ZOMATO EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "29fd5f7c",
   "metadata": {},
   "source": [
    "# CS CULT PROJECT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8534604e",
   "metadata": {},
   "source": [
    "# ZOMATO EDA BY SHIJALI KHARE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "524687c4",
   "metadata": {},
   "source": [
    "## IMPORTING LIBRARIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4175a367",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "889e5c65",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\shija\\Downloads\\zomato.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d614a4da",
   "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>url</th>\n",
       "      <th>address</th>\n",
       "      <th>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>phone</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>dish_liked</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>reviews_list</th>\n",
       "      <th>menu_item</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.zomato.com/bangalore/jalsa-banasha...</td>\n",
       "      <td>942, 21st Main Road, 2nd Stage, Banashankari, ...</td>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>775</td>\n",
       "      <td>080 42297555\\r\\n+91 9743772233</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.zomato.com/bangalore/spice-elephan...</td>\n",
       "      <td>2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...</td>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>787</td>\n",
       "      <td>080 41714161</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://www.zomato.com/SanchurroBangalore?cont...</td>\n",
       "      <td>1112, Next to KIMS Medical College, 17th Cross...</td>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>918</td>\n",
       "      <td>+91 9663487993</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.zomato.com/bangalore/addhuri-udupi...</td>\n",
       "      <td>1st Floor, Annakuteera, 3rd Stage, Banashankar...</td>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7/5</td>\n",
       "      <td>88</td>\n",
       "      <td>+91 9620009302</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Masala Dosa</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300</td>\n",
       "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.zomato.com/bangalore/grand-village...</td>\n",
       "      <td>10, 3rd Floor, Lakshmi Associates, Gandhi Baza...</td>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>166</td>\n",
       "      <td>+91 8026612447\\r\\n+91 9901210005</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Panipuri, Gol Gappe</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  \\\n",
       "0  https://www.zomato.com/bangalore/jalsa-banasha...   \n",
       "1  https://www.zomato.com/bangalore/spice-elephan...   \n",
       "2  https://www.zomato.com/SanchurroBangalore?cont...   \n",
       "3  https://www.zomato.com/bangalore/addhuri-udupi...   \n",
       "4  https://www.zomato.com/bangalore/grand-village...   \n",
       "\n",
       "                                             address                   name  \\\n",
       "0  942, 21st Main Road, 2nd Stage, Banashankari, ...                  Jalsa   \n",
       "1  2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...         Spice Elephant   \n",
       "2  1112, Next to KIMS Medical College, 17th Cross...        San Churro Cafe   \n",
       "3  1st Floor, Annakuteera, 3rd Stage, Banashankar...  Addhuri Udupi Bhojana   \n",
       "4  10, 3rd Floor, Lakshmi Associates, Gandhi Baza...          Grand Village   \n",
       "\n",
       "  online_order book_table   rate  votes                             phone  \\\n",
       "0          Yes        Yes  4.1/5    775    080 42297555\\r\\n+91 9743772233   \n",
       "1          Yes         No  4.1/5    787                      080 41714161   \n",
       "2          Yes         No  3.8/5    918                    +91 9663487993   \n",
       "3           No         No  3.7/5     88                    +91 9620009302   \n",
       "4           No         No  3.8/5    166  +91 8026612447\\r\\n+91 9901210005   \n",
       "\n",
       "       location            rest_type  \\\n",
       "0  Banashankari        Casual Dining   \n",
       "1  Banashankari        Casual Dining   \n",
       "2  Banashankari  Cafe, Casual Dining   \n",
       "3  Banashankari          Quick Bites   \n",
       "4  Basavanagudi        Casual Dining   \n",
       "\n",
       "                                          dish_liked  \\\n",
       "0  Pasta, Lunch Buffet, Masala Papad, Paneer Laja...   \n",
       "1  Momos, Lunch Buffet, Chocolate Nirvana, Thai G...   \n",
       "2  Churros, Cannelloni, Minestrone Soup, Hot Choc...   \n",
       "3                                        Masala Dosa   \n",
       "4                                Panipuri, Gol Gappe   \n",
       "\n",
       "                         cuisines approx_cost(for two people)  \\\n",
       "0  North Indian, Mughlai, Chinese                         800   \n",
       "1     Chinese, North Indian, Thai                         800   \n",
       "2          Cafe, Mexican, Italian                         800   \n",
       "3      South Indian, North Indian                         300   \n",
       "4        North Indian, Rajasthani                         600   \n",
       "\n",
       "                                        reviews_list menu_item  \\\n",
       "0  [('Rated 4.0', 'RATED\\n  A beautiful place to ...        []   \n",
       "1  [('Rated 4.0', 'RATED\\n  Had been here for din...        []   \n",
       "2  [('Rated 3.0', \"RATED\\n  Ambience is not that ...        []   \n",
       "3  [('Rated 4.0', \"RATED\\n  Great food and proper...        []   \n",
       "4  [('Rated 4.0', 'RATED\\n  Very good restaurant ...        []   \n",
       "\n",
       "  listed_in(type) listed_in(city)  \n",
       "0          Buffet    Banashankari  \n",
       "1          Buffet    Banashankari  \n",
       "2          Buffet    Banashankari  \n",
       "3          Buffet    Banashankari  \n",
       "4          Buffet    Banashankari  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5dc01ad4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51717, 17)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b25eca2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51717 entries, 0 to 51716\n",
      "Data columns (total 17 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   url                          51717 non-null  object\n",
      " 1   address                      51717 non-null  object\n",
      " 2   name                         51717 non-null  object\n",
      " 3   online_order                 51717 non-null  object\n",
      " 4   book_table                   51717 non-null  object\n",
      " 5   rate                         43942 non-null  object\n",
      " 6   votes                        51717 non-null  int64 \n",
      " 7   phone                        50509 non-null  object\n",
      " 8   location                     51696 non-null  object\n",
      " 9   rest_type                    51490 non-null  object\n",
      " 10  dish_liked                   23639 non-null  object\n",
      " 11  cuisines                     51672 non-null  object\n",
      " 12  approx_cost(for two people)  51371 non-null  object\n",
      " 13  reviews_list                 51717 non-null  object\n",
      " 14  menu_item                    51717 non-null  object\n",
      " 15  listed_in(type)              51717 non-null  object\n",
      " 16  listed_in(city)              51717 non-null  object\n",
      "dtypes: int64(1), object(16)\n",
      "memory usage: 6.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a27daefa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['url', 'address', 'name', 'online_order', 'book_table', 'rate', 'votes',\n",
       "       'phone', 'location', 'rest_type', 'dish_liked', 'cuisines',\n",
       "       'approx_cost(for two people)', 'reviews_list', 'menu_item',\n",
       "       'listed_in(type)', 'listed_in(city)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "512255a9",
   "metadata": {},
   "source": [
    "# CLEANING DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6484d5af",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['url', 'address','phone', 'menu_item','dish_liked', 'reviews_list'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a057ddc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51717, 11)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "459c3380",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51717 entries, 0 to 51716\n",
      "Data columns (total 11 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   name                         51717 non-null  object\n",
      " 1   online_order                 51717 non-null  object\n",
      " 2   book_table                   51717 non-null  object\n",
      " 3   rate                         43942 non-null  object\n",
      " 4   votes                        51717 non-null  int64 \n",
      " 5   location                     51696 non-null  object\n",
      " 6   rest_type                    51490 non-null  object\n",
      " 7   cuisines                     51672 non-null  object\n",
      " 8   approx_cost(for two people)  51371 non-null  object\n",
      " 9   listed_in(type)              51717 non-null  object\n",
      " 10  listed_in(city)              51717 non-null  object\n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 4.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9bf35e30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                              0\n",
       "online_order                      0\n",
       "book_table                        0\n",
       "rate                           7775\n",
       "votes                             0\n",
       "location                         21\n",
       "rest_type                       227\n",
       "cuisines                         45\n",
       "approx_cost(for two people)     346\n",
       "listed_in(type)                   0\n",
       "listed_in(city)                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1ea100f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "04a1b2a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51609, 11)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a23dff9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4.1/5', '3.8/5', '3.7/5', '3.6/5', '4.6/5', '4.0/5', '4.2/5',\n",
       "       '3.9/5', '3.1/5', '3.0/5', '3.2/5', '3.3/5', '2.8/5', '4.4/5',\n",
       "       '4.3/5', 'NEW', '2.9/5', '3.5/5', nan, '2.6/5', '3.8 /5', '3.4/5',\n",
       "       '4.5/5', '2.5/5', '2.7/5', '4.7/5', '2.4/5', '2.2/5', '2.3/5',\n",
       "       '3.4 /5', '-', '3.6 /5', '4.8/5', '3.9 /5', '4.2 /5', '4.0 /5',\n",
       "       '4.1 /5', '3.7 /5', '3.1 /5', '2.9 /5', '3.3 /5', '2.8 /5',\n",
       "       '3.5 /5', '2.7 /5', '2.5 /5', '3.2 /5', '2.6 /5', '4.5 /5',\n",
       "       '4.3 /5', '4.4 /5', '4.9/5', '2.1/5', '2.0/5', '1.8/5', '4.6 /5',\n",
       "       '4.9 /5', '3.0 /5', '4.8 /5', '2.3 /5', '4.7 /5', '2.4 /5',\n",
       "       '2.1 /5', '2.2 /5', '2.0 /5', '1.8 /5'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3f3645b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rates(value):\n",
    "    if(value == 'NEW' or value == '-'):\n",
    "        return np.nan\n",
    "    else:\n",
    "        value = str(value).split('/')\n",
    "        value = value[0]\n",
    "        return float(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6d819ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['rate'] = df['rate'].apply(rates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "18732021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.1\n",
       "1    4.1\n",
       "2    3.8\n",
       "3    3.7\n",
       "4    3.8\n",
       "Name: rate, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "92e93646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].fillna(df['rate'].mean(), inplace = True)\n",
    "df['rate'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b341df1f",
   "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>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1</td>\n",
       "      <td>775</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1</td>\n",
       "      <td>787</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>918</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7</td>\n",
       "      <td>88</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>166</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name online_order book_table  rate  votes      location  \\\n",
       "0                  Jalsa          Yes        Yes   4.1    775  Banashankari   \n",
       "1         Spice Elephant          Yes         No   4.1    787  Banashankari   \n",
       "2        San Churro Cafe          Yes         No   3.8    918  Banashankari   \n",
       "3  Addhuri Udupi Bhojana           No         No   3.7     88  Banashankari   \n",
       "4          Grand Village           No         No   3.8    166  Basavanagudi   \n",
       "\n",
       "             rest_type                        cuisines  \\\n",
       "0        Casual Dining  North Indian, Mughlai, Chinese   \n",
       "1        Casual Dining     Chinese, North Indian, Thai   \n",
       "2  Cafe, Casual Dining          Cafe, Mexican, Italian   \n",
       "3          Quick Bites      South Indian, North Indian   \n",
       "4        Casual Dining        North Indian, Rajasthani   \n",
       "\n",
       "  approx_cost(for two people) listed_in(type) listed_in(city)  \n",
       "0                         800          Buffet    Banashankari  \n",
       "1                         800          Buffet    Banashankari  \n",
       "2                         800          Buffet    Banashankari  \n",
       "3                         300          Buffet    Banashankari  \n",
       "4                         600          Buffet    Banashankari  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b8c5e5f1",
   "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>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>Cost2plates</th>\n",
       "      <th>Type</th>\n",
       "      <th>listed_in(city)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1</td>\n",
       "      <td>775</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1</td>\n",
       "      <td>787</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>918</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7</td>\n",
       "      <td>88</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>166</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name online_order book_table  rate  votes      location  \\\n",
       "0                  Jalsa          Yes        Yes   4.1    775  Banashankari   \n",
       "1         Spice Elephant          Yes         No   4.1    787  Banashankari   \n",
       "2        San Churro Cafe          Yes         No   3.8    918  Banashankari   \n",
       "3  Addhuri Udupi Bhojana           No         No   3.7     88  Banashankari   \n",
       "4          Grand Village           No         No   3.8    166  Basavanagudi   \n",
       "\n",
       "             rest_type                        cuisines Cost2plates    Type  \\\n",
       "0        Casual Dining  North Indian, Mughlai, Chinese         800  Buffet   \n",
       "1        Casual Dining     Chinese, North Indian, Thai         800  Buffet   \n",
       "2  Cafe, Casual Dining          Cafe, Mexican, Italian         800  Buffet   \n",
       "3          Quick Bites      South Indian, North Indian         300  Buffet   \n",
       "4        Casual Dining        North Indian, Rajasthani         600  Buffet   \n",
       "\n",
       "  listed_in(city)  \n",
       "0    Banashankari  \n",
       "1    Banashankari  \n",
       "2    Banashankari  \n",
       "3    Banashankari  \n",
       "4    Banashankari  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns = {'approx_cost(for two people)':'Cost2plates', 'listed_in(type)':'Type'}, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2642d41b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Banashankari', 'Basavanagudi', 'Mysore Road', 'Jayanagar',\n",
       "       'Kumaraswamy Layout', 'Rajarajeshwari Nagar', 'Vijay Nagar',\n",
       "       'Uttarahalli', 'JP Nagar', 'South Bangalore', 'City Market',\n",
       "       'Nagarbhavi', 'Bannerghatta Road', 'BTM', 'Kanakapura Road',\n",
       "       'Bommanahalli', 'CV Raman Nagar', 'Electronic City', 'HSR',\n",
       "       'Marathahalli', 'Wilson Garden', 'Shanti Nagar',\n",
       "       'Koramangala 5th Block', 'Koramangala 8th Block', 'Richmond Road',\n",
       "       'Koramangala 7th Block', 'Jalahalli', 'Koramangala 4th Block',\n",
       "       'Bellandur', 'Sarjapur Road', 'Whitefield', 'East Bangalore',\n",
       "       'Old Airport Road', 'Indiranagar', 'Koramangala 1st Block',\n",
       "       'Frazer Town', 'RT Nagar', 'MG Road', 'Brigade Road',\n",
       "       'Lavelle Road', 'Church Street', 'Ulsoor', 'Residency Road',\n",
       "       'Shivajinagar', 'Infantry Road', 'St. Marks Road',\n",
       "       'Cunningham Road', 'Race Course Road', 'Commercial Street',\n",
       "       'Vasanth Nagar', 'HBR Layout', 'Domlur', 'Ejipura',\n",
       "       'Jeevan Bhima Nagar', 'Old Madras Road', 'Malleshwaram',\n",
       "       'Seshadripuram', 'Kammanahalli', 'Koramangala 6th Block',\n",
       "       'Majestic', 'Langford Town', 'Central Bangalore', 'Sanjay Nagar',\n",
       "       'Brookefield', 'ITPL Main Road, Whitefield',\n",
       "       'Varthur Main Road, Whitefield', 'KR Puram',\n",
       "       'Koramangala 2nd Block', 'Koramangala 3rd Block', 'Koramangala',\n",
       "       'Hosur Road', 'Rajajinagar', 'Banaswadi', 'North Bangalore',\n",
       "       'Nagawara', 'Hennur', 'Kalyan Nagar', 'New BEL Road', 'Jakkur',\n",
       "       'Rammurthy Nagar', 'Thippasandra', 'Kaggadasapura', 'Hebbal',\n",
       "       'Kengeri', 'Sankey Road', 'Sadashiv Nagar', 'Basaveshwara Nagar',\n",
       "       'Yeshwantpur', 'West Bangalore', 'Magadi Road', 'Yelahanka',\n",
       "       'Sahakara Nagar', 'Peenya'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['location'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "54f2b433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Banashankari', 'Bannerghatta Road', 'Basavanagudi', 'Bellandur',\n",
       "       'Brigade Road', 'Brookefield', 'BTM', 'Church Street',\n",
       "       'Electronic City', 'Frazer Town', 'HSR', 'Indiranagar',\n",
       "       'Jayanagar', 'JP Nagar', 'Kalyan Nagar', 'Kammanahalli',\n",
       "       'Koramangala 4th Block', 'Koramangala 5th Block',\n",
       "       'Koramangala 6th Block', 'Koramangala 7th Block', 'Lavelle Road',\n",
       "       'Malleshwaram', 'Marathahalli', 'MG Road', 'New BEL Road',\n",
       "       'Old Airport Road', 'Rajajinagar', 'Residency Road',\n",
       "       'Sarjapur Road', 'Whitefield'], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['listed_in(city)'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cf9f7ce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['listed_in(city)'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8d33dc8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['800', '300', '600', '700', '550', '500', '450', '650', '400',\n",
       "       '900', '200', '750', '150', '850', '100', '1,200', '350', '250',\n",
       "       '950', '1,000', '1,500', '1,300', '199', '80', '1,100', '160',\n",
       "       '1,600', '230', '130', '50', '190', '1,700', '1,400', '180',\n",
       "       '1,350', '2,200', '2,000', '1,800', '1,900', '330', '2,500',\n",
       "       '2,100', '3,000', '2,800', '3,400', '40', '1,250', '3,500',\n",
       "       '4,000', '2,400', '2,600', '120', '1,450', '469', '70', '3,200',\n",
       "       '60', '560', '240', '360', '6,000', '1,050', '2,300', '4,100',\n",
       "       '5,000', '3,700', '1,650', '2,700', '4,500', '140'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cost2plates'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "572832da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 800.,  300.,  600.,  700.,  550.,  500.,  450.,  650.,  400.,\n",
       "        900.,  200.,  750.,  150.,  850.,  100., 1200.,  350.,  250.,\n",
       "        950., 1000., 1500., 1300.,  199.,   80., 1100.,  160., 1600.,\n",
       "        230.,  130.,   50.,  190., 1700., 1400.,  180., 1350., 2200.,\n",
       "       2000., 1800., 1900.,  330., 2500., 2100., 3000., 2800., 3400.,\n",
       "         40., 1250., 3500., 4000., 2400., 2600.,  120., 1450.,  469.,\n",
       "         70., 3200.,   60.,  560.,  240.,  360., 6000., 1050., 2300.,\n",
       "       4100., 5000., 3700., 1650., 2700., 4500.,  140.])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def handlecomma(value):\n",
    "    value = str(value)\n",
    "    if ',' in value:\n",
    "        value = value.replace(',', '')\n",
    "        return float(value)\n",
    "    else:\n",
    "        return float(value)\n",
    "    \n",
    "df['Cost2plates'] = df['Cost2plates'].apply(handlecomma)\n",
    "df['Cost2plates'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "313b13bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quick Bites                   19010\n",
       "Casual Dining                 10253\n",
       "Cafe                           3682\n",
       "Delivery                       2574\n",
       "Dessert Parlor                 2242\n",
       "                              ...  \n",
       "Cafe, Food Court                  2\n",
       "Food Court, Beverage Shop         2\n",
       "Bakery, Food Court                2\n",
       "Quick Bites, Kiosk                1\n",
       "Sweet Shop, Dessert Parlor        1\n",
       "Name: rest_type, Length: 93, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_types = df['rest_type'].value_counts(ascending  = False)\n",
    "rest_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fd4df07a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Beverage Shop                 863\n",
       "Bar                           686\n",
       "Food Court                    616\n",
       "Sweet Shop                    468\n",
       "Bar, Casual Dining            411\n",
       "                             ... \n",
       "Cafe, Food Court                2\n",
       "Food Court, Beverage Shop       2\n",
       "Bakery, Food Court              2\n",
       "Quick Bites, Kiosk              1\n",
       "Sweet Shop, Dessert Parlor      1\n",
       "Name: rest_type, Length: 85, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_types_lessthan1000 = rest_types[rest_types<1000]\n",
    "rest_types_lessthan1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a3347359",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quick Bites           19010\n",
       "Casual Dining         10253\n",
       "others                 9003\n",
       "Cafe                   3682\n",
       "Delivery               2574\n",
       "Dessert Parlor         2242\n",
       "Takeaway, Delivery     2008\n",
       "Bakery                 1140\n",
       "Casual Dining, Bar     1130\n",
       "Name: rest_type, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def handle_rest_type(value):\n",
    "    if(value in rest_types_lessthan1000):\n",
    "        return 'others'\n",
    "    else:\n",
    "        return value\n",
    "        \n",
    "df['rest_type'] = df['rest_type'].apply(handle_rest_type)\n",
    "df['rest_type'].value_counts()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0a4b62f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTM                      5056\n",
       "others                   4954\n",
       "HSR                      2494\n",
       "Koramangala 5th Block    2479\n",
       "JP Nagar                 2218\n",
       "Whitefield               2105\n",
       "Indiranagar              2026\n",
       "Jayanagar                1916\n",
       "Marathahalli             1805\n",
       "Bannerghatta Road        1609\n",
       "Bellandur                1268\n",
       "Electronic City          1246\n",
       "Koramangala 1st Block    1236\n",
       "Brigade Road             1210\n",
       "Koramangala 7th Block    1174\n",
       "Koramangala 6th Block    1127\n",
       "Sarjapur Road            1047\n",
       "Koramangala 4th Block    1017\n",
       "Ulsoor                   1011\n",
       "Banashankari              902\n",
       "MG Road                   893\n",
       "Kalyan Nagar              841\n",
       "Richmond Road             803\n",
       "Malleshwaram              721\n",
       "Frazer Town               714\n",
       "Basavanagudi              684\n",
       "Residency Road            671\n",
       "Brookefield               656\n",
       "New BEL Road              644\n",
       "Banaswadi                 640\n",
       "Kammanahalli              639\n",
       "Rajajinagar               591\n",
       "Church Street             566\n",
       "Lavelle Road              518\n",
       "Shanti Nagar              508\n",
       "Shivajinagar              498\n",
       "Cunningham Road           490\n",
       "Domlur                    482\n",
       "Old Airport Road          437\n",
       "Ejipura                   433\n",
       "Commercial Street         370\n",
       "St. Marks Road            343\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location = df['location'].value_counts(ascending  = False)\n",
    "\n",
    "location_lessthan300 = location[location<300]\n",
    "\n",
    "\n",
    "\n",
    "def handle_location(value):\n",
    "    if(value in location_lessthan300):\n",
    "        return 'others'\n",
    "    else:\n",
    "        return value\n",
    "        \n",
    "df['location'] = df['location'].apply(handle_location)\n",
    "df['location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "124d4089",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "others                                 26159\n",
       "North Indian                            2852\n",
       "North Indian, Chinese                   2351\n",
       "South Indian                            1820\n",
       "Biryani                                  903\n",
       "                                       ...  \n",
       "South Indian, Chinese, North Indian      105\n",
       "South Indian, Fast Food                  104\n",
       "North Indian, Mughlai, Chinese           104\n",
       "Italian, Pizza                           102\n",
       "North Indian, Chinese, Seafood           102\n",
       "Name: cuisines, Length: 70, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cuisines = df['cuisines'].value_counts(ascending  = False)\n",
    "\n",
    "\n",
    "cuisines_lessthan100 = cuisines[cuisines<100]\n",
    "\n",
    "\n",
    "\n",
    "def handle_cuisines(value):\n",
    "    if(value in cuisines_lessthan100):\n",
    "        return 'others'\n",
    "    else:\n",
    "        return value\n",
    "        \n",
    "df['cuisines'] = df['cuisines'].apply(handle_cuisines)\n",
    "df['cuisines'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5bc258f",
   "metadata": {},
   "source": [
    "# VISUALISATION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7bbc900",
   "metadata": {},
   "source": [
    "## COUNT PLOT OF LOCATIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a9127041",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\shija\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "        34, 35, 36, 37, 38, 39, 40, 41]),\n",
       " [Text(0, 0, 'Banashankari'),\n",
       "  Text(1, 0, 'Basavanagudi'),\n",
       "  Text(2, 0, 'others'),\n",
       "  Text(3, 0, 'Jayanagar'),\n",
       "  Text(4, 0, 'JP Nagar'),\n",
       "  Text(5, 0, 'Bannerghatta Road'),\n",
       "  Text(6, 0, 'BTM'),\n",
       "  Text(7, 0, 'Electronic City'),\n",
       "  Text(8, 0, 'HSR'),\n",
       "  Text(9, 0, 'Marathahalli'),\n",
       "  Text(10, 0, 'Shanti Nagar'),\n",
       "  Text(11, 0, 'Koramangala 5th Block'),\n",
       "  Text(12, 0, 'Richmond Road'),\n",
       "  Text(13, 0, 'Koramangala 7th Block'),\n",
       "  Text(14, 0, 'Koramangala 4th Block'),\n",
       "  Text(15, 0, 'Bellandur'),\n",
       "  Text(16, 0, 'Sarjapur Road'),\n",
       "  Text(17, 0, 'Whitefield'),\n",
       "  Text(18, 0, 'Old Airport Road'),\n",
       "  Text(19, 0, 'Indiranagar'),\n",
       "  Text(20, 0, 'Koramangala 1st Block'),\n",
       "  Text(21, 0, 'Frazer Town'),\n",
       "  Text(22, 0, 'MG Road'),\n",
       "  Text(23, 0, 'Brigade Road'),\n",
       "  Text(24, 0, 'Lavelle Road'),\n",
       "  Text(25, 0, 'Church Street'),\n",
       "  Text(26, 0, 'Ulsoor'),\n",
       "  Text(27, 0, 'Residency Road'),\n",
       "  Text(28, 0, 'Shivajinagar'),\n",
       "  Text(29, 0, 'St. Marks Road'),\n",
       "  Text(30, 0, 'Cunningham Road'),\n",
       "  Text(31, 0, 'Commercial Street'),\n",
       "  Text(32, 0, 'Domlur'),\n",
       "  Text(33, 0, 'Ejipura'),\n",
       "  Text(34, 0, 'Malleshwaram'),\n",
       "  Text(35, 0, 'Kammanahalli'),\n",
       "  Text(36, 0, 'Koramangala 6th Block'),\n",
       "  Text(37, 0, 'Brookefield'),\n",
       "  Text(38, 0, 'Rajajinagar'),\n",
       "  Text(39, 0, 'Banaswadi'),\n",
       "  Text(40, 0, 'Kalyan Nagar'),\n",
       "  Text(41, 0, 'New BEL Road')])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16,10))\n",
    "ax = sns.countplot(df['location'])\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbdeefb1",
   "metadata": {},
   "source": [
    "## ONLINE ORDERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a8f15702",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\shija\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='online_order', ylabel='count'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (6,6))\n",
    "sns.countplot(df['online_order'], palette = 'inferno')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a3bb497",
   "metadata": {},
   "source": [
    "## Book TABLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "569ae47f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\shija\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='book_table', ylabel='count'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (6,6))\n",
    "sns.countplot(df['book_table'], palette = 'rainbow')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b186568",
   "metadata": {},
   "source": [
    "## ONLINE ORDER VS RATE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c509e8bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='online_order', ylabel='rate'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (6,6))\n",
    "sns.boxplot(x = 'online_order', y = 'rate', data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7700bc5",
   "metadata": {},
   "source": [
    "## BOOK TABLE VS RATE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "aa3c2614",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='book_table', ylabel='rate'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (6,6))\n",
    "sns.boxplot(x = 'book_table', y = 'rate', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7c8a9299",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>online_order</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BTM</th>\n",
       "      <td>1763</td>\n",
       "      <td>3293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banashankari</th>\n",
       "      <td>397</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banaswadi</th>\n",
       "      <td>302</td>\n",
       "      <td>338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bannerghatta Road</th>\n",
       "      <td>685</td>\n",
       "      <td>924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basavanagudi</th>\n",
       "      <td>243</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bellandur</th>\n",
       "      <td>517</td>\n",
       "      <td>751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brigade Road</th>\n",
       "      <td>552</td>\n",
       "      <td>658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brookefield</th>\n",
       "      <td>239</td>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Church Street</th>\n",
       "      <td>226</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Commercial Street</th>\n",
       "      <td>228</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cunningham Road</th>\n",
       "      <td>168</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Domlur</th>\n",
       "      <td>247</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ejipura</th>\n",
       "      <td>214</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic City</th>\n",
       "      <td>676</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frazer Town</th>\n",
       "      <td>287</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSR</th>\n",
       "      <td>584</td>\n",
       "      <td>1910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiranagar</th>\n",
       "      <td>697</td>\n",
       "      <td>1329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP Nagar</th>\n",
       "      <td>911</td>\n",
       "      <td>1307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jayanagar</th>\n",
       "      <td>552</td>\n",
       "      <td>1364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kalyan Nagar</th>\n",
       "      <td>350</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kammanahalli</th>\n",
       "      <td>264</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 1st Block</th>\n",
       "      <td>384</td>\n",
       "      <td>852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 4th Block</th>\n",
       "      <td>459</td>\n",
       "      <td>558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 5th Block</th>\n",
       "      <td>866</td>\n",
       "      <td>1613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 6th Block</th>\n",
       "      <td>445</td>\n",
       "      <td>682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 7th Block</th>\n",
       "      <td>389</td>\n",
       "      <td>785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lavelle Road</th>\n",
       "      <td>315</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MG Road</th>\n",
       "      <td>520</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malleshwaram</th>\n",
       "      <td>309</td>\n",
       "      <td>412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marathahalli</th>\n",
       "      <td>701</td>\n",
       "      <td>1104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New BEL Road</th>\n",
       "      <td>255</td>\n",
       "      <td>389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Old Airport Road</th>\n",
       "      <td>221</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajajinagar</th>\n",
       "      <td>286</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residency Road</th>\n",
       "      <td>424</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richmond Road</th>\n",
       "      <td>557</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sarjapur Road</th>\n",
       "      <td>323</td>\n",
       "      <td>724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanti Nagar</th>\n",
       "      <td>289</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shivajinagar</th>\n",
       "      <td>354</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St. Marks Road</th>\n",
       "      <td>176</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ulsoor</th>\n",
       "      <td>389</td>\n",
       "      <td>622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whitefield</th>\n",
       "      <td>986</td>\n",
       "      <td>1119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>others</th>\n",
       "      <td>2064</td>\n",
       "      <td>2890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       name      \n",
       "online_order             No   Yes\n",
       "location                         \n",
       "BTM                    1763  3293\n",
       "Banashankari            397   505\n",
       "Banaswadi               302   338\n",
       "Bannerghatta Road       685   924\n",
       "Basavanagudi            243   441\n",
       "Bellandur               517   751\n",
       "Brigade Road            552   658\n",
       "Brookefield             239   417\n",
       "Church Street           226   340\n",
       "Commercial Street       228   142\n",
       "Cunningham Road         168   322\n",
       "Domlur                  247   235\n",
       "Ejipura                 214   219\n",
       "Electronic City         676   570\n",
       "Frazer Town             287   427\n",
       "HSR                     584  1910\n",
       "Indiranagar             697  1329\n",
       "JP Nagar                911  1307\n",
       "Jayanagar               552  1364\n",
       "Kalyan Nagar            350   491\n",
       "Kammanahalli            264   375\n",
       "Koramangala 1st Block   384   852\n",
       "Koramangala 4th Block   459   558\n",
       "Koramangala 5th Block   866  1613\n",
       "Koramangala 6th Block   445   682\n",
       "Koramangala 7th Block   389   785\n",
       "Lavelle Road            315   203\n",
       "MG Road                 520   373\n",
       "Malleshwaram            309   412\n",
       "Marathahalli            701  1104\n",
       "New BEL Road            255   389\n",
       "Old Airport Road        221   216\n",
       "Rajajinagar             286   305\n",
       "Residency Road          424   247\n",
       "Richmond Road           557   246\n",
       "Sarjapur Road           323   724\n",
       "Shanti Nagar            289   219\n",
       "Shivajinagar            354   144\n",
       "St. Marks Road          176   167\n",
       "Ulsoor                  389   622\n",
       "Whitefield              986  1119\n",
       "others                 2064  2890"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.groupby(['location','online_order'])['name'].count()\n",
    "df1.to_csv('location_online.csv')\n",
    "df1 = pd.read_csv('location_online.csv')\n",
    "df1 = pd.pivot_table(df1, values=None, index=['location'], columns=['online_order'], fill_value=0, aggfunc=np.sum)\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "773e85e7",
   "metadata": {},
   "source": [
    "## ONLINE ORDER FACILITY LOCATION WISE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c6e2474c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8WHfccYcef/zxmffZdtttJUmDBw/WGmusoaWXXlr9+vXTyiuvrBdeeEG33367xo4dqxEjRmjo0KG6/fbbNXny5Nm276CDDtKyyy6rjTfeuNMYK6+8siZPnqzvfe97uummm7TwwgvPvP+SSy6pl19+ufgDlVAABQAAAEBLmH/++TV16tR2x/r16zfz6759+2ratGmaOnWqDjzwQI0ZM0bLL7+8Ro4c2e5+tfv06dOn3f379OmjadOmyd2155576qSTTsrUvj59+qhPnxgP6yrGo48+qptvvllnn322rrjiCp1//vmSpKlTp2r++efP9Du7bE9pkQAAAACggEUXXVTTp0+fJaHrqPb9xRdfXO+9956uuuqqTL9ns80201VXXaUpU6ZIkt588009//zzkqQ99thDDz/8cO4Yr7/+umbMmKGvf/3rOvHEEzVu3LiZ9/nnP/+pQYMGZWprVxiZAwAAANBYN7YSKNsWW2yh++67T1/60pc6/ZlFFllE++23nwYNGqRPfepTGjFiRKbfsfrqq+vEE0/UFltsoRkzZmjeeefVmWeeqRVXXFETJkzQMssskzvG/PPPr7333lszZsyQpJkjdx9//LEmTZqk4cOHZ2prV6xW9aUVDR8+3MeMGTP7H2y0/0UPPPEAAACAqnnyySe12mqr9XQzZho3bpxOO+00XXLJJXP8d7/zzjvad999deWVV5Ye+5prrtG4ceN0wgkndPozjf4XZjbW3RtmgEyzBAAAANAyhg0bpk022aTLrQKaZeGFF25KIidJ06ZN0w9+8INSYzLNEgAAAEBL2WeffXq6CaXbaaedSo/JyBwAAADQy7Xy0qveIs//gGQOAAAA6MX69++vN954g4SuB7m73njjDfXv3z/T/WY7zdLM+ku6R1K/9PNXufuxZraSpMskfVLSWEnfcvePzKyfpIslrS3pDUk7u/tzKdbRkvaVNF3SIe5+c6bWAgAAACjVcsstpxdffFGvvfZaTzelV+vfv7+WW265TPfpzpq5/0na1N3fM7N5Jd1nZn+X9H1Jp7n7ZWZ2tiJJOyt9/q+7f8bMdpH0f5J2NrPVJe0iaQ1Jy0i6zcw+6+5zfmUjAAAAAEnSvPPOq5VWWqmnm4EcZjvN0sN76ea86cMlbSqptjvfRZK2T19vl24rfX8zM7N0/DJ3/5+7PytpkqR1yvgjAAAAAKC36daaOTPra2bjJU2RdKukZyS95e7T0o+8KGnZ9PWykl6QpPT9txVTMWceb3AfAAAAAEAG3Urm3H26uw+VtJxiNG3VZjXIzPY3szFmNoZ5uwAAAADQWKZqlu7+lqQ7Ja0vaREzq625W07SS+nrlyQtL0np+wMUhVBmHm9wn/rfcY67D3f34UsssUSW5gEAAABArzHbZM7MljCzRdLX80vaXNKTiqRux/Rje0q6Nn19Xbqt9P07POqcXidpFzPrlyphriLp4ZL+DgAAAADoVbpTzXJpSReZWV9F8neFu99gZk9IuszMTpT0iKTz0s+fJ+kSM5sk6U1FBUu5++NmdoWkJyRNk3QQlSwBAAAAIJ/ZJnPuPkHSWg2OT1aDapTuPlXSTp3E+rmkn2dvJgAAAACgXqY1cwAAAACA1kAyBwAAAAAVRDIHAAAAABVEMgcAAAAAFUQyBwAAAAAVRDIHAAAAABVEMgcAAAAAFUQyBwAAAAAVRDIHAAAAABU0T083AAAAAAB6nZEDGhx7O1MIRuYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggmabzJnZ8mZ2p5k9YWaPm9mh6fhIM3vJzManjy/X3edoM5tkZk+b2ZZ1x7dKxyaZ2VHN+ZMAAAAAYO43Tzd+ZpqkH7j7ODNbSNJYM7s1fe80dz+1/ofNbHVJu0haQ9Iykm4zs8+mb58paXNJL0oabWbXufsTZfwhAAAAANCbzDaZc/dXJL2Svn7XzJ6UtGwXd9lO0mXu/j9Jz5rZJEnrpO9NcvfJkmRml6WfJZkDAAAAgIwyrZkzs4GS1pL0UDp0sJlNMLPzzWzRdGxZSS/U3e3FdKyz4wAAAACAjLqdzJnZgpL+Iukwd39H0lmSPi1pqGLk7ldlNMjM9jezMWY25rXXXisjJAAAAADMdbqVzJnZvIpEbpS7Xy1J7v4fd5/u7jMknau2qZQvSVq+7u7LpWOdHW/H3c9x9+HuPnyJJZbI+vcAAAAAQK/QnWqWJuk8SU+6+6/rji9d92NfkzQxfX2dpF3MrJ+ZrSRpFUkPSxotaRUzW8nM5lMUSbmunD8DAAAAAHqX7lSz3FDStyQ9Zmbj07EfS9rVzIZKcknPSTpAktz9cTO7QlHYZJqkg9x9uiSZ2cGSbpbUV9L57v54aX8JAAAAAPQi3almeZ8ka/CtG7u4z88l/bzB8Ru7uh8AAAAAoHsyVbMEAAAAALQGkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKigeXq6AQAAAAAwtxt41N/a3X6uf/GYjMwBAAAAQAWRzAEAAABABZHMAQAAAEAFkcwBAAAAQAXNNpkzs+XN7E4ze8LMHjezQ9PxxczsVjP7V/q8aDpuZvZbM5tkZhPMbFhdrD3Tz//LzPZs3p8FAAAAAHO37ozMTZP0A3dfXdJ6kg4ys9UlHSXpdndfRdLt6bYkbS1plfSxv6SzpEj+JB0raV1J60g6tpYAAgAAAACymW0y5+6vuPu49PW7kp6UtKyk7SRdlH7sIknbp6+3k3SxhwclLWJmS0vaUtKt7v6mu/9X0q2StirzjwEAAACA3iLTmjkzGyhpLUkPSVrK3V9J33pV0lLp62UlvVB3txfTsc6OAwAAAAAy6nYyZ2YLSvqLpMPc/Z3677m7S/IyGmRm+5vZGDMb89prr5UREgAAAADmOt1K5sxsXkUiN8rdr06H/5OmTyp9npKOvyRp+bq7L5eOdXa8HXc/x92Hu/vwJZZYIsvfAgAAAAC9RneqWZqk8yQ96e6/rvvWdZJqFSn3lHRt3fE9UlXL9SS9naZj3ixpCzNbNBU+2SIdAwAAAABkNE83fmZDSd+S9JiZjU/HfizpZElXmNm+kp6X9I30vRslfVnSJEkfSNpbktz9TTM7QdLo9HPHu/ubZfwRAAAAANDbzDaZc/f7JFkn396swc+7pIM6iXW+pPOzNBAAAAAAMKtM1SwBAAAAAK2BZA4AAAAAKohkDgAAAAAqiGQOAAAAACqIZA4AAAAAKohkDgAAAAAqqDv7zAEAyjByQINjb8/5dgAAgLkCI3MAAAAAUEEkcwAAAABQQSRzAAAAAFBBJHMAAAAAUEEkcwAAAABQQSRzAAAAAFBBJHMAAAAAUEEkcwAAAABQQSRzAAAAAFBBJHMAAAAAUEEkcwAAAABQQSRzAAAAAFBBJHMAAAAAUEEkcwAAAABQQSRzAAAAAFBBJHMAAAAAUEEkcwAAAABQQfP0dAOyGnjU32Y59lz/HmgIAAAAAPQgRuYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCC5unpBgAAgDlg5IAGx96e8+0AAJSGkTkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKig2SZzZna+mU0xs4l1x0aa2UtmNj59fLnue0eb2SQze9rMtqw7vlU6NsnMjir/TwEAAACA3qM7I3MXStqqwfHT3H1o+rhRksxsdUm7SFoj3ef3ZtbXzPpKOlPS1pJWl7Rr+lkAAAAAQA7zzO4H3P0eMxvYzXjbSbrM3f8n6VkzmyRpnfS9Se4+WZLM7LL0s09kbzIAAAAAoMiauYPNbEKahrloOraspBfqfubFdKyz4wAAAACAHPImc2dJ+rSkoZJekfSrshpkZvub2RgzG/Paa6+VFRYAAAAA5iq5kjl3/4+7T3f3GZLOVdtUypckLV/3o8ulY50dbxT7HHcf7u7Dl1hiiTzNAwAAAIC5Xq5kzsyWrrv5NUm1SpfXSdrFzPqZ2UqSVpH0sKTRklYxs5XMbD5FkZTr8jcbAAAAAHq32RZAMbNLJW0saXEze1HSsZI2NrOhklzSc5IOkCR3f9zMrlAUNpkm6SB3n57iHCzpZkl9JZ3v7o+X/ccAAAAAQG/RnWqWuzY4fF4XP/9zST9vcPxGSTdmah0AAAAAoKEi1SwBAAAAAD2EZA4AAAAAKohkDgAAAAAqiGQOAAAAACqIZA4AAAAAKohkDgAAAAAqiGQOAAAAACqIZA4AAAAAKohkDgAAAAAqiGQOAAAAACqIZA4AAAAAKohkDgAAAAAqaJ6ebgAAYC41ckCDY2/P+XYAADCXYmQOAAAAACqIZA4AAAAAKohkDgAAAAAqiGQOAAAAACqIAigAUFUUGAEAoFdjZA4AAAAAKohkDgAAAAAqiGmWAIDAtE0AACqFkTkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIJI5AAAAAKggkjkAAAAAqKB5eroBAAAAAFAJIwc0OPb2nG9HwsgcAAAAAFQQI3MAAABV1mIjBQDmHEbmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJI5gAAAACggkjmAAAAAKCCSOYAAAAAoIJmm8yZ2flmNsXMJtYdW8zMbjWzf6XPi6bjZma/NbNJZjbBzIbV3WfP9PP/MrM9m/PnAAAAAEDv0J2RuQslbdXh2FGSbnf3VSTdnm5L0taSVkkf+0s6S4rkT9KxktaVtI6kY2sJIAAAAAAgu9kmc+5+j6Q3OxzeTtJF6euLJG1fd/xiDw9KWsTMlpa0paRb3f1Nd/+vpFs1a4IIAAAAAOimvGvmlnL3V9LXr0paKn29rKQX6n7uxXSss+MAAAAAgBwKF0Bxd5fkJbRFkmRm+5vZGDMb89prr5UVFgAAAADmKnmTuf+k6ZNKn6ek4y9JWr7u55ZLxzo7Pgt3P8fdh7v78CWWWCJn8wAAAABg7jZPzvtdJ2lPSSenz9fWHT/YzC5TFDt5291fMbObJf2irujJFpKOzt9sAAAAAGiegUf9bZZjz/XvgYZ0YbbJnJldKmljSYub2YuKqpQnS7rCzPaV9Lykb6Qfv1HSlyVNkvSBpL0lyd3fNLMTJI1OP3e8u3csqgIAAAAA6KbZJnPuvmsn39qswc+6pIM6iXO+pPMztQ4AAAAA0FDhAigAAAAAgDmPZA4AAAAAKohkDgAAAAAqiGQOAAAAACqIZA4AAAAAKijvPnPoSSMHNDj29pxvBwAAAIAeQzIHAL1QFTZCBQAAXWOaJQAAAABUEMkcAAAAAFQQyRwAAAAAVBDJHAAAAABUEMkcAAAAAFQQ1SwBAJjLUK0UAHoHRuYAAAAAoIIYmQMAoAwjBzQ49vacbwcAoNdgZA4AAAAAKohkDgAAAAAqiGQOAAAAACqIZA4AAAAAKohkDgAAAAAqiGQOAAAAACqIZA4AAAAAKoh95gAAQM9jnz4AyIyROQAAAACoIJI5AAAAAKggkjkAAAAAqCCSOQAAAACoIAqgoHwsYgcAAACajpE5AAAAAKggkjkAAAAAqCCmWQIAAACtiuUr6AIjcwAAAABQQSRzAAAAAFBBTLMEgAoYeNTfZjn2XP8eaAiAHsW5AEA9RuYAAAAAoIIYmWtx9MABAFoSRRkAoMeRzAGorI6dHc+dvE0PtQQAAGDOY5olAAAAAFQQI3NofUzlAQAAAGbByBwAAAAAVBDJHAAAAABUEMkcAAAAAFQQyRwAAAAAVBAFUAAAAADMnebyQnqMzAEAAABABZHMAQAAAEAFkcwBAAAAQAWRzAEAAABABVEABQCaYOBRf5vl2HP9e6AhAABgrkUyB2DuMZdXrAIAAKjHNEsAAAAAqCBG5gAAaEWMNAMtr+GU+pO36YGWoLcimUNLYZ0RUE28dgEAmPOYZgkAAAAAFVQomTOz58zsMTMbb2Zj0rHFzOxWM/tX+rxoOm5m9lszm2RmE8xsWBl/AAAAAAD0RmWMzG3i7kPdfXi6fZSk2919FUm3p9uStLWkVdLH/pLOKuF3AwAAAECv1Iw1c9tJ2jh9fZGkuyT9KB2/2N1d0oNmtoiZLe3urzShDZhDWCcDAJjbdHxvo6AFgFZVdGTOJd1iZmPNbP90bKm6BO1VSUulr5eV9ELdfV9MxwAAAAAAGRUdmdvI3V8ysyUl3WpmT9V/093dzDxLwJQU7i9JK6ywQsHm5UQ5aAAAAAAtrlAy5+4vpc9TzOwaSetI+k9t+qSZLS1pSvrxlyQtX3f35dKxjjHPkXSOJA0fPjxTIggAmIvR0QYAQDu5p1ma2QJmtlDta0lbSJoo6TpJe6Yf21PStenr6yTtkaparifpbdbLAQAAAEA+RUbmlpJ0jZnV4vzZ3W8ys9GSrjCzfSU9L+kb6edvlPRlSZMkfSBp7wK/GwAAAAB6tdzJnLtPlrRmg+NvSNqswXGXdFDe3wcAAAAAaNOMrQkAAEAGbPMCAMijjE3DAQAAAABzGCNzAACgS4wcAkBrIpkD5b4BAACACiKZA4DO0NEBAABaGMkcAAAAeoWGU4ZP3qYHWgKUg2QOvQ+jLQCAnsT7EICSUM0SAAAAACqo14/MUaELAAAAQBX1+mQOAADMWXSktjimgaKieuO5hWQOAICMeuMFAwCg9ZDMAQB6J0Yf0F08VwC0KAqgAAAAAEAFkcwBAAAAQAUxzRIA0HJYkwYArYdN11sPyRxQFGspAAAA0ANI5uYELvYBAAAAlIxkDgAAAGgBTDFHViRzwNyOkWEAcznW8QDorUjmAAAAmoSRFgDNRDJXMk7aAAAATcSME2Am9pkDAAAAgApiZA4AxKg6ALQizs3FsJ507sfIHAAAAABUEMkcAAAAAFQQ0ywBAHM9pmoBAOZGJHMAAAC9FB0dQLWRzPUynLSRBQunAQBzvbK3OmDrBMxBJHOYq5G8NgFvUgAAAC2BZA7oQYx8AQAAIC+SOSADRvoAAOgcnZTAnEUyB2COaZwMf3PWH2TaJgDMPZie31r4f8xV2GcOAAAAACqIkTkAADD3YfQBQC/AyBwAAAAAVBDJHAAAAABUENMsAQAAAMxxVD8tjmQOmIuwdQIAAKg01rtmwjRLAAAAAKggkjkAAAAAqCCmWQKthukFAACgKrhu6VGMzAEAAABABZHMAQAAAEAFkcwBAAAAQAWRzAEAAABABZHMAQAAAEAFkcwBAAAAQAWRzAEAAABABZHMAQAAAEAFkcwBAAAAQAWRzAEAAABABZHMAQAAAEAFkcwBAAAAQAWRzAEAAABABc3xZM7MtjKzp81skpkdNad/PwAAAADMDeZoMmdmfSWdKWlrSatL2tXMVp+TbQAAAACAucGcHplbR9Ikd5/s7h9JukzSdnO4DQAAAABQeXM6mVtW0gt1t19MxwAAAAAAGZi7z7lfZrajpK3c/dvp9rckrevuB9f9zP6S9k83Pyfp6W6EXlzS6yU2lXjEI14147Vy24hHPOJVN14rt414xCNedeN1N9aK7r5Eo2/MU1JDuuslScvX3V4uHZvJ3c+RdE6WoGY2xt2HF28e8YhHvCrHa+W2EY94xKtuvFZuG/GIR7zqxisj1pyeZjla0ipmtpKZzSdpF0nXzeE2AAAAAEDlzdGROXefZmYHS7pZUl9J57v743OyDQAAAAAwN5jT0yzl7jdKurHksJmmZRKPeMSba+O1ctuIRzziVTdeK7eNeMQjXnXjFY41RwugAAAAAADKMafXzAEAAAAASkAyBwC9kJn1MbMNSoxnZrb87H+yZ+IBQG9U9rkerYdplijMzEzScu7+wmx/eC5gZju5+5WzO5Yh3qHu/pvZHcOszOx7kv7k7v/t6bZUkZk94u5rlRjvMXcf3KrxymZmK7n7sx2OjXD30S0Sb213H9vh2Ffc/Ya5MV4ZzOz7XX3f3X89p9pSRWa2Q1ffd/er51Rb5gQzW6yr77v7mxnjNeXxK/Ncb2Z9JO3o7leUEIvXWwnmeAGUVmNmu7v7nzp7QmV9IpnZpu5+R2cvyCwvRDO7XlKn2ba7b5ulbc3i7m5mN0oqdNFlZu+q67934Rwxh3X1fXcflzWmpKMldUzcGh3rrj0ldUzc9mpwrEtm9pi6fvyGZG5ZxO0r6WJ33y3P/ZtsKUmjzWycpPMl3ewFeqjMbAlJ+0kaqLrzo7vvU7CdHX/PCu7+7xz36yvpNnffpKSm3G5mX5d0dZHHrc64IsnHHIhXtr+Y2Vfd/SVJMrMvSvqd8p8Ly453rpnt4e4TU7xdJR0mKW+y1JLxzOwMdX3uOyRDuIXS589JGqG27ZO+KunhLO3qyMwWkbSHZj2/ZGlfqUp+7KR4nCRpSUkbSLoj3d5E0j8k5U1G+kr6P3c/Is/96+KUfZ0xNsUzSStI+m/6ehFJ/5a0UsZ4TXn8VOK53t1nmNkPJRVO5tSk11uJz5dSk/VOfseikpZ39wl5Y1QumTOz33b1/RwnngXS54W6/Knu+6LixffVBt9zZXshnpo+7yDpU5L+lG7vKuk/eRpnZve5+0YNTmimyMsyJ0xJ4Ysud18otfEESa9IuiS1azdJS+cM+6v0ub+k4ZIeTTGHSBojaf3uBjKzrSV9WdKyHZ6HC0ualrVh6cLlm5JWMrP6/RYXkpTnBPGV9Pmg9PmS9LlQEubu081sRTObz90/KhKrxszWk3SGpNUkzafYquT9rM8/d/+pmR0jaQtJe0v6nZldIek8d38mR9OulXSvpNskTc9x/3bMbH1Jy0q6x92nmNkQSUdJ+rykzFMI0/9ihpkNcPe3i7ZP0gGSvi9pmplNVfHzwLqSdjOz5yW9XxcvV0dCmfHM7Ifu/svOLl5zXlAfIOmvZvZVScMknaQ4R+RVdrwdJV1lZt9UPOf2ULxW5rZ4Y9LnDSWtLunydHsnSU9kCeTux0mSmd0jaZi7v5tuj5T0txxtq3ejpAclPSZpRt4gJT+XS3vs0u/eO7XxFkmru/sr6fbSki7MGq8u7nQz2yjv/evilHqd4e4rpXjnSromVWyvXS9snyNeUx4/lX+uv83MjlA8X96vHcya3DTr9VbW80XlJ+uSJDO7S9K2ijxsrKQpZna/u3c5UtlpvKpNszSzjyRNVPQIvKx4UGdy94tyxOwr6RB3P62URpbMGuwO3+hYTzKzpyR9RlIZF12PuvuaszuWMebVko5198fS7UGSRrr7jhlirClpqKTjJf2s7lvvSroz61Q/M1tRcSI4SXGBXx9vgrtnThBT3FmmU5jZOHfvcpRyNjEvViRe16n9iTvXFAgzGyNpF8Vo5nDFRdxn3f3onPHWVCRzW0m6U9J6km519x9mjDPe3YfmaUODWKcoEuzxitfGzZK+rfh//8Hdp+aMe62ktSTdqvb/ix7r3a9Jz+lZuPvzPR0vjXhdb2Z7dhIz83tHiru+pD9ImippG3d/LU+cJsb7rKS/Ki48vubuH86t8czsQUkb1c6dZjavpHvdfb0csZ6WNMTd/5du91Oclz9XoH2FzsN1cUp/Lpf52KX7P+nuq9Xd7iPp8fpjOWKepegcu1Ltz32ZR6vKvs6wBlPCGx3LEK/0x69MZvZsg8Pu7ivnjNeM11uZz5eGybq7H5CzbY+4+1pm9m3FqNyxZjYhb8dn5UbmFD0nO0naWTEacrmkq9z9rbwBUwa/q6TCyZw1Z/7vAma2srtPTr9jJbWNKGZtX7OGjLfMeb9G3jez3SRdpugR2VV1L8ScPldL5CTJ3SeaWaaTors/KulRM/uz4rWzgrs/nbdB6YL0eUnrp4vWVdz9NjObX9L8iqQuDzOzDd39/nRjAxUvdvRM+uijkkax3X2SmfV19+mSLjCzRxTTVbvNzA5VJIKvS/qjpCPd/eP0xvcvSZmSOUk3mNmXayfsgraRtJa7T03TKF6QNMjdnysY92rln2ozi9S2VRSj15Ikd78nT6xakmVmS9bHy6vMeO5+ffqcK2mrZ7NOgf+EpLclnWdmmafANyFexynXiylGvx9K8TJdMLR6vDqLKmZK1N7HFkzH8rhY0sNmdk26vb2kos+dS8xsP8U00v/VDuYYzSjtuVynzMdOiml9N0u6NN3eWTHjoYj+kt6QtGndsawznmrKvs542cx+qrYZVLspBhzyKv3xK/lcn2tEqgvNeL2V+XxZz933mxnE/e9m9ssCbZsnjbZ+Q9JPCsSRVMGRuXpmtpyid//7kn7k7pfM5i5dxTpN0ryadcg405oqMzs2fdlw/q+7756jbVspNhWcrBjxWlHSAe5+c45Yz6qLIeO8L1AzW6HRcc+3LmigYr3Yhqmt90s6rMhFsJldqvi/1p9oF3T3XXPE+qpiCux87r6SmQ2VdHzWC666ePtJ2l/SYu7+aTNbRdLZ7r5ZznhrK9aPDVD8b/8raZ+sz+VmSlMqvqRIwF5VTHfZK2uvqJkdJ+n8RiM1Zraauz+ZMd67io6S/0n6WAWmonTshW80YtrTUq/goZKWU4wgrifpAXfftKv7dRFvW8XU5mUkTVGcq5509zV6Ol6DhKmdLK9fi7VsnXL3uzM0rRnxGo5o1sXLNLLZ6vHq4u4taaRidN4kfUExAyPvqOvakmpTte5x90fyxKmLd5Ckn0t6S23PxcyjGWU+l+tilvrYpZg7KKbPSvH4XdPVz89JZV9npI7yYxWPmyTdI+m4Ah3kpT5+ZZ/rU8xBiqm59cnhxQXilfp6K1NKrO9V+2vIL7h7roEMM9tRMcPrPnc/0MxWlnSKu389V7yqJnMWhS12lbS5Yr7pr9w98/zuunh3NjjsBS5q7lFMkanN/11I0t/c/Qtd37PTeP0krZpuPlUbis6rCUPGtZ5WU7ywV5L0dN6LuLKZWX9J31X7E+1ZnmOqm5mNVfT03FW7OC84nWK8pHUkPVRGvLq4AyTJS1hblV4fjdZm5H19rKhY9zmfpMMViefv3X1SxjiXuPu3ZnesJ5jZW4rnWc0X6m8XSP5XUUzV7Pgmmnl6S3rdjpD0oLsPNbNVJf3C3busqNZFvEcVr43b0hSSTSTt7u779nS8shOmFHMlSa/UziNpVH2pAheEZcdbTzE1q/Y+tLCk1dz9obkxXorxKcVaSynOqa/mjZXitRsVztNBWRdrsqR13P31gm0q/bmc4pb62JUtvY/vK2kNtf+flFqgam7UhHP9sZI2VrwP3Shpa0Vi0u2lK53ELfP1tpxibf6G6dC9kg519xdzxCotWbcmLO2q3DRLMzteMX3pScXw+NGec21RPS+vOlzNUpLqi0V8lI7ltYpitK+/pDXTVJTcPSAqeci4Y+KRku0D88Rqxgk7XRydphKm0kr62N3fNmu/XLNAvP+5+0e1eGY2T8F4MrNtlB6/Wlx3P75AyPqKUP0lfV05ir6ktvVVvInsplgXdFyBdrXrLEix184bzMwuUZyk73X3pwq0S5K263D7Vw1/KrsLFG8qp0naRLFWMO802qke00BlZv3c/Skzy71GQfHaeMNiX6M+7n6nmZ3eCvHyXuDOxpWKinM109OxES0S7yxFIZWa9xocm5viSTGq/oriPPVZM/tsnqlkDUaFV5D0lDqcczKaJOmDAveX1LTnslTSYyfNTNQLF7nq4BLF/2BLxdr13RTXgnnaV+p1hkUl5B82iJe3w7Psx6/sc/2OktaU9Ii7721mS6lt1CqzJr3eLpD0Z8XSLEnaPR3bPGuglLQdWqAt9bFKW9pVU7lkTtJPJT2reBKtKekX6WK1aNW0dhfAtWMFLoBLm//bWQ9I+h15lT2/ux13H2dm687+Jxsq84TdjHL9j1tUX+ubRkkOUZQMzutuM/uxpPnNbHNFEnx93mBmdrZizc0mimmMO6pgSW3vsBeUpPvNLFdML6E6ppkdLan2mL1TO6zoNDknT8zkfMW0ljPM7NOSHlFM98i851/HCy6LggKDJL3k7lMKtHF+d7/dzCxNRxuZRot/Nrs7NvCiRbn0v0q61cz+q1jHmddbZragIiEeZWZTVGwdSmnxmnQumKf+OZw6ZebL074mxTOvm37jUVK8yPt+S8ezTqaSqf2ame46Id2/3ahw3rYl70san2Y61K+Zy1S8qBnP5ZIfOym21JilyFXOWDWfcfedzGw7d7/IYv36vTljlXadkYxSLNP5iqTvKLYcKlK8qOzHr+xz/Yfp9TotjahPUY4KzXWa8Xpbwt0vqLt9oZkdliWANW+LsPvN7HcquLSrporJXNmLLiWVfwHs7j83s5vUNv93b88//7fUHpBkV0Xvfi3ZvCcdy8XaF37po+hZzZsclnnC/srsfySz7ykWrP5P0etzs6QTC8Q7StFD+JiifPCNiudgXhu4+xCLykjHmdmvJP29QLzaFIOaPorRrwEFQk5WnMxyVcd095MknWRmJ3nOCpidxL3TYor0CMW54DuKDp7MyVw6p5zh7o9bTHl9QDHSspiZHeHul3YdoVP/s1TgxcwOlvSSolhBZu7+tfTlyHSBOUDSTTnbJcVo5FTF1NndUrwiI8JlxmvGueA1M9vW3a+TJDPbTlGMp1XiTTazQxSjXVJ0FE2ei+MdqrapZJvUppLljFX2KLMUF9J/LRhDas5zuczHTlI5Ra46+Dh9fstivdariv3Y8ijzOkOSPunu55nZoakj724zK7Q/ZpmPXxPO9WNScniuYqnTe4r3uLya8Xp7w8x2V1sRmV0VBVGyOHX2P5LL0PS5/v3MlbPzpIrJ3LnuXmRfm86UfgHs7mPN7AWlkT7LuVGwyu8BKXXIOKmvcDhNsT/IX3LGKu2E7XUL6W3WipG5nv/u/oGkn5jZz9PXhaT/7Z8UI0C5q2PWqZX2/sDMllGcvPLu01dTv9fKNMXoeK51UEmh6phmtmqaBnmlNdgYPm/vlpndriiA8oDijX1EgVG0z7v7d9LXe0v6p7tvb7Eu5e9qe4PJ6lBFx9Mhit7MTRW9wJl1SNJr1V5zT/F19/pRs8KV9sqM5zmLaszGdxQjhmem2y9IKrJesxnxfquY0SJFNbz95+J4ZU4lq40K36tyRplLqz7ZpOdy2dPwPkijyuMtlnC8ouJVlc+xqMh4jKK43ILKNyNBKjcxrI/3isUsr5cVVVrzKvXxa8K5vraM5uw0cLGwF9j0Wk14vUnaRzFVtTad8X7Fe3G31c+wSdeNhaqY18Utd2mXu1fqQzE61Yy4D6XPDyrm7PaTNKlAvG0VpdHfV1z4Tlcs9M4T6/eKapPfSTEfkXRBwb/3TsXm5u0+evr/m9r2bUVJ5C8qemmnKKp3Fom5n6TRkp5Jt1eRdHvOWBsoNlP9d7q9pqJ4R5HnytOSnk23h0q6rkC8Y9Lz5etqqxR5fE//X0t+jpybPt/Z4CP381hx0r9HsYfbSEWiNH/OWI/Uff03RbXOWb7Xw4/jc+nc9Loi6Z+uGOkbJ2ntHPHelfROh48XFDMAVu7peCnmeulc8J5iWu50Se8UfBwXVFTHLev/Umq83vKRnheLpNfuPZKulXRjzlgLKNYpzaPoLDlEMfpSpH2rSLoqvX9Mrn0UiFfac7nMxy7FW1HRkb2wYhbQrxWjYT3+PEntK/U6QzFaOkAxlf5ORQfotq3y+DXhXD+swcenFVPF87Sv9Ndbyc+Xr6rc67SlJJ0n6e/p9uqS9s0br3LVLC2qQR3R2fc9x2aAKe4xigx+M0lnKnos/ujux+SMV2pVt7q4A1W8B6RWArZmZkELz77JcrPmE5fKSqwYaWYPKaa+XlcXa6K7D8rZtlKrY3aI3U9Sf89Z0dKiNHKnCrze7lSJ1THLZlF9di/FueZT7t4vR4w7FQu6X1K8ua/q7q9arAma6O6rdhmg87iNXnNvSxqjjJuRW1S1vcrTNidmtoXiXHCBpN+4e6Z1r2Z2gqQXFdOPTbHm49OKC4bvuvvGPRkvxSxtw/o0ffZYtVU4u1vRcZL39VZ2vNKquVUhXofYX1SaSub51+YupbbiMw97sbWuMrP71Fa86KtKxYvcPdfoUpnP5Q5xCz92Kc58alvn9bS7f9zVz3cj3vcbHH5b0lh3H18kdisq8/Frwrn+QUUCN0Fxbh4k6XHF8+a77n5LjjaW/Xors5pl2VXM/6547H/i7mum64JH8sYrOuTdEwYoekC+2uCjyDzyX7r7W+7+F0WPyKoqVmnmY3d/Q9LM+b+Kk20uZratmZ0q6eDUvkLcfWzdx/3u/n1FkZWsTlVcsD6rmN53bvp4TzGNLjMzG2Bmp5nZmPRxarrIKeJ/9W9KVrBipLu/0OHQ9LyxlKpjdvwVeYOZ2bxmdoiZXaVYlP0ti+IbeTR6nZXxejtC0pHp4xjFgvsx3b2zme1uZrNMPzOzb1kUp8nFzA42s8sVo9/bKQqibJ0z3AGK1+sFiv2LamW+N1OM1OU1WfH6qr3W3lGMYH023c5iPa/brzK9Aa/v7g8qZidkta27/8Hd33X3d9z9HElbuvvlyrcBcdnxJMVaFEl93X26xwL5rXKGOl/x2H8jfbyj+H/nVXa8CxTT0ZZJH9fP5fFkZmtarCUdLOn5AoncNxTr5ndS/C8estgbqoj53f12ReGX5919pKI6d24lPpdLe+xSrI0VM4nOVMwu+qeZ5dqaqc5wxQylZdPHAYq/91wzy9oRXep1hpktZ2bXmNlrZjbFzP6Skom88TZWuY9f2ef6lyWt5e7D3X1tSWsp3ps2l5S5MnqTXm9lnl9KvU6TtLi7XyFphiR5VOXPfx2Zd0ivpz4kjWtS3L9Jmrfu9qcUvT15492mmCpzhmJtzG8k/SNnrJMl3a6Y/7uPYgrYLwr+vYvVfSyuqOj0dIF4Y7pzrJux/qIoV79y+jhW0tUF/95fKqofPqU42Vwj6ec5Y12lmGo5TrHR/BGSLivQtvMkfVPRw7VKes6cXSDeHxXrizZNHxcoRplzP35z4kPRE9fdn31IDaahKaZqFHndHqHYZynXVJE59DiN7uyYMk7llnSLpB8pOohWVJTWvlUx3SXzuVax1vAbio7CPunrB9P3xvd0vHS/exSlvi9O54XDJT2aM9YsbcjbLuKVEu9QSRMVRQWOV6wN+l7OWI9KWrLu9hJ5nyd1Mf6RnsdXKzp6vqZi77tlPpdLe+xSvLGSPld3+7NFzs11f++CdbcXVIxezy/piYyxSr3OSOfNvRXTBOdRzOy4tVUevyac6yd2diznub4Zr7dZ2lHgfaPs67S7JH2y9tgrpkzfnTtekQeqJz7UvDVz+yku8PtKGpj+YVsUiLdAOmkXnv+b2tKn7nZfSRMK/r3PKnpRnlX0/twiaaMC8Z5U3RoWRdXRJ3PGGt+dYxlj9kn/4ysVydh+it7RPLEWV4x4/Ucxz/5Pef+3Kd4nJP1csfZhtKIyZv8C8WY5AZZwUlwkPYd/rShY8FtJvy0Qr1BnQldvPiW8NtZUXGgdLGnNAnHOqH+sOn4UiPukYhF27fYKtdda1vNjeuzPUIxEPqIoh72E4gIx8/oMxUXR9Yp1Ga+lrz+juNjKfH4pO16KuaJKWouiSDY3qru9oaQHCvxvy453u6K8d9/0sbtyrhWuSLwJkhaou71A3vOBpMc63O7T8ViOmCMUCchyik62vyhGTPLGK/O5XNpjV4vXnWMZYz6l9p3u/SQ9lb5+JGOs8d051oPxSn38mnCuv1xRhfaL6eP3kq5I/5NZOhy7Ea8Zr7fSzi9qf502Jn1d5DptmKIgy9vp8z8lDckbr4rVLBtNrVpc0hueHqE83P3cND/5r4pk7gB3L7J32C6K6oT/UglV3RQX07Wd5gcUDebuZW/xcLiku9KaRlO8yRyQM9aHZraRu98nSWa2odoqNObi7jPUNi0tN4tNqX/jseF1YSne3zwqG/2kjJiSppvZp939mfQ7VlaxaaBSbJfwoKK3dkbBWFLx6pjzm9kC3r7aYW2tW+59uSzKpO+v6DmXpD+Z2TnufkaOcPXTRo9TXGyV4QeS7jOzZxSP30qSDjSzBZTxXOPuryu22mhkUtaGuftkxRTcRu7r6XgpZq0SYNEN6yXpu5IuStOzTHGO3quF4tVXc3PFyFCmam4Vi2dqf66bno7lcZOZ3ay2qrM7q0CF63Su39ndj1BMky7yd0oq/blc5mMnRen6P6ptG6XdlWEqfSdGKabfXZtuf1XSn9O574mMscq+ziijDH69Uh+/ss/1ivPSgZIOS7fvV8xs+VixrU9Wpb7eksLVLGs8VTFXSddpHnsxf1HS5xSvs2JrIgvkPz3CzNZTTDt8U1GW+xJFj0MfSXu4e6Z9MzosqDXFAuIJip4LeTf3vWoQ9zjF5sMrKV6A90i613Ms0rXYKf5kRREFUyyOP8pj3UgunRS2eFvRE5Jr0WkqtlEr6vCUu/+vq5/vIs5QxUVp/QXNnp6j6IuZXeHu37BONln1fJur3idpUy+wnqBDvNsl7eA5ixw0iLeZote3PrHe22PdZt6Y49x9li0AeoqZHaFYe/ad2gWNRXGgMxULlE/JGXeCYh3B++n2AoqRkTwbStfHfcTToukydHitPe0Zip50iLOEYrrNGkpbqEj5C9GYWX9FUt4x3j6tEC/F3FBRsW9F1W1P4u4rF4i5cIrxTt4YzYzXW6T38z3Vtn/q9pIucvfTOr1T1/G+rvbFE/5apNPYzB509/Xy3r9BvNKey0147PpJOkhte+3eo6j6XOh908xGKJY5SNL97p4rwSnzOiPFW1GROKyvto6J7/ms6+u7G6/Ux6/sc30zlP16K4OZne7uh1knxf68QJE/M9tAMXhU/9q9OFesCiZzYxRrnwZIOkfS1u7+oMUGl5dmvWAysy57y929UG+Xxb4U+yl6LJZ197454yytuio/kubzfHvW1eL9TXHSqV3gb6wYLVlJUT3tkhwxS3tipngLpy/fl7SLu4/KEWMZd385nWhn4Tn26zGziyWtplhYm3nD6wbxrlUsHr61Q7xD8sRLMfspenykKKc73N3vLxDvcEVv8g2KzdJrbXyz0zt1Ha+/oldvI8UJ8l7F/PMslRi/o9hAtbZh9nuSTnb3szq/12xjPqbYW25qXTtHe8HKomUnwxb7Iq2u9m/KmV9rZnaLYrrMEYrCAntKes3df5SzXVcqpkJ9U7HuZjfFFNBDWyFeivmUYibBWNWNRHgUrOpujEZV9WbKei5oQrwz1HWV4UznllaP1yH2MLVdAN+r2N8x035VaRRt0TSaUasquJekw919tQJtO0tRuONKtT/X560KXPi53CFe4ceui9hbSDrS3TcvIdaSan/uK3ItVPg6o5O4i0o60N1/XlK8Qo9fE871q0g6SbO+D+XpSGjW6+2XimUrHyo2SB+SYv6pyzu2jzGsbhRtFl63D13Gtl2iqMw8Xm2vXc977qviNMt5PJU8NbPjPSrxyGODy8zBiiZrnTGznyp6GBZUjPIdoTg5Zo2zvuLkf4+7X2dmQxTz4j+vYhuHzyNpNXf/T/o9SykWUa+r6AHKlMx19sRMMbsbY2FFT9Syij1ubku3f6AYLc1zkr1BMTf5RHcvsvluvUYbXhfpFblabdP6ajLHSyfEbygev7+7+wQz+4qksxVrjIqMDH0k6RTFFINa21yxpimPixVV+2rTF7+peM7t1N0A7n62YsPShdLtd3O2RWZ2obvvpRjRfMjM6nunz8sbtxlSB9TGijfRGxXVNu9ThtdanU+6+3lmdmh6U7rbzEYXaN5n3H0nM9vO3S8ysz8rx3mvifEk6W13Lzp9J/NG93M4XtHpbFWLJzNbVtLSinVF49IF/2GKi8JlMsTZRdIfJL1vZv9SrI05X7FWpuj0+v6KqXf1oyGuWc//3VXGc7m0xy7F2lTxnrOMYtnK/ynOq6Z4LIu0c1tF9exlFOvVV1B09qyRIUap1xlmtryiIvMyilHNyxRTXvdQ25TBLPGa9fiVfa6/QG3bbGyitM1G1iBNfr1t4e4/NLOvKfbZ20FxfdvtZE5x3bOZpC/nTXw7MVzS6mWNPFYxmatfr9NxfnOR6Q+fVSRcA9V+ZCnvEPQOirVAf1NUW3og67RDMztFUf59vKQfWcwn/raiNyT3NKNk+Voil0xJx940szzzdst4Yl4i6b+KIgD7KRIHk/Q1z7+HzHwWpeo3sAZTS3P2iD7h7lfWHzCzbichDSzi7r/pEC/PyMN5igT/YUlnmNnLktaWdLS7/7VA+6R4o/tMreesBIPcffW623eaWdY1D5KKJXF1hqRYvzazu9TWO723uz+SJ6CZvau2c9InzKw2Zc7iV/nCje85WzsqirQ84u57p46YLG9O9Wqv9VfMbBtFuenFcsaqj/dWGj18VdKSrRAvjTpI8Vw7RXEBXT/KPK67scruBGxCvDLWaVcmnpkdpni/mCSpn5n9XnERfLHiHJjFTxWbKE9Kz5kHJO3o7tcXbae7F14nJ5X7XC75sZMi2dpf8bhtnT4f5e6/yxGroxMUVf/a7d+bMUbZ1xkXK67x/qLYJmGM4pptiLdtR5NFsx6/ss/187v77WZmaYbTSIu92LLumdi015varuW3kXSlu7+dY9Bn6TTrbFszu0wd1pFmea11MFFRNf+VnPdvp4rJ3JrposgURRDqL5D6d3632bpS0RvyRxUvFiF3H5Z6gDZUlMI/x8ymuPtGs7lrvW0U+3hMTUP2Lygugp8r2j5FsZIbFH+3FJtH3mWxRuitHPHKeGKuXJvOZrHw9xVF1b5c64GS7yh6dxbRrIUU8vaIHq22x62rY921p2Lrinp7NTg2O8MVbyAz0vTAVyV9Ou+Umw4mSfqghDg148xsvdrIupmtqyb01mfwCTNbS20n6lpxDatNs8ga0N3LHm2p+TD9j6elc8wU5R+lP9Gi2MYPFKOkCyumbeV1TjpXHaOYhrygsr+5Nyverzrcrt/309V+tKRL6fW1s+Ki8HrFfolfUIzYn5C106MJ8RZXjDb8V9HLfYpiNsczkn7gsTfZXBNPcfH7udQZuYKiMtyG7j42YxxJ+qj2+9Mo1b9KurAscw1oac9llfvYSdFRdVf6+q9m9lJJiZyU9u81s5n795rZ6RljlH2dsZjHfoGSdHPq2N3No+haHs16/Mo+1//PzPpI+pfF3oQvqW3JQxZNe71JusFiKvKHkr5rsW4w6//5Z4r3n+UUs+LqZX2tydrW3i0k6Qkze1jtO2JyrcGrXDLnOdecdcM0L7DWpqPUi/x5RcnW4YpELOv0oKm1E4y7/zc9yZ8rqYkHKRK42mLTiyX9JY2s5alEtLiKPzFnjgi6+3Qze7FgIiePSlX3mdkYdy80Xc7Mtpb0ZUnLmtlv6761sGIUNmu8XRXTC1cys+s6xMuzFu2j2htI6gCYXFIiJ8V6gvFmdqfa/3/zrm1ZW9I/zKy21mEFSU9bKlTjBQuO5LCs4gKpUbdd5hN2k40xs0UUlVnHKtYKPpAnkLvfkL58W/le9x3j/TF9ebfyT8FtSjyPirFluVhxvlpAcXE0UVHqeyNJFypmVPRkvD8rOkdWUYzUX6DoHPq8osNy47ks3lRP63fd/d9m9nSBZGRJa7+GcZH6255zbXRyiWJa4JaqWwOaNUjJz+UyHzspHq/6WTDz1N/OORum5i0zW1AxVW6UmU1R3drDbir9OiN1ONXeO96QNMDSEJBnX1felMev7HO9Yl/CTyi2LDpB8R65Z444TXu9uftRFuvm3k7/6/clbZcxxlWSrjKzY9z9hLxtqXNqCTFmUbkCKM1iZiMVPdzXqJwCDzcoTjj3KQooZJ66aGZvpRg1X6i/nTeDbwYrYXGomU1X24nZFOu8PlDxaWlKI46HK3rg9rdYvPu5uhNcd2KsKWmo4k24fnTgXUl3uvt/M7ZpRUXBmZMkHdUh3gR3z5QgmtkHaisxbIo1jJPU9vjlTpDMrOFJOu90KeukIE1d3G4VpjGzgySNcve30u1FJe3q7r/P2J5HvMRqk3OKRQXPhT1jBTYz62p0y/O+aVkU3vm6Zp2ufnzGOHvMpn2ZCzR1iL+NZh0d6XYbzWyiuw8ys3kkvejun6r73qPuvmbG9pQd71F3XzNdUD7v7ivUfW+8uw+dy+JNUaxVqtml/naWTidrYlG02nnGzCa4+xAzm1dR5Tp3hcsSnsulPXYp3gVdfNtzjELWx15AMbJiikR4gOL8n6V4UanXGWb2nGL5T8OOQM9YEKTsx69Z5/qyNOP1Zo2rtdfHzJwQp1HIbypGdo9Po9ifcveHs8ZK8f7PO6zBa3Ssuyo3MtdEtYvVI+uO5S7w4O4ze1LNbFEzWz7rBZdm7UHoOLUiN4stHs5QVGWcT7Gh4vt5E6YsSVsXMZo16irFVJ6xaitp/JJiWmS3kzl3f1TSo2b2Z3f/OL0RD5L0UtZELsV7XtLziqqiMrNPKhL297Imcknuqk+z41F8Yj5Jn02Hcu2JYmYLe5Rbb7jOLUfnyX7ufmbd/f9rZvspNjCda1kULJhZjtzMvuDu93R9r3Ya9WYvoJgC9klFT2se1yp6fseqrlMshxGdHN9WMYqaO5kzs7MVPcqbKEaBdlSMDmXxkSS5+zSLtan18kzTLzve9BTPzazjFM08079aPd6RHW7nHlkqkqx1Q6lrSkt6Lpf22EnlrQvsJHb9eStXR2LZ1xnuPrDkeGU/fk0511vUmThSs26LkWkWS5Neb19V+xoa9Yl23uU1ZyrOTZsqOvTfVayT7Oy9anY2l9Qxcdu6wbFuIZlLvORNtC2KKGyreIzHSppiZv9w927PUS4jQerC7xQ9cFcqpoHuobYL9czKTg6b4NPuvnOa2ih3/6A2DaK70hvnGe7+uMXc8wcUFyWLmdkR7p6pclUavT3K3SdabD0xTjH16NMWG1WfniVed0ez8jCzjRVvns8pTozLm9meGRMIKaZXfUXtNw2vydN50tfMzNMUA4uKnnk2DS+zSlVTmdn/KdZXPaH2lWO7/b9w95kdQxbVQA9VVCO7TMU6jZZz960K3F+S5O4zN7dNr9PdFP+jB1WwIp6kDdKoyAR3P87MfqXsm9MuZzHV2uq+Vrq9bI42lR1vZYup21b3dS1enve6lo6Xd4ZAD2i0BvSYAvEKP5cr9NjVRlz+T5EAm0qYtTO3a+K5vlZn4lyVUGeiZBM73HZJr0m6z92fzRlzXY9aGLU9qP+bOrgzMbPvKrZl+rTFvrY1Cyn2JsyFZK6OlbR3UzLA3d8xs29Lutjdj+3wj+txHtWD+rr7dEkXpCfp0TnDlZocNsFHFnv+1S76P63sIwefd/fvpK/3VuzBs72ZfUrxBpq1DPFK7l476ewt6VZ33yOdcO+XdHrGeM30K0WZ36elmb1ylypjtbPaiHWJnSc3SbrczP6Qbh+QjmXiabuTitheMUW4yMiXzGwxSd9XJEoXSRqWZ4S5g3+Y2WB3f6xgHKUph3spqgw/qKhw9nTRuGqrgvyBmS2jWOOydMYY9aMZHQv35CnkU3a8+lkdHddo5Fmz0erxKsFLXlOqcp7LVfJLSV9198zrDHuzJp3rS60zUbJGhVhWlPQTMxvp7pc1+P7sfJw6i2vXkEso3yyCPyuuF0+SdLJiNpYUiWauytkSydxMVu7eTVIsWl1ase/XT8poY8k+SL0K4y0WiL6iHHuE1Cs5OSzbsYqL/OXNbJSi8MteGWN8VPf15krVK9391YyDfDX10xQ3U/Rwyd3fNbO8lbCaZd76C2l3/2eaZpqJma3qsSdkow20XdKbGUcYf6RI4L6bbt+qmG40N5ssaV4VmMZoUc58B0nnSBrs7u+V1LaNJO1lZs+m9uVar2mxFvJQSbdL2srLK/wkRYWzRRQVFMcpnneZnjNlj2Y0IV6pszpaPV5VmNkzio6JexVr5R4vGLLwc7lZzKxfxw6nRscy+k9vSOTS+qz13D33SE1drGad6683swNVUp2JMnU2dTMltbep/RrR7vqt4m9d0sx+rpjSnHlU3d3flvS2mT2o2FLoasX75EVmdq67n9FlgE5QACWxqKJX27tpTUt7N7n75jnj7aT4R9/n7gea2cqSTnH3r5fX6vwsClD8RzEl7XDFQuLfe/aS0LV490j6kuKN5FVFcriXZ1y436F9q7j7bWlEbR4vtin0YooXzHrp84OSFsoy5G5RyfFXivV2d0paNSVy80ia6O6rZmzT9ZJukfSiYk3fSu7+Vvp7x7h7tzdCbTYzO1/RC1Xbz2w3SX09+2Lsc919v/RYNvJJSY96eRu8d1vqdfs/dz9iTv/uLMzsL4pz1e3KWVk0dRb8T1GFtePagtzTlqyTwjZZpwCn9k1RTI1p1L5Sqp1aFGzpn95gAZnZcpIGelRClkVlvVpP/5/zvkemWP0krauo2rmhpM8pil19rVirm/NcNrPDsk73r7vvOHcfNrtjGWP+RrEF0l/V/tyXp6DF9xTXeIVGqNK1RafyJjdWUlGuJp7rG107uWcs+FIX71BFVdt3FdeRaymWoZQ6a6bI42pmqyo63k3S7UU6FtJMvfU9rQO1KO7zQN73Nkbm2pS5d5M8NpW+su72ZEWVt8zScO6PNOsU0Nzl0usurqZKKmMB6rcU6+QOViSHyyv/37ufYu+bxRQVGZdTzM3erED7rpe0tbv/Lf2O1RT/n0EZYhyg6J35lKTDvG1D0M0Um8Nnta9iIe2XJO3sqSKjIuHsqqJVl8zsK4pFzbWFyWWsK/iuYjuLWsJwr3IUGXH3/dLnTksjm9lsT95mdoW7fyN1wszSI5XnhOhRujjLPpA95br0kZu7FxqF7yLu85JkZkuq2L6fpa5h7shiE9iBaisgU2RKPVpEmv59lqSlPKqDDpG0rbufmCHMKZJG1d0+QDGq8QnFe+VuBZo4XTEjY7qic2xK+sityc/l7yvjdP+07GBZxT7Aa6ltXfTCisewiIUVlSe3qDuWt6DFUpJGm9k4RWfqzZ5vdKN+/fcKir0TTbG37b+V/1x2u5l9XdLVOdslqann+rLP0fu4+2/MbEtJiyquKS9RdHiXwmKT+VzJu5ldkjqZn2pwLFdItV9rOF2NK6J2Lxgjc8HMfi/px4p1Xz9Q7N003nNWFrLyNgetXdxerlg78h1F5c3XPGcJ0xRzQ0kjNWslojLm8RdiZuMlrSPpoVoPipk95mmjz5wxt5H0Q8U+casqps/u5u7jCze4xZjZJMW0iseKvAk0iLuEJLn7awVi/NDdf5m+3il1etS+9wt3/3E34yzt7q+UNRJUF/csxYXIlaqrApan57c3MrNtFaPXyyguUleU9GSLjTJfougkGq+6AjJZRjbrYm3o7vfP7hjmDDO7W7H+8A917x0T3b3bnXYdR4/qe/LN7F53/3yB9n0g6THF5sO3ecF9QMt8LncS/wV3z9SpbbGNzV6KtfOj1XaB+o6ki1rpXGqxPmILxZr14ZKukHSeuz+TI9a5kq5x9xvT7a0lbe/uB+Rs27uKqpPT1LYdQ9FO2cLMbFN3v8M6Kf+f9/9rbdt1/EbSXe5+Td5RtE46eReT9LKkPdz9qVnvNduYHc8LfRXXWKtnjZXu/33Ftfw16dD2ki7MPRJOMjcry7l3U4cYVyoy+G+qbnNQdz80R6yx7r527cmejo1297wlUWVmTylG0Maqrncg65tLZyMjdfEyj5CY2UPuvq617ckzj6RxRadWmdn2ioRuIUlfd/d/FonXqtIUxs08bSBeMJYp1hserLY1ldMVVT0z7R2W4s08ITY4ORaaglMGa7zHj+fphGkWiz0ST9KsI/Wt0BHzqKJ0823ptbuJpN3dfd8ebtpMZvakpNXL6OgoeypZ6jDZT7Pu05fr+WcllQ+vULzR7j6iQwKWad86M3ui/gLNzBarTZUzsyfdPfcWMGa2nWJd6TqKNdj/kHSPu9+eM15pz+VO4v/b6/YAzHC/Por9PkfN9oe7F++H7v5LMztDjWdi5E5eLfaP3VvSVorlE+spipH9MGOcWTqci3ZCtyIzO86joF+p75Up3rKKkcw1FTO97nL3TEXWUqyOnbwu6Q1vv7VFd2MdrRjoqd+PUIrX7znunrsuhEXtgNpsoHudAijlsOJ7N9X7jLvvZGbbeezR9WfF1LQ8aoUyXkkjTC8rehmKeNvds5bjbuQrs/+RzO42sx8rpmlsrijjen2eQA1O/gMkPSPp4DQdpZQezBbzQ0k3pl7q+nUFv84R63DF2o4RntYXWqz/PMvMDnf30zLGs06+bnR79sFKLlWddyR+DrtAkWCfJmkTxYVIU6bS5PCxu79hZn3MrI+732lmp/d0ozqYqJgq/UreAGa2vmLPyiVSD2vNwoqLkLyuVbxP3KZyyn2XXT681eO9blGpuFZxbkdl/z+/a2afrXX21SVyq6qT/TG7y92vlXRtirW1pMMU5+v5c4Ys47n8rhp3yFrednksWTlc7aerFlFbm5SnsmtDFmu09pD0umKN1pEe+8f2kfQvxf8li5fN7Kdqv668456RWdr3hUbHC1yTlsLdj02fy36v3FfSUEmTPbaO+qTivS2zvDNzOol1kqSTzOykIolbJ7HHKQoXFUYyl1gJezd1UObmoCda7Gv2A8Vebgsr3gSKuNOiytHVan/Bn+mJVeaLps5Rihf2Y4r1Cjcqf4Wujif/QhuiVsTPFdOE+yvfnmv1viVpc3efubGvu082s90Vc9mzJnPeydeNbndHqaWqrZw1N802v7vfbmaWXn8jzWyspJ9lCZJGqj+jmCpyc0lte8vMFlScN0eZ2RQ13rS2Jy0u6Qkze1jtz33bZogxn6IoxjyKkf6adxRVzvL6hBeYPt9A2eXDWz3eQYr1baua2UuSnpW0e8YYxyqqRP5cbRdaayt65zPPrKlnbcWLnlG8RvaQ9FCOONcrzpcLqeBz2d0Xmv1P5XKbmR2hWCJSP2U9c0EQd78+fS6z6utiknboeA2TEtE8ndS7Kp47tWlz96RjedVvV9JfMZo7VjHzoceY2e7u/qcOnVg1LulNSdd59sIyI929/j3sLUWNgiJrVAuzVIFb0pXWoAp31mvmZmGaZWJmT0sa4gX3bqqL923F7vBDFD3pC0r6mbufnSNW6esyrHE1Qc86vaVDr15tZKW2GLjH53fXs9iKobb33dPu/nFXP99FnH6K4i4D1X5qUKZph51NGamLl2vU0DKuEckbK8/vMbPpijf2Wq/vB7VvKSqxZdruwMzud/cNs9xnNvEKr7lpNjP7h2JqxlWS7lBUVz3Z3T+XIcbvFet5/6Eo4HO9u59QQtsWUNv6jt0UI+Gjsk7frovXcW1v7bySe0qpmX2x0XHPUS7fzFb0tqIvfSQt6O7vFGjbiZL+4WndTYE4tZkbhyjWLhYqH97q8RrEX0BSH89ZATl1wP5Q8RqRpMcl/dLb9gXN267hiorZhUYhO3sO12R5LltaA5W+XsnrKjyb2Q6efw1UadUOzex0dz+sLoltF1ORQPzB3R/MEHM9SY/XniMWhe9Wc/fMyfWcYGbLSzrdM1ZEN7P73H2jBiOwua7RzOwAd/+DxXZejXxS0jruvl7GuBco9u49KV1jXaF4rYzMEqdsZnaOu+9f1jVzs5DMJWb2d0k7eXl7cJTGmlDitxVZE9bf1cXeWLFZ5nOKk9jykvbMM2XBzG6S9LZmXW/4q4xx9uzq+3l7IS32DbzNSyjp29XzrBWeg1ZiqeoUr/Cam2YzsxGKaUeLKKqWLqzY9iTLhcxESWt6VPD8hGK+fua1CQ3illLuuy5eKWt7myVNn/+Oom2jFf+L37j7KRnj1C60TFH04H+K2R15L7ierYvXUeYL6grEazRKUB8wzxTzprAO1Scl5a4+aWb/13Ekt9Gx2cRo6XXMqR1ru/vYLpLYxSWd4BmKUVjsgzvM00Vw6owZk/fvTbM6jtCs/9tSLvbNzBTJZ66CG3OSmR3fYZStO/cxxbTcxxTLB/7u2Zdx9Fq9fppl3ejIB4oNtHPv3dQhbuHRG2veuoxa/G00a7XNzEUt6uJtpNgb7gIzW1wZ93FT2/q7g9LnS9Ln3ZVvCl69X0nawtPG1+nEe6li+kxWy7n7VgXbU/pGwXW+K+kIMyt0QZisaWaNRhpMxUrPl6XMUtVSOWtumsaigtbOHnvhvaecawokfVQbHfBYn5C7JHIHZZX7rilrbe9MqUf+DEmrKaZL9pX0fs7Xx+ru/o6Z7Sbp74op4mMV5e27zUue6uapbLiZ9Xf3qfXfs6i0PFfFU/uproWYWZfbfni26bgdYzesPqmorpzH5opti+pt3eBYl83q5OtGt7sf1GxexXtRbe3XXYrRs8wzYtx9bPrc6YijmX2UtYn15yaP6ZVFrolr6z//qBLWf3aYudNHsZ4s95Q+a1BGv9GxDPE6rdieJZHrMHXxN5L+IOl+Re2EYd4i0xilcjtiytbrkzm1rakaq4J7N3VwrdpGb/JO3WzWugyZ2dmKPV82UZx8dpT0cIF4xypK+35OMa10PsVC4G5PgaubrrS5ty9H+6N0cXhU3vZJmreWyKXf9c/0ZpPHP8xssLs/VqA9M1nJ+wiWeWHo7oU7DZrJy1+EXcaam6bx8vbCW81i09Laxdqn62573lFwd/+pmR2jtnLfvzOz3OW+VdLa3g5+p9iC5krFOWsPtU2/zmredB7ZXtLvPAoo5E5ezex2d99sdscy+IekjiMNjY5VOp67l7FXas36kl5QdPY9pAIJTQPDVUL1STP7rqIw2MrpdVuzkOLxy6Lsdcw1Z0maV237kX4rHft23oDWRSVfT+vqMphsZoekNknxeE7O2zaVv/6zfr3/NEmXerEtT9ptD5MS1yKzMS5RVGzfUnUV23PE6Tij6b+K/++vFM+/1pjGWH5HTKl6fTLXaHTEzBaVtLwX2JpAJYzepF6ou83sQ097c9W1cSdFxaW8NvDY02OCux9nZr9S9Czn9TVJayn1HLn7y2aWN6kwq1sTmHpDilbrG2Nmf1T7SlOZKmPVTQOdR9LeZjZZcYFZ6AJYMbXgcknbqG4fwZyxam1dVNIqav+G16NVsJrBzJZTjLLUOg3ulXSou7+YJ567T5b0JSu45qbJHkmjB0X2wltVxUe7G3J3N7NXFUWfpik2gL3KzDKX+5a0bvo8vP5XqOAbvLtPMrO+aXTygjTlKk+lsj8opm4/Kukei5LYmdfMpV7uBSQtnl679RstL5sjXqkbN1cg3m+7+n7GGTafUox47arYWuhvigvpx7O2q4HC1SeTPyver09S+07Odz37esOV0/nE6r5Wul1kY+gR7r5m3e07LLYuKaLMSr7fURTY+KninHK7pP0LtO16MztQJa3/9KiCPp/aztVPz+YuDVldaf26WTamVFo/T8yklIrt7r5JmuK6k7tfXqA9zVZKR0yzsGYuMbO7JG2ruFAfq1iUfb+7dzkXv4t45yj24io8etNo3nrRuezWtpfbg4oNpt9QzMf+TM54D7v7OrV2pYvhB/IkOGa2tmKK1gDFSee/kvYp0hufpr0epLo9PST93jMUvLFONqiu8fwbVZe6j6BF8Z1DJS2n6EVaT/G/aIkerjKZ2a2KC5v6Kbm7ufvmOeNNV0yRO7puLUVLrBupsRL297HOy5FLcSHyjKSfeMb9r2zWct9/9bpy3+7+6SzxmsHM7pH0JUX7XlVcWO/V4cIzb2yT1Nfdp2W836GKCsXLKAra1G+0fK67/y5jvD3VeOPmdxUb02aahjwH4+XaWNqat/64nyKpO0XScVn/D3Vx6qtPDlXMgslbSbVR/CXVvuPu3xnuW1oxlQ5xxyku0J9Jt1eWdFXB65bae+XM/dtqx/LGLIuVWPAlxfuyorPoGbUl1gfknXZuJZfWr7vmu0cxqvmqpIcL/L1j3H347H+yZ1jsHX2Iu7fMsot6JHOJtW1Q/W3FqNyx9RfXOeI9oSj7/axyjt6Y2daSvizpG4qRm5qFFT0E6+RpW4p9jGJEYzNJZyreaM7NMte5Q7wjFCNBmyt6C/eR9Gd3P6NAGwdIkru/nTdGh3jzKaaBuopVsyy1CpaZPeju65nZzYqewpcVb3q5LnzTCOIISQ+6+1CLPY1+4e475InXyqxBcZJGxzLEmyDpJsUo887u/qbVFUPpDSzW5Q1SVKHMWq30OEnnN+rYMLPVPMcWElb+2t4VFZ118yqKqwxQdOxMyhuzQ/y93b1Rwt2d+36vyDmzQbyvu/tfeku8urifcPcPZv+Tnd6/n2KmxK6KNTLXKZ7XL+WM16yE6auSfq3oBJiiqPr6pLuv0eUd5wAz20wxkjZZcf2zoqS93b1RVcDuxixcybcu1hKS9tOsa6BybXpdNoviT1+pnZcs1nL/zd1XLRCz3V7KUv4ZO9a4Yvsx7v6HnPFOVnQCFt7KokzN7ogpC8lcki6At1BUPPyJu48umMw1HMXJMnpjZmsqnjzHq/0eUu9KutPLqxjXT1EWvlDSZLHB9xaKE/fN7n5rgVhlX8BtrPKqWZZdBesripHC5dW2j+Bx7p5rDae1VWQcL2ldd/+fmT3eCm/wZbMoWHSBYn2LFBdfe3vONUZ1I8s7K15zeyg6OXp8ZM7Mfujuv7ROtrTwnMWauvh9BxR4Y849UtAhTsO1ve6+b554c4KZ/dvdVyghzsXuvkdJbdpIsU/VY3nOy2a2riJJeMfM5ldM7Rum2Jf1F1nfOyzWKl3tOadDdxF3fUnnKbaIWCG9hx7g7gdmiHGxojPjRkmXecHtCDrEXkDShx7FNj6rmEL39wIdi48qphzfljqjN5G0e5bXh8U6tB8rZsD8WrGB++cVI0L7unvujbrTtUUt0Xo6y0yYTuJ1rOQ7QLFlRLcr+dbF+ofifbdjpdxcnQtWYsGXFK/d7Jw06v9wgRk7JyvWCrfbSzlvMmJt09RLUfbIZlnM7HBFx984te0fPVPejpiykcwlFmvQjpF0n7sfmKYEnOIZ9/RoELfwRY2ZzZv3hNBFzAmSLpN0uecrTNBV7MUlveE5n1zNuICz2FT5m96hmmWe6RmdjAblTvzLZmbXKNYSHKZ4o/+vogDMl3uyXc2QOk3OUBQtcMXi/0MKJA8zR+Es9pr6s6QV3H2Rclqcn5l91d2v72xKmTevOmq3lT1SUHtd1X1eUHHx+/kCbfyK4kKw49513a5mae2LTrT7lqTPunu/jG3q2HFjivPfHVL23t/aFKj09X6KKebXKDrbrnf3kzPGe1yxlcU0iyUEHyhGRzZLxzON+pvZ24re92cUHTFXunuhdcIp7kOK94vrPOc+kWY2Q20jA4X35eoQe6wiUVpUUbFvtKKybK6NkWtT01JSt1ZKEh/1DFOGzew+RRGHhRUj1YdJuj6180R3X7fze3cZt79i+t1GisfxXklne4fqpT2lyAyOTuL9UXHRXzsPf0vSdHfPVfDFzM5SnKOuUDx+O0n6t6TbpOzb71j5eyn/WzGL5XJJd+S93mt1Znaqoqr8apImKF63/1DsB9qjo4b1SOaaxMy2VVTjKXxRY11UcCrQvhUl7Zw+ZihekFdkvQhOUw5PVmzaeYJi7dLiikXJe7j7TTna1owLuFmSrbwJmJldreh1q6+CtYm7b58xTtNHWtL0ngGSbnL3rKWbW1qaDnhx3guhTmKu7akMdro9QNJ23iLlh1tdGSMFHeKVurY3xZyUYj1WoMPpP4oqbh1nR5jiTX6ZjPHGKXrM/6i2/dcuVfSkZ+797dApMVrSl939tTQy9KCn9UYZ4j3p7qvV2urt9yLLfFGcZjesrVi7uLNivfpYxd98teff7Lv2fKn/+zMlN81UN/L/PUnzp/N/7vaZ2W2KSqonKd53pygKj2yQIcbM/5+ZTap/bRVJeCyq2L6rtqJj35S0iLvvlCNWMzYNP1HxWr0xa3s6iTfL/7Hg/7arqdruGaeDWsl7KVvsT/oVxTlqbUUHwGXufl+BmIM063VuS7z3WizTGa5I7NZPH295i+z71+urWdZYF3tm5Ax5gqLwRLuLmpyxyqzgJGnmdM9fSvplShaPkfR/yr5/3e8UUzQGKHqRt3b3By3WaV2q6LnJ6sP0+QMzW0ZxAbd0jjj1xlrBapZ1yqqCVVs/lHsaS2dSorOUYs2mFBXUco1WtSqPMv0rmtl8ZSWqHhvTzvKG0go6uZCZKe90mZJ97O5vmFkfM+vj7nea2ekF4t1gZosoClCMU1rbW7CNL0iaWLAn+QbFVL7xHb9hUUwrq+GKokU/kXSku4+3qGKcdwpPH4uqmH0UnbavSZK7v29mmYqzJBOtbS3go2Y23N3HpBkOeWaNuLvPkHSLpFvSFLWtFdOkT5W0RI6YkvSCRfVjTzEPVb5y6c1iFlNBd1Ncb0jF3su3U7xfHp5iDlAsy8hiRt3XHSuxzlB+gzpc6N5pUUsgj1qBq1M7+f7iiqJpWS6sD5X0Y4v96T5S8ZHX6Wb2aW9f8CX3NEQvf9udUvdS9liTeoWkK9K55jeS7lbOPZAttrfaWPE/vFFxPqiNGreC+RWj1wPSx8uKDc5bAslcm7L2zKgp86Jmfne/3cwsJWEj03SNXMVKajqMzk2XlLVsuCTN4+63pHjH13rG3P0py78PcaMLuD/mDZZ8RzHVqHbiuldt+990W0qSTnP3XQq2R572xSl7elzq9T1W0n/U9mbsioXKc5vJku5P09TqF03/Ok+wFn9DqV3I7KBIzmsdE7sq/tet4K00kn6vpFFmNkV1/5es3P2E9OVfzOwGlbC2V3Geu9HM7lb7i5puP2e6Gml0929mbVBKbE6zqJh2Whr5K/L+PEAx0mWKxGZpd38l/W/ynJi/Lek3ZvZTRZGCB8zsBUVinGcaWbs2eCwjuE7SdanHP6/vKC4ql1UUx7hFcd5vFYcptsC4xt0fTxf8uQqCpPeiG9x9E8V5Pu/7yKrWtsdkbb9JpdtF1iuNM7P1atcEFusuc3VcehM2DfcS92NNjlQkrO0KvuQN1oQBhutU7l7KtZk/O0vaSvG//UaBcDtKWlPSI+6+t5ktpbb3uB5jMa18DcUo80OKKZa/9pJqVpSFZK5NKXtm1Kld1Nyj4hc1/7NU2tvMDla8SS1YoG21tQXzKvaq2sljf6086nvuPuzwvVw932VfwKU3vUc9qkDlusiva1vpo0EW5fV3cve30u1FFdMVtswZ8lBJn3P3N8poX4t7Jn30UVSbkortn9aSbyhS24WMmf3K25dwvt7MSh/dzWlbSVMVz8HdlYr55A2WOq3OV1TG/a/qkq8Cfi7pPcUF0nwlxCuNR0GQnSwKQGXer64uzsBOvjVDsSdo1nhvS9rLonLvSoprhxfdPW8nws5d/K7cVSgVo5ClTbsum7ftHfuJdHuy2joYs8aabmYzzGxAwQ6O1QrctytrS/qHxdoqSVpB0tOW9mv1fEscSts03KK3eTdJK7n7CWa2vKSl3f3hrO1Kbbg9ta+sgi+lDjA0odP4OUmPKEbnjnT33J12Sa0w0LR0npmiKArX01aQ1E+xr/NLkl6U9FZPNqgRkrk2takib6VpVq9KWrJAvDKmP9QcqigIcohi+uamio2li9jDUzGQgta02IjSNOumlLmmqZnZLIvpLRbMP+buU7LGS296T5vZCp6zMEYHpY4GSVqilsilOP+1KJyT1wuSStnOoQKecPcr6w9YFDPKq1XfUOotYGYr1zpgzGwlxYbTPcYa71tXG335mZnl2rdOcdG/t6TRKWG9QNItBadILuMZt1yY09z9b4rNqsuO+4Hapl7nuf87ig3Si7bjn0VjdOL+dJF5uaS/1J9XW4HVVduUlKvaZgfvSXosdQjWvxd1O0H0nPujdsNWTYhZ5pKT3ys6NzZVXFe9p9imKW+1yL6KxGug4tr6S2ZW5Lqg1AGGrhLhnCGHpPNBWcakGVnnKmYVvCfpgRLj5+LuW6XEfw3FerkfSBpkZm8q9u89tkcbmJDMtTknjYgcoxiKXlAFpjHW9VLMMLO/qUB1R3cfnb58TwWG7TvEfNpKKP/v7rnmR8/GvorFpbXpJxsrXtwrpamcl3R2xy4sKulxM3tY7d/08qwzajQaVMT0+kQzTX/N/Fwxs9oG95Ml3ZWed7mmkVXI0YrR5dkd666WfEPp4HDF/7d+Os8BPdmgrqYsWd2+delzlriTJP3EYl/MryhG6aZbFAf4jeerJnajmW1Rmx6OuYe7f9bM1lEUZfiJxRqty9y9JUbXJZ2uuOC/TpLc/VEz+0KX9+ja1emjpaTX/M1eYE+0TpS55GRdj2I0j0gzO1GLjNRfr5iV8JiKrTWsKXuAoZRE2FLhNkknNlpKU2ANXq1D42wzu0nSwu7eWdXgOSpdu080s7cUHeVvK96P1lE8pj2OZC5x99qarLtVYJ64dVHd0czyVnf8rGI+dsfNHjct0M6G5f/zxivZPIpNuP8jSWmq28WS1lVMW82TzB1TVuPcPfe0sU78WNJ9aQ2PKUpC5ymoUrug/nf6mE8tNo2sLGa2taQvS1rWzH5b962FJeUp8FCbdnNS6s1vuTeUGne/KfWy1i6Unio4naepPPYietSiamtmZjZEceHxZcUmtaMU5c7vUOzDmdV3JR1hZv9TXDAVLjmP1pGmyT1sZr9QTKu/SC0yVVqS3P2FDhfBRYpk9Ph2JI00YTZMTZlLTj5OSWdtv9glVCwJWy7P1NEulDrAoPIS4dpUz7Fd/lQ3mVmne7ia2TB3H1fG78nLYk/MDdLHx0rbEig6FimA0mosNrf8utqGyCXl2qi6GdUdr5R0tmK0oKxNGjfwtvL/x5nZryT9vaTYRS3fYS3GlHTsTTPLtd+e1y2ctuL74N2pxlsJZE6u0xvTAMUGvOulw4e5++tZYzUhyWxlLysWXNdKmte8qxi5yszd3cxulDQ43X6uYBubaW21navWTNN5WqFIS6c8xwbk6WLjLcXUtKPqktaHzGzDnO0orfBB6rw7Q7HuaD5FJbf3SQx7Rpoa/TXFyNynFXvrrdOjjWqv1GqbZU6dM7PNFKX6O659z6vM2TA1jZac7JEz1m8Vz48lzeznig7tnxZo29/LHPEva4ChTimJsJdfuG2MpImKwkpS++JIrvgf96SBimvww939lR5uS6fYZy5JvfBvKy4MZyZM7v6rjHHq92yZuTdPuv2Ip71vMsYc6zk2t55NzNL3byqLmf1esei0NlXu64pFp0eqrXpXd2M1Yx+8+v9F/9S+ae6epxqoLG38mue+ncRrVML+bcVJ8w/eIpu2liFdvL2fRn9q03v6ec4iCmZ2kaTf1U1tbjlmdoniQnW82s5Vnnd6SyurXxtYctwhmrXjLvN0tbSObxfFuWq44sLys+5+dM52lZoc9sJ4z0r6q2LP1FabHl3rSPyNYn89U1TbPNRzFquy2PC7NnXuq0pT59w98whOOvetr3ivvFcxC+Y+z1m1z6LS4Sw8/3YbjX5HX0m7uPuonPdfVbHxvUm63d2LJNZfU4wA91GBEX8z293d/1S3bKJebU+967L+X8xshKLjYBHFtdAASb/0DHvzdYj3WUlHaNbzaKbky8wOUyTSb0u6TFHptZS98HoTkrnEzCZ6CYvirW5DVZt1c9V2t7sRa7H05SGK0alr1H4NVO7d59MalDMUJ7Izlcr/u3tp0xHzStPdvi6p1vN+v2Ixe551ZGPUNlJ6jjqMlOZJrjv5PQ+7e64eYDM7WdErdbna92Dm+v+a2W8U+zRdmg7trKiM54ppg9/KE7cVpc6IL9VO/hYVZG/xDJvmdoj3lKRVJD2n+F/U3pBbZlsHM3tS0up5R5aroJMLmZm8wPpPMztfsU3H46rbusNzlPyudcSkGQ5D0rFcnXa1eCo3Oext8Wxufl10VOvoNbPHPG0EX7Tz12Jv1x0VF+rLuHuPz+BKnXYHKbacuE7Sren2DyRNcPftMsRarKvvF3jffVZR+O6xIs9BMzvA3f9gsU1OI5+UtI67r9fJ9+cIM3tUMWOs4wBIrumXFtt07KJ4DJ+X9AtvsI8nGuvxF2kL+YeZDXb3onNgy6zuOFZxAV4bdj6y7nuuAkPv3pz9m0qRToRXpY+iSt8Hr8ObQR/FlLcBBdpYK9Ndvx9Skf/vBu5eX5HrejMb7e4jzOzxnDFbVf/6Xjx3f89y7FNVt7Yj73YQc9JExT5zLTvlowRl7wFVbz1vv5lxER9YFE0Yb2a/VPxPimwCLXefZGZ902jzBRYFGnIlN70w3uJm9kPNWtirR6dqWawX7fQCv8CoemlryMxsd8V67cGKzsXfKUf1RDO7z903slkr3BZZm3qJpP8qilF9W9FBa5K+luOCv/66aoUU1xQjVv9WbL2RxwuSJhbtTEiJXF9J77j7aY1+xswyV0a38msvTHP3s3LedxbuPtnMrlVszv0tSZ9VzD5BN5DMtdlIsY/Os4rRr1w98l5idUd3z3tSmS2L8u03ufu7ihf4MDM7wd0fadbv7EabmvEmUPo+eGr/ZjBNUeq7002EZ6cJ/+cFrX11zBXU9gZfyt54LeR9q1sknabA5lnz8VdJw9z9eTP7i7t/vcxGlmxxSU+ktSj1I/VF1qK0FG/u+s8HzGx1d3+ihFjfUkwNPFixVnN5xayCvMpODntbvFGKGQ5fUWwgvqek1wrEK0uz9oEsc9ui0xVVms+WdKfnXDPs7hulz2V2yKxcN/L4R8XzZAXPsWSg9n5rZucqpvTdmG5vLWn7Am2sVZH+uwpWkfYoILOrYvpso+/nKYRSSu2Fus7s683sQBWcMdZhRO4FxVTLX3h5azd7BaZZJhbl4GfhzduDpduswb5rivnFufZdSzEneBRA2UjSiZJOkfQzd1+3QFNbjplNV9t0ufkl1dZSmWJUZ96ealtNGkn6vuLNaX9LG4+6+w05431ZcdJ+RvF3riTpQEl3SdrP3U8vo92tIK0DuExREMUUI1Y7Z53qUT81rsg0uTlhTqxFaRVm1l/RUdJxpCXzlMi6mF9UTNV6VQU67pohvQ9NkTSvIjkcIOn3Hls0EG/28WrTDuunvY7uMFMBnTCzNSR9QdG5vYpi4+vC0/LNbAFFYZpd3X2bHPcvtGSlk5gzp6Z2dSxDvIbTIvN2TJnZaYrXRcflF7mqOxadflsX51m1nzHWLonwjMV3zGyGpAmSrlXbcpD6eHPjlkqlI5nrwGKz5vqLhjLL6uZisV9Yw33XJOXad612wWpmJymSwj+3wkVsml7wuJe/P02pLCqSDVT76Qq5qgma2eWK/+ce7j4oJXf/8FRIJ2fMfmorXf90nh7MqrCoCve5dPNpd89c8dS6WOuKnmNmV0p6StI3JR0vaTdJT7r7oQViTlJ0nrTbDypLx52ZPaaup831eGLYG5nZg+6+npndrKhW+LKkq9z90z3crkZFqWbKOqpuZtd19f08o/RpXdqGkr6omG65uKQH3T3XSF8acd1G8drdUrGtyNWeqiFmjFXrlJXad8zmnrWTniP3qm3bit0kfcHdW2KqvUXV7I4867RIK7n2gsU+ji94quxoZnsqZiM8J2lkjngj1fVrozdV6c6NZC4xs20l/UrSMoon/IqKi4Y1erRhmnnS2cNn3XdtV0n3eI7CLWmd3EuSNleUxf9Q0sPuvmZpDc8pzZv+Xisk0o1YydUEra2IQv3o0KNF/hdlJputrG5Uc0V33y/vqOZsRnDzTvEtVYPpxzO/pRZpY9nqOp1qMwnmlXSvF1j8b2YPuPv6BdvVcCZHTdYZHWUnh70tXl3crygu0JdXFPhaWHGBmTmBKFNno+k1WUfVzew1xZS0SyU9JLUr555rlN7MJki6L33c4+4vZo2R4myhuDbZQtEBfbmkM9x9YJ54zZKSnGMVI5FSVO88LmsyUhevlOqOZWswklbPc4ykjVMUHXvTYsP7yyR9T7Hn52ruvmPBJiMH1sy1OUGxz9dt6eJhE0m793Cbakrfd03SNyRtJelUd3/LzJZW+wIrPakZ+9OUabjKrSb4kZnNr7bNSz+tup6zrDpLNhUdAHObCxSjmrWL85cUawMyJXNe4lrXZvFy16BURe389paZDVJMjVyyYMxHzOzPkq5X+x7qbm9N0ITp918hXnF1nThvS9pEkixKn/eoJkyB/pSiI3ZXxcjX3xTVmXMXuKqblvoJz7m1S3KTIqHeyN2fTTF/UyBeU6SkLfcIfwO1NWl/VEn7AZvZNpp1inmm4ide/pr8vnUJ786SznH3vyiK6Y0v+Xehm0jm2nzs7m+YWR8z6+Pud5rZ6T3dqOSuNJJWv+/aXWke+lt5AqaT9dVmtqRFgQwppjO1gh7fHmE2yq4meKziDXB5MxulmOqyV4F4ZSebrezT7r5zWiwud//ALGeZUrSic8xsUcVmvtcpCvkUPT/Mr0jitqg75pLy7DNXyj5pZSeHvS3ebHxfUdyjx1lJm3x7VP68SdJNaUr9roprguPc/Xc527a+pPMUr7EVzGxNSQe4+4EZQw1TFLS4zcwmK0ZuWqazzMxOd/fDOpv6WqDTuNTqjmZ2tqK4zSaKBHFHSQ/niDNCMS3y1XR7D8U15PPKMS1SUl8zm8fdpym2ttq/7nvkFD2EB77NWxZ7VN0jaZSZTVHdqFBPcvcDzezrikXJUoyw1PZd6/YG2vUaTCtdQZHM9fi00vpeTItNVt9ohcSk7uS/kEqsJujut6apC+sppkIc6u6vF2hqbyhdX1PqqCZag5kt7+4vuPsf06F7lLbqSFPpcnP3vYu2r87v1GCftLzBykoOe2u8zn5NibGKukBtm3xvorTJd55AKYnbRpHIDVSsEbymQNtOV6xtu06S3P3RNI0uE4+tAsZLOipN999V0rwWVR6vcfdzCrSxDLUaA6eWEcxKru5YZ4M0tXyCux9nZr+S9Pcccf6g2KRe6f95stqmRZ6jSBKzuFTS3Wb2umJ5zr0p9mcUI+LoAayZS9Io14eKE+tuiqpao9z9jR5uV1MKglhs+LipOkwrdffcJfZLaNN6ihPNm4ppr5coFmH3UawZvKmn2iaVv+6hQ+whmnWufeaRghTrTsWJeq4tXV9jZpsrRm1Wl3SL0qimu9/Vk+1CMRabt2/lHcqjm9nekn7qBQpaWIkVMo1Nw1sqXie/49/uvsLsf7L5rKRNvs3sYkmDJN0o6TJ3n1hC2x5y93XLXLtdF7uPIqHYJc/rrNnS6P/y7j4hx31LXZNWF7f2/3hQ0g6S3lBcC34mY5yZ/0MzO1PSa+4+Mt0e7zkKraVrtaUl3eLu76djn5W0oOesttnJ7xlWZry5GSNzSe0JKWmGRfXIlhgN8thv5Gmr2zesJK04rfR3is1AB0i6Q9LW7v6gma2q6A3q0WROsR5rKXe/v/6gxfYOuUfBzOx8SUMkPa626nq5pn0lI/O2pWqaMKqJ1vB9SbeY2Tbu/i9JMrOjFeuDuuxU6YZLFLMQtlRdhcycsdg0vAXiWdfFgebP27YmKGuT790VM4cOlXRI3czyIsWQXkgjaW5RaOhQ5X9dtOPuMxSdbbeUEa8MZnaXpG0V18FjJU0xs/vd/ftZ4jRhTVrNDWa2iGLbqHGK5/e5OeKUPi3S3R9scOyfeWLNxncl7deEuHOdXp/MdTUaZGY9PhqUNKMgSCtOK53H3W+RJDM7vnbCcPenWmQZ1OlqfJHxdvreV3PGXc/dV89531kUGSGsCjPruHVALZleIXV80JtXYe5+o5n9T9LfzWx7Sd+WtI6idPh/C4b/jLvvZGbbuftFFsVQ7s0Zi03DWyCeV6c4UCmbfLt7oQ6DTnxH0m8kLatIMm+RdFATfk+rGODu75jZtyVd7O7HWlT0zMXMDlLM5nor3V5Usa/e7/PEc/cT0pd/saiZ0N/d80xjrOy0SHcnkeumXj/NMk3zqI0GnaMOo0F5p8uUqbPpfQWn9bXctFLrYq+vjrd7gnWx+awV22z0PEm/cvcnCrbvPnffqEEv9VxXut4a78FT497D5aBRDjP7vGINyj8kfcNL2C/RzB5293XM7B5JByoqZD6cdzpUmaz1N+Vu6XhAd1lsj7GFpIsk/cTdR9dPl84Rb5Ypi3mmXFv5BUvm2LTIIszsdnffbHbH0BjJXN0L0MyedPfV6r6Xe+1D2dKb3irufpvF3lp93f3dkmK3RJER63qvr/7uPm9PtU2SzOxf7r5KJ9+blHUue919v6hYdP6qYo1bLfli42H0SnUdEiapn2KLgukqoWMi9cT/RdJgSRcqVch09z/kiNVov7S3JY2RdGJPdo6hdVgTNvkui5n9rItve90IUXfjLdbV9/MkI81gZjspKuPe7+7fNbOVJZ3i7rlG1tO5YEjtOsqi3sEEz7hXsfWyfdws1jB/QrEn4cZqW3u4sKSbvOR6EXOrXj/NUm1rlKQYqarXEpmume2nmOe8mGL/sGUV+5lk7rFo5Wml3vp7fY0xs/3cvd289XRxOLZA3PMU07UeU/vnY27pjWQptS+o0pKbsOdhZj9091+mr3dy9yvrvvcLd/9xz7UORTVj2pw1p0Lm3xVJ5p/T7V0UFyavKhLFTFOvy04Oe1u8Fra+utjku4c1Wl6xgKJI0CcV1wlZjFVbR8wKkv6bvl5E0r8lNWuNWSbpPePKutuTVWyK9E2SLjezWqfQAcq3zr+37eN2gKTDFJXVx6rttfGOoo4CuoGRuRYfDZJi9FCxXuQhb6sylWtaXxWmlbYqM1tKMeXrI7Ulb8MVJbW/VpsWkSPuA+6+/ux/stvxvqcof/0f1RVUmZtG+lp9Si5ajzWhQmaj51rtWJ5zdFo31llyuJG7Z00Oe1W8VpU612qbfA9RCZt8N4OZLaRY17evpCsU0/+n5Ix1rmIrghvT7a0lbe/uB5TV3iLSNMOzFEXNBllUlN7W3U/MGa+PIjGpdbLfKumPHoV9ssSZKGmou09L56z93f2e2vfcfVCe9rU6M/ueu5/R0+2oql4/MleB0SBJ+p+7f1QrAmJm8yj/qGGrFxlpWe7+H0kbWGzjUDuh/s3d7ygY+pFUhOF6td9KIG81y0MlfW4u6tVuxDr5utFtQGpOhcy+ZraOuz+c4o1Q2wbJ03LE+1KH5PCxuuRwd+JVkzdhk+8ypamR31esn79I0jAvXmhoPa8rYOHuf0/Je6s4V9KRin3Y5O4T0vtwrmTOo2LnWemjiMoWLMmjbo3gGel24TWCvVGvT+Yq4m4z+7Gk+S321TpQceGfR8tPK2117n6nYn53WeZXJHFb1P8a5d+a4AXNhSf9DryTrxvdBppVIfPbks63qAxsiqlB37YoMHVSjnhlJ4e9LV7LsvI3+S6FmZ2i2MfsHEmD3f29kkK/bGY/lfSndHs3SS+XFLsMn3D3hzt0Ymd+zpnZFe7+jU6mDCvrjBh3/7mZ3a62giW1mH0Ua+fmNmVvat4r9fppllWQhu/3VVzsm6SbFcP3mf95VZhWimIsqmN+TjGVp36k79c91qiS8TxGXtacCpkDJMnzlQ6vjzNC0vmKoiwzk0PFHpTbuPsVxKsea8Im32UxsxmK94lpKrEKchrtO1bSF9KheyQd1yojLWb2d8WWIlemkeAdJe3r7ltnjLO0u79iUaRuFu7+fAnNnWtZEzY1741I5oAeZmYrKXqiBqp9wZJcFc7M7NhGx939uDzxgLmBNaFCZhpt+bpmfe0eX7CtpSSHvTVeq0kJU63QyFy9bUxVWFSvPEfSBooiLc9K2q2s5Ct1wu/q7qPKiDe36q1rBMvGNMsKMLMNJY2UtKLif1Z7A+jxfZFQir8qKlperxKqWZK0AbPy5mwsfa1iSvNY1Y2C59UxOaxNAcubHPa2eK3Km7PJd0sys+vVxVT3vJ2UZfOoXvmlNCW6j2J2xy6KtVrdZmYLKzZXX1axxdCtihG/H0h6VBLJXNd61RrBZiGZq4bzFBuqjlX0JGPuMtXdf1tWsFSl6wjNOlrARtpAuZZz961KjFdqctgL46HnndrTDehKh+TrWkm3pds/kDRB2ZOvSxQjew8opgj/WNHhvr27jy+n1XOvXrhGsCmYZlkBZvaQu6/b0+1Ac5jZNyWtIukWtV/jNi5nvEcV+xC2S/7dvcheeAA6MLNzJJ3h7o+VFK/UaUW9LR5ah8Vm0J9JNyeVsTa1DGZ2rdqSr80kLalIvg7Nk3xZ3RYkaQuKVySt0Cp/L3oHRuaq4c5UcepqlXCxj5YzWLFp+Kaq2xcu3c5jmrsXLY8MYPY2krSXmT2rODfXpsDn3dPxH2Y2uKzksBfGQw9LWyf9QtI+iimLJml5M7tA0k/c/eOebJ+kleuSrz+qePI18+9x9+lm9iKJHOY0RuYqwMwalcF3ps3NHcxskqTV3f2jkuKNlDRFUbGvPvlviSpiwNyi7Ap2ZvaEYjSjlOSwt8VDzzOz0yQtJOlwd383HVtYMf3yQ3c/tIfbN65+b8OOt3PEq1VWltpXV6a4DeYYkjmgh5nZXxUVnKaUFO/ZBocpmAM0iZktKal/7ba7/ztnnLKTw14VDz3PzP4l6bMdt05KUxCfcvdVeqZlM9tB8oW5DtMsK6BZ5a/RMhaR9JSZjVb7kbRcVb/cfaWS2gWgC2a2raRfSVpGMRq+oqQnJa2RJ14tiemYHObV2+KhJXijPXDTFMQeHz1w976z/ymgWkjmqoGKX3O3hvvC5WVmezQ67u4Xl/l7AOgESetJus3d1zKzTSTtnjdY2clhb4uHlvCEme3R8f3GzHaX9FQPtQmYq5HMVUPZ5a/RQtz9bjNbStKIdOjhglMuR9R93V9RsWucJJI5oFwfu/sbZtbHzPq4+51mdnqBeKUmh70wHnreQZKuNrN9FB3QkjRcMZ3xaz3WKmAuRjJXDVT8mouZ2TcknSLpLsW8/TPM7Eh3vypPPHdvtzeLmS0i6bKCzQQwq7fMbEFJ90gaZWZT1LYeJ4+yk8PeFg89zN1fkrSumW2qthHWG9399h5sFjBXI5mrhrLLX6O1/ETSiNponJktodjINFcy18D7klhHB5RvO0lTJR0uaTdJAyQVWctcdnLY2+KhRbj7HZLu6Ol2AL0B1SwrgIpfc7f6TUfT7T6SHq0/ljHe9Yp96iSpj6TVJV3h7kcVbiyAWaTS6/XFqXJtA2JmCyiSQ1NbcjjK3d8gHgCgEZK5Cimr/DVaS9oQfoikS9OhnSU95u4/zBnvi3U3p0l63t1fLNZKAB2Z2QGSjlMkJDPUNmui0DYgZSWHvTUeAPQmJHMV0FnFL3en4tdcwsx2UEynlaR73f2aHDE+I2kpd7+/w/ENJb3q7s8UbymAmrSn1vru/npJ8UpNDntbPADojUjmKsDMHpW0qTpU/HL3fXu4aSigi+RrI0mvZE2+zOwGSUd3LJRjZoMl/cLdv1q0zQDamNlNknZw9w9Kild2ctir4gFAb0QBlGqg4tfc6XRJRzc4/nb6Xtbka6lGFU/d/TEzG5i1cQBm62hFteGHVLcHqLsfkjPeM5JKSQx7aTwA6HVI5qqBil9zp7KTr0W6+N78OeIB6NofFBX7HlNMEyyq7OSwt8UDgF6HZK4atpP0ocorf43WsEgX38uTfI0xs/3c/dz6g2b2bbVt3gqgPPO6+/dLjFd2ctjb4gFAr8OauYoxs8UlveH84yrPzC6VdEcnydfm7r5zxnhLSbpG0kdqS96GS5pP0tfc/dXirQZQY2a/kPScpOvVfmQp79YEj7j7WuW0rvfFA4DeiGSuhZnZepJOlvSmpBMkXSJpccXeYXu4+0092DwU1KzkKxXIGZRuPp42bwVQMjN7tsHhItUdy04Oe1U8AOiNSOZamJmNkfRjxbTKcyRt7e4Pmtmqki6lR3PuQPIFQGpKctir4gFAb0Qy18LMbLy7D01fP+nuq9V9j+kpANDDzGyQpNUl9a8dc/eLe65FAIDehAIora1+QfiHHb5HFg4APcjMjpW0sSKZu1HS1pLuk5Q7mSs7Oext8QCgt2FkroWZ2XTFFgSmqG5Y24/HJPV393l7qm0A0NuZ2WOS1pT0iLuvmdbB/sndN88Zr2Fy6O47Eg8A0Eifnm4AOufufd19YXdfyN3nSV/XbpPIAUDP+tDdZ0iaZmYLS5oiafkC8XaUtJmkV919b0WiOIB4AIDOMM0SAIB8xpjZIpLOVVSkfU/SAwXifejuM8ysrOSwt8UDgF6HZA4AgIzMzCSd5O5vSTrbzG6StLC7TygQtuzksLfFA4BehzVzAADkYGaPufvgkmKZpOXc/YV0e6AKJIe9LR4A9FYkcwAA5GBmF0n6nbuPLileaclhb4wHAL0RBVAAAMhnXUkPmNkzZjbBzB4zsyIjS+PMbERZjeuF8QCg12FkDgCAHMxsxUbH3f35nPGekvQZSc+rbVsad/chxAMANEIyBwBATma2pqTPp5v3uvujBWKVnRz2qngA0BsxzRIAgBzM7FBJoyQtmT7+ZGbfyxsvJTGLSPpq+likSGLT2+IBQG9EMgcAQD77SlrX3X/m7j+TtJ6k/fIGKzs57G3xAKA3YpolAAA5mNljkka4+9R0u7+k0XkrNKbiKeu7+/vp9gKSHiiwJq1XxQOA3ohNwwEAyOcCSQ+Z2TXp9vaSzisQzyRNr7s9PR0jHgCgIZI5AAAyMLPl3f0Fd/+1md0laaP0rb0lLVsgdNnJYW+LBwC9DtMsAQDIIJXU38rdn+twfB9JP3H3T2eMt7y7v5C+Hqa25PBeScu6+w3EA4D/b+9+QqUq4zCOfx8qFLEWQYt2goZRhhJYEKlQbiwwiegupMWlXAhtiqw2LnQluXFnq7qtCiqFCIuiKCSh/5qB/SFqEVQQtVAiCfu5OO9wh0hk7p2LnJnvZ3M473vmOZzZPbxz3tH/scxJkjSCJPcBh4D7q+r7NvYssBPYVlU/j5g37nI4VXmSNM3czVKSpBFU1TFgN/BWknVJDgHbgc2jFrnmSeCdJDcNBlo5fALYYp4k6VJ8Z06SpBFV1XtJZoEPgBPAPYNdLReQdSzJebpyuAN4DLiDrhz+aZ4k6VL8maUkSSNIchYoup0XlwH/ML8TY1XVdQvM3QQcpSuHDy+0HE5rniRNI8ucJElX0LjL4bTlSdI0s8xJkiRJUg+5AYokSZIk9ZBlTpIkSZJ6yDInSZIkST1kmZMkTZwk58actyPJLUPn+5NsHec9JEkalRugSJImTpJzVbVyjHlzwJtV9dq4MiVJWixX5iRJEyudg0m+TnI6yczQ3DNt7FSSA21sV5JP29jrSVYkuQvYDhxMcjLJ6iRzSR5qn7k3yZct64Uky9r4T0n2Jfmizd18Jb4DSdLkssxJkibZg8AGYD2wla6Q3ZhkG/AAcGdVrQeea9cfqaqNbewM8GhVnQDeAPZU1Yaq+mEQnmQ5MAfMVNVtwNXA7qH7/15VtwOHgaeW8DklSVPIMidJmmR3Ay9X1YWq+g34ENhIV+xerKq/AKrqj3b9uiTHk5wGdgK3XiZ/LfBjVX3Xzl8CNg/NH2nHz4FVi30YSZKGWeYkSZo3BzzeVtn2AcsXmXe+HS/QrdpJkjQ2ljlJ0iQ7DswkuSrJDXSrZp8A7wKzSVYAJLm+XX8t8EuSa+hW5gbOtrn/+hZYlWRNO3+EbvVPkqQlZ5mTJE2yo8BXwCngfeDpqvq1qt6mew/usyQnmX+fbS/wMfAR8M1QzivAnrbRyerBYFX9DcwCr7afZv4LPL+0jyRJUse/JpAkSZKkHnJlTpIkSZJ6yDInSZIkST1kmZMkSZKkHrLMSZIkSVIPWeYkSZIkqYcsc5IkSZLUQ5Y5SZIkSeohy5wkSZIk9dBFmKHACXtB5iAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot(kind = 'bar', figsize = (15,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "63c7cf24",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>book_table</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BTM</th>\n",
       "      <td>4889</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banashankari</th>\n",
       "      <td>839</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banaswadi</th>\n",
       "      <td>632</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bannerghatta Road</th>\n",
       "      <td>1510</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basavanagudi</th>\n",
       "      <td>668</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bellandur</th>\n",
       "      <td>1170</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brigade Road</th>\n",
       "      <td>1034</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brookefield</th>\n",
       "      <td>582</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Church Street</th>\n",
       "      <td>385</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Commercial Street</th>\n",
       "      <td>370</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cunningham Road</th>\n",
       "      <td>315</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Domlur</th>\n",
       "      <td>427</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ejipura</th>\n",
       "      <td>433</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic City</th>\n",
       "      <td>1148</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frazer Town</th>\n",
       "      <td>706</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSR</th>\n",
       "      <td>2277</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiranagar</th>\n",
       "      <td>1578</td>\n",
       "      <td>448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP Nagar</th>\n",
       "      <td>1903</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jayanagar</th>\n",
       "      <td>1637</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kalyan Nagar</th>\n",
       "      <td>692</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kammanahalli</th>\n",
       "      <td>590</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 1st Block</th>\n",
       "      <td>1186</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 4th Block</th>\n",
       "      <td>695</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 5th Block</th>\n",
       "      <td>1787</td>\n",
       "      <td>692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 6th Block</th>\n",
       "      <td>1015</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 7th Block</th>\n",
       "      <td>1012</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lavelle Road</th>\n",
       "      <td>290</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MG Road</th>\n",
       "      <td>546</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malleshwaram</th>\n",
       "      <td>632</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marathahalli</th>\n",
       "      <td>1642</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New BEL Road</th>\n",
       "      <td>588</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Old Airport Road</th>\n",
       "      <td>378</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajajinagar</th>\n",
       "      <td>550</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residency Road</th>\n",
       "      <td>522</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richmond Road</th>\n",
       "      <td>687</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sarjapur Road</th>\n",
       "      <td>893</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanti Nagar</th>\n",
       "      <td>451</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shivajinagar</th>\n",
       "      <td>475</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St. Marks Road</th>\n",
       "      <td>219</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ulsoor</th>\n",
       "      <td>834</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whitefield</th>\n",
       "      <td>1852</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>others</th>\n",
       "      <td>4587</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       name     \n",
       "book_table               No  Yes\n",
       "location                        \n",
       "BTM                    4889  167\n",
       "Banashankari            839   63\n",
       "Banaswadi               632    8\n",
       "Bannerghatta Road      1510   99\n",
       "Basavanagudi            668   16\n",
       "Bellandur              1170   98\n",
       "Brigade Road           1034  176\n",
       "Brookefield             582   74\n",
       "Church Street           385  181\n",
       "Commercial Street       370    0\n",
       "Cunningham Road         315  175\n",
       "Domlur                  427   55\n",
       "Ejipura                 433    0\n",
       "Electronic City        1148   98\n",
       "Frazer Town             706    8\n",
       "HSR                    2277  217\n",
       "Indiranagar            1578  448\n",
       "JP Nagar               1903  315\n",
       "Jayanagar              1637  279\n",
       "Kalyan Nagar            692  149\n",
       "Kammanahalli            590   49\n",
       "Koramangala 1st Block  1186   50\n",
       "Koramangala 4th Block   695  322\n",
       "Koramangala 5th Block  1787  692\n",
       "Koramangala 6th Block  1015  112\n",
       "Koramangala 7th Block  1012  162\n",
       "Lavelle Road            290  228\n",
       "MG Road                 546  347\n",
       "Malleshwaram            632   89\n",
       "Marathahalli           1642  163\n",
       "New BEL Road            588   56\n",
       "Old Airport Road        378   59\n",
       "Rajajinagar             550   41\n",
       "Residency Road          522  149\n",
       "Richmond Road           687  116\n",
       "Sarjapur Road           893  154\n",
       "Shanti Nagar            451   57\n",
       "Shivajinagar            475   23\n",
       "St. Marks Road          219  124\n",
       "Ulsoor                  834  177\n",
       "Whitefield             1852  253\n",
       "others                 4587  367"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.groupby(['location','book_table'])['name'].count()\n",
    "df2.to_csv('location_booktable.csv')\n",
    "df2 = pd.read_csv('location_booktable.csv')\n",
    "df2 = pd.pivot_table(df2, values=None, index=['location'], columns=['book_table'], fill_value=0, aggfunc=np.sum)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffd99078",
   "metadata": {},
   "source": [
    "## BOOOK TABLE FACILITY LOCATION WISE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "aeba0783",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot(kind = 'bar', figsize = (15,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e7bf78f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Type', ylabel='rate'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14, 8))\n",
    "sns.boxplot(x = 'Type', y = 'rate', data = df, palette = 'inferno')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "68bfcfcb",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th>Buffet</th>\n",
       "      <th>Cafes</th>\n",
       "      <th>Delivery</th>\n",
       "      <th>Desserts</th>\n",
       "      <th>Dine-out</th>\n",
       "      <th>Drinks &amp; nightlife</th>\n",
       "      <th>Pubs and bars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</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>BTM</th>\n",
       "      <td>21</td>\n",
       "      <td>83</td>\n",
       "      <td>3053</td>\n",
       "      <td>198</td>\n",
       "      <td>1660</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banashankari</th>\n",
       "      <td>7</td>\n",
       "      <td>36</td>\n",
       "      <td>418</td>\n",
       "      <td>71</td>\n",
       "      <td>356</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banaswadi</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>310</td>\n",
       "      <td>37</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bannerghatta Road</th>\n",
       "      <td>9</td>\n",
       "      <td>46</td>\n",
       "      <td>828</td>\n",
       "      <td>137</td>\n",
       "      <td>578</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basavanagudi</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>344</td>\n",
       "      <td>66</td>\n",
       "      <td>251</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bellandur</th>\n",
       "      <td>28</td>\n",
       "      <td>36</td>\n",
       "      <td>617</td>\n",
       "      <td>75</td>\n",
       "      <td>479</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brigade Road</th>\n",
       "      <td>25</td>\n",
       "      <td>46</td>\n",
       "      <td>497</td>\n",
       "      <td>108</td>\n",
       "      <td>455</td>\n",
       "      <td>57</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brookefield</th>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>339</td>\n",
       "      <td>45</td>\n",
       "      <td>245</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Church Street</th>\n",
       "      <td>19</td>\n",
       "      <td>51</td>\n",
       "      <td>193</td>\n",
       "      <td>29</td>\n",
       "      <td>215</td>\n",
       "      <td>36</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Commercial Street</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>121</td>\n",
       "      <td>77</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cunningham Road</th>\n",
       "      <td>29</td>\n",
       "      <td>34</td>\n",
       "      <td>194</td>\n",
       "      <td>26</td>\n",
       "      <td>184</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Domlur</th>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>261</td>\n",
       "      <td>35</td>\n",
       "      <td>135</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ejipura</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>245</td>\n",
       "      <td>16</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic City</th>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>570</td>\n",
       "      <td>71</td>\n",
       "      <td>516</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frazer Town</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>470</td>\n",
       "      <td>56</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSR</th>\n",
       "      <td>19</td>\n",
       "      <td>49</td>\n",
       "      <td>1694</td>\n",
       "      <td>120</td>\n",
       "      <td>580</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiranagar</th>\n",
       "      <td>38</td>\n",
       "      <td>97</td>\n",
       "      <td>1091</td>\n",
       "      <td>140</td>\n",
       "      <td>529</td>\n",
       "      <td>65</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP Nagar</th>\n",
       "      <td>45</td>\n",
       "      <td>76</td>\n",
       "      <td>1151</td>\n",
       "      <td>166</td>\n",
       "      <td>722</td>\n",
       "      <td>51</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jayanagar</th>\n",
       "      <td>27</td>\n",
       "      <td>77</td>\n",
       "      <td>1043</td>\n",
       "      <td>182</td>\n",
       "      <td>575</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kalyan Nagar</th>\n",
       "      <td>9</td>\n",
       "      <td>45</td>\n",
       "      <td>366</td>\n",
       "      <td>88</td>\n",
       "      <td>315</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kammanahalli</th>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>329</td>\n",
       "      <td>35</td>\n",
       "      <td>240</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 1st Block</th>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>716</td>\n",
       "      <td>70</td>\n",
       "      <td>398</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 4th Block</th>\n",
       "      <td>21</td>\n",
       "      <td>53</td>\n",
       "      <td>464</td>\n",
       "      <td>81</td>\n",
       "      <td>302</td>\n",
       "      <td>62</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 5th Block</th>\n",
       "      <td>65</td>\n",
       "      <td>146</td>\n",
       "      <td>1075</td>\n",
       "      <td>209</td>\n",
       "      <td>842</td>\n",
       "      <td>84</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 6th Block</th>\n",
       "      <td>18</td>\n",
       "      <td>43</td>\n",
       "      <td>511</td>\n",
       "      <td>70</td>\n",
       "      <td>411</td>\n",
       "      <td>51</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 7th Block</th>\n",
       "      <td>25</td>\n",
       "      <td>52</td>\n",
       "      <td>503</td>\n",
       "      <td>127</td>\n",
       "      <td>417</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lavelle Road</th>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "      <td>127</td>\n",
       "      <td>50</td>\n",
       "      <td>191</td>\n",
       "      <td>59</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MG Road</th>\n",
       "      <td>51</td>\n",
       "      <td>76</td>\n",
       "      <td>266</td>\n",
       "      <td>68</td>\n",
       "      <td>343</td>\n",
       "      <td>53</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malleshwaram</th>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>269</td>\n",
       "      <td>85</td>\n",
       "      <td>291</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marathahalli</th>\n",
       "      <td>34</td>\n",
       "      <td>32</td>\n",
       "      <td>980</td>\n",
       "      <td>105</td>\n",
       "      <td>630</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New BEL Road</th>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>338</td>\n",
       "      <td>33</td>\n",
       "      <td>224</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Old Airport Road</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>35</td>\n",
       "      <td>164</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajajinagar</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>258</td>\n",
       "      <td>55</td>\n",
       "      <td>251</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residency Road</th>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>187</td>\n",
       "      <td>63</td>\n",
       "      <td>289</td>\n",
       "      <td>55</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richmond Road</th>\n",
       "      <td>63</td>\n",
       "      <td>21</td>\n",
       "      <td>257</td>\n",
       "      <td>78</td>\n",
       "      <td>356</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sarjapur Road</th>\n",
       "      <td>25</td>\n",
       "      <td>22</td>\n",
       "      <td>558</td>\n",
       "      <td>82</td>\n",
       "      <td>319</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanti Nagar</th>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>198</td>\n",
       "      <td>39</td>\n",
       "      <td>229</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shivajinagar</th>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>143</td>\n",
       "      <td>37</td>\n",
       "      <td>280</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St. Marks Road</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>111</td>\n",
       "      <td>10</td>\n",
       "      <td>145</td>\n",
       "      <td>40</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ulsoor</th>\n",
       "      <td>16</td>\n",
       "      <td>56</td>\n",
       "      <td>456</td>\n",
       "      <td>71</td>\n",
       "      <td>359</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whitefield</th>\n",
       "      <td>28</td>\n",
       "      <td>51</td>\n",
       "      <td>1041</td>\n",
       "      <td>137</td>\n",
       "      <td>768</td>\n",
       "      <td>47</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>others</th>\n",
       "      <td>83</td>\n",
       "      <td>133</td>\n",
       "      <td>2787</td>\n",
       "      <td>276</td>\n",
       "      <td>1553</td>\n",
       "      <td>75</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name                                   \\\n",
       "Type                  Buffet Cafes Delivery Desserts Dine-out   \n",
       "location                                                        \n",
       "BTM                       21    83     3053      198     1660   \n",
       "Banashankari               7    36      418       71      356   \n",
       "Banaswadi                  0    24      310       37      262   \n",
       "Bannerghatta Road          9    46      828      137      578   \n",
       "Basavanagudi               7    11      344       66      251   \n",
       "Bellandur                 28    36      617       75      479   \n",
       "Brigade Road              25    46      497      108      455   \n",
       "Brookefield                6    17      339       45      245   \n",
       "Church Street             19    51      193       29      215   \n",
       "Commercial Street          0    13      121       77      159   \n",
       "Cunningham Road           29    34      194       26      184   \n",
       "Domlur                    15    13      261       35      135   \n",
       "Ejipura                    0     0      245       16      172   \n",
       "Electronic City           23    24      570       71      516   \n",
       "Frazer Town                1    11      470       56      172   \n",
       "HSR                       19    49     1694      120      580   \n",
       "Indiranagar               38    97     1091      140      529   \n",
       "JP Nagar                  45    76     1151      166      722   \n",
       "Jayanagar                 27    77     1043      182      575   \n",
       "Kalyan Nagar               9    45      366       88      315   \n",
       "Kammanahalli               2    27      329       35      240   \n",
       "Koramangala 1st Block      3    26      716       70      398   \n",
       "Koramangala 4th Block     21    53      464       81      302   \n",
       "Koramangala 5th Block     65   146     1075      209      842   \n",
       "Koramangala 6th Block     18    43      511       70      411   \n",
       "Koramangala 7th Block     25    52      503      127      417   \n",
       "Lavelle Road              30    27      127       50      191   \n",
       "MG Road                   51    76      266       68      343   \n",
       "Malleshwaram              11    31      269       85      291   \n",
       "Marathahalli              34    32      980      105      630   \n",
       "New BEL Road               4    29      338       33      224   \n",
       "Old Airport Road          12     5      200       35      164   \n",
       "Rajajinagar               10     4      258       55      251   \n",
       "Residency Road            20    31      187       63      289   \n",
       "Richmond Road             63    21      257       78      356   \n",
       "Sarjapur Road             25    22      558       82      319   \n",
       "Shanti Nagar               9    22      198       39      229   \n",
       "Shivajinagar               6    17      143       37      280   \n",
       "St. Marks Road             5    10      111       10      145   \n",
       "Ulsoor                    16    56      456       71      359   \n",
       "Whitefield                28    51     1041      137      768   \n",
       "others                    83   133     2787      276     1553   \n",
       "\n",
       "                                                        \n",
       "Type                  Drinks & nightlife Pubs and bars  \n",
       "location                                                \n",
       "BTM                                   22            19  \n",
       "Banashankari                          14             0  \n",
       "Banaswadi                              6             1  \n",
       "Bannerghatta Road                      9             2  \n",
       "Basavanagudi                           5             0  \n",
       "Bellandur                             17            16  \n",
       "Brigade Road                          57            22  \n",
       "Brookefield                            4             0  \n",
       "Church Street                         36            23  \n",
       "Commercial Street                      0             0  \n",
       "Cunningham Road                       16             7  \n",
       "Domlur                                12            11  \n",
       "Ejipura                                0             0  \n",
       "Electronic City                       21            21  \n",
       "Frazer Town                            2             2  \n",
       "HSR                                   14            18  \n",
       "Indiranagar                           65            66  \n",
       "JP Nagar                              51             7  \n",
       "Jayanagar                             12             0  \n",
       "Kalyan Nagar                          18             0  \n",
       "Kammanahalli                           6             0  \n",
       "Koramangala 1st Block                  7            16  \n",
       "Koramangala 4th Block                 62            34  \n",
       "Koramangala 5th Block                 84            58  \n",
       "Koramangala 6th Block                 51            23  \n",
       "Koramangala 7th Block                 25            25  \n",
       "Lavelle Road                          59            34  \n",
       "MG Road                               53            36  \n",
       "Malleshwaram                          20            14  \n",
       "Marathahalli                          22             2  \n",
       "New BEL Road                           8             8  \n",
       "Old Airport Road                      12             9  \n",
       "Rajajinagar                            3            10  \n",
       "Residency Road                        55            26  \n",
       "Richmond Road                         16            12  \n",
       "Sarjapur Road                         19            22  \n",
       "Shanti Nagar                           9             2  \n",
       "Shivajinagar                           7             8  \n",
       "St. Marks Road                        40            22  \n",
       "Ulsoor                                23            30  \n",
       "Whitefield                            47            33  \n",
       "others                                75            47  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df.groupby(['location','Type'])['name'].count()\n",
    "df3.to_csv('location_Type.csv')\n",
    "df3 = pd.read_csv('location_Type.csv')\n",
    "df3 = pd.pivot_table(df3, values=None, index=['location'], columns=['Type'], fill_value=0, aggfunc=np.sum)\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0fd883a",
   "metadata": {},
   "source": [
    "## TYPE OF RESTAURANTS LOCATION WISE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "759d6600",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.plot(kind = 'bar', figsize = (20,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4008d772",
   "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>votes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cuisines</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>others</th>\n",
       "      <td>11542182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Indian</th>\n",
       "      <td>516310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Indian, Chinese</th>\n",
       "      <td>258225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Indian</th>\n",
       "      <td>161975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Indian, Mughlai</th>\n",
       "      <td>103706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          votes\n",
       "cuisines                       \n",
       "others                 11542182\n",
       "North Indian             516310\n",
       "North Indian, Chinese    258225\n",
       "South Indian             161975\n",
       "North Indian, Mughlai    103706"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = df[['cuisines', 'votes']]\n",
    "df6.drop_duplicates()\n",
    "df7 = df6.groupby(['cuisines'])['votes'].sum()\n",
    "df7 = df7.to_frame()\n",
    "df7 = df7.sort_values('votes', ascending=False)\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b771d063",
   "metadata": {},
   "source": [
    "## TOP CUISINES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "00d87257",
   "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>votes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cuisines</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>North Indian</th>\n",
       "      <td>516310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Indian, Chinese</th>\n",
       "      <td>258225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Indian</th>\n",
       "      <td>161975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Indian, Mughlai</th>\n",
       "      <td>103706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chinese</th>\n",
       "      <td>101728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        votes\n",
       "cuisines                     \n",
       "North Indian           516310\n",
       "North Indian, Chinese  258225\n",
       "South Indian           161975\n",
       "North Indian, Mughlai  103706\n",
       "Chinese                101728"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7 = df7.iloc[1:, :]\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f9b5ca99",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\shija\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "        51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "        68]),\n",
       " [Text(0, 0, 'North Indian'),\n",
       "  Text(1, 0, 'North Indian, Chinese'),\n",
       "  Text(2, 0, 'South Indian'),\n",
       "  Text(3, 0, 'North Indian, Mughlai'),\n",
       "  Text(4, 0, 'Chinese'),\n",
       "  Text(5, 0, 'Cafe'),\n",
       "  Text(6, 0, 'North Indian, South Indian'),\n",
       "  Text(7, 0, 'Desserts'),\n",
       "  Text(8, 0, 'Chinese, Momos'),\n",
       "  Text(9, 0, 'Ice Cream, Desserts'),\n",
       "  Text(10, 0, 'Cafe, Continental'),\n",
       "  Text(11, 0, 'Biryani, North Indian, Chinese'),\n",
       "  Text(12, 0, 'Chinese, Thai, Momos'),\n",
       "  Text(13, 0, 'South Indian, North Indian, Chinese'),\n",
       "  Text(14, 0, 'Desserts, Beverages'),\n",
       "  Text(15, 0, 'Biryani'),\n",
       "  Text(16, 0, 'Finger Food'),\n",
       "  Text(17, 0, 'Bakery, Desserts'),\n",
       "  Text(18, 0, 'Burger, Fast Food'),\n",
       "  Text(19, 0, 'Continental'),\n",
       "  Text(20, 0, 'Cafe, Desserts'),\n",
       "  Text(21, 0, 'Andhra, Biryani'),\n",
       "  Text(22, 0, 'North Indian, Continental'),\n",
       "  Text(23, 0, 'Pizza, Fast Food'),\n",
       "  Text(24, 0, 'Chinese, Thai'),\n",
       "  Text(25, 0, 'North Indian, Chinese, Biryani'),\n",
       "  Text(26, 0, 'South Indian, North Indian, Chinese, Street Food'),\n",
       "  Text(27, 0, 'Mithai, Street Food'),\n",
       "  Text(28, 0, 'North Indian, South Indian, Chinese'),\n",
       "  Text(29, 0, 'Desserts, Ice Cream'),\n",
       "  Text(30, 0, 'Biryani, North Indian'),\n",
       "  Text(31, 0, 'North Indian, Street Food'),\n",
       "  Text(32, 0, 'Fast Food'),\n",
       "  Text(33, 0, 'North Indian, Chinese, Seafood'),\n",
       "  Text(34, 0, 'Chinese, North Indian'),\n",
       "  Text(35, 0, 'North Indian, Biryani'),\n",
       "  Text(36, 0, 'Biryani, Kebab'),\n",
       "  Text(37, 0, 'Italian, Pizza'),\n",
       "  Text(38, 0, 'North Indian, Chinese, Fast Food'),\n",
       "  Text(39, 0, 'North Indian, Chinese, Continental'),\n",
       "  Text(40, 0, 'Desserts, Bakery'),\n",
       "  Text(41, 0, 'Arabian'),\n",
       "  Text(42, 0, 'North Indian, Chinese, South Indian'),\n",
       "  Text(43, 0, 'Pizza'),\n",
       "  Text(44, 0, 'South Indian, North Indian'),\n",
       "  Text(45, 0, 'North Indian, Mughlai, Chinese'),\n",
       "  Text(46, 0, 'Beverages, Fast Food'),\n",
       "  Text(47, 0, 'South Indian, Biryani'),\n",
       "  Text(48, 0, 'Fast Food, Rolls'),\n",
       "  Text(49, 0, 'Bakery'),\n",
       "  Text(50, 0, 'Andhra'),\n",
       "  Text(51, 0, 'Street Food'),\n",
       "  Text(52, 0, 'Kerala'),\n",
       "  Text(53, 0, 'Cafe, Bakery'),\n",
       "  Text(54, 0, 'Biryani, Fast Food'),\n",
       "  Text(55, 0, 'South Indian, Fast Food'),\n",
       "  Text(56, 0, 'North Indian, Fast Food'),\n",
       "  Text(57, 0, 'Bakery, Fast Food'),\n",
       "  Text(58, 0, 'Fast Food, Burger'),\n",
       "  Text(59, 0, 'Cafe, Fast Food'),\n",
       "  Text(60, 0, 'South Indian, Chinese'),\n",
       "  Text(61, 0, 'Beverages, Desserts'),\n",
       "  Text(62, 0, 'Ice Cream'),\n",
       "  Text(63, 0, 'Beverages'),\n",
       "  Text(64, 0, 'Biryani, South Indian'),\n",
       "  Text(65, 0, 'Kerala, South Indian'),\n",
       "  Text(66, 0, 'Fast Food, Beverages'),\n",
       "  Text(67, 0, 'South Indian, Chinese, North Indian'),\n",
       "  Text(68, 0, 'Mithai')])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,8))\n",
    "sns.barplot(df7.index , df7['votes'])\n",
    "plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84c29d54",
   "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": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}