Game_Streamer_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dc68e5de",
   "metadata": {},
   "source": [
    "### Project :   Game Streamer Analysis\n",
    "### Author :    Sanjoy Kumar\n",
    "### Email :      sanjoy.eee32@gmail.com\n",
    "### Portfolio : imsanjoykb.github.io"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9e5131",
   "metadata": {},
   "source": [
    "## Import Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "74ba3fae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "import datetime\n",
    "import missingno as msno\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beb83c0f",
   "metadata": {},
   "source": [
    "## Import Data Source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "baf2116d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Game</th>\n",
       "      <th>Total Follower</th>\n",
       "      <th>Broadcast Hours</th>\n",
       "      <th>PaidStarPerWatchedHour</th>\n",
       "      <th>Character_Facet_Cont_Rigidity</th>\n",
       "      <th>Character_Facet_Cont_AchievementStriving</th>\n",
       "      <th>Personal_Values_Facet_Cont_Hedonism</th>\n",
       "      <th>Character_Facet_Cont_Dutifulness</th>\n",
       "      <th>...</th>\n",
       "      <th>Personal_Values_Facet_Cont_UniversalismNature</th>\n",
       "      <th>Role_Charismatic</th>\n",
       "      <th>Personal_Values_Facet_Cont_Tradition</th>\n",
       "      <th>Self_Esteem_Cont_SELevelAppearance</th>\n",
       "      <th>Character_Facet_Cont_Assertiveness</th>\n",
       "      <th>Character_Facet_Cont_Cautiousness</th>\n",
       "      <th>Role_Curator</th>\n",
       "      <th>Personal_Values_Cont_Conservation</th>\n",
       "      <th>Self_Esteem_Cont_SELevelIntelligence</th>\n",
       "      <th>Temperament_Centric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID</td>\n",
       "      <td>Male</td>\n",
       "      <td>MLBB</td>\n",
       "      <td>96751</td>\n",
       "      <td>104</td>\n",
       "      <td>0.004113</td>\n",
       "      <td>0.663442</td>\n",
       "      <td>0.718679</td>\n",
       "      <td>0.288008</td>\n",
       "      <td>0.471456</td>\n",
       "      <td>...</td>\n",
       "      <td>0.523116</td>\n",
       "      <td>0.408444</td>\n",
       "      <td>0.318747</td>\n",
       "      <td>0.684412</td>\n",
       "      <td>0.494384</td>\n",
       "      <td>0.751974</td>\n",
       "      <td>0.524306</td>\n",
       "      <td>0.299052</td>\n",
       "      <td>0.803920</td>\n",
       "      <td>0.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID</td>\n",
       "      <td>Male</td>\n",
       "      <td>8 Ball Pool</td>\n",
       "      <td>4781</td>\n",
       "      <td>174</td>\n",
       "      <td>0.002855</td>\n",
       "      <td>0.543255</td>\n",
       "      <td>0.427887</td>\n",
       "      <td>0.430609</td>\n",
       "      <td>0.493514</td>\n",
       "      <td>...</td>\n",
       "      <td>0.444493</td>\n",
       "      <td>0.407624</td>\n",
       "      <td>0.382594</td>\n",
       "      <td>0.635493</td>\n",
       "      <td>0.468738</td>\n",
       "      <td>0.571198</td>\n",
       "      <td>0.100202</td>\n",
       "      <td>0.304527</td>\n",
       "      <td>0.751291</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID</td>\n",
       "      <td>Female</td>\n",
       "      <td>Free Fire - Battlegrounds</td>\n",
       "      <td>68246</td>\n",
       "      <td>83</td>\n",
       "      <td>0.003007</td>\n",
       "      <td>0.527279</td>\n",
       "      <td>0.399873</td>\n",
       "      <td>0.753009</td>\n",
       "      <td>0.472405</td>\n",
       "      <td>...</td>\n",
       "      <td>0.581657</td>\n",
       "      <td>0.443453</td>\n",
       "      <td>0.325263</td>\n",
       "      <td>0.398686</td>\n",
       "      <td>0.515929</td>\n",
       "      <td>0.309322</td>\n",
       "      <td>0.070988</td>\n",
       "      <td>0.305975</td>\n",
       "      <td>0.759900</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID</td>\n",
       "      <td>Male</td>\n",
       "      <td>MLBB</td>\n",
       "      <td>142037</td>\n",
       "      <td>127</td>\n",
       "      <td>0.005171</td>\n",
       "      <td>0.677222</td>\n",
       "      <td>0.603534</td>\n",
       "      <td>0.407923</td>\n",
       "      <td>0.467787</td>\n",
       "      <td>...</td>\n",
       "      <td>0.385822</td>\n",
       "      <td>0.408994</td>\n",
       "      <td>0.440821</td>\n",
       "      <td>0.703669</td>\n",
       "      <td>0.516412</td>\n",
       "      <td>0.535322</td>\n",
       "      <td>0.214652</td>\n",
       "      <td>0.298592</td>\n",
       "      <td>0.734010</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID</td>\n",
       "      <td>Male</td>\n",
       "      <td>Free Fire - Battlegrounds</td>\n",
       "      <td>15416</td>\n",
       "      <td>86</td>\n",
       "      <td>0.001213</td>\n",
       "      <td>0.238194</td>\n",
       "      <td>0.543285</td>\n",
       "      <td>0.392993</td>\n",
       "      <td>0.583099</td>\n",
       "      <td>...</td>\n",
       "      <td>0.582025</td>\n",
       "      <td>0.135650</td>\n",
       "      <td>0.405968</td>\n",
       "      <td>0.439668</td>\n",
       "      <td>0.586193</td>\n",
       "      <td>0.567604</td>\n",
       "      <td>0.324364</td>\n",
       "      <td>0.298716</td>\n",
       "      <td>0.814530</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 126 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country  Gender                       Game Total Follower  Broadcast Hours  \\\n",
       "0      ID    Male                       MLBB          96751              104   \n",
       "1      ID    Male                8 Ball Pool           4781              174   \n",
       "2      ID  Female  Free Fire - Battlegrounds          68246               83   \n",
       "3      ID    Male                       MLBB         142037              127   \n",
       "4      ID    Male  Free Fire - Battlegrounds          15416               86   \n",
       "\n",
       "   PaidStarPerWatchedHour  Character_Facet_Cont_Rigidity  \\\n",
       "0                0.004113                       0.663442   \n",
       "1                0.002855                       0.543255   \n",
       "2                0.003007                       0.527279   \n",
       "3                0.005171                       0.677222   \n",
       "4                0.001213                       0.238194   \n",
       "\n",
       "   Character_Facet_Cont_AchievementStriving  \\\n",
       "0                                  0.718679   \n",
       "1                                  0.427887   \n",
       "2                                  0.399873   \n",
       "3                                  0.603534   \n",
       "4                                  0.543285   \n",
       "\n",
       "   Personal_Values_Facet_Cont_Hedonism  Character_Facet_Cont_Dutifulness  ...  \\\n",
       "0                             0.288008                          0.471456  ...   \n",
       "1                             0.430609                          0.493514  ...   \n",
       "2                             0.753009                          0.472405  ...   \n",
       "3                             0.407923                          0.467787  ...   \n",
       "4                             0.392993                          0.583099  ...   \n",
       "\n",
       "   Personal_Values_Facet_Cont_UniversalismNature  Role_Charismatic  \\\n",
       "0                                       0.523116          0.408444   \n",
       "1                                       0.444493          0.407624   \n",
       "2                                       0.581657          0.443453   \n",
       "3                                       0.385822          0.408994   \n",
       "4                                       0.582025          0.135650   \n",
       "\n",
       "   Personal_Values_Facet_Cont_Tradition  Self_Esteem_Cont_SELevelAppearance  \\\n",
       "0                              0.318747                            0.684412   \n",
       "1                              0.382594                            0.635493   \n",
       "2                              0.325263                            0.398686   \n",
       "3                              0.440821                            0.703669   \n",
       "4                              0.405968                            0.439668   \n",
       "\n",
       "   Character_Facet_Cont_Assertiveness  Character_Facet_Cont_Cautiousness  \\\n",
       "0                            0.494384                           0.751974   \n",
       "1                            0.468738                           0.571198   \n",
       "2                            0.515929                           0.309322   \n",
       "3                            0.516412                           0.535322   \n",
       "4                            0.586193                           0.567604   \n",
       "\n",
       "   Role_Curator  Personal_Values_Cont_Conservation  \\\n",
       "0      0.524306                           0.299052   \n",
       "1      0.100202                           0.304527   \n",
       "2      0.070988                           0.305975   \n",
       "3      0.214652                           0.298592   \n",
       "4      0.324364                           0.298716   \n",
       "\n",
       "   Self_Esteem_Cont_SELevelIntelligence  Temperament_Centric  \n",
       "0                              0.803920                0.625  \n",
       "1                              0.751291                0.125  \n",
       "2                              0.759900                0.000  \n",
       "3                              0.734010                0.000  \n",
       "4                              0.814530                0.125  \n",
       "\n",
       "[5 rows x 126 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_source =  'Maingames_DS_dataset.csv'\n",
    "df = pd.read_csv(data_source, index_col=\"Unnamed: 0\")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be4f5e69",
   "metadata": {},
   "source": [
    "## Data Internal Insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9e2bcf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(631, 126)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "274ff60e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                                 0\n",
       "Gender                                  1\n",
       "Game                                    2\n",
       "Total Follower                          1\n",
       "Broadcast Hours                         0\n",
       "                                       ..\n",
       "Character_Facet_Cont_Cautiousness       0\n",
       "Role_Curator                            0\n",
       "Personal_Values_Cont_Conservation       0\n",
       "Self_Esteem_Cont_SELevelIntelligence    0\n",
       "Temperament_Centric                     0\n",
       "Length: 126, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f9e29046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Broadcast Hours</th>\n",
       "      <th>PaidStarPerWatchedHour</th>\n",
       "      <th>Character_Facet_Cont_Rigidity</th>\n",
       "      <th>Character_Facet_Cont_AchievementStriving</th>\n",
       "      <th>Personal_Values_Facet_Cont_Hedonism</th>\n",
       "      <th>Character_Facet_Cont_Dutifulness</th>\n",
       "      <th>Character_Cont_Conscientiousness</th>\n",
       "      <th>Character_Facet_Cont_Sympathy</th>\n",
       "      <th>Personal_Values_Facet_Cont_UniversalismTolerance</th>\n",
       "      <th>Temperament_Choleric</th>\n",
       "      <th>...</th>\n",
       "      <th>Personal_Values_Facet_Cont_UniversalismNature</th>\n",
       "      <th>Role_Charismatic</th>\n",
       "      <th>Personal_Values_Facet_Cont_Tradition</th>\n",
       "      <th>Self_Esteem_Cont_SELevelAppearance</th>\n",
       "      <th>Character_Facet_Cont_Assertiveness</th>\n",
       "      <th>Character_Facet_Cont_Cautiousness</th>\n",
       "      <th>Role_Curator</th>\n",
       "      <th>Personal_Values_Cont_Conservation</th>\n",
       "      <th>Self_Esteem_Cont_SELevelIntelligence</th>\n",
       "      <th>Temperament_Centric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>631.000000</td>\n",
       "      <td>629.000000</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>631.000000</td>\n",
       "      <td>631.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>127.870048</td>\n",
       "      <td>0.012047</td>\n",
       "      <td>0.602272</td>\n",
       "      <td>0.511264</td>\n",
       "      <td>0.504968</td>\n",
       "      <td>0.495829</td>\n",
       "      <td>0.475071</td>\n",
       "      <td>0.398333</td>\n",
       "      <td>0.558301</td>\n",
       "      <td>0.192155</td>\n",
       "      <td>...</td>\n",
       "      <td>0.486182</td>\n",
       "      <td>0.483384</td>\n",
       "      <td>0.354377</td>\n",
       "      <td>0.628526</td>\n",
       "      <td>0.498150</td>\n",
       "      <td>0.509117</td>\n",
       "      <td>0.264708</td>\n",
       "      <td>0.299209</td>\n",
       "      <td>0.769686</td>\n",
       "      <td>0.330626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>53.127032</td>\n",
       "      <td>0.103878</td>\n",
       "      <td>0.098965</td>\n",
       "      <td>0.132756</td>\n",
       "      <td>0.110216</td>\n",
       "      <td>0.099318</td>\n",
       "      <td>0.089500</td>\n",
       "      <td>0.093354</td>\n",
       "      <td>0.095576</td>\n",
       "      <td>0.215062</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072933</td>\n",
       "      <td>0.133083</td>\n",
       "      <td>0.052241</td>\n",
       "      <td>0.113400</td>\n",
       "      <td>0.095051</td>\n",
       "      <td>0.132999</td>\n",
       "      <td>0.115095</td>\n",
       "      <td>0.007841</td>\n",
       "      <td>0.059291</td>\n",
       "      <td>0.231695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.238194</td>\n",
       "      <td>0.092290</td>\n",
       "      <td>0.203574</td>\n",
       "      <td>0.176896</td>\n",
       "      <td>0.192784</td>\n",
       "      <td>0.136315</td>\n",
       "      <td>0.234030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.333361</td>\n",
       "      <td>0.089912</td>\n",
       "      <td>0.153680</td>\n",
       "      <td>0.342775</td>\n",
       "      <td>0.214129</td>\n",
       "      <td>0.131429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.267636</td>\n",
       "      <td>0.302305</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>0.000910</td>\n",
       "      <td>0.548895</td>\n",
       "      <td>0.378814</td>\n",
       "      <td>0.438643</td>\n",
       "      <td>0.437895</td>\n",
       "      <td>0.418463</td>\n",
       "      <td>0.350123</td>\n",
       "      <td>0.510763</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.429169</td>\n",
       "      <td>0.397342</td>\n",
       "      <td>0.329644</td>\n",
       "      <td>0.523061</td>\n",
       "      <td>0.412747</td>\n",
       "      <td>0.387027</td>\n",
       "      <td>0.179912</td>\n",
       "      <td>0.292658</td>\n",
       "      <td>0.742862</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>113.000000</td>\n",
       "      <td>0.002594</td>\n",
       "      <td>0.592589</td>\n",
       "      <td>0.523694</td>\n",
       "      <td>0.538550</td>\n",
       "      <td>0.505687</td>\n",
       "      <td>0.488704</td>\n",
       "      <td>0.389375</td>\n",
       "      <td>0.552733</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.471135</td>\n",
       "      <td>0.474822</td>\n",
       "      <td>0.341652</td>\n",
       "      <td>0.660061</td>\n",
       "      <td>0.496871</td>\n",
       "      <td>0.512096</td>\n",
       "      <td>0.247404</td>\n",
       "      <td>0.301182</td>\n",
       "      <td>0.776426</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>151.500000</td>\n",
       "      <td>0.007003</td>\n",
       "      <td>0.668015</td>\n",
       "      <td>0.615077</td>\n",
       "      <td>0.571307</td>\n",
       "      <td>0.567108</td>\n",
       "      <td>0.523962</td>\n",
       "      <td>0.450524</td>\n",
       "      <td>0.625677</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.544211</td>\n",
       "      <td>0.586773</td>\n",
       "      <td>0.377048</td>\n",
       "      <td>0.721947</td>\n",
       "      <td>0.555224</td>\n",
       "      <td>0.613752</td>\n",
       "      <td>0.367443</td>\n",
       "      <td>0.304341</td>\n",
       "      <td>0.807230</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>571.000000</td>\n",
       "      <td>2.561621</td>\n",
       "      <td>0.869732</td>\n",
       "      <td>0.803543</td>\n",
       "      <td>0.853163</td>\n",
       "      <td>0.783423</td>\n",
       "      <td>0.732078</td>\n",
       "      <td>0.677443</td>\n",
       "      <td>0.842938</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.671015</td>\n",
       "      <td>0.832664</td>\n",
       "      <td>0.598359</td>\n",
       "      <td>0.838676</td>\n",
       "      <td>0.786141</td>\n",
       "      <td>0.910022</td>\n",
       "      <td>0.601515</td>\n",
       "      <td>0.323636</td>\n",
       "      <td>0.897407</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 121 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Broadcast Hours  PaidStarPerWatchedHour  Character_Facet_Cont_Rigidity  \\\n",
       "count       631.000000              629.000000                     631.000000   \n",
       "mean        127.870048                0.012047                       0.602272   \n",
       "std          53.127032                0.103878                       0.098965   \n",
       "min           0.000000                0.000000                       0.238194   \n",
       "25%          92.000000                0.000910                       0.548895   \n",
       "50%         113.000000                0.002594                       0.592589   \n",
       "75%         151.500000                0.007003                       0.668015   \n",
       "max         571.000000                2.561621                       0.869732   \n",
       "\n",
       "       Character_Facet_Cont_AchievementStriving  \\\n",
       "count                                631.000000   \n",
       "mean                                   0.511264   \n",
       "std                                    0.132756   \n",
       "min                                    0.092290   \n",
       "25%                                    0.378814   \n",
       "50%                                    0.523694   \n",
       "75%                                    0.615077   \n",
       "max                                    0.803543   \n",
       "\n",
       "       Personal_Values_Facet_Cont_Hedonism  Character_Facet_Cont_Dutifulness  \\\n",
       "count                           631.000000                        631.000000   \n",
       "mean                              0.504968                          0.495829   \n",
       "std                               0.110216                          0.099318   \n",
       "min                               0.203574                          0.176896   \n",
       "25%                               0.438643                          0.437895   \n",
       "50%                               0.538550                          0.505687   \n",
       "75%                               0.571307                          0.567108   \n",
       "max                               0.853163                          0.783423   \n",
       "\n",
       "       Character_Cont_Conscientiousness  Character_Facet_Cont_Sympathy  \\\n",
       "count                        631.000000                     631.000000   \n",
       "mean                           0.475071                       0.398333   \n",
       "std                            0.089500                       0.093354   \n",
       "min                            0.192784                       0.136315   \n",
       "25%                            0.418463                       0.350123   \n",
       "50%                            0.488704                       0.389375   \n",
       "75%                            0.523962                       0.450524   \n",
       "max                            0.732078                       0.677443   \n",
       "\n",
       "       Personal_Values_Facet_Cont_UniversalismTolerance  Temperament_Choleric  \\\n",
       "count                                        631.000000            631.000000   \n",
       "mean                                           0.558301              0.192155   \n",
       "std                                            0.095576              0.215062   \n",
       "min                                            0.234030              0.000000   \n",
       "25%                                            0.510763              0.000000   \n",
       "50%                                            0.552733              0.250000   \n",
       "75%                                            0.625677              0.250000   \n",
       "max                                            0.842938              1.000000   \n",
       "\n",
       "       ...  Personal_Values_Facet_Cont_UniversalismNature  Role_Charismatic  \\\n",
       "count  ...                                     631.000000        631.000000   \n",
       "mean   ...                                       0.486182          0.483384   \n",
       "std    ...                                       0.072933          0.133083   \n",
       "min    ...                                       0.333361          0.089912   \n",
       "25%    ...                                       0.429169          0.397342   \n",
       "50%    ...                                       0.471135          0.474822   \n",
       "75%    ...                                       0.544211          0.586773   \n",
       "max    ...                                       0.671015          0.832664   \n",
       "\n",
       "       Personal_Values_Facet_Cont_Tradition  \\\n",
       "count                            631.000000   \n",
       "mean                               0.354377   \n",
       "std                                0.052241   \n",
       "min                                0.153680   \n",
       "25%                                0.329644   \n",
       "50%                                0.341652   \n",
       "75%                                0.377048   \n",
       "max                                0.598359   \n",
       "\n",
       "       Self_Esteem_Cont_SELevelAppearance  Character_Facet_Cont_Assertiveness  \\\n",
       "count                          631.000000                          631.000000   \n",
       "mean                             0.628526                            0.498150   \n",
       "std                              0.113400                            0.095051   \n",
       "min                              0.342775                            0.214129   \n",
       "25%                              0.523061                            0.412747   \n",
       "50%                              0.660061                            0.496871   \n",
       "75%                              0.721947                            0.555224   \n",
       "max                              0.838676                            0.786141   \n",
       "\n",
       "       Character_Facet_Cont_Cautiousness  Role_Curator  \\\n",
       "count                         631.000000    631.000000   \n",
       "mean                            0.509117      0.264708   \n",
       "std                             0.132999      0.115095   \n",
       "min                             0.131429      0.000000   \n",
       "25%                             0.387027      0.179912   \n",
       "50%                             0.512096      0.247404   \n",
       "75%                             0.613752      0.367443   \n",
       "max                             0.910022      0.601515   \n",
       "\n",
       "       Personal_Values_Cont_Conservation  \\\n",
       "count                         631.000000   \n",
       "mean                            0.299209   \n",
       "std                             0.007841   \n",
       "min                             0.267636   \n",
       "25%                             0.292658   \n",
       "50%                             0.301182   \n",
       "75%                             0.304341   \n",
       "max                             0.323636   \n",
       "\n",
       "       Self_Esteem_Cont_SELevelIntelligence  Temperament_Centric  \n",
       "count                            631.000000           631.000000  \n",
       "mean                               0.769686             0.330626  \n",
       "std                                0.059291             0.231695  \n",
       "min                                0.302305             0.000000  \n",
       "25%                                0.742862             0.125000  \n",
       "50%                                0.776426             0.250000  \n",
       "75%                                0.807230             0.500000  \n",
       "max                                0.897407             1.000000  \n",
       "\n",
       "[8 rows x 121 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40a86c64",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "913626fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop NA value (value = '-')\n",
    "df = df[df.Game != '-']\n",
    "df = df[df.Game != 'No MLBB Video']\n",
    "# Merge same game name\n",
    " # Arena of Valor\n",
    "df['Game'] = df['Game'].replace(['Garena Liên Quân Mobile','Liên Quân Mobile'],'Arena of Valor')\n",
    " # Age of Empires\n",
    "df['Game'] = df['Game'].replace(['Agge of Empires'],'Age of Empires')\n",
    " # Audition online\n",
    "df['Game'] = df['Game'].replace(['Audition'],'Audition Online')\n",
    "df['Game'] = df['Game'].replace(['Call of Duty: Mobile VN'],'Call of Duty: Mobile')\n",
    "df['Total Follower'] = df['Total Follower'].replace(['250,98'],25098)\n",
    "\n",
    "# change datatype\n",
    "df['Total Follower'] = pd.to_numeric(df['Total Follower'])\n",
    "\n",
    "# Other\n",
    "df['PaidStarPerWatchedHourPerFollower'] = df['PaidStarPerWatchedHour']/df['Total Follower']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3f4c3d4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 Ball Pool\n",
      "Age of Empires\n",
      "Apex Legends\n",
      "Arena of Valor\n",
      "Assassin's Creed Odyssey\n",
      "Audition Online\n",
      "Auto Chess\n",
      "Blade & Soul\n",
      "CABAL ONLINE\n",
      "Call of Duty: Mobile\n",
      "Call of Duty: Warzone\n",
      "Coin Master\n",
      "Counter-Strike: Global Offensive\n",
      "Crazy Kart\n",
      "Crossfire\n",
      "Days Gone\n",
      "Dead by Daylight\n",
      "Dota 2\n",
      "EA Sports UFC 3\n",
      "Euro Truck Simulator 2\n",
      "FIFA Online 4\n",
      "Five Nights at Freddy's 2\n",
      "Free Fire - Battlegrounds\n",
      "God of War\n",
      "Grand Theft Auto V\n",
      "Green Hell\n",
      "Identity V\n",
      "J-League Jikkyou Winning Eleven\n",
      "League of Legends\n",
      "League of Legends: Wild Rift\n",
      "Little Big Snake\n",
      "MLBB\n",
      "MU Online\n",
      "Minecraft\n",
      "Moon of Madness\n",
      "MotoGP\n",
      "NBA 2K20\n",
      "Naruto Shippuden: Ultimate Ninja Storm 4\n",
      "Ngọc Rồng Online\n",
      "PUBG\n",
      "Persona 5 Royal\n",
      "Point Blank Indonesia\n",
      "RULES OF SURVIVAL\n",
      "Ragnarok M: Eternal Love\n",
      "Roblox\n",
      "Sea of Thieves\n",
      "Star Wars Jedi: Fallen Order\n",
      "Teamfight Tactics\n",
      "The Last of Us\n",
      "The Last of Us: Part II\n",
      "The Warriors\n",
      "Township Mobile\n",
      "Valorant\n",
      "World War Z\n",
      "Wormate.io\n",
      "Worms Zone\n",
      "Yakuza: Kiwami 2\n",
      "eFootball PES 2020\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(58,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show all games\n",
    "for game in df.groupby('Game').groups.keys():\n",
    "  print(game)\n",
    "df.groupby('Game').groups.keys()\n",
    "df.groupby('Game').count().iloc[:,0].shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c8ca1c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate game count\n",
    "df_game_count = df.groupby('Game').count().sort_values('Country', ascending=False).iloc[:, 0:1].rename(columns={'Country':'Count'})\n",
    "# df_game_count.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff10de9",
   "metadata": {},
   "source": [
    "## Check Per Followers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c6e066a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(491, 127)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['Total Follower','PaidStarPerWatchedHour'] # The columns you want to search for outliers in\n",
    "\n",
    "# Calculate quantiles and IQR\n",
    "Q1 = df[cols].quantile(0.25) # Same as np.percentile but maps (0,1) and not (0,100)\n",
    "Q3 = df[cols].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Return a boolean array of the rows with (any) non-outlier column values\n",
    "condition = ~((df[cols] < (Q1 - 1.5 * IQR)) | (df[cols] > (Q3 + 1.5 * IQR))).any(axis=1)\n",
    "\n",
    "# Filter our dataframe based on condition\n",
    "filtered_df = df[condition]\n",
    "filtered_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e3055e77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Total Follower', ylabel='PaidStarPerWatchedHourPerFollower'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G5qQxxpjO89t0dmDQgfRGzS1R7n3hHW5ftI7KsM1JY4wxneG36SwC/BA4zl20GLhDVeMBxdXjnCrOK4gmnMF26Qc4ZzywkkmjBtudjTHG+OT3gc3f4fTL/Nb9mugu65N2VnHOG9GdmZPGGGOMP377aD6tqt7plZ8UkRVBBNQbtFfF2TvVszHGmNL83tEkReQT6TcichBQuMxxHzBiUA3xVKGpAqwcjTHGdJTfRHMZsEhEFovIU8CTwCWldhKRWSLyoYisLrJeROQ2EVknIitF5FOedeeIyOvu1zk+4+wWdbVV3HTaznpnVRXCJV84hH/+2/E2EMAYYzrI76izf4jIwcCh7qJXVTXqY9e7gZnAPUXWnwQc7H4dhdPvc5T73M41QD3OsOrlIrJAVTf5ibc7WL0zY4zpHu0mGhH5epFVo0QEVf1re/ur6tMickA7m5yKM6maAs+LyJ4iMhSYDCxU1Y/dOBYCJwL3t3e+7mb1zowxputK3dF8Jef1Q573CrSbaHwYDrzred/oLiu2PI+ITAemAwwZMoTFixd3KICWlpa8fZIpJZZMURkOEQ5Jh47X3QrF15tYfF1j8XWNxbdrKDWV8/fSr0XkZe/73kJV7wTuBKivr9fJkyd3aP/Fixfj3Wd+QxOXP7CSSKh3PKCZG19vY/F1jcXXNRbfrsHvYAAoME1AN2gCRnrej3CXFVseqPTzM23xFNuiCdriKWY8sJLmFj/dUcYYYwrpSKIJwgLgO+7os6OBLar6HvAE8EURGSQig4AvussCtWbDVkJkN5XZA5rGGNM1pQYDPMTOO5mDRGSBd72qnlJi//txOvYHi0gjzkiyiLvv74FHgS8D64AdwPfcdR+LyPXAUvdQP00PDAjK/IYmLpu7glgy+8YtlkyxpTVOc0vUBgYYY0wnlBoMcIvn9a0dPbiqnllivQLnF1k3C5jV0XN2RnNLlEvmNJDIeUazIgTJVIrz732pV/TXGGPMrqjUYICnyhVIT1qzYWtekklLpGBb1JlM1ApqGmNMx5VqOltFO4MAVHVct0fUIwp/i2EREp516f4aSzTGGONfqaazqe7/081bf3L//y2CGYXWI8YMG0gkLMQ9/TNhEaI5/TVWUNMYYzqu3VFnqvq2qr4NfEFVZ6jqKvfrcpyRYH1CXW0Vt35jPFUVIfaoDFNVEUIkP49edfJou5sxxpgO8jtNgIjIJFVd4r75DD0/NLpbeWubbWmNc/69L2X6ZgD6VYUZO3xgD0ZojDG7Jr+J5lxgloikr7Sbge8HElEPStc2a26J5k0TkEypNZsZY0wn+LorUdXl7sRn44HxqjpBVV8KNrSe0dwSpXFTK1dNHZ2ZJqA6YvPQGGNMZ/m6oxGRIcDPgWGqepKIjAaOUdU/BBpdmd37/Ntc99AaIuEQSVWuOnk0Y4cPtGkCjDGmC/z2s9yNUwJmmPv+NeBHAcTTY+59/m2ufHA1saSyPZakLZ7i+kfWWpIxxpgu8ptoBqvqHCAFoKoJ+tBUzs0tUa57eG3e8rCI1Tkzxpgu8ptototIHe6zM+kCmIFFVWaNm1qpDOfPOxNP2nMzxhjTVX5Hnf0Ep9LyJ0RkCbA3MC2wqMpsxKAaEqn852au+coYazYzxpgu8jvq7CXgc8BngH8FxqjqyiADK6e62ipuOm0c1ZEQ/arCVFaE+NnXxnL20fv3dGjGGLPL83tHA3AkcIC7z6dEBFW9J5CoeoD3gU0bAGCMMd3H7/DmPwGfABrYOQhAgT6TaGDnA5vGGGO6j987mnpgtDt/jDHGGOOb31Fnq4F9gwzEGGNM3+R3Kuf+wFoReRGIpteXmsq5r0iXpbG+G2OM6biOTOW8W5rf0MTlD6wkEgrZdM7GGNMJvqZyFpEDgfdUtc19XwMMKXVwETkR+DUQBu5S1Rty1v8KmOK+3QPYR1X3dNclgVXuund64u6puSXK5Q+spC2eos0pimDTORtjTAf5HQwwF+cZmrSku+zTxXYQkTBwO/AFoBFYKiILVDVT60VVf+zZ/kLgCM8hWlV1gs/4uo23maxxUyuRUCiTZMCmczbGmI7ym2gqVDWWfqOqMRGpLLHPkcA6VV0PICKzgVOB/KJijjOBa3zGE4jcZrKrpo7Om5fGpnM2xpiOET8jlkVkIfAbVV3gvj8VuEhVT2hnn2nAiap6nvv+28BRqnpBgW33B54HRqhq0l2WwHluJwHcoKoPFjnPdGA6wJAhQybOnj275Pfj1dLSQm1tLcmU8sr720h5Po+QCEMHVvPeljYEZ1TEiEE1DKyJdOgcXZGOr7ey+LrG4usai6/rpkyZslxV64M8h987mh8A94rITPd9I/DtbozjDGBeOsm49lfVJhE5CHhSRFap6hu5O6rqncCdAPX19Tp58uQOnXjx4sVMnjyZFe9u5vdPvZA3ffN95x3J59xmtJ4YdZaOr7ey+LrG4usai2/X4DfRpFT1aBGpBVDVFneAQHuagJGe9yPcZYWcAZzvXaCqTe7/14vIYpz+m7xE011GDKrJaybbHk2yumkL40fuaX0yxhjTSX4f2HwAnASjqi3usnkl9lkKHCwiB7r9OWfgVIDOIiKHAYOA5zzLBolIlft6MDCJ4n073aKutoqrpo7OW379I2tpbonmLW9uibLi3c0F1xljjNmp1AObhwFjgIEi8nXPqgFAdXv7qmpCRC7AmZkzDMxS1TUi8lNgWbq/BycBzc4pb/NJ4A4RSeEkwxu8o9WCMnbYQPaIhNgRb3+UmT1bY4wx/pVqOjsUmArsCXzFs3wb8C+lDq6qjwKP5iy7Ouf9tQX2+ydweKnjd7fVTVuykgzkjzKzZ2uMMaZjSj2wOR+YLyLHqOpz7W27q1v3wbaC0zlfdfLorARiz9YYY0zH+B0M8LKInI/TjJZpMlPV7wcSVZnNb2jisrkriCWzh3r3qwozdvjArGWFBg3YszXGGFOc38EAf8Kp3vwl4CmcEWTbggqqnJIp5fIHVuYlmfS63ATinY2zf1UF1ZEQN502zu5mjDGmCL93NKNU9Rsicqqq/lFE7gOeCTKwcoklU3lNYQCVFcUTiM3GaYwx/vlNNHH3/5tFZCzwPrBPMCGVV2U4lNcUVhkW7jv3SCIVYZpbogUTic3GaYwx/vhNNHeKyCDgKpxnYWrd17u8cEi46bRxzPAMVz594gi+NetFG75sjDHdoNRzNP8F/BN4TFU34fTPHFSGuMrK2xTWrzLM1JnP2vBlY4zpJqUGA6wDvgosEZG3ROQ+EblARI4QEb8DCXYJdbVVjB+5J9tjSSKh7G8tPXw5zaoCGGOMf6Weo5kJzAQQkWE4c9J8BvgxsDdOhYA+o7klypbWOLFkdp9NLJliS2uc5pYoz67baFUBjDGmA0r20YiI4Dyl/xmcmmOjgdeBe4INrby8ZWWSqRSRsFBdEaYtkSSZSnH+vS8RSyZJKcSTas1qxhjjU6k+moU4dy0NOPPF/FxV/68McZVVobIyVRXwi68fziVzG4gmyZo+wMuqAhhjTPtK9bOsB1LAwe7XKLeacp+SLivjVRkO0xZPUhkOt7uvVQUwxpj2leqj+VcAERkAHI3TfHa+iOwNrFbVc4IPMXjFyspMGLln3vKKEIRDoczzN1YVwBhj2uf3OZoosANodV+PACqDCqrc0mVlZuR08o8a0r/gcqsKYIwx/pXqo/kVzl3MwcDLOJOT/R44R1U3Bx5dGeU+S7M9lqS5JVq03IwlGGOM8afUHc2bwH3AB6r6Thni6VF1tVU8u24jM+atJBwSkinl5mnO8OVSiaW5JcqaDVsBZcywgZaIjDHGVaqP5jYAEVlFD0xEVm7NLVEunbuCuKeS8yVzVzBplDP+oVhz2fyGJi6Z00DC7c6JhIVbvzHenq8xxhj899G8JCKfVtWlgUbTw9Zs2JKVZMB5ZuauZ9Yza8lbeXc54CSnGfNWZJJMep/L5tnzNcYYA/7nozkKeE5E3hCRlSKySkRWBhlYOaVLyjR5ysx43fnMm0QTKXbEkkQTKS6ZuyJTfqZxUyvhAtV4wiHJKltjjDG7K793NF/qzMFF5ETg10AYuEtVb8hZ/13gZqDJXTRTVe9y150D/Ie7/D9V9Y+diaGULa1xJt34JJpSogUmPwNnAjSveFJZs2ELxx2yDyMG1ZDUVMF97PkaY4zxeUejqm8DI4Hj3dc7Su0rImHgduAknLI1Z4rI6AKb/kVVJ7hf6SSzF3ANzp3UkcA17jQF3aq5JUrjplba4qmiSaY4AZwBBDdPG0+F59OIhIWbp9nzNcYYAz7vaETkGqAeOBT4HyAC/Bmn9lkxRwLrVHW9e4zZwKnAWh+n/BKwUFU/dvddCJwI3O8nXr8aN7W66aJjwgJbW2Os+2Ab22NJJo0azAv//nkbdWaMMQWIaum/5EWkATgCeElVj3CXrVTVce3sMw04UVXPc99/GzhKVS/wbPNd4BfAR8BrwI9V9V0RuRSoVtX/dLe7CmhV1VsKnGc6MB1gyJAhE2fPnu3n+wac5q2PN2/lfZ9dKSJC7ucVEidVjRhUw8CaiO9z+9XS0kJtbW23H7e7WHxdY/F1jcXXdVOmTFmuqvVBnsNvH01MVVVEFEBE+nXT+R8C7lfVqIj8K/BH4PiOHEBV7wTuBKivr9fJkyd3KID5jy3k9pcTmT6a6kiIZEpJJJV0SomEhcu+dCg3P/EK8WTh41RHEiy5/DjqaqsyTXLdUTlg8eLFdPR7KieLr2ssvq6x+HYNfhPNHBG5A9hTRP4F+D7w3yX2acLp10kbwc5OfwBUtdnz9i7gJs++k3P2Xewz1g4ZWBNhyeXHZVUE6FcZZsOWNra2xhhQE2HMsIFu4c0w8WThTJOu4mzz1RhjTDZfiUZVbxGRLwBbcfpprlbVhSV2WwocLCIH4iSOM4CzvBuIyFBVfc99ewqQnoLgCeDnngEAXwSu8BNrZ9TVVmXuPLzz0niLZm7aHiuaZMApwtmvMpw33YDNV2OM2d2VqnV2MHAL8AlgFXCpqja1t0+aqiZE5AKcpBEGZqnqGhH5KbBMVRcAF4nIKUAC+Bj4rrvvxyJyPU6yAvhpemBAkArNSzPjgZVsa0tw/SNrCbsVm72qwoKEhKtOHk3Du5upCGUPL7D5aowxu7tSdzSzcGbSfBr4CvAb4Ot+D66qjwKP5iy72vP6CorcqajqLPf8ZZOelyadZMB58PK6h9YQ8wx/joSFX50+gcP27c/2WJLVTVucRCTC9lj2XY/NV2OM2d2VSjT9VTXdF/OqiLwUdEA9pbklypbWGLGc5rF4UomEQ1nLqyvCjNxrD0YN6U9zS5Rv3vkcbfHsO51+VWGSKbX5aowxu71SiaZaRI6AzOMmNd73qtonEo+3XyalzuRmNZEK4qkUV00dzfUPZz/6471LKXQX1K8yzHVfGcOUw/axJGOM2e2VSjTvAb/0vH/f817p4FDk3iiZ0rx+maqKELef/SnGDBtAXW0V/asq8iY/SyeQQrNzJlUtyRhjjKvUNAFTRCQEHKOqS8oUU1nFkqm8O5LKcAhQ1mzYytbWGCERbpk2ngE1FQWf+j9/8ihmLnqdynDYpnc2xpgcJYc3q2pKRGbiVAbocyoLjCRrSyT53v8sJbf8WUUIfnn6hMxzMd4mNxCmH3cQZx21nyUZY4zx8DtNwD9E5DQR6UxpsF4tHBJuOm0c1ZEQ/asqqKoIkUppXpIBSKTgsnnOFAHeodDbogmiiRS3L15X/m/AGGN6Ob+VAf4V+AmQEJE2nMEAqqoDAousjE6ZMJxJowbTuKmVLa1xfvCn5ewoUmsmldLMPDO5TW72zIwxxuTzWxmgf9CB9LR0dYDmlmjB+WXS4ilnVNmgfpV5TW7Fnpnpztpnxhizq/HbdIaIDBKRI0XkuPRXkIH1lELzy3hVR0JsjyWpq63KanKrjoQKDgKY39DEpBuf5Ft3vcCkG59kQYOvwgrGGNNn+J2P5jzgYpzilg3A0cBz9IHhzYWkm9Kee6OZH//lZXKexczctXib3NJ3K967F8Bqnxljdnt++2guBj4NPO8OeT4M+HlwYZVXoaatutoqpo4fRkq16DM06e2KFeQ8f/Io68cxxuz2/CaaNlVtExFEpEpVXxGRQwONrEy2tMaZdOOTRcv6F7prgfzkVKgg58xFr0POHJ5W+8wYs7vxm2gaRWRP4EFgoYhsAt4OKqhySSeLtni43aYt710LFJ5KYP+6fgUe/Awz/biDuH3xuqJ3RMYY09f5HXX2NffltSKyCBgIPBZYVGXSuKmV3AeDSjVtFZtK4OELji04Cu2so/bjrKP2s1Fnxpjdlq9RZyLyp/RrVX3KnUumrCX8gzBiUA25z2WWatpKF9H0ioRCbNjSyvmTR1FVkT8Kra62ivEj97QkY4zZLfltOhvjfSMiYWBi94dTXnW1VYwYVEN1JJHVtAWw4t3NBe9AChXRbEsk+Zd7llEZDgNqpWiMMcaj1AybVwD/jjM9wNb0YiAG3BlwbGUxsCbCksuPyzRtPbtuI5+54UnCISGZUm6elj04IP38THokWiyZIplKEU1CNJEA4PbF6zjrqP2Anf1A/SrDbI8lrfnMGLPbKVW9+RfAL0TkF+5smH2StyrApXNXEPcUOrtk7oq8wQG5JWvOv/cltkUTmfVhERa98iHRRIrrH3HmsmmLpzLTPueObDPGmL6s1B3N/sDmdJIRkSnAV4G3gNtVNRZ0gOW0ZsOWrCQDzgybazZs4bhD9slank5O6z7YRjSRXRdteyzJNQtWsz2W3cQWTSoklUvnrmD00AGMGtLnK/sYY0zJwQBzgH4AIjIBmAu8A0wAflvq4CJyooi8KiLrROTfCqz/iYisFZGVIvIPN7Gl1yVFpMH9WuD/W+qKYsWpCy+f39DE1JnPEgo56yvDO7fLTTJesaTy5d8826lyNM0tUVa8u5nmlmiH9zXGmJ5QajBAjapucF9/C5ilqre6k6E1tLejO2DgduALQCOwVEQWqKp3XuSXgXpV3SEiPwRuAr7prmtV1Qkd+m66aNjAat/LvcOc0xToVxlqN8mkxRIpX+VokinNDEx4dt3GvOd3rAnOGNPblUo03j/ljweugMxkaKWOfSSwTlXXA4jIbOBUIJNoVHWRZ/vncZJZj9keS1IdCWUlj3QRzVyNm1oJ53wGlRUh4on8JFNVESJaYHmpZ3bmNzTR+P42fv/UC5lBB4kUVjfNGLNLEdUCM3ylV4r8GhgKvAecAhyiqnERGQo8pKr17ew7DThRVc9z338bOEpVLyiy/UzgfVX9T/d9AueuKQHcoKoPFtlvOjAdYMiQIRNnz57d7jecq6WlhdraWsC5e3jl/W2kPJ9JSITD9u1POJSdVD7eHqNpc2vWspAIQwdW896WNmfCHmDowGpqKsOowvqN29GcY+9ftwc1kXDe8dOx7F2tfJB9moywCAfu3Y+aSLhD33N38n5+vZHF1zUWX9f09vgApkyZsry9a3l3KHVH8yOcpqyhwLGqGneX7wtc2V1BiMi3gHrgc57F+6tqk4gcBDwpIqtU9Y3cfVX1Ttyh1vX19Tp58uQOnXvx4sV499ne0JRXRPOEnOap5pYok258krZ49scXFvjR5w/kpPp9Cw5lXtDQxGXzVhIOCdF4klBIqK5IEk/F85rBbvvH6/xy5WtccniCW1cV/jFVR0IsOeXYHr2jyf38ehuLr2ssvq7p7fGVS6nhzQrk3SKo6ss+jt0EjPS8H+EuyyIin8dJWp9T1UwPt6o2uf9fLyKLgSOAvETT3dJDl9ds2AIIY4YNyCv9v+iVD6kI5TcdJhVuXfgaMxet4+Zp4xg/cs+s9er+V1POtsmkEk86w6LTzWAAazZs5fZF+dNCV4Scqacrw2Grm2aM2WWUGt68DfKqtGSUmMp5KXCwiByIk2DOAM7KOf4RwB04TWwfepYPAnaoalREBgOTcAYKlIW30701nkBEqK4IsyPmvK6qkHY7/KMFOvrTgweiCaXQRxoJhbj3hXf47eJ1hJCCfToXn3CI1U0zxuxySt3R9AcQketx+mn+hDNA4Gyc5rT29k2IyAXAE0AYZ8TaGhH5KbDMrZd2M1ALzHUHF7yjqqcAnwTuEJEUzhDsG3JGqwWmUNFM2HnngSqJWPF+rbTcjv50jTRvdWevWDLF7YtedxNRvqqKUFZZm8ZNTsdNoWRjU0cbY3oTv7XOTlHV8Z73vxORFcDV7e2kqo8Cj+Ysu9rz+vNF9vsncLjP2LpVqYTgtUdliK9NGMHc5e+QOzAttzhnoRppAP2qwiRTyvmTR3Hn0+szZWzSQiJZBTrnNzQxY94KwhIiqSlunjY+q2+n0BQGXRkCbUnLGNNVfhPNdhE5G6e/RoEzge2BRdVDmluivPvxDrbHEqU3BmIJ5WtHDONLY/fluTc2MmvJW1SGi8/E6a2RFk+luOrk0YwdPjCTkG5fnN0vU1XhjEpbcvlnMyVyLpnTgNOq5mS2n8xpyDTRNbdEmTFvJdFE9hQGo4cO6FSdte5OWsaY3ZPfRHMW8Gv3S4El5PS37OrmNzTl1TkrJaXKtDuez9Qwu3rqzsRRaCbOYrN1puUmoptOG0ft5tcz263ZsJXcrptEyll+3CF7c+8L7xTs2/nybc9QVRHuULIoNu+OPbdjjOkovxOfvYXzsGWfVKiYph8pd/N0DbPrHl7LoxfuHG7svSOIJVNcMGUUJ43dly2tcba0xhgzbGDRYp3pRLR48eueMxaOb/1H2xg2sJrbF72ety798GksZ3RbqWRRqAmx1AOmuazZzRgDpUedzVDVm0TkNxS4yqnqRYFFVkaFiml2RiyR4su3PcMt3xjPpFGD8+4Ibl34GrcufC2zfSQs3PqN8XnTEORelNMX7GEDa4iEJS/Wm594jZ8/9ipS4FvI3d5vsijUp1RqUjhvYrFyOcaYtFJ3NP/n/n9Z0IH0rJLldDJOO2IYDzZsoFheirnVmX95+viSgwriSeWyefl3GN4L9pbWOJNufDJzwf7aEcN58OUmQiHJ3K0UKpEDUBkGZzTfzmBLJYu0utoqTp84gnuefyez7PT6Ee2Wy9l595Ykpc73Z81uxphSw5sfcv//x/KE0zOGDawmBD7GmcGZR+7HgpXvkWznDiiWVH4yZyUpLX3EcEiy7jByL9gXjk7QFg9nLthzljWyR8RpiquqkKzh0NWREKmUZvXHAJ5J2pKcP3lUyZiaW6Ks2bCVvyxrzFo+Z1kjF59wSME7rvwh4dk62uxmjOk7fPXRiMjewOXAaCBTylhVjw8orrLZ0hrnBzOfJZJz0S7m/hffoboivPO5miJiyRSRsFAZdhJPMdF4kn6VTq2yQhfsQrXodrh3MolU/rpHL/ps3gizSaMGc+8L73D7onX87qk3MpULCjVlpROdKHkDC4olCz9Dwv3eSRlj+p5S89Gk3YvTjHYgcB3OxGdLA4qpbNJNVG3xlK8kA/DAyxtoifob/hwJC3edU8/ZR+5XdJukwsm/eYYFDU2ZC7ZfVWGhsiJE/6qKzLM2o4b0Z/zIPTPDnVe8u5lN22P85snXiSZS7IgliSZSXDJ3Rd6cNt5E11pg9FpusmhuidLqJsrc/pyKkPOQqTc2u5sxZvfkd3hznar+QUQuVtWngKdEZJdPNI2bWjvQO7OT32EDO2Ip3t3Uyk++eAjzXmosOPQYIJpQZjywkj9//8i82ToFp1hnoZsiCQmPXHBswWdkvE1wbYlkwZlDH1qxgWNHDc7s37iptehfHlUVkpUs0se/6JNxfjzzWU6vH8GcZY1Znf/tDeU2xuw+/CaadNXm90TkZGADsFcwIZXPiEE1vpNGZ13/8FpOHLMvN08bx2Xuw5SFaEo56w8vOrN1JpVIWAiJMGzPGirCSZI5+6Uv/KOG9M/cmQGZO5lSfSYA//nwWhLq9O0AnDJ+WKZZzqsmEuKOb0/MTGftPX5SlbZ4ijnLGnm4QNKzBGOM8Zto/lNEBgKXAL8BBgA/DiyqMqmrrWLEoBqqIwnCIsSSKRJJ7dbkk+7XSD8jc98L7/CbJ1/P67eJJrOLbcaTSmVYSaRCVIbzJ0776hHDOWXC8MydRViEeDLFNV8Zw8i99iDk414t3VqYHr02J6fzP02BMcMGZt4Xe8ZmeyyZV7HaGGNKPUdTDfwAGAUMB/6gqlPKEVi5DKyJcNXJh3DdQ2uoCDljz1IpRXAuxFUVQjyhvkakFeLt16irreLCEw7mpLH78vlfPV1y31gSPtoWJZaM5K37y9JG2mJJnlj7QdaMoFc+uJqKkBQcKNAZlRX5/SudecYmV3c9zGkPhRrT+5W6o/kjTrPZM8BJOKPOLg46qHJKppRrH1rj9GEknf4RASrCQr9wiHhK+eKYvXl8zYftH6iAcCi7X8MZNryFVY1bfB9DgNM+NZz7Xnw3b938Fe9RU5F/59JdSSYSFu4790giFWGaW6JZzWGn14/gnufyn7FZ98E2Gt7dzISRezJqSP+8Yza3RDMj4MIhIZnSoiPg2uM9jre+XPo46QSUbOezsCRlTHmUSjSjVfVwABH5A/Bi8CGVV2s8v6NccZqu4m7i6UySASeJjR46IHNRvO0fr+XVKvMKyc6yNmkpVT7zicH8Zem7BQcEtPocLdcZyZTyzTufp6YyTCKlmQt5c0s0r5ltzrLGTF9N2un1w5k6bhjpCeQeX/0+1y5YTW430CVzVxR9mLNQ4kpXsE6PFEw3K6YfCvVWJfjBoVG2NzTlJTLnGCu7lOzAkpUxfpRKNOlBAOn5ZQIOp++5ev4alr+zqeggAK9Cf3wrcNm8FRR4nKZbVIeFtiLP+aTjaYk6CfeyeSsIibCxJZo3w2hI8vt45ixrYs4yZ1LV7PoE2eJJZc2GLZnBBmlXP7gqqzLBd47Zj4tPOMQzgVy2SCjEmg1bsgZCpFQLTkKXW9vukrkr2HOPSsYMG1A0YeQmlWLVrS35GJOtVKIZLyJb3dcC1LjvBWem5/Zm2Nwl1ETCVIRo906jK/65vrnLx2gtMBKsO4SFokmmkGhCueD+wrN4l0qkpc/iJK70RTqeSGYlGYB7nnuHIw+oK/pwaCyZAqRkMdBCte3iSWX6PctAyKvLtrOZ7vXMNNpXTR3N9Q+vzatuva0twfWPrM0anHH20fuX/O6LKVfSSjfrpu8+LUH2frvSHzSlStCEyxVITwmHhOtOGcuVD67u6VDKznut3aMyTDKVIpbo3Kg7afeepbRhA6uzRtC1xQvXb9vY0kZrvPADs/FEiufe2Egsmb2vd6BCc0uU9R8VnkqpLacJrq62inuff3tnHx5kJqa77qG1RHLu6sIiXPfwWmKJ7MEZCJx91M5k471AFFOqD6qj0ufsVxnOG4KeO0VGRQh+efqEkucK4kK3K108e9KuNleU3+HNfdrY4QPpVxlieyyg25oAhAX2GVDFe1uipTf2IZpIcvLhQ1mw4r1O7Z9IacE+Jr+WvvUxV8/P77/JNXLQHnmFQtNSwO+eWk84JIQEKsOCiHDV1NFZTV25zX650ndAj69+v+gfIJGwEM+5i4u7ZYdiOdtet2ANIwftwZhhAzL9RxUhIZZUbjgm/2+5Un1QxQqwtlfwdMa8lag6d33p+ZPSD9XOmLcy6w4vkXKaSSeNGlz0M/L2cSWSKS48/uCsqcY7Y1e7eHZGdyTSQs/JXTp3BaOHDig4AKc3sESDM1y3u0ZqlUtS6bYkA5BM0ekkk5ZSp6aR4iTCjoxTuOJvpe8ow8C//nk5oRJ9hemRZm0JRVW5Zv5qULj+kbVZQ8GLiadS9KsMc93Da4tu0xZL8tNTx2Y1k/3wc5/ImyUVnFp30+9ZRjKVAsmetqFpc4x7n3+bE8fuy5oNW9naGs9KMl65TYDexBlLKtd8ZTQnjtk360JWqD8qPX/SZfNWcMVJn6Rg3lVhzYatBVYU7uO6deFr3Pbk61zbyabCck+0V+qC77cpsdRxkillxbubi06d0ZnqGY2bWtGc61UsqXz5N89ySycHtQQt0EQjIifizMoZBu5S1Rty1lcB9wATgWbgm+4ka4jIFcC5OHMWX6SqTwQZ6xc+OYSHV70f5Cl2C+nLeBCD4ZKZE3Ts4IkUXLtgNZGwv8EsJxy6Dxu2tFEZForO6i3OM1hn1I/k3hffIQT8+sl1VBSp4dOWufvJj/3KB1dz9fzVRaeeSGuNJxgxqCZzEcxNSFf+bTXXzF9NTaQicyELSf78RWnRhHLj468U7AOMJlOc98el3HxsJO9iWmz+pnhSufLB1TRu2sF5nz2o4CyzxS6mhS6emlJfFb+LHb9QcyGQ19+We+eU25QYFvjpqWPzEmipO7B7n3+bTe9v43eLnyeRUpKpFIkUmUT6kzkNhEOhDjeN9qsMuw94Z4slUr12Oo7AEo2IhIHbgS8AjcBSEVmgqt4/E88FNqnqKBE5A7gR+KaIjAbOAMYAw4D/FZFDVLVww30XpOd7SXXDxGem90qkIO7zrvWR1e+z8P8+INXOUL9kioIDIzo7qMTPPz9FeHz1+1z/yFpCUrjaeCIF29yir5fMXZF38c7V3kCTWFJ55+MdfOaGJ7MuhiVaHvndU+uZteRNbp42HgVfzWEvrG/Ou3hGk5qpbF5M7nxN6eOnk4CmlGhSqY6ESKaUVEozn3W6v817cW5uieY1JSY1v6+t1B3Yvc+/zZUPruaSw7XofFGJFCRSqXabRgvZHktSHQkVvDvvrdNx+C8V3HFHAutUdb2qxoDZ5E8HfSrOQ6EA84ATxGmAPxWYrapRVX0TWOcer1t5qzfHdrGmM9MxHf3pxpKKiPi+CyqH9MPFbXGnCncp8aR2y51lNJFiWzRBW9z5i9nPRIHRhDOp34x5K2iLZ+9fqGr4LX9/Ne8YVRWhohfp9H7p31/v8dd9sC2TBNLJqy2eIp7Uggk9fXEG584qXCSTXvfQ2kzshSqtp4/T3BJtt9m1GG8c7WlvEElvnY5DCs130i0HFpkGnKiq57nvvw0cpaoXeLZZ7W7T6L5/AzgKuBZ4XlX/7C7/A/CYqs4rcJ7pwHSAIUOGTJw9e7bvGFvjSVq2tfB+6Z9tjxBgSA29Nj5w4vugj8YXFmH4oBre3dRacF6g7tDR+EIi7d5pdbfc+MIi7Fe3B29t3F4yeaf70rzxhkU4cO9+1ER23qm0xpOs/2h73vclInxy3/5FL/yFfn/DIgzds4b3NreS9Pk5hUQ4zD1PMqW88v62gp9xSISD3NgLbZc+TiyZ4s2PtpNUzfr8xP2e0kNZlOz5prxxlLKlNZ5JSinVzGc9YlANA2vyS1a1Z8qUKctVtb5DO3XQLj8YQFXvBO4EqK+v18mTJ/vet7klyuwFT3Drqp3/6CvDcPEJB/Or/13XpQEClWGo61fJe1tzxyA5Ss3oWVkR4tELj2XNS8/z62XxTCwVIZjxpcO45e+vtjuhWrlccniCW1dl/zOqCAkXn3AwVZEQP3/0Fd/H+sXXxtK/OsKaDVuYteRNNKUUGgjYkVpulxyeYHVqGE8UqO5w8fGjmLl4HckiP4jqSIgllx/HknUbmeEOu44lk6RSxZu60k00hfow9qgMkVKnXM9fljYSDgnnHxbjo9oDue+Fd0o2u4UFKnIKrEbCAqpUVoSJJpKICNUVYWLJVKZPICuGSIgUZKZ1CIeE7dHidw25P1/nM/ksqXUbuWzeCsIScqa2EMkr91NV4QwNyZ0Fdskpx+b1pfz4xifzmoJ+9rWxnOAZFp6r0O9vdSTEwxcczRUzn/U18KOqQrh52nhO8DTnbW9o4idzGvI+u9zYtzc0ZWavTTfbneA+sJv+fryf38++OpYTx+4crJH+d5W7v1/tDVnvbYJMNE3ASM/7Ee6yQts0ikgFMBBnUICffbvMW705t4135F79uMxTouSCKaM466j9eOClRm564lW3o9j5l5hKZSeNk8fuy0+/Opa62ir+sfZ9fvHY/7Huox2Z9d85Zj/q99+LGZ425PScM+mS/ekpABprIrzw78fljYDZd2B11hTNF0xxinVu2NJG06YdRBMpYskUNzz2SrtDjkMAApGQU8QzFBIqw0IyBV8ZP5T6/Qfx4dY23mzeQf+qMH9Z1pgp0VNZoFnp85/chxs99d36VVVw3UNrQbXdxPidY/bjTPeiMnX8MM777EE0bmrlhfXN3LLwNSJhZxjtBVOcYbSPr34/qwM9Eha+dsRwHmzYkBl6/I2JIzlkwEdc+PlPc/X8VVm12b5zzH78+IuH8p3PHMCaDVvY2ppgzXtbmPXsm1mdxHW1VZnK294LhPdCFAkLFx3vfP7pX/jH17zvPGsTdv79XDV1NGOHDcxcDC4+4RAaN7Wy8fWXOf/4w7n4hEPcUV7Kux+3cv0jazP/Npxh2nDztPEAeRcnb2xA0QtZsRhWN23h+kfWEgmFaI0n3CZDJ2HW1VZSHSHrfIU+E4D7XniHmTmd7IXizb0Y1tVWcdNp4zLJPPOgaztJJr1fod/fUUP6Z46X20ejqtREKoglU5nf6dx40t/bXc+s5w/PvkWkwvkZ5sae+xl4awGmzx8SZ3LCa74yOvP9pLcrtr9fdbVVvTax5Aqy6awCeA04ASdJLAXOUtU1nm3OBw5X1R+4gwG+rqqni8gY4D6cfplhwD+Ag0sNBqivr9dly5Z1KM7FixdzeP0x7Y5aaW85kHmS/a3mHUWLSRaq2ZX7F0mhv0wWL15Msbs0P6N5mluiPPfGRja2xBg7bADrPmxh3UfbOeagvRjcvzrv4uR9XWzYpzfmja+/zP5j6ksW0kzvs2FLK1tb4wyoqWSPSKjdz6zU9+mMvnIuzmOGDcx05nq39X5+pQp++v1Mi527M8cq9vNt76/VjjyL0ZHvp9C/gVXLniv6++H3fJ2Jwe8F1M/vb+6os46coyvPvTS3RHnxuWc58phje3VCEJHAm85Q1cC+gC/jJJs3gCvdZT8FTnFfVwNzcTr7XwQO8ux7pbvfq8BJfs43ceJE7ahFixZ1eJ9ysvi6xuLrGouva3p7fKqqwDINMA+oarB9NKr6KPBozrKrPa/bgG8U2fdnwM+CjM8YY0zwghzebIwxxliiMcYYEyxLNMYYYwJlicYYY0ygAhve3BNE5CPg7Q7uNhjYGEA43cXi6xqLr2ssvq7p7fEBHKqqgc4vsMtXBvBS1b07uo+ILNOgx5B3gcXXNRZf11h8XdPb4wMnxqDPYU1nxhhjAmWJxhhjTKAs0bgFOXsxi69rLL6usfi6prfHB2WIsU8NBjDGGNP72B2NMcaYQFmiMcYYE6ygq3b21i/gRJzK0OuAfyvD+d4CVgENuNVSgb2AhcDr7v8HucsFuM2NbSXwKc9xznG3fx04x7N8onv8de6+UiKeWcCHwGrPssDjKXYOn/FdizPlRIP79WXPuivcc70KfKnUzxk4EHjBXf4XoNJdXuW+X+euP6BIfCOBRcBaYA1wcW/6DNuJr1d8hjiV218EVrjxXdfZY3ZX3D7juxt40/P5Teip3xF32zDwMvBwb/r88uLsrgvprvTl/nDeAA4CKt1/TKMDPudbwOCcZTelf4DAvwE3uq+/DDzm/uM9GnjB8w9wvfv/Qe7r9IXsRXdbcfdtd2oF4DjgU2RfyAOPp9g5fMZ3LXBpgW1Huz/DKveX4A33Z1z05wzMAc5wX/8e+KH7+v8Bv3dfnwH8pUh8Q3EvJkB/nOkwRveWz7Cd+HrFZ+h+T7Xu6wjOhevojh6zO+P2Gd/dwLQC25f9d8Rd/xOcubsebu97K/fnlxdnEBfV3v4FHAM84Xl/BXBFwOd8i/xE8yow1H09FHjVfX0HcGbudsCZwB2e5Xe4y4YCr3iWZ23XTkwHkH0hDzyeYufwGd+1FL5IZv38gCfcn3HBn7P7i70RqMj995De131d4W7X7t2hu+184Au97TMsEF+v+wyBPYCXgKM6eszujNtnfHdTONGU/eeLM/PwP4DjgYc78zMpx+enqrttH81w4F3P+0Z3WZAU+LuILBeR6e6yIar6nvv6fWBIifjaW95YYHlHlSOeYufw6wIRWSkis0RkUCfjqwM2q2qiQHyZfdz1W9ztixKRA4AjcP7q7XWfYU580Es+QxEJi0gDThPpQpy/oDt6zO6Mu934VDX9+f3M/fx+JSLpqTN74uf7X8AMds4k35mfSWCfn9fummh6wrGq+ingJOB8ETnOu1KdPw+0RyIroBzxdOIcvwM+AUwA3gNuDSCsDhGRWuAB4EequtW7rjd8hgXi6zWfoaomVXUCzl/mRwKH9VQsheTGJyJjcf6qPwz4NE5z2OUBx1Dw5ysiU4EPVXV5kOfvLrtromnC6SxNG+EuC4yqNrn//xD4G84v1gciMhTA/f+HJeJrb/mIAss7qhzxFDtHSar6gfvLnwL+G+cz7Ex8zcCeIlKRszzrWO76ge72eUQkgnMRv1dV/1ri+yv7Z1govt72GboxbcYZuHBMJ47ZnXGXiu9EVX1PHVHgf+j859fVn+8k4BQReQuYjdN89ut2vrce+/yA3baPpgKnU+5AdnZ0jQnwfP2A/p7X/8QZ0XEz2Z1+N7mvTya7Y/FFd/leOCNeBrlfbwJ7uetyOxa/7COuA8juAwk8nmLn8BnfUM/rHwOz3ddjyO7QXI/TmVn05wzMJbtD8/+5r88nu9N0TpHYBLgH+K+c5b3iM2wnvl7xGQJ7A3u6r2uAZ4CpHT1md8btM76hns/3v4AbevJ3xN1mMjsHA/SKzy8vxu6+qO4qXzijRF7DaRe+MuBzHeT+oNJDJa90l9fhdOa9Dvyv5x+gALe7sa0C6j3H+j7OsMJ1wPc8y+uB1e4+Myk9vPl+nKaTOE4767nliKfYOXzG9yf3/CuBBWRfNK90z/UqnhF3xX7O7s/kRTfuuUCVu7zafb/OXX9QkfiOxWnSWIlnqHBv+Qzbia9XfIbAOJxhuSvd7/Hqzh6zu+L2Gd+T7ue3GvgzO0emlf13xHOcyexMNL3i88v9shI0xhhjArW79tEYY4wpE0s0xhhjAmWJxhhjTKAs0RhjjAmUJRpjjDGBskRj+jQRqRORBvfrfRFp8ryvzNn2RyKyh49jLhaR+iLLX/Ucf1o7x/iuiMx0X18rIpd25vszZldQUXoTY3ZdqtqMU24FEbkWaFHVW4ps/iOcZyN2dOGUZ6vqsi7sHxgRqdCdNaqMKRu7ozG7HRE5QUReFpFVbmHJKhG5CBgGLBKRRe52vxORZSKyRkSu6+S59hKRB90ijM+LyLgS209wt1spIn8TkUEiso+ILHfXjxcRFZH93PdviMgeIrK3iDwgIkvdr0nu+mtF5E8isgTnYU1jys4SjdndVOOUev+mqh6Oc1f/Q1W9DdgATFHVKe62V6pqPc5T4p8rlSRc93qazuqA64CXVXUc8O84ZWHacw9wubv9KuAaderjVYvIAOCzwDLgsyKyP05hxR04da5+paqfBk4D7vIcczTweVU900f8xnQ7azozu5sw8Kaqvua+/yNOHaj/KrDt6e6UDhU484KMxilJ0p6spjMRORbnwo+qPun2GQ0otKOIDMSpr/WUJ7a57ut/4hRSPA74OU6tPMGpwQXweWC0iKQPN8Ct3AywQFVbS8RtTGAs0RhTgIgcCFwKfFpVN4nI3Th3Qz3laZy7mf1xJjG7HKeW2SPu+hBwtKq2eXdyE8/28oVpTD5rOjO7myRwgIiMct9/G0jfQWzDmfYYYADOBXqLiAzBmUeoM54BzgYQkcnARs2ZtyZNVbcAm0TkswViewb4FvC6OiX+P8Ypevisu/7vwIXpY4nIhE7Ga0y3szsas7tpA74HzHXn1FiKU+oc4E7gcRHZoKpTRORl4BWcmQaXdPJ81wKzRGQlzmi2c0psfw7we3eY9Xo3VlT1LXFuT552t3sWGKGqm9z3FwG3u+epcLf7QSdjNqZbWfVmY4wxgbKmM2OMMYGyRGOMMSZQlmiMMcYEyhKNMcaYQFmiMcYYEyhLNMYYYwJlicYYY0yg/j/XxE3K5p9vLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered_df[['Total Follower', 'PaidStarPerWatchedHourPerFollower']].plot(x='Total Follower', y='PaidStarPerWatchedHourPerFollower', grid=True, kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5bf9fe4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select data of PaidStarPerWatchedHourPerFollower in Q3-Q4\n",
    "cols = ['PaidStarPerWatchedHourPerFollower']\n",
    "Q1 = df[cols].quantile(0.25)\n",
    "Q3 = df[cols].quantile(0.75)\n",
    "condition = (df[cols]>Q3).any(axis=1)\n",
    "df_filtered_by_PSPWPF = df[condition]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2d5c9c62",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eclbd/.local/lib/python3.6/site-packages/ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PaidStarPerWatchedHour</th>\n",
       "      <th>PSPW_abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Role_Philanthropist</th>\n",
       "      <td>-0.282005</td>\n",
       "      <td>0.282005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Role_Manager</th>\n",
       "      <td>-0.254636</td>\n",
       "      <td>0.254636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Character_Cont_Conscientiousness</th>\n",
       "      <td>-0.245829</td>\n",
       "      <td>0.245829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Character_Facet_Cont_AchievementStriving</th>\n",
       "      <td>-0.245191</td>\n",
       "      <td>0.245191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Role_Marshal</th>\n",
       "      <td>-0.237801</td>\n",
       "      <td>0.237801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personal_Values_Facet_Cont_Hedonism</th>\n",
       "      <td>0.236380</td>\n",
       "      <td>0.236380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Character_Cont_Openness</th>\n",
       "      <td>-0.230269</td>\n",
       "      <td>0.230269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Role_Commander</th>\n",
       "      <td>-0.228296</td>\n",
       "      <td>0.228296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Character_Facet_Cont_Morality</th>\n",
       "      <td>0.221398</td>\n",
       "      <td>0.221398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personal_Values_Facet_Cont_ConformityRules</th>\n",
       "      <td>-0.215039</td>\n",
       "      <td>0.215039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            PaidStarPerWatchedHour  PSPW_abs\n",
       "Role_Philanthropist                                      -0.282005  0.282005\n",
       "Role_Manager                                             -0.254636  0.254636\n",
       "Character_Cont_Conscientiousness                         -0.245829  0.245829\n",
       "Character_Facet_Cont_AchievementStriving                 -0.245191  0.245191\n",
       "Role_Marshal                                             -0.237801  0.237801\n",
       "Personal_Values_Facet_Cont_Hedonism                       0.236380  0.236380\n",
       "Character_Cont_Openness                                  -0.230269  0.230269\n",
       "Role_Commander                                           -0.228296  0.228296\n",
       "Character_Facet_Cont_Morality                             0.221398  0.221398\n",
       "Personal_Values_Facet_Cont_ConformityRules               -0.215039  0.215039"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = df_filtered_by_PSPWPF.corr()\n",
    "c['PSPW_abs'] = c['PaidStarPerWatchedHour'].apply(lambda x: abs(x))\n",
    "c_rank = c[['PaidStarPerWatchedHour', 'PSPW_abs']].sort_values('PSPW_abs', ascending=False)[c['PSPW_abs'] > c['PSPW_abs'].mean()]\n",
    "c_rank = c_rank.iloc[3:]\n",
    "c_rank.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e082276c",
   "metadata": {},
   "source": [
    "## Correlation Matrix with General Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "13e54530",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = df.corr()\n",
    "corr_matrix['PSPW_abs'] = corr_matrix['PaidStarPerWatchedHour'].apply(lambda x: abs(x))\n",
    "corr_core = corr_matrix[['PaidStarPerWatchedHour', 'PSPW_abs']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "18f7044a",
   "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>PaidStarPerWatchedHour</th>\n",
       "      <th>PSPW_abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PaidStarPerWatchedHour</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaidStarPerWatchedHour.1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaidStarPerWatchedHourPerFollower</th>\n",
       "      <td>0.945065</td>\n",
       "      <td>0.945065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personal_Values_Facet_Cont_Hedonism</th>\n",
       "      <td>0.142095</td>\n",
       "      <td>0.142095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Role_Philanthropist</th>\n",
       "      <td>-0.140508</td>\n",
       "      <td>0.140508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personal_Values_Facet_Cont_ConformityRules</th>\n",
       "      <td>-0.140314</td>\n",
       "      <td>0.140314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Character_Facet_Cont_AchievementStriving</th>\n",
       "      <td>-0.134136</td>\n",
       "      <td>0.134136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Role_Manager</th>\n",
       "      <td>-0.130683</td>\n",
       "      <td>0.130683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Character_Cont_Conscientiousness</th>\n",
       "      <td>-0.126654</td>\n",
       "      <td>0.126654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Role_Marshal</th>\n",
       "      <td>-0.116596</td>\n",
       "      <td>0.116596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            PaidStarPerWatchedHour  PSPW_abs\n",
       "PaidStarPerWatchedHour                                    1.000000  1.000000\n",
       "PaidStarPerWatchedHour.1                                  1.000000  1.000000\n",
       "PaidStarPerWatchedHourPerFollower                         0.945065  0.945065\n",
       "Personal_Values_Facet_Cont_Hedonism                       0.142095  0.142095\n",
       "Role_Philanthropist                                      -0.140508  0.140508\n",
       "Personal_Values_Facet_Cont_ConformityRules               -0.140314  0.140314\n",
       "Character_Facet_Cont_AchievementStriving                 -0.134136  0.134136\n",
       "Role_Manager                                             -0.130683  0.130683\n",
       "Character_Cont_Conscientiousness                         -0.126654  0.126654\n",
       "Role_Marshal                                             -0.116596  0.116596"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_core.sort_values('PSPW_abs', ascending=False).head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78ffeee3",
   "metadata": {},
   "source": [
    "## Divide into several group: Country, Game, Genger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9dd6a048",
   "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>PaidStarPerWatchedHour</th>\n",
       "      <th>PSPW_abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total Follower</th>\n",
       "      <td>-0.195180</td>\n",
       "      <td>0.195180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broadcast Hours</th>\n",
       "      <td>0.220304</td>\n",
       "      <td>0.220304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaidStarPerWatchedHour</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Character_Facet_Cont_Rigidity</th>\n",
       "      <td>-0.099999</td>\n",
       "      <td>0.099999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Character_Facet_Cont_AchievementStriving</th>\n",
       "      <td>0.079238</td>\n",
       "      <td>0.079238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Role_Curator</th>\n",
       "      <td>0.174295</td>\n",
       "      <td>0.174295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personal_Values_Cont_Conservation</th>\n",
       "      <td>0.126991</td>\n",
       "      <td>0.126991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Self_Esteem_Cont_SELevelIntelligence</th>\n",
       "      <td>0.114209</td>\n",
       "      <td>0.114209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temperament_Centric</th>\n",
       "      <td>-0.020632</td>\n",
       "      <td>0.020632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaidStarPerWatchedHourPerFollower</th>\n",
       "      <td>0.639993</td>\n",
       "      <td>0.639993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          PaidStarPerWatchedHour  PSPW_abs\n",
       "Total Follower                                         -0.195180  0.195180\n",
       "Broadcast Hours                                         0.220304  0.220304\n",
       "PaidStarPerWatchedHour                                  1.000000  1.000000\n",
       "Character_Facet_Cont_Rigidity                          -0.099999  0.099999\n",
       "Character_Facet_Cont_AchievementStriving                0.079238  0.079238\n",
       "...                                                          ...       ...\n",
       "Role_Curator                                            0.174295  0.174295\n",
       "Personal_Values_Cont_Conservation                       0.126991  0.126991\n",
       "Self_Esteem_Cont_SELevelIntelligence                    0.114209  0.114209\n",
       "Temperament_Centric                                    -0.020632  0.020632\n",
       "PaidStarPerWatchedHourPerFollower                       0.639993  0.639993\n",
       "\n",
       "[123 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distribution of VN, PUBG, Male\n",
    "vpm = df.loc[df['Game'] == 'PUBG'].loc[df['Country'] == 'VN'].loc[df['Gender'] == 'Male']\n",
    "test_corr = vpm.corr()\n",
    "test_corr['PSPW_abs'] = test_corr['PaidStarPerWatchedHour'].apply(lambda x: abs(x))\n",
    "test_corr = test_corr[['PaidStarPerWatchedHour', 'PSPW_abs']]\n",
    "test_corr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b66376a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "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": [
    "# select data of PaidStarPerWatchedHourPerFollower in Q3-Q4\n",
    "cols = ['PSPW_abs']\n",
    "Q3 = test_corr[cols].quantile(0.75)\n",
    "condition = (test_corr[cols]>Q3).any(axis=1)\n",
    "test_corr = test_corr[condition]\n",
    "test_corr = test_corr.sort_values('PSPW_abs',ascending= False).iloc[3:,1:]\n",
    "test_corr\n",
    "test_corr.head(5).plot(kind='barh')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6eb13ac2",
   "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>PaidStarPerWatchedHour</th>\n",
       "      <th>PSPW_abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total Follower</th>\n",
       "      <td>-0.160742</td>\n",
       "      <td>0.160742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broadcast Hours</th>\n",
       "      <td>0.074013</td>\n",
       "      <td>0.074013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaidStarPerWatchedHour</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Character_Facet_Cont_Rigidity</th>\n",
       "      <td>-0.387625</td>\n",
       "      <td>0.387625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Character_Facet_Cont_AchievementStriving</th>\n",
       "      <td>0.008814</td>\n",
       "      <td>0.008814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Role_Curator</th>\n",
       "      <td>-0.200469</td>\n",
       "      <td>0.200469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personal_Values_Cont_Conservation</th>\n",
       "      <td>-0.347054</td>\n",
       "      <td>0.347054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Self_Esteem_Cont_SELevelIntelligence</th>\n",
       "      <td>0.367693</td>\n",
       "      <td>0.367693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temperament_Centric</th>\n",
       "      <td>0.172117</td>\n",
       "      <td>0.172117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaidStarPerWatchedHourPerFollower</th>\n",
       "      <td>0.618702</td>\n",
       "      <td>0.618702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          PaidStarPerWatchedHour  PSPW_abs\n",
       "Total Follower                                         -0.160742  0.160742\n",
       "Broadcast Hours                                         0.074013  0.074013\n",
       "PaidStarPerWatchedHour                                  1.000000  1.000000\n",
       "Character_Facet_Cont_Rigidity                          -0.387625  0.387625\n",
       "Character_Facet_Cont_AchievementStriving                0.008814  0.008814\n",
       "...                                                          ...       ...\n",
       "Role_Curator                                           -0.200469  0.200469\n",
       "Personal_Values_Cont_Conservation                      -0.347054  0.347054\n",
       "Self_Esteem_Cont_SELevelIntelligence                    0.367693  0.367693\n",
       "Temperament_Centric                                     0.172117  0.172117\n",
       "PaidStarPerWatchedHourPerFollower                       0.618702  0.618702\n",
       "\n",
       "[123 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distribution of VN, PUBG, Female\n",
    "vpf = df.loc[df['Game'] == 'PUBG'].loc[df['Country'] == 'VN'].loc[df['Gender'] == 'Female']\n",
    "test_corr_f = vpf.corr()\n",
    "test_corr_f['PSPW_abs'] = test_corr_f['PaidStarPerWatchedHour'].apply(lambda x: abs(x))\n",
    "test_corr_f = test_corr_f[['PaidStarPerWatchedHour', 'PSPW_abs']]\n",
    "test_corr_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "820a6ff1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eclbd/.local/lib/python3.6/site-packages/ipykernel_launcher.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "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": [
    "# select data of PaidStarPerWatchedHourPerFollower in Q3-Q4\n",
    "cols = ['PSPW_abs']\n",
    "Q3 = test_corr_f[cols].quantile(0.75)\n",
    "condition = (test_corr_f[cols]>Q3).any(axis=1)\n",
    "test_corr_f = test_corr_f[condition]\n",
    "test_corr_f = test_corr_f.sort_values('PSPW_abs',ascending= False)[test_corr_f['PSPW_abs'] > test_corr_f['PSPW_abs'].mean()].iloc[3:,1:]\n",
    "test_corr_f.plot(kind='barh')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "01d3b2f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country\n",
      "ID    0.000022\n",
      "PH    0.000202\n",
      "VN    0.000025\n",
      "Name: PaidStarPerWatchedHour, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Country'>"
      ]
     },
     "execution_count": 19,
     "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": [
    "# print(df.groupby('Country').mean()['PaidStarPerWatchedHour'])\n",
    "print(df.groupby('Country').mean()['PaidStarPerWatchedHour']/df.groupby('Country').count()['PaidStarPerWatchedHour'])\n",
    "(df.groupby('Country').mean()['PaidStarPerWatchedHour']/df.groupby('Country').count()['PaidStarPerWatchedHour']).plot(kind='barh')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b889dc65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Country'>"
      ]
     },
     "execution_count": 20,
     "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[df['Gender'] == 'Male'].groupby('Country').mean()['PaidStarPerWatchedHour'].plot(kind='barh')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "84fdc09f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Country'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD4CAYAAADlwTGnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAANaElEQVR4nO3df6zddX3H8eeb3kBhzKK0MwrTC9vItFTBFcmYSzbI5rBhom5a5w/4YxLiZCMGJ479wbbMFWO2ZZKMNG5DjQGBgJPgj0zWMLcgzW0pFobdoJZIXWSUpFrKMHTv/XG+HafX297v7b3f8z2n7+cjubnnfL7f7/m+8s2553W+3++53xOZiSSpnuP6DiBJ6ocFIElFWQCSVJQFIElFWQCSVNRU3wEWYuXKlTk9Pd13DEmaGFu2bHk6M1fNNW2iCmB6epqZmZm+Y0jSxIiIJw43zUNAklSUBSBJRVkAklSUBSBJRVkAklSUBSBJRVkAklSUBSBJRVkAklSUBSBJRVkAklSUBSBJRVkAklSUBSBJRVkAklSUBSBJRU3UF8Js372X6Wvv6TtGp3ZtWNd3BElFuAcgSUVZAJJUlAUgSUVZAJJUlAUgSUVZAJJUlAUgSUVZAJJUlAUgSUVZAJJUlAUgSUVZAJJUlAUgSUVZAJJUVCcFEBGbIuLNs8aujoivRERGxFVD4zdGxOVd5JAkHV5XewC3AOtnja0H/gJ4CviDiDi+o3VLklroqgDuANYdfJGPiGnglcB3gf8G7gUu62jdkqQWOimAzHwG2Axc3AytB24Dsrl/A3BNRCzrYv2SpPl1eRJ4+DDQ+uY+AJm5E3gA+J35HiQiroiImYiYObB/bydBJamiLgvgH4GLIuINwEmZuWXW9I8DHwXiSA+SmRszc21mrl120oqOokpSPZ0VQGbuAzYBf8/Qu/+h6d8G/h24pKsMkqTD6/r/AG4BXs8cBdD4c+D0jjNIkuYw1eWDZ+YXGTrEk5m7gLOH7j+E/4wmSb3wxVeSirIAJKkoC0CSirIAJKkoC0CSirIAJKkoC0CSirIAJKkoC0CSirIAJKkoC0CSirIAJKkoC0CSiur0aqBLbc1pK5jZsK7vGJJ0THAPQJKKsgAkqSgLQJKKsgAkqSgLQJKKsgAkqSgLQJKKsgAkqSgLQJKKsgAkqSgLQJKKsgAkqSgLQJKKsgAkqSgLQJKKsgAkqSgLQJKKsgAkqSgLQJKKsgAkqSgLQJKKsgAkqSgLQJKKsgAkqSgLQJKKsgAkqSgLQJKKsgAkqSgLQJKKsgAkqSgLQJKKsgAkqSgLQJKKsgAkqSgLQJKKmuo7wEJs372X6Wvv6TuGJI3Mrg3rOnvsVnsAEXFqZwkkSb1oewjomxFxe0S8JSKi00SSpJFoWwBnARuB9wH/GREfj4izuoslSepaqwLIgX/KzHcDHwAuAzZHxH0R8YudJpQkdaLVSeDmHMB7GewBfB+4CvgScA5wO3BGR/kkSR1p+ymg+4HPAZdm5pND4zMRcdPSx5IkdW3eAoiIZcDdmflnc03PzBuWPJUkqXPzngPIzAPABSPIIkkaobaHgLZFxJcYHO9/9uBgZt7ZSSpJUufaFsByYA9w4dBYAhaAJE2otgXw6cz8t+GBiPilDvJIkkak7T+CfarlmCRpQhxxD6D5J68LgFUR8eGhSS8BlnUZTJLUrfkOAR0PnNzM95ND4z8AfqurUJKk7h2xADLzPuC+iLg5M59Y6INHxAFge7OeR4HLMnN/ROzLzJOH5rscWJuZH1roOiRJR6ftSeATImIjMD28TGZeeNglBp7LzHMAIuLzwJXAXy48piRpqbUtgNuBm4BPAweOcl3fAF53lMtKkpZY2wJ4ITP/9mhXEhFTwMXAV5uhEyNi29AsL2Nwcbm5lr0CuAJg2UtWHW0ESdIsbQvg7oj4IHAX8PzBwcx8Zp7lhl/ovwH8XXP7/w8NwYvnAOZ6gMzcyOC7CDjhFT+XLfNKkubRtgAua35/ZGgsgTPnWe6QF3pJ0vhoVQCZ6fX+JekY0/YLYd4/13hmfnZp40iSRqXtIaDzhm4vBy4CtgJHLIDhz/ofaTwzbwZubplFkrQE2h4Cumr4fkScAtzaRSBJ0mi0vRjcbM/i9wBL0kRrew7gbgaf+oHBReBeA9zWVShJUvfangP45NDtF4AnZn05vCRpwrQ6BNRcFO7bDK4I+lLgR12GkiR1r1UBRMQ7gc3AbwPvBB6ICC8HLUkTrO0hoOuA8zLzKYCIWAV8Hbijq2CSpG61/RTQcQdf/Bt7FrCsJGkMtd0D+GpEfA24pbn/LuDL3USSJI3CfN8J/LPAyzPzIxHxduBNzaT7gc93HU6S1J359gD+GvgYQGbeCdwJEBFrmmmXdJhNktSh+Y7jvzwzt88ebMamO0kkSRqJ+fYATjnCtBOXMEcra05bwcyGdaNerSQdk+bbA5iJiA/MHoyI3wW2dBNJkjQK8+0BXA3cFRHv4cUX/LXA8cDbOswlSerYEQsgM78PXBARvwqc3Qzfk5n/3HkySVKn2n4fwCZgU8dZJEkj5H/zSlJRFoAkFWUBSFJRFoAkFWUBSFJRFoAkFWUBSFJRFoAkFWUBSFJRFoAkFWUBSFJRFoAkFWUBSFJRFoAkFWUBSFJRFoAkFWUBSFJRFoAkFWUBSFJRFoAkFWUBSFJRFoAkFWUBSFJRFoAkFWUBSFJRFoAkFWUBSFJRFoAkFWUBSFJRFoAkFWUBSFJRU30HWIjtu/cyfe09fcdg14Z1fUeQpEVzD0CSirIAJKkoC0CSirIAJKkoC0CSirIAJKkoC0CSirIAJKkoC0CSirIAJKkoC0CSirIAJKkoC0CSirIAJKmokRVAROxrfk9HxHMR8WBEPBoRmyPi8lHlkCQN9PV9AI9n5rkAEXEmcGdERGb+Q095JKmc3g8BZeZO4MPA7/edRZIq6b0AGluBn+87hCRVMi4FEIedEHFFRMxExMyB/XtHmUmSjmnjUgDnAo/ONSEzN2bm2sxcu+ykFSOOJUnHrt4LICKmgU8Cn+o5iiSV0tengH4mIh4ElgM/BP4mM2/uKYsklTSyAsjMk5vfu4ATR7VeSdLcej8EJEnqhwUgSUVZAJJUlAUgSUVZAJJUlAUgSUVZAJJUlAUgSUVZAJJUlAUgSUVZAJJUlAUgSUVZAJJUVF+Xgz4qa05bwcyGdX3HkKRjgnsAklSUBSBJRVkAklSUBSBJRVkAklSUBSBJRVkAklSUBSBJRVkAklSUBSBJRVkAklSUBSBJRVkAklSUBSBJRVkAklSUBSBJRVkAklRUZGbfGVqLiB8CO/rO0dJK4Om+QyyAebtl3m6Z9/BenZmr5powUV8JCezIzLV9h2gjImYmJSuYt2vm7ZZ5j46HgCSpKAtAkoqatALY2HeABZikrGDerpm3W+Y9ChN1EliStHQmbQ9AkrRELABJKmosCiAifiMidkTEYxFx7RzTT4iILzTTH4iI6aFpH2vGd0TEm8c5b0ScGhGbImJfRNw4iqyLzPtrEbElIrY3vy8c87xvjIhtzc9DEfG2cc47NP1VzXPimnHNGhHTEfHc0Pa9qeusi8nbTHtdRNwfEY80z+Hl45o3It4ztG23RcT/RsQ5XeclM3v9AZYBjwNnAscDDwGvnTXPB4GbmtvrgS80t1/bzH8CcEbzOMvGOO9PAG8CrgRunIDtey7wyub22cDuMc97EjDV3H4F8NTB++OYd2j6HcDtwDXjmhWYBh4exXN2ifJOAd8CXt/cP3WcXxtmzbMGeHwU23gc9gDeCDyWmTsz80fArcBbZ83zVuAzze07gIsiIprxWzPz+cz8DvBY83hjmTczn83MfwX+p+OMwxaT98HM/F4z/ghwYkScMMZ592fmC834cmAUn3BYzPOXiLgU+A6D7TvWWXuwmLy/DnwrMx8CyMw9mXlgjPMOe3ezbOfGoQBOA747dP/JZmzOeZo/8L0MGr3NskttMXn7sFR53wFszcznO8r5Y1kaC8obEedHxCPAduDKoUIYu7wRcTLwUeBPOs74YzkaC30unBERD0bEfRHxy12HZXF5zwIyIr4WEVsj4g/HPO+wdwG3dJTxEJN2KQj1ICJWAzcweFc11jLzAWB1RLwG+ExEfCUzR7nHtRDXA3+Vmfv6e5Pd2n8Br8rMPRHxC8AXI2J1Zv6g72CHMcXgcOt5wH7g3ojYkpn39hvryCLifGB/Zj48ivWNwx7AbuCnh+6f3ozNOU9ETAErgD0tl11qi8nbh0XljYjTgbuA92fm452nXaLtm5mPAvsYnLvo0mLyng98IiJ2AVcDfxQRHxrHrM1h1j0AmbmFwbHuszrMuqi8DN59/0tmPp2Z+4EvA28Y47wHrWdE7/6BsTgJPAXsZHAS9+CJk9Wz5vk9Dj1xcltzezWHngTeSfcneo4679D0yxndSeDFbN9TmvnfPiHPhzN48STwq4HvASvHNe+sea6n+5PAi9m2qw7+bTE4ybkbeNkY530psJXmgwHA14F145q3uX9cs13P7DLnIXlGtaJ5NtxbgP9g8K7iumbsT4HfbG4vZ/ApiceAzcMbCLiuWW4HcPEE5N0FPMPg3emTzPqUwDjlBf4YeBbYNvTzU2Oc930MTqZua/74Lx3358PQY1xPxwWwyG37jlnb9pJx37bAe5vMDwOfmIC8vwJ8cxQ5D/54KQhJKmoczgFIknpgAUhSURaAJBVlAUhSURaAJBVlAUhSURaAJBX1f0weVbRJxUW8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Gender'] == 'Female'].groupby('Country').mean()['PaidStarPerWatchedHour'].plot(kind='barh')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "78698a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Gender'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAD4CAYAAADPccAIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAODUlEQVR4nO3df6zd9V3H8efLFtqualUKsQKjYKqTgg7b4dgPg5votkZ+bCTuh1NiIqA44hJjYKCZzh+dzqiTPzZMMMzFMbdsCDLZUCjJwkZtodB1rI5hzWCLbEPrgFkU3v5xvmWnl9t7T+nne849t89HcnLP99fnvN/93t7X/f4456aqkCSple+YdAGSpMXFYJEkNWWwSJKaMlgkSU0ZLJKkppZOuoBJW716da1du3bSZUjSVNm+ffvXq+rY2ZYd8cGydu1atm3bNukyJGmqJPn3gy3zVJgkqSmDRZLUlMEiSWrKYJEkNWWwSJKaMlgkSU0ZLJKkpgwWSVJTBoskqSmDRZLUlMEiSWrKYJEkNWWwSJKaMlgkSU0ZLJKkpgwWSVJTBoskqSmDRZLUlMEiSWrKYJEkNWWwSJKaMlgkSU0ZLJKkpgwWSVJTBoskqSmDRZLU1NJJFzBpOx/Zy9orbpl0Gc/Lns2bJl2CJD2HRyySpKYMFklSUwaLJKkpg0WS1JTBIklqymCRJDVlsEiSmjJYJElNGSySpKYMFklSUwaLJKkpg0WS1JTBIklqymCRJDVlsEiSmjJYJElNGSySpKYMFklSUwaLJKkpg0WS1JTBIklqymCRJDVlsEiSmlpQwZKkknxwaHppkq8l+Yd5tjt7vnUkSeOxoIIFeAI4LcmKbvoc4JEJ1iNJOkQLLVgAPgFs6p6/CfjQ/gVJzkzymST3JrkryQ/P3DjJyiTXJdnarXfemOqWJLEwg+UG4I1JlgM/Ctw9tOwLwCur6gzgd4A/nGX7q4Dbq+pM4KeAP0mycniFJBcn2ZZk29NP7u2lCUk6Ui2ddAEzVdX9SdYyOFr5xIzFq4Drk6wDCjhqliF+Bjg3yW9208uBFwIPDL3GtcC1AMvWrKumDUjSEW7BBUvnJuA9wNnAMUPz3wXcUVUXdOGzZZZtA7yhqnb3XKMkaRYL8VQYwHXA71bVzhnzV/Hti/kXHWTbTwJvSxKAJGf0UqEkaVYLMliq6uGqeu8si/4Y+KMk93Lwo613MThFdn+SXd20JGlMFtSpsKr6zlnmbaE75VVVnwF+aGjx1bOs8y3gkl4LlSQd1II8YpEkTS+DRZLUlMEiSWrKYJEkNWWwSJKaMlgkSU0ZLJKkpgwWSVJTBoskqSmDRZLUlMEiSWrKYJEkNWWwSJKaMlgkSU0ZLJKkpgwWSVJTBoskqSmDRZLUlMEiSWrKYJEkNWWwSJKaWjrpAibt9ONXsW3zpkmXIUmLhkcskqSmDBZJUlMGiySpKYNFktSUwSJJaspgkSQ1ZbBIkpoyWCRJTRkskqSmDBZJUlPzBkuSJUnuGEcxkqTpN2+wVNXTwDNJVo2hHknSlBv1QygfB3YmuQ14Yv/Mqrq8l6okSVNr1GD5WPeQJGlOIwVLVV2fZAXwwqra3XNNkqQpNtJdYUl+DtgB3NpNvzjJTT3WJUmaUqPebvxO4EzgvwCqagdwSi8VSZKm2qjB8r9VtXfGvGdaFyNJmn6jXrzfleTNwJIk64DLgbv6K0uSNK1GPWJ5G7Ae2Ad8CPhv4Dd6qkmSNMVGvSvsSeCq7iFJ0kHNGSxJbgbqYMur6tzmFUmSptp8Ryzv6b6+Hvh+4IPd9JuA/+irKEnS9JozWKrqToAkf1pVG4cW3ZxkW6+VSZKm0qgX71cmefZ9K0lOBlb2U5IkaZqNervx24EtSR4CApwEXNJbVZKkqTXqXWG3du9feVE36wtVta+/siRJ02rUIxaADcDabpsfS0JVfaCXqiRJU2ukYEnyN8APMvggyqe72QUYLJKkA4x6xLIROLWqDvqeFkmSYPS7wj7H4H0skiTNadQjltXA55NsZfB5YYDvvJckPdeowfLOPouQJC0eo95ufGeSk4B1VfVPSV4ALOm3NEnSNBr1TxP/CvBR4P3drOOBG3uqSZI0xUa9eH8Z8HIGf4eFqvoicFxfRUmSpteowbKvqp7aP5FkKXN8nL4k6cg1arDcmeQdwIok5wAfAW7uryxJ0rQaNViuAL4G7AQuBm6pKv+apCTpOeYMliTnJbmsqp6pqr9i8KnGG4F3JLlwLBVKkqbKfEcsvwXcNDR9NIMPozwb+NWeapIkTbH53sdydFV9eWj601X1GPBYEv/QlyTpOeY7Yvne4Ymq+vWhyWPblyNJmnbzBcvd3ZsjD5DkEmBrPyVJkqbZfKfC3g7cmOTNwD3dvA3AMuD8HuuSJE2pOYOlqh4FXpbkVcD6bvYtVXV775VJkqbSqB9CeTtgmEiS5jXqGyQlSRqJwSJJaspgkSQ1ZbBIkpoyWCRJTRkskqSmRrrdeDHb+che1l5xy6TLkKSx2rN5U29je8QiSWrKYJEkNWWwSJKaMlgkSU0ZLJKkpgwWSVJTBoskqSmDRZLUlMEiSWrKYJEkNWWwSJKaMlgkSU0ZLJKkpgwWSVJTBoskqSmDRZLUlMEiSWrKYJEkNWWwSJKaMlgkSU0ZLJKkpgwWSVJTBoskqanegiXJ00l2DD3W9vhae5Ks7mt8SdLolvY49req6sU9ji9JWoDGeiosyYYkdybZnuSTSdZ087ck+bMk25I8kOQlST6W5ItJfn9o+xu7bXclufggr/ELSbZ2R0nvT7JkXP1JkvoNlhVDp8E+nuQo4C+BC6tqA3Ad8AdD6z9VVRuB9wF/D1wGnAZclOSYbp1f7rbdCFw+NB+AJD8C/Dzw8u5o6WngLTMLS3JxF2Lbnn5yb8ueJemIN7ZTYUlOYxAUtyUBWAJ8dWj9m7qvO4FdVfXVbruHgBOBbzAIkwu69U4E1nXz93s1sAH4l+41VgCPziysqq4FrgVYtmZdHU6TkqQD9RksM4VBYJx1kOX7uq/PDD3fP700ydnATwNnVdWTSbYAy2d5jeur6spWRUuSDs04r7HsBo5NchZAkqOSrD+E7VcB/9mFyouAl86yzj8DFyY5rnuN70ty0uEWLkka3diCpaqeAi4E3p3kPmAH8LJDGOJWBkcuDwCbgc/O8hqfB64GPpXkfuA2YM1hli5JOgSpOrIvMSxbs67W/NKfT7oMSRqrPZs3Hdb2SbZ3N1w9h++8lyQ1ZbBIkpoyWCRJTRkskqSmDBZJUlMGiySpKYNFktSUwSJJaspgkSQ1ZbBIkpoyWCRJTRkskqSmDBZJUlMGiySpKYNFktSUwSJJaspgkSQ1ZbBIkpoyWCRJTRkskqSmDBZJUlMGiySpqaWTLmDSTj9+Fds2b5p0GZK0aHjEIklqymCRJDVlsEiSmjJYJElNGSySpKYMFklSUwaLJKkpg0WS1JTBIklqymCRJDVlsEiSmjJYJElNGSySpKYMFklSUwaLJKkpg0WS1JTBIklqymCRJDVlsEiSmjJYJElNGSySpKYMFklSUwaLJKkpg0WS1JTBIklqymCRJDWVqpp0DROV5JvA7knX0chq4OuTLqIB+1h4Fksv9tHOSVV17GwLlo67kgVod1VtnHQRLSTZthh6sY+FZ7H0Yh/j4akwSVJTBoskqSmDBa6ddAENLZZe7GPhWSy92McYHPEX7yVJbXnEIklqymCRJDW16IIlyWuS7E7yYJIrZlm+LMmHu+V3J1k7tOzKbv7uJD876phT1MeeJDuT7EiybSH3keSYJHckeTzJNTO22dD18WCS9ybJFPeypRtzR/c4bgH3cU6S7d2//fYkrxraZuz7pKc+xr4/DrOXM4dqvS/JBaOO2auqWjQPYAnwJeAU4GjgPuDUGev8GvC+7vkbgQ93z0/t1l8GnNyNs2SUMaehj27ZHmD1lOyPlcArgEuBa2ZssxV4KRDgH4HXTnEvW4CNU7JPzgB+oHt+GvDIpPZJj32MdX806OUFwNLu+RrgUQbvTxz7z63hx2I7YjkTeLCqHqqqp4AbgPNmrHMecH33/KPAq7vfrs4DbqiqfVX1b8CD3XijjDkNfUzC8+6jqp6oqk8D/zO8cpI1wHdX1Wdr8L/pA8D5fTbRad7LhBxOH/dW1Ve6+buAFd1v0pPYJ8376LneuRxOL09W1f9185cD++/GmsTPrWcttmA5Hvjy0PTD3bxZ1+l2yF7gmDm2HWXM1vroAwbfdJ/qDv8v7qHumQ6nj7nGfHieMfvQRy/7/XV3KuO3x3AKqVUfbwDuqap9TGaf9NHHfuPcHwfU2TmkXpL8RJJdwE7g0m75JH5uPWuxBYvm9oqq+nHgtcBlSX5y0gWJt1TV6cAru8dbJ1zPvJKsB94NXDLpWg7HQfqYuv1RVXdX1XrgJcCVSZZPuqbFFiyPACcOTZ/QzZt1nSRLgVXAN+bYdpQxW+ujD6pq/9dHgY/T/ymyw+ljrjFPmGfMPvTRy/A++SbwtyzwfZLkBAbfO79YVV8aWn/c+6SPPiaxPw6os/O8vreq6gHgcbrrRiOM2Z9xXqTq+8HgotVDDC5a779gtX7GOpdx4EWwv+uer+fAi94PMbgANu+YU9LHSuC7unVWAncBr1mofQwtv4j5L96/biF/bx2sl27M1d3zoxicO790ofYBfE+3/utnGXes+6SPPiaxPxr0cjLfvnh/EvAVBp98PPafWwfUO64XGltD8DrgXxncEXFVN+/3gHO758uBjzC4qL0VOGVo26u67XYzdFfLbGNOWx8M7g65r3vsmpI+9gCPMfgt7GG6u1qAjcDnujGvofsEiWnrhUHAbwfu7/bJX9DdwbcQ+wCuBp4Adgw9jpvUPmndx6T2x2H28tau1h3APcD5c405rocf6SJJamqxXWORJE2YwSJJaspgkSQ1ZbBIkpoyWCRJTRkskqSmDBZJUlP/D9vmw5xT3/vPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Gender').mean()['PaidStarPerWatchedHour'].plot(kind='barh')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6e19cb42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Gender'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zPUQp/t7lYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Country'] == 'PH'].groupby('Gender').mean()['PaidStarPerWatchedHour'].plot(kind='barh')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7982a1e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender\n",
      "Female    0.000183\n",
      "Male      0.000010\n",
      "Name: PaidStarPerWatchedHour, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Gender'>"
      ]
     },
     "execution_count": 24,
     "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": [
    "print(df.groupby('Gender').mean()['PaidStarPerWatchedHour']/df.groupby('Gender').count()['PaidStarPerWatchedHour'])\n",
    "(df.groupby('Gender').mean()['PaidStarPerWatchedHour']/df.groupby('Gender').count()['PaidStarPerWatchedHour']).plot(kind='barh')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c087c25c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Gender'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Country'] == 'VN'].groupby('Gender').mean()['PaidStarPerWatchedHour'].plot(kind='barh')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e32460dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Gender'>"
      ]
     },
     "execution_count": 26,
     "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[df['Country'] == 'ID'].groupby('Gender').mean()['PaidStarPerWatchedHour'].plot(kind='barh')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "013155b1",
   "metadata": {},
   "source": [
    "## Thank you"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15b476f7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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