycwecLzppkLzhwNHSZqU77sVmA5gewawrqTFgHMlrW17Sp7/4sA7SRHIZ0h6k+tcnmxPyGP1pXcQhGE4CLqRXjAMV11Z7B5J6wNbkyKGL7V9SD79sqQTga/ma58l+//nQLL78qfY39OSriTtMKaQoonPyYv+DTkeYUlSrqGBoZ3qoFA9BUHrhE1gZBHDiwEv5iyhnwMm2n42B5a9mgXA/MDmwBF5iPNI5SqvlLQGaXcxcIXmY7EMgu4kbAIjixh+M3CypBmktBC75/Zlgd9mu8AY4AzbF+RzJwAnSJpCSi/92XpV0CDQr1lEQ7gFvU4vpI2IiOEgCIImjDZi+GMrb9vyenPWP88fdixJHyAV2RoLHGf78Lrzn2JWZubngT1tTx6qz4gYDoIgqIgy1UFZG/IrYAuSPfRGSefbvrNw2X3A+2w/JWkrkkPMO4bqt+1CINsCfgh8nFRL4Rjbv5C0HSmOYCbJK2h/29fkexpKP0mnA2vmrhcDnra9bvuepjsIY20QdCczXKpVYENgqu17AST9nhQs+5oQsP3XwvXXkdzsh6QTO4FdgBWBtWzPLET5Xg6cn1NHv40UZLbWUNLP9g61TiX9BHimnQ8yiITACYLWGYkIKMYzZSZk9/YaywMPFo6nMfRb/u7An4YbtxMRw3sCn8yFYF6L8q0VlMksyKyo4GGlX95dfILkKRRUSCzMQdA6I3ERLcYzNaGRzaDhADmlzu7Ae4YbtxMRw78GdpC0Pcmff1/bf8/ntyeljlga+GDuoxXptzGpIM3fq3qQbiYW5iDoTkr2DppG0qLUWAF4uP6irEk5DtjK9hPDddqJGsOvA/5jezypJOQJtYttn2t7LVL8QC3PUCvSbyfgtGaT6PdC80EQdCe2W/60wI3A6pJWyQW2dgTOL14gaSXgHODTubzvsLQ9Ypgkzc7Ol5xLqjNcf99ESasWagw3lX6S5iHlHFp/iHn0fdqIIAi6jzLNwranS9obuISkXj/B9h2SvpjPHwscDLwB+HXSkjM9v3A3pe0Rw8yK8j0BeB9wT752NeAf2TC8Hin69wngabL0Ax4iSb9PFobZHLjb9rQqnyUIgmCkzCg5Ztj2RcBFdW3HFr5/jpRxoWU6ETE8lRQ1fAApmKE24Y8Cn5H0KqnG8A45+reh9CuMsSNDqIKCIAg6RS8E40bEcBAEQRNGGzG86QpbtLzeXDHtsqgxHARB0E8MfBbRIAiCQaYXisp0otC8JB0q6R5Jd0nat3Buk1x8/g5JV+W2NQtF6SdJelbS/vncOpKulXS7pD9KWqTdzxMEQdCMGbjlT6fomrQRuZ7Ar4EP2H6g1m77b8C6+ZqxJA+hc3Nfx5FKSl4laTfga8B32vcoQRAEzemFVNKV7gQkLSjpQkmTJU2RtAPJQ+iQ+rQRJLfPc2w/UNde5P0kN9J/5uM1gYn5+2UkD6MgCIKuoORgsUroprQRawDz5vKRCwNH2T65rr96d9ApwLbAH0hZSVekAf1eaD6SugVBd9ILO4FOFJp/LW2EpI+QgsY2znNZn/S2Pz9wraTraqHPOUx6W+DAQv+7Ab+QdDApfPqVRpPo94jhWJiDoDsZeO+gEaaNmAY8bvsF4AVJE4F1yBHFwFbALbYfLfR/N7AlQK4xXEs6FwRB0HF6IQ6rapvAcqTi8acARwLrMSttBBTSRpBUOhtLmkfSAqRMoXcVupsjSVzBqDwG+DZwLEEQBF3CDM9s+dMpuiZthO27JF0M3EbKu3Sc7SkAWShsAXyhrv+dJO2Vv59Dg2R0g0DYBIKgO+kFm0CkjegDQggEQTWMNm3E2974rpbXm9v+dW2kjQjmjliYg6A76YWI4U4Umr+a5AIKqYLYDbY/nEtEHkUyIr8I7GL7lnzP/cBzpML0r+XHlrQEcDowDrgf+ITtp9r2MF1Cv+4E2vVcIUSDqhh476BG2H7tX5yks0kGYUjeP6vnzzuAY5i9jOSmth+v6+6bwOW2D5f0zXz8jarmHrSXWJyDXqeTBt9WaXuhedun53MLk7yEds2XbwecnGsIXCdpMUnL2n5kiCG2AzbJ338LXMkACoF+fDuHEAJB7xPqoMYRwzW2J73FP5uPGxWUXx54hFRT+FJJBn6Tg78AlqkJCduP1FxG64mI4fKIhTkIWifUQQ0ihgvndiIlgKsxVEH5jWw/nBf5yyTdbXtig+sb0u8Rw/1KCLeg1xn4nUCjiGHbh0h6A7AhaTdQo2lBedu1n49JOjffOxF4tKYykrQs0CjpXNCjxMIc9DoDvxNoUmgeUrK3C2z/p3D5+cDekn5PMgg/kxf3BYExtp/L37cEDinc81ng8PzzDwwgsVgGQXfiQTcM0zhiGFI20MPrrr2ItGOYSnIRrRmMlwHOTR6kzAP8zvbF+dzhwBmSdgceIAmXIAiCrqAXvIMiYrgPCN15EFTDaCOGV1hi7ZbXm2lPTomI4WDuiIU5CLqTXnjJ7kSN4asL9YIflnRe3fkNJM2Q9LF8vKKkK3I94jsk7Ve49uO5baak8W1+lK6hnTuBIAhaZ6bd8qdTdFPEcK2G8BHAJYVbpgNfsX1LDjC7WdJltu8kVRb7CPCbtky+S4mdQBB0J73gHdSJGsO1c7WI4fMKt+xDKjjzmqun7UdqOYRsP0eqMbB8Pr4rF6IPgiDoOqLG8AgihiUtn9s2AzZo1JmkccDbgesrnHPPEYbhIOhOesE7qJsihn8OfMP2jOwOOhuSFiLtEvYvpJpoiX5PGxELcxB0J70QMVy5i2hO97w18EWgGDF8D7B8LWBM0n3MSh2xJClWYA/b50maF7gAuMT2TxuMcSXwVds3DTeffnQRhUi7HARVMFoX0cUXWq3l9eap56f2n4voSCKGba9SuO+kfP68XGfgeOCuRgIgSMTiHATdRy+Ul6zaRfStwA2SJgEHAT/M7TtSVzR+CDYCPg1sVnAt3RpA0vaSpgHvAi6UdMlQHQVBELSTXjAMR8RwHxCG4SCohtGqgxZcYFzL680LL97ff+qgoD3EwhwE3UkvGIZDCPQBsRMIgu6kFzQtIQSCriU8noJep+yIYUkfAI4iles9zvbhdeeVz29N8rDcpRZs24wQAkHXEotz0OuUuRPIaXV+BWxBKsJ1o6TzcwqdGlsBq+fPO4Bj8s+mhBDoA6LQfBB0JyWrgzYEptq+FyAX4NoOKAqB7YCTnQa+TtJiteqLzTodOCEwN9Z+SXsUittXSow1i+mvPNSWceaWGKt3xmrnMxV5dQTrTTGzQWZC3ZyXBx4sHE9jzrf8RtcsDzQVAm1PJd2j7DH8JTFWl4zVj88UY/XOOHON7Qm2xxc+9UKrkUCp32q0cs1shBAIgiDoDaYBKxaOVwAenotrZiOEQBAEQW9wI7C6pFUkzUfKvHB+3TXnA59R4p3AM0PZA2AAbQJzSTt1iTFWb4wTY/XWWG23B5SN7emS9iYV3RoLnGD7DklfzOePBS4iuYdOJbmI7jpcvwOXNiIIgiCYRaiDgiAIBpgQAkEQBANMCIEgCNqGpPklrdnpeQSzCCHQhByi3Y5x1pB0uaQp+fhtkr5d0ViStOLwV/YWuXpd0OVI2gaYBFycj9eVVO/dErSZMAw3IZe7PAs4sS43R9njXAV8DfiN7bfntim2165ovJttr19F33XjXG77/cO1lTTW30mLy4nAn9yGP2pJKwOr2/6zpPmBeWw/V/IYH2nQ/Axwu+3HSh5rKeDzwDgKXoO2dytxjJuBzYArC3/rt9l+W1ljNBhzLLAMsz/TA1WN14uEi2hz3kbywz1O0hjgBOD3Iy1y3wIL2L4hJf97jeklj1HkOkkb2L6xis4lvR5YAFhS0uLMimBcBFiuijGBNYDNgd2AoyWdDpxk+54qBpP0eVIE6hLAqqSAnGOBsgXc7qSqeVfk402A64A1JB1i+39LHOsPwNXAn4EZJfZbZLrtZ+r+1itD0j7Ad4FHgZm52aR/20GNkZQ/G9QP8F7gIeAF4LfAaiX2/SfSQnJLPv4Y6W22qme5kyRk/gHcBtwO3FZi//sB9wEvA/fm7/cBk4G92/D/atP8/+pp4CrgXRWMMQmYD7i10HZ7BeP8EVimcLwMcA5J+Ewp+5na8P/meOCT+e9udeBo4NgKx5sKvKHq5+r1T+wEmpC3kR8kBVuMA34CnApsTArIWKOkofYiBbKsJekh0oK5c0l9N2KrCvvG9lHAUZL2sX10lWPVkPQG0u/s06S3vn1IkZPrAmcCq5Q85Mu2X6m90Uqah2Hys8wl42w/Wjh+DFjD9pOSXi15rAskbW37opL7LbIPqdb4y6Qa45cAP6hwvAdJ6rNgCEIINOfvpG34j23/tdB+lqT3ljWIU1rYzSUtCIxxyXrlBuP9U9I6JGEGcLXtyRUM9S9JC9t+Lhu61wN+6GEKXMwl1wL/C3zY9rRC+02Sjq1gvKskfQuYX9IWwJdIb+1lc7WkC0iCDOCjwMT8t/J0yWPtB3xL0itATcDY9iJlDWD7ReAgSUfkviv9WyftRK+UdCFJ8NTm8dOKx+0pwjDcgLwLOMj2IRWO8eWhzlf1hyppP5IB8JzctD0pZW2pb+01g5+k9wCHAUcC37I9ZIGLuRhnLElQD/n7LHnMMSR9/ZYkm8cltv+ngnFEWvg3yuNcA5ztHv1HK2kDkm1t4dz0DLCb7ZsrGu+7jdptf7+K8XqVEAJNkHSF7U0r7L/hH2jGVQkgSbeR9OQv5OMFgWtdsoeGpFttv13SYSR9+e9qbWWOk8eqxOtoiPH2y2qvIdt6DUnbkuxfkDx4Lii5/9uAvWxfnY/fA/y67L+9BuMuTPo39XyV4/QqoQ5qzl8l/RI4nWQQBqAsdUbtbUTSRrb/UjwnaaMyxmiCmN37YwaNc5CPlock/YbktXOEpNdRXVzKpOxvfiaz/786p/kto+KzpDquRXZp0DYqsovoEcDSpP9HomQVTWGsw4ENSHYvgP0kvcf2N0sc5rmaAACwfY2kylRCktYmqQmXyMePA5+xfUdVY/YisRNogqQrGjTb9mYlj3OL7fWGaytxvC+TFrFzc9OHgd/a/lnJ4ywAfIC0C/i7pGWBt9q+tMxx8lgnNmi2S/Rxz+PsRPJueQ/JnbLGwsAM25uXPN5UYBvbd5XZb5OxbgPWtT0zH48leT+V9pYu6Wck9+HTSIb0HYCngLOhvBeswnh/Jal1r8jHmwD/z/a7yxyn14mdQBOqVAUBSHoX8G5gqTr7wCKkNLGVYPunkq4kLWQCdrV9awXjvAicI2lpSSvl5rvLHidzXJt2U38llelbkuQtVuM5kttj2TzaDgFQYDHgyfx90Qr6Xzf/rFeFvpskFEp9wQIWrAkAANtXZvVnUCCEQBMkLUr6Y63pSK8CDrFdlsvZfMBCpP8HCxfanyXFClSCpP+1/WnglgZtZY6zLWmhXI7k2rgSSQj8V5njZI4meR8N1zYqbP8T+CcpgKsd3JQD385jdu+WKtRchwG35h2wSH/3B5Y8xua2qwpEa8S9kr5DUglBciO+r43j9wShDmqCpLOBKaTgMEg+6OvYbhTKP5pxVs6LS1uoVzXlbf/ttt9S8jiTSW92f84G4k2BnWyXVuu1sJvaHyiqsxYBtre9Tllj1Y37TpKQeTNJmI8FXihbV98uNVdhvGVJdgEB19v+V8n911KxnNAmFdfiwPeZteudCHzP9lNVj91LxE6gOava/mjh+PuSJpXVuaRf2t4b+KWkOSSx7W3LGiuPdyBQ822vpb4Q8ArVVF161fYTksZIGmP7iuwfXiYd2U0BvySlFDkTGA98Blit7EFsD1sVarRIWsv23ZJqLwa1OIvlJC1Xsp6+lorl+IpTsQCQF/t9q+i7n4idQBMkXQt8zfY1+Xgj4EjbpagCJD1rexFJ72t03vZVZYzTYNzDbJe9zW80zp9JRufDSDr0x4ANqjDKdWA3dZPt8SokP5P017KeTdLXbf9I0tE0iES2XdrCJmmC7T2qdISQNI/t6XVt7yUZiBcj7Q5+YHvqaMfKff/c9v6S/kjj31+pL1i9TuwEmrMn8NtsGxDJYLZLif3/A6pb7IfgBkmL1mwbkhYDNrF9XhmdS1qNlONmO+Al4ADgU8DKpLQBVfA6SROYMwNm2YbGGi8qFfqeJOlHJGNxmQbHmqrkphL7bEhBPbeV7f8UzyklAyyDG4D1NCsVy26kv4eqUrHUbABHltRfXxM7gWGQtAhA2VtWSdOAplHBri5ieJLtdevaSgviUkpz8C3bt9W1jwe+a3ubMsap63syKYvnzRRiIFxdJOrKpBxF85GE3KKkoKdS3mQL43zc9pnDtZU0VmWuyrV+JN1LSsVynO1r6675RZk7nNxnXwb1lU0IgSaocVqHZ4CbbU8qof9HgGNoEqjlikLb1SB/u6Tbbb+1pP6b1kIoc5y6fttSI6FuzKUAbP+7wjEqjyGR9EZgeeAUUgxEMfX3sbbXKmGM2gvPAqTd4WyLToUvPI1+f6W98PQLoQ5qzvj8qSUG+yBwI/BFSWfa/tEo+3/EFeYmGoKbJP0U+BXpH+M+pDfoshhKhTB/ieMU+aOkL5EC4IqulE82v2XkSBLJbXhv0mI5RtJ04Ogy/19K2grYGlhe0i8Kpxah/FoT/01Sc67A7DvT50iOBGUwlmTAV/5ZKYWgvlU0e+WyhYEnqh6/14idQBMkXQJ81DnfiKSFSAas7Um7gVG5VHbqjSQHy3yHlM4B4FLgUOdcQiX0fxrwf65LqCZpd2BL2zuUMU5d3418v237TSWPcwBpcd7D9n257U2kHd3FLinqWinL67rAIcDBhVPPAVdU4eIo6aO2zy6739x3ZRHwTcZbmZQ+/DCgmPbiOVLtjCqLNvUcIQSaIOkuUlzAK/n4daTCG28uYwGXtETZb6ojHH8hV5BQS9IypDfyV5i1wxhP0p9vX7bveTuRdCuwhe3H69qXAi4tW6hLmtd22XUDhhrvg6Rgvtd2c2XscDqpgsnqrg1Ju94be/nvrypCHdSc35FKMf4hH28DnJbfpEddc7hTAkDSu4HjSNvylfJb5xdsf6mM/p2KoLw7B4fVbAMX2v6/MvpvhKTPNJnLySUPNW+9AMjj/FvSvCWPBTBOKQvrW5h9YS51hwOgVHdhAWBT0t/Hx0hePWXQtgyvRfLu87vA/5FUUUcrleU8oRPz6VZiJzAEktZnVrThNbYrd9mrGknXk/6Bn+82FLZvB9mfvsbrSYvOLbZLDRgbSq1RhcpD0jWkRexnpJeQXUn/ZodKQz63Y9XqP9R+LgScY3vLssdqF5L+Brzb9hP5+A3AX22v2dmZdRexExia+YFnbZ8oaSlJq9R0wb2M7Qc1e7HvduZzKR3bs8Uf5NiOMouw11hHs6KtZxuSoQ3ic8v8ti+XpBwM9z1JVzNnArYyqMUIvChpOZIBteyynO1mGskOUOM5UsnJoEAIgSYoFX0ZD6wJnAjMS3KjqzLXfzt4MKuEnAOe9mVWcFK/8CKpkHmp2G4pu6ukxUsy3v5HKb3C3yXtDTxEqi1QBX/MgYM/JiUXNFB6tbQ28xBwfVbpmhTAeEPN/bsq19ReI4RAc7YH3k7Otmn7YaUKRb3OF0nFT5YnvSldSip237PUpQcYS0rsdkbnZsTllJPBdH+Snn5fUkH2zUh5ikolC5rLbT8NnJ0D/l7v8jLmdop/5E+Nmn2vH/4dl0YIgea8YtvKyd3UB3nIc9j+z21/qtNzKZlieoDpwD89e8H5dlNKpTbbN+avzwO7SpqHVIjl+jL6L4wzU9JPyCmybb9MId6iV6kq4LLfqKrcXz9whlJ5xMUkfR74M8lromdxyuW+VFYD9Q05/9LdpDe8xUnuqZ1kVN4WkhaRdKCkX0raUom9ganAJ8qZ4hxcKumjqjMW9SKSfp5//lHS+fWfDk+v6wjvoCGQtAWwJenN7hLbl3V4SqMmC7b1gPOZvR5vz+pHJX2CpMu+kvT/amNSBtizOjSfUXkKZR32U8C1JE+nxUlxFvuVkbKkyZjPkZLgTScZiSurZ1w1kta3fbPanKG3Vwkh0ARJR9j+xnBtvUY2eM9BL2+dlRLIbWH7sXy8FKmYTSVFZVqYz6iCo1TIsZRVeI8DK9murCh7v5F/b7+1vXOn59LthE2gOVsA9Qv+Vg3aeopeXuyHYExNAGSeoGJVp6T3AKvX3IeBhQruw6MNjnotStj2DEn3VS0AlPL7z4HtiVWOWxX597aUpPlqUf9BY0II1CFpT+BLwJskFdMhLwz8pfFd3Y/6u9DGxTnX02n5eAdSfvpKGM59uIRo8GI8gphVDa5KFc3XCt9fT0q1cDPlF39vJ/cDf8l2gL5QfVZBCIE5+R3wJxokn+pkrp8S6LtCG8oFbGx/TdJHmBXdfS2pWElVVOo+3Go8Qpm4rs6DpBWB0WbK7TQP588Ywi20KWETGAZJSzN73pYHOjidUlAbcuG3A3WggE3u/wbbG2pWsZQFgWtdV6ehl8leQre5gvoPQXcRO4EmSNqGlF99OVJ93JVJkbX/1cl5zS35H3XlufDbzLh6AQBg+yZJ4yoct959eDd6PLpWs9czHkNKZT25YxMqAUmXAR/PQXBIWpxU2P6/OzqxLiOEQHN+CLyT5GXy9pwVc6cOz2k07E/SWW9Qnwtf0gFl5cJvM50oYIPtI7P78LMku8DBfeA+XEyOOB04zXbP2sAyS9UEAIDtp/LOPigQQqA5r9p+QtIYSWNsXyHpiE5PahR8hrpc+LbvlbQzKXVELwqBGyV93o0L2FRSXzj3vyCpcM5lktYE1lSbc/+Xje3f9ouasMAMSSvVVLhKxWZC/11HCIHmPJ3T6U4ETpX0GOWX9msn7c6F3w72B86V9CkaFLCpcNyJwMZZvfBn0lv0DkDPpePoUzVhjYOAayTVgsPeC+zRwfl0JWEYrqPmcQJMIhXFHkP6x70yqThKZW+YVTJUFOtoI1w7jWYvYHNHlQVs8ng1g/A+pHTPPxptgFinUJtKZnYKSUuS1LoiGe/neBEadEII1NEpj5OqkTSDgq908RQpY2Sv7gbajlKZyS+RVGi7276jGOXbS6jNJTPbiaSNSCVhX8hqz/WAo3JthiATCeTmpKnHCTCu/dMpB9tjbS/S4LNwCIARsx9wIHBuFgBvAq7o8JzmlqZqQlIQXC9zDKlIzjqkYLh/AmWXHO15wiYwJx3xOAl6h5xKYWLh+F5Szv9eZKiUCr2ebmF6Tge/HfAL28dL+mynJ9VthBCYk454nAS9Q1aVfJ0UM1IMJOzFFAvtLpnZTp6TdCDwaZIhfyy9v7spnbAJ1CFpGeBc0lvQHB4ntv/VqbkF3YGkS4HTga+SKrV9Fvh3r2eY7TckvRH4JHCj7aslrQRsYjtUQgVCCDSh3R4nQe8g6Wbb60u6rZYqQtJVthvmrw86R44NWN32nyUtAIyNlNyzE+qgJti+gt419gXVUgsKe0TSB0lJylbo4HyCBuSUHnsASwCrkupqH8voU333FSEEgmDk/FDSosBXgKOBRYADOjuloAF7kVJiXw9g+++RNmJOQggEwQixfUH++gywaSfnEgzJy7ZfUS6bLGkeIm3EHEScQBCMEElrSLpc0pR8/DZJ3+70vII5uErSt0hFebYAzgT+2OE5dR1hGA6CEZJz0XwN+E0tolbSFNtrD31n0E4kjQF2B7YkubxeAhznWPRmI9RBQTByFrB9Q03NkOnl5IJ9ie2Zks4DzuujzKilE+qgIBg5j0talaxflvQx4JHOTimoocT3JD0O3A38TdK/JR3c6bl1IyEEgmDk7AX8BlhL0kOklNZ7dnRGQZH9mVVA6Q22lwDeAWyUs6YGBcImEARzSS4uMyaCj7qLfs6MWgVhEwiCFpH05SbtANj+aVsnFDSjHwsoVUYIgSBonYU7PYGgJfo5M2rphDooCIK+IgoojYwwDAfBCIlgse4mCiiNjBACQTBy/odUWexVgFyJbseOzigI5pIQAkEwchawfUNdWwSLBT1JCIEgGDkRLBb0DWEYDoIRkgvLTwDeDTwF3Ad8yvY/OzqxIJgLQggEwVxSCxYDXgJ2sH1qh6cUBCMm1EFB0CKSFpF0oKRf5tTEL5LqC08FPtHZ2QXB3BE7gSBoEUl/IKl/riWVKFwcmA/Yz/akDk4tCOaaEAJB0CKSbrf91vx9LPA4sFLkDgp6mVAHBUHr1ArMY3sGcF8IgKDXiZ1AELRIXToCAfOT7AICbHuRTs0tCOaWEAJBEAQDTKiDgiAIBpgQAkEQBANMCIEgCIIBJoRAEATBAPP/Adfym3vlpYMNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cb7c2552",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1902c088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4809, 10)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "96d4da24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Show_Id'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2ecedaef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://youtu.be/b7Kd0fLwgO4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a7643df7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Time']=pd.to_datetime(df['Release_Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3a02443a",
   "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>Category</th>\n",
       "      <th>Title</th>\n",
       "      <th>Director</th>\n",
       "      <th>Cast</th>\n",
       "      <th>Country</th>\n",
       "      <th>Release_Date</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Show_Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>s2</th>\n",
       "      <td>Movie</td>\n",
       "      <td>07:19</td>\n",
       "      <td>Jorge Michel Grau</td>\n",
       "      <td>Demián Bichir, Héctor Bonilla, Oscar Serrano, ...</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>December 23, 2016</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>93 min</td>\n",
       "      <td>Dramas, International Movies</td>\n",
       "      <td>After a devastating earthquake hits Mexico Cit...</td>\n",
       "      <td>2016-12-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s3</th>\n",
       "      <td>Movie</td>\n",
       "      <td>23:59</td>\n",
       "      <td>Gilbert Chan</td>\n",
       "      <td>Tedd Chan, Stella Chung, Henley Hii, Lawrence ...</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>December 20, 2018</td>\n",
       "      <td>R</td>\n",
       "      <td>78 min</td>\n",
       "      <td>Horror Movies, International Movies</td>\n",
       "      <td>When an army recruit is found dead, his fellow...</td>\n",
       "      <td>2018-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s4</th>\n",
       "      <td>Movie</td>\n",
       "      <td>9</td>\n",
       "      <td>Shane Acker</td>\n",
       "      <td>Elijah Wood, John C. Reilly, Jennifer Connelly...</td>\n",
       "      <td>United States</td>\n",
       "      <td>November 16, 2017</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>80 min</td>\n",
       "      <td>Action &amp; Adventure, Independent Movies, Sci-Fi...</td>\n",
       "      <td>In a postapocalyptic world, rag-doll robots hi...</td>\n",
       "      <td>2017-11-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s5</th>\n",
       "      <td>Movie</td>\n",
       "      <td>21</td>\n",
       "      <td>Robert Luketic</td>\n",
       "      <td>Jim Sturgess, Kevin Spacey, Kate Bosworth, Aar...</td>\n",
       "      <td>United States</td>\n",
       "      <td>January 1, 2020</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>123 min</td>\n",
       "      <td>Dramas</td>\n",
       "      <td>A brilliant group of students become card-coun...</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s6</th>\n",
       "      <td>TV Show</td>\n",
       "      <td>46</td>\n",
       "      <td>Serdar Akar</td>\n",
       "      <td>Erdal Beşikçioğlu, Yasemin Allen, Melis Birkan...</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>July 1, 2017</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>International TV Shows, TV Dramas, TV Mysteries</td>\n",
       "      <td>A genetics professor experiments with a treatm...</td>\n",
       "      <td>2017-07-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Category  Title           Director  \\\n",
       "Show_Id                                      \n",
       "s2         Movie  07:19  Jorge Michel Grau   \n",
       "s3         Movie  23:59       Gilbert Chan   \n",
       "s4         Movie      9        Shane Acker   \n",
       "s5         Movie     21     Robert Luketic   \n",
       "s6       TV Show     46        Serdar Akar   \n",
       "\n",
       "                                                      Cast        Country  \\\n",
       "Show_Id                                                                     \n",
       "s2       Demián Bichir, Héctor Bonilla, Oscar Serrano, ...         Mexico   \n",
       "s3       Tedd Chan, Stella Chung, Henley Hii, Lawrence ...      Singapore   \n",
       "s4       Elijah Wood, John C. Reilly, Jennifer Connelly...  United States   \n",
       "s5       Jim Sturgess, Kevin Spacey, Kate Bosworth, Aar...  United States   \n",
       "s6       Erdal Beşikçioğlu, Yasemin Allen, Melis Birkan...         Turkey   \n",
       "\n",
       "              Release_Date Rating  Duration  \\\n",
       "Show_Id                                       \n",
       "s2       December 23, 2016  TV-MA    93 min   \n",
       "s3       December 20, 2018      R    78 min   \n",
       "s4       November 16, 2017  PG-13    80 min   \n",
       "s5         January 1, 2020  PG-13   123 min   \n",
       "s6            July 1, 2017  TV-MA  1 Season   \n",
       "\n",
       "                                                      Type  \\\n",
       "Show_Id                                                      \n",
       "s2                            Dramas, International Movies   \n",
       "s3                     Horror Movies, International Movies   \n",
       "s4       Action & Adventure, Independent Movies, Sci-Fi...   \n",
       "s5                                                  Dramas   \n",
       "s6         International TV Shows, TV Dramas, TV Mysteries   \n",
       "\n",
       "                                               Description       Time  \n",
       "Show_Id                                                                \n",
       "s2       After a devastating earthquake hits Mexico Cit... 2016-12-23  \n",
       "s3       When an army recruit is found dead, his fellow... 2018-12-20  \n",
       "s4       In a postapocalyptic world, rag-doll robots hi... 2017-11-16  \n",
       "s5       A brilliant group of students become card-coun... 2020-01-01  \n",
       "s6       A genetics professor experiments with a treatm... 2017-07-01  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8f290324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category                object\n",
       "Title                   object\n",
       "Director                object\n",
       "Cast                    object\n",
       "Country                 object\n",
       "Release_Date            object\n",
       "Rating                  object\n",
       "Duration                object\n",
       "Type                    object\n",
       "Description             object\n",
       "Time            datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a1a54390",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019    1337\n",
       "2020    1229\n",
       "2018    1115\n",
       "2017     752\n",
       "2016     207\n",
       "2021      78\n",
       "2015      50\n",
       "2014      14\n",
       "2011      13\n",
       "2013       7\n",
       "2012       3\n",
       "2009       2\n",
       "2008       1\n",
       "2010       1\n",
       "Name: Time, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Time'].dt.year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "6ec8e697",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Time'].dt.year.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "532efdb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Time'>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Time'].dt.year.value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "cda9fbce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "Movie      4673\n",
       "TV Show     136\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Category').Category.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "06c47b97",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SantoshKumar\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Category', ylabel='count'>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "278a5141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([358., 349., 156., 217.,  56.,  28.,  55.,  26.,  61.]),\n",
       " array([ 10,  20,  30,  40,  50,  60,  70,  80,  90, 100]),\n",
       " <BarContainer object of 9 artists>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3 =df.Country\n",
    "bin = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
    "plt.hist(df3, bin, histtype='bar', rwidth = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3cda37b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}