Exploratory Data Analysis 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 780,
   "metadata": {},
   "outputs": [],
   "source": [
    "english_geotagged_tweets['method'] = 'Geotagged'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1142cf65108>"
      ]
     },
     "execution_count": 609,
     "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(english_geotagged_tweets.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114d00916c8>"
      ]
     },
     "execution_count": 1186,
     "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(location_bio_ready_followers.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114d792f908>"
      ]
     },
     "execution_count": 1221,
     "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(arabic_profile_info_tweets.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1245,
   "metadata": {},
   "outputs": [],
   "source": [
    "followers_df = pd.DataFrame(data = {'Type of Followers':  ['All Collected Followers','Unique Followers','Public Followers','Tweeted Followers','Joined Before Deadline Followers','Location or Bio Followers','Egyptian Location or Bio Followers'],\n",
    "                                    'No. of Followers': [437013,224754,212135,131719,65033,45293,4170]\n",
    "                                    },\n",
    "                            columns=['Type of Followers','No. of Followers']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1246,
   "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>Type of Followers</th>\n",
       "      <th>No. of Followers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All Collected Followers</td>\n",
       "      <td>437013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Unique Followers</td>\n",
       "      <td>224754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Public Followers</td>\n",
       "      <td>212135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tweeted Followers</td>\n",
       "      <td>131719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Joined Before Deadline Followers</td>\n",
       "      <td>65033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Location or Bio Followers</td>\n",
       "      <td>45293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Egyptian Location or Bio Followers</td>\n",
       "      <td>4170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Type of Followers  No. of Followers\n",
       "0             All Collected Followers            437013\n",
       "1                    Unique Followers            224754\n",
       "2                    Public Followers            212135\n",
       "3                   Tweeted Followers            131719\n",
       "4    Joined Before Deadline Followers             65033\n",
       "5           Location or Bio Followers             45293\n",
       "6  Egyptian Location or Bio Followers              4170"
      ]
     },
     "execution_count": 1246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "followers_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1285,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x='Type of Followers'\n",
    "y='No. of Followers'\n",
    "\n",
    "plt.figure(figsize=(16,7))\n",
    "sns.lineplot(x,y, data=followers_df, sort=False, marker='o',lw=3,markersize=12)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1289,
   "metadata": {},
   "outputs": [],
   "source": [
    "nearby_df = pd.DataFrame(data = {'Language':  ['Arabic','Arabic','English','English'],\n",
    "                                'Worked': ['Worked',\"Didn't Work\",'Worked',\"Didn't Work\"],\n",
    "                                 \"Governorates Count\": [15,12,22,5]\n",
    "                                    },\n",
    "                            columns=['Language','Worked',\"Governorates Count\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1290,
   "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>Language</th>\n",
       "      <th>Worked</th>\n",
       "      <th>Governorates Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arabic</td>\n",
       "      <td>Worked</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arabic</td>\n",
       "      <td>Didn't Work</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>English</td>\n",
       "      <td>Worked</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>English</td>\n",
       "      <td>Didn't Work</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Language       Worked  Governorates Count\n",
       "0   Arabic       Worked                  15\n",
       "1   Arabic  Didn't Work                  12\n",
       "2  English       Worked                  22\n",
       "3  English  Didn't Work                   5"
      ]
     },
     "execution_count": 1290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nearby_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1299,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_nearby_df = nearby_df[nearby_df['Language'] == 'Arabic']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1302,
   "metadata": {},
   "outputs": [],
   "source": [
    "english_nearby_df = nearby_df[nearby_df['Language'] == 'English']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1333,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:728: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x114e9a41088>"
      ]
     },
     "execution_count": 1333,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1584x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(22,8))\n",
    "h = sns.FacetGrid(nearby_df,col='Language')\n",
    "h.map(sns.barplot, 'Governorates Count','Worked')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1334,
   "metadata": {},
   "outputs": [],
   "source": [
    "search_tweets = pd.read_csv('Search Approach/EgyptianSearchTweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1336,
   "metadata": {},
   "outputs": [],
   "source": [
    "search_tweets.drop('Unnamed: 0', axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1339,
   "metadata": {},
   "outputs": [],
   "source": [
    "search_tweets.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1340,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 161336 entries, 0 to 161835\n",
      "Data columns (total 16 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   date            161336 non-null  object \n",
      " 1   time            161336 non-null  object \n",
      " 2   username        161336 non-null  object \n",
      " 3   name            161317 non-null  object \n",
      " 4   place           142 non-null     object \n",
      " 5   tweet           161336 non-null  object \n",
      " 6   urls            161336 non-null  object \n",
      " 7   replies_count   161336 non-null  int64  \n",
      " 8   retweets_count  161336 non-null  int64  \n",
      " 9   likes_count     161336 non-null  int64  \n",
      " 10  hashtags        161336 non-null  object \n",
      " 11  near            0 non-null       float64\n",
      " 12  geo             0 non-null       float64\n",
      " 13  governorate     161336 non-null  object \n",
      " 14  language        161336 non-null  object \n",
      " 15  method          161336 non-null  object \n",
      "dtypes: float64(2), int64(3), object(11)\n",
      "memory usage: 20.9+ MB\n"
     ]
    }
   ],
   "source": [
    "search_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1341,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile_info_tweets = pd.read_csv('Profile Info Approach/EgyptianProfileInfoTweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1343,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile_info_tweets.drop('Unnamed: 0', axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1347,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 40518 entries, 0 to 40517\n",
      "Data columns (total 16 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   date            40518 non-null  object \n",
      " 1   time            40518 non-null  object \n",
      " 2   username        40518 non-null  object \n",
      " 3   name            40518 non-null  object \n",
      " 4   place           11 non-null     object \n",
      " 5   tweet           40518 non-null  object \n",
      " 6   urls            40518 non-null  object \n",
      " 7   replies_count   40518 non-null  int64  \n",
      " 8   retweets_count  40518 non-null  int64  \n",
      " 9   likes_count     40518 non-null  int64  \n",
      " 10  hashtags        40518 non-null  object \n",
      " 11  near            0 non-null      float64\n",
      " 12  geo             0 non-null      float64\n",
      " 13  governorate     40518 non-null  object \n",
      " 14  language        40518 non-null  object \n",
      " 15  method          40518 non-null  object \n",
      "dtypes: float64(2), int64(3), object(11)\n",
      "memory usage: 4.9+ MB\n"
     ]
    }
   ],
   "source": [
    "profile_info_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1348,
   "metadata": {},
   "outputs": [],
   "source": [
    "nearby_tweets = pd.read_csv('Nearby Approach/EgyptianNearbyTweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1350,
   "metadata": {},
   "outputs": [],
   "source": [
    "nearby_tweets.drop('Unnamed: 0', axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1354,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 180599 entries, 0 to 180598\n",
      "Data columns (total 16 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   date            180599 non-null  object \n",
      " 1   time            180599 non-null  object \n",
      " 2   username        180599 non-null  object \n",
      " 3   name            180593 non-null  object \n",
      " 4   place           4027 non-null    object \n",
      " 5   tweet           180599 non-null  object \n",
      " 6   urls            180599 non-null  object \n",
      " 7   replies_count   180599 non-null  int64  \n",
      " 8   retweets_count  180599 non-null  int64  \n",
      " 9   likes_count     180599 non-null  int64  \n",
      " 10  hashtags        180599 non-null  object \n",
      " 11  near            180599 non-null  object \n",
      " 12  geo             0 non-null       float64\n",
      " 13  governorate     180599 non-null  object \n",
      " 14  language        180599 non-null  object \n",
      " 15  method          180599 non-null  object \n",
      "dtypes: float64(1), int64(3), object(12)\n",
      "memory usage: 22.0+ MB\n"
     ]
    }
   ],
   "source": [
    "nearby_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1357,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "geotagged_tweets.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1360,
   "metadata": {},
   "outputs": [],
   "source": [
    "geotagged_tweets.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1361,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 648747 entries, 0 to 648746\n",
      "Data columns (total 16 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   date            648747 non-null  object \n",
      " 1   time            648747 non-null  object \n",
      " 2   username        648747 non-null  object \n",
      " 3   name            648745 non-null  object \n",
      " 4   place           5190 non-null    object \n",
      " 5   tweet           648747 non-null  object \n",
      " 6   urls            648747 non-null  object \n",
      " 7   replies_count   648747 non-null  int64  \n",
      " 8   retweets_count  648747 non-null  int64  \n",
      " 9   likes_count     648747 non-null  int64  \n",
      " 10  hashtags        648747 non-null  object \n",
      " 11  near            0 non-null       float64\n",
      " 12  geo             648747 non-null  object \n",
      " 13  governorate     648747 non-null  object \n",
      " 14  language        648747 non-null  object \n",
      " 15  method          648747 non-null  object \n",
      "dtypes: float64(1), int64(3), object(12)\n",
      "memory usage: 79.2+ MB\n"
     ]
    }
   ],
   "source": [
    "geotagged_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1365,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_per_approach_df = pd.DataFrame(data = {'Approach':  ['Geocoded','Keyword Search','Nearby','Profile Info'],\n",
    "                                'No. of tweets': [648747,180599,161336,40518]\n",
    "                                    },\n",
    "                            columns=['Approach','No. of tweets']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1149dbe1b88>"
      ]
     },
     "execution_count": 1371,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.barplot(x='Approach', y='No. of tweets', data=tweets_per_approach_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1375,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_tweets = pd.concat([search_tweets,profile_info_tweets,nearby_tweets,geotagged_tweets])\n",
    "all_tweets.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_search_tweets = pd.read_csv('Search Approach/Arabic/EgyptianArabicSearchTweets3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_search_tweets.drop('Unnamed: 0', axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 113928 entries, 0 to 113927\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   date            113928 non-null  object \n",
      " 1   time            113928 non-null  object \n",
      " 2   username        113928 non-null  object \n",
      " 3   name            113926 non-null  object \n",
      " 4   place           54 non-null      object \n",
      " 5   tweet           113928 non-null  object \n",
      " 6   urls            113928 non-null  object \n",
      " 7   replies_count   113928 non-null  int64  \n",
      " 8   retweets_count  113928 non-null  int64  \n",
      " 9   likes_count     113928 non-null  int64  \n",
      " 10  hashtags        113928 non-null  object \n",
      " 11  near            0 non-null       float64\n",
      " 12  geo             0 non-null       float64\n",
      " 13  governorate     113928 non-null  object \n",
      " 14  language        113928 non-null  object \n",
      "dtypes: float64(2), int64(3), object(10)\n",
      "memory usage: 13.0+ MB\n"
     ]
    }
   ],
   "source": [
    "arabic_search_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59593"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arabic_search_tweets['username'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "english_search_tweets = pd.read_csv('Search Approach/English/EgyptianEnglishSearchTweets3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "english_search_tweets.drop('Unnamed: 0', axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "english_search_tweets.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "english_search_tweets.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 47904 entries, 0 to 47903\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   date            47904 non-null  object \n",
      " 1   time            47904 non-null  object \n",
      " 2   username        47904 non-null  object \n",
      " 3   name            47887 non-null  object \n",
      " 4   place           88 non-null     object \n",
      " 5   tweet           47904 non-null  object \n",
      " 6   urls            47904 non-null  object \n",
      " 7   replies_count   47904 non-null  int64  \n",
      " 8   retweets_count  47904 non-null  int64  \n",
      " 9   likes_count     47904 non-null  int64  \n",
      " 10  hashtags        47904 non-null  object \n",
      " 11  near            0 non-null      float64\n",
      " 12  geo             0 non-null      float64\n",
      " 13  governorate     47904 non-null  object \n",
      " 14  language        47904 non-null  object \n",
      "dtypes: float64(2), int64(3), object(10)\n",
      "memory usage: 5.5+ MB\n"
     ]
    }
   ],
   "source": [
    "english_search_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29581"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "english_search_tweets['username'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_profile_info_tweets = pd.read_csv('Profile Info Approach/Arabic/EgyptianArabicProfileInfoTweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_profile_info_tweets.drop('Unnamed: 0', axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 38813 entries, 0 to 38812\n",
      "Data columns (total 16 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   date            38813 non-null  object \n",
      " 1   time            38813 non-null  object \n",
      " 2   username        38813 non-null  object \n",
      " 3   name            38813 non-null  object \n",
      " 4   place           9 non-null      object \n",
      " 5   tweet           38813 non-null  object \n",
      " 6   urls            38813 non-null  object \n",
      " 7   replies_count   38813 non-null  int64  \n",
      " 8   retweets_count  38813 non-null  int64  \n",
      " 9   likes_count     38813 non-null  int64  \n",
      " 10  hashtags        38813 non-null  object \n",
      " 11  near            0 non-null      float64\n",
      " 12  geo             0 non-null      float64\n",
      " 13  governorate     38813 non-null  object \n",
      " 14  language        38813 non-null  object \n",
      " 15  method          38813 non-null  object \n",
      "dtypes: float64(2), int64(3), object(11)\n",
      "memory usage: 4.7+ MB\n"
     ]
    }
   ],
   "source": [
    "arabic_profile_info_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "english_profile_info_tweets = pd.read_csv('Profile Info Approach/English/EgyptianEnglishProfileInfoTweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "english_profile_info_tweets.drop('Unnamed: 0', axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1705 entries, 0 to 1704\n",
      "Data columns (total 16 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   date            1705 non-null   object \n",
      " 1   time            1705 non-null   object \n",
      " 2   username        1705 non-null   object \n",
      " 3   name            1705 non-null   object \n",
      " 4   place           2 non-null      object \n",
      " 5   tweet           1705 non-null   object \n",
      " 6   urls            1705 non-null   object \n",
      " 7   replies_count   1705 non-null   int64  \n",
      " 8   retweets_count  1705 non-null   int64  \n",
      " 9   likes_count     1705 non-null   int64  \n",
      " 10  hashtags        1705 non-null   object \n",
      " 11  near            0 non-null      float64\n",
      " 12  geo             0 non-null      float64\n",
      " 13  governorate     1705 non-null   object \n",
      " 14  language        1705 non-null   object \n",
      " 15  method          1705 non-null   object \n",
      "dtypes: float64(2), int64(3), object(11)\n",
      "memory usage: 213.2+ KB\n"
     ]
    }
   ],
   "source": [
    "english_profile_info_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_nearby_tweets = pd.read_csv('Nearby Approach/Arabic/EgyptianArabicNearbyTweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_nearby_tweets.drop('Unnamed: 0', axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 56539 entries, 0 to 56538\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   date            56539 non-null  object \n",
      " 1   time            56539 non-null  object \n",
      " 2   username        56539 non-null  object \n",
      " 3   name            56539 non-null  object \n",
      " 4   place           1731 non-null   object \n",
      " 5   tweet           56539 non-null  object \n",
      " 6   urls            56539 non-null  object \n",
      " 7   replies_count   56539 non-null  int64  \n",
      " 8   retweets_count  56539 non-null  int64  \n",
      " 9   likes_count     56539 non-null  int64  \n",
      " 10  hashtags        56539 non-null  object \n",
      " 11  near            56539 non-null  object \n",
      " 12  geo             0 non-null      float64\n",
      " 13  governorate     56539 non-null  object \n",
      " 14  language        56539 non-null  object \n",
      "dtypes: float64(1), int64(3), object(11)\n",
      "memory usage: 6.5+ MB\n"
     ]
    }
   ],
   "source": [
    "arabic_nearby_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "english_nearby_tweets = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 124060 entries, 0 to 124059\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   date            124060 non-null  object \n",
      " 1   time            124060 non-null  object \n",
      " 2   username        124060 non-null  object \n",
      " 3   name            124054 non-null  object \n",
      " 4   place           2296 non-null    object \n",
      " 5   tweet           124060 non-null  object \n",
      " 6   urls            124060 non-null  object \n",
      " 7   replies_count   124060 non-null  int64  \n",
      " 8   retweets_count  124060 non-null  int64  \n",
      " 9   likes_count     124060 non-null  int64  \n",
      " 10  hashtags        124060 non-null  object \n",
      " 11  near            124060 non-null  object \n",
      " 12  geo             0 non-null       float64\n",
      " 13  governorate     124060 non-null  object \n",
      " 14  language        124060 non-null  object \n",
      "dtypes: float64(1), int64(3), object(11)\n",
      "memory usage: 14.2+ MB\n"
     ]
    }
   ],
   "source": [
    "english_nearby_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_geocoded_tweets = pd.read_csv('Geotagged Approach/Arabic/EgyptianArabicGeoTweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_geocoded_tweets.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 605400 entries, 0 to 605399\n",
      "Data columns (total 16 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   date            605400 non-null  object \n",
      " 1   time            605400 non-null  object \n",
      " 2   username        605400 non-null  object \n",
      " 3   name            605400 non-null  object \n",
      " 4   place           4288 non-null    object \n",
      " 5   tweet           605400 non-null  object \n",
      " 6   urls            605400 non-null  object \n",
      " 7   replies_count   605400 non-null  int64  \n",
      " 8   retweets_count  605400 non-null  int64  \n",
      " 9   likes_count     605400 non-null  int64  \n",
      " 10  hashtags        605400 non-null  object \n",
      " 11  near            0 non-null       float64\n",
      " 12  geo             605400 non-null  object \n",
      " 13  governorate     605400 non-null  object \n",
      " 14  language        605400 non-null  object \n",
      " 15  method          605400 non-null  object \n",
      "dtypes: float64(1), int64(3), object(12)\n",
      "memory usage: 73.9+ MB\n"
     ]
    }
   ],
   "source": [
    "arabic_geocoded_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "english_geocoded_tweets = pd.read_csv('Geotagged Approach/English/EgyptianEnglishGeoTweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "english_geocoded_tweets.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 43353 entries, 0 to 43352\n",
      "Data columns (total 16 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   date            43353 non-null  object \n",
      " 1   time            43353 non-null  object \n",
      " 2   username        43353 non-null  object \n",
      " 3   name            43351 non-null  object \n",
      " 4   place           902 non-null    object \n",
      " 5   tweet           43353 non-null  object \n",
      " 6   urls            43353 non-null  object \n",
      " 7   replies_count   43353 non-null  int64  \n",
      " 8   retweets_count  43353 non-null  int64  \n",
      " 9   likes_count     43353 non-null  int64  \n",
      " 10  hashtags        43353 non-null  object \n",
      " 11  near            0 non-null      float64\n",
      " 12  geo             43353 non-null  object \n",
      " 13  governorate     43353 non-null  object \n",
      " 14  language        43353 non-null  object \n",
      " 15  method          43353 non-null  object \n",
      "dtypes: float64(1), int64(3), object(12)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "english_geocoded_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_tweets_approach_df = pd.DataFrame(data={'Language': ['Arabic','Arabic','Arabic','Arabic','English','English','English','English'],\n",
    "                                           'Approach': ['Geocoded','Nearby','Profile Info', 'Keyword Search','Geocoded','Nearby','Profile Info', 'Keyword Search'],\n",
    "                                           'No. of Tweets': [605400,56539,38813,113928,43353,124060,1705,47904],\n",
    "                                           'No. of Unique Users': [18451,4268,1436,59593,6512,8631,423,29581]},\n",
    "\n",
    "                                     columns=['Language','Approach','No. of Tweets','No. of Unique Users']\n",
    "                                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Language</th>\n",
       "      <th>Approach</th>\n",
       "      <th>No. of Tweets</th>\n",
       "      <th>No. of Unique Users</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arabic</td>\n",
       "      <td>Geocoded</td>\n",
       "      <td>605400</td>\n",
       "      <td>18451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arabic</td>\n",
       "      <td>Nearby</td>\n",
       "      <td>56539</td>\n",
       "      <td>4268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arabic</td>\n",
       "      <td>Profile Info</td>\n",
       "      <td>38813</td>\n",
       "      <td>1436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arabic</td>\n",
       "      <td>Keyword Search</td>\n",
       "      <td>113928</td>\n",
       "      <td>59593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>English</td>\n",
       "      <td>Geocoded</td>\n",
       "      <td>43353</td>\n",
       "      <td>6512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>English</td>\n",
       "      <td>Nearby</td>\n",
       "      <td>124060</td>\n",
       "      <td>8631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>English</td>\n",
       "      <td>Profile Info</td>\n",
       "      <td>1705</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>English</td>\n",
       "      <td>Keyword Search</td>\n",
       "      <td>47904</td>\n",
       "      <td>29581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Language        Approach  No. of Tweets  No. of Unique Users\n",
       "0   Arabic        Geocoded         605400                18451\n",
       "1   Arabic          Nearby          56539                 4268\n",
       "2   Arabic    Profile Info          38813                 1436\n",
       "3   Arabic  Keyword Search         113928                59593\n",
       "4  English        Geocoded          43353                 6512\n",
       "5  English          Nearby         124060                 8631\n",
       "6  English    Profile Info           1705                  423\n",
       "7  English  Keyword Search          47904                29581"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_tweets_approach_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ace10f3308>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.set_context('paper')\n",
    "sns.set_style('whitegrid')\n",
    "sns.barplot(x='Approach',y='No. of Tweets',hue='Language',data=all_tweets_approach_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59593"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arabic_search_tweets['username'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1436"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arabic_profile_info_tweets['username'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4268"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arabic_nearby_tweets['username'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18451"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arabic_geocoded_tweets['username'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29581"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "english_search_tweets['username'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "423"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "english_profile_info_tweets['username'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8631"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "english_nearby_tweets['username'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6512"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "english_geocoded_tweets['username'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ace2343e08>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.set_context('paper')\n",
    "sns.set_style('whitegrid')\n",
    "sns.barplot(x='Approach',y='No. of Unique Users',hue='Language',data=all_tweets_approach_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_per_approach_df = pd.DataFrame(data = {'Approach':  ['Geocoded','Keyword Search','Nearby','Profile Info'],\n",
    "                                'No. of tweets': [648747,180599,161336,40518]\n",
    "                                    },\n",
    "                            columns=['Approach','No. of tweets'],\n",
    "                            \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_per_approach_df.set_index('Approach',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2aceb4df508>"
      ]
     },
     "execution_count": 89,
     "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": [
    "tweets_per_approach_df['No. of tweets'].plot.pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 874,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_sentiment_df = pd.DataFrame({'Ngrams':['Unigram','Unigram','Unigram','Unigram','Unigram','Unigram','Unigram+Bigrams','Unigram+Bigrams','Unigram+Bigrams','Unigram+Bigrams','Unigram+Bigrams','Unigram+Bigrams','Unigram+Bigrams+Trigrams','Unigram+Bigrams+Trigrams','Unigram+Bigrams+Trigrams','Unigram+Bigrams+Trigrams','Unigram+Bigrams+Trigrams','Unigram+Bigrams+Trigrams'],\n",
    "                                 'Preprocess':['Raw','Raw','Normalized','Normalized','Stemmed','Stemmed','Raw','Raw','Normalized','Normalized','Stemmed','Stemmed','Raw','Raw','Normalized','Normalized','Stemmed','Stemmed'],\n",
    "                                'Classifier':['Support Vector Machines','Naive-Bayes','Support Vector Machines','Naive-Bayes','Support Vector Machines','Naive-Bayes','Support Vector Machines','Naive-Bayes','Support Vector Machines','Naive-Bayes','Support Vector Machines','Naive-Bayes','Support Vector Machines','Naive-Bayes','Support Vector Machines','Naive-Bayes','Support Vector Machines','Naive-Bayes'],\n",
    "                                'Accuracy':[81,82,81,83,83,84,82,83,82,83,84,84,82,82,82,83,84,84]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 875,
   "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>Ngrams</th>\n",
       "      <th>Preprocess</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Unigram</td>\n",
       "      <td>Raw</td>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Unigram</td>\n",
       "      <td>Raw</td>\n",
       "      <td>Naive-Bayes</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Unigram</td>\n",
       "      <td>Normalized</td>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Unigram</td>\n",
       "      <td>Normalized</td>\n",
       "      <td>Naive-Bayes</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Unigram</td>\n",
       "      <td>Stemmed</td>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Unigram</td>\n",
       "      <td>Stemmed</td>\n",
       "      <td>Naive-Bayes</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Unigram+Bigrams</td>\n",
       "      <td>Raw</td>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Unigram+Bigrams</td>\n",
       "      <td>Raw</td>\n",
       "      <td>Naive-Bayes</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Unigram+Bigrams</td>\n",
       "      <td>Normalized</td>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Unigram+Bigrams</td>\n",
       "      <td>Normalized</td>\n",
       "      <td>Naive-Bayes</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Unigram+Bigrams</td>\n",
       "      <td>Stemmed</td>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Unigram+Bigrams</td>\n",
       "      <td>Stemmed</td>\n",
       "      <td>Naive-Bayes</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Unigram+Bigrams+Trigrams</td>\n",
       "      <td>Raw</td>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Unigram+Bigrams+Trigrams</td>\n",
       "      <td>Raw</td>\n",
       "      <td>Naive-Bayes</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Unigram+Bigrams+Trigrams</td>\n",
       "      <td>Normalized</td>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Unigram+Bigrams+Trigrams</td>\n",
       "      <td>Normalized</td>\n",
       "      <td>Naive-Bayes</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Unigram+Bigrams+Trigrams</td>\n",
       "      <td>Stemmed</td>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Unigram+Bigrams+Trigrams</td>\n",
       "      <td>Stemmed</td>\n",
       "      <td>Naive-Bayes</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Ngrams  Preprocess               Classifier  Accuracy\n",
       "0                    Unigram         Raw  Support Vector Machines        81\n",
       "1                    Unigram         Raw              Naive-Bayes        82\n",
       "2                    Unigram  Normalized  Support Vector Machines        81\n",
       "3                    Unigram  Normalized              Naive-Bayes        83\n",
       "4                    Unigram     Stemmed  Support Vector Machines        83\n",
       "5                    Unigram     Stemmed              Naive-Bayes        84\n",
       "6            Unigram+Bigrams         Raw  Support Vector Machines        82\n",
       "7            Unigram+Bigrams         Raw              Naive-Bayes        83\n",
       "8            Unigram+Bigrams  Normalized  Support Vector Machines        82\n",
       "9            Unigram+Bigrams  Normalized              Naive-Bayes        83\n",
       "10           Unigram+Bigrams     Stemmed  Support Vector Machines        84\n",
       "11           Unigram+Bigrams     Stemmed              Naive-Bayes        84\n",
       "12  Unigram+Bigrams+Trigrams         Raw  Support Vector Machines        82\n",
       "13  Unigram+Bigrams+Trigrams         Raw              Naive-Bayes        82\n",
       "14  Unigram+Bigrams+Trigrams  Normalized  Support Vector Machines        82\n",
       "15  Unigram+Bigrams+Trigrams  Normalized              Naive-Bayes        83\n",
       "16  Unigram+Bigrams+Trigrams     Stemmed  Support Vector Machines        84\n",
       "17  Unigram+Bigrams+Trigrams     Stemmed              Naive-Bayes        84"
      ]
     },
     "execution_count": 875,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_sentiment_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 876,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2add455c7c8>"
      ]
     },
     "execution_count": 876,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(pre_sentiment_df.pivot_table(values='Accuracy',index='Classifier',columns=['Ngrams','Preprocess']),annot=True,cmap = sns.cm.rocket_r,linewidths=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "governorates = pd.read_csv('Governorates.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "governorates.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>Name</th>\n",
       "      <th>Abbr.</th>\n",
       "      <th>Native</th>\n",
       "      <th>Capital</th>\n",
       "      <th>Area</th>\n",
       "      <th>Population</th>\n",
       "      <th>Density</th>\n",
       "      <th>% Urban</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>Urban Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Cairo</td>\n",
       "      <td>QAH</td>\n",
       "      <td>القاهرة</td>\n",
       "      <td>Al-Qāhirah</td>\n",
       "      <td>3085</td>\n",
       "      <td>9539673</td>\n",
       "      <td>3092.276499</td>\n",
       "      <td>100.0</td>\n",
       "      <td>30.077080</td>\n",
       "      <td>31.285909</td>\n",
       "      <td>9539673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Port Said</td>\n",
       "      <td>BSD</td>\n",
       "      <td>بور سعيد</td>\n",
       "      <td>Būr Sa'īd</td>\n",
       "      <td>1345</td>\n",
       "      <td>749371</td>\n",
       "      <td>557.153160</td>\n",
       "      <td>100.0</td>\n",
       "      <td>31.256541</td>\n",
       "      <td>32.284115</td>\n",
       "      <td>749371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Suez</td>\n",
       "      <td>SWS</td>\n",
       "      <td>السويس</td>\n",
       "      <td>As-Suways</td>\n",
       "      <td>9002</td>\n",
       "      <td>728180</td>\n",
       "      <td>80.890913</td>\n",
       "      <td>100.0</td>\n",
       "      <td>29.973714</td>\n",
       "      <td>32.526267</td>\n",
       "      <td>728180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Alexandria</td>\n",
       "      <td>ISK</td>\n",
       "      <td>الإسكندرية</td>\n",
       "      <td>Al-Iskandariyah</td>\n",
       "      <td>2300</td>\n",
       "      <td>5163750</td>\n",
       "      <td>2245.108696</td>\n",
       "      <td>98.8</td>\n",
       "      <td>31.215645</td>\n",
       "      <td>29.955266</td>\n",
       "      <td>5101785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Red Sea</td>\n",
       "      <td>BAH</td>\n",
       "      <td>البحر الأحمر</td>\n",
       "      <td>Al-Ghurdaqah</td>\n",
       "      <td>120000</td>\n",
       "      <td>359888</td>\n",
       "      <td>2.999067</td>\n",
       "      <td>95.1</td>\n",
       "      <td>26.729177</td>\n",
       "      <td>33.936511</td>\n",
       "      <td>342253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Matrouh</td>\n",
       "      <td>MTR</td>\n",
       "      <td>مطروخ</td>\n",
       "      <td>Marsā Maṭrūḥ</td>\n",
       "      <td>166563</td>\n",
       "      <td>425624</td>\n",
       "      <td>2.555333</td>\n",
       "      <td>70.6</td>\n",
       "      <td>31.352539</td>\n",
       "      <td>27.245275</td>\n",
       "      <td>300490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>North Sinai</td>\n",
       "      <td>SSH</td>\n",
       "      <td>شمال سيناء</td>\n",
       "      <td>Al-'Arīsh</td>\n",
       "      <td>27564</td>\n",
       "      <td>450328</td>\n",
       "      <td>16.337542</td>\n",
       "      <td>60.2</td>\n",
       "      <td>31.128660</td>\n",
       "      <td>33.797117</td>\n",
       "      <td>271097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Giza</td>\n",
       "      <td>JIZ</td>\n",
       "      <td>الجيزة</td>\n",
       "      <td>Al-Jīzah</td>\n",
       "      <td>13184</td>\n",
       "      <td>8632021</td>\n",
       "      <td>654.734603</td>\n",
       "      <td>58.6</td>\n",
       "      <td>30.008079</td>\n",
       "      <td>31.210931</td>\n",
       "      <td>5058364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>South Sinai</td>\n",
       "      <td>SJN</td>\n",
       "      <td>جنوب سيناء</td>\n",
       "      <td>Aṭ-Ṭūr</td>\n",
       "      <td>31272</td>\n",
       "      <td>102018</td>\n",
       "      <td>3.262279</td>\n",
       "      <td>51.1</td>\n",
       "      <td>28.236381</td>\n",
       "      <td>33.625404</td>\n",
       "      <td>52131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>New Valley</td>\n",
       "      <td>WJD</td>\n",
       "      <td>الوادي الجديد</td>\n",
       "      <td>Al-Khārijah</td>\n",
       "      <td>440098</td>\n",
       "      <td>241247</td>\n",
       "      <td>0.548167</td>\n",
       "      <td>48.0</td>\n",
       "      <td>25.451405</td>\n",
       "      <td>30.546346</td>\n",
       "      <td>115798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ismailia</td>\n",
       "      <td>ISM</td>\n",
       "      <td>الإسماعيلية</td>\n",
       "      <td>Al-Ismā'īliyah</td>\n",
       "      <td>5067</td>\n",
       "      <td>1303993</td>\n",
       "      <td>257.350109</td>\n",
       "      <td>45.4</td>\n",
       "      <td>30.604272</td>\n",
       "      <td>32.272252</td>\n",
       "      <td>592012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Qalyubia</td>\n",
       "      <td>QLY</td>\n",
       "      <td>القليوبية</td>\n",
       "      <td>Banhā</td>\n",
       "      <td>1124</td>\n",
       "      <td>5627420</td>\n",
       "      <td>5006.601423</td>\n",
       "      <td>44.7</td>\n",
       "      <td>30.459065</td>\n",
       "      <td>31.178577</td>\n",
       "      <td>2515456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Aswan</td>\n",
       "      <td>ASW</td>\n",
       "      <td>أسوان</td>\n",
       "      <td>Aswān</td>\n",
       "      <td>62726</td>\n",
       "      <td>1473975</td>\n",
       "      <td>23.498629</td>\n",
       "      <td>42.3</td>\n",
       "      <td>24.093433</td>\n",
       "      <td>32.907038</td>\n",
       "      <td>623491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Damietta</td>\n",
       "      <td>DMY</td>\n",
       "      <td>دمياط</td>\n",
       "      <td>Dumyāṭ</td>\n",
       "      <td>910</td>\n",
       "      <td>1496765</td>\n",
       "      <td>1644.796703</td>\n",
       "      <td>38.7</td>\n",
       "      <td>31.416477</td>\n",
       "      <td>31.813316</td>\n",
       "      <td>579248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Luxor</td>\n",
       "      <td>UQS</td>\n",
       "      <td>الأقصر</td>\n",
       "      <td>Al-Uqṣur</td>\n",
       "      <td>2960</td>\n",
       "      <td>1250209</td>\n",
       "      <td>422.367905</td>\n",
       "      <td>37.8</td>\n",
       "      <td>25.695858</td>\n",
       "      <td>32.643592</td>\n",
       "      <td>472579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gharbia</td>\n",
       "      <td>GHB</td>\n",
       "      <td>الغربية</td>\n",
       "      <td>Ṭanṭā</td>\n",
       "      <td>1942</td>\n",
       "      <td>4999633</td>\n",
       "      <td>2574.476313</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.788471</td>\n",
       "      <td>31.001921</td>\n",
       "      <td>1499889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dakahlia</td>\n",
       "      <td>DQH</td>\n",
       "      <td>الدقهلية</td>\n",
       "      <td>Al-Manṣūrah</td>\n",
       "      <td>3538</td>\n",
       "      <td>6492381</td>\n",
       "      <td>1835.042679</td>\n",
       "      <td>28.2</td>\n",
       "      <td>31.036373</td>\n",
       "      <td>31.380691</td>\n",
       "      <td>1830851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Asyut</td>\n",
       "      <td>ASY</td>\n",
       "      <td>أسيوط</td>\n",
       "      <td>Asyūṭ</td>\n",
       "      <td>13720</td>\n",
       "      <td>4383289</td>\n",
       "      <td>319.481706</td>\n",
       "      <td>26.5</td>\n",
       "      <td>27.180956</td>\n",
       "      <td>31.183683</td>\n",
       "      <td>1161571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Beni Suef</td>\n",
       "      <td>BSW</td>\n",
       "      <td>بنى سويف</td>\n",
       "      <td>Banī Suwayf</td>\n",
       "      <td>10954</td>\n",
       "      <td>3154100</td>\n",
       "      <td>287.940478</td>\n",
       "      <td>23.2</td>\n",
       "      <td>29.074409</td>\n",
       "      <td>31.097848</td>\n",
       "      <td>731751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sharqia</td>\n",
       "      <td>SQY</td>\n",
       "      <td>الشرقية</td>\n",
       "      <td>Az-Zaqāzīq</td>\n",
       "      <td>4911</td>\n",
       "      <td>7163824</td>\n",
       "      <td>1458.730198</td>\n",
       "      <td>23.1</td>\n",
       "      <td>30.587676</td>\n",
       "      <td>31.501997</td>\n",
       "      <td>1654843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Kafr el-Sheikh</td>\n",
       "      <td>KSH</td>\n",
       "      <td>كفر الشيخ</td>\n",
       "      <td>Kafr ash-Shaykh</td>\n",
       "      <td>3467</td>\n",
       "      <td>3362185</td>\n",
       "      <td>969.767811</td>\n",
       "      <td>23.1</td>\n",
       "      <td>31.114304</td>\n",
       "      <td>30.940116</td>\n",
       "      <td>776664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fayoum</td>\n",
       "      <td>FYM</td>\n",
       "      <td>الفيوم</td>\n",
       "      <td>Al-Fayyūm</td>\n",
       "      <td>6068</td>\n",
       "      <td>3596954</td>\n",
       "      <td>592.774225</td>\n",
       "      <td>22.5</td>\n",
       "      <td>29.309949</td>\n",
       "      <td>30.841804</td>\n",
       "      <td>809314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Sohag</td>\n",
       "      <td>SWJ</td>\n",
       "      <td>سوهاج</td>\n",
       "      <td>Sawhāj</td>\n",
       "      <td>11218</td>\n",
       "      <td>4967409</td>\n",
       "      <td>442.807007</td>\n",
       "      <td>21.4</td>\n",
       "      <td>26.556952</td>\n",
       "      <td>31.694785</td>\n",
       "      <td>1063025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Monufia</td>\n",
       "      <td>MNF</td>\n",
       "      <td>المنوفية</td>\n",
       "      <td>Shibīn al-Kawm</td>\n",
       "      <td>2499</td>\n",
       "      <td>4301601</td>\n",
       "      <td>1721.328932</td>\n",
       "      <td>20.6</td>\n",
       "      <td>30.597200</td>\n",
       "      <td>30.987600</td>\n",
       "      <td>886129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Qena</td>\n",
       "      <td>QNA</td>\n",
       "      <td>قنا</td>\n",
       "      <td>Qinā</td>\n",
       "      <td>8980</td>\n",
       "      <td>3164281</td>\n",
       "      <td>352.369822</td>\n",
       "      <td>19.7</td>\n",
       "      <td>26.164179</td>\n",
       "      <td>32.726710</td>\n",
       "      <td>623363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beheira</td>\n",
       "      <td>BHR</td>\n",
       "      <td>البحيرة</td>\n",
       "      <td>Damanhūr</td>\n",
       "      <td>9826</td>\n",
       "      <td>6171613</td>\n",
       "      <td>628.090067</td>\n",
       "      <td>19.5</td>\n",
       "      <td>31.032821</td>\n",
       "      <td>30.425270</td>\n",
       "      <td>1203464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Al-Minya</td>\n",
       "      <td>MNY</td>\n",
       "      <td>المنيا</td>\n",
       "      <td>Al-Minyā</td>\n",
       "      <td>32279</td>\n",
       "      <td>5497095</td>\n",
       "      <td>170.299421</td>\n",
       "      <td>18.9</td>\n",
       "      <td>28.109884</td>\n",
       "      <td>30.750299</td>\n",
       "      <td>1038950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name Abbr.         Native          Capital    Area  Population  \\\n",
       "10           Cairo   QAH        القاهرة       Al-Qāhirah    3085     9539673   \n",
       "19       Port Said   BSD       بور سعيد        Būr Sa'īd    1345      749371   \n",
       "15            Suez   SWS         السويس        As-Suways    9002      728180   \n",
       "5       Alexandria   ISK     الإسكندرية  Al-Iskandariyah    2300     5163750   \n",
       "1          Red Sea   BAH   البحر الأحمر     Al-Ghurdaqah  120000      359888   \n",
       "22         Matrouh   MTR          مطروخ     Marsā Maṭrūḥ  166563      425624   \n",
       "26     North Sinai   SSH     شمال سيناء        Al-'Arīsh   27564      450328   \n",
       "7             Giza   JIZ         الجيزة         Al-Jīzah   13184     8632021   \n",
       "25     South Sinai   SJN     جنوب سيناء           Aṭ-Ṭūr   31272      102018   \n",
       "13      New Valley   WJD  الوادي الجديد      Al-Khārijah  440098      241247   \n",
       "6         Ismailia   ISM    الإسماعيلية   Al-Ismā'īliyah    5067     1303993   \n",
       "11        Qalyubia   QLY      القليوبية            Banhā    1124     5627420   \n",
       "16           Aswan   ASW          أسوان            Aswān   62726     1473975   \n",
       "20        Damietta   DMY          دمياط           Dumyāṭ     910     1496765   \n",
       "12           Luxor   UQS         الأقصر         Al-Uqṣur    2960     1250209   \n",
       "4          Gharbia   GHB        الغربية            Ṭanṭā    1942     4999633   \n",
       "0         Dakahlia   DQH       الدقهلية      Al-Manṣūrah    3538     6492381   \n",
       "17           Asyut   ASY          أسيوط            Asyūṭ   13720     4383289   \n",
       "18       Beni Suef   BSW       بنى سويف      Banī Suwayf   10954     3154100   \n",
       "14         Sharqia   SQY        الشرقية       Az-Zaqāzīq    4911     7163824   \n",
       "21  Kafr el-Sheikh   KSH      كفر الشيخ  Kafr ash-Shaykh    3467     3362185   \n",
       "3           Fayoum   FYM         الفيوم        Al-Fayyūm    6068     3596954   \n",
       "24           Sohag   SWJ          سوهاج           Sawhāj   11218     4967409   \n",
       "8          Monufia   MNF       المنوفية   Shibīn al-Kawm    2499     4301601   \n",
       "23            Qena   QNA            قنا             Qinā    8980     3164281   \n",
       "2          Beheira   BHR        البحيرة         Damanhūr    9826     6171613   \n",
       "9         Al-Minya   MNY         المنيا         Al-Minyā   32279     5497095   \n",
       "\n",
       "        Density  % Urban        lat        lng  Urban Population  \n",
       "10  3092.276499    100.0  30.077080  31.285909           9539673  \n",
       "19   557.153160    100.0  31.256541  32.284115            749371  \n",
       "15    80.890913    100.0  29.973714  32.526267            728180  \n",
       "5   2245.108696     98.8  31.215645  29.955266           5101785  \n",
       "1      2.999067     95.1  26.729177  33.936511            342253  \n",
       "22     2.555333     70.6  31.352539  27.245275            300490  \n",
       "26    16.337542     60.2  31.128660  33.797117            271097  \n",
       "7    654.734603     58.6  30.008079  31.210931           5058364  \n",
       "25     3.262279     51.1  28.236381  33.625404             52131  \n",
       "13     0.548167     48.0  25.451405  30.546346            115798  \n",
       "6    257.350109     45.4  30.604272  32.272252            592012  \n",
       "11  5006.601423     44.7  30.459065  31.178577           2515456  \n",
       "16    23.498629     42.3  24.093433  32.907038            623491  \n",
       "20  1644.796703     38.7  31.416477  31.813316            579248  \n",
       "12   422.367905     37.8  25.695858  32.643592            472579  \n",
       "4   2574.476313     30.0  30.788471  31.001921           1499889  \n",
       "0   1835.042679     28.2  31.036373  31.380691           1830851  \n",
       "17   319.481706     26.5  27.180956  31.183683           1161571  \n",
       "18   287.940478     23.2  29.074409  31.097848            731751  \n",
       "14  1458.730198     23.1  30.587676  31.501997           1654843  \n",
       "21   969.767811     23.1  31.114304  30.940116            776664  \n",
       "3    592.774225     22.5  29.309949  30.841804            809314  \n",
       "24   442.807007     21.4  26.556952  31.694785           1063025  \n",
       "8   1721.328932     20.6  30.597200  30.987600            886129  \n",
       "23   352.369822     19.7  26.164179  32.726710            623363  \n",
       "2    628.090067     19.5  31.032821  30.425270           1203464  \n",
       "9    170.299421     18.9  28.109884  30.750299           1038950  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "governorates.sort_values('% Urban',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Governorate')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.barplot(y='Name',x='% Urban',data=governorates.sort_values('% Urban',ascending=False),palette='viridis')\n",
    "plt.ylabel('Governorate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "governorates['Urban Population'] = governorates['Population'] * (governorates['% Urban'] /100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "governorates['Urban Population'] = governorates['Urban Population'].apply(math.floor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Governorate')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.barplot(y='Name',x='Urban Population',data=governorates.sort_values('Urban Population',ascending=False),palette='viridis')\n",
    "plt.ylabel('Governorate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_nearby_tweets['method'] = 'Nearby'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_search_tweets['method'] = 'Keyword Search'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gov_changer(gov):\n",
    "    if gov == 'Az-Zaqaziq':\n",
    "        return 'Sharqia'\n",
    "    if gov == 'Tanta':\n",
    "        return 'Gharbia'\n",
    "    return gov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_search_tweets['governorate'] = arabic_search_tweets['governorate'].apply(gov_changer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_tweets = pd.concat([arabic_geocoded_tweets,arabic_nearby_tweets,arabic_profile_info_tweets,arabic_search_tweets])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_tweets.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_no_nearby_tweets = arabic_tweets[arabic_tweets['method'] != 'Nearby']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_no_nearby_tweets.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2aca5e2f5c8>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(arabic_tweets.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_tweets_count = arabic_no_nearby_tweets.groupby('governorate').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>date</th>\n",
       "      <th>time</th>\n",
       "      <th>username</th>\n",
       "      <th>name</th>\n",
       "      <th>place</th>\n",
       "      <th>tweet</th>\n",
       "      <th>urls</th>\n",
       "      <th>replies_count</th>\n",
       "      <th>retweets_count</th>\n",
       "      <th>likes_count</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>near</th>\n",
       "      <th>geo</th>\n",
       "      <th>language</th>\n",
       "      <th>method</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>governorate</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Al-Minya</th>\n",
       "      <td>16162</td>\n",
       "      <td>16162</td>\n",
       "      <td>16162</td>\n",
       "      <td>16162</td>\n",
       "      <td>68</td>\n",
       "      <td>16162</td>\n",
       "      <td>16162</td>\n",
       "      <td>16162</td>\n",
       "      <td>16162</td>\n",
       "      <td>16162</td>\n",
       "      <td>16162</td>\n",
       "      <td>0</td>\n",
       "      <td>12591</td>\n",
       "      <td>16162</td>\n",
       "      <td>16162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alexandria</th>\n",
       "      <td>12610</td>\n",
       "      <td>12610</td>\n",
       "      <td>12610</td>\n",
       "      <td>12610</td>\n",
       "      <td>215</td>\n",
       "      <td>12610</td>\n",
       "      <td>12610</td>\n",
       "      <td>12610</td>\n",
       "      <td>12610</td>\n",
       "      <td>12610</td>\n",
       "      <td>12610</td>\n",
       "      <td>0</td>\n",
       "      <td>378</td>\n",
       "      <td>12610</td>\n",
       "      <td>12610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aswan</th>\n",
       "      <td>13128</td>\n",
       "      <td>13128</td>\n",
       "      <td>13128</td>\n",
       "      <td>13128</td>\n",
       "      <td>48</td>\n",
       "      <td>13128</td>\n",
       "      <td>13128</td>\n",
       "      <td>13128</td>\n",
       "      <td>13128</td>\n",
       "      <td>13128</td>\n",
       "      <td>13128</td>\n",
       "      <td>0</td>\n",
       "      <td>7399</td>\n",
       "      <td>13128</td>\n",
       "      <td>13128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asyut</th>\n",
       "      <td>10173</td>\n",
       "      <td>10173</td>\n",
       "      <td>10173</td>\n",
       "      <td>10173</td>\n",
       "      <td>27</td>\n",
       "      <td>10173</td>\n",
       "      <td>10173</td>\n",
       "      <td>10173</td>\n",
       "      <td>10173</td>\n",
       "      <td>10173</td>\n",
       "      <td>10173</td>\n",
       "      <td>0</td>\n",
       "      <td>5926</td>\n",
       "      <td>10173</td>\n",
       "      <td>10173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beheira</th>\n",
       "      <td>83932</td>\n",
       "      <td>83932</td>\n",
       "      <td>83932</td>\n",
       "      <td>83932</td>\n",
       "      <td>373</td>\n",
       "      <td>83932</td>\n",
       "      <td>83932</td>\n",
       "      <td>83932</td>\n",
       "      <td>83932</td>\n",
       "      <td>83932</td>\n",
       "      <td>83932</td>\n",
       "      <td>0</td>\n",
       "      <td>81742</td>\n",
       "      <td>83932</td>\n",
       "      <td>83932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beni Suef</th>\n",
       "      <td>10980</td>\n",
       "      <td>10980</td>\n",
       "      <td>10980</td>\n",
       "      <td>10980</td>\n",
       "      <td>40</td>\n",
       "      <td>10980</td>\n",
       "      <td>10980</td>\n",
       "      <td>10980</td>\n",
       "      <td>10980</td>\n",
       "      <td>10980</td>\n",
       "      <td>10980</td>\n",
       "      <td>0</td>\n",
       "      <td>8641</td>\n",
       "      <td>10980</td>\n",
       "      <td>10980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cairo</th>\n",
       "      <td>65773</td>\n",
       "      <td>65773</td>\n",
       "      <td>65773</td>\n",
       "      <td>65772</td>\n",
       "      <td>661</td>\n",
       "      <td>65773</td>\n",
       "      <td>65773</td>\n",
       "      <td>65773</td>\n",
       "      <td>65773</td>\n",
       "      <td>65773</td>\n",
       "      <td>65773</td>\n",
       "      <td>0</td>\n",
       "      <td>15957</td>\n",
       "      <td>65773</td>\n",
       "      <td>65773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dakahlia</th>\n",
       "      <td>87842</td>\n",
       "      <td>87842</td>\n",
       "      <td>87842</td>\n",
       "      <td>87842</td>\n",
       "      <td>187</td>\n",
       "      <td>87842</td>\n",
       "      <td>87842</td>\n",
       "      <td>87842</td>\n",
       "      <td>87842</td>\n",
       "      <td>87842</td>\n",
       "      <td>87842</td>\n",
       "      <td>0</td>\n",
       "      <td>82291</td>\n",
       "      <td>87842</td>\n",
       "      <td>87842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Damietta</th>\n",
       "      <td>32647</td>\n",
       "      <td>32647</td>\n",
       "      <td>32647</td>\n",
       "      <td>32647</td>\n",
       "      <td>117</td>\n",
       "      <td>32647</td>\n",
       "      <td>32647</td>\n",
       "      <td>32647</td>\n",
       "      <td>32647</td>\n",
       "      <td>32647</td>\n",
       "      <td>32647</td>\n",
       "      <td>0</td>\n",
       "      <td>22371</td>\n",
       "      <td>32647</td>\n",
       "      <td>32647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fayoum</th>\n",
       "      <td>4679</td>\n",
       "      <td>4679</td>\n",
       "      <td>4679</td>\n",
       "      <td>4679</td>\n",
       "      <td>39</td>\n",
       "      <td>4679</td>\n",
       "      <td>4679</td>\n",
       "      <td>4679</td>\n",
       "      <td>4679</td>\n",
       "      <td>4679</td>\n",
       "      <td>4679</td>\n",
       "      <td>0</td>\n",
       "      <td>2890</td>\n",
       "      <td>4679</td>\n",
       "      <td>4679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gharbia</th>\n",
       "      <td>34295</td>\n",
       "      <td>34295</td>\n",
       "      <td>34295</td>\n",
       "      <td>34295</td>\n",
       "      <td>95</td>\n",
       "      <td>34295</td>\n",
       "      <td>34295</td>\n",
       "      <td>34295</td>\n",
       "      <td>34295</td>\n",
       "      <td>34295</td>\n",
       "      <td>34295</td>\n",
       "      <td>0</td>\n",
       "      <td>25838</td>\n",
       "      <td>34295</td>\n",
       "      <td>34295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Giza</th>\n",
       "      <td>89558</td>\n",
       "      <td>89558</td>\n",
       "      <td>89558</td>\n",
       "      <td>89558</td>\n",
       "      <td>749</td>\n",
       "      <td>89558</td>\n",
       "      <td>89558</td>\n",
       "      <td>89558</td>\n",
       "      <td>89558</td>\n",
       "      <td>89558</td>\n",
       "      <td>89558</td>\n",
       "      <td>0</td>\n",
       "      <td>83585</td>\n",
       "      <td>89558</td>\n",
       "      <td>89558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ismailia</th>\n",
       "      <td>9447</td>\n",
       "      <td>9447</td>\n",
       "      <td>9447</td>\n",
       "      <td>9447</td>\n",
       "      <td>42</td>\n",
       "      <td>9447</td>\n",
       "      <td>9447</td>\n",
       "      <td>9447</td>\n",
       "      <td>9447</td>\n",
       "      <td>9447</td>\n",
       "      <td>9447</td>\n",
       "      <td>0</td>\n",
       "      <td>7946</td>\n",
       "      <td>9447</td>\n",
       "      <td>9447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kafr el-Sheikh</th>\n",
       "      <td>41593</td>\n",
       "      <td>41593</td>\n",
       "      <td>41593</td>\n",
       "      <td>41592</td>\n",
       "      <td>210</td>\n",
       "      <td>41593</td>\n",
       "      <td>41593</td>\n",
       "      <td>41593</td>\n",
       "      <td>41593</td>\n",
       "      <td>41593</td>\n",
       "      <td>41593</td>\n",
       "      <td>0</td>\n",
       "      <td>38424</td>\n",
       "      <td>41593</td>\n",
       "      <td>41593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxor</th>\n",
       "      <td>9377</td>\n",
       "      <td>9377</td>\n",
       "      <td>9377</td>\n",
       "      <td>9377</td>\n",
       "      <td>21</td>\n",
       "      <td>9377</td>\n",
       "      <td>9377</td>\n",
       "      <td>9377</td>\n",
       "      <td>9377</td>\n",
       "      <td>9377</td>\n",
       "      <td>9377</td>\n",
       "      <td>0</td>\n",
       "      <td>4306</td>\n",
       "      <td>9377</td>\n",
       "      <td>9377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matrouh</th>\n",
       "      <td>58303</td>\n",
       "      <td>58303</td>\n",
       "      <td>58303</td>\n",
       "      <td>58303</td>\n",
       "      <td>340</td>\n",
       "      <td>58303</td>\n",
       "      <td>58303</td>\n",
       "      <td>58303</td>\n",
       "      <td>58303</td>\n",
       "      <td>58303</td>\n",
       "      <td>58303</td>\n",
       "      <td>0</td>\n",
       "      <td>57629</td>\n",
       "      <td>58303</td>\n",
       "      <td>58303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monufia</th>\n",
       "      <td>20055</td>\n",
       "      <td>20055</td>\n",
       "      <td>20055</td>\n",
       "      <td>20055</td>\n",
       "      <td>130</td>\n",
       "      <td>20055</td>\n",
       "      <td>20055</td>\n",
       "      <td>20055</td>\n",
       "      <td>20055</td>\n",
       "      <td>20055</td>\n",
       "      <td>20055</td>\n",
       "      <td>0</td>\n",
       "      <td>16223</td>\n",
       "      <td>20055</td>\n",
       "      <td>20055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Valley</th>\n",
       "      <td>31436</td>\n",
       "      <td>31436</td>\n",
       "      <td>31436</td>\n",
       "      <td>31436</td>\n",
       "      <td>251</td>\n",
       "      <td>31436</td>\n",
       "      <td>31436</td>\n",
       "      <td>31436</td>\n",
       "      <td>31436</td>\n",
       "      <td>31436</td>\n",
       "      <td>31436</td>\n",
       "      <td>0</td>\n",
       "      <td>31035</td>\n",
       "      <td>31436</td>\n",
       "      <td>31436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Sinai</th>\n",
       "      <td>11649</td>\n",
       "      <td>11649</td>\n",
       "      <td>11649</td>\n",
       "      <td>11649</td>\n",
       "      <td>119</td>\n",
       "      <td>11649</td>\n",
       "      <td>11649</td>\n",
       "      <td>11649</td>\n",
       "      <td>11649</td>\n",
       "      <td>11649</td>\n",
       "      <td>11649</td>\n",
       "      <td>0</td>\n",
       "      <td>10980</td>\n",
       "      <td>11649</td>\n",
       "      <td>11649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Port Said</th>\n",
       "      <td>7752</td>\n",
       "      <td>7752</td>\n",
       "      <td>7752</td>\n",
       "      <td>7752</td>\n",
       "      <td>18</td>\n",
       "      <td>7752</td>\n",
       "      <td>7752</td>\n",
       "      <td>7752</td>\n",
       "      <td>7752</td>\n",
       "      <td>7752</td>\n",
       "      <td>7752</td>\n",
       "      <td>0</td>\n",
       "      <td>4060</td>\n",
       "      <td>7752</td>\n",
       "      <td>7752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qalyubia</th>\n",
       "      <td>15334</td>\n",
       "      <td>15334</td>\n",
       "      <td>15334</td>\n",
       "      <td>15334</td>\n",
       "      <td>57</td>\n",
       "      <td>15334</td>\n",
       "      <td>15334</td>\n",
       "      <td>15334</td>\n",
       "      <td>15334</td>\n",
       "      <td>15334</td>\n",
       "      <td>15334</td>\n",
       "      <td>0</td>\n",
       "      <td>14784</td>\n",
       "      <td>15334</td>\n",
       "      <td>15334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qena</th>\n",
       "      <td>5757</td>\n",
       "      <td>5757</td>\n",
       "      <td>5757</td>\n",
       "      <td>5757</td>\n",
       "      <td>31</td>\n",
       "      <td>5757</td>\n",
       "      <td>5757</td>\n",
       "      <td>5757</td>\n",
       "      <td>5757</td>\n",
       "      <td>5757</td>\n",
       "      <td>5757</td>\n",
       "      <td>0</td>\n",
       "      <td>2172</td>\n",
       "      <td>5757</td>\n",
       "      <td>5757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Red Sea</th>\n",
       "      <td>19908</td>\n",
       "      <td>19908</td>\n",
       "      <td>19908</td>\n",
       "      <td>19908</td>\n",
       "      <td>156</td>\n",
       "      <td>19908</td>\n",
       "      <td>19908</td>\n",
       "      <td>19908</td>\n",
       "      <td>19908</td>\n",
       "      <td>19908</td>\n",
       "      <td>19908</td>\n",
       "      <td>0</td>\n",
       "      <td>17138</td>\n",
       "      <td>19908</td>\n",
       "      <td>19908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharqia</th>\n",
       "      <td>36713</td>\n",
       "      <td>36713</td>\n",
       "      <td>36713</td>\n",
       "      <td>36713</td>\n",
       "      <td>195</td>\n",
       "      <td>36713</td>\n",
       "      <td>36713</td>\n",
       "      <td>36713</td>\n",
       "      <td>36713</td>\n",
       "      <td>36713</td>\n",
       "      <td>36713</td>\n",
       "      <td>0</td>\n",
       "      <td>32334</td>\n",
       "      <td>36713</td>\n",
       "      <td>36713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sohag</th>\n",
       "      <td>5706</td>\n",
       "      <td>5706</td>\n",
       "      <td>5706</td>\n",
       "      <td>5706</td>\n",
       "      <td>22</td>\n",
       "      <td>5706</td>\n",
       "      <td>5706</td>\n",
       "      <td>5706</td>\n",
       "      <td>5706</td>\n",
       "      <td>5706</td>\n",
       "      <td>5706</td>\n",
       "      <td>0</td>\n",
       "      <td>2325</td>\n",
       "      <td>5706</td>\n",
       "      <td>5706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Sinai</th>\n",
       "      <td>2315</td>\n",
       "      <td>2315</td>\n",
       "      <td>2315</td>\n",
       "      <td>2315</td>\n",
       "      <td>59</td>\n",
       "      <td>2315</td>\n",
       "      <td>2315</td>\n",
       "      <td>2315</td>\n",
       "      <td>2315</td>\n",
       "      <td>2315</td>\n",
       "      <td>2315</td>\n",
       "      <td>0</td>\n",
       "      <td>1932</td>\n",
       "      <td>2315</td>\n",
       "      <td>2315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suez</th>\n",
       "      <td>21017</td>\n",
       "      <td>21017</td>\n",
       "      <td>21017</td>\n",
       "      <td>21017</td>\n",
       "      <td>81</td>\n",
       "      <td>21017</td>\n",
       "      <td>21017</td>\n",
       "      <td>21017</td>\n",
       "      <td>21017</td>\n",
       "      <td>21017</td>\n",
       "      <td>21017</td>\n",
       "      <td>0</td>\n",
       "      <td>14503</td>\n",
       "      <td>21017</td>\n",
       "      <td>21017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date   time  username   name  place  tweet   urls  \\\n",
       "governorate                                                          \n",
       "Al-Minya        16162  16162     16162  16162     68  16162  16162   \n",
       "Alexandria      12610  12610     12610  12610    215  12610  12610   \n",
       "Aswan           13128  13128     13128  13128     48  13128  13128   \n",
       "Asyut           10173  10173     10173  10173     27  10173  10173   \n",
       "Beheira         83932  83932     83932  83932    373  83932  83932   \n",
       "Beni Suef       10980  10980     10980  10980     40  10980  10980   \n",
       "Cairo           65773  65773     65773  65772    661  65773  65773   \n",
       "Dakahlia        87842  87842     87842  87842    187  87842  87842   \n",
       "Damietta        32647  32647     32647  32647    117  32647  32647   \n",
       "Fayoum           4679   4679      4679   4679     39   4679   4679   \n",
       "Gharbia         34295  34295     34295  34295     95  34295  34295   \n",
       "Giza            89558  89558     89558  89558    749  89558  89558   \n",
       "Ismailia         9447   9447      9447   9447     42   9447   9447   \n",
       "Kafr el-Sheikh  41593  41593     41593  41592    210  41593  41593   \n",
       "Luxor            9377   9377      9377   9377     21   9377   9377   \n",
       "Matrouh         58303  58303     58303  58303    340  58303  58303   \n",
       "Monufia         20055  20055     20055  20055    130  20055  20055   \n",
       "New Valley      31436  31436     31436  31436    251  31436  31436   \n",
       "North Sinai     11649  11649     11649  11649    119  11649  11649   \n",
       "Port Said        7752   7752      7752   7752     18   7752   7752   \n",
       "Qalyubia        15334  15334     15334  15334     57  15334  15334   \n",
       "Qena             5757   5757      5757   5757     31   5757   5757   \n",
       "Red Sea         19908  19908     19908  19908    156  19908  19908   \n",
       "Sharqia         36713  36713     36713  36713    195  36713  36713   \n",
       "Sohag            5706   5706      5706   5706     22   5706   5706   \n",
       "South Sinai      2315   2315      2315   2315     59   2315   2315   \n",
       "Suez            21017  21017     21017  21017     81  21017  21017   \n",
       "\n",
       "                replies_count  retweets_count  likes_count  hashtags  near  \\\n",
       "governorate                                                                  \n",
       "Al-Minya                16162           16162        16162     16162     0   \n",
       "Alexandria              12610           12610        12610     12610     0   \n",
       "Aswan                   13128           13128        13128     13128     0   \n",
       "Asyut                   10173           10173        10173     10173     0   \n",
       "Beheira                 83932           83932        83932     83932     0   \n",
       "Beni Suef               10980           10980        10980     10980     0   \n",
       "Cairo                   65773           65773        65773     65773     0   \n",
       "Dakahlia                87842           87842        87842     87842     0   \n",
       "Damietta                32647           32647        32647     32647     0   \n",
       "Fayoum                   4679            4679         4679      4679     0   \n",
       "Gharbia                 34295           34295        34295     34295     0   \n",
       "Giza                    89558           89558        89558     89558     0   \n",
       "Ismailia                 9447            9447         9447      9447     0   \n",
       "Kafr el-Sheikh          41593           41593        41593     41593     0   \n",
       "Luxor                    9377            9377         9377      9377     0   \n",
       "Matrouh                 58303           58303        58303     58303     0   \n",
       "Monufia                 20055           20055        20055     20055     0   \n",
       "New Valley              31436           31436        31436     31436     0   \n",
       "North Sinai             11649           11649        11649     11649     0   \n",
       "Port Said                7752            7752         7752      7752     0   \n",
       "Qalyubia                15334           15334        15334     15334     0   \n",
       "Qena                     5757            5757         5757      5757     0   \n",
       "Red Sea                 19908           19908        19908     19908     0   \n",
       "Sharqia                 36713           36713        36713     36713     0   \n",
       "Sohag                    5706            5706         5706      5706     0   \n",
       "South Sinai              2315            2315         2315      2315     0   \n",
       "Suez                    21017           21017        21017     21017     0   \n",
       "\n",
       "                  geo  language  method  \n",
       "governorate                              \n",
       "Al-Minya        12591     16162   16162  \n",
       "Alexandria        378     12610   12610  \n",
       "Aswan            7399     13128   13128  \n",
       "Asyut            5926     10173   10173  \n",
       "Beheira         81742     83932   83932  \n",
       "Beni Suef        8641     10980   10980  \n",
       "Cairo           15957     65773   65773  \n",
       "Dakahlia        82291     87842   87842  \n",
       "Damietta        22371     32647   32647  \n",
       "Fayoum           2890      4679    4679  \n",
       "Gharbia         25838     34295   34295  \n",
       "Giza            83585     89558   89558  \n",
       "Ismailia         7946      9447    9447  \n",
       "Kafr el-Sheikh  38424     41593   41593  \n",
       "Luxor            4306      9377    9377  \n",
       "Matrouh         57629     58303   58303  \n",
       "Monufia         16223     20055   20055  \n",
       "New Valley      31035     31436   31436  \n",
       "North Sinai     10980     11649   11649  \n",
       "Port Said        4060      7752    7752  \n",
       "Qalyubia        14784     15334   15334  \n",
       "Qena             2172      5757    5757  \n",
       "Red Sea         17138     19908   19908  \n",
       "Sharqia         32334     36713   36713  \n",
       "Sohag            2325      5706    5706  \n",
       "South Sinai      1932      2315    2315  \n",
       "Suez            14503     21017   21017  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arabic_tweets_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 5 governorates with # of tweets of all methods')"
      ]
     },
     "execution_count": 305,
     "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": [
    "sns.barplot(x='tweet',y=arabic_tweets_count.sort_values('tweet',ascending=False).head(5).index,data=arabic_tweets_count.sort_values('tweet',ascending=False).head(5),palette='magma')\n",
    "plt.title('Top 5 governorates with # of tweets of all methods')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_grouped_geo = arabic_geocoded_tweets.groupby('governorate').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2aca71a2f08>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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efPBBq3PVq1ePM2fOsH79erp27coLL7yAm5sb27dv5/vvv2fo0KEUFxfzxhtv0LRpU/71r39x5MgRfH19mTFjBp9++inFxcXUr18fgO+++44FCxaQlJREWVkZ//jHPzh69Ch9+/YlOjraFt8uEREREbtjk5nEhQsXUlBQwMmTJyu3HTp0iNdee43ly5eTnZ1NaGgonTp1Ytq0adSvX5+AgABWrVpV2SACJCYmMm/ePJYvX87q1au5ePGi1bnc3d2ZO3cuGRkZ9OjRg1GjRlFQUACAyWQiNTWVAQMGsGnTJjZv3kxAQABpaWk8+uijVsvLBw4cICkpiVmzZuHp6UlRURFjxoxh2bJlrFq1yhbfKhERERG7ZJOZxPvvv58XX3yRvn370r17d2655RaCgoKYOnUq9erV49y5c5SXl1c5pnnz5lbjHDhwgGeeeQaAgoICTp06BcA333zDvHm/PL9sxowZmEwm3njjDcrLy5k7dy6LFi2ic+fOtG7dGgB/f3+OHTvGwYMH+fzzz9m5cydms5nbb7+d4ODgyvNlZmbi7e2Nh4cHAH5+fjRp0gQAFxeXGv4uiYiIiNgvmzSJISEheHp6Mn78eMaNG8fKlSuZOXMmaWlpuLq60rt3bywWS5VjXF2tJzVbtGjBggULqFevHvPmzaNRo0YA3H333dx9990AZGdnM336dN555x1MJhM333wzP/3002XHbNasGQ899BBPPPEE33//PadPn+bcuXOV7w8cOBBXV1eSk5OJj49XYygiIiJOy6aPwLn77rsJCQkhJSWF++67j4EDBzJ8+HACAwM5ffp0tcfHx8czfPhwoqKiKC8vx8vLy2qf2267jV69etGvXz9iY2PJyMhg+PDhlx0vMjKSgwcPEhMTw7Rp07j11lut9omOjuarr77i8OHDf7peERERkbrCxfLfU3pyTbKysni652tGxxCRGrB+zzwaXR9Q7X45OTmEhobWQiL75Mz1O3PtoPrrQv1ZWVm0b9/+su/pYdoiIiIiYkVNooiIiIhYUZMoIiIiIlbUJIqIiIiIFZs8AsfZrd8zz+gIhjGXmXH3cM5fK2euHepm/SZPD6MjiIgYpm79i24nruRuyLqqLtzpdbWcuXZQ/SIidY2Wm0VERETEippEEREREbGi5WYbOP1zgdERDOPvG+i09Ttz7XBt9Zs8PbjO36eGE4mIyLVQk2gDA+94zugIIg5l5fdvGh1BRET+i5abRURERMSKmkQRERERseKwy83l5eVMmzaNgwcPUlFRwcMPP0xeXh5t2rShW7du1zT2q6++yvPPP4+Xl1cNpRURERFxLA7bJG7ZsgVPT09SUlKoqKggOjqa2267rUbGnjBhQo2MIyIiIuKoHLZJDAoKYuvWrdx77720b9+e5cuXs2DBAlavXk1KSgo+Pj7Mnj2b48eP88orr1BaWoq3tzdz585lwoQJFBYWEhQUREREBG+++SbBwcGcPHmS9PR0YmNjefvtt9m+fTupqamUlJTQqVMn4uPjjS5bREREpFY4bJN4++23Ex8fT3JyMjk5OTz88MPUr1+fsLAwnn76aRISEsjJyaGgoICEhARuvfVWnn76aX766ScAHn30UXr06MHAgQNJTU3F1dWV++67r8o5jh07RnJyMm5ubjz44INqEkVERMRpOGyTuH//ftq3b0/37t25cOECzz33HIWFhYwYMQIAf39/SkpKCAoKYv78+ZhMJvLy8igvLwegWbNmAJjNZvz8/ABo2bJllXP4+/szbtw4fHx8Ko8TERERcQYO2yRu3bqVgoICRo8ejY+PD02aNKGkpARX16o3bM+ePZvnn3+ekJAQBgwYgMViAajcz8PDg/z8fOrXr8+hQ4esjv3oo484c+YMn3/+ee0UJiIiImIHHLZJHDx4MFOnTiUqKgpvb286dOhAo0aNrPbr3r078fHxNGjQAC8vL06dOlXl/TFjxjBy5Ej8/Pzw8PCo8l54eDhRUVH4+Pjg7+9PUVER3t7eNq1LRERExB44bJPo4eHBpEmTfvf9sWPHAtCxY0f69u1b5b0uXbpUfv0///M/rF69GoCoqCgA0tLSAJgyZUqNZhYRERFxFHqYtoiIiIhYUZP4G+np6UZHEBEREbELahJFRERExIqaRBERERGx4rA3rtizld+/aXQEw5jNZtzdnfPXyplrh2ur3+TpUf1OIiJSq5z3L5oNBV7vb3QEw+Tk5BAaGmp0DEM4c+2g+kVE6hotN4uIiIiIFTWJIiIiImJFy802kP/zWaMjGKahb6DT1u/MtcPv1+/h5Y5vAx8DEomIyLVQk2gDw8OfNzqCiN14JzPR6AgiInIVtNwsIiIiIlbUJIqIiIiIFTWJIiIiImLF8CYxPT2dwYMHV77etm0bM2bMuOrxBgwYwIkTJypf79q1i3Hjxl1239jYWIqKioiKirrq84mIiIjURYY3iQAHDhzggw8+qJGxevXqxWeffVb5etOmTfztb3+rkbFFREREnIVdNIlxcXEsWrSICxcuVNn+1ltvMWjQIIYNG8b//d//MWLECPLz8/n+++956KGHAJg/fz7ffPNN5TEPPPBAZZNosVjYsWMHHTt25MiRIwwfPpzY2FhGjBhBeXm5VY7NmzczYMAABgwYwDfffMNnn33G7NmzAdi+fTuJibpLU0RERJyDXTSJfn5+xMbGMmfOnMpte/fuJTc3lxUrVjBmzBjmzp3LX//6VzIzM8nMzKS8vJxz586xc+dO2rdvX3lco0aNcHV1rWwm77jjDtzc3Dh8+DAJCQmkpaXh5ubGTz/9VCVDRUUF8+bNY+nSpSxevJikpCQ6d+7Mf/7zHwA++ugjevXqVTvfEBERERGD2c1zEvv27cvgwYNp1qwZAAcPHmT37t3ExsYCvzSS9957L8uWLaOgoIB+/frx9ddf4+XlhclkqjJWz5492bx5M/v37+fBBx8EICgoiPnz52MymcjLy7OaSczPzycvL4/HH3+88jVASEgIP/74IwcOHNDn0oqIiIjTsIuZRAAXFxfGjx9PUlISADfeeCP33HMPaWlpvPLKK3Tp0oWQkBByc3MB+Mtf/sKCBQu4++67rca6//77+eqrrzhw4ABt27YFYPbs2YwcOZIpU6ZgMpmwWCxVjvH39yckJITU1FTeeecdevfujclk4sEHH+Stt94iPDzcxt8BEREREfthN00iQJs2bYiMjAQgLCwMb29vYmJi+Mc//sGtt94KQOPGjWnZsiWtWrXi559/plOnTlbjXHfddQDccccdldu6d+9OfHw8MTExeHl5cerUqSrHuLm5ERcXR0xMDP379ycwMBD4pRndtWuXlppFRETEqbhY/ntKTaq4dOkSTz/9NAsXLryi/bOysnj1kXk2TiXiON7JTCTg+gZGx7CpnJwcp74cxZnrd+baQfXXhfqzsrKq3NvxW3Y1k2hvTp06Rd++fYmJiTE6ioiIiEitspsbV+xRo0aNWLdundExRERERGqdZhJFRERExIpmEm3gnUznfei22VyGu7uH0TEM4cy1w+/X7+Glf2ZERByR/vW2gbp+kf4fqQsX8V4tZ64dVL+ISF2j5WYRERERsaImUURERESsaLnZBgpOnDU6gmECrwt02vrtsXYPT3d8GvgYHUNERByQmkQbePYvY4yOIALAW9++bnQEERFxUFpuFhERERErahJFRERExIqaRBERERGx4rBN4u7duxkyZAiDBg1i8ODBbN68mTlz5pCRkfGnxklISGDfvn1Vtr366qtcvHixJuOKiIiIOBSHvHGlsLCQl156iXnz5hEcHMyFCxd48sknadu2bY2MP2HChBoZR0RERMRROWSTmJGRQWRkJMHBwQD4+PiwbNkykpKSWL16NSkpKfj4+DB79myOHz/OK6+8QmlpKd7e3sydO5cJEyZQWFhIUFAQAHPmzOHMmTN07NiRZ599ltjYWN5++222b99OamoqJSUldOrUifj4eCPLFhEREak1DrncfOLECW666SYAtmzZQmxsLI888gjffvstYWFhpKWl4efnR05ODocPHyYhIYG0tDTc3Nz46aefAHj00Ud56aWXAHjggQdYsWIF2dnZ5OXlVZ7n2LFjJCcns2LFCjZs2FDrdYqIiIgYxSFnEhs1asSpU6cA6Ny5M507d668HrF169YA+Pv7U1JSQlBQEPPnz8dkMpGXl0d5eTkAzZo1qxwvLCwMgJYtW3L06NHK7f7+/owbNw4fH5/K40REREScgUPOJEZERLB+/XpOnjwJQGlpKTk5Obi4uODqWrWk2bNnM3LkSKZMmYLJZMJisQBU2e/HH38EIDs7m+bNm1c5NjExkWeeeYaSkhJblyUiIiJiNxxyJrFBgwZMnDiRcePGUVZWxqVLl7j33ntp0aKF1b7du3cnPj6eBg0a4OXlVTkD+VsfffQR77zzDhERETRp0qRye3h4OFFRUfj4+ODv709RURHe3t42rU1ERETEHjhkkwjQpk0bFi1a9Lvvjx07FoCOHTvSt2/fKu916dKl8uvp06dbHZuWlgbAlClTaiKqiIiIiMNxyOVmEREREbEtNYkiIiIiYkVNooiIiIhYcdhrEu3ZW9++bnQEw5jNZbi7exgdwxD2WLuHp/4TFxGRq6O/IDbgH9zA6AiGycnJITQ01OgYhnDm2kVEpO7RcrOIiIiIWFGTKCIiIiJWtNxsA2dPnDU6gmEaXRfotPVfS+0enh54N9CD2kVExH6oSbSB8Z1fMDqCOJipW14zOoKIiEgVWm4WEREREStqEkVERETEippEEREREbHiEE1ieno6PXr0IDY2lujoaKZOnUpFRcWfHmfixIm/+957770HQF5eHtu3b7/qrCIiIiJ1gUM0iQBxcXGkpaWxevVqfHx8WLNmzZ8e4+WXX/7d99LS0gDYvn07+/btu+qcIiIiInWBQ97dHB0dzcyZM8nJySE3N5fCwkImTZpESUkJixYtory8HIvFwl/+8hc+//xz7rzzTsaOHUtUVBTp6els3ryZhQsXAvDss89SWFjIoUOHSExMZOvWrVy4cIF77rmHjRs3kpWVRWFhIfHx8XTr1s3gykVERERqh8PMJP5Ww4YN2bdvH2FhYaSkpDBy5Eg2bdoEQHl5OYsWLaJRo0b4+/uzcuVKtm7dWnlsRUUF8+bNY+nSpSxevJikpCR69uxJSEgIzz//PDExMcTFxdGsWTN8fX1ZvHgxU6dOrVyOFhEREXEGDjmTePr0aZo1a0Z2djaZmZkUFxcTHBwMQIsWLQAICAigWbNmuLi44O7+/8vMz88nLy+Pxx9/vPL1pUuXrM7h5uZGfn4+Y8eOxdXVFbPZXAuViYiIiNgHh5xJXLJkCZGRkXh6ejJ9+nTCw8OxWCwAuLi4/OGx/v7+hISEkJqayjvvvEPv3r0xmUyV77u4uGCxWMjJyeHw4cPMmDGDyMjIyvFFREREnIHDzCQuXbqUjz/+GIvFQlhYGB07diQlJYUdO3YQFBSEq+uV9btubm7ExcURExNDcXExAwcOBOCmm25iypQpPPTQQ4wbN47w8HBOnDhBdHQ0wcHBnD9/3pbliYiIiNgVF4umyGpUVlYWiwa9bXQMcTBTt7xGg+AGRse4Jjk5OYSGhhodwxDOXDs4d/3OXDuo/rpQf1ZWFu3bt7/sew653CwiIiIitqUmUURERESsqEkUERERESsOc+OKI5m65TWjIxjGbC7D3d3D6BiGuJbaPTyd83smIiL2S02iDTj6DQjXoi5cxHu1nLl2ERGpe7TcLCIiIiJW1CSKiIiIiBUtN9vAuZNnjY5gmCC/Rg5Xv7unB/X9vI2OISIiYlfUJNrAq91fNDqC/AkTNs80OoKIiIjd0XKziIiIiFhRkygiIiIiVtQkioiIiIgVh7gmcdu2bbz44os0a9YMi8XCuXPnePnll7njjjv+8LiMjAx++OEH4uPjK7fl5uYyZcoUzGYzHh4e/POf/+TGG2+0dQkiIiIiDsUhmkSAXr16MXbsWAD27t1LUlISSUlJf3qc119/nbFjx3LLLbewd+9epkyZwoIFC2o6roiIiIhDc8jl5hMnTuDr6wvAqlWrGDBgAIMHD2bv3r1cunSJUaNGERsby8qVK/ihOGMAACAASURBVK2ODQ4OZvny5Rw8eJDWrVszZ84cADZv3syAAQMYMGAA33zzDQBJSUk89thjREVFkZGRUXsFioiIiBjMYZrEjRs3MmjQIDp16sS///1vRo8eTX5+Phs3bmTFihW8+eabzJo1i4yMDFq0aEFaWhodOnSwGueFF17A19eXUaNG0aNHD7799lsqKiqYN28eS5cuZfHixSQlJWE2m/H19WXx4sVMnTqV9957z4CqRURERIzhcMvNn3/+OampqQQGBrJnzx4OHz7MkCFDALh06RJ5eXm0bt0agLCwML777rsq4+zatYvRo0czevRosrOzefbZZ1m5ciV5eXk8/vjjAOTn51NRUUF+fj5jx47F1dUVs9lcuwWLiIiIGMhhZhJ/FRERQfPmzVm5ciVNmjThtttuIy0tjbfeeosePXpw8803s3v3buCXaxf/27Rp0/jpp58AaNasGb6+vvj7+xMSEkJqairvvPMOvXv35sCBAxw+fJgZM2YQGRmJxWKp1TpFREREjOQwM4m/NXr0aAYMGMBDDz3E3XffzaBBgygqKiI+Pp6uXbvy8ccfExMTQ4MGDWjVqlWVYxMTE5kwYQIVFRWVdze7ubkRFxdHTEwMxcXFDBw4kObNm3PixAmio6MJDg7m/PnzBlUrIiIiUvscokns2LEjHTt2rHwdEBDAJ598AsCQIUMql5t/NX369N8dKyQkhBUrVlht79WrF7169aqybdWqVdcSW0RERMRhOdxys4iIiIjYnppEEREREbGiJlFERERErDjENYmOZsLmmUZHMEyZ2YyHu2P9Wrl7ehgdQURExO441l9zB3FdUAOjIxgmJyeH0NBQo2OIiIjINdJys4iIiIhYUZMoIiIiIla03GwD50+dNTqCYa73a2S39bubPKjn5210DBEREYegJtEG3ur5otER5DKe/ch5bygSERH5s7TcLCIiIiJW1CSKiIiIiBU1iSIiIiJixSGbxPT0dNq0aUNRUVHltjFjxpCQkHDVY549e5a+ffsya9YsJk6cWBMxRURERByWw9640rhxY7Zu3UqPHj0wm83s27eP22677arH279/P3feeSejR4+uwZQiIiIijslhm8SIiAgyMjLo0aMHmZmZhIeHc+HCBcaNG0dubi4mk4lp06Zx5MgRFi9eTFlZGQUFBbz55pt89913FBcXExMTw7Jly6hfvz5r1qzh+PHjtG/fnuTkZNLT01mzZg0bNmzg/PnzREdH079/f6PLFhEREakVDrncDHDjjTdy/PhxKioq2Lx5MxEREXz22WcEBASwfPlyRowYwezZswEoKytj0aJFREdH89FHH112vOeee45evXrRs2fPym0XLlxg8eLFLFq0iDVr1tRKXSIiIiL2wGFnEgHuuOMOdu3axZkzZ2jUqBFlZWW0bdu28r0333wTgBYtWgAQFBTEyZMnq4xhsVh+d3xXV1deeOEFfH19KS8vt1EVIiIiIvbHYWcSAbp37878+fMJDQ0FwM3Njd27dwPw/fff07RpUwBcXFyqHOfp6cmZM2eAX65FvJzCwkLWr1/P66+/zoABAzCbzbYqQ0RERMTuOPRMYlhYGNnZ2SQkJGA2m4mMjCQ/P59Bgwbh5ubGG2+8wcGDB62Ou+eee0hLS2PHjh0EBgZedmxfX18CAgLo168ffn5+uLu7Y7FYrBpOERERkbrIIZvEqKioyq+//vrryq9nzrT+2LVGjRrRsWNHALp160a3bt0AWLVqldW+v+6Xnp4OQHJycs2FFhEREXEgDr3cLCIiIiK2oSZRRERERKyoSRQRERERKw55TaK9e/Yj62sjnYW5zIy7h33+WrmbPIyOICIi4jDs86+5g/Nt1MDoCIbJycmpfCSRiIiIOC4tN4uIiIiIFTWJIiIiImJFy802cOHUWaMjGKZxg0C7q9/d5I6Xn4/RMURERByKmkQbWPTQGKMjyG88vu51oyOIiIg4HC03i4iIiIgVNYkiIiIiYkVNooiIiIhYqfPXJG7atInly5cDEBAQwLRp0/D29q6yT05ODjt37mTQoEFGRBQRERGxO3V6JnH//v289957LF68mGXLltGpUyfefPNNq/1CQ0PVIIqIiIj8Rp2eSdy4cSP9+/fHw+OXj2OLioqipKSEyZMnk5ubS2FhIZMmTaKkpIQvvviCrl27kpiYiKurK2+99RYvvfQS586d47rrrmPmzJn4+OgxKiIiIuIc6vRM4unTpwkKCqp87ebmhtlsJiwsjJSUFEaOHMmmTZuqHBMQEMCqVavYtGkT9957L8uWLeO+++5jyZIltR1fRERExDB1eiYxODiYY8eO0bZtWwDMZjPfffcd2dnZZGZmUlxcTHBwcJVjmjdvDsChQ4fo168fAO3ateM///lP7YYXERERMVCdnkmMjIzk3//+N2azGYC1a9cya9YsPD09mT59OuHh4VgslirHuLr+8i1p3rw5u3fvBmDXrl00bdq0dsOLiIiIGKhOzyS2atWKBx54gJiYGFxcXGjYsCELFy5kxIgR7Nixg6CgoMqm8L9FR0fz4osv8uGHH3LdddeRmJhYy+lFREREjFOnm0SAPn360KdPnyrb1q1bZ7Vfx44dq/yvt7c3c+fOtX1AERERETtUp5ebRUREROTqqEkUEREREStqEkVERETESp2/JtEIj6973egIhjGby3B39zA6RhXuJv2ai4iI/Fn662kDPo0aGB3BMDk5OYSGhhodQ0RERK6RlptFRERExIqaRBERERGxoiZRRERERKzomkQbKDpdYHQEwzTxDzSkfjeTB17X+dT6eUVEROoqNYk2sLrv80ZHcDrRa/WxiSIiIjVJy80iIiIiYkVNooiIiIhYcZgmMSMjgwEDBhATE8Pw4cM5cuTIZfeLjY2lqKjoisacM2cOGRkZVbYlJydz9OjRa84rIiIi4siqvSYxPz+fOXPmcObMGSIiImjdujWtW7eujWyVjhw5wsKFC1m4cCG+vr4cOnSIF198kRUrVuDm5laj53riiSdqdDwRERERR1TtTOKECROIiIigoKCAW265hUmTJtVGrio+/PBDhg4diq+vLwAhISG0a9eOnTt3Mnz4cGJjYxkxYgTl5eUAnD9/nqFDhwJQUlLC0KFDq8wazpgxg23btgGwYsUKYmNjmTBhAgAJCQns27eP3bt389hjjzFo0KDK90REREScRbVNYnFxMZ06dcLFxYWwsDA8PGr/c3l//vlnGjduXGXb9ddfz9GjR0lISCAtLQ03Nzd++uknAHx9fTGZTJw8eZIvv/ySrl27/u7Yd911F2lpaXh4eLB9+/bK7YcOHeK1115j+fLlZGdnX/EStoiIiEhdUO1ys7e3Nx988AGlpaV8/vnnlbN5tSk4OJhjx44RFhZWue3o0aO0b9+e+fPnYzKZyMvLq5xJBOjVqxeffvopWVlZjB07lnfffbfyPYvFUvn1r2O2bt2a3Nzcyu1BQUFMnTqVevXqce7cuSpji4iIiNR11c4kTps2jR9//BE/Pz+2b9/O1KlTayNXFX/7299ITU3l3LlzrFu3jsTERPbu3cvcuXMZOXIkU6ZMwWQyVWn+7rvvPr788kuKi4sJDg7G09OTM2fOALB///7K/fbu3QvADz/8QEhISOX2mTNn8vLLLzNhwgQqKiqqjC0iIiJS11U7k7hw4UJefPHFytevvPIK//rXv2wa6r81b96ckSNH8uSTT2KxWHB3d6d+/fp06dKF+Ph4GjRogJeXF6dOnao8xsfHBy8vL+6++24AevTowejRo9mwYQPe3t6V+2VlZfHZZ5/RsmVL2rdvz5o1a4BfmsyBAwfi6+tLYGAgp0+fxs/Pr1brFhERETHK7zaJa9asYf78+Zw+fZpNmzZVbm/SpEmtBPtvXbp0oUuXLpWvCwoKKCoqYvDgwVb7/crFxYX7778fgJtuuon33nuvyr4dO3a0Os/06dMBaNmyJU8//XSN5RcRERFxJL/bJPbr149+/fqRnJxsl4+F8ff3x9/f/3ffHzFiBLfffjsBAQG1mEpERESkbqh2ublTp0689tprmM1mLBYLp06dYtasWbWR7Zq8/fbbRkcQERERcVjV3rgyceJEWrRoQV5eXpVr+URERESk7qq2SfT19eWRRx7Bz8+PZ599tsrNISIiIiJSN1W73Ozu7s7u3bspLS3lm2++UZN4BaLXJhodwTBmsxl392p/rWqcm6n2H/IuIiJSl1X713zSpEkcOnSIESNG8NZbbzFq1KjayOXQvAN//4aaui4nJ4fQ0FCjY4iIiMg1qrZJfP311ytvVElKSrJ5IBERERExXrVNosVi4euvv+amm27C1fWXSxiNelaiiIiIiNSOapvEM2fOsGDBAlxcXLBYLLi4uLB06dLayOawSs4UGB3BMDcEBNZq/a4mDzx9fWrtfCIiIs6i2iYxLS2NgoICcnNzufHGG/Vw6iuwIeZZoyM4jd7L3jI6goiISJ1UbZP44YcfsnDhQlq0aMHevXsZMmQI/fr1q41sIiIiImKQK5pJXLNmDSaTidLSUgYNGqQmUURERKSOq/Zh2haLBZPJBICnp6chz8ATERERkdpVbcd33333MWTIENq1a8f3339PREREbeSqMeXl5UybNo2DBw9SUVHBww8/zKOPPmp0LBERERG7Vm2T+MQTT9ClSxcOHTpE7969adWqVW3kqjFbtmzB09OTlJQUKioqiI6OplOnTgQGBhodTURERMRuVbvcnJ2dzcqVK/nyyy9JTU1l3LhxtZGrxgQFBbF161a++eYbzGYzy5cv5/XXX2ffvn0APPPMM+Tl5XHo0CHi4uIYOHAgixYt4vz588TGxhIbG8t9993HlClTDK5EREREpPZUO5M4fvx4/v73v3P99dfXRp4ad/vttxMfH09ycjI5OTk8/PDDVFRUWO2XmJjISy+9REhICPHx8TzwwAOkpaVx5swZnnvuOeLj4w1ILyIiImKMapvEwMBAevfuXRtZbGL//v20b9+e7t27c+HCBZ577jmys7MZPnw48MuNOQBHjhxh4sSJAJw7d45jx47RqFEjXnzxRSZMmICfn59hNYiIiIjUtmqbRG9vb5555hlatmxZuW3UqFE2DVWTtm7dSkFBAaNHj8bHx4cmTZpw9OhRzpw5g9ls5tChQwDccMMNTJ48meDgYJYsWULz5s155ZVXePjhh2ndurXBVYiIiIjUrmqbxHvvvdehH3szePBgpk6dSlRUFN7e3nTo0IFp06YxadIkGjduTFBQEACjR4/mhRdeoKSkhFatWnHnnXeyfv16cnNzWbNmDS1btuRf//qXwdWIiIiI1I5qu7+NGzeyePHi2shiEx4eHkyaNMlq+/r16622/fdnUu/cudNmuURERETsWbVNoqenJ6tWrSIkJARX119uhg4PD7d5MBERERExTrVNor+/P7t27WLXrl2V29QkioiIiNRt1TaJ06ZN48SJExw9epSbb76ZgICA2sglIiIiIgaqtkn897//zQcffEBhYSEPPPAAhYWFjB07tjayOazey94yOoJhzGZzrd7o5GryqLVziYiIOJNq/5qvXbuW5cuXM2TIEEaMGEFUVFRt5HJo9Rr6Gx3BMDk5OYSGhhodQ0RERK5RtR/LV1FRQUVFBS4uLsAvN7KIiIiISN1W7UxiTEwM/fr14+TJkwwcOJD+/fvXRi4RERERMVC1TWL79u3p1KkTubm53HDDDbpx5QpczM83OoJhbgxoeE31u3qYMPn61GAiERERuRrVNonjx4/H1dWVRx55hFatWtVGJof3xeOO87GF9qbroiSjI4iIiAhX0CSmpqZy4sQJ1q9fz9///vfKzzQWERERkbqr2htXAC5cuMD58+cpKyvD19fX1plERERExGDVziT27duXxo0bExUVxTPPPIObm1tt5BIRERERA1U7k5icnMzLL79Mw4YNKSwsrI1M1YqNjeXtt98GICEhgX379lm9//zzz1e+LioqIiwsjG3btpGens7OnTtrNa+IiIiIo6m2SfzPf/7D0KFDWbp0KXFxcaxZs6Y2cv2u48ePExQUxBdffPGH+x0+fBiz2QzAV199RcOGDQGIiorizjvvtHVMEREREYdW7XJzWloaa9aswWQyUVpayqBBg+jXr19tZLusDRs20KVLF7799lt27dr1u/t16NCBHTt2cNddd/HVV1/RuXNnAObMmUObNm344YcfyMvL4+eff8bDw4P58+cTGxtLSkoK9evX5/HHH+fNN98kMTGR3NxcCgsLmTRpEm3btq2tUkVEREQMU+1MosViwWQyAWAymWr1c3kvZ/PmzURERNCzZ0/ef//9392ve/fuZGRkYDabuXjxIt7e3lb73HLLLSxZsgQ/Pz/+93//l4iICL744gtOnz5N/fr1KS8vJywsjJSUFEaOHMmmTZtsWZqIiIiI3ai244uMjGTIkCG0a9eOXbt20b1799rIdVmHDh0iNzeXZ599loqKCg4cOMBdd90FwLBhwygrK2Po0KEA3H777cybN4/MzEzCw8M5cuSI1Xi33norAEFBQZSWltK7d29mzpzJ2bNn6dmzJ/Xr1yc7O5vMzEyKi4sJDg6utVpFREREjFTtTOLBgwe57rrrWLp0Kb6+vhw9epSkpCTyDfhUkQ0bNpCQkMCiRYtYvHgxkZGRZGRkAJCSkkJaWlqVJvamm25iyZIlREREXHa8Xz+P+ldNmzblwoULbNmyhW7duvHll1/i6enJ9OnTCQ8Px2Kx2K44ERERETtyRcvNnTt3Jj09ne7du1NYWEjTpk1JSEiojXxVfPLJJ3Tr1q3yda9evfjLX/7yu/t3796dgoICgoKCrvgc3bp1o379+tSvX5+wsDC+/vprBgwYQGZmJqdPn76m/CIiIiKOotrl5uPHjzN9+nQAbr75ZtatW8ejjz7K2rVrbR7uv61bt67K6w4dOtChQwer/dLS0gDo1KkTnTp1AmDs2LEAdOzYEaBKs/nrewDl5eX07t0bgOuvv97qnCIiIiLOoNqZRBcXF95//30OHz7M+++/j6urK3v37uXSpUu1ka9WJSUl8e2339K1a1ejo4iIiIgYqtqZxFmzZpGSksKmTZu4+eabee2119izZw9Tp06tjXy1atSoUUZHEBEREbEL1TaJAQEBjBkzpsq2Ll262CyQiIiIiBjP2Ice1lFdFyUZHcEw5jIz7h5X/2vl6mGqwTQiIiJytdQk2oBXQIDREQyTk5NDaGio0TFERETkGlV744qIiIiIOB81iSIiIiJiRcvNNlBqwKfR2ItmDRtetn5XkwcePr4GJBIREZGroSbRBraPetLoCHbnrqQFRkcQERGRP0HLzSIiIiJiRU2iiIiIiFhRkygiIiIiVgxvEtPT0xk8eHDl623btjFjxoxqjystLWXjxo0AxMbGUlRU9Lv7zp8/n8cee4zHHnuM5ORkALZs2cKnn376p3Lu3LnzivcXERERcWSGN4kABw4c4IMPPvhTx5w6dYqPPvqo2v327dvHjz/+yOLFi1m8eDG7d+9mz549dO7cmcjIyCs+X1RUFHfeeeefyigiIiLiqOzi7ua4uDgWLVpE9+7dK7dZLBbGjx9Pbm4uJpOJadOmceTIERITE3F1deXmm29m+/btlbOJkydP5ujRo9xzzz0888wzleMEBASQk5PD5s2bueeee5g1axbu7u6kp6dTXFxM/fr1+fLLLzl//jwXL15k3rx5VFRU8M9//pPi4mLKy8uZO3cuS5YsoU2bNnTr1q3Wvz8iIiIitc0uZhL9/PyIjY1lzpw5lds+++wzAgICWL58OSNGjGD27NnAL03fqlWreOqpp7jrrrvo1asXANHR0axcudJqdjEwMJBXX32VtWvX0rVrVyZMmEBpaanV+VNSUujQoQNbt24lLy+PYcOGkZqaSmhoKFlZWTb+DoiIiIjYF7toEgH69u3Lnj17OHDgAACHDh2ibdu2ANxxxx0cOnQIgObNm1/2+NatW+Pi4oKXl1eV7UePHuXGG29k/vz5fPXVV5WziL/VokULAIKCgigtLSUgIIB3332XcePG8cMPP1BeXl6jtYqIiIjYO7tpEl1cXBg/fjxJSUnAL83g7t27Afj+++9p2rQpAK6urpX7WyyWKsdfzt69e3n99dexWCyYTCaaN2+OyWSyOvdvpaam0rt3b6ZNm0ZgYGCV84iIiIg4A7tpEgHatGlTeTNJZGQk+fn5DBo0iKSkJMaOHVtl34CAAHJzc3n//ff/cMzIyEiaN2/Oo48+SlxcHMePH+eRRx75w2M6d+7M1KlTGThwIEVFRZw6deraChMRERFxMC4WTZPVqKysLIoTpxsdw+7clbQAz4AAo2PYVE5ODqGhoUbHMIwz1+/MtYNz1+/MtYPqrwv1Z2Vl0b59+8u+Z1cziSIiIiJiH9QkioiIiIgVNYkiIiIiYsUuHqZd19yVtMDoCIYxm824u1v/WrmaPAxIIyIiIldLTaIN1PUbNP7IwTpwEa+IiIhouVlERERELkNNooiIiIhY0XKzDVwqOGN0BJtw9TDh7uNrdAwRERGpBWoSbWDX80ONjmAT7RJTjY4gIiIitUTLzSIiIiJiRU2iiIiIiFhRkygiIiIiVuyySUxPT6dHjx7ExsbSp08f3n333T91fHJyMkePHq2ybe3atQwdOpRhw4Yxffp0ysvLr3i8I0eO8Le//Y1ly5b9qRwiIiIijsoum0SAuLg40tLSWL16NStXrqSiouKKj33iiSe48cYbK1+fP3+etWvXkpqaSkpKChaLhU8//fSKx9u5cyd9+vQhJibmT9UgIiIi4qjs/u7mixcv4uXlhaurK6dPn2bcuHFcvHiR0NBQxo8fT0JCAp6enhw4cIAbbriBGTNmkJCQwLBhw2jZsiUA9erV48yZM6xfv56uXbvywgsv4ObmRnp6OsXFxcTExLBs2TLq16/PAw88wNixYykoKOD6669n8uTJvP3221RUVNCmTRs6dOhg8HdERERExPbsdiZx6dKlxMbG8tBDD9GmTRvgl2XkIUOGkJaWRkVFBVlZWQDcddddLF++nCNHjnDq1Cmrsdzd3Zk7dy4ZGRn06NGDUaNGUVBQcNnzvvvuu/z1r38lLS2N22+/nc2bN/PEE08QFxenBlFEREScht3OJMbFxRETE4PFYmHMmDFs27aNgwcPsmfPHhYsWEBRURHt2rUD4NZbbwUgKCiI0tJSq7FOnTqFyWTijTfeoLy8nLlz57Jo0SJuueWWyn0sFgtA5Tk+/PBDSktLiYyMpGHDhrVQsYiIiIj9sNuZxF+5uLgQGBhIWVkZzZo1IyEhgbS0NOLi4mjdunXlPn/k1KlTTJw4kUuXLuHm5sbNN9+MyWTC09OTM2d++XSU/fv3A9CsWTOGDx9OWloaTz31FHfeeadtCxQRERGxQ3Y7k7h06VI+/vhjXFxcaNq0Kffccw8tWrRgwoQJXLhwgYYNG9KzZ88rGuu2226jV69e9OvXj+uuu46goCBefvllLl26RFpaGjt27CAwMBCA6OhoEhISWLZsGR4eHsyaNYvc3FxblioiIiJid1wsv66zSo3IysqiYu5ko2PYRLvEVEz+f7z0npOTQ2hoaC0lsi/OXDs4d/3OXDs4d/3OXDuo/rpQf1ZWFu3bt7/se3a/3CwiIiIitU9NooiIiIhYUZMoIiIiIlbs9sYVR9YuMdXoCDbh6mEyOoKIiIjUEjWJNlDdzR0iIiIi9k7LzSIiIiJiRU2iiIiIiFjRcrMNlJ09bXSEGuHiYcLd+zqjY4iIiIgB1CTaQPaEgUZHqBG3vbrS6AgiIiJiEC03i4iIiIgVNYkiIiIiYkVNooiIiIhYcehrEvPy8pg5cyazZ8++pnFycnLYuXMnXl5eFBcX06xZM0pLS4mMjKyhpCIiIiKOxaGbxJoSGhpKaGgo6enpAHTu3NngRCIiIiLGqhNN4vPPP8+JEycwmUzMmjWLpUuXkpuby88//0yrVq0oKysjKyuL+Ph47r//fiZPnkxubi6FhYVMmjSJkpISvvjiC1q0aAFAeno6xcXF9OrVi3/+858UFxdTXl7O3Llzue46PRJGRERE6j6HvyaxoqKC3NxcFi5cyIgRIzh37hwALVq0IC0tjc2bNzNs2DDmzJnDhg0bOPv/2rv3sKrqfI/jH+4hbBkIIUVlNDuDlTlzNG08GaPjbfBUIyqCgIOYd9F0RqGjaTiKl3QqL+MtlVLU0tBMsvLkrXHyRiNMaaN0CEExb4SCisBe54+eeKJtUyqwYO/365/2Wntdvt+93Dyffr+99v76a7Vr105r1qzR6NGjtXPnzh88dkFBgeLj45Wamqq2bdsqMzOzrtoCAAAwVYMfSXR2dtbQoUM1fvx4eXp6KikpSZLUpk0bSZKvr6+Cg4N14cIFlZWVqVGjRjp+/LiOHDmia9euKTAw8AeP7efnp0WLFumtt95SXl6eOnXqVCc9AQAAmK3BjyQWFxfr7NmzWrVqlbp3766tW7dKkpycnG65/b59++Th4aG5c+fq0UcflWEYP3js1NRU9e3bV3PmzJG/v/+/3RYAAMCeNPiQ2LhxY508eVIDBw5Uenq6/vu///vfbt+uXTv97W9/U2RkpI4cOaKLF3/4J/SeeOIJpaSkKCoqSqWlpbpw4UJNlw8AAFAvNejp5ubNm2vx4sU26xMSEqoef3vHcpMmTbRixQpJ0vbt22326dy58y3PwZ3OAADAETX4kUQAAADUPEIiAAAAbBASAQAAYKNBfyaxvnpw9kazS6gRTm7uZpcAAABMQkisBW4/8ze7BAAAgLvCdDMAAABsEBIBAABgg+nmWlBe3DC/dNvJzV2ujXzMLgMAANQDhMRakDOrn9kl3JE207aaXQIAAKgnmG4GAACADUIiAAAAbBASAQAAYMPuQmJsbKyWL19udhkAAAANRRJ86AAAIABJREFUml2FxLNnzyogIEB79+41uxQAAIAGza7ubs7IyFBoaKgOHjyorKwsnTlzRqmpqbJarUpISNDevXvVv39/3Xffffrd736nQ4cOaceOHSorK5O/v79SU1N1/fp1de3aVQkJCYqJiVFQUJBOnjypgQMHavDgwWa3CAAAUCfsaiTxww8/VPfu3dWnTx9t27ZNO3fu1LRp07R69WqVl5era9euOnz4sA4fPix/f3+dPHlSf//73/XEE0/ozJkzWrlypTZs2KCMjAxJUmFhoSZNmqS0tDRt3Ggfv8cMAADwU9jNSGJubq5Onz6tCRMmyGq1KicnR6+//rqWLVum8+fPKyIiQqGhodq6dauaNGmi+Ph4ZWZm6uLFiwoMDJSvr6+ee+45eXt7q7KyUpLk4+OjwMBASZKbm5uZ7QEAANQpuwmJGRkZSkpK0lNPPSVJmjlzpsaPH6+NGzfKzc1NAwYMUFhYmCoqKnThwgU9++yz+sMf/qD/+q//kiQtWrRI7733ni5duqTdu3eb2QoAAIDp7CYkfvDBB0pLS6taDgsLU3p6ugYPHixvb29FR0dLkn71q18pPz9fjRs3lmEY6tq1qyTp0UcfVXh4uLy9veXr66vS0lJT+gAAAKgP7CYkbt++vdpyx44ddezYMZvtRowYUfV427ZtVY9nzZpls216evotHwMAANg7u7pxBQAAADWDkAgAAAAbhEQAAADYsJvPJNYnbaZtNbuEO+Lk5m52CQAAoJ4gJNYCN58mZpcAAABwV5huBgAAgA1CIgAAAGww3VwLyq+cN7sEG86uHnJp5GN2GQAAoIEgJNaC3L/0MbsEG60mvScXs4sAAAANBtPNAAAAsEFIBAAAgA1CIgAAAGw4REiMjY3V8uXL7+oY7777rsrKymqoIgAAgPrN7kPi2bNnFRAQoL17997VcTZu3KiKioqaKQoAAKCes/uQmJGRodDQULVu3VpZWVl69913FRERoQEDBmjfvn1KTU3VW2+9JUnaunWrNm3apPDw8Kr9w8PDdfToUZ04cULJyclmtQEAAFCn7D4kfvjhh+revbv69Omjbdu2aefOnZo2bZpWr16t8vJyhYWFadeuXZKkXbt2qVevXjbH6Nixo9q2basZM2bUdfkAAACmsOvvSczNzdXp06c1YcIEWa1W5eTk6PXXX9eyZct0/vx5RUREKCAgQOXl5crPz5dhGPLz8zO7bAAAANPZdUjMyMhQUlKSnnrqKUnSzJkzNX78eG3cuFFubm4aMGCAwsLC1Lt3b82ZM0c9e/aUJFVWVuratWsqLi7W1atXq45nGIYpfQAAANQ1u55u/uCDD9StW7eq5bCwMOXn52vw4MEaOnSooqOjJUm9evXSwYMHq0LiwIEDNXjwYP3lL39R06ZNJUnt2rVTYmJi3TcBAABgArseSdy+fXu15Y4dO+rYsWM221mtVoWGhspisUiSYmJiFBMTU22bKVOm1F6hAAAA9YxdjyT+FMePH1dcXJyGDx9udikAAAD1hl2PJP4UDz74oM2IIwAAgKNz+JFEAAAA2CIkAgAAwIbDTzfXhlaT3jO7BBvOrh5mlwAAABoQQmItcGscYHYJAAAAd4XpZgAAANggJAIAAMAG0821oKLkqzo5j5OLh1w8f1Yn5wIAAI6FkFgLvlweWifn+fmofXVyHgAA4HiYbgYAAIANQiIAAABs2G1ITE9P1/r1680uAwAAoEGy25AIAACAO+cwITEpKUknT56UJI0fP14FBQUaOnSosrOztW/fPiUmJur69esaM2aMYmJiNGbMGJWUlCg9PV0xMTGKiYnRzZs3Te4CAACgbjj03c3Jycl67rnnVF5erlWrVumNN97Q448/rsGDBys9PV2vvfaamjZtqpCQEE2bNs3scgEAAOqMw4wkfpdhGJKkli1bytfXV+3bt5ePj49yc3P1yCOPSJLat2+v3Nzcqu0AAAAcicOERHd3d126dEkVFRVV4e+TTz6Rq6urcnJylJubq+DgYGVnZ0uSsrKyFBQUJElydnaYlwkAAECSnU83r169Wunp6ZKknj17KiUlRU2bNlVAQIDKy8s1Z84cLVmyREVFRUpOTtaSJUuUmJioHTt2qHHjxlq4cKHef/99k7sAAACoe3YbEsPDwxUeHl5t3ejRo6stb968WZIUGBio1NRUSdLSpUttjgMAAOBomEcFAACADUIiAAAAbBASAQAAYIOQCAAAABt2e+OKmX4+al+dnMfJxaNOzgMAABwPIbEWuHoHml0CAADAXWG6GQAAADYIiQAAALDBdHMtqCj9qtbP4eTiIZd7flbr5wEAAI6JkFgLTq9/otbP0TJmf62fAwAAOC6mmwEAAGCDkAgAAAAbhEQAAADYqNchMT09Xb169VJsbKxiY2M1btw4lZWV/eh+H3/8sQoLC6utO336tEaMGKH4+HiNHDlS+fn5P7j/9OnTqy2fPHlSSUlJd9YEAABAA1SvQ6IkDRkyROvWrdO6det0//3368MPP/zRfd5++21dvXq12roFCxYoMTFRa9as0cSJEzVr1qwf3H/mzJl3XTcAAEBD1qDubr5y5Yq8vLx09OhRvfjii5Kk3/3ud4qLi1N8fLw8PT3Vtm1bffTRR8rPz1daWlrVvoGBgUpLS1NMTIxCQkK0ePFiSdLmzZuVkZGhq1evatCgQYqIiFB4eLjS09M1ffp0nTp1Sk2aNFGjRo1M6RkAAMAM9X4k8fXXX1dsbKyGDBkib29vPfHEE1qwYIGWLVumTZs2ad++fSooKFB5ebnGjBmjcePGqWvXrpoxY0a140yePFkWi0Xjxo1Tr169dPDgQUlSSUmJ1q5dq9WrV2vz5s1V23/66ae6ceOGNm7cqN69e9dpzwAAAGar9yOJQ4YMUUxMTLV1FRUV8vPzkyQ99NBDOn36tCQpODj4B4+TlZWliRMnauLEiTp+/LgmTJigXbt2ydnZuSpAVlZWVm1fUFCgkJAQSVK7du300Ucf1XRrAAAA9Va9H0m8FRcXF12+fFmGYSg7O1vNmjWTJDk5OVX91zCMavvMmTNHX3zxhSSpZcuWslgsKi4u1jvvvKMFCxYoMjJSFRUVVdu3bt1a2dnZkqTPP/+8LtoCAACoN+r9SOKtTJ48WWPGjFFFRYWefPJJ/fznP6/2/EMPPaRp06Zp06ZNcnFxkSQtXLhQU6dOldVqlZubm6ZNmyaLxSI/Pz8NHDhQPj4+cnV1rQqX//Ef/6GWLVtq0KBBat68udzc3Oq6TQAAANPU65AYHh5+y/UdO3bUpk2bqq1bt25d1eOYmBibKepWrVppw4YNNsdauXKlzbr09HRJ0qRJk267ZgAAAHvQIKebAQAAULsIiQAAALBBSAQAAICNev2ZxIaqZcz+Wj+Hk4tHrZ8DAAA4LkJiLXD1CjS7BAAAgLvCdDMAAABsEBIBAABgg+nmWlBx7fxdH8PJxUMuHj41UA0AAMDtIyTWgoK3+9z1MZo//V4NVAIAAHBnmG4GAACADUIiAAAAbBASAQAAYKNBfibx0KFDmjJlilq2bClJ8vDw0KuvvmpyVQAAAPajQYZESQoLC1NiYqLZZQAAANilBhsSv2/JkiXKzMxUcXGxEhISlJeXJ4vFov79+2vr1q0qKyuTn5+fVq1aJScnJ8XFxSksLEzh4eFKT09XaWmpRo0apXXr1qlfv34KCgrS2bNn1b9/f+3evVs3b97UmjVr5ObmZnarAAAAta7Bfibx3XffVWxsrGJjY7V582ZZLBatXbtWKSkp2rp1q8LCwrRr1y5J0q5du9SzZ08tXbpU69ev1/r16/Xqq6/qxo0btzz22bNnNW/ePEVHR+sf//iHVq9erYCAAOXm5tZliwAAAKZpsCOJ351uNgxDL7/8shITE+Xs7KyKigoFBASovLxc+fn5MgxDhmGoadOm8vDwkCQFBwfrq6++qjqeYRhVj4OCguTl5SU/Pz8FBwdLkry8vFRWVlaHHQIAAJinwY4kfteJEyf05Zdfat68eerZs2dV4Ovdu7fmzJmjnj17ytfXV2fPnlVZWZlu3rypnJwc+fv7q7S0VFarVadOnTK5CwAAgPqjwY4kfte3o4KDBg1SYGCgrl69Kknq1auX5s6dq3nz5snFxUVjx45VbGysKisrNWbMGHl5ealnz56KiIhQu3bt5OnpaXInAAAA9UODDImdO3dW586dq5a9vLy0adMmm+2sVqtCQ0NlsVgkfTOy2Lt372rb/OlPf7LZLz09XZLUrVs3devWTZI0c+bMGqsfAACgvrOL6eZbOX78uOLi4jR8+HCzSwEAAGhwGuRI4k/x4IMPavv27WaXAQAA0CDZ7UgiAAAA7pzdjiSaqfnT7931MZxcPGqgEgAAgDtDSKwFro0CzC4BAADgrjDdDAAAABuERAAAANhgurkWVNy4eEf7OTm7y8W9cQ1XAwAAcPsIibXgzO5Bd7RfUPc3argSAACAO8N0MwAAAGwQEgEAAGCDkAgAAAAbdhUSKysrNWvWLMXHxysuLk5bt2695XZJSUk6efJkHVcHAADQcNjVjSv79++Xh4eH1qxZI6vVqkGDBqlr167y9/c3uzQAAIAGxa5CYkBAgA4cOKDHH39cHTp0UFpamvLz8zVhwgRZrVb98pe/VGJioiRpyZIlunz5soKDgzV79mxlZ2frpZdeUllZmVq1aqXZs2dr27Ztev311xUUFKQvv/xS77zzjskdAgAA1A27CokPPfSQEhIStHLlSp04cUJPP/208vLylJycrDZt2igpKUlHjx6VJHXv3l2///3vFRsbq0uXLik3N1cvvvii7r33XoWHh6u0tFTr16/Xpk2b9NVXXykyMtLk7gAAAOqOXYXEU6dOqUOHDvrtb3+rkpISPfvssyoqKlKbNm0kSe3bt1dubq4kKSQkRJLk6+ur69evKyAgQCkpKfL09NSVK1d048YN+fv7y93dXS1atJCfn59pfQEAANQ1u7px5cCBA1q7dq0kydvbW82aNVNubq5ycnIkSVlZWQoKCpIkOTtXb33+/PmaOXOmpk6dKqvVKicnJ50/f17l5eUqLCxUUVFR3TYDAABgIrsaSYyOjlZKSorCw8Pl5eWljh076s0339QLL7yg8vJydejQQV26dNH27dtt9u3Ro4eioqJksVjk7++voqIiDRkyRFFRUWrRooU8PDxM6AgAAMAcdhUS3dzcNGPGDJv169evr7Y8d+7cqseLFi2SJI0dO1Zjx46ttt2BAwe0ZcsWnTt3zuY5AAAAe2ZXIbGmXbt2Tf3791d5ebmmTJlidjkAAAB1hpD4b4waNUqjRo0yuwwAAIA6Z1c3rgAAAKBmMJJYC4K6v3FH+zk5u9dwJQAAAHeGkFgLXO/hZwABAEDDxnQzAAAAbBASAQAAYIPp5lpQUXb5tvdxcnaTi5ulFqoBAAC4fYTEWnAmc8xt7xPU4a+1UAkAAMCdYboZAAAANgiJAAAAsEFIBAAAgA27/kzinj17tGLFCrm6uuqee+7R888/r+DgYLPLAgAAqPfsNiTm5eVp1apVWrVqlSwWi3JzczVlyhRt2LBBLi4uZpcHAABQr9ltSNyxY4fi4uJksXzztTKtWrVS+/btlZmZqVWrVunGjRtq27at/ud//kdJSUny8PBQTk6Omjdvrnnz5ik7O1svvfSSysrK1KpVK82ePdvkjgAAAOqO3X4m8dy5c2ratGm1dffdd5+SkpL0hz/8QevWrZPValVmZqYkqVOnTkpLS1NeXp4uXLig3Nxcvfjii0pLS9Px48dVWlpqRhsAAACmsNuRxMDAQJ05c0bt2rWrWpefn6/WrVtr6dKlWrFihUpLS9W+fXtJUps2bSRJAQEBKisrU0BAgFJSUuTp6akrV66osrLSlD4AAADMYLch8cknn1RiYqK6dOmivXv36tSpU/r888/Vtm1bPf3002rfvr22bdumkJAQHThwQE5OTtX2nz9/vtatWydnZ2f17dtXhmGY1AkAAEDds9uQGBwcrNGjR2vkyJEyDEOurq5q1KiR+vbtq8WLF6ukpET33nuv+vTpc8v9e/TooaioKFksFvn7++vixYvy8fGp4y4AAADMYbchUZJCQ0MVGhpatVxUVKTS0lK9+uqr1babO3du1eNFixZJksaOHauxY8fWTaEAAAD1jF2HxO/z9fWVr6+v2WUAAADUe3Z7dzMAAADuHCERAAAANhxqurmuBHX4623v4+TsVguVAAAA3BlCYi1w9fAzuwQAAIC7wnQzAAAAbBASAQAAYIPp5lpQUV78k7d1cnKVi6tXLVYDAABw+wiJteDMqRd+8rZBD/z0bQEAAOoK080AAACwQUgEAACADUIiAAAAbJgWEisrKzVr1izFx8crLi5OW7duva39P/74YxUWFurQoUOaN2/eD253+vRpjRgxQvHx8Ro5cqTy8/MlSdOnT7+t893u9gAAAA2ZaSFx//798vDw0Jo1a7RmzRpt2LBBFy9e/Mn7v/3227p69eqPbrdgwQIlJiZqzZo1mjhxombNmiVJmjlz5m3Ve7vbAwAANGSmhcSAgAAdOHBAH3/8sSoqKpSWliZ/f3998cUXio6OVlRUVNUIYWxsrEpLSyVJ4eHhysvL00cffaTk5GRJ0rFjxxQXF6eIiAgVFBRUO09gYKDS0tL0f//3fwoJCdHixYurjiNJMTExSkxMVL9+/bRhwwZJ0ubNmxUXF6f+/fvrzTffrLY9AACAIzAtJD700ENKSEjQypUr9cQTT2jhwoWyWq1asGCBkpOTtXHjRhUVFeno0aM2+wYHB6tr166aMWOGJMnd3V2pqamKjIzUzp07q207efJkWSwWjRs3Tr169dLBgwerPV9YWKhJkyYpLS1NGzdulCSVlJRo7dq1Wr16tTZv3lxLrwAAAED9Zdr3JJ46dUodOnTQb3/7W5WUlOjZZ5/VRx99pAsXLqhNmzaSpPbt2ys3N/dHjxUSEiJJ8vX11ZkzZ6o9l5WVpYkTJ2rixIk6fvy4JkyYoF27dlU97+Pjo8DAQEmSm5ubJMnZ2bkqXFZWVtZIvwAAAA2JaSOJBw4c0Nq1ayVJ3t7eatasmdzd3eXv76+cnBxJ3wS8oKAgubu76/LlyyopKdHZs2clSU5OTjIMQ9I3oe6HzJkzR1988YUkqWXLlrJYLP+2ruLiYr3zzjtasGCBIiMjVVFRcde9AgAANDSmjSRGR0crJSVF4eHh8vLyUseOHfXrX/9aTZo00QsvvKDy8nJ16NBBXbp0UWlpqcaMGaPg4GA1b95c0jfT1dOmTdOkSZP+7XkWLlyoqVOnymq1ys3NTdOmTfu321ssFvn5+WngwIHy8fGRq6trVRgFAABwFE4GCahGZWZmyt9z/U/ePuiBF+Tq5lOLFdWtEydOqG3btmaXYQpH7l1y7P4duXfJsft35N4l+reH/jMzM9WhQ4dbPseXaQMAAMAGIREAAAA2CIkAAACwYdqNK/Ys6IEXfvK2Tk5cAgAAUP+QUGqBPd2IAgAAHBPTzQAAALBBSAQAAIANQmItqKwskdV6w+wyAAAA7hghsRac+2q1DIOf8wMAAA0XIREAAAA2CIkAAACwQUgEAACAjXoZEmNjY7V8+XJJUlJSkk6ePFlr5xo/frwKCgqqrVu5cqXy8/Nr7ZwAAAD1Xb0LiWfPnlVAQID27t1rWg0jRoxQixYtTDs/AACA2erdL65kZGQoNDRUBw8eVFZWVtX63NxczZgxQ+Xl5erRo4diY2MVGRmpJUuWaM+ePbp06ZJiYmI0bdo0Xbt2TZWVlVq6dKlee+01FRQU6Ny5c3Jzc9OyZcv0+eefa8aMGfL19dWlS5ckfTNiWVxcrICAAJWVlSk+Pl43btzQSy+9pLKyMrVq1UqzZ88262UBAACoU/VuJPHDDz9U9+7d1adPH23btq1q/cKFC5WcnKyNGzfq2LFjunjxoqZOnarnn39eO3bs0OjRo1VQUKD4+Hilpqaqbdu2yszMlCTdf//9eu211+Tj46PPPvtMS5cu1csvv6wVK1aosrKy6hz9+vVTcnJy1XJubq5efPFFpaWl6fjx4yotLa27FwIAAMBE9WokMTc3V6dPn9aECRNktVqVk5OjTp06SZLy8vI0ffp0SdKVK1d05swZPfroo7p8+bKefvppubm5yc/PT4sWLdJbb72lvLy8qn3btGkjSVWjhJcvX1bLli0lSSEhIVXn/3bdtwICApSSkiJPT09duXKlWqAEAACwZ/VqJDEjI0NJSUlavXq11q5dq549e2rPnj2SpObNm2vBggVat26dwsPDFRwcrIyMDHXo0EE7d+5UUVGRUlNT1bdvX82ZM0f+/v4yDEOS5OTkVO089913n7744gtZrVadOnWqar2zc/WXY/78+Zo5c6amTp0qq9VadTwAAAB7V69GEj/44AOlpaVVLYeFhencuXOSpIkTJ2ry5Mm6fv26fvGLX+ipp57S6tWrtX79eh05ckRz585V3759NXv2bC1fvlyNGjXShQsXbnmeP/7xj/rjH/8oi8UiDw+PH6ynR48eioqKksVikb+/vy5evCgfH5+abRoAAKAecjIYHqtRmZmZuq/p33Rf4DC5uHibXU6dO3HihNq2bWt2GaZw5N4lx+7fkXuXHLt/R+5don976D8zM1MdOnS45XP1aroZAAAA9QMhEQAAADYIiQAAALBBSKwF9wUOk5NTvbonCAAA4LaQZGqBI96wAgAA7AsjiQAAALDBV+DUsG9/ChAAAKAh+KGvwCEkAgAAwAbTzQAAALBBSAQAAIANQiIAAABsEBIBAABgg5AIAAAAG4REAAAA2CAk1pCKigo9++yzGjx4sObOnWt2OTVuzpw52rNnj0pKSjRs2DBFRUVp7dq1kqTCwkJFR0crMjJSO3bskCSdOHFCgwYNUmRkpA4dOiRJOnDggAYMGKDo6GidOnXKtF5+qpKSEg0fPlyxsbEaP368rl696jC9S9/0/8wzz2jQoEFauXKlQ137b/3973/X+PHjb/n+vpvXoyHo1q2bYmNjFRsbq8OHDzvUtTcMQ3/+858VFRWluLg4Xb582WGu/7Jly6qu+69//Wtt3brVoa59WVmZRo4cqaioKM2aNcsh/+5VY6BGZGRkGH/9618NwzCM5557zsjKyjK5oppRUVFhTJ482ejWrZuxe/duY8WKFcbbb79tWK1WY9iwYcb58+eN6dOnG0ePHjXKysqMyMhIo6yszBgxYoRx5swZ48qVK8bgwYMNwzCMQYMGGVevXjXy8/ONUaNGmdzZj1u1apWxZcsWwzAM4+WXX3ao3g3DMNatW2e88cYbhmEYRlxcnMP1X1lZaURGRhoJCQm3fH/fzetR3xUUFBhJSUlVy4527Xfv3m3MnTvXMAzD2Lt3r7FixQqHuv6GYRiFhYVGbGysw137Xbt2GfPnzzcMwzASEhKMxYsXO1T/38dIYg05duyYOnfuLEnq0qWLPvnkE5MrqhmVlZV68skn1a9fP0lSVlaWOnfuLCcnJz366KM6duyYTpw4of/8z/+Uu7u7HnjgAeXk5Ojy5ctq1qyZLBaL7rnnHp05c0aNGjWSt7e3mjdvrvPnz5vc2Y+LjIzUk08+Kemb12HVqlUO07skxcTEqH///rp586auXbvmUNdekrZs2aLQ0FBJt35/3+nrUVxcbGZbP8nJkyf1r3/9S9HR0Zo9e7bDXfsjR45IkoYOHar9+/fr4sWLDnX9JWn58uUaN26cw137+++/X5WVlTIMQzdu3NChQ4ccqv/vIyTWkJKSEnl5eUmSPD09VVpaanJFNcPd3V1du3atWr5Vn1arVU5OTlXrrl27JuM7P+Tj6ekpZ2fnqv0aCm9vb7m7uysrK0uHDx/Wgw8+6DC9f6u0tFR9+/bVvffe61DXvqSkRLt371bfvn2rlr/f+52+HteuXavjbm6fn5+fxowZo7S0NEnS7t27HebaS1JxcbHKysq0du1aeXh46H//938d6vrfvHlTubm56tSpk0O97yXJzc1N+/fvV58+feTs/E1EcqT+v4+QWEO8vLyq3vzXrl2TxWIxuaLacas+v30jfbvO29u76g0kSdevX5fVaq32x9HFxaXuir4LmZmZmjlzpl555RWH612SGjdurF27dikkJETZ2dkO0/+qVav0zDPPVPVyq2t/p/8evL2967CTO/OLX/yiahT18ccfV2hoqMNce+mbf/ePPfaYJOmxxx7Tb37zG4e6/vv379dvfvMbSY73N3/dunUaOnSo3n//fbVv396h/u7dCiGxhjz88MM6fPiwJOngwYN65JFHTK6odny3zyNHjujhhx/WAw88oH/84x8qLy/Xv/71L7Vu3Vo+Pj4qLCzU1atXVVpaqqCgIJWUlKikpEQFBQX62c9+ZnInPy43N1cpKSlavny5AgMDHap3SVqzZo327dsn6Zv/Mx4+fLjD9P/JJ5/olVde0aRJk3T48GH5+PjYvL/v9N9DQ/gfyNTUVG3ZskWSdPToUT3yyCMOc+0l6ZFHHtHBgwclSf/85z+r9e8I1//jjz/WL3/5S0mO9Tdf+iYUfxvk/f39Herv3i2Z9mlIO1NWVmZMmDDBiIiIMJ5//nmzy6lxixYtMnbv3m18/fXXxrBhw4z+/ftXfZA7Pz/fiI6ONvr161d1o8enn35qREREGP369TP27dtnGIZh7N+/3xgwYIARHh5ufPrpp6b18lMlJSUZPXv2NGJiYoyYmBjjgw8+cJjeDcMwzp07ZwwZMsSIiYkxJk2aZFy4cMGh+jeMb/pLSEi45fv7bt4L9d2VK1eMYcOGGTExMcbzzz9vXL582aGufXl5uZGUlGREREQ45PV/5plnjHPnzhmGcXd9NsRrX1RUZAwfPtyIjo42Ro4caRQUFDhU/9/nZBjfmUgHAAAAxHQzAAAAboGQCAAAABtOPdDAAAACH0lEQVSERAAAANggJAIAAMAGIREAAAA2CIkAYJLi4mK9//77NXa8N998s8aOBQCERAAwyeeff649e/bU2PGWL19eY8cCAEIiAJhk5cqV2rFjhx5++GFJ0rJlyzRp0iRJ0pIlS/T+++/rwIEDioyM1ODBg7VkyRJJ0tdff63Ro0crNjZWo0aN0qVLl7Ry5UpduHBB8+fPN60fAPbF1ewCAMBRjRgxQk2aNNGJEydktVqVlZWloqIiSdKhQ4cUHx+vAQMGaOPGjfLx8dGkSZOUnZ2tnTt3qnfv3vr973+v/fv3a/HixXrhhRe0adMmTZkyxeSuANgLQiIAmOxXv/qVMjMz5erqqtatW+vo0aOyWCy6fv26vvrqK40bN06SVFJSory8POXk5Ojo0aN66623ZLVa5evra3IHAOwRIREATOLk5CTDMNStWze98sor6t69u/z9/TVv3jxFRETI19dXzZo108qVK+Xp6an169frwQcf1D//+U916tRJPXr0UHZ2tnJycsxuBYAd4jOJAGCSFi1aKDs7W4WFhfrss8/02GOPqUuXLvrss8/UvXt3OTs7609/+lPVtPOxY8fUokULjRo1Slu2bFF0dLT+/Oc/KyQkRJLUtGlTJScnm9wVAHvhZBiGYXYRAAAAqF8YSQQAAIANQiIAAABsEBIBAABgg5AIAAAAG4REAAAA2CAkAgAAwAYhEQAAADYIiQAAALDx/zYerDRnZzKAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.barplot(x='tweet',y=arabic_grouped_geo.sort_values('tweet',ascending=False).index,data=arabic_grouped_geo.sort_values('tweet',ascending=False),palette='inferno')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_grouped_profile = arabic_profile_info_tweets.groupby('governorate').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 5 governorates with # of tweets with Profile Info')"
      ]
     },
     "execution_count": 302,
     "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": [
    "\n",
    "sns.barplot(x='tweet',y=arabic_grouped_profile.sort_values('tweet',ascending=False).head(5).index,data=arabic_grouped_profile.sort_values('tweet',ascending=False).head(5),palette='inferno')\n",
    "plt.title('Top 5 governorates with # of tweets with Profile Info')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_grouped_search = arabic_search_tweets.groupby('governorate').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 5 governorates with # of tweets with Keyword Search')"
      ]
     },
     "execution_count": 303,
     "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": [
    "\n",
    "sns.barplot(x='tweet',y=arabic_grouped_search.sort_values('tweet',ascending=False).head(5).index,data=arabic_grouped_search.sort_values('tweet',ascending=False).head(5),palette='inferno')\n",
    "plt.title('Top 5 governorates with # of tweets with Keyword Search')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_users_geo = arabic_geocoded_tweets.reset_index().groupby('governorate')['username'].nunique().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_users_gov_geo = arabic_geocoded_tweets.reset_index().groupby('username')['governorate'].nunique().to_frame().sort_values('governorate', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "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>governorate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>username</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mohamed_samy14</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aboelwafa005</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mohamedk7airyp1</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fatmasa82696760</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shepoozamalek14</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 governorate\n",
       "username                    \n",
       "mohamed_samy14            14\n",
       "aboelwafa005              13\n",
       "mohamedk7airyp1           11\n",
       "fatmasa82696760           11\n",
       "shepoozamalek14           11"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arabic_users_gov_geo.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top hundered user occurrences in common governorates')"
      ]
     },
     "execution_count": 254,
     "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": [
    "x=arabic_users_gov_geo.head(100).index\n",
    "sns.set_style('white')\n",
    "ax = plt.gca()\n",
    "ax.axes.xaxis.set_visible(False)\n",
    "sns.scatterplot(x,y='governorate',data=arabic_users_gov_geo.head(100))\n",
    "plt.title('Top hundered user occurrences in common governorates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_users_gov_search = arabic_search_tweets.reset_index().groupby('username')['governorate'].nunique().to_frame().sort_values('governorate', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "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>governorate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>username</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alahram</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alahramgate</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vetogate</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aja_egypt</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eldostoregypt</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               governorate\n",
       "username                  \n",
       "alahram                 27\n",
       "alahramgate             27\n",
       "vetogate                26\n",
       "aja_egypt               25\n",
       "eldostoregypt           25"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arabic_users_gov_search.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "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>governorate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>username</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mohamed_samy14</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aboelwafa005</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuckinamr</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mohamedk7airyp1</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fatmasa82696760</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gom3a_tarek</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>haniadepoon</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h0llland</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hazemelzooz70</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h__t__l</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10854 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 governorate\n",
       "username                    \n",
       "mohamed_samy14            14\n",
       "aboelwafa005              13\n",
       "fuckinamr                 12\n",
       "mohamedk7airyp1           11\n",
       "fatmasa82696760           11\n",
       "...                      ...\n",
       "gom3a_tarek                2\n",
       "haniadepoon                2\n",
       "h0llland                   2\n",
       "hazemelzooz70              2\n",
       "h__t__l                    2\n",
       "\n",
       "[10854 rows x 1 columns]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arabic_users_gov_geo[arabic_users_gov_geo['governorate'] > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "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>governorate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>username</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mohamed_samy14</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aboelwafa005</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuckinamr</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mohamedk7airyp1</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fatmasa82696760</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ashrafbeso6</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ashrafe04771646</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mohmedxo</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ashrafelbatal24</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mostafa12339944</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18451 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 governorate\n",
       "username                    \n",
       "mohamed_samy14            14\n",
       "aboelwafa005              13\n",
       "fuckinamr                 12\n",
       "mohamedk7airyp1           11\n",
       "fatmasa82696760           11\n",
       "...                      ...\n",
       "ashrafbeso6                1\n",
       "ashrafe04771646            1\n",
       "mohmedxo                   1\n",
       "ashrafelbatal24            1\n",
       "mostafa12339944            1\n",
       "\n",
       "[18451 rows x 1 columns]"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arabic_users_gov_geo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "represented_by_more_gov_df = pd.DataFrame({'No. of followers represented by':[10854,7597]},\n",
    "                                         index=['More than one gov','Only one gov'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2adcec6bf88>"
      ]
     },
     "execution_count": 281,
     "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": [
    "represented_by_more_gov_df['No. of followers represented by'].plot.pie(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_users_geo.sort_values('username', ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 5 Governorates with Active Users using Geocode')"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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USCIiYggKJBERMQQFkoiIGIICSUREDEGBJCIihqBAEhERQ1AgiYiIISiQRETEEBRIIiJiCHf0xVUPHjxIeHg4+fn5mEwmXnzxRdLS0vD29uahhx6yd3kiIvIn3LGBlJaWxhtvvMG7776Lp6cnmZmZDB48mIiICJydne1dnoiI/El3bCB9++23dOzYEU9PTwBcXFyIiYlhyZIlNG3alK+//prk5GSysrIoKChg48aNTJs2jVOnTpGWlkZoaCjNmjWzcy9EROSaOzaQzp8/T8OGDQH45z//SWRkJOnp6bi4uNC0aVNmz55NUVERI0eOZPDgwVy+fBlfX1+mTZvGli1b2Lx5swJJRMRA7thAql27NikpKQC0adOGNm3asHjxYr799lvbOuHh4bRt25ZmzZqRl5fHoUOHiI+PJzs72zayEhERY7hjz7Jr3749mzZt4sKFCwDk5uZy+PBhTCYTAJs2bSI9PZ3evXsDsH37dpycnJgzZw6tWrXCarXarXYREbneHTtC+stf/sLUqVMJCQkhPz+fvLw8WrdujY+PDwBTpkyhWbNmBAcHAzBnzhwWL17M/v378fDwwMHhjs1iEZG70h0bSABNmzbl/fffv+FzCQkJ1y3buHFjWZckIiI3ScMEERExBAWSiIgYggJJREQMQYEkIiKGoEASERFDUCCJiIghKJBERMQQFEgiImIICiQRETEEBZKIiBiCAklERAxBgSQiIoagQBIREUO4o6/2XV6+nNLL3iWIiJQrc2XHct+nAqkUartVs3cJIiJ3PU3ZiYiIISiQRETEEBRIIiJiCAokERExBAWSiIgYggJJREQMQYEkIiKGoL9DKoWCjAv2LkFEbpGpkhOOVd3sXYb8FwqkUjj7Vgd7lyAit6ju6K32LkFKoCk7ERExBAWSiIgYggJJREQMQYEkIiKGoEASERFDUCCJiIghKJBERMQQFEgiImIICiQRETEEBZKIiBiCAklERAxBgSQiIoZgmECKi4vjqaeeIjg4mJ49e/LRRx/dcL2JEydy9OjREtubNWsWOTk5t7tMEREpI4a62veAAQMICgqioKCAgIAAevXqhYPDzWXm5MmTb3N1IiJSlgwVSNfk5ORQpUoVUlNTCQkJIScnhyZNmjBp0iQAlixZQmpqKvfeey+zZs3ixIkThIaGkp+fz5NPPsmgQYMIDg5m2bJljBw5kqpVq9KkSRMefPBBVq5cyZUrV/Dz82PkyJF27qmIiFxjmCk7gKioKIKDg+nevTtNmzYlIiKCgQMHEh0dTVFREfv27QOgffv2xMTEcOrUKS5evEh4eDhvvPEGa9as4cCBA5w9e9bWZn5+PsOGDWPEiBGcOXOGiIgIVq9ezeeff26vboqIyA0YaoR0bcrOarUyduxYvvnmG3766SeWL19OVlYWzZs3B+D+++8HwN3dnStXrnDy5EmmTp0KQHp6OmfOnCnW7r333mtbPyQkBBcXFwoLC8uxZyIiUhJDBdI1JpOJWrVq4eXlxcSJE2nevDnr16/n/vvvZ+fOndd9r+Tl5cW0adPw9PTkww8/tAXQ79sDWLRoEV9++SUXL15k27Zt5dYfEREpmaECKSoqiq+++gqTyUS9evX44IMPmDx5MpmZmdSsWZNnnnnmhtuNHj2acePGceXKFSwWC8HBwTdcr1WrVvj7++Pi4oK7uztZWVk4OzuXZZdERKSUTFar1WrvIozMYrHwTX+zvcsQkVtUd/RWKrl62LuMCsNisZCYmPintjHUSQ0iIlJxKZBERMQQFEgiImIICiQRETEEBZKIiBiCAklERAxBgSQiIoagQBIREUNQIImIiCEokERExBAUSCIiYggKJBERMQQFkoiIGIKhbj9hVHVHb7V3CSJyi0yVnOxdgpRAgVQKumS9iEjZ05SdiIgYQokjpNTUVBYvXszFixdp3749999/P/fff3951CYiIhVIiSOkyZMn0759ey5dusR9991HaGhoedQlIiIVTImBlJ2djZ+fHyaTCV9fXypXrlwedYmISAVTYiA5OzuzYcMGcnNz2bZtG66uruVRl4iIVDAlBtLs2bNJTEzEzc2NvXv3EhYWVh51iYhIBVPiSQ2RkZGMHz/e9njGjBm8/vrrZVqU0aRkpti7BJEKz1zJjFsVN3uXIWXoDwMpNjaWpUuX8ttvv7F582bb8rp165ZLYUbS44Me9i5BpMJb/8J6e5cgZewPAykgIICAgAAiIiJ45ZVXyrMmERGpgEqcsvPz82P+/PkUFBRgtVpJSUnhrbfeKo/aRESkAinxpIapU6fi4+NDcnIyzs7O5VGTiIhUQCUGkqurKz169MDNzY1XX32VlBR9wS8iIrdfiYFUqVIlDh48SG5uLrt371YgiYhImSgxkEJDQ0lPT2fIkCGsWrWKESNGlEddIiJSwZR4UsOCBQtsJzEsWbKkzAsSEZGKqcRAslqt7Nixg4YNG+LgcHVAVRH/FklERMpWiYF08eJFli9fjslkwmq1YjKZiIqKKo/aRESkAikxkKKjo7l06RKnTp2ifv361KhRozzqEhGRCqbEQPrss8+IjIzEx8eHI0eOMHDgQAICAsqjNhERqUBKNUKKjY3FbDaTm5tLv379FEgiInLblXjat9VqxWw2A+Dk5ESlSiVmWKnExcXx1FNPERwcTJ8+fQgLC6OoqOi69fbs2cPcuXNLbM/f3/8Pl02dOvXWCxYRkTJVYro8+eSTDBw4kObNm3PgwAHat29/23Y+YMAAgoKCAFi0aBGxsbH06dPntrV/zfTp0297myIicnuVGEivvPIKbdu25cSJE3Tp0gWLxVImhfTp04d58+Zx+PBhTp06RVpaGqGhobbnjx8/TmhoKIsWLeLAgQOsXLmSK1eu4Ofnx8iRI8nPz2fMmDGcPn2aXr16FQs2f39/4uLiWLJkCfv27SMtLY2RI0fSrl27MumLiIj8eSVO2R06dIg1a9awfft2Vq5cSUhISJkUUrNmTY4ePYqvry8rVqxg6NChtvswXbhwgdDQUMLDw3F3d+fMmTNERESwevVqPv/8cwCysrIYO3YsMTExrF279rr2CwoKcHV15YMPPiAsLIxPP/20TPohIiI3p8QR0qRJk3j55ZepU6dOmRby22+/0aBBAw4dOkR8fDzZ2dl4enoCsHfvXjw9PW3fX7m7uxMSEoKLiwuFhYUAuLm52f5g12QyXde+o6MjqampTJgwAQcHBwoKCsq0PyIi8ueUOEKqVasWXbp04eGHH7b9KwsffvghHTt2xMnJiTlz5tCqVSusVisAXbt2ZeTIkcybNw+4+n1TeHg4o0aN4sqVK8CNQ+j3Dh8+zC+//MLcuXPp2LGjrW0RETGGEkdIzs7OjBo1isaNG9uW3a4LrEZFRfHVV19htVrx9fXlkUceYcWKFezfvx8PDw/bpYoA2rZty5o1a4iPj6dVq1b4+/vj4uKCu7s7WVlZJe7r3nvv5fz58/Tp0wdPT08yMjJuSx9EROT2MFlLGCrExsZed6r3c889V6ZFGYnFYqHWiFr2LkOkwlv/wnpqu9S2dxlSShaLhcTExD+1TYkjpC+++IIPPvjgposSEREpjRIDycnJibVr1+Lt7W2bQmvVqlWZFyYiIhVLiYHk7u5OQkICCQkJtmUKJBERud1KDKTZs2dz/vx5Tp8+TaNGjXS1bxERKRMlBtInn3zChg0bSEtLo1OnTqSlpTFhwoTyqE1ERCqQEv8O6eOPP2blypVUr16dIUOGsGfPnvKoS0REKpgSA6moqIiioiLbH546OTmVeVEiIlLxlDhlFxQUREBAABcuXKBv37707t27POoSEZEKpsRAevjhh/Hz8+PUqVN4eXnppAYRESkTpbq4qoODAz169CizW0+IiIiUGEgrV67k/PnzbNq0iZdffpl7772XGTNmlEdtIiJSgZR4UgNAZmYmGRkZ5Ofn4+rqWtY1iYhIBVTiCKlXr17cc889+Pv7M2rUKBwdHcujLhERqWBKDKSIiAhMJhOnT58mLS2tQp7UsP6F9fYuQaTCM1cy27sEKWMlBtKuXbuIjIzEx8eHI0eOMHDgQAICAsqjNsPQJe9FRMpeiYEUHR1NbGwsZrOZ3Nxc+vXrV+ECSUREyl6JJzVYrVbM5qtDZbPZfN3N+kRERG6HEtOlY8eODBw4kObNm5OQkECHDh3Koy4REalgShwhHT9+nOrVqxMVFYWrqyunT59myZIlpKamlkd9IiJSQZRqyq5NmzbExcXRoUMH0tLSqFevHhMnTiyP+kREpIIoMZDOnj1LQEAAjRo14rnnniMjI4PnnnuOrKys8qhPREQqiBK/QzKZTKxfv54WLVpw4MABHBwcOHLkCHl5eeVRn4iIVBAmq9Vq/W8rpKamsmLFCo4dO0ajRo14+eWX+emnn6hbty4+Pj7lVafdWCwWDu7cae8yRG6Jg9lM5erV7V2GVCAWi4XExMQ/tU2JgVTRWSwWIjzr2LsMkVvyaNwnONWqZe8ypAK5mUAq1cVVRUREypoCSUREDEGBJCIihqBAEhERQ1AgiYiIISiQRETEEBRIIiJiCAokERExBAWSiIgYggJJREQMQYEkIiKGoEASERFDsHsgxcXF0bRp02L3Vxo7duwNbwCYm5vLF198cdP78vf3v+ltRUSkbNk9kADuuecedv7/WzwUFBRw9OjRG66XkpLCl19+WZ6liYhIOTFEILVv355vv/0WgPj4eFq1agXAtGnTePHFF+nZsycHDx5k1apV7N27ly+++IIXX3yR4cOHs2TJEr7++msCAgLo3bu3bQR1bTSUlZVFcHAwAJcvX2bo0KH4+/vb9iciIsZgiECqX78+Z8+epaioiK1bt9K+fXsuX76Mr68vK1asYOjQoWzevJn+/fvzt7/9jc6dO5Ofn8+wYcMYNmwY77zzDjExMcTExPDee++Rk5Nzw/1kZGQwf/58Fi1axOrVq8u5lyIi8t+UeAvz8tKiRQsSEhK4ePEitWvXplq1ahw6dIj4+Hiys7Px9PS8bpt7772X1NRU7rnnHpycnGzLzp8/b1vn9/cf9PLywsXFBZPJ9IehJSIi9mGIERJAhw4dWLp0KU2aNAFg+/btODk5MWfOHFq1aoXVasVkMhULGJPJhLu7O2fPniU3N5e8vDySkpKoVasWWVlZFBUVcezYsWLri4iIMRlmhOTr68uhQ4eYOHEiBQUF+Pr6smPHDvbv34+HhwcODg7UqFGDU6dOsX79ett2jo6ODB8+nODgYAoLCxk2bBjOzs507NiR3r174+vrS9WqVe3YMxERKQ2T9fdDDrmOxWIhwrOOvcsQuSWPxn2CU61a9i5DKhCLxUJiYuKf2sYwU3YiIlKxKZBERMQQFEgiImIICiQRETEEBZKIiBiCAklERAxBgSQiIoagQBIREUNQIImIiCEokERExBAUSCIiYggKJBERMQQFkoiIGIJhbj9hZI/GfWLvEkRuiYPZbO8SREqkQCoFXbZfRKTsacpOREQMQYEkIiKGoEASERFDUCCJiIghKJBERMQQFEgiImIIOu27FDLTcuxdgthRpUoOVHHW3/GIlDUFUimsnP6NvUsQO3p+akd7lyBSIWjKTkREDEGBJCIihqBAEhERQ1AgiYiIISiQRETEEBRIIiJiCAokERExBAWSiIgYggJJREQMQYEkIiKGoEASERFDUCCJiIgh3PZAiouLo3///rbHe/bsYe7cuTfdXmBgIOfPn7c9TkhIICQk5IbrBgcHk5WVhb+//03vT0RE7KNMRkhJSUls2LDhtrTVuXNntmzZYnu8efNmunXrdlvaFhER4yiTQBowYADvv/8+mZmZxZYvXLiQfv368eKLL3Lu3DmGDBlCamoqBw4coHv37gAsXbqU3bt327bp1KmTLZCsViv79+/nkUce4eTJk7z00ksEBwczZMgQCgsLr6tj69atBAYGEhgYyO7du9myZQuLFi0CYO/evYSHh5dF90VE5CaUSSC5ubkRHBzM4sWLbcuOHDnCqVOnWL16NWPHjuWdd97hiSeeID4+nvj4eAoLC0lPT+fHH3/k4Ycftm1Xu3ZtHBwcbMHVokULHB0d+eWXX5g4cSLR0dE4Ojry73//u1gNRUVFvHRk0b4AAAo/SURBVPvuu0RFRfHBBx+wZMkS2rRpw65duwD48ssv6dy5c1l0X0REbkKZ3aCvV69e9O/fnwYNGgBw/PhxDh48SHBwMHA1tFq3bk1MTAyXLl0iICCAHTt2UKVKFczm4nfnfOaZZ9i6dSvHjh2ja9euAHh4eLB06VLMZjPJycnXjZBSU1NJTk5m0KBBtscA3t7eJCYmkpSURJMmTcqq+yIi8ieV2Vl2JpOJSZMmsWTJEgDq16/P448/TnR0NDNmzKBt27Z4e3tz6tQpAB599FGWL1/OY489dl1bTz/9NN9//z1JSUk0a9YMgEWLFjF06FBmzpyJ2WzGarUW28bd3R1vb29WrlzJe++9R5cuXTCbzXTt2pWFCxfSqlWrsuq6iIjchDI97btp06Z07Hj19s++vr44OzsTFBTEmDFj+J//+R8A7rnnHho3bozFYuHXX3/Fz8/vunaqV68OQIsWLWzLOnTowMiRIwkKCqJKlSqkpKQU28bR0ZEBAwYQFBRE7969qVWrFnA1+BISEjRdJyJiMCbrfw4t7nJ5eXkMHz6cyMjIUq1vsVgY2XVBGVclRvb81I64uFWxdxkidxSLxUJiYuKf2qZC/WFsSkoKvXr1IigoyN6liIjIfyizkxqMqHbt2mzcuNHeZYiIyA1UqBGSiIgYlwJJREQMQYEkIiKGoEASERFDUCCJiIghKJBERMQQFEgiImIICiQRETEEBZKIiBiCAklERAxBgSQiIoZQoa5ld7Oen9rR3iWIHVWqpN/bRMqDAqkUdOsBEZGyp1/9RETEEBRIIiJiCAokERExBH2HVAoWi8XeJYiI3PVMVqvVau8iRERENGUnIiKGoEASERFDUCCJiIghKJBERMQQFEgiImIICiQRETEEx2nTpk2zdxFGVFBQwJgxY4iKiuLYsWO0bt3a3iXdtNmzZ1NQUEDt2rUZOnQoa9euJSMjg4ceeohz584xZMgQYmNjqVKlCo0bN+bw4cOMGDGCTz75hPr16+Pl5WXvLhSTmZnJ8OHDiY2N5bvvvqN169YMGzbsju/XNZmZmQwbNoxVq1Zx6dIlLBbLXXHcfm/Xrl3Mnz+fjh07XvdzlpmZWar+GlG7du3YunUrn376KV5eXkydOvWuOG5Wq5WZM2eybNkyPvvsM9q0aUNISMjtP25WuaHPP//c+u6771qtVqs1JCTEmpCQYOeK/ryCggLruHHjrO3atbNu27bNunz5cuuGDRusRUVF1kGDBlkvXLhgnTp1qvWHH36w5ubmWgMDA625ubnWV155xXrmzBlrenq6tV+/fvbuxnUiIyOtH3/8sdVqtVrffvvtu6Zf10RHR1vXrVtntVqt1ueff/6u619hYaE1MDDQOnLkyBv+nJW2v0aTnJxsnThxou3x3XTctm3bZp0zZ47VarVav/vuO+vy5cvL5Lhpyu4PHDhwgEceeQSAxx9/nP3799u5oj+vsLCQbt268dxzzwGQkJDAI488gslkolWrVhw4cIDDhw/TsmVLzGYzPj4+JCUlkZqaSt26dXF1daVKlSqkpaXZuSfFBQYG0q1bN+BqHyMjI++Kfl0TFBREz549ycvLIzs7+645btd8/PHHtG3bFrjxz1lp+2s0R48eJTExkf79+zNr1qy76rjFx8cD8MILL/DPf/6T3377rUyOmwLpD2RmZuLs7AxA1apVycrKsnNFf57ZbMbPz8/2+EZ9KioqwmQy2ZZlZ2dj/d3FO64tMxIXFxfMZjMJCQns3buXBx544K7o1+9lZWXRpUsXatasedccN7j6Hty2bRtdunSxPf7PvpW2v0ZTo0YN21QrwLZt2+6a45aWlkZubi4ffPABTk5ObNmypUyOmwLpDzg7O9tevOzsbFxdXe1c0a27UZ8cHP7vLZCdnY2Li4vtDQRw5coVXFxcyr3Wkuzbt4/p06ezcOHCu6pf11SvXp1vvvmG+++/n4MHD941/YuMjOSll16y1XqjY1fa42k0FovFNvJr3bo1bdu2vWuOW/Xq1Xn00UcBePTRR/n73/9eJsdNgfQHmjZtyt69ewH417/+RbNmzexc0a37fZ/i4+Np2rQpPj4+/Pjjj+Tn55OYmEijRo1wc3Pj3LlzZGRkkJWVZbgwPnHiBGFhYSxbtgxPT8+7pl/XrFixgu3btwNXf6t8+eWX75r+7d+/n4ULFzJmzBj27t2Lm5vbdT9npT2eRrNy5Uo+/vhjAH744QeaNWt21xy3Zs2a8a9//QuAn376qVjfbudx08VV/0BeXh7jx4/n3LlzWCwWpk+fbu+SbtrixYtp2rQpLVu25B//+AeXL1+mQ4cODB06lOTkZCZOnEh2djb9+/enZ8+e/O///i/Tp08nPz+f1157jTZt2ti7C8WEhISwb98+PD09ARgwYADr1q274/t1zfnz5xk/fjxFRUV4eHgQEhLCxIkT75r+ASQnJzNv3jwWLFhw3c9ZWlpaqd6nRpORkcHo0aPJzc3F29ub0aNHM27cuLviuBUUFPD6669z/PhxPD09y+y4KZBERMQQNGUnIiKGoEASERFDUCCJiIghKJBERMQQFEgiImIICiQRETEEBZKIiBhCJXsXIFJRxMXFcebMGUaOHMkPP/zAqlWruHLlCllZWRQWFhIWFkbDhg0JCwvj559/xmQyMX78eJo3b06PHj2oU6cOXl5eZGZmUrlyZU6dOkVRURHvvPMOVquVyZMnk52dzeXLlxk9ejR+fn506tSJBx54gJMnT/L0009z7NgxDh06RM+ePXnhhRfYuXMnixcvxsHBgccff5wRI0bY+2WSCkyBJGInhYWFZGdnExERQVJSEunp6Xz33XdkZGSwevVqUlNTGTx4MLGxsaSnpxMeHs59993HxIkT8fHxYcaMGUydOpXvv/+e+vXrExAQQNu2bYmPjyc6Oho/Pz+Sk5OJiYmhqKiIDh068P333wPQp08fnn/+eWbNmsWaNWtwc3NjzJgxHDx48K64TJbcmRRIInZSrVo1/Pz8GDFiBI6OjowaNYpjx46xf/9+goODAUhPTycvLw8Ab29v27bXbnRWp04dcnNz8fDwYNWqVWzevJmCggIKCgoAqFWrFjVr1rT9383NDbh6aazU1FTOnz9vGxVlZmZy8uRJBZLYjQJJpJw4OTlx8eJFAA4dOkRWVhY5OTm8//777Nmzh4iICLp3707btm2ZNGkSly9f5sMPP8RsNgMUu3Ly768QDVcvyPrXv/6VgIAAYmNj2bZt2w3X+z13d3fq1q1LREQEVatWJSYmhgceeOB2d1uk1BRIIuXkiSeeICYmhuDgYBo1aoSjoyO7du1i48aNVK5cmTFjxvDwww+ze/dugoKCSE9PZ9CgQaVqu127dkybNo24uDjq1q1Lampqids4ODgwduxYXnzxRfLz82nYsCG9e/e+1W6K3DRdXFVERAxBp32LiIghKJBERMQQFEgiImIICiQRETEEBZKIiBiCAklERAxBgSQiIoagQBIREUP4f6MhGVTI4TGDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y=arabic_users_geo.head(5).index,x='username',data=arabic_users_geo.head(5))\n",
    "plt.title('Top 5 Governorates with Active Users using Geocode')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_users_search = arabic_search_tweets.reset_index().groupby('governorate')['username'].nunique().to_frame()\n",
    "arabic_users_search.sort_values('username', ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 5 Governorates with Active Users using Keyword Search')"
      ]
     },
     "execution_count": 290,
     "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": [
    "sns.barplot(y=arabic_users_search.head(5).index,x='username',data=arabic_users_search.head(5))\n",
    "plt.title('Top 5 Governorates with Active Users using Keyword Search')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_users_profile_info = arabic_profile_info_tweets.reset_index().groupby('governorate')['username'].nunique().to_frame()\n",
    "arabic_users_profile_info.sort_values('username', ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 5 Governorates with Active Users using Profile Info')"
      ]
     },
     "execution_count": 293,
     "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": [
    "sns.barplot(y=arabic_users_profile_info.head(5).index,x='username',data=arabic_users_profile_info.head(5))\n",
    "plt.title('Top 5 Governorates with Active Users using Profile Info')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "trial_df = arabic_tweets[['governorate','method']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "trial_df1 = trial_df[trial_df['method'] == 'Geotagged'].groupby('governorate').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "trial_df2 = trial_df[trial_df['method'] == 'Keyword Search'].groupby('governorate').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "trial_df3 = trial_df[trial_df['method'] == 'Profile Info'].groupby('governorate').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "trial_df1.rename({'method':'Geocoded'},axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "trial_df2.rename({'method':'Keyword Search'},axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "trial_df3.rename({'method':'Profile Info'},axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "trial_df1 = trial_df1.join(trial_df2,on='governorate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "trial_df1 = trial_df1.join(trial_df3,on='governorate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "trial_df1.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Geocoded',\n",
       " 'Profile Info',\n",
       " 'Geocoded',\n",
       " 'Geocoded',\n",
       " 'Geocoded',\n",
       " 'Geocoded',\n",
       " 'Keyword Search',\n",
       " 'Geocoded',\n",
       " 'Geocoded',\n",
       " 'Geocoded',\n",
       " 'Geocoded',\n",
       " 'Geocoded',\n",
       " 'Geocoded',\n",
       " 'Geocoded',\n",
       " 'Keyword Search',\n",
       " 'Geocoded',\n",
       " 'Geocoded',\n",
       " 'Geocoded',\n",
       " 'Geocoded',\n",
       " 'Geocoded',\n",
       " 'Geocoded',\n",
       " 'Profile Info',\n",
       " 'Geocoded',\n",
       " 'Geocoded',\n",
       " 'Keyword Search',\n",
       " 'Geocoded',\n",
       " 'Geocoded']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_approach=[]\n",
    "for i in range(0,len(trial_df1)):\n",
    "    row = trial_df1.iloc[i]\n",
    "    best = row.max()\n",
    "    if best == row['Geocoded']:\n",
    "        best_approach.append('Geocoded')\n",
    "    if best == row['Profile Info']:\n",
    "        best_approach.append('Profile Info')\n",
    "    if best == row['Keyword Search']:\n",
    "        best_approach.append('Keyword Search')\n",
    "best_approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "trial_df1['Most Approach'] = best_approach\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Geocoded</th>\n",
       "      <th>Keyword Search</th>\n",
       "      <th>Profile Info</th>\n",
       "      <th>Most Approach</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>governorate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Al-Minya</th>\n",
       "      <td>12591</td>\n",
       "      <td>3312</td>\n",
       "      <td>259.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alexandria</th>\n",
       "      <td>378</td>\n",
       "      <td>5773</td>\n",
       "      <td>6459.0</td>\n",
       "      <td>Profile Info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aswan</th>\n",
       "      <td>7399</td>\n",
       "      <td>4673</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asyut</th>\n",
       "      <td>5926</td>\n",
       "      <td>4123</td>\n",
       "      <td>124.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beheira</th>\n",
       "      <td>81742</td>\n",
       "      <td>1808</td>\n",
       "      <td>382.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beni Suef</th>\n",
       "      <td>8641</td>\n",
       "      <td>2270</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cairo</th>\n",
       "      <td>15957</td>\n",
       "      <td>38054</td>\n",
       "      <td>11762.0</td>\n",
       "      <td>Keyword Search</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dakahlia</th>\n",
       "      <td>82291</td>\n",
       "      <td>4220</td>\n",
       "      <td>1331.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Damietta</th>\n",
       "      <td>22371</td>\n",
       "      <td>8024</td>\n",
       "      <td>2252.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fayoum</th>\n",
       "      <td>2890</td>\n",
       "      <td>1664</td>\n",
       "      <td>125.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gharbia</th>\n",
       "      <td>25838</td>\n",
       "      <td>4889</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Giza</th>\n",
       "      <td>83585</td>\n",
       "      <td>3626</td>\n",
       "      <td>2347.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ismailia</th>\n",
       "      <td>7946</td>\n",
       "      <td>1428</td>\n",
       "      <td>73.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kafr el-Sheikh</th>\n",
       "      <td>38424</td>\n",
       "      <td>3129</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxor</th>\n",
       "      <td>4306</td>\n",
       "      <td>4381</td>\n",
       "      <td>690.0</td>\n",
       "      <td>Keyword Search</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matrouh</th>\n",
       "      <td>57629</td>\n",
       "      <td>637</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monufia</th>\n",
       "      <td>16223</td>\n",
       "      <td>3141</td>\n",
       "      <td>691.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Valley</th>\n",
       "      <td>31035</td>\n",
       "      <td>401</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Sinai</th>\n",
       "      <td>10980</td>\n",
       "      <td>422</td>\n",
       "      <td>247.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Port Said</th>\n",
       "      <td>4060</td>\n",
       "      <td>3685</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qalyubia</th>\n",
       "      <td>14784</td>\n",
       "      <td>491</td>\n",
       "      <td>59.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qena</th>\n",
       "      <td>2172</td>\n",
       "      <td>152</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>Profile Info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Red Sea</th>\n",
       "      <td>17138</td>\n",
       "      <td>2770</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharqia</th>\n",
       "      <td>32334</td>\n",
       "      <td>2840</td>\n",
       "      <td>1539.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sohag</th>\n",
       "      <td>2325</td>\n",
       "      <td>2562</td>\n",
       "      <td>819.0</td>\n",
       "      <td>Keyword Search</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Sinai</th>\n",
       "      <td>1932</td>\n",
       "      <td>378</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suez</th>\n",
       "      <td>14503</td>\n",
       "      <td>5075</td>\n",
       "      <td>1439.0</td>\n",
       "      <td>Geocoded</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Geocoded  Keyword Search  Profile Info   Most Approach\n",
       "governorate                                                           \n",
       "Al-Minya           12591            3312         259.0        Geocoded\n",
       "Alexandria           378            5773        6459.0    Profile Info\n",
       "Aswan               7399            4673        1056.0        Geocoded\n",
       "Asyut               5926            4123         124.0        Geocoded\n",
       "Beheira            81742            1808         382.0        Geocoded\n",
       "Beni Suef           8641            2270          69.0        Geocoded\n",
       "Cairo              15957           38054       11762.0  Keyword Search\n",
       "Dakahlia           82291            4220        1331.0        Geocoded\n",
       "Damietta           22371            8024        2252.0        Geocoded\n",
       "Fayoum              2890            1664         125.0        Geocoded\n",
       "Gharbia            25838            4889        3568.0        Geocoded\n",
       "Giza               83585            3626        2347.0        Geocoded\n",
       "Ismailia            7946            1428          73.0        Geocoded\n",
       "Kafr el-Sheikh     38424            3129          40.0        Geocoded\n",
       "Luxor               4306            4381         690.0  Keyword Search\n",
       "Matrouh            57629             637          37.0        Geocoded\n",
       "Monufia            16223            3141         691.0        Geocoded\n",
       "New Valley         31035             401           0.0        Geocoded\n",
       "North Sinai        10980             422         247.0        Geocoded\n",
       "Port Said           4060            3685           7.0        Geocoded\n",
       "Qalyubia           14784             491          59.0        Geocoded\n",
       "Qena                2172             152        3433.0    Profile Info\n",
       "Red Sea            17138            2770           0.0        Geocoded\n",
       "Sharqia            32334            2840        1539.0        Geocoded\n",
       "Sohag               2325            2562         819.0  Keyword Search\n",
       "South Sinai         1932             378           5.0        Geocoded\n",
       "Suez               14503            5075        1439.0        Geocoded"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trial_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trial_df1.groupby('Most Approach').count()['Geocoded'].plot.pie(cmap='viridis')\n",
    "plt.xlabel('Approach with the most collected per governorate')\n",
    "plt.ylabel('')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_tweets = arabic_tweets[arabic_tweets['corona'] == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 56885 entries, 23 to 758139\n",
      "Data columns (total 17 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   date            56885 non-null  object\n",
      " 1   time            56885 non-null  object\n",
      " 2   username        56885 non-null  object\n",
      " 3   name            56885 non-null  object\n",
      " 4   place           308 non-null    object\n",
      " 5   tweet           56885 non-null  object\n",
      " 6   urls            56885 non-null  object\n",
      " 7   replies_count   56885 non-null  int64 \n",
      " 8   retweets_count  56885 non-null  int64 \n",
      " 9   likes_count     56885 non-null  int64 \n",
      " 10  hashtags        56885 non-null  object\n",
      " 11  near            0 non-null      object\n",
      " 12  geo             31704 non-null  object\n",
      " 13  governorate     56885 non-null  object\n",
      " 14  language        56885 non-null  object\n",
      " 15  method          56885 non-null  object\n",
      " 16  corona          56885 non-null  bool  \n",
      "dtypes: bool(1), int64(3), object(13)\n",
      "memory usage: 7.4+ MB\n"
     ]
    }
   ],
   "source": [
    "corona_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 758141 entries, 0 to 758140\n",
      "Data columns (total 17 columns):\n",
      " #   Column          Non-Null Count   Dtype \n",
      "---  ------          --------------   ----- \n",
      " 0   date            758141 non-null  object\n",
      " 1   time            758141 non-null  object\n",
      " 2   username        758141 non-null  object\n",
      " 3   name            758139 non-null  object\n",
      " 4   place           4351 non-null    object\n",
      " 5   tweet           758141 non-null  object\n",
      " 6   urls            758141 non-null  object\n",
      " 7   replies_count   758141 non-null  int64 \n",
      " 8   retweets_count  758141 non-null  int64 \n",
      " 9   likes_count     758141 non-null  int64 \n",
      " 10  hashtags        758141 non-null  object\n",
      " 11  near            0 non-null       object\n",
      " 12  geo             605400 non-null  object\n",
      " 13  governorate     758141 non-null  object\n",
      " 14  language        758141 non-null  object\n",
      " 15  method          758141 non-null  object\n",
      " 16  corona          758141 non-null  bool  \n",
      "dtypes: bool(1), int64(3), object(13)\n",
      "memory usage: 93.3+ MB\n"
     ]
    }
   ],
   "source": [
    "arabic_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_mentions = pd.DataFrame({'No. of mentions':[56885,701256]},\n",
    "                              index=['Corona mentions','Non-corona mentions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae1a78d9c8>"
      ]
     },
     "execution_count": 146,
     "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": [
    "sns.barplot(x=corona_mentions.index,y='No. of mentions',data=corona_mentions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corona_mentions['No. of mentions'].plot.pie(cmap='PiYG',autopct='%.1f')\n",
    "plt.ylabel('')\n",
    "plt.title('COVID-19 mentions in tweets opposite to non COVID-19 mentions')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_date = corona_tweets.groupby('date').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>time</th>\n",
       "      <th>username</th>\n",
       "      <th>name</th>\n",
       "      <th>place</th>\n",
       "      <th>tweet</th>\n",
       "      <th>urls</th>\n",
       "      <th>replies_count</th>\n",
       "      <th>retweets_count</th>\n",
       "      <th>likes_count</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>near</th>\n",
       "      <th>geo</th>\n",
       "      <th>governorate</th>\n",
       "      <th>language</th>\n",
       "      <th>method</th>\n",
       "      <th>corona</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-08</th>\n",
       "      <td>2175</td>\n",
       "      <td>2175</td>\n",
       "      <td>2175</td>\n",
       "      <td>11</td>\n",
       "      <td>2175</td>\n",
       "      <td>2175</td>\n",
       "      <td>2175</td>\n",
       "      <td>2175</td>\n",
       "      <td>2175</td>\n",
       "      <td>2175</td>\n",
       "      <td>0</td>\n",
       "      <td>818</td>\n",
       "      <td>2175</td>\n",
       "      <td>2175</td>\n",
       "      <td>2175</td>\n",
       "      <td>2175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09</th>\n",
       "      <td>2381</td>\n",
       "      <td>2381</td>\n",
       "      <td>2381</td>\n",
       "      <td>6</td>\n",
       "      <td>2381</td>\n",
       "      <td>2381</td>\n",
       "      <td>2381</td>\n",
       "      <td>2381</td>\n",
       "      <td>2381</td>\n",
       "      <td>2381</td>\n",
       "      <td>0</td>\n",
       "      <td>1081</td>\n",
       "      <td>2381</td>\n",
       "      <td>2381</td>\n",
       "      <td>2381</td>\n",
       "      <td>2381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-10</th>\n",
       "      <td>2676</td>\n",
       "      <td>2676</td>\n",
       "      <td>2676</td>\n",
       "      <td>28</td>\n",
       "      <td>2676</td>\n",
       "      <td>2676</td>\n",
       "      <td>2676</td>\n",
       "      <td>2676</td>\n",
       "      <td>2676</td>\n",
       "      <td>2676</td>\n",
       "      <td>0</td>\n",
       "      <td>1432</td>\n",
       "      <td>2676</td>\n",
       "      <td>2676</td>\n",
       "      <td>2676</td>\n",
       "      <td>2676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-11</th>\n",
       "      <td>2382</td>\n",
       "      <td>2382</td>\n",
       "      <td>2382</td>\n",
       "      <td>12</td>\n",
       "      <td>2382</td>\n",
       "      <td>2382</td>\n",
       "      <td>2382</td>\n",
       "      <td>2382</td>\n",
       "      <td>2382</td>\n",
       "      <td>2382</td>\n",
       "      <td>0</td>\n",
       "      <td>1155</td>\n",
       "      <td>2382</td>\n",
       "      <td>2382</td>\n",
       "      <td>2382</td>\n",
       "      <td>2382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-12</th>\n",
       "      <td>2902</td>\n",
       "      <td>2902</td>\n",
       "      <td>2902</td>\n",
       "      <td>32</td>\n",
       "      <td>2902</td>\n",
       "      <td>2902</td>\n",
       "      <td>2902</td>\n",
       "      <td>2902</td>\n",
       "      <td>2902</td>\n",
       "      <td>2902</td>\n",
       "      <td>0</td>\n",
       "      <td>1518</td>\n",
       "      <td>2902</td>\n",
       "      <td>2902</td>\n",
       "      <td>2902</td>\n",
       "      <td>2902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-13</th>\n",
       "      <td>2880</td>\n",
       "      <td>2880</td>\n",
       "      <td>2880</td>\n",
       "      <td>8</td>\n",
       "      <td>2880</td>\n",
       "      <td>2880</td>\n",
       "      <td>2880</td>\n",
       "      <td>2880</td>\n",
       "      <td>2880</td>\n",
       "      <td>2880</td>\n",
       "      <td>0</td>\n",
       "      <td>1713</td>\n",
       "      <td>2880</td>\n",
       "      <td>2880</td>\n",
       "      <td>2880</td>\n",
       "      <td>2880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-14</th>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "      <td>17</td>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "      <td>0</td>\n",
       "      <td>2849</td>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "      <td>4293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-15</th>\n",
       "      <td>4300</td>\n",
       "      <td>4300</td>\n",
       "      <td>4300</td>\n",
       "      <td>32</td>\n",
       "      <td>4300</td>\n",
       "      <td>4300</td>\n",
       "      <td>4300</td>\n",
       "      <td>4300</td>\n",
       "      <td>4300</td>\n",
       "      <td>4300</td>\n",
       "      <td>0</td>\n",
       "      <td>2936</td>\n",
       "      <td>4300</td>\n",
       "      <td>4300</td>\n",
       "      <td>4300</td>\n",
       "      <td>4300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-16</th>\n",
       "      <td>4944</td>\n",
       "      <td>4944</td>\n",
       "      <td>4944</td>\n",
       "      <td>23</td>\n",
       "      <td>4944</td>\n",
       "      <td>4944</td>\n",
       "      <td>4944</td>\n",
       "      <td>4944</td>\n",
       "      <td>4944</td>\n",
       "      <td>4944</td>\n",
       "      <td>0</td>\n",
       "      <td>2707</td>\n",
       "      <td>4944</td>\n",
       "      <td>4944</td>\n",
       "      <td>4944</td>\n",
       "      <td>4944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-17</th>\n",
       "      <td>5416</td>\n",
       "      <td>5416</td>\n",
       "      <td>5416</td>\n",
       "      <td>21</td>\n",
       "      <td>5416</td>\n",
       "      <td>5416</td>\n",
       "      <td>5416</td>\n",
       "      <td>5416</td>\n",
       "      <td>5416</td>\n",
       "      <td>5416</td>\n",
       "      <td>0</td>\n",
       "      <td>2626</td>\n",
       "      <td>5416</td>\n",
       "      <td>5416</td>\n",
       "      <td>5416</td>\n",
       "      <td>5416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-18</th>\n",
       "      <td>4833</td>\n",
       "      <td>4833</td>\n",
       "      <td>4833</td>\n",
       "      <td>35</td>\n",
       "      <td>4833</td>\n",
       "      <td>4833</td>\n",
       "      <td>4833</td>\n",
       "      <td>4833</td>\n",
       "      <td>4833</td>\n",
       "      <td>4833</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>4833</td>\n",
       "      <td>4833</td>\n",
       "      <td>4833</td>\n",
       "      <td>4833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-19</th>\n",
       "      <td>4876</td>\n",
       "      <td>4876</td>\n",
       "      <td>4876</td>\n",
       "      <td>24</td>\n",
       "      <td>4876</td>\n",
       "      <td>4876</td>\n",
       "      <td>4876</td>\n",
       "      <td>4876</td>\n",
       "      <td>4876</td>\n",
       "      <td>4876</td>\n",
       "      <td>0</td>\n",
       "      <td>2795</td>\n",
       "      <td>4876</td>\n",
       "      <td>4876</td>\n",
       "      <td>4876</td>\n",
       "      <td>4876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-20</th>\n",
       "      <td>7283</td>\n",
       "      <td>7283</td>\n",
       "      <td>7283</td>\n",
       "      <td>23</td>\n",
       "      <td>7283</td>\n",
       "      <td>7283</td>\n",
       "      <td>7283</td>\n",
       "      <td>7283</td>\n",
       "      <td>7283</td>\n",
       "      <td>7283</td>\n",
       "      <td>0</td>\n",
       "      <td>4114</td>\n",
       "      <td>7283</td>\n",
       "      <td>7283</td>\n",
       "      <td>7283</td>\n",
       "      <td>7283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-21</th>\n",
       "      <td>4976</td>\n",
       "      <td>4976</td>\n",
       "      <td>4976</td>\n",
       "      <td>34</td>\n",
       "      <td>4976</td>\n",
       "      <td>4976</td>\n",
       "      <td>4976</td>\n",
       "      <td>4976</td>\n",
       "      <td>4976</td>\n",
       "      <td>4976</td>\n",
       "      <td>0</td>\n",
       "      <td>2953</td>\n",
       "      <td>4976</td>\n",
       "      <td>4976</td>\n",
       "      <td>4976</td>\n",
       "      <td>4976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            time  username  name  place  tweet  urls  replies_count  \\\n",
       "date                                                                  \n",
       "2020-03-08  2175      2175  2175     11   2175  2175           2175   \n",
       "2020-03-09  2381      2381  2381      6   2381  2381           2381   \n",
       "2020-03-10  2676      2676  2676     28   2676  2676           2676   \n",
       "2020-03-11  2382      2382  2382     12   2382  2382           2382   \n",
       "2020-03-12  2902      2902  2902     32   2902  2902           2902   \n",
       "2020-03-13  2880      2880  2880      8   2880  2880           2880   \n",
       "2020-03-14  4293      4293  4293     17   4293  4293           4293   \n",
       "2020-03-15  4300      4300  4300     32   4300  4300           4300   \n",
       "2020-03-16  4944      4944  4944     23   4944  4944           4944   \n",
       "2020-03-17  5416      5416  5416     21   5416  5416           5416   \n",
       "2020-03-18  4833      4833  4833     35   4833  4833           4833   \n",
       "2020-03-19  4876      4876  4876     24   4876  4876           4876   \n",
       "2020-03-20  7283      7283  7283     23   7283  7283           7283   \n",
       "2020-03-21  4976      4976  4976     34   4976  4976           4976   \n",
       "\n",
       "            retweets_count  likes_count  hashtags  near   geo  governorate  \\\n",
       "date                                                                         \n",
       "2020-03-08            2175         2175      2175     0   818         2175   \n",
       "2020-03-09            2381         2381      2381     0  1081         2381   \n",
       "2020-03-10            2676         2676      2676     0  1432         2676   \n",
       "2020-03-11            2382         2382      2382     0  1155         2382   \n",
       "2020-03-12            2902         2902      2902     0  1518         2902   \n",
       "2020-03-13            2880         2880      2880     0  1713         2880   \n",
       "2020-03-14            4293         4293      4293     0  2849         4293   \n",
       "2020-03-15            4300         4300      4300     0  2936         4300   \n",
       "2020-03-16            4944         4944      4944     0  2707         4944   \n",
       "2020-03-17            5416         5416      5416     0  2626         5416   \n",
       "2020-03-18            4833         4833      4833     0  2631         4833   \n",
       "2020-03-19            4876         4876      4876     0  2795         4876   \n",
       "2020-03-20            7283         7283      7283     0  4114         7283   \n",
       "2020-03-21            4976         4976      4976     0  2953         4976   \n",
       "\n",
       "            language  method  corona  \n",
       "date                                  \n",
       "2020-03-08      2175    2175    2175  \n",
       "2020-03-09      2381    2381    2381  \n",
       "2020-03-10      2676    2676    2676  \n",
       "2020-03-11      2382    2382    2382  \n",
       "2020-03-12      2902    2902    2902  \n",
       "2020-03-13      2880    2880    2880  \n",
       "2020-03-14      4293    4293    4293  \n",
       "2020-03-15      4300    4300    4300  \n",
       "2020-03-16      4944    4944    4944  \n",
       "2020-03-17      5416    5416    5416  \n",
       "2020-03-18      4833    4833    4833  \n",
       "2020-03-19      4876    4876    4876  \n",
       "2020-03-20      7283    7283    7283  \n",
       "2020-03-21      4976    4976    4976  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_date.drop('2020-03-22')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.set_style('whitegrid')\n",
    "sns.lineplot(x=corona_date.drop('2020-03-22').index,y='tweet',data=corona_date.drop('2020-03-22'),marker='o',markersize=8,lw=2)\n",
    "plt.title('# of COVID-19 tweets per day')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_gov = corona_tweets.groupby('governorate').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_gov = corona_gov.sort_values('tweet',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 5 governorates with # of tweets')"
      ]
     },
     "execution_count": 153,
     "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": [
    "sns.barplot(y=corona_gov.head(5).index,x='tweet',data=corona_gov.head(5),palette='viridis')\n",
    "plt.title('Top 5 governorates with # of tweets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_tweet_count = arabic_tweets.groupby('governorate').count()['tweet'].to_frame().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_tweet_count.rename({'governorate':'Name'}, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "governorates = pd.merge(governorates,arabic_tweet_count,on='Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "governorates.rename({'tweet':'Tweet Count'}, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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>Name</th>\n",
       "      <th>Abbr.</th>\n",
       "      <th>Native</th>\n",
       "      <th>Capital</th>\n",
       "      <th>Area</th>\n",
       "      <th>Population</th>\n",
       "      <th>Density</th>\n",
       "      <th>% Urban</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>Urban Population</th>\n",
       "      <th>Radius</th>\n",
       "      <th>Tweet Count</th>\n",
       "      <th>Corona Tweets</th>\n",
       "      <th>% Corona</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dakahlia</td>\n",
       "      <td>DQH</td>\n",
       "      <td>الدقهلية</td>\n",
       "      <td>Al-Manṣūrah</td>\n",
       "      <td>3538</td>\n",
       "      <td>6492381</td>\n",
       "      <td>1835.042679</td>\n",
       "      <td>28.2</td>\n",
       "      <td>31.036373</td>\n",
       "      <td>31.380691</td>\n",
       "      <td>1830851</td>\n",
       "      <td>33.558611</td>\n",
       "      <td>87842</td>\n",
       "      <td>6086</td>\n",
       "      <td>6.928349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Red Sea</td>\n",
       "      <td>BAH</td>\n",
       "      <td>البحر الأحمر</td>\n",
       "      <td>Al-Ghurdaqah</td>\n",
       "      <td>120000</td>\n",
       "      <td>359888</td>\n",
       "      <td>2.999067</td>\n",
       "      <td>95.1</td>\n",
       "      <td>26.729177</td>\n",
       "      <td>33.936511</td>\n",
       "      <td>342253</td>\n",
       "      <td>195.441005</td>\n",
       "      <td>19908</td>\n",
       "      <td>1553</td>\n",
       "      <td>7.800884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beheira</td>\n",
       "      <td>BHR</td>\n",
       "      <td>البحيرة</td>\n",
       "      <td>Damanhūr</td>\n",
       "      <td>9826</td>\n",
       "      <td>6171613</td>\n",
       "      <td>628.090067</td>\n",
       "      <td>19.5</td>\n",
       "      <td>31.032821</td>\n",
       "      <td>30.425270</td>\n",
       "      <td>1203464</td>\n",
       "      <td>55.925959</td>\n",
       "      <td>83932</td>\n",
       "      <td>4386</td>\n",
       "      <td>5.225659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fayoum</td>\n",
       "      <td>FYM</td>\n",
       "      <td>الفيوم</td>\n",
       "      <td>Al-Fayyūm</td>\n",
       "      <td>6068</td>\n",
       "      <td>3596954</td>\n",
       "      <td>592.774225</td>\n",
       "      <td>22.5</td>\n",
       "      <td>29.309949</td>\n",
       "      <td>30.841804</td>\n",
       "      <td>809314</td>\n",
       "      <td>43.948884</td>\n",
       "      <td>7479</td>\n",
       "      <td>392</td>\n",
       "      <td>5.241342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gharbia</td>\n",
       "      <td>GHB</td>\n",
       "      <td>الغربية</td>\n",
       "      <td>Ṭanṭā</td>\n",
       "      <td>1942</td>\n",
       "      <td>4999633</td>\n",
       "      <td>2574.476313</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.788471</td>\n",
       "      <td>31.001921</td>\n",
       "      <td>1499889</td>\n",
       "      <td>24.862779</td>\n",
       "      <td>34295</td>\n",
       "      <td>1843</td>\n",
       "      <td>5.373961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Alexandria</td>\n",
       "      <td>ISK</td>\n",
       "      <td>الإسكندرية</td>\n",
       "      <td>Al-Iskandariyah</td>\n",
       "      <td>2300</td>\n",
       "      <td>5163750</td>\n",
       "      <td>2245.108696</td>\n",
       "      <td>98.8</td>\n",
       "      <td>31.215645</td>\n",
       "      <td>29.955266</td>\n",
       "      <td>5101785</td>\n",
       "      <td>27.057582</td>\n",
       "      <td>13007</td>\n",
       "      <td>1196</td>\n",
       "      <td>9.195049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ismailia</td>\n",
       "      <td>ISM</td>\n",
       "      <td>الإسماعيلية</td>\n",
       "      <td>Al-Ismā'īliyah</td>\n",
       "      <td>5067</td>\n",
       "      <td>1303993</td>\n",
       "      <td>257.350109</td>\n",
       "      <td>45.4</td>\n",
       "      <td>30.604272</td>\n",
       "      <td>32.272252</td>\n",
       "      <td>592012</td>\n",
       "      <td>40.160630</td>\n",
       "      <td>13955</td>\n",
       "      <td>837</td>\n",
       "      <td>5.997850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Giza</td>\n",
       "      <td>JIZ</td>\n",
       "      <td>الجيزة</td>\n",
       "      <td>Al-Jīzah</td>\n",
       "      <td>13184</td>\n",
       "      <td>8632021</td>\n",
       "      <td>654.734603</td>\n",
       "      <td>58.6</td>\n",
       "      <td>30.008079</td>\n",
       "      <td>31.210931</td>\n",
       "      <td>5058364</td>\n",
       "      <td>64.781151</td>\n",
       "      <td>90735</td>\n",
       "      <td>6100</td>\n",
       "      <td>6.722874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Monufia</td>\n",
       "      <td>MNF</td>\n",
       "      <td>المنوفية</td>\n",
       "      <td>Shibīn al-Kawm</td>\n",
       "      <td>2499</td>\n",
       "      <td>4301601</td>\n",
       "      <td>1721.328932</td>\n",
       "      <td>20.6</td>\n",
       "      <td>30.597200</td>\n",
       "      <td>30.987600</td>\n",
       "      <td>886129</td>\n",
       "      <td>28.203837</td>\n",
       "      <td>20055</td>\n",
       "      <td>1372</td>\n",
       "      <td>6.841187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Al-Minya</td>\n",
       "      <td>MNY</td>\n",
       "      <td>المنيا</td>\n",
       "      <td>Al-Minyā</td>\n",
       "      <td>32279</td>\n",
       "      <td>5497095</td>\n",
       "      <td>170.299421</td>\n",
       "      <td>18.9</td>\n",
       "      <td>28.109884</td>\n",
       "      <td>30.750299</td>\n",
       "      <td>1038950</td>\n",
       "      <td>101.364317</td>\n",
       "      <td>16162</td>\n",
       "      <td>1182</td>\n",
       "      <td>7.313451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Cairo</td>\n",
       "      <td>QAH</td>\n",
       "      <td>القاهرة</td>\n",
       "      <td>Al-Qāhirah</td>\n",
       "      <td>3085</td>\n",
       "      <td>9539673</td>\n",
       "      <td>3092.276499</td>\n",
       "      <td>100.0</td>\n",
       "      <td>30.077080</td>\n",
       "      <td>31.285909</td>\n",
       "      <td>9539673</td>\n",
       "      <td>31.336656</td>\n",
       "      <td>67017</td>\n",
       "      <td>10766</td>\n",
       "      <td>16.064581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Qalyubia</td>\n",
       "      <td>QLY</td>\n",
       "      <td>القليوبية</td>\n",
       "      <td>Banhā</td>\n",
       "      <td>1124</td>\n",
       "      <td>5627420</td>\n",
       "      <td>5006.601423</td>\n",
       "      <td>44.7</td>\n",
       "      <td>30.459065</td>\n",
       "      <td>31.178577</td>\n",
       "      <td>2515456</td>\n",
       "      <td>18.915082</td>\n",
       "      <td>15334</td>\n",
       "      <td>809</td>\n",
       "      <td>5.275858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Luxor</td>\n",
       "      <td>UQS</td>\n",
       "      <td>الأقصر</td>\n",
       "      <td>Al-Uqṣur</td>\n",
       "      <td>2960</td>\n",
       "      <td>1250209</td>\n",
       "      <td>422.367905</td>\n",
       "      <td>37.8</td>\n",
       "      <td>25.695858</td>\n",
       "      <td>32.643592</td>\n",
       "      <td>472579</td>\n",
       "      <td>30.695232</td>\n",
       "      <td>13624</td>\n",
       "      <td>2467</td>\n",
       "      <td>18.107751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>New Valley</td>\n",
       "      <td>WJD</td>\n",
       "      <td>الوادي الجديد</td>\n",
       "      <td>Al-Khārijah</td>\n",
       "      <td>440098</td>\n",
       "      <td>241247</td>\n",
       "      <td>0.548167</td>\n",
       "      <td>48.0</td>\n",
       "      <td>25.451405</td>\n",
       "      <td>30.546346</td>\n",
       "      <td>115798</td>\n",
       "      <td>374.282706</td>\n",
       "      <td>31436</td>\n",
       "      <td>1989</td>\n",
       "      <td>6.327141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sharqia</td>\n",
       "      <td>SQY</td>\n",
       "      <td>الشرقية</td>\n",
       "      <td>Az-Zaqāzīq</td>\n",
       "      <td>4911</td>\n",
       "      <td>7163824</td>\n",
       "      <td>1458.730198</td>\n",
       "      <td>23.1</td>\n",
       "      <td>30.587676</td>\n",
       "      <td>31.501997</td>\n",
       "      <td>1654843</td>\n",
       "      <td>39.537575</td>\n",
       "      <td>36713</td>\n",
       "      <td>1856</td>\n",
       "      <td>5.055430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Suez</td>\n",
       "      <td>SWS</td>\n",
       "      <td>السويس</td>\n",
       "      <td>As-Suways</td>\n",
       "      <td>9002</td>\n",
       "      <td>728180</td>\n",
       "      <td>80.890913</td>\n",
       "      <td>100.0</td>\n",
       "      <td>29.973714</td>\n",
       "      <td>32.526267</td>\n",
       "      <td>728180</td>\n",
       "      <td>53.529670</td>\n",
       "      <td>21089</td>\n",
       "      <td>1008</td>\n",
       "      <td>4.779743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Aswan</td>\n",
       "      <td>ASW</td>\n",
       "      <td>أسوان</td>\n",
       "      <td>Aswān</td>\n",
       "      <td>62726</td>\n",
       "      <td>1473975</td>\n",
       "      <td>23.498629</td>\n",
       "      <td>42.3</td>\n",
       "      <td>24.093433</td>\n",
       "      <td>32.907038</td>\n",
       "      <td>623491</td>\n",
       "      <td>141.302179</td>\n",
       "      <td>13171</td>\n",
       "      <td>1117</td>\n",
       "      <td>8.480753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Asyut</td>\n",
       "      <td>ASY</td>\n",
       "      <td>أسيوط</td>\n",
       "      <td>Asyūṭ</td>\n",
       "      <td>13720</td>\n",
       "      <td>4383289</td>\n",
       "      <td>319.481706</td>\n",
       "      <td>26.5</td>\n",
       "      <td>27.180956</td>\n",
       "      <td>31.183683</td>\n",
       "      <td>1161571</td>\n",
       "      <td>66.084882</td>\n",
       "      <td>10190</td>\n",
       "      <td>1061</td>\n",
       "      <td>10.412169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Beni Suef</td>\n",
       "      <td>BSW</td>\n",
       "      <td>بنى سويف</td>\n",
       "      <td>Banī Suwayf</td>\n",
       "      <td>10954</td>\n",
       "      <td>3154100</td>\n",
       "      <td>287.940478</td>\n",
       "      <td>23.2</td>\n",
       "      <td>29.074409</td>\n",
       "      <td>31.097848</td>\n",
       "      <td>731751</td>\n",
       "      <td>59.048848</td>\n",
       "      <td>11007</td>\n",
       "      <td>668</td>\n",
       "      <td>6.068865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Port Said</td>\n",
       "      <td>BSD</td>\n",
       "      <td>بور سعيد</td>\n",
       "      <td>Būr Sa'īd</td>\n",
       "      <td>1345</td>\n",
       "      <td>749371</td>\n",
       "      <td>557.153160</td>\n",
       "      <td>100.0</td>\n",
       "      <td>31.256541</td>\n",
       "      <td>32.284115</td>\n",
       "      <td>749371</td>\n",
       "      <td>20.691225</td>\n",
       "      <td>11925</td>\n",
       "      <td>969</td>\n",
       "      <td>8.125786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Damietta</td>\n",
       "      <td>DMY</td>\n",
       "      <td>دمياط</td>\n",
       "      <td>Dumyāṭ</td>\n",
       "      <td>910</td>\n",
       "      <td>1496765</td>\n",
       "      <td>1644.796703</td>\n",
       "      <td>38.7</td>\n",
       "      <td>31.416477</td>\n",
       "      <td>31.813316</td>\n",
       "      <td>579248</td>\n",
       "      <td>17.019459</td>\n",
       "      <td>55069</td>\n",
       "      <td>4205</td>\n",
       "      <td>7.635875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Kafr el-Sheikh</td>\n",
       "      <td>KSH</td>\n",
       "      <td>كفر الشيخ</td>\n",
       "      <td>Kafr ash-Shaykh</td>\n",
       "      <td>3467</td>\n",
       "      <td>3362185</td>\n",
       "      <td>969.767811</td>\n",
       "      <td>23.1</td>\n",
       "      <td>31.114304</td>\n",
       "      <td>30.940116</td>\n",
       "      <td>776664</td>\n",
       "      <td>33.220180</td>\n",
       "      <td>54682</td>\n",
       "      <td>2628</td>\n",
       "      <td>4.805969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Matrouh</td>\n",
       "      <td>MTR</td>\n",
       "      <td>مطروخ</td>\n",
       "      <td>Marsā Maṭrūḥ</td>\n",
       "      <td>166563</td>\n",
       "      <td>425624</td>\n",
       "      <td>2.555333</td>\n",
       "      <td>70.6</td>\n",
       "      <td>31.352539</td>\n",
       "      <td>27.245275</td>\n",
       "      <td>300490</td>\n",
       "      <td>230.257789</td>\n",
       "      <td>58303</td>\n",
       "      <td>3195</td>\n",
       "      <td>5.479992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Qena</td>\n",
       "      <td>QNA</td>\n",
       "      <td>قنا</td>\n",
       "      <td>Qinā</td>\n",
       "      <td>8980</td>\n",
       "      <td>3164281</td>\n",
       "      <td>352.369822</td>\n",
       "      <td>19.7</td>\n",
       "      <td>26.164179</td>\n",
       "      <td>32.726710</td>\n",
       "      <td>623363</td>\n",
       "      <td>53.464220</td>\n",
       "      <td>5764</td>\n",
       "      <td>544</td>\n",
       "      <td>9.437890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Sohag</td>\n",
       "      <td>SWJ</td>\n",
       "      <td>سوهاج</td>\n",
       "      <td>Sawhāj</td>\n",
       "      <td>11218</td>\n",
       "      <td>4967409</td>\n",
       "      <td>442.807007</td>\n",
       "      <td>21.4</td>\n",
       "      <td>26.556952</td>\n",
       "      <td>31.694785</td>\n",
       "      <td>1063025</td>\n",
       "      <td>59.756174</td>\n",
       "      <td>8022</td>\n",
       "      <td>546</td>\n",
       "      <td>6.806283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>South Sinai</td>\n",
       "      <td>SJN</td>\n",
       "      <td>جنوب سيناء</td>\n",
       "      <td>Aṭ-Ṭūr</td>\n",
       "      <td>31272</td>\n",
       "      <td>102018</td>\n",
       "      <td>3.262279</td>\n",
       "      <td>51.1</td>\n",
       "      <td>28.236381</td>\n",
       "      <td>33.625404</td>\n",
       "      <td>52131</td>\n",
       "      <td>99.770671</td>\n",
       "      <td>2315</td>\n",
       "      <td>218</td>\n",
       "      <td>9.416847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>North Sinai</td>\n",
       "      <td>SSH</td>\n",
       "      <td>شمال سيناء</td>\n",
       "      <td>Al-'Arīsh</td>\n",
       "      <td>27564</td>\n",
       "      <td>450328</td>\n",
       "      <td>16.337542</td>\n",
       "      <td>60.2</td>\n",
       "      <td>31.128660</td>\n",
       "      <td>33.797117</td>\n",
       "      <td>271097</td>\n",
       "      <td>93.669065</td>\n",
       "      <td>11649</td>\n",
       "      <td>643</td>\n",
       "      <td>5.519787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name Abbr.         Native          Capital    Area  Population  \\\n",
       "0         Dakahlia   DQH       الدقهلية      Al-Manṣūrah    3538     6492381   \n",
       "1          Red Sea   BAH   البحر الأحمر     Al-Ghurdaqah  120000      359888   \n",
       "2          Beheira   BHR        البحيرة         Damanhūr    9826     6171613   \n",
       "3           Fayoum   FYM         الفيوم        Al-Fayyūm    6068     3596954   \n",
       "4          Gharbia   GHB        الغربية            Ṭanṭā    1942     4999633   \n",
       "5       Alexandria   ISK     الإسكندرية  Al-Iskandariyah    2300     5163750   \n",
       "6         Ismailia   ISM    الإسماعيلية   Al-Ismā'īliyah    5067     1303993   \n",
       "7             Giza   JIZ         الجيزة         Al-Jīzah   13184     8632021   \n",
       "8          Monufia   MNF       المنوفية   Shibīn al-Kawm    2499     4301601   \n",
       "9         Al-Minya   MNY         المنيا         Al-Minyā   32279     5497095   \n",
       "10           Cairo   QAH        القاهرة       Al-Qāhirah    3085     9539673   \n",
       "11        Qalyubia   QLY      القليوبية            Banhā    1124     5627420   \n",
       "12           Luxor   UQS         الأقصر         Al-Uqṣur    2960     1250209   \n",
       "13      New Valley   WJD  الوادي الجديد      Al-Khārijah  440098      241247   \n",
       "14         Sharqia   SQY        الشرقية       Az-Zaqāzīq    4911     7163824   \n",
       "15            Suez   SWS         السويس        As-Suways    9002      728180   \n",
       "16           Aswan   ASW          أسوان            Aswān   62726     1473975   \n",
       "17           Asyut   ASY          أسيوط            Asyūṭ   13720     4383289   \n",
       "18       Beni Suef   BSW       بنى سويف      Banī Suwayf   10954     3154100   \n",
       "19       Port Said   BSD       بور سعيد        Būr Sa'īd    1345      749371   \n",
       "20        Damietta   DMY          دمياط           Dumyāṭ     910     1496765   \n",
       "21  Kafr el-Sheikh   KSH      كفر الشيخ  Kafr ash-Shaykh    3467     3362185   \n",
       "22         Matrouh   MTR          مطروخ     Marsā Maṭrūḥ  166563      425624   \n",
       "23            Qena   QNA            قنا             Qinā    8980     3164281   \n",
       "24           Sohag   SWJ          سوهاج           Sawhāj   11218     4967409   \n",
       "25     South Sinai   SJN     جنوب سيناء           Aṭ-Ṭūr   31272      102018   \n",
       "26     North Sinai   SSH     شمال سيناء        Al-'Arīsh   27564      450328   \n",
       "\n",
       "        Density  % Urban        lat        lng  Urban Population      Radius  \\\n",
       "0   1835.042679     28.2  31.036373  31.380691           1830851   33.558611   \n",
       "1      2.999067     95.1  26.729177  33.936511            342253  195.441005   \n",
       "2    628.090067     19.5  31.032821  30.425270           1203464   55.925959   \n",
       "3    592.774225     22.5  29.309949  30.841804            809314   43.948884   \n",
       "4   2574.476313     30.0  30.788471  31.001921           1499889   24.862779   \n",
       "5   2245.108696     98.8  31.215645  29.955266           5101785   27.057582   \n",
       "6    257.350109     45.4  30.604272  32.272252            592012   40.160630   \n",
       "7    654.734603     58.6  30.008079  31.210931           5058364   64.781151   \n",
       "8   1721.328932     20.6  30.597200  30.987600            886129   28.203837   \n",
       "9    170.299421     18.9  28.109884  30.750299           1038950  101.364317   \n",
       "10  3092.276499    100.0  30.077080  31.285909           9539673   31.336656   \n",
       "11  5006.601423     44.7  30.459065  31.178577           2515456   18.915082   \n",
       "12   422.367905     37.8  25.695858  32.643592            472579   30.695232   \n",
       "13     0.548167     48.0  25.451405  30.546346            115798  374.282706   \n",
       "14  1458.730198     23.1  30.587676  31.501997           1654843   39.537575   \n",
       "15    80.890913    100.0  29.973714  32.526267            728180   53.529670   \n",
       "16    23.498629     42.3  24.093433  32.907038            623491  141.302179   \n",
       "17   319.481706     26.5  27.180956  31.183683           1161571   66.084882   \n",
       "18   287.940478     23.2  29.074409  31.097848            731751   59.048848   \n",
       "19   557.153160    100.0  31.256541  32.284115            749371   20.691225   \n",
       "20  1644.796703     38.7  31.416477  31.813316            579248   17.019459   \n",
       "21   969.767811     23.1  31.114304  30.940116            776664   33.220180   \n",
       "22     2.555333     70.6  31.352539  27.245275            300490  230.257789   \n",
       "23   352.369822     19.7  26.164179  32.726710            623363   53.464220   \n",
       "24   442.807007     21.4  26.556952  31.694785           1063025   59.756174   \n",
       "25     3.262279     51.1  28.236381  33.625404             52131   99.770671   \n",
       "26    16.337542     60.2  31.128660  33.797117            271097   93.669065   \n",
       "\n",
       "    Tweet Count  Corona Tweets   % Corona  \n",
       "0         87842           6086   6.928349  \n",
       "1         19908           1553   7.800884  \n",
       "2         83932           4386   5.225659  \n",
       "3          7479            392   5.241342  \n",
       "4         34295           1843   5.373961  \n",
       "5         13007           1196   9.195049  \n",
       "6         13955            837   5.997850  \n",
       "7         90735           6100   6.722874  \n",
       "8         20055           1372   6.841187  \n",
       "9         16162           1182   7.313451  \n",
       "10        67017          10766  16.064581  \n",
       "11        15334            809   5.275858  \n",
       "12        13624           2467  18.107751  \n",
       "13        31436           1989   6.327141  \n",
       "14        36713           1856   5.055430  \n",
       "15        21089           1008   4.779743  \n",
       "16        13171           1117   8.480753  \n",
       "17        10190           1061  10.412169  \n",
       "18        11007            668   6.068865  \n",
       "19        11925            969   8.125786  \n",
       "20        55069           4205   7.635875  \n",
       "21        54682           2628   4.805969  \n",
       "22        58303           3195   5.479992  \n",
       "23         5764            544   9.437890  \n",
       "24         8022            546   6.806283  \n",
       "25         2315            218   9.416847  \n",
       "26        11649            643   5.519787  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "governorates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_gov_count = corona_gov['tweet'].to_frame().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_gov_count.rename({'governorate':'Name'},axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "governorates = pd.merge(governorates,corona_gov_count,on='Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "governorates.rename({'tweet':'Corona Tweets'}, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "governorates['% Corona'] = ((governorates['Corona Tweets']/governorates['Tweet Count'])*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>Name</th>\n",
       "      <th>Abbr.</th>\n",
       "      <th>Native</th>\n",
       "      <th>Capital</th>\n",
       "      <th>Area</th>\n",
       "      <th>Population</th>\n",
       "      <th>Density</th>\n",
       "      <th>% Urban</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>Urban Population</th>\n",
       "      <th>Radius</th>\n",
       "      <th>Tweet Count</th>\n",
       "      <th>Corona Tweets</th>\n",
       "      <th>% Corona</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Luxor</td>\n",
       "      <td>UQS</td>\n",
       "      <td>الأقصر</td>\n",
       "      <td>Al-Uqṣur</td>\n",
       "      <td>2960</td>\n",
       "      <td>1250209</td>\n",
       "      <td>422.367905</td>\n",
       "      <td>37.8</td>\n",
       "      <td>25.695858</td>\n",
       "      <td>32.643592</td>\n",
       "      <td>472579</td>\n",
       "      <td>30.695232</td>\n",
       "      <td>13624</td>\n",
       "      <td>2467</td>\n",
       "      <td>18.107751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Cairo</td>\n",
       "      <td>QAH</td>\n",
       "      <td>القاهرة</td>\n",
       "      <td>Al-Qāhirah</td>\n",
       "      <td>3085</td>\n",
       "      <td>9539673</td>\n",
       "      <td>3092.276499</td>\n",
       "      <td>100.0</td>\n",
       "      <td>30.077080</td>\n",
       "      <td>31.285909</td>\n",
       "      <td>9539673</td>\n",
       "      <td>31.336656</td>\n",
       "      <td>67017</td>\n",
       "      <td>10766</td>\n",
       "      <td>16.064581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Asyut</td>\n",
       "      <td>ASY</td>\n",
       "      <td>أسيوط</td>\n",
       "      <td>Asyūṭ</td>\n",
       "      <td>13720</td>\n",
       "      <td>4383289</td>\n",
       "      <td>319.481706</td>\n",
       "      <td>26.5</td>\n",
       "      <td>27.180956</td>\n",
       "      <td>31.183683</td>\n",
       "      <td>1161571</td>\n",
       "      <td>66.084882</td>\n",
       "      <td>10190</td>\n",
       "      <td>1061</td>\n",
       "      <td>10.412169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Qena</td>\n",
       "      <td>QNA</td>\n",
       "      <td>قنا</td>\n",
       "      <td>Qinā</td>\n",
       "      <td>8980</td>\n",
       "      <td>3164281</td>\n",
       "      <td>352.369822</td>\n",
       "      <td>19.7</td>\n",
       "      <td>26.164179</td>\n",
       "      <td>32.726710</td>\n",
       "      <td>623363</td>\n",
       "      <td>53.464220</td>\n",
       "      <td>5764</td>\n",
       "      <td>544</td>\n",
       "      <td>9.437890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>South Sinai</td>\n",
       "      <td>SJN</td>\n",
       "      <td>جنوب سيناء</td>\n",
       "      <td>Aṭ-Ṭūr</td>\n",
       "      <td>31272</td>\n",
       "      <td>102018</td>\n",
       "      <td>3.262279</td>\n",
       "      <td>51.1</td>\n",
       "      <td>28.236381</td>\n",
       "      <td>33.625404</td>\n",
       "      <td>52131</td>\n",
       "      <td>99.770671</td>\n",
       "      <td>2315</td>\n",
       "      <td>218</td>\n",
       "      <td>9.416847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Alexandria</td>\n",
       "      <td>ISK</td>\n",
       "      <td>الإسكندرية</td>\n",
       "      <td>Al-Iskandariyah</td>\n",
       "      <td>2300</td>\n",
       "      <td>5163750</td>\n",
       "      <td>2245.108696</td>\n",
       "      <td>98.8</td>\n",
       "      <td>31.215645</td>\n",
       "      <td>29.955266</td>\n",
       "      <td>5101785</td>\n",
       "      <td>27.057582</td>\n",
       "      <td>13007</td>\n",
       "      <td>1196</td>\n",
       "      <td>9.195049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Aswan</td>\n",
       "      <td>ASW</td>\n",
       "      <td>أسوان</td>\n",
       "      <td>Aswān</td>\n",
       "      <td>62726</td>\n",
       "      <td>1473975</td>\n",
       "      <td>23.498629</td>\n",
       "      <td>42.3</td>\n",
       "      <td>24.093433</td>\n",
       "      <td>32.907038</td>\n",
       "      <td>623491</td>\n",
       "      <td>141.302179</td>\n",
       "      <td>13171</td>\n",
       "      <td>1117</td>\n",
       "      <td>8.480753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Port Said</td>\n",
       "      <td>BSD</td>\n",
       "      <td>بور سعيد</td>\n",
       "      <td>Būr Sa'īd</td>\n",
       "      <td>1345</td>\n",
       "      <td>749371</td>\n",
       "      <td>557.153160</td>\n",
       "      <td>100.0</td>\n",
       "      <td>31.256541</td>\n",
       "      <td>32.284115</td>\n",
       "      <td>749371</td>\n",
       "      <td>20.691225</td>\n",
       "      <td>11925</td>\n",
       "      <td>969</td>\n",
       "      <td>8.125786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Red Sea</td>\n",
       "      <td>BAH</td>\n",
       "      <td>البحر الأحمر</td>\n",
       "      <td>Al-Ghurdaqah</td>\n",
       "      <td>120000</td>\n",
       "      <td>359888</td>\n",
       "      <td>2.999067</td>\n",
       "      <td>95.1</td>\n",
       "      <td>26.729177</td>\n",
       "      <td>33.936511</td>\n",
       "      <td>342253</td>\n",
       "      <td>195.441005</td>\n",
       "      <td>19908</td>\n",
       "      <td>1553</td>\n",
       "      <td>7.800884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Damietta</td>\n",
       "      <td>DMY</td>\n",
       "      <td>دمياط</td>\n",
       "      <td>Dumyāṭ</td>\n",
       "      <td>910</td>\n",
       "      <td>1496765</td>\n",
       "      <td>1644.796703</td>\n",
       "      <td>38.7</td>\n",
       "      <td>31.416477</td>\n",
       "      <td>31.813316</td>\n",
       "      <td>579248</td>\n",
       "      <td>17.019459</td>\n",
       "      <td>55069</td>\n",
       "      <td>4205</td>\n",
       "      <td>7.635875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Al-Minya</td>\n",
       "      <td>MNY</td>\n",
       "      <td>المنيا</td>\n",
       "      <td>Al-Minyā</td>\n",
       "      <td>32279</td>\n",
       "      <td>5497095</td>\n",
       "      <td>170.299421</td>\n",
       "      <td>18.9</td>\n",
       "      <td>28.109884</td>\n",
       "      <td>30.750299</td>\n",
       "      <td>1038950</td>\n",
       "      <td>101.364317</td>\n",
       "      <td>16162</td>\n",
       "      <td>1182</td>\n",
       "      <td>7.313451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dakahlia</td>\n",
       "      <td>DQH</td>\n",
       "      <td>الدقهلية</td>\n",
       "      <td>Al-Manṣūrah</td>\n",
       "      <td>3538</td>\n",
       "      <td>6492381</td>\n",
       "      <td>1835.042679</td>\n",
       "      <td>28.2</td>\n",
       "      <td>31.036373</td>\n",
       "      <td>31.380691</td>\n",
       "      <td>1830851</td>\n",
       "      <td>33.558611</td>\n",
       "      <td>87842</td>\n",
       "      <td>6086</td>\n",
       "      <td>6.928349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Monufia</td>\n",
       "      <td>MNF</td>\n",
       "      <td>المنوفية</td>\n",
       "      <td>Shibīn al-Kawm</td>\n",
       "      <td>2499</td>\n",
       "      <td>4301601</td>\n",
       "      <td>1721.328932</td>\n",
       "      <td>20.6</td>\n",
       "      <td>30.597200</td>\n",
       "      <td>30.987600</td>\n",
       "      <td>886129</td>\n",
       "      <td>28.203837</td>\n",
       "      <td>20055</td>\n",
       "      <td>1372</td>\n",
       "      <td>6.841187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Sohag</td>\n",
       "      <td>SWJ</td>\n",
       "      <td>سوهاج</td>\n",
       "      <td>Sawhāj</td>\n",
       "      <td>11218</td>\n",
       "      <td>4967409</td>\n",
       "      <td>442.807007</td>\n",
       "      <td>21.4</td>\n",
       "      <td>26.556952</td>\n",
       "      <td>31.694785</td>\n",
       "      <td>1063025</td>\n",
       "      <td>59.756174</td>\n",
       "      <td>8022</td>\n",
       "      <td>546</td>\n",
       "      <td>6.806283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Giza</td>\n",
       "      <td>JIZ</td>\n",
       "      <td>الجيزة</td>\n",
       "      <td>Al-Jīzah</td>\n",
       "      <td>13184</td>\n",
       "      <td>8632021</td>\n",
       "      <td>654.734603</td>\n",
       "      <td>58.6</td>\n",
       "      <td>30.008079</td>\n",
       "      <td>31.210931</td>\n",
       "      <td>5058364</td>\n",
       "      <td>64.781151</td>\n",
       "      <td>90735</td>\n",
       "      <td>6100</td>\n",
       "      <td>6.722874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>New Valley</td>\n",
       "      <td>WJD</td>\n",
       "      <td>الوادي الجديد</td>\n",
       "      <td>Al-Khārijah</td>\n",
       "      <td>440098</td>\n",
       "      <td>241247</td>\n",
       "      <td>0.548167</td>\n",
       "      <td>48.0</td>\n",
       "      <td>25.451405</td>\n",
       "      <td>30.546346</td>\n",
       "      <td>115798</td>\n",
       "      <td>374.282706</td>\n",
       "      <td>31436</td>\n",
       "      <td>1989</td>\n",
       "      <td>6.327141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Beni Suef</td>\n",
       "      <td>BSW</td>\n",
       "      <td>بنى سويف</td>\n",
       "      <td>Banī Suwayf</td>\n",
       "      <td>10954</td>\n",
       "      <td>3154100</td>\n",
       "      <td>287.940478</td>\n",
       "      <td>23.2</td>\n",
       "      <td>29.074409</td>\n",
       "      <td>31.097848</td>\n",
       "      <td>731751</td>\n",
       "      <td>59.048848</td>\n",
       "      <td>11007</td>\n",
       "      <td>668</td>\n",
       "      <td>6.068865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ismailia</td>\n",
       "      <td>ISM</td>\n",
       "      <td>الإسماعيلية</td>\n",
       "      <td>Al-Ismā'īliyah</td>\n",
       "      <td>5067</td>\n",
       "      <td>1303993</td>\n",
       "      <td>257.350109</td>\n",
       "      <td>45.4</td>\n",
       "      <td>30.604272</td>\n",
       "      <td>32.272252</td>\n",
       "      <td>592012</td>\n",
       "      <td>40.160630</td>\n",
       "      <td>13955</td>\n",
       "      <td>837</td>\n",
       "      <td>5.997850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>North Sinai</td>\n",
       "      <td>SSH</td>\n",
       "      <td>شمال سيناء</td>\n",
       "      <td>Al-'Arīsh</td>\n",
       "      <td>27564</td>\n",
       "      <td>450328</td>\n",
       "      <td>16.337542</td>\n",
       "      <td>60.2</td>\n",
       "      <td>31.128660</td>\n",
       "      <td>33.797117</td>\n",
       "      <td>271097</td>\n",
       "      <td>93.669065</td>\n",
       "      <td>11649</td>\n",
       "      <td>643</td>\n",
       "      <td>5.519787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Matrouh</td>\n",
       "      <td>MTR</td>\n",
       "      <td>مطروخ</td>\n",
       "      <td>Marsā Maṭrūḥ</td>\n",
       "      <td>166563</td>\n",
       "      <td>425624</td>\n",
       "      <td>2.555333</td>\n",
       "      <td>70.6</td>\n",
       "      <td>31.352539</td>\n",
       "      <td>27.245275</td>\n",
       "      <td>300490</td>\n",
       "      <td>230.257789</td>\n",
       "      <td>58303</td>\n",
       "      <td>3195</td>\n",
       "      <td>5.479992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gharbia</td>\n",
       "      <td>GHB</td>\n",
       "      <td>الغربية</td>\n",
       "      <td>Ṭanṭā</td>\n",
       "      <td>1942</td>\n",
       "      <td>4999633</td>\n",
       "      <td>2574.476313</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.788471</td>\n",
       "      <td>31.001921</td>\n",
       "      <td>1499889</td>\n",
       "      <td>24.862779</td>\n",
       "      <td>34295</td>\n",
       "      <td>1843</td>\n",
       "      <td>5.373961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Qalyubia</td>\n",
       "      <td>QLY</td>\n",
       "      <td>القليوبية</td>\n",
       "      <td>Banhā</td>\n",
       "      <td>1124</td>\n",
       "      <td>5627420</td>\n",
       "      <td>5006.601423</td>\n",
       "      <td>44.7</td>\n",
       "      <td>30.459065</td>\n",
       "      <td>31.178577</td>\n",
       "      <td>2515456</td>\n",
       "      <td>18.915082</td>\n",
       "      <td>15334</td>\n",
       "      <td>809</td>\n",
       "      <td>5.275858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fayoum</td>\n",
       "      <td>FYM</td>\n",
       "      <td>الفيوم</td>\n",
       "      <td>Al-Fayyūm</td>\n",
       "      <td>6068</td>\n",
       "      <td>3596954</td>\n",
       "      <td>592.774225</td>\n",
       "      <td>22.5</td>\n",
       "      <td>29.309949</td>\n",
       "      <td>30.841804</td>\n",
       "      <td>809314</td>\n",
       "      <td>43.948884</td>\n",
       "      <td>7479</td>\n",
       "      <td>392</td>\n",
       "      <td>5.241342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beheira</td>\n",
       "      <td>BHR</td>\n",
       "      <td>البحيرة</td>\n",
       "      <td>Damanhūr</td>\n",
       "      <td>9826</td>\n",
       "      <td>6171613</td>\n",
       "      <td>628.090067</td>\n",
       "      <td>19.5</td>\n",
       "      <td>31.032821</td>\n",
       "      <td>30.425270</td>\n",
       "      <td>1203464</td>\n",
       "      <td>55.925959</td>\n",
       "      <td>83932</td>\n",
       "      <td>4386</td>\n",
       "      <td>5.225659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sharqia</td>\n",
       "      <td>SQY</td>\n",
       "      <td>الشرقية</td>\n",
       "      <td>Az-Zaqāzīq</td>\n",
       "      <td>4911</td>\n",
       "      <td>7163824</td>\n",
       "      <td>1458.730198</td>\n",
       "      <td>23.1</td>\n",
       "      <td>30.587676</td>\n",
       "      <td>31.501997</td>\n",
       "      <td>1654843</td>\n",
       "      <td>39.537575</td>\n",
       "      <td>36713</td>\n",
       "      <td>1856</td>\n",
       "      <td>5.055430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Kafr el-Sheikh</td>\n",
       "      <td>KSH</td>\n",
       "      <td>كفر الشيخ</td>\n",
       "      <td>Kafr ash-Shaykh</td>\n",
       "      <td>3467</td>\n",
       "      <td>3362185</td>\n",
       "      <td>969.767811</td>\n",
       "      <td>23.1</td>\n",
       "      <td>31.114304</td>\n",
       "      <td>30.940116</td>\n",
       "      <td>776664</td>\n",
       "      <td>33.220180</td>\n",
       "      <td>54682</td>\n",
       "      <td>2628</td>\n",
       "      <td>4.805969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Suez</td>\n",
       "      <td>SWS</td>\n",
       "      <td>السويس</td>\n",
       "      <td>As-Suways</td>\n",
       "      <td>9002</td>\n",
       "      <td>728180</td>\n",
       "      <td>80.890913</td>\n",
       "      <td>100.0</td>\n",
       "      <td>29.973714</td>\n",
       "      <td>32.526267</td>\n",
       "      <td>728180</td>\n",
       "      <td>53.529670</td>\n",
       "      <td>21089</td>\n",
       "      <td>1008</td>\n",
       "      <td>4.779743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name Abbr.         Native          Capital    Area  Population  \\\n",
       "12           Luxor   UQS         الأقصر         Al-Uqṣur    2960     1250209   \n",
       "10           Cairo   QAH        القاهرة       Al-Qāhirah    3085     9539673   \n",
       "17           Asyut   ASY          أسيوط            Asyūṭ   13720     4383289   \n",
       "23            Qena   QNA            قنا             Qinā    8980     3164281   \n",
       "25     South Sinai   SJN     جنوب سيناء           Aṭ-Ṭūr   31272      102018   \n",
       "5       Alexandria   ISK     الإسكندرية  Al-Iskandariyah    2300     5163750   \n",
       "16           Aswan   ASW          أسوان            Aswān   62726     1473975   \n",
       "19       Port Said   BSD       بور سعيد        Būr Sa'īd    1345      749371   \n",
       "1          Red Sea   BAH   البحر الأحمر     Al-Ghurdaqah  120000      359888   \n",
       "20        Damietta   DMY          دمياط           Dumyāṭ     910     1496765   \n",
       "9         Al-Minya   MNY         المنيا         Al-Minyā   32279     5497095   \n",
       "0         Dakahlia   DQH       الدقهلية      Al-Manṣūrah    3538     6492381   \n",
       "8          Monufia   MNF       المنوفية   Shibīn al-Kawm    2499     4301601   \n",
       "24           Sohag   SWJ          سوهاج           Sawhāj   11218     4967409   \n",
       "7             Giza   JIZ         الجيزة         Al-Jīzah   13184     8632021   \n",
       "13      New Valley   WJD  الوادي الجديد      Al-Khārijah  440098      241247   \n",
       "18       Beni Suef   BSW       بنى سويف      Banī Suwayf   10954     3154100   \n",
       "6         Ismailia   ISM    الإسماعيلية   Al-Ismā'īliyah    5067     1303993   \n",
       "26     North Sinai   SSH     شمال سيناء        Al-'Arīsh   27564      450328   \n",
       "22         Matrouh   MTR          مطروخ     Marsā Maṭrūḥ  166563      425624   \n",
       "4          Gharbia   GHB        الغربية            Ṭanṭā    1942     4999633   \n",
       "11        Qalyubia   QLY      القليوبية            Banhā    1124     5627420   \n",
       "3           Fayoum   FYM         الفيوم        Al-Fayyūm    6068     3596954   \n",
       "2          Beheira   BHR        البحيرة         Damanhūr    9826     6171613   \n",
       "14         Sharqia   SQY        الشرقية       Az-Zaqāzīq    4911     7163824   \n",
       "21  Kafr el-Sheikh   KSH      كفر الشيخ  Kafr ash-Shaykh    3467     3362185   \n",
       "15            Suez   SWS         السويس        As-Suways    9002      728180   \n",
       "\n",
       "        Density  % Urban        lat        lng  Urban Population      Radius  \\\n",
       "12   422.367905     37.8  25.695858  32.643592            472579   30.695232   \n",
       "10  3092.276499    100.0  30.077080  31.285909           9539673   31.336656   \n",
       "17   319.481706     26.5  27.180956  31.183683           1161571   66.084882   \n",
       "23   352.369822     19.7  26.164179  32.726710            623363   53.464220   \n",
       "25     3.262279     51.1  28.236381  33.625404             52131   99.770671   \n",
       "5   2245.108696     98.8  31.215645  29.955266           5101785   27.057582   \n",
       "16    23.498629     42.3  24.093433  32.907038            623491  141.302179   \n",
       "19   557.153160    100.0  31.256541  32.284115            749371   20.691225   \n",
       "1      2.999067     95.1  26.729177  33.936511            342253  195.441005   \n",
       "20  1644.796703     38.7  31.416477  31.813316            579248   17.019459   \n",
       "9    170.299421     18.9  28.109884  30.750299           1038950  101.364317   \n",
       "0   1835.042679     28.2  31.036373  31.380691           1830851   33.558611   \n",
       "8   1721.328932     20.6  30.597200  30.987600            886129   28.203837   \n",
       "24   442.807007     21.4  26.556952  31.694785           1063025   59.756174   \n",
       "7    654.734603     58.6  30.008079  31.210931           5058364   64.781151   \n",
       "13     0.548167     48.0  25.451405  30.546346            115798  374.282706   \n",
       "18   287.940478     23.2  29.074409  31.097848            731751   59.048848   \n",
       "6    257.350109     45.4  30.604272  32.272252            592012   40.160630   \n",
       "26    16.337542     60.2  31.128660  33.797117            271097   93.669065   \n",
       "22     2.555333     70.6  31.352539  27.245275            300490  230.257789   \n",
       "4   2574.476313     30.0  30.788471  31.001921           1499889   24.862779   \n",
       "11  5006.601423     44.7  30.459065  31.178577           2515456   18.915082   \n",
       "3    592.774225     22.5  29.309949  30.841804            809314   43.948884   \n",
       "2    628.090067     19.5  31.032821  30.425270           1203464   55.925959   \n",
       "14  1458.730198     23.1  30.587676  31.501997           1654843   39.537575   \n",
       "21   969.767811     23.1  31.114304  30.940116            776664   33.220180   \n",
       "15    80.890913    100.0  29.973714  32.526267            728180   53.529670   \n",
       "\n",
       "    Tweet Count  Corona Tweets   % Corona  \n",
       "12        13624           2467  18.107751  \n",
       "10        67017          10766  16.064581  \n",
       "17        10190           1061  10.412169  \n",
       "23         5764            544   9.437890  \n",
       "25         2315            218   9.416847  \n",
       "5         13007           1196   9.195049  \n",
       "16        13171           1117   8.480753  \n",
       "19        11925            969   8.125786  \n",
       "1         19908           1553   7.800884  \n",
       "20        55069           4205   7.635875  \n",
       "9         16162           1182   7.313451  \n",
       "0         87842           6086   6.928349  \n",
       "8         20055           1372   6.841187  \n",
       "24         8022            546   6.806283  \n",
       "7         90735           6100   6.722874  \n",
       "13        31436           1989   6.327141  \n",
       "18        11007            668   6.068865  \n",
       "6         13955            837   5.997850  \n",
       "26        11649            643   5.519787  \n",
       "22        58303           3195   5.479992  \n",
       "4         34295           1843   5.373961  \n",
       "11        15334            809   5.275858  \n",
       "3          7479            392   5.241342  \n",
       "2         83932           4386   5.225659  \n",
       "14        36713           1856   5.055430  \n",
       "21        54682           2628   4.805969  \n",
       "15        21089           1008   4.779743  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "governorates.sort_values('% Corona',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_search_tweets = corona_tweets[corona_tweets['method'] == 'Keyword Search']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_search_tweets = corona_search_tweets.groupby('governorate').count()['tweet'].to_frame().reset_index().sort_values('tweet',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 5 governorates searched with COVID-19 mentions')"
      ]
     },
     "execution_count": 159,
     "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": [
    "sns.barplot(x='tweet',y='governorate',data=corona_search_tweets.head(5),palette='viridis')\n",
    "plt.title('Top 5 governorates searched with COVID-19 mentions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(training_data['stem_tweet'], training_data['sentiment'], test_size=0.2, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('bow', CountVectorizer(ngram_range=(1,2))),\n",
    "    ('tfidf', TfidfTransformer()),\n",
    "    ('classifier', MultinomialNB())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('bow',\n",
       "                 CountVectorizer(analyzer='word', binary=False,\n",
       "                                 decode_error='strict',\n",
       "                                 dtype=<class 'numpy.int64'>, encoding='utf-8',\n",
       "                                 input='content', lowercase=True, max_df=1.0,\n",
       "                                 max_features=None, min_df=1,\n",
       "                                 ngram_range=(1, 2), preprocessor=None,\n",
       "                                 stop_words=None, strip_accents=None,\n",
       "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "                                 tokenizer=None, vocabulary=None)),\n",
       "                ('tfidf',\n",
       "                 TfidfTransformer(norm='l2', smooth_idf=True,\n",
       "                                  sublinear_tf=False, use_idf=True)),\n",
       "                ('classifier',\n",
       "                 MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.87      0.84       341\n",
      "           1       0.87      0.82      0.84       368\n",
      "\n",
      "    accuracy                           0.84       709\n",
      "   macro avg       0.84      0.84      0.84       709\n",
      "weighted avg       0.85      0.84      0.84       709\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "corona_tweets['clean_tweet'] = corona_tweets['tweet'].apply(normalize_stem_tweet_ar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "corona_tweets['sentiment'] = pipeline.predict(corona_tweets['clean_tweet'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentiment_str(sentiment):\n",
    "    if sentiment == 0:\n",
    "        return 'Negative'\n",
    "    else:\n",
    "        return 'Positive'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "corona_tweets['sentiment_str'] = corona_tweets['sentiment'].apply(sentiment_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'COVID-19 Negative and Positive tweets count')"
      ]
     },
     "execution_count": 165,
     "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": [
    "sns.set_style('whitegrid')\n",
    "sns.set_context('paper')\n",
    "sns.countplot('sentiment_str',data=corona_tweets,palette='viridis')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('tweets')\n",
    "plt.title('COVID-19 Negative and Positive tweets count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}