zomato_banglore_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "978cd6c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "375b6551",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('zomato.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d2f1aa99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51717, 17)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0607bdf9",
   "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": 5,
   "id": "cfe42915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "url                                0\n",
       "address                            0\n",
       "name                               0\n",
       "online_order                       0\n",
       "book_table                         0\n",
       "rate                            7775\n",
       "votes                              0\n",
       "phone                           1208\n",
       "location                          21\n",
       "rest_type                        227\n",
       "dish_liked                     28078\n",
       "cuisines                          45\n",
       "approx_cost(for two people)      346\n",
       "reviews_list                       0\n",
       "menu_item                          0\n",
       "listed_in(type)                    0\n",
       "listed_in(city)                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f2e10160",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>4.1/5</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>4.1/5</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>3.8/5</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>3.7/5</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>3.8/5</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  4.1/5    775  Banashankari   \n",
       "1         Spice Elephant          Yes         No  4.1/5    787  Banashankari   \n",
       "2        San Churro Cafe          Yes         No  3.8/5    918  Banashankari   \n",
       "3  Addhuri Udupi Bhojana           No         No  3.7/5     88  Banashankari   \n",
       "4          Grand Village           No         No  3.8/5    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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['url','phone','address','menu_item','dish_liked','reviews_list'],axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a9cec2de",
   "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": 8,
   "id": "799caf66",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>4.1/5</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>4.1/5</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>3.8/5</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>3.7/5</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>3.8/5</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51712</th>\n",
       "      <td>Best Brews - Four Points by Sheraton Bengaluru...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.6 /5</td>\n",
       "      <td>27</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Continental</td>\n",
       "      <td>1,500</td>\n",
       "      <td>Pubs and bars</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51713</th>\n",
       "      <td>Vinod Bar And Restaurant</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Finger Food</td>\n",
       "      <td>600</td>\n",
       "      <td>Pubs and bars</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51714</th>\n",
       "      <td>Plunge - Sheraton Grand Bengaluru Whitefield H...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Finger Food</td>\n",
       "      <td>2,000</td>\n",
       "      <td>Pubs and bars</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51715</th>\n",
       "      <td>Chime - Sheraton Grand Bengaluru Whitefield Ho...</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.3 /5</td>\n",
       "      <td>236</td>\n",
       "      <td>ITPL Main Road, Whitefield</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Finger Food</td>\n",
       "      <td>2,500</td>\n",
       "      <td>Pubs and bars</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51716</th>\n",
       "      <td>The Nest - The Den Bengaluru</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.4 /5</td>\n",
       "      <td>13</td>\n",
       "      <td>ITPL Main Road, Whitefield</td>\n",
       "      <td>Bar, Casual Dining</td>\n",
       "      <td>Finger Food, North Indian, Continental</td>\n",
       "      <td>1,500</td>\n",
       "      <td>Pubs and bars</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51609 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    name online_order  \\\n",
       "0                                                  Jalsa          Yes   \n",
       "1                                         Spice Elephant          Yes   \n",
       "2                                        San Churro Cafe          Yes   \n",
       "3                                  Addhuri Udupi Bhojana           No   \n",
       "4                                          Grand Village           No   \n",
       "...                                                  ...          ...   \n",
       "51712  Best Brews - Four Points by Sheraton Bengaluru...           No   \n",
       "51713                           Vinod Bar And Restaurant           No   \n",
       "51714  Plunge - Sheraton Grand Bengaluru Whitefield H...           No   \n",
       "51715  Chime - Sheraton Grand Bengaluru Whitefield Ho...           No   \n",
       "51716                       The Nest - The Den Bengaluru           No   \n",
       "\n",
       "      book_table    rate  votes                    location  \\\n",
       "0            Yes   4.1/5    775                Banashankari   \n",
       "1             No   4.1/5    787                Banashankari   \n",
       "2             No   3.8/5    918                Banashankari   \n",
       "3             No   3.7/5     88                Banashankari   \n",
       "4             No   3.8/5    166                Basavanagudi   \n",
       "...          ...     ...    ...                         ...   \n",
       "51712         No  3.6 /5     27                  Whitefield   \n",
       "51713         No     NaN      0                  Whitefield   \n",
       "51714         No     NaN      0                  Whitefield   \n",
       "51715        Yes  4.3 /5    236  ITPL Main Road, Whitefield   \n",
       "51716         No  3.4 /5     13  ITPL Main Road, Whitefield   \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",
       "51712                  Bar                             Continental   \n",
       "51713                  Bar                             Finger Food   \n",
       "51714                  Bar                             Finger Food   \n",
       "51715                  Bar                             Finger Food   \n",
       "51716   Bar, Casual Dining  Finger Food, North Indian, Continental   \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  \n",
       "...                           ...             ...             ...  \n",
       "51712                       1,500   Pubs and bars      Whitefield  \n",
       "51713                         600   Pubs and bars      Whitefield  \n",
       "51714                       2,000   Pubs and bars      Whitefield  \n",
       "51715                       2,500   Pubs and bars      Whitefield  \n",
       "51716                       1,500   Pubs and bars      Whitefield  \n",
       "\n",
       "[51609 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e8c5a46a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51609, 11)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4233b739",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 51609 entries, 0 to 51716\n",
      "Data columns (total 11 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   name                         51609 non-null  object\n",
      " 1   online_order                 51609 non-null  object\n",
      " 2   book_table                   51609 non-null  object\n",
      " 3   rate                         43854 non-null  object\n",
      " 4   votes                        51609 non-null  int64 \n",
      " 5   location                     51588 non-null  object\n",
      " 6   rest_type                    51382 non-null  object\n",
      " 7   cuisines                     51564 non-null  object\n",
      " 8   approx_cost(for two people)  51265 non-null  object\n",
      " 9   listed_in(type)              51609 non-null  object\n",
      " 10  listed_in(city)              51609 non-null  object\n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 4.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f63dd1",
   "metadata": {},
   "source": [
    "Cleaning \"Rate\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a4de6a5b",
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ab02a5db",
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def handlerate(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)\n",
    "    \n",
    "df['rate'] = df['rate'].apply(handlerate)\n",
    "df['rate'].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "03bae1f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10019"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20b706f5",
   "metadata": {},
   "source": [
    "Replacing null values in rate column with mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a6261b6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].fillna(df['rate'].mean(), inplace=True)\n",
    "df['rate'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "96ac5df7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 51609 entries, 0 to 51716\n",
      "Data columns (total 11 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   name                         51609 non-null  object \n",
      " 1   online_order                 51609 non-null  object \n",
      " 2   book_table                   51609 non-null  object \n",
      " 3   rate                         51609 non-null  float64\n",
      " 4   votes                        51609 non-null  int64  \n",
      " 5   location                     51588 non-null  object \n",
      " 6   rest_type                    51382 non-null  object \n",
      " 7   cuisines                     51564 non-null  object \n",
      " 8   approx_cost(for two people)  51265 non-null  object \n",
      " 9   listed_in(type)              51609 non-null  object \n",
      " 10  listed_in(city)              51609 non-null  object \n",
      "dtypes: float64(1), int64(1), object(9)\n",
      "memory usage: 4.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70d42b00",
   "metadata": {},
   "source": [
    "Dropping null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5253f5b3",
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "22d32eb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                           0\n",
       "online_order                   0\n",
       "book_table                     0\n",
       "rate                           0\n",
       "votes                          0\n",
       "location                       0\n",
       "rest_type                      0\n",
       "cuisines                       0\n",
       "approx_cost(for two people)    0\n",
       "listed_in(type)                0\n",
       "listed_in(city)                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b5a4717",
   "metadata": {},
   "source": [
    "Renaming columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d2eaf4b2",
   "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": 18,
     "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": 19,
   "id": "b3428e48",
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['location'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e85352c9",
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['listed_in(city)'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "04075a32",
   "metadata": {},
   "outputs": [],
   "source": [
    "#location and listed_in(city) both are locations, so keep one\n",
    "\n",
    "df = df.drop(['listed_in(city)'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e0afa1f9",
   "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cd8dc2c4",
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cost2plates'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46e8550c",
   "metadata": {},
   "source": [
    "Removing ',' from Cost2plates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "af574bc6",
   "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": 25,
     "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": 26,
   "id": "fc70af0d",
   "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",
       "    </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>800.0</td>\n",
       "      <td>Buffet</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>800.0</td>\n",
       "      <td>Buffet</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>800.0</td>\n",
       "      <td>Buffet</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>300.0</td>\n",
       "      <td>Buffet</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>600.0</td>\n",
       "      <td>Buffet</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        800.0  Buffet  \n",
       "1        Casual Dining     Chinese, North Indian, Thai        800.0  Buffet  \n",
       "2  Cafe, Casual Dining          Cafe, Mexican, Italian        800.0  Buffet  \n",
       "3          Quick Bites      South Indian, North Indian        300.0  Buffet  \n",
       "4        Casual Dining        North Indian, Rajasthani        600.0  Buffet  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d994bfb",
   "metadata": {},
   "source": [
    "Cleaning \"rest_type\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "106efa2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Casual Dining', 'Cafe, Casual Dining', 'Quick Bites',\n",
       "       'Casual Dining, Cafe', 'Cafe', 'Quick Bites, Cafe',\n",
       "       'Cafe, Quick Bites', 'Delivery', 'Mess', 'Dessert Parlor',\n",
       "       'Bakery, Dessert Parlor', 'Pub', 'Bakery', 'Takeaway, Delivery',\n",
       "       'Fine Dining', 'Beverage Shop', 'Sweet Shop', 'Bar',\n",
       "       'Beverage Shop, Quick Bites', 'Confectionery',\n",
       "       'Quick Bites, Beverage Shop', 'Dessert Parlor, Sweet Shop',\n",
       "       'Bakery, Quick Bites', 'Sweet Shop, Quick Bites', 'Kiosk',\n",
       "       'Food Truck', 'Quick Bites, Dessert Parlor',\n",
       "       'Beverage Shop, Dessert Parlor', 'Takeaway', 'Pub, Casual Dining',\n",
       "       'Casual Dining, Bar', 'Dessert Parlor, Beverage Shop',\n",
       "       'Quick Bites, Bakery', 'Dessert Parlor, Quick Bites',\n",
       "       'Microbrewery, Casual Dining', 'Lounge', 'Bar, Casual Dining',\n",
       "       'Food Court', 'Cafe, Bakery', 'Dhaba', 'Quick Bites, Sweet Shop',\n",
       "       'Microbrewery', 'Food Court, Quick Bites', 'Pub, Bar',\n",
       "       'Casual Dining, Pub', 'Lounge, Bar', 'Food Court, Dessert Parlor',\n",
       "       'Casual Dining, Sweet Shop', 'Food Court, Casual Dining',\n",
       "       'Casual Dining, Microbrewery', 'Sweet Shop, Dessert Parlor',\n",
       "       'Bakery, Beverage Shop', 'Lounge, Casual Dining',\n",
       "       'Cafe, Food Court', 'Beverage Shop, Cafe', 'Cafe, Dessert Parlor',\n",
       "       'Dessert Parlor, Cafe', 'Dessert Parlor, Bakery',\n",
       "       'Microbrewery, Pub', 'Bakery, Food Court', 'Club',\n",
       "       'Quick Bites, Food Court', 'Bakery, Cafe', 'Bar, Cafe',\n",
       "       'Pub, Cafe', 'Casual Dining, Irani Cafee', 'Fine Dining, Lounge',\n",
       "       'Bar, Quick Bites', 'Bakery, Kiosk', 'Pub, Microbrewery',\n",
       "       'Microbrewery, Lounge', 'Fine Dining, Microbrewery',\n",
       "       'Fine Dining, Bar', 'Mess, Quick Bites', 'Dessert Parlor, Kiosk',\n",
       "       'Bhojanalya', 'Casual Dining, Quick Bites', 'Pop Up', 'Cafe, Bar',\n",
       "       'Casual Dining, Lounge', 'Bakery, Sweet Shop', 'Microbrewery, Bar',\n",
       "       'Cafe, Lounge', 'Bar, Pub', 'Lounge, Cafe', 'Club, Casual Dining',\n",
       "       'Quick Bites, Mess', 'Quick Bites, Meat Shop',\n",
       "       'Quick Bites, Kiosk', 'Lounge, Microbrewery',\n",
       "       'Food Court, Beverage Shop', 'Dessert Parlor, Food Court',\n",
       "       'Bar, Lounge'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rest_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e169006f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quick Bites                   19010\n",
       "Casual Dining                 10253\n",
       "Cafe                           3682\n",
       "Delivery                       2574\n",
       "Dessert Parlor                 2242\n",
       "                              ...  \n",
       "Dessert Parlor, Kiosk             2\n",
       "Food Court, Beverage Shop         2\n",
       "Dessert Parlor, Food Court        2\n",
       "Quick Bites, Kiosk                1\n",
       "Sweet Shop, Dessert Parlor        1\n",
       "Name: rest_type, Length: 93, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rest_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dd74e33d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quick Bites                   19010\n",
       "Casual Dining                 10253\n",
       "Cafe                           3682\n",
       "Delivery                       2574\n",
       "Dessert Parlor                 2242\n",
       "                              ...  \n",
       "Dessert Parlor, Kiosk             2\n",
       "Food Court, Beverage Shop         2\n",
       "Dessert Parlor, Food Court        2\n",
       "Quick Bites, Kiosk                1\n",
       "Sweet Shop, Dessert Parlor        1\n",
       "Name: rest_type, Length: 93, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_types = df['rest_type'].value_counts(ascending = False)\n",
    "rest_types\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "839016ed",
   "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",
       "Dessert Parlor, Kiosk           2\n",
       "Food Court, Beverage Shop       2\n",
       "Dessert Parlor, Food Court      2\n",
       "Quick Bites, Kiosk              1\n",
       "Sweet Shop, Dessert Parlor      1\n",
       "Name: rest_type, Length: 85, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_types_lessthan1000 = rest_types[rest_types<1000]\n",
    "rest_types_lessthan1000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "553c78b6",
   "metadata": {},
   "source": [
    "Making rest_types_lessthan1000 in frequency as 'others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "635c21dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Casual Dining\n",
       "1    Casual Dining\n",
       "2           others\n",
       "3      Quick Bites\n",
       "4    Casual Dining\n",
       "Name: rest_type, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def handle_rest(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)\n",
    "df['rest_type'].head()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6a3a8358",
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rest_type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12103fc5",
   "metadata": {},
   "source": [
    "Cleaning location column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bbd1d5a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTM                      5056\n",
       "HSR                      2494\n",
       "Koramangala 5th Block    2479\n",
       "JP Nagar                 2218\n",
       "Whitefield               2105\n",
       "                         ... \n",
       "West Bangalore              6\n",
       "Yelahanka                   5\n",
       "Jakkur                      3\n",
       "Rajarajeshwari Nagar        2\n",
       "Peenya                      1\n",
       "Name: location, Length: 93, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "183710a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "location = df['location'].value_counts(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "274bc085",
   "metadata": {},
   "outputs": [],
   "source": [
    "location_lessthan300 = location[location<300]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6ccd100b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Banashankari\n",
       "1    Banashankari\n",
       "2    Banashankari\n",
       "3    Banashankari\n",
       "4    Basavanagudi\n",
       "Name: location, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def handle_loc(value):\n",
    "    if(value in location_lessthan300):\n",
    "        return 'others'\n",
    "    else:\n",
    "        return value\n",
    "    \n",
    "df['location'] = df['location'].apply(handle_loc)\n",
    "df['location'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0d7061c9",
   "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['location'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e13c2ef",
   "metadata": {},
   "source": [
    "Cleaning cuisines column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d524e911",
   "metadata": {},
   "outputs": [],
   "source": [
    "cuisines = df['cuisines'].value_counts(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7d25ba0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cuisines_lessthan100 = cuisines[cuisines<100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "73499993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    North Indian, Mughlai, Chinese\n",
       "1                            others\n",
       "2                            others\n",
       "3        South Indian, North Indian\n",
       "4                            others\n",
       "Name: cuisines, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9e79e0c8",
   "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",
       "North Indian, Mughlai, Chinese           104\n",
       "South Indian, Fast Food                  104\n",
       "Italian, Pizza                           102\n",
       "North Indian, Chinese, Seafood           102\n",
       "Name: cuisines, Length: 70, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cuisines'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fcb0a660",
   "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",
       "    </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>800.0</td>\n",
       "      <td>Buffet</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>others</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</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>others</td>\n",
       "      <td>others</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</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>300.0</td>\n",
       "      <td>Buffet</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>others</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Buffet</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        800.0  Buffet  \n",
       "1  Casual Dining                          others        800.0  Buffet  \n",
       "2         others                          others        800.0  Buffet  \n",
       "3    Quick Bites      South Indian, North Indian        300.0  Buffet  \n",
       "4  Casual Dining                          others        600.0  Buffet  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b321f596",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delivery              25579\n",
       "Dine-out              17562\n",
       "Desserts               3559\n",
       "Cafes                  1703\n",
       "Drinks & nightlife     1084\n",
       "Buffet                  869\n",
       "Pubs and bars           686\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e15166",
   "metadata": {},
   "source": [
    "# Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a45db05",
   "metadata": {},
   "source": [
    "Count plot of various locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6833e19c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sanika\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(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": 50,
     "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)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa6d45f7",
   "metadata": {},
   "source": [
    "Visualizing online order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c0f28964",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sanika\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='online_order', ylabel='count'>"
      ]
     },
     "execution_count": 51,
     "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": "967e318d",
   "metadata": {},
   "source": [
    "visualizing book table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2a747f25",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sanika\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='book_table', ylabel='count'>"
      ]
     },
     "execution_count": 52,
     "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 = 'inferno')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64e2ed0b",
   "metadata": {},
   "source": [
    "visualizing online order vs rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "66bc9d25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='online_order', ylabel='rate'>"
      ]
     },
     "execution_count": 55,
     "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": "114082f7",
   "metadata": {},
   "source": [
    "visualizing book table vs rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "04731b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='book_table', ylabel='rate'>"
      ]
     },
     "execution_count": 56,
     "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": "markdown",
   "id": "6a109d40",
   "metadata": {},
   "source": [
    "Visualizing online order facility location wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0a1c37d8",
   "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": 57,
     "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": "code",
   "execution_count": 58,
   "id": "26687694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 58,
     "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": [
    "df1.plot(kind = 'bar', figsize=(15,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e154f385",
   "metadata": {},
   "source": [
    "Visualizing book table facility location wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "46a6c10c",
   "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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.groupby(['location','book_table'])['name'].count()\n",
    "df2.to_csv('location_book.csv')\n",
    "df2 = pd.read_csv('location_book.csv')\n",
    "df2 = pd.pivot_table(df2, values=None, index=['location'], columns=['book_table'], fill_value=0, aggfunc = np.sum)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2a55a014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 60,
     "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": "markdown",
   "id": "062c771e",
   "metadata": {},
   "source": [
    "Visualizing types of restuarants vs rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0217e2a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Type', ylabel='rate'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hI6AGdXV1af3jq9Vw+NjQUcoy5s38trdz4/rAScrXvWNP6AgADhAFEGpSQ0OD3nj1TTXWzQodZcTq6l2thoaG1Nb/R6MP13lHfji19cfu7tcfCR0BNarh8LG68c+mho4xYl3zi+dCRwBwgDgGCAAAAEA0KIAAAAAARIMCCAAAAEA0KIAAAAAARIMCCAAAAEA0KIAAAAAARIMCCAAAAEA0KIAAAAAARIMCCAAAAEA0KIAAAAAARGN06ABp6rU3tXf0C6FjlK3XeiRJdV4fOEn5eu3N0BEAABgRNm17W1f9eHfoGGV76fVeSdKxR2Znf/qmbW/r5EmhUyC0EVsANTY2ho4wZJ2dnZKkGTNmBE4yNFl8rAEAqCVZfC3t2ZF/3zJ2Unbet5w8KZuPNSprxBZA7e3toSMMWUtLiyRp+fLlgZMAAIBq4n0LUD3Z6VkCAAAAwAGiAAIAAAAQDQogAAAAANGgAAIAAAAQDQogAAAAANGgAAIAAAAQDQogAAAAANGgAAIAAAAQDQogAAAAANGgAAIAAAAQjdGhAwAD2a2d6updHTpG2fZqtyTpYI0LnKQ8u7UztXV3d3frtbd26O7XH0ntd8Tutbd2SN3dFV9vd3e3Xt+zUwufW1vxdSPvpT07tSuFuZPy87djxx5d84vnUlk/pOd37NHhKc0fsu1Xv96jy2/YGDpG2V7a8qYk6diJBwVOUp5f/XqPTn5PZdZFAYSa1NjYGDrCkHV2dkqSZsyYHjhJ+bL4OAMAUGuy+Hr65iv59y0Hv2dG4CTlOfk9lXucKYBQk9rb20NHGLKWlhZJ0vLlywMnCa+hoUF6rUfnHfnh0FFGrLtffyT/OFdYQ0ODtu3cq/lTT6v4upG38Lm1Gp/C3En5+dvZs1M3/tnUVNYP6ZpfPKfDUpo/ZBfvW7KFY4AAAAAARIMCCAAAAEA0KIAAAAAARIMCCAAAAEA0KIAAAAAARIMCCAAAAEA0KIAAAAAARIMCCAAAAEA0KIAAAAAARGN06AAAgNry8p5dWvjc2tAxyrbtzd2SpPEHjQucpDwv79ml8aFDAEDEKIAAAPs0NjaGjjBkWzs7JUnjZ/xJ4CTlGa9sPs4AMFJQAAEA9mlvbw8dYchaWlokScuXLw+cBACQBRwDBAAAACAaFEAAAAAAokEBBAAAACAaFEAAAAAAokEBBAAAACAaFEAAAAAAokEBBAAAACAaFEAAAAAAopFaAWRmB5vZY2b2lJl1mtn1/SxzppltN7N1yc+CtPIAAAAAwOgU1/2mpI+4+y4zGyPpITP7D3d/tM9yD7r7+SnmAAAAAABJKRZA7u6SdiXDMcmPp/X7ANSW3721Q3e//kjoGGXZ8fYbkqTDRx0SOEn5fvfWDjWEDoGa1L1jj675xXOhY5TlN2+8KUk65pCDAicpX/eOPToldAgAByTNDpDMbJSktZKmSfq2u6/uZ7EPm9lTkl6R9AV37+xnPfMkzZOkyZMnp5gYQCU0NjaGjjAknZ35p52GGScGTlK+BmXvcUb6svZ/4tfJtnfYiTMCJynfKcre4wygVKoFkLu/LWmmmR0p6Q4zO8ndNxQt8oSk9yUfkztX0kpJx/ezniWSlkhSU1MTXSSgxrW3t4eOMCQtLS2SpOXLlwdOAhwYtj0A2L+qnAXO3V+X9ICkc/pcv8PddyWX75E0xsyOqkYmAAAAAPFJ8yxw45POj8xsrKS/kPTLPsscbWaWXP5gkue1tDIBAAAAiFuaH4E7RtLS5DigOkn/5u53mdmlkuTuiyW1SLrMzN6StEfSRcnJEwAAAACg4tI8C9x6Saf2c/3ioss3S7o5rQwAAAAAUKwqxwABAAAAQC2gAAIAAAAQDQogAAAAANGgAAIAAAAQDQogAAAAANGgAAIAAAAQjbIKIDMbZ2ZfMrPvJOPjzez8dKMBAAAAQGWV2wG6RdKbkj6cjF+SdEMqiQAAAAAgJeUWQFPd/X9K+oMkufseSZZaKgAAAABIQbkFUI+ZjZXkkmRmU5XvCAEAAABAZowuc7kvS/qJpElmdpuk2ZLmphUKAAAAANJQVgHk7vea2VpJH1L+o29XuftvU00GAAAAABVW7lng7nP319z9bne/y91/a2b3pR0OAAAAACpp0A6QmR0saZyko8zsPXrnxAeHS/rjlLMBAAAAQEXt7yNwfyOpTfliZ63eKYB2SPp2erEAAAAAoPIGLYDc/VuSvmVm8919YZUyAQAAAEAqyj0JwkIzO0lSo6SDi66/Na1gAAAAAFBpZRVAZvb3ks5UvgC6R9JHJT0kiQIIAAAAQGaU+0WoLZLmSHrV3edK+lNJB6WWCgAAAABSUG4BtNfdeyW9ZWaHS9oq6bj0YgEAAABA5e33I3BmZpLWm9mRkr6j/Nngdkl6LN1oAAAAAFBZ+y2A3N3NbKa7vy5psZn9RNLh7r4+9XQAAAAAUEHlfgTuUTP7gCS5+2aKHwAAAABZVNZZ4CSdJelvzOwFSW8o/4Wo7u6npJYMAAAAACqs3ALoo6mmAAAAAIAqKPeLUF9IOwgAAAAApK3cY4AAAAAAIPMogAAAAABEgwIIAAAAQDQogAAAAABEgwIIAAAAQDTKPQ02MCIsWLBAXV1dqay7s7NTktTS0lLxdTc2Nqq9vb3i6wUAAIgNBRBQIePGjQsdAQAAAPtBAYSo0EUBAACIG8cAAQAAAIgGBRAAAACAaFAAAQAAAIgGBRAAAACAaFAAAQAAAIgGBRAAAACAaFAAAQAAAIgGBRBQIaeccopyuZxmzpwZOgoAAAAGQAEEVMhrr70mSdq2bVvgJAAAABgIBRBQAaecckrJmC4QAABAbRodOkAWLViwQF1dXRVfb2dnpySppaWl4utubGxUe3t7xdeLvEL3p4AuEACgVvC+BShFAVRDxo0bFzoCAABAWXjfgqyiABoG9kgAAICs4H0LUIpjgIAKeO9731syHj9+fKAkAAAAGAwFEFAB69evLxmvW7cuTBAAAAAMigIIqJBCF4juDwAAQO3iGCCgQvp2gQAAAFB76AABAAAAiAYFEAAAAIBoUAABAAAAiEZqBZCZHWxmj5nZU2bWaWbX97OMmdlNZrbJzNab2fvTypMFp556qnK5nE477bTQUTAMs2fPVi6X0+mnnx46CgAAqTvjjDOUy+V01llnhY4CDEmaJ0F4U9JH3H2XmY2R9JCZ/Ye7P1q0zEclHZ/8zJK0KPk3Slu3bpUkvfrqq4GTYDg2b94sSXr++efDBgEAoAo2bdokSXr22WcDJwGGJrUOkOftSoZjkh/vs9jHJN2aLPuopCPN7Ji0MtWyU089tWRMFyhbZs+eXTKmCwQAGMnOOOOMkjFdIGRJqqfBNrNRktZKmibp2+6+us8iOUkvFo1fSq77TZq5alGh+1NAFyhbCt2fArpA6ViwYIG6uroqvt7Ozk5JUktLS8XXLUmNjY1qb29PZd1ZkdbcSenOH3OXx7aHvgrdnwK6QMiSVAsgd39b0kwzO1LSHWZ2krtvKFrE+rtb3yvMbJ6keZI0efLkNKICiNi4ceNCR8ABYP6yi7kDEEJVvgjV3V83swcknSOpuAB6SdKkovGxkl7p5/5LJC2RpKampncVSADiwJ7c7GLuso35AzCSpHkWuPFJ50dmNlbSX0j6ZZ/F7pR0SXI2uA9J2u7u0X38TZImTJhQMj766KMDJcFwTJkypWR83HHHhQkCAEAVTJs2rWR8wgknBEoCDF2a3wN0jKT7zWy9pMcl/dTd7zKzS83s0mSZeyQ9L2mTpO9I+lyKeWrak08+WTJeu3ZtoCQYjocffrhk/OCDDwZKAgBA+latWlUyvv/++wMlAYYutY/Auft6Saf2c/3iossu6fK0MmTNhAkTtHXrVro/GTVlyhRt3ryZ7g8AIArTpk3Tpk2b6P4gc6pyDBDK07cLhGzp2wUCAGAk69sFArIizY/AAQAAAEBNoQACAAAAEA0KIAAAAADRoACqIdddd51yuZyuv/760FEwDFOnTlUul3vXqUFR+3K53L4fZA/zl13MXbbxvgVZRQFUQ2655RZJ0pIlSwInwXDs3btXkrRnz57ASQAASB/vW5BVFEA14rrrrisZszclW6ZOnVoypguUHX33PLMnOluYv+xi7rKN9y3IMst/FU92NDU1+Zo1a0LHqLj+nvhffvnlAEkwHMxfdjF32cb8ZRdzl23MX3UsWLBAXV1dqay7s7NTkjRjxoyKr7uxsVHt7e0VX+9QmNlad2/q7za+BwgAAACIzLhx40JHCIYCCAAAAKhBobsoIxXHANWIuXPnloznzZsXKAmG4+CDDy4Zjx07NlASAADSx/sWZBnHANWQ4s/T8jna7GH+sou5yzbmL7uYu2xj/lDLBjsGiA5QDSnsTWEvSjYVukB0fwAAMeB9C7KKDhAAAACAEYUOEAAAAACIAggAAABARCiAAAAAAESDAqiGzJkzR7lcTmeffXboKBiG2bNnK5fL6fTTTw8dBUOUy+X2/SB7zjnnHOVyOZ133nmho2CI2PaybeXKlcrlcrrrrrtCR8EwbNmyRc3Nzdq6dWvoKFVHAVRDfvnLX0qSOjs7AyfBcGzevFmS9Pzzz4cNAkTm6aefliStW7cubBAgMldffbUkaf78+YGTYDg6Ojq0evVqdXR0hI5SdRRANWLOnDklY7pA2TJ79uySMV2g7Oi755k90dlyzjnnlIzpAmUH2162rVy5Uj09PZKknp4eukAZs2XLFi1btkzurmXLlkXXBaIAqhGF7k8BXaBsKXR/CugCAdVR6P4U0AUCqqPQ/SmgC5QtHR0dKnwVTm9vb3RdIAogAAAADEmh+zPQGLXt9ttvL+ngrVixInCi6qIAAgAAwJDU19cPOkZtu/DCC/fNWX19vZqbmwMnqi4KoBoxffr0kvGMGTMCJcFwTJkypWR83HHHhQkCRObkk08uGc+cOTNMECAy3/zmN0vGCxcuDJQEw9HW1iYzkyTV1dWpra0tbKAqowCqEffdd1/J+N577w2UBMPx8MMPl4wffPDBQEkwVC+//PKgY9S2n/zkJyXju+++O1ASDBXbXrZ9/OMfL+kgnH/++YETYSgmTpyo1tZWmZlaW1s1YcKE0JGqigKohhS6QHR/sqnQBaL7A1RXoQtE9weorkIXiO5PNrW1tWnWrFnRdX8kyQpngMiKpqYmX7NmTegYAAAAAGqUma1196b+bqMDBAAAACAaFEAAAAAAokEBVENWrlypXC7Htyln1Oc//3nlcjn93d/9XegoGKJcLrfvB9nDtpddbHvZtnDhQuVyOS1evDh0FAzDqlWrNHnyZD300EOho1QdxwDVkIaGBvX09Ki+vl7d3d2h42CIil/AOZtRtjB32cb8ZRdzl23MX7Y1NjZq+/btOuKII9TV1RU6TsVxDFAGrFy5suQbeekCZcvnP//5kjF7orOj755n9kRnC9tedrHtZVvfM7/RBcqWVatWafv27ZKk7du3R9cFogNUIwrdnwK6QNnS3ws3e8OygbnLNuYvu5i7bGP+sq3Q/SkYiV0gOkAZUFz89DcGAAAAKqG4+OlvPNJRANWIwrcpDzQGAAAAKuGII44YdDzSUQDViMK3KRfwrcrZ0traWjL+9Kc/HSgJEBe2PSCMa665pmT8pS99KVASDMeiRYtKxkuWLAmUJAyOAaohnAUu2zgbTnYxd9nG/GUXc5dtzF+2cRY41IRCF4juTzYV9kSzBxqoLrY9IIxCF4juTzYtWrRIo0aNiq77I9EBAgAAADDC0AECAAAAAFEAAQAAAIgIBRAAAACAaFAA1ZBVq1Zp8uTJeuihh0JHwTBcdtllyuVyuvLKK0NHwRDlcrl9P8iepUuXKpfL6bbbbgsdBUPEtpdtbHvZtmXLFjU3N2vr1q2ho1QdJ0GoISP9dIQjHacDzS7mLtsmTZqk3t5e1dXV6cUXXwwdB0PAtpdtbHvZdu211+oHP/iBLrnkEn31q18NHafiOAlCBqxatUrbt2+XJG3fvp0uUMZcdtllJWO6QNnRd88ze6KzZenSpert7ZUk9fb2sic6Q9j2so1tL9u2bNmiZcuWyd21bNmy6LpAdIBqRKH7U0AXKFv6e+Fmb2Y2MHfZVtgDXcCe6Oxg28s2tr1su/baa/WjH/1IPT09qq+v1yc/+ckR1wWiA5QBxcVPf2MAwLsVvwHrbwwgHWx72Xb77berp6dHktTT06MVK1YETlRdFEA14ogjjhh0DAB4t7q6ukHHANLBtpdtF154oerr6yVJ9fX1am5uDpyouvjfWiMWLVpUMl6yZEmgJBiOCy64oGQc2xMJEMoNN9xQMr7xxhsDJQHiwraXbW1tbTIzSfnita2tLWygKuMYoBrCWeCyjbMZZRdzl22ciSq72PayjW0v2zgLHGrCokWLNGrUKLo/GVXoAtH9AaqrsCeaPdBAdbHtZVtbW5tmzZoVXfdHogMEAAAAYIShAwQAAAAAogACAAAAEBEKIAAAAADRSK0AMrNJZna/mT1jZp1mdlU/y5xpZtvNbF3ysyCtPFmwZcsWNTc3a+vWraGjYBg2bNig6dOncwa/DMrlcvt+kD08d2YX2162se1lW8zzl2YH6C1Jf+vufyLpQ5IuN7PGfpZ70N1nJj/tKeapeR0dHVq9erU6OjpCR8EwzJ8/Xzt37tQVV1wROgoQFZ47gTDY9rIt5vlLrQBy99+4+xPJ5Z2SnpHELp4BbNmyRcuWLZO7a9myZVFW41m2YcMGPfvss5KkjRs30gXKkL57ntkTnS08d2YX2162se1lW+zzV5VjgMxsiqRTJa3u5+YPm9lTZvYfZjajGnlqUUdHhwqnJO/t7Y2yGs+y+fPnl4zpAgHVwXMnEAbbXrbFPn+pfw+QmR0qaZWkf3D32/vcdrikXnffZWbnSvqWux/fzzrmSZonSZMnTz7thRdeSDVzCCeeeKJ27dq1b3zooYdq48aNARNhKPrbc8m3mmcDc5dtPHdmF9tetrHtZVsM8xfse4DMbIykFZJu61v8SJK773D3XcnleySNMbOj+lluibs3uXvT+PHj04wczIUXXqj6+npJUn19vZqbmwMnwlCccMIJJeMTTzwxUBIgLjx3AmGw7WVb7POX5lngTNI/S3rG3f9xgGWOTpaTmX0wyfNaWplqWVtbm5KHQnV1dWprawsbCEOycOHCkvHNN98cKAkQF547gTDY9rIt9vlLswM0W9JfSfpI0WmuzzWzS83s0mSZFkkbzOwpSTdJusjT/kxejZo4caJaW1tlZmptbdWECRNCR8IQnHTSSfu6QCeeeKIaG/s74SFqUd+P3PARnGzhuTO72PayjW0v22Kfv9FprdjdH5Jk+1nmZknsKk+0tbXp2Wefja4KHykWLlyolpYWuj9AlfHcCYTBtpdtMc9f6idBqLSmpiZfs2ZN6BgAAAAAalSwkyAAAAAAQC2hAAIAAAAQDQogAAAAANGgAAIqZMOGDZo+fbq6urpCR8EQTZkyRblcTg0NDaGjYBi2bNmi5uZmbd26NXQUDNHUqVOVy+U0bdq00FEwDLzuZVvM80cBBFTI/PnztXPnTl1xxRWho2CI/vCHP0iSenp6AifBcHR0dGj16tXq6OgIHQVDtHfvXknSnj17AifBcPC6l20xzx8FEFABGzZs0LPPPitJ2rhxY5R7U7JqypQpJWO6QNmyZcsWLVu2TO6uZcuW0QXKkKlTp5aM6QJlC6972Rb7/FEAARUwf/78knGMe1OyqtD9KaALlC0dHR0qfJ1Db28vXaAMKXR/CugCZQuve9kW+/xRAAEVUNiLUrBx48ZASYC43H777fuK1p6eHq1YsSJwIiAOvO5lW+zzRwEEVMAJJ5xQMj7xxBMDJQHicuGFF6q+vl6SVF9fr+bm5sCJgDjwupdtsc8fBRBQAQsXLiwZ33zzzYGSYKjGjBlTMi68mUY2tLW1ycwkSXV1dWprawsbCGU7+OCDS8Zjx44NlATDwetetsU+fxRAQAWcdNJJ+/amnHjiiWpsbAycCOXavHlzybi7uztMEAzLxIkT1draKjNTa2urJkyYEDoSyvTcc8+VjDdt2hQoCYaD171si33+KICAClm4cKEOO+yw6PaijASFLhDdn2xqa2vTrFmz6P5kUKELRPcnm3jdy7aY588KZ8/JiqamJl+zZk3oGAAAAABqlJmtdfem/m6jAwQAAAAgGhRAAAAAAKJBAVRDli5dqlwup9tuuy10FAzDli1b1NzczDfRZ9App5yiXC6nmTNnho4CROWkk05SLpfTKaecEjoKgIhQANWQ6667TpJ0zTXXBE6C4ejo6NDq1av5JvoMeu211yRJ27ZtC5wEiMvvf/97Se9sgwBQDRRANWLp0qXq7e2VJPX29tIFypgtW7Zo2bJlcnctW7aMLlCG9N3zTBcIqI6TTjqpZEwXCEC1UADViEL3p4AuULZ0dHSocEbF3t5eukAZ0nfPM10goDoK3Z8CukAAqoUCqEYUuj8DjVHbbr/9dvX09EiSenp6tGLFisCJAAAA0B8KoBpRV1c36Bi17cILL9z3JZr19fVqbm4OnAgAAAD94V12jbjhhhtKxjfeeGOgJBiOtrY2mZmkfPHKN9Jnx3vf+96S8fjx4wMlAeLynve8p2Tcd1sEgLRQANWIz3zmM/u6PnV1dbr44osDJ8JQTJw4Ua2trTIztba2asKECaEjoUzr168vGa9bty5MECAyGzZsKBn33RYBIC0UQDWk0AWi+5NNbW1tmjVrFt2fDCrseab7A1RXoQtE9wdANVnhzFVZ0dTU5GvWrAkdAwAAAECNMrO17t7U3210gAAAAABEgwIIAAAAQDQogAAAAABEgwIIQPTmzp2rXC6nefPmhY4CROWcc85RLpfTeeedFzoKgIhQAAGI3r333itJuvvuuwMnAeLy9NNPS+L08wCqiwIIQNTmzp1bMqYLBFTHOeecUzKmCwSgWiiAAESt0P0poAsEVEeh+1NAFwhAtVAAAQAAAIgGBRAAAACAaFAAAYja2WefXTLmOASgOk4++eSS8cyZM8MEARAdc/fQGYakqanJ16xZEzoGgBEkl8vtu/zyyy8HTALEhW0PQFrMbK27N/V3Gx0gANErdIHo/gDVVegC0f0BUE10gAAAAACMKHSAAAAAAEAUQAAAAAAiQgEEAAAAIBoUQACiN2fOHOVyuXedEhtAuq677jrlcjldf/31oaMAiAgnQQAQPU7FC4TBtgcgLZwEAQAGMGfOnJIxXSCgOq677rqSMV0gANVCBwhA1Ir3QBewJxpIH9segDTRAQIAAAAAUQABAAAAiAgFEICoTZ8+vWQ8Y8aMQEmAuMydO7dkPG/evEBJAMSGY4AARI8zUQFhsO0BSAvHAAHAIApdILo/QHUVukB0fwBUEx0gAAAAACMKHSAAAAAAEAUQAAAAgIhQAAEAAACIRmoFkJlNMrP7zewZM+s0s6v6WcbM7CYz22Rm683s/WnlAYCBbNmyRc3Nzdq6dWvoKEBUFi5cqFwup8WLF4eOgmFYuXKlcrmc7rrrrtBRgCFJ7SQIZnaMpGPc/QkzO0zSWkkfd/euomXOlTRf0rmSZkn6lrvPGmy9nAQBQKVde+21+sEPfqBLLrlEX/3qV0PHAaLBabCzraGhQT09Paqvr1d3d3foOECJICdBcPffuPsTyeWdkp6RlOuz2Mck3ep5j0o6MimcAKAqtmzZomXLlsndtWzZMrpAQJUsXLiwZEwXKFtWrlypnp4eSVJPTw9dIGRKVY4BMrMpkk6VtLrPTTlJLxaNX9K7iyQASE1HR4cKnfDe3l51dHSEDQRE4sYbbywZf+UrXwmUBMNx9dVXl4znz58fKAkwdKkXQGZ2qKQVktrcfUffm/u5y7s+k2dm88xsjZmt2bZtWxoxAUTq9ttvL9mLuWLFisCJAKD2FZ43BxoDtSzVAsjMxihf/Nzm7rf3s8hLkiYVjY+V9Erfhdx9ibs3uXvT+PHj0wkLIEoXXnih6uvrJUn19fVqbm4OnAgAal/heXOgMVDL0jwLnEn6Z0nPuPs/DrDYnZIuSc4G9yFJ2939N2llAoC+2tralH+6kurq6tTW1hY2EBCJa665pmT8pS99KVASDMc3v/nNknHfY7qAWpZmB2i2pL+S9BEzW5f8nGtml5rZpcky90h6XtImSd+R9LkU8wDAu0ycOFGtra0yM7W2tmrChAmhIwFR6HvMyKWXXjrAkqhFH//4x0u65+eff37gRED50jwL3EPubu5+irvPTH7ucffF7r44Wcbd/XJ3n+ruJ7s757cGUHVtbW2aNWsW3R+gygpdILo/2VToAtH9Qdak9j1AaeF7gAAAAAAMJsj3AAEAAABAraEAAgAAABANCiAAAAAA0aAAAgAAABANCiAAAAAA0aAAAgAAABANCiAAAAAA0aAAAgAAABANCiAAAAAA0aAAAgAAABANCiAAAAAA0aAAAgAAABANCiAAAAAA0aAAAgAAABANCiAAAAAA0aAAAgAAQSxdulS5XE633XZb6CgAImLuHjrDkDQ1NfmaNWtCxwAAAAdo0qRJ6u3tVV1dnV588cXQcQCMIGa21t2b+ruNDhAAAKi6pUuXqre3V5LU29tLFwhA1dABAgAAVVfo/hTQBQJQSXSAAABATSkufvobA0BaKIAAAEDV1dXVDToGgLTwbAMAAKruhhtuKBnfeOONgZIAiA0FEAAAqLrPfOYz+7o+dXV1uvjiiwMnAhALCiAAABBEoQtE9wdANXEWOAAAAAAjCmeBAwAAAABRAAEAAACICAUQAAAAgGhQAAEAAACIBgUQAAAAgGhQAAEAAACIBgUQAAAAgGhQAAEAAACIBgUQAAAAgGhQAAEAAACIBgUQAAAAgGhQAAEAAACIBgUQAAAAgGhQAAEAAACIhrl76AxDYmbbJL0QOkeKjpL029AhMGzMX3Yxd9nG/GUXc5dtzF+2jeT5e5+7j+/vhswVQCOdma1x96bQOTA8zF92MXfZxvxlF3OXbcxftsU6f3wEDgAAAEA0KIAAAAAARIMCqPYsCR0AB4T5yy7mLtuYv+xi7rKN+cu2KOePY4AAAAAARIMOEAAAAIBoUAClxMzeNrN1ZvaUmT1hZn9Wxn1ON7PO5H5jzezryfjrg9znzHLWjaEzs6PN7Edm9pyZdZnZPWZ2wiDLX2lmz5jZbdXMGauibawz2c4+b2aDPqeZ2RQz25BcbjKzm6qTFsWGM3cp5eD5cxgGm7/Q25WZtZnZuFC/v1KGso2Y2R+b2fIy1rlrmFnMzJYkr4NPm9mHh7GOC8zsmv0sc6aZ3TXAbSXzamabzeyo5PIviq7f7/umWlA0vxvM7Mf7+z9b/PdWU/FrZp/rB5yrrBgdOsAItsfdZ0qSmf1XSV+TdMZ+7nOxpG+4+y3J/f5G0nh3f3OQ+5wpaZekXwyyDIbIzEzSHZKWuvtFyXUzJU2U9OwAd/ucpI+6e3dVQqJ4G5sg6YeSjpD09+Xc2d3XSFpzoCHMbLS7v3Wg64nMAc1dJZjZaPH8OVwDzl+ltqsD0CbpXyTtDpihEsraRpLnn1cktaSY5c8lHS9phqSxkg4b6grc/U5Jdx5AhjYNMK/uXrwTo5z3TbWgeH5vk3SppH8MmqiKzGyUu78dMgMdoOo4XNLvpXdXzWZ2s5l91sz+WtJfSlpgZreZ2Z2SDpG02sxazWy8ma0ws8eTn9lmNkX5jebqZE/C6dX/00assyT9wd0XF65w93WSnjSz+5Ku3tNm9jFJMrPFko6TdKeZXW1mh5jZ95K5erJouRlm9lgyX+vN7PgAf9uI4+5bJc2TdEWyt3JUsifw8eRx/pu+9ylsi2ZWl+xdO7Lotk1mNrG/7S65/cvJHtF7Jd1qZg8mBXLh/g+b2Smp/+EjQLlzZ2bHmNnPi/aanp4s+/1k/LSZXZ0sO9XMfmJma5O5mZ5c/30z+0czu1/SMvV5/jSzTyTresrMfh7oIcmUfuZv32tcsp18z8weMLPnzezKwv3M7NNFz4X/y8xG9V13sr6vF81va3L9QK+jV0r6Y0n3J3M8IvTzGH/W8l2Df5d0r5V2tj9rZrcn//9/ZWb/s+/6zOwoM3vEzM7rb7vqJ0KP8jv/xrj7bnffMlDW5Ln0envnNbKw7X3WzG5OLk81s0eTbbzdSjtTh5rZcjP7peXfC9n+5rVwfyvjfVNZD3j1PShp2kD/r4uW++/JNvOYmU1Llhn0OcvMDrX+37NMsfwnVr5j+Y7ZvWY2NrnttGR9j0i6fJDch5vZHZbvDC62d7rAi8xsTbLe64uybDazBWb2kKRPWP5TM12Wf57/0fAfvmFyd35S+JH0tqR1kn4pabuk05Lrz5R0V9FyN0v6bHL5+5Jaim7bVXT5h5L+PLk8WdIzyeUvS/pC6L93pP1IulLSN/u5frSkw5PLR0napHdOJrJZ0lHJ5a9K+nRy+Ujlu0aHSFoo6eLk+npJY0P/rVn9Kd4+iq77vfIv1PMkXZdcd5Dye6QbJE2RtCG5ft+2KOlbkuYml2dJ+j/J5cG2u7WF+ZP0GUkdyeUTJK0J/fjU8s8w5+5vJf2P5PpRyu+FPk3ST4vWcWTy732Sji+az58ll78v6S5Jo4rm8QtF939aUq54XfwMef6Kt6svK99dOyh5vnxN0hhJfyLp35V/Qy1J/yTpkn7W2Szpp8l8T5T0a0nHaPDX0c1Knoez/LOfx/izkl6S9EfJ9cXPa5+V9Lzy3aKDJb0gaVJhncn9V0v6v5Lr3rVd9fN7pyS/74dKXu8Gyb1Z0vzk8uckfbco183J5bskfTK5fGnhb03mdbukY5XfQf+I3nn+LZlXlb7e7urvcdMAz9+18FP0N4+W9L8lXVbG/+vCPF2id7axQZ+zNMB7lmRO35I0M7nt3/TOe5b1ks5ILn+98H+rz3rPlLRX+R2/o5TfTluS2wr/L0dJekDSKUV/wxeL1vGKpIMGyp72Dx2g9Oxx95nuPl3SOcrvJbYDWN9fSLrZzNYp30Y+3MyG3IbGATNJXzWz9ZL+j6Sc8i8ofZ0t6Zpkvh5Q/oVosvJP6P+fmf2dpPe5+55qhI5IYRs7W9IlyeO/WtJ7lf8Ix0CWSWpNLl+UjKXBt7s7i+bvx5LON7Mxkv6b8m+0MTT7m7vHJc01sy9LOtnddyr/Ru84M1toZudI2mFmh0r6M0k/Ttbxv5R/01zwYx/4oxcPS/q+mf2/yr94o3wDvb7d7e5vuvtvJW1V/vlyjvLF6+PJHM1R/o1UX38u6V/d/W3Pdx1WSfpAxZNnR/Fj/FN3/90Ay93n7tvdfa+kLknvS64fo/zOgS+6+0+T6/rbrvparvwc7Zb0TUkys38ys/MG+P23J/+uVf6Ndl8fVv45U8oXKcUec/eX3L1X+Z3I/d2/XLX8vmlskmuN8oX9P5dxn38t+rdwHNb+nrMGe8/S7flPtkjJXJnZEcoXI6uS638wSJ7H3P355Pn0X5XfXiXpL83sCUlPKv+xycai+ywrurxe0m1m9mnli7Gq4higKnD3Ryx/8Np45Se5uPA8uMzV1En6cN83zAdWU2EQner/M9UXKz+Pp7n7H8xss/qfQ5PU7O4b+1z/jJmtlnSepP80s792959VMHe0zOw45TuvW5V//Oe7+3/2WWbKAHd/RPmPIIyX9HFJNyTXD7bdvVEYu/tuM/uppI8p/1HWpgP8c6JSztwly/0X5bedH5jZ1939VjP7U0n/VfmPavyl8scKvO7J5+v78cYA18vdLzWzWcnvWGdmM939teH/ZXHoM39/0ufm4mMx3lb+fYcpf3zltX3W83/rnWNc/loDF1XDfR3NrD6PsTTI/2P1/5hL+cdtrfLbyypJcvef97ddFf3eCcp3WjZa/uOoK8zs75V/jvvv+/n9xb+7XANlH45+n79rxJ6+z1Fmtr//1973chnPWYO9Z+n7WI9Vfpsr9/tx+i7nZtYg6QuSPuDuvzez7/f5O4r/354n6b9IukDSl8xshlfxeFo6QFVg+c/AjlK+/f+CpEYzOyiptOeUuZp7JV1RtM6ZycWdGsYBidivn0k6KNmrIkkysw8ovydta/JEcpbe2bPW139Kml/o+pnZqcm/x0l63t1vUn6PFMeJVEBSuCxW/iMWrvzjf1nSkZGZnWBmhwx0/+Q+dyh/EOozRS8gA213/fmupJskPT7Inln0Ue7cmVlh2/uO8ntL35/sWKpz9xWSviTp/e6+Q1K3mX0iub8lRVJ/Sp4/zWyqu6929wWSfitpUip/9AjSz/yV4z5JLcmba5nZH5nZ+9z9juSTEzM9fzKFn0tqtfyxXuOVf7P0mAZ/HR1xr4nDfIz748p3qKdbcka2/rarPvfZll/Mzkr29M+TdJWkJ9x9sCJsMI8q//FGKd9xL8dw5nUoz9+1YH/vD1uL/n1EKus56wiV955FkuTur0vabmaFbs7Fgyz+QTNrSI79aZX0kPLHvL+RrGOipI/2d8fkPpPc/X5JX1T+UIFDB8tWaXSA0lNob0r5ivozyZPHi2b2b8q3/n6lfIuwHFdK+nbSxhyt/AvDpcp/jnq55Q9sm+/uD1bwb4iWu3uyN7IjeaHYq/znV78s6SYzW6N3jvHqz1ckdUhanxRBmyWdr/yTxKfN7A+SXpXUntofMfIVtrExyu/Z/IHeOYvOd5X/6MQTyeO/TfnOzmCWKf9xkM8WXTfQdvcu7r7WzHZIumXof0p0hjN3Zyp/EPAflD+W4RLlP85xi71zeuBCR+FiSYvM7Lrkd/xI0lP95Ch5/lT+hAjHK/+cfd8A98Hg87df7t6VzM29ydz9QfkO3gt9Fr1D+Y/6PKX8m/cvuvurkjTI6+gSSf9hZr9x97OG8bfVigN6jAfi7m+b2UWS/j15vnpD796uipd3M2tW/nVvnPIfg7tC0hfNrMXd93v67X60SfoXM/tbSXcrf9zP/gxnXst+/q4F7r6/94cHJZ8gqZP0yeS6r+/nOes25ed6f+9Zis2V9D0z2638DqmBPCLpRkknK//Y3uHuvWb2pPKfonle+Y/o9WeU8v8HjkiyfzMpvqrGDmxnAgBAyn8Xh/LHe01PPr8OAOgjKaT2JMXVRcqfEOFjoXMhLnSAAOAAmdklkv5B0ucpfgBgUKcpf3ICk/S68h/LA6qKDhAAAACAaHASBAAAAADRoAACAAAAEA0KIAAAAADR4CQIAICaY2bvVf60rpJ0tPJf1LctGX/Q3XuCBAMAZB4nQQAA1DQz+7KkXe7+jdBZAADZx0fgAABZMNbMus1sjCSZ2eFmttnMxpjZA2bWYWa/MLMNZvbBZJlDzOx7Zva4mT2ZfOEpACByFEAAgCzYo/wXzZ6XjC+StMLd/5CMD3H3P5P0OUnfS677H5J+5u4fkHSW8t+afkj1IgMAahEFEAAgK74raW5yea6kW4pu+1dJcvefSzrczI6UdLaka8xsnfLF08GSJlcpKwCgRnESBABAJrj7w2Y2xczOkDTK3TcU39x3cUkmqdndN1YtJACg5tEBAgBkya3Kd3tu6XN9qySZ2Z9L2u7u2yX9p6T5ZmbJbadWMygAoDZRAAEAsuQ2Se9R8pG3Ir83s19IWizp/0mu+4qkMZLWm9mGZAwAiBynwQYAZIaZtUj6mLv/VdF1D0j6gruvCRYMAJAZHAMEAMgEM1so6aOSzg2dBQCQXXSAAAAAAESDY4AAAAAARIMCCAAAAEA0KIAAAAAARIMCCAAAAEA0KIAAAAAARIMCCAAAAEA0/n/LOPlOF31yZQAAAABJRU5ErkJggg==\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": "markdown",
   "id": "baa84b4e",
   "metadata": {},
   "source": [
    "Grouping types of restuarants location wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "cae3d7ad",
   "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": 62,
     "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": "code",
   "execution_count": 64,
   "id": "e4bd79f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2592x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.head().plot(kind = 'bar', figsize=(36,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "279b3a1c",
   "metadata": {},
   "source": [
    "Visualizing top cuisines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "574a16c6",
   "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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df[['cuisines','votes']]\n",
    "df4.drop_duplicates()\n",
    "df5 = df4.groupby(['cuisines'])['votes'].sum()\n",
    "df5 = df5.to_frame()\n",
    "df5 = df5.sort_values('votes', ascending=False)\n",
    "df5.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e3e6a605",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>others</td>\n",
       "      <td>others</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</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>300.0</td>\n",
       "      <td>Buffet</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>others</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Timepass Dinner</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>286</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Rosewood International Hotel - Bar &amp; Restaurant</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.6</td>\n",
       "      <td>8</td>\n",
       "      <td>others</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>others</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              name online_order book_table  \\\n",
       "2                                  San Churro Cafe          Yes         No   \n",
       "3                            Addhuri Udupi Bhojana           No         No   \n",
       "4                                    Grand Village           No         No   \n",
       "5                                  Timepass Dinner          Yes         No   \n",
       "6  Rosewood International Hotel - Bar & Restaurant           No         No   \n",
       "\n",
       "   rate  votes      location      rest_type                    cuisines  \\\n",
       "2   3.8    918  Banashankari         others                      others   \n",
       "3   3.7     88  Banashankari    Quick Bites  South Indian, North Indian   \n",
       "4   3.8    166  Basavanagudi  Casual Dining                      others   \n",
       "5   3.8    286  Basavanagudi  Casual Dining                North Indian   \n",
       "6   3.6      8        others  Casual Dining                      others   \n",
       "\n",
       "   Cost2plates    Type  \n",
       "2        800.0  Buffet  \n",
       "3        300.0  Buffet  \n",
       "4        600.0  Buffet  \n",
       "5        600.0  Buffet  \n",
       "6        800.0  Buffet  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df5 = df5.iloc[1:, :]\n",
    " df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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