Restaurant EDA Bangalore .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bdc87df9",
   "metadata": {},
   "source": [
    "# Importing libraries \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "691023ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "plt.style.use('dark_background')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8336918",
   "metadata": {},
   "source": [
    "# reading csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e542ef57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>address</th>\n",
       "      <th>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>phone</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>dish_liked</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>reviews_list</th>\n",
       "      <th>menu_item</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.zomato.com/bangalore/jalsa-banasha...</td>\n",
       "      <td>942, 21st Main Road, 2nd Stage, Banashankari, ...</td>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>775</td>\n",
       "      <td>080 42297555\\r\\n+91 9743772233</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.zomato.com/bangalore/spice-elephan...</td>\n",
       "      <td>2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...</td>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>787</td>\n",
       "      <td>080 41714161</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://www.zomato.com/SanchurroBangalore?cont...</td>\n",
       "      <td>1112, Next to KIMS Medical College, 17th Cross...</td>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>918</td>\n",
       "      <td>+91 9663487993</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.zomato.com/bangalore/addhuri-udupi...</td>\n",
       "      <td>1st Floor, Annakuteera, 3rd Stage, Banashankar...</td>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7/5</td>\n",
       "      <td>88</td>\n",
       "      <td>+91 9620009302</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Masala Dosa</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300</td>\n",
       "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.zomato.com/bangalore/grand-village...</td>\n",
       "      <td>10, 3rd Floor, Lakshmi Associates, Gandhi Baza...</td>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>166</td>\n",
       "      <td>+91 8026612447\\r\\n+91 9901210005</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Panipuri, Gol Gappe</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  \\\n",
       "0  https://www.zomato.com/bangalore/jalsa-banasha...   \n",
       "1  https://www.zomato.com/bangalore/spice-elephan...   \n",
       "2  https://www.zomato.com/SanchurroBangalore?cont...   \n",
       "3  https://www.zomato.com/bangalore/addhuri-udupi...   \n",
       "4  https://www.zomato.com/bangalore/grand-village...   \n",
       "\n",
       "                                             address                   name  \\\n",
       "0  942, 21st Main Road, 2nd Stage, Banashankari, ...                  Jalsa   \n",
       "1  2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...         Spice Elephant   \n",
       "2  1112, Next to KIMS Medical College, 17th Cross...        San Churro Cafe   \n",
       "3  1st Floor, Annakuteera, 3rd Stage, Banashankar...  Addhuri Udupi Bhojana   \n",
       "4  10, 3rd Floor, Lakshmi Associates, Gandhi Baza...          Grand Village   \n",
       "\n",
       "  online_order book_table   rate  votes                             phone  \\\n",
       "0          Yes        Yes  4.1/5    775    080 42297555\\r\\n+91 9743772233   \n",
       "1          Yes         No  4.1/5    787                      080 41714161   \n",
       "2          Yes         No  3.8/5    918                    +91 9663487993   \n",
       "3           No         No  3.7/5     88                    +91 9620009302   \n",
       "4           No         No  3.8/5    166  +91 8026612447\\r\\n+91 9901210005   \n",
       "\n",
       "       location            rest_type  \\\n",
       "0  Banashankari        Casual Dining   \n",
       "1  Banashankari        Casual Dining   \n",
       "2  Banashankari  Cafe, Casual Dining   \n",
       "3  Banashankari          Quick Bites   \n",
       "4  Basavanagudi        Casual Dining   \n",
       "\n",
       "                                          dish_liked  \\\n",
       "0  Pasta, Lunch Buffet, Masala Papad, Paneer Laja...   \n",
       "1  Momos, Lunch Buffet, Chocolate Nirvana, Thai G...   \n",
       "2  Churros, Cannelloni, Minestrone Soup, Hot Choc...   \n",
       "3                                        Masala Dosa   \n",
       "4                                Panipuri, Gol Gappe   \n",
       "\n",
       "                         cuisines approx_cost(for two people)  \\\n",
       "0  North Indian, Mughlai, Chinese                         800   \n",
       "1     Chinese, North Indian, Thai                         800   \n",
       "2          Cafe, Mexican, Italian                         800   \n",
       "3      South Indian, North Indian                         300   \n",
       "4        North Indian, Rajasthani                         600   \n",
       "\n",
       "                                        reviews_list menu_item  \\\n",
       "0  [('Rated 4.0', 'RATED\\n  A beautiful place to ...        []   \n",
       "1  [('Rated 4.0', 'RATED\\n  Had been here for din...        []   \n",
       "2  [('Rated 3.0', \"RATED\\n  Ambience is not that ...        []   \n",
       "3  [('Rated 4.0', \"RATED\\n  Great food and proper...        []   \n",
       "4  [('Rated 4.0', 'RATED\\n  Very good restaurant ...        []   \n",
       "\n",
       "  listed_in(type) listed_in(city)  \n",
       "0          Buffet    Banashankari  \n",
       "1          Buffet    Banashankari  \n",
       "2          Buffet    Banashankari  \n",
       "3          Buffet    Banashankari  \n",
       "4          Buffet    Banashankari  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('zomato.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3ae0b210",
   "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": "ac1c21b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['url', 'address', 'name', 'online_order', 'book_table', 'rate', 'votes',\n",
       "       'phone', 'location', 'rest_type', 'dish_liked', 'cuisines',\n",
       "       'approx_cost(for two people)', 'reviews_list', 'menu_item',\n",
       "       'listed_in(type)', 'listed_in(city)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2d462a1",
   "metadata": {},
   "source": [
    "Dropping Unwanted Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c87f0bf3",
   "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>4.1/5</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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= df.drop(['url','address','phone','menu_item','dish_liked','reviews_list'],axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c8b1134c",
   "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": 7,
   "id": "1b9d5d0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51609, 11)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(inplace = True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3de3fc47",
   "metadata": {},
   "source": [
    "# cleaning rate columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "12b8ed45",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "69fc3d21",
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def changeRate (value):\n",
    "    if(value=='NEW' or value=='-'):\n",
    "        return np.nan\n",
    "    \n",
    "    else:\n",
    "        value = str(value).split('/')\n",
    "        value = value[0]\n",
    "        \n",
    "        return float(value)\n",
    "    \n",
    "df['rate'] = df['rate'].apply(changeRate);\n",
    "df['rate'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6297fd1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10019"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25f9b25d",
   "metadata": {},
   "source": [
    "# filling null values in Rate with mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "647af2d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].fillna(df['rate'].mean(),inplace = True)\n",
    "df['rate'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "467cf644",
   "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": "d259864e",
   "metadata": {},
   "source": [
    "# dropping Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "96623617",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2310d83c",
   "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": 14,
     "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": 15,
   "id": "b40330a8",
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['location'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d1e5501a",
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['listed_in(city)'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db3df743",
   "metadata": {},
   "source": [
    "# Deleting extra city columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "eb43779d",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['listed_in(city)'],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ab5957",
   "metadata": {},
   "source": [
    "# flitering cost2plates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a74d5fe8",
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cost2plates'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9edf3393",
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def checkCost(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(checkCost)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3661bfac",
   "metadata": {},
   "source": [
    "# Filtering Rest Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6cf16830",
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rest_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eb48945f",
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_type = df['rest_type'].value_counts(ascending= False )\n",
    "rest_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "61a42e23",
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_type_less_than_1000 = rest_type[rest_type<1000]\n",
    "rest_type_less_than_1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b0d67e0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#making this as OTHERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "15461a72",
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def changeRestType(value):\n",
    "    if(value in rest_type_less_than_1000):\n",
    "        return 'Others'\n",
    "    \n",
    "    else:\n",
    "        return value\n",
    "    \n",
    "df['rest_type'] = df['rest_type'].apply(changeRestType)\n",
    "df['rest_type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33111bbd",
   "metadata": {},
   "source": [
    "# Filtering Location columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "879c9c43",
   "metadata": {},
   "outputs": [],
   "source": [
    "location = df['location'].value_counts(ascending = False)\n",
    "location_less_than_300 = location[location<300]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "839bf202",
   "metadata": {},
   "outputs": [],
   "source": [
    "def changeLocation(value):\n",
    "    if (value in location_less_than_300):\n",
    "        return 'Others'\n",
    "    else:\n",
    "        return value\n",
    "    \n",
    "df['location'] = df['location'].apply(changeLocation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "33f619b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTM                      5056\n",
       "Others                   4954\n",
       "HSR                      2494\n",
       "Koramangala 5th Block    2479\n",
       "JP Nagar                 2218\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['location'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26429ea8",
   "metadata": {},
   "source": [
    "# Filtering Cusine columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "10e29d24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North Indian                                2852\n",
       "North Indian, Chinese                       2351\n",
       "South Indian                                1820\n",
       "Biryani                                      903\n",
       "Bakery, Desserts                             898\n",
       "                                            ... \n",
       "North Indian, Chinese, Oriya, Mithai           1\n",
       "Beverages, Burger                              1\n",
       "North Indian, Mughlai, Lucknowi                1\n",
       "Continental, Thai, North Indian, Chinese       1\n",
       "North Indian, Chinese, Arabian, Momos          1\n",
       "Name: cuisines, Length: 2704, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cuisines'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "77ae4841",
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cuisines = df['cuisines'].value_counts(ascending = False)\n",
    "cuisines_less_than_100 = cuisines[cuisines<100]\n",
    "\n",
    "def changeCuisines(value):\n",
    "    if(value in cuisines_less_than_100):\n",
    "        return 'Others'\n",
    "    \n",
    "    else:\n",
    "        return value\n",
    "    \n",
    "df['cuisines'] = df['cuisines'].apply(changeCuisines)\n",
    "df['cuisines'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3bd0c767",
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dbaf31a",
   "metadata": {},
   "source": [
    "# DataSet has been cleaned "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94bdc681",
   "metadata": {},
   "source": [
    "Data Vizualizations "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5fcf1cf",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d558a9f0",
   "metadata": {},
   "source": [
    "# Count plot of various Location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "af0e6aee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vivek\\AppData\\Local\\Programs\\Python\\Python310\\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": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "ax = sns.countplot(df['location'])\n",
    "plt.xticks(rotation= 90)   #To rotate the values below"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e04d9516",
   "metadata": {},
   "source": [
    "# Checking for Online Orders "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "88943e74",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vivek\\AppData\\Local\\Programs\\Python\\Python310\\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": {
      "image/png": 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os88+U2FhoVJTU+Xr6+uu0wEAoNVxWwTU19dr7ty56t+/vwYNGqSZM2eqb9++kqRly5YpLCxMYWFh2r59uySpb9++io2NVf/+/RUdHa1Vq1bJy8tLXl5eWrlypUaNGqV+/frpqaeech1nyZIlWrZsmUJDQ1VTU6OpU6e663QAAGh13BYBlZWVysnJkSSdO3dOR44cUUBAwE33j4mJUWpqqi5fvqySkhIVFRUpIiJCERERKioqUnFxserq6pSamqqYmBhJ0ogRI7Rp0yZJUkpKisaMGeOu0wEAoNVpltcEBAcHKywsTFlZWZKkWbNmKTc3V2vWrJHD4ZAkBQQEqLS01HWbsrIyBQQE3HS9e/fuqq2tVUNDwzXrN5KQkCCn0ymn0yk/Pz83nSUAAC2L2yOgU6dO2rx5s+bMmaOzZ89q9erVuu+++zRgwABVVFRo6dKl7h5BycnJCg8PV3h4uKqrq91+fwAAtAQ+bj24j482b96s999/X1u2bJEkVVVVua5PTk7WRx99JEkqLy9XUFCQ67rAwECVl5dL0g3XT548KYfDIW9vbzU0NFyzPwAA+HZufSRgzZo1OnLkiJYtW+Za8/f3d309duxY5eXlSZLS09MVGxurtm3bKiQkRKGhocrOzpbT6VRoaKhCQkLk6+ur2NhYpaenS5L27NmjCRMmSJLi4+OVlpbmztMBAKBVcdsjAY8++qji4uJ08OBB1wsEX3zxRT311FMaMGCAjDEqKSnR9OnTJUn5+fnauHGj8vPzVV9fr5kzZ+rKlSuSrr6GICMjQ97e3nrnnXeUn58vSZo3b55SU1P16quvKicnR2vWrHHX6QAA0Oq0kWQ8PURzcjqdCg8Pb/Ljrsr+pMmPCXjCjIihnh4BQBO61e89/mIgAACWIgIAALAUEQAAgKWIAAAALEUEAABgKSIAAABLEQEAAFiKCAAAwFJEAAAAliICAACwFBEAAICliAAAACxFBAAAYCkiAAAASxEBAABYiggAAMBSRAAAAJYiAgAAsBQRAACApYgAAAAsRQQAAGApIgAAAEsRAQAAWIoIAADAUkQAAACWIgIAALAUEQAAgKWIAAAALEUEAABgKSIAAABLEQEAAFiKCAAAwFJEAAAAliICAACwFBEAAICliAAAACxFBAAAYCkiAAAASxEBAABYiggAAMBSRAAAAJYiAgAAsBQRAACApYgAAAAsRQQAAGApIgAAAEsRAQAAWIoIAADAUkQAAACWIgIAALAUEQAAgKWIAAAALOXj6QEA4B9x/sJOT48ANIlOHX/W7PfJIwEAAFiKCAAAwFJEAAAAliICAACwFBEAAICliAAAACzltggIDAzU7t27dfjwYeXl5Wn27NmSpK5duyozM1NHjx5VZmamHA6H6zbLly9XYWGhcnNzFRYW5lqPi4vT0aNHdfToUcXFxbnWH3roIR08eFCFhYVavny5u04FAIBWyW0RUF9fr7lz56p///4aNGiQZs6cqb59+yoxMVG7du1Snz59tGvXLiUmJkqSRo0apdDQUIWGhmratGlavXq1pKvRkJSUpIEDByoiIkJJSUmucFi9erUSEhJct4uOjnbX6QAA0Oq4LQIqKyuVk5MjSTp37pyOHDmigIAAxcTEKCUlRZKUkpKiMWPGSJJiYmK0fv16SVJWVpYcDof8/f0VFRWlHTt2qKamRrW1tdqxY4eio6Pl7++vLl26KCsrS5K0fv1617EAAMC3a5bXBAQHByssLExZWVnq2bOnKisrJV0NhZ49e0qSAgICVFpa6rpNWVmZAgICbrleVlZ23ToAAGgct//Z4E6dOmnz5s2aM2eOzp49e931xhh3j6CEhARNmzZNkuTn5+f2+wMAoCVw6yMBPj4+2rx5s95//31t2bJFknTixAn5+/tLkvz9/VVVVSVJKi8vV1BQkOu2gYGBKi8vv+V6YGDgdes3kpycrPDwcIWHh6u6urrJzxMAgJbIrRGwZs0aHTlyRMuWLXOtpaenKz4+XpIUHx+vtLQ01/rXr/wfOHCgTp8+rcrKSmVkZCgyMlIOh0MOh0ORkZHKyMhQZWWlzpw5o4EDB0q6+g6Cr48FAAC+ndueDnj00UcVFxengwcPul4g+OKLL+q1117Txo0bNXXqVB07dkwTJ06UJG3btk2jR49WUVGRLly4oClTpkiSampqtGjRIjmdTknSwoULVVNTI0maMWOG1q1bpw4dOmj79u3avn27u04HAIBWp40k9z8pfwdxOp0KDw9v8uOuyv6kyY8JeMKMiKGeHuG28FHCaC3c9VHCt/q9x18MBADAUkQAAACWIgIAALAUEQAAgKWIAAAALEUEAABgKSIAAABLEQEAAFiKCAAAwFJEAAAAliICAACwFBEAAICliAAAACxFBAAAYCkiAAAASxEBAABYiggAAMBSRAAAAJYiAgAAsBQRAACApYgAAAAsRQQAAGApIgAAAEsRAQAAWIoIAADAUkQAAACWIgIAALAUEQAAgKWIAAAALEUEAABgKSIAAABLEQEAAFiKCAAAwFJEAAAAliICAACwFBEAAICliAAAACxFBAAAYCkiAAAASxEBAABYqlERsHPnzkatAQCAlsPnVle2a9dOHTt2lJ+fnxwOh9q0aSNJ6tKliwICApplQAAA4B63jIDp06drzpw56t27tz7//HNXBJw5c0ZvvvlmswwIAADc45YRsGLFCq1YsUKzZs3ilz4AAK3MLSPga2+++aYGDx6skJAQ+fj8/5u8++67bhsMAAC4V6MiYP369brvvvv0xRdfqKGhQZJkjCECAABowRoVAY888oj69evn7lkAAEAzatRbBPPy8uTv7+/uWQAAQDNq1CMBfn5+ys/PV3Z2ti5duuRaj4mJcdtgAADAvRoVAfPnz3fzGAAAoLk1KgI+/fRTd88BAACaWaMi4MyZMzLGSJLatm0rX19fnT9/XnfffbdbhwMAAO7TqAjo0qXLNZdjYmI0aNAgtwwEAACax3f6FMG0tDRFRUU19SwAAKAZNeqRgLFjx7q+9vLy0iOPPKKLFy+6bSgAAOB+jYqAJ554wvV1fX29SkpKeHsgAAAtXKMi4Nlnn3X3HAAAoJk16jUBAQEB+vDDD3XixAmdOHFCmzZtUkBAgLtnAwAAbtSoCFi7dq3S09PVu3dv9e7dW1u3btXatWvdPRsAAHCjRkVAjx49tG7dOjU0NKihoUEpKSnq0aOHu2cDAABu1KgIOHnypCZNmiQvLy95eXlp0qRJOnny5C1vs2bNGp04cUKHDh1yrSUlJamsrEw5OTnKycnRqFGjXNclJiaqsLBQBQUFioyMdK1HRUWpoKBAhYWFmjdvnms9JCREn332mQoLC5WamipfX99GnzQAAGhkBDz77LOaOHGiKisrVVFRoQkTJmjy5Mm3vM26desUHR193fqyZcsUFhamsLAwbd++XZLUt29fxcbGqn///oqOjtaqVatcwbFy5UqNGjVK/fr101NPPaW+fftKkpYsWaJly5YpNDRUNTU1mjp16m2eOgAAdmtUBCxcuFDx8fG655571LNnTz377LNasGDBLW+zd+9enTp1qlFDxMTEKDU1VZcvX1ZJSYmKiooUERGhiIgIFRUVqbi4WHV1dUpNTXW9NXHEiBHatGmTJCklJUVjxoxp1H0BAICrGhUBP/rRj1RbW+u6XFNTo7CwsO90h7NmzVJubq7WrFkjh8Mh6eq7D0pLS137lJWVKSAg4Kbr3bt3V21trRoaGq5Zv5mEhAQ5nU45nU75+fl9p7kBAGhtGhUBXl5erl/YktS1a1f5+DTqTwxcY/Xq1brvvvs0YMAAVVRUaOnSpbd9jO8iOTlZ4eHhCg8PV3V1dbPcJwAAd7pG/SZfunSpDhw4oP/5n/+RJD355JP67W9/e9t3VlVV5fo6OTlZH330kSSpvLxcQUFBrusCAwNVXl4uSTdcP3nypBwOh7y9vdXQ0HDN/gAAoHEa9UjAu+++q3Hjxrn+WNC4ceP03nvv3fad+fv7u74eO3as8vLyJEnp6emKjY1V27ZtFRISotDQUGVnZ8vpdCo0NFQhISHy9fVVbGys0tPTJUl79uzRhAkTJEnx8fFKS0u77XkAALBZox/TP3LkiI4cOdLoA3/wwQcaNmyY/Pz8VFpaqqSkJA0bNkwDBgyQMUYlJSWaPn26JCk/P18bN25Ufn6+6uvrNXPmTF25ckXS1dcQZGRkyNvbW++8847y8/MlSfPmzVNqaqpeffVV5eTkaM2aNbdz3gAAWK+NJOPpIZqT0+lUeHh4kx93VfYnTX5MwBNmRAz19Ai35fyFnZ4eAWgSnTr+zC3HvdXvvUY9HQAAAFofIgAAAEsRAQAAWIoIAADAUkQAAACWIgIAALAUEQAAgKWIAAAALEUEAABgKSIAAABLEQEAAFiKCAAAwFJEAAAAliICAACwFBEAAICliAAAACxFBAAAYCkiAAAASxEBAABYiggAAMBSRAAAAJYiAgAAsBQRAACApYgAAAAsRQQAAGApIgAAAEsRAQAAWIoIAADAUkQAAACWIgIAALAUEQAAgKWIAAAALEUEAABgKSIAAABLEQEAAFiKCAAAwFJEAAAAliICAACwFBEAAICliAAAACxFBAAAYCkiAAAASxEBAABYiggAAMBSRAAAAJYiAgAAsBQRAACApYgAAAAsRQQAAGApIgAAAEsRAQAAWIoIAADAUkQAAACWIgIAALAUEQAAgKWIAAAALEUEAABgKSIAAABLEQEAAFjKbRGwZs0anThxQocOHXKtde3aVZmZmTp69KgyMzPlcDhc1y1fvlyFhYXKzc1VWFiYaz0uLk5Hjx7V0aNHFRcX51p/6KGHdPDgQRUWFmr58uXuOg0AAFott0XAunXrFB0dfc1aYmKidu3apT59+mjXrl1KTEyUJI0aNUqhoaEKDQ3VtGnTtHr1aklXoyEpKUkDBw5URESEkpKSXOGwevVqJSQkuG739/cFAABuzW0RsHfvXp06deqatZiYGKWkpEiSUlJSNGbMGNf6+vXrJUlZWVlyOBzy9/dXVFSUduzYoZqaGtXW1mrHjh2Kjo6Wv7+/unTpoqysLEnS+vXrXccCAACN06yvCejZs6cqKyslSZWVlerZs6ckKSAgQKWlpa79ysrKFBAQcMv1srKy69YBAEDj+Xjyzo0xzXI/CQkJmjZtmiTJz8+vWe4TAIA7XbM+EnDixAn5+/tLkvz9/VVVVSVJKi8vV1BQkGu/wMBAlZeX33I9MDDwuvWbSU5OVnh4uMLDw1VdXd3UpwUAQIvUrBGQnp6u+Ph4SVJ8fLzS0tJc61+/8n/gwIE6ffq0KisrlZGRocjISDkcDjkcDkVGRiojI0OVlZU6c+aMBg4cKOnqOwi+PhYAAGgctz0d8MEHH2jYsGHy8/NTaWmpkpKS9Nprr2njxo2aOnWqjh07pokTJ0qStm3bptGjR6uoqEgXLlzQlClTJEk1NTVatGiRnE6nJGnhwoWqqamRJM2YMUPr1q1Thw4dtH37dm3fvt1dpwIAQKvURlLzPDF/h3A6nQoPD2/y467K/qTJjwl4woyIoZ4e4bacv7DT0yMATaJTx5+55bi3+r3HXwwEAMBSRAAAAJYiAgAAsBQRAACApYgAAAAsRQQAAGApIgAAAEsRAQAAWIoIAADAUkQAAACWIgIAALAUEQAAgKWIAAAALEUEAABgKSIAAABLEQEAAFiKCAAAwFJEAAAAliICAACwFBEAAICliAAAACxFBAAAYCkiAAAASxEBAABYiggAAMBSRAAAAJYiAgAAsBQRAACApYgAAAAsRQQAAGApIgAAAEsRAQAAWIoIAADAUkQAAACWIgIAALAUEQAAgKWIAAAALEUEAABgKSIAAABLEQEAAFiKCAAAwFJEAAAAliICAACwFBEAAICliAAAACxFBAAAYCkiAAAASxEBAABYiggAAMBSRAAAAJYiAgAAsBQRAACApYgAAAAsRQQAAGApIgAAAEsRAQAAWIoIAADAUkQAAACWIgIAALAUEQAAgKU8EgHFxcU6ePCgcnJy5HQ6JUldu3ZVZmamjh49qszMTDkcDtf+y5cvV2FhoXJzcxUWFuZaj4uL09GjR3X06FHFxcU192kAANCieeyRgOHDhyssLEzh4eGSpMTERO3atUt9+vTRrl27lJiYKEkaNWqUQkNDFRoaqmnTpmn16tWSrkZDUlKSBg4cqIiICCUlJV0TDgAA4NbumKcDYmJilJKSIklKSUnRmDFjXOvr16+XJGVlZcnhcMjf319RUVHasWOHampqVFtbqx07dig6OtpT4wMA0OJ4JAKMMcrMzNSf/vQnJSQkSJJ69uypyspKSVJlZaV69uwpSQoICFBpaanrtmVlZQoICLjp+o0kJCTI6XTK6XTKz8/PXacFAECL4uOJOx0yZIiOHz+uHj16aMeOHSooKLhuH2NMk91fcnKykpOTJcn1GgQAAGznkUcCjh8/Lkn629/+pi1btigiIkInTpyQv7+/JMnf319VVVWSpPLycgUFBbluGxgYqPLy8puuAwCAxmn2COjYsaPuuusu19eRkZHKy8tTenq64uPjJUnx8fFKS0uTJKWnp7te+T9w4ECdPn1alZWVysjIUGRkpBwOhxwOhyIjI5WRkdHcpwMAQIvV7E8H9OzZU1u2bLl65z4++uCDD5SRkSGn06mNGzdq6tSpOnbsmCZOnChJ2rZtm0aPHq2ioiJduHBBU6ZMkSTV1NRo0aJFrof3Fy5cqJqamuY+HQAAWqw2kpruyfcWwOl0ut6W2JRWZX/S5McEPGFGxFBPj3Bbzl/Y6ekRgCbRqePP3HLcW/3eu2PeIggAAJoXEQAAgKWIAAAALEUEAABgKSIAAABLEQEAAFiKCAAAwFJEAAAAliICAACwFBEAAICliAAAACxFBAAAYCkiAAAASxEBAABYiggAAMBSRAAAAJYiAgAAsBQRAACApYgAAAAsRQQAAGApIgAAAEsRAQAAWIoIAADAUkQAAACWIgIAALAUEQAAgKWIAAAALEUEAABgKSIAAABLEQEAAFiKCAAAwFJEAAAAliICAACwFBEAAICliAAAACxFBAAAYCkiAAAASxEBAABYiggAAMBSRAAAAJYiAgAAsBQRAACApYgAAAAsRQQAAGApIgAAAEsRAQAAWIoIAADAUkQAAACWIgIAALAUEQAAgKWIAAAALEUEAABgKSIAAABLEQEAAFiKCAAAwFJEAAAAliICAACwFBEAAICliAAAACzV4iMgKipKBQUFKiws1Lx58zw9DgAALUaLjgAvLy+tXLlSo0aNUr9+/fTUU0+pb9++nh4LAIAWoUVHQEREhIqKilRcXKy6ujqlpqYqJibG02MBANAitOgICAgIUGlpqetyWVmZAgICPDgRAAAth4+nB2gOCQkJmjZtmiTpBz/4gZxOp4cnwnfh5+en6upqT4/R6rW0n4/8w56ewA78/Lmfu372goODb3pdi46A8vJyBQUFuS4HBgaqvLz8uv2Sk5OVnJzcnKPBDZxOp8LDwz09BmAlfv5apxb9dIDT6VRoaKhCQkLk6+ur2NhYpaene3osAABahBb9SEBDQ4NmzZqljIwMeXt765133lF+fr6nxwIAoEVo0REgSdu3b9f27ds9PQaawdtvv+3pEQBr8fPXOrWRZDw9BAAAaH4t+jUBAADguyMCcEfZu3evoqOjXZcnTJjA0z1AMzLG6PXXX3ddnjt3rpKSkjw4EdyJCMAd5bnnntN//ud/ql27durUqZMWL16smTNnenoswBoXL17UuHHj1L17d0+PgmZABOCOcvjwYW3dulXz5s3TK6+8ovfee08vvfSSsrKy9Oc//1k///nPJUn9+vVTVlaWcnJylJubq+9///senhxoHerr6/X222/rl7/85XXXBQcHa9euXcrNzdXOnTuv+TstaLkMG9udtHXs2NEUFBSYgwcPmsWLF5tJkyYZSebuu+82//d//2c6duxoVqxYYZ5++mkjyfj6+pr27dt7fG42ttawnT171nTu3NkUFxebLl26mLlz55qkpCQjyaSnp5u4uDgjyUyZMsVs2bLF4/Oy/WMb7w7AHWnBggU6d+6cJk6cqPbt26u+vl6S1K1bN0VFRSksLEwvvfSS1q9frw8//FBFRUUenhhoHc6ePavOnTtrwYIFqqur01dffaW77rpLCxYs0N/+9jf16tVL9fX18vHxUUVFhXr06OHpkfEP4OkA3JGuXLmiK1euqE2bNho/frzCwsIUFham4OBgFRQUaMOGDfr5z3+ur776Stu2bdPw4cM9PTLQqrzxxhuaOnWqOnXq5OlR4EZEAO5oGRkZev75512XBwwYIEm699579Ze//EX/9V//pbS0NP3oRz/y0IRA61RTU6ONGzdq6tSprrU//vGPio2NlSRNmjRJe/fu9dR4aCJEAO5oixYtkq+vrw4ePKi8vDwtWrRIkjRx4kTl5eUpJydHP/zhD7V+/XoPTwq0PkuXLpWfn5/r8vPPP68pU6YoNzdXzzzzjF544QUPToemwGsCAACwFI8EAABgKSIAAABLEQEAAFiKCAAAwFJEAAAAliICAACwFBEA4KbWrl2r8ePHS5KSk5PVt29fD08kFRcX8wl3QBPx8fQAAFqGhISEZr9Pb29vNTQ0/EPH8PLy0pUrV5poIqB14ZEAwDK//OUvdejQIR06dEgvvPCCgoODlZ+fr7ffflt5eXnKyMhQ+/btr7vdnj179PDDD0u6+iEzr776qr744gsdOHBA99xzjyTJz89PmzZtUnZ2trKzs/XjH//4pnN07dpVW7ZsUW5urg4cOKAHHnhAkpSUlKT169dr3759evfdd9WtWzdlZGQoLy9PycnJatOmjesYkyZNcn2k9FtvvSUvLy/XfK+//rq++OILDR48uMm+d0Br5PGPMmRjY2ue7aGHHjIHDx40HTt2NJ06dTJ5eXlmwIABpq6uzjz44INGkvn973/v+vjmtWvXmvHjxxtJZs+ePebhhx82kowxxjz++ONGklmyZIl56aWXjCTz/vvvm0cffdRIMkFBQSY/P/+ms6xYscK88sorRpIZPny4ycnJMZJMUlKS+dOf/uT6eOjly5ebf/u3fzOSzOjRo40xxnTv3t3cf//9Jj093fj4+BhJZuXKleaZZ55xzffkk096/PvNxnanbzwdAFhkyJAh2rJliy5cuCBJ+vDDD/WTn/xExcXFys3NlSR9/vnnCgkJueVxLl26pI8++si1/2OPPSZJ+tnPfqZ+/fq59uvSpYs6deqk8+fP33CWr19vsGfPHnXv3l2dO3eWJKWnp+vixYuSpJ/+9KcaN26cJGnbtm06deqUJGnkyJF6+OGH5XQ6JUkdOnRQVVWVJKm+vl6bN2++ze8OYB8iAIAuXbrk+rqhoUEdOnS45f51dXXX7O/jc/V/JV5eXho0aNA1x/subhQNf69NmzZKSUnRiy++eN11Fy9e5HUAQCPwmgDAInv37tWYMWPUoUMHdezYUWPHjm3Sj4PNzMy85qOfH3zwwVvOMmnSJEnS0KFDVV1drbNnz16336effqqnn35akhQdHa1u3bpJknbt2qUJEyaoR48ekq6+xuB73/tek50LYAMiALBITk6O1q1bp+zsbGVlZel3v/udampqmuz4s2fP1iOPPKLc3FwdPnxYzz333E33nT9/vh5++GHl5ubqtddeU3x8/A33W7BggX76058qLy9P48aN07FjxyRJR44c0csvv6zMzEzl5uZqx44d6tWrV5OdC2ADPkoYAABL8UgAAACW4oWBANxq8uTJeuGFF65Z279/v2bNmuWhiQB8jacDAACwFE8HAABgKSIAAABLEQEAAFiKCAAAwFJEAAAAlvp/YxPDETjXvgoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plot = sns.countplot(df['online_order']).set(title=\" Taking Online Orders ? \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "79963cd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vivek\\AppData\\Local\\Programs\\Python\\Python310\\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": {
      "image/png": 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iAgAAMIoIAADAKCIAAACjiAAAAIwiAgAAMIoIAADAKCIAAACjiAAAAIwiAgAAMIoIAADAKCIAAACjiAAAAIwiAgAAMIoIAADAKCIAAACjiAAAAIwiAgAAMIoIAADAKCIAAACjAhYBvXv3Vmlpqf72t79p9+7d+uUvfylJSkpK0o4dO+T1elVYWKiIiAhJUq9evVRYWCiv16sdO3YoMTHRv6/58+fL6/WqqqpK48eP969PmDBBVVVV8nq9mjdvXqBOBQCAHilgEXDixAmNHTtWo0aN0qhRo5SamqoxY8Zo4cKFWrRokdxut3w+n3JyciRJOTk58vl8crvdWrRokRYuXChJGj58uDIzM3XllVcqNTVVzzzzjEJDQxUaGqolS5YoLS1NI0aM0O23367hw4cH6nQAAOhxAvp2wLFjxyRJERERioiIkOM4Gjt2rNasWSNJKigo0KRJkyRJ6enpKigokCStWbNG48aN868XFhbq5MmT2rdvn6qrq5WSkqKUlBRVV1erpqZGp06dUmFhodLT0wN5OgAA9CgBjYDQ0FCVlZXp0KFD2rhxo9577z01NTWpra1NknTw4EG5XC5JksvlUm1trSSpra1Nzc3NGjBgQIf1f37OudbPJjc3Vx6PRx6PR3FxcYE6XQAAupWARkB7e7tGjx6thIQEpaSk6Iorrgjk4c4pPz9fycnJSk5O1uHDh4MyAwAAF5su+emA5uZmbdmyRddff71iYmIUFhYmSUpISFBdXZ0kqa6uTkOHDpUkhYWFKTo6WkeOHOmw/s/POdc6AADonIBFQFxcnKKjoyVJffr00Xe/+11VVlZqy5YtysjIkCRlZ2dr7dq1kqTi4mJlZ2dLkjIyMvT666/71zMzM9WrVy8lJSXJ7XZr586d8ng8crvdSkpKUkREhDIzM1VcXByo0wEAoMcJD9SOBw8erIKCAoWFhSk0NFRFRUV67bXXVFFRocLCQv3qV79SWVmZli1bJklatmyZVq5cKa/Xq8bGRmVmZkqSKioqVFRUpIqKCrW2tmrWrFlqb2+XJM2ePVslJSUKCwvT8uXLVVFREajTAQCgxwmR5AR7iK7k8XiUnJwc7DEAXCDHjm8K9gjABdG/380B2e/5Xvf4jYEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGdSoCNm3a1Kk1AADQfYSf787evXurX79+iouLU0xMjEJCQiRJUVFRcrlcXTIgAAAIjPNGwF133aV7771XQ4YM0dtvv+2PgKNHj+rpp5/ukgEBAEBgnDcCnnzyST355JOaPXs2L/oAAPQw542A055++mldf/31SkpKUnj4/3/KypUrAzYYAAAIrE5FwIoVK3TZZZfpb3/7m9ra2iRJjuMQAQAAdGOdioBrr71WI0aMCPQsAACgC3XqRwR3796t+Pj4QM8CAAC6UKeuBMTFxamiokI7d+7UiRMn/Ovp6ekBGwwAAARWpyLgl7/8ZYDHAAAAXa1TEfCXv/wl0HMAAIAu1qkIOHr0qBzHkST16tVLEREROnbsmKKjowM6HAAACJxORUBUVFSH2+np6bruuusCMhAAAOgaX+ivCK5du1YTJky40LMAAIAu1KkrAf/xH//h/3doaKiuvfZaffLJJwEbCgAABF6nIuDWW2/1/7u1tVX79u3jxwMBAOjmOhUBd955Z6DnAAAAXaxTnwlwuVx6+eWX1dDQoIaGBq1Zs0YulyvQswEAgADqVAQ899xzKi4u1pAhQzRkyBC9+uqreu655wI9GwAACKBORcDAgQP1/PPPq62tTW1tbSooKNDAgQMDPRsAAAigTkXAkSNHNG3aNIWGhio0NFTTpk3TkSNHAj0bAAAIoE5FwJ133qkpU6aovr5eH374oTIyMjR9+vQAjwYAAAKpUz8d8OCDDyo7O1tNTU2SpNjYWD322GPKyckJ5GwAACCAOnUlYOTIkf4AkCSfz6fRo0cHaiYAANAFOhUBoaGhiomJ8d+OjY1VeHinLiIAAICLVKdeyR9//HFt375dL774oiTpP//zP/Xwww8HdDAAABBYnYqAlStX6q233tLYsWMlSZMnT1ZlZWVABwMAAIHV6Wv6lZWVvPADANCDfKE/JQwAALo/IgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjApYBCQkJOj111/Xnj17tHv3bv3kJz+R9OmfId6wYYP27t2rDRs2dPgTxYsXL5bX69WuXbs0evRo/3pWVpb27t2rvXv3Kisry79+9dVXq7y8XF6vV4sXLw7UqQAA0CMFLAJaW1s1d+5cXXnllbruuus0a9YsDR8+XPPnz9fmzZt1+eWXa/PmzZo/f74kKS0tTW63W263WzNnztTSpUslfRoNeXl5GjNmjFJSUpSXl+cPh6VLlyo3N9f/vNTU1ECdDgAAPU7AIqC+vl5lZWWSpH/84x+qrKyUy+VSenq6CgoKJEkFBQWaNGmSJCk9PV0rVqyQJJWWliomJkbx8fGaMGGCNm7cKJ/Pp6amJm3cuFGpqamKj49XVFSUSktLJUkrVqzw7wsAAPxrXfKZgMTERI0ePVqlpaUaNGiQ6uvrJX0aCoMGDZIkuVwu1dbW+p9z8OBBuVyu864fPHjwjHUAANA54YE+QP/+/fXSSy/p3nvvVUtLyxn3O44T6BGUm5urmTNnSpLi4uICfjwAALqDgF4JCA8P10svvaTf//73euWVVyRJDQ0Nio+PlyTFx8fr0KFDkqS6ujoNHTrU/9yEhATV1dWddz0hIeGM9bPJz89XcnKykpOTdfjw4Qt+ngAAdEcBjYBly5apsrJSixYt8q8VFxcrOztbkpSdna21a9f6109/8n/MmDFqbm5WfX29SkpKNH78eMXExCgmJkbjx49XSUmJ6uvrdfToUY0ZM0bSpz9BcHpfAADgXwvY2wE33HCDsrKyVF5e7v+A4P33369HH31URUVFysnJ0f79+zVlyhRJ0rp163TLLbeourpax48f14wZMyRJPp9PDz30kDwejyTpwQcflM/nkyTdfffdev7559W3b1+tX79e69evD9TpAADQ44RICvyb8hcRj8ej5OTkYI8B4AI5dnxTsEcALoj+/W4OyH7P97rHbwwEAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwKmARsGzZMjU0NOjdd9/1r8XGxmrDhg3au3evNmzYoJiYGP99ixcvltfr1a5duzR69Gj/elZWlvbu3au9e/cqKyvLv3711VervLxcXq9XixcvDtRpAADQYwUsAp5//nmlpqZ2WJs/f742b96syy+/XJs3b9b8+fMlSWlpaXK73XK73Zo5c6aWLl0q6dNoyMvL05gxY5SSkqK8vDx/OCxdulS5ubn+5332WAAA4PwCFgHbtm1TY2Njh7X09HQVFBRIkgoKCjRp0iT/+ooVKyRJpaWliomJUXx8vCZMmKCNGzfK5/OpqalJGzduVGpqquLj4xUVFaXS0lJJ0ooVK/z7AgAAndOlnwkYNGiQ6uvrJUn19fUaNGiQJMnlcqm2ttb/uIMHD8rlcp13/eDBg2esAwCAzgsP5sEdx+mS4+Tm5mrmzJmSpLi4uC45JgAAF7suvRLQ0NCg+Ph4SVJ8fLwOHTokSaqrq9PQoUP9j0tISFBdXd151xMSEs5YP5f8/HwlJycrOTlZhw8fvtCnBQBAt9SlEVBcXKzs7GxJUnZ2ttauXetfP/3J/zFjxqi5uVn19fUqKSnR+PHjFRMTo5iYGI0fP14lJSWqr6/X0aNHNWbMGEmf/gTB6X0BAIDOCdjbAatXr9a3v/1txcXFqba2Vnl5eXr00UdVVFSknJwc7d+/X1OmTJEkrVu3Trfccouqq6t1/PhxzZgxQ5Lk8/n00EMPyePxSJIefPBB+Xw+SdLdd9+t559/Xn379tX69eu1fv36QJ0KAAA9Uoikrnlj/iLh8XiUnJwc7DEAXCDHjm8K9gjABdG/380B2e/5Xvf4jYEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGhQd7gJ7imZ1bgz0C8KXdnXJTsEcA0IW4EgAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAY1e0jYMKECaqqqpLX69W8efOCPQ4AAN1Gt46A0NBQLVmyRGlpaRoxYoRuv/12DR8+PNhjAQDQLXTrCEhJSVF1dbVqamp06tQpFRYWKj09PdhjAQDQLXTrCHC5XKqtrfXfPnjwoFwuVxAnAgCg+wgP9gBdITc3VzNnzpQkff3rX5fH4wnyRPgi4uLidPjw4WCP0aN1x++Nij3BnqDn43uvawTq+y8xMfGc93XrCKirq9PQoUP9txMSElRXV3fG4/Lz85Wfn9+VoyEAPB6PkpOTgz0GYA7fez1Xt347wOPxyO12KykpSREREcrMzFRxcXGwxwIAoFvo1lcC2traNHv2bJWUlCgsLEzLly9XRUVFsMcCAKBb6NYRIEnr16/X+vXrgz0GusDvfve7YI8AmMT3Xs8VIskJ9hAAAKDrdevPBAAAgC+OCMBFZdu2bUpNTfXfzsjI4O0eoAs5jqPHHnvMf3vu3LnKy8sL4kQIJCIAF5X/+q//0m9+8xv17t1b/fv31yOPPKJZs2YFeyzAjE8++USTJ0/WgAEDgj0KugARgIvKnj179Oqrr2revHn6xS9+oVWrVmnBggUqLS3VO++8o4kTJ0qSRowYodLSUpWVlWnXrl36t3/7tyBPDvQMra2t+t3vfqef/vSnZ9yXmJiozZs3a9euXdq0aVOH39OC7sthY7uYtn79+jlVVVVOeXm588gjjzjTpk1zJDnR0dHO3//+d6dfv37Ok08+6UydOtWR5ERERDh9+vQJ+txsbD1ha2lpcSIjI52amhonKirKmTt3rpOXl+dIcoqLi52srCxHkjNjxgznlVdeCfq8bF9u46cDcFF64IEH9I9//ENTpkxRnz591NraKkm69NJLNWHCBI0ePVoLFizQihUr9PLLL6u6ujrIEwM9Q0tLiyIjI/XAAw/o1KlT+vjjj3XJJZfogQce0EcffaTBgwertbVV4eHh+vDDDzVw4MBgj4wvgbcDcFFqb29Xe3u7QkJC9IMf/ECjR4/W6NGjlZiYqKqqKr3wwguaOHGiPv74Y61bt07f+c53gj0y0KM88cQTysnJUf/+/YM9CgKICMBFraSkRPfcc4//9qhRoyRJX/3qV/X+++/rqaee0tq1azVy5MggTQj0TD6fT0VFRcrJyfGvvfnmm8rMzJQkTZs2Tdu2bQvWeLhAiABc1B566CFFRESovLxcu3fv1kMPPSRJmjJlinbv3q2ysjJdddVVWrFiRZAnBXqexx9/XHFxcf7b99xzj2bMmKFdu3bpjjvu0Jw5c4I4HS4EPhMAAIBRXAkAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAMC4xMREvfvuu196PzU1NZ36y3PR0dH68Y9//KXm2rJli6655prPPSOAjogAAF0qJiZGd999d7DHACAiAICk8PBwrVq1ShUVFXrxxRfVt29fjR07Vu+8847Ky8u1bNky9erVS5LOuX5anz59tG7dOv3oRz8667EeffRRXXbZZSorK9Ovf/1r9e/fX5s2bdLbb7+t8vJy/5+LPtdcn/Xd735Xb775pt5++20VFRXxu+6Bzynof8qQjY0teFtiYqLjOI7z7//+744kZ9myZc6CBQucAwcOOG6325HkFBQUOHPmzHF69+591nVJTk1NjZOYmOhs3LjRueOOO857vHfffdd/OywszImMjHQkOQMGDHC8Xu8555o7d64jydmyZYtzzTXXOAMGDHC2bt3q9OvXz5Hk3Hfffc7Pf/7zoH9N2di6y8aVAAA6cOCA3nzzTUnSqlWrNG7cONXU1Mjr9UqSCgoK9K1vfUtf//rXz7p+2tq1a/Xcc89p5cqVnT52SEiIHnnkEe3atUubNm2Sy+XSoEGDzjrXjTfe2OG51113nUaMGKE33nhDZWVlys7OVmJi4hf/QgDGhAd7AADB5zhOh9tNTU2d+pDfZ73xxhtKTU3V6tWrO/2cadOmaeDAgbrmmmvU2tqqmpoa9enT56xzffZ2SEiINm7cqKlTp37uWQHwmQAA+vST+Nddd50kaerUqXrrrbeUlJSkyy67TJJ0xx13aOvWrfr73/9+1vXTfvGLX8jn82nJkiXnPFZLS4siIyP9t6Ojo3Xo0CG1trbq29/+tpKSks4511//+tcO+9qxY4duuOEG/zz9+vWT2+3+El8JwBYiAICqqqo0a9YsVVRUKDY2VosWLdKMGTP04osvqry8XO3t7Xr22Wd14sSJs67/szlz5qhv375auHDhWY/V2NioN954Q++++65+/etf6/e//72uvfZalZeXKysrS5WVleeca+nSpR32dfjwYU2fPl0vvPCCdu3ape3bt+uKK6648F8goIfiTwkDAGAUVwIAADCKDwYCCIhLL71UmzdvPmN93LhxamxsDMJEAD6LtwMAADCKtwMAADCKCAAAwCgiAAAAo4gAAACMIgIAADDq/wH5byzh69IohQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plot = sns.countplot(df['book_table']).set(title=\" Having Book Table ? \")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b81f282",
   "metadata": {},
   "source": [
    "# Online Order vs Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "97599094",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='online_order', ylabel='rate'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "sns.boxplot(x='online_order', y = 'rate' , data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1460c197",
   "metadata": {},
   "source": [
    "# Book Table vs Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f22801c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='book_table', ylabel='rate'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "sns.boxplot(x='book_table', y = 'rate' , data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e60c6e9e",
   "metadata": {},
   "source": [
    "# Online Order facility location wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "18611dd5",
   "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>Others</th>\n",
       "      <td>2064</td>\n",
       "      <td>2890</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",
       "  </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",
       "Others                 2064  2890\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"
      ]
     },
     "execution_count": 36,
     "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": 37,
   "id": "e10976c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df1.plot(kind= 'bar',figsize=(15,10),color=['red','blue'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b927215d",
   "metadata": {},
   "source": [
    "# Book Table facility location wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e70305ca",
   "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>Others</th>\n",
       "      <td>4587</td>\n",
       "      <td>367</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",
       "  </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",
       "Others                 4587  367\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"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2= df.groupby(['location','book_table'])['name'].count()\n",
    "df2.to_csv('location_booktable.csv')\n",
    "df2 = pd.read_csv('location_booktable.csv')\n",
    "df2 = pd.pivot_table(df2 , values=None ,index=['location'],columns=['book_table'],fill_value=0,aggfunc=np.sum)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b56ba082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot(kind= 'bar',figsize=(15,10),color=['purple','green'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96e294ab",
   "metadata": {},
   "source": [
    "# Types of Restaurant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bde4d3f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Type', ylabel='rate'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.boxplot(x= 'Type' , y='rate', data = df , palette='inferno')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a226e50e",
   "metadata": {},
   "source": [
    "# Types of restraunts location wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "071a4ae3",
   "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>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",
       "    <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",
       "  </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",
       "Others                    83   133     2787      276     1553   \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",
       "\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",
       "Others                                75            47  \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  "
      ]
     },
     "execution_count": 41,
     "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": 42,
   "id": "110a8dd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAIVIcHCw7YJTLVq0kNlsVnh4uNLS0rRgwQLb/UaNGqW4uDglJydrxowZtulms1kTJkyQxWJRamqqGjVqpCVLlmjHjh0aM2ZMnjqbNm2S1WrV9OnTb3oE04gRIzRw4ECdPHlSknTy5EnNmzfvun3p0KGDhg4dqoEDB9oulHWtmm5ubgoNDVVycrKSkpI0dOhQSdL+/ftVtmxZlStX7g4f0XsLQREAAAAAAIWEp6enqlevrn379tmm+fv7a+jQoapdu7aqV6+uZs2aSZImT56soKAgPfbYYypatKg6depkm+f8+fMKDAzU9OnTFRERoUGDBqlu3bp65ZVXVKZMGdWsWVM9evRQs2bN5O/vr6ysLAUHB1+3Xz4+PvLx8dGePXuuefu1+vLrr79q+vTpmjhxolq1anXdmg0aNFDFihX12GOPqV69egoNDbUtNz4+3ra+uDUERQAAAAAAFBK+vr46fvx4nmlxcXE6ePCgDMNQQkKCqlWrJklq2bKlNm7cqKSkJLVq1Up16tSxzRMZGSnp0gWutm7dqoyMDJ0/f17p6emqXLmyWrdurYCAAFksFlmtVrVu3TrfYwhd7kZ9yXW9munp6apevbq++uortWvXTn///bdtniNHjujBBx+87X7di2561TMAAAAAAFAwnDlzRt7e3nmmnTt3zvb/rKwseXh4yMvLS1OnTlWjRo104MABhYSE5Jkvd57s7Ow882dnZ8vDw0Mmk0lhYWEaMWLELfXr5MmTOnXqlB566KGrjiq6WV9y3ahm/fr11a5dO73xxhvq3r27+vfvL+nSwN5nzpy5pT7iEo4oAgAAAADAQdxMne3abub48eNyd3eXl5fXDe+XG8RkZmaqePHi6tatW77WKzY2Vt26ddP9998vSSpdurSqVKkiSQoLC1NgYOBV84wdO1ZTpkyRj4+PJKl48eJ6+eWXb7kv16tZtmxZubm5aenSpRo5cqQaNmxom+fRRx9VSkpKvtbtXscRRQAAAAAAFCLR0dFq3ry5YmNjr3ufEydOaNasWUpJSVFGRoYsFku+aqSlpWnkyJGKjo6Wm5ubLly4oEGDBmn//v2qV6+eDh06dNU806ZNU4kSJWSxWHThwgVduHBB48ePv+W+XK/mmTNnFBoaahtM+9///rckycPDQ4888og2b96cr3WDZNytzWKx3PJ9zTHbDXPMdpf3mUaj0Wg0Go1Go9Fo926bN2+ey/vg7+/vsn74+PgYixYtcvljIMl49tlnjY8//tjl/XB1u9a2cKO8hVPPAAAAAAAoRKxWq8xm800vV+8IJ0+eVPfu3Z1e91o8PDw0fvx4V3ejwOHUMwAAAAAACpnLLxF/r1q8eLGru1AgcUQRAAAAAAAAJBEUAQAAAAAAIAdBEQAAAAAAACQxRhEAAAAAAA5jjtlu1+W1bFPjpvfx9vbWihUr1KpVK2VnZ9u1/p0IDQ1VixYt9Pfff6to0aLauHGjRowYoYMHD95wPrPZrPfff19btmzR8uXL1atXL504ccIhffT09NTKlSvVqlUrZWVlOaTG3Y4jigAAAAAAKET69eunpUuX3lUhUa5hw4apQYMGqlGjhqxWq1atWiVPT89bnr9jx452CYlMJtM1p1+4cEGxsbHq0aPHHdcoqAiKAAAAAAAoRIKDgxURESFJatGihcxms8LDw5WWlqYFCxbY7jdq1CjFxcUpOTlZM2bMsE03m82aMGGCLBaLUlNT1ahRIy1ZskQ7duzQmDFj8tTZtGmTrFarpk+fLje3/EUMkyZNUkZGhjp06CBJatOmjX777Tdt2bJFixYtUvHixa+aZ8+ePSpbtqzGjh2rN9980zY9JCRE7733niTp/fffV1xcnBITE/XRRx9JkqpWrapt27YpLCxMKSkpGjVqlCZOnGib/9VXX9WECRMkScuWLVNwcHC+1qUwISgCAAAAAKCQ8PT0VPXq1bVv3z7bNH9/fw0dOlS1a9dW9erV1axZM0nS5MmTFRQUpMcee0xFixZVp06dbPOcP39egYGBmj59uiIiIjRo0CDVrVtXr7zyisqUKaOaNWuqR48eatasmfz9/ZWVlXVb4Up8fLxq1qypsmXLauTIkXr66acVEBCgzZs36913373ufD/88IO6d+9u+7t79+764Ycf1KZNG/n5+SkoKEgNGjRQQECAnnjiCUmSn5+fpk6dqrp162r8+PHq3LmzPDwujcjTt29fzZ07V5KUkpKiwMDAfK9LYcEYRQAAAAAAFBK+vr46fvx4nmlxcXG2cYASEhJUrVo1rV+/Xi1bttQHH3ygYsWKqUyZMtq6dat+/vlnSVJkZKQkKTk5WVu3blVGRoYkKT09XZUrV1bz5s0VEBAgi8UiSSpatKiOHDmS7/7mngLWpEkT1a5dW+vXr5ckFSlSRBs2bLjufAkJCXrggQdUoUIF3X///Tp27JgOHDigt99+W23btpXVapUklShRQn5+ftq/f7/27dunTZs2SZJOnz6tVatWqVOnTkpLS5Onp6dSUlIkSdnZ2Tp//rxKlCihU6dO5XudCjqCIgAAAAAACokzZ87I29s7z7Rz587Z/p+VlSUPDw95eXlp6tSpatSokQ4cOKCQkJA88+XOk52dnWf+7OxseXh4yGQyKSwsTCNGjLij/vr7+ys2NlYmk0kxMTHq1avXLc8bHh6ubt26qXz58vrhhx8kXQqexo4dq5kzZ+a5b9WqVXX69Ok802bPnq0RI0Zo27ZtCg0NzXObl5eXzp49e5trVbBx6hkAAAAAAIXE8ePH5e7uLi8vrxveLzcUyszMVPHixdWtW7d81YmNjVW3bt10//33S5JKly6tKlWqSJLCwsJu6dStwYMHq0KFClqxYoU2btyoZs2a6eGHH5YkFStWTH5+fjec/4cfflDPnj3VrVs3hYeHS5KioqLUr18/2/hGDz74oK2PV4qLi1PlypXVq1cvff/997bpZcqUUWZmpi5evHjTdSiMOKIIAAAAAAAHuZXL2dtbdHS0mjdvrtjY2Ove58SJE5o1a5ZSUlKUkZFhO4XsVqWlpWnkyJGKjo6Wm5ubLly4oEGDBmn//v2qV6+eDh06dM35xo0bp1GjRqlYsWLauHGjWrZsqQsXLigzM1OvvPKKvv/+e1vINXLkSO3cufO6fUhNTZWPj48OHjxoOzUuJiZGtWrVsp22durUKb300kvXvdT9okWL1KBBgzyn67Vs2VLLly/P1+NR2Bh3a7NYLLd8X3PMdsMcs93lfabRaDQajUaj0Wg02r3b5s2b5/I++Pv7u6wfPj4+xqJFi1z+GNxq++mnn4xWrVrlmbZkyRLDz8/P5X2zV7vWtnCjvIVTzwAAAAAAKESsVqvMZnO+L1dvDydPnsxzNbK7VcmSJbV9+3adOXNGq1atsk339PTUsmXLbngkU2HHqWcAAAAAABQyVw7OjLxOnDihGjWuPi3wwoULmj9/vgt6dPfgiCIAAAAAAABIIigCAAAAAABADoIiAAAAAAAASCIoAgAAAAAAQA4GswYAAAAAwEHOj37frssrEvIfuy4PuBJHFAEAAADAPcocs13mmO2u7gbszNvbW6tXr5ab2931lT80NFTp6elKSEjQ9u3bFRYWpooVKzq9HyVLltTAgQNver+YmBiVKlXK8R26y9xdWw0AAAAAALgj/fr109KlS5Wdne3qrlxl2LBhatCggWrUqCGr1apVq1bJ09PTafXd3d1VqlQpvfnmmze97/z582/pfoUNQREAAAAAAIVIcHCwIiIiJEktWrSQ2WxWeHi40tLStGDBAtv9Ro0apbi4OCUnJ2vGjBm26WazWRMmTJDFYlFqaqoaNWqkJUuWaMeOHRozZkyeOps2bZLVatX06dPzfQTTpEmTlJGRoQ4dOkiS2rRpo99++01btmzRokWLVLx4cUnS2LFjtXXrViUmJmrcuHGSpG7duik5OVkJCQlas2aNJMnNzU1ffPGF4uLilJiYqAEDBtgeg7Vr1yoiIkKpqan67LPP9PDDD8tqteqLL75Q+fLltWbNGlmtViUnJ6t58+aSpMjISL344ov5WqfCgDGKAAAAAAAoJDw9PVW9enXt27fPNs3f31916tTRoUOHtH79ejVr1kzr16/X5MmTbcHPvHnz1KlTJ/3888+SpPPnzyswMFBDhgxRRESEAgIC9Ndff2n37t2aOHGiHnjgAfXo0UPNmjXTxYsXNWXKFAUHB2v+/Pn56m98fLxq1qyp9evXa+TIkXr66af1zz//6IMPPtC7776rKVOm6LnnnlPNmjUlXTptTJI+/PBDtWvXTocOHbJN69+/v06cOKGgoCAVKVJE69evV3R0tCSpYcOGqlu3rvbu3auqVauqbt268vf3lyS9++67ioqK0qeffio3NzcVK1ZMknT8+HF5eXmpTJky+uuvv273KSlwCIoAAAAAACgkfH19dfz48TzT4uLidPDgQUlSQkKCqlWrpvXr16tly5b64IMPVKxYMZUpU0Zbt261BUWRkZGSpOTkZG3dulUZGRmSpPT0dFWuXFnNmzdXQECALBaLJKlo0aI6cuRIvvtrMpkkSU2aNFHt2rW1fv16SVKRIkW0YcMGnThxQmfPntWcOXP0888/2/q3fv16ffPNN1q0aJGWLl0qSWrbtq3q1aunbt26SboUKvn5+en8+fOKi4vT3r17r9kHi8WiuXPnytPTU8uWLVNiYqLttiNHjujBBx8kKAIAAAAAAAXPmTNn5O3tnWfauXPnbP/PysqSh4eHvLy8NHXqVDVq1EgHDhxQSEhInvly58nOzs4zf3Z2tjw8PGQymRQWFqYRI0bcUX/9/f0VGxsrk8mkmJgY9erV66r7BAUFqXXr1urWrZveeusttW7dWgMHDlRQUJA6duyoLVu2KCAgQCaTSYMHD7YdRZSrRYsWOn369HX7sG7dOj355JPq2LGjvvnmG02YMMF2ZJS3t7fOnDlzR+tY0BAUAQAAAADgIM6+nP3x48fl7u4uLy+vPAHPlXJDoczMTBUvXlzdunXT4sWLb7lObGysIiIiNHHiRB09elSlS5eWj4+P9u/fr7CwME2ePNl2tNH1DB48WBUqVNCKFStUsmRJTZkyRQ8//LB2796tYsWKqWLFijp06JCKFSumX3/9VevXr1d6erokqXr16oqLi1NcXJw6dOigypUrKyoqSgMHDtSqVat08eJF+fn52Y6kutzJkyfl4+Nj+7tKlSo6cOCAZs+eLS8vLzVs2NAWFJUvX/66RyIVVgRFAAAAAAAUItHR0WrevLliY2Ove58TJ05o1qxZSklJUUZGxk1DnSulpaVp5MiRio6Olpubmy5cuKBBgwZp//79qlevng4dOnTN+caNG6dRo0apWLFi2rhxo1q2bKkLFy4oMzNTr7zyir7//nt5eXlJkkaOHKmTJ08qIiJC3t7eMplMevfdd23L8fPzk8lkUmxsrBITE5WUlKRq1aopPj5eJpNJR48e1bPPPntVH/766y+tX79eycnJ+vXXX5WSkqJhw4bpwoULOnXqlHr37i1JCggI0MaNG5WVlZWvx6YwMO7WZrFYbvm+5pjthjlmu8v7TKPRaDQajUaj0WgFpfE9yv5t3rx5Lu+Dv7+/y/rh4+NjLFq0yOWPgT3apEmTjFatWrm8H3farrUt3Chvyd+16wAAAAAAwF3NarXKbDbn+3L19nDy5El1797d6XUdISUlRatWrXJ1N5yOU88AAAAAAChkQkNDXd2FAm/27Nmu7oJLcEQRAAAAAAAAJBEUAQAAAAAAIAdBEQAAAAAAACQxRhEAAAAAAA4zOiLJrssL6VLPrssDrsQRRQAAAAAAFCLe3t5avXq1S656diOhoaFKT09XQkKCtm/frrCwMFWsWNF2+/Lly1WyZEmX9K1FixZq2rSp7e9Bgwapb9++LumLq91dWw0AAAAAALgj/fr109KlS5Wdne3qrlxl2LBhatCggWrUqCGr1apVq1bJ09NTktSxY0edOHHCJf166qmn9Pjjj9v+njt3rgYPHuySvrgaQREAAAAAAIVIcHCwIiIiJF06UsZsNis8PFxpaWlasGCB7X6jRo1SXFyckpOTNWPGDNt0s9msCRMmyGKxKDU1VY0aNdKSJUu0Y8cOjRkzJk+dTZs2yWq1avr06fk+gmnSpEnKyMhQhw4dJEl79uxR2bJlVbVqVaWmpmrmzJlKSUlRVFSUvL29JUnVq1fXr7/+qs2bN2vt2rWqUaPGNZfdqlUrxcfHKykpSXPmzFGRIkXy1JCkgIAAmc1mVa1aVW+88YbeeecdWa1WNW/eXGfOnNHevXsVGBiYr3UqDAiKAAAAAAAoJDw9PVW9enXt27fPNs3f319Dhw5V7dq1Vb16dTVr1kySNHnyZAUFBemxxx5T0aJF1alTJ9s858+fV2BgoKZPn66IiAgNGjRIdevW1SuvvKIyZcqoZs2a6tGjh5o1ayZ/f39lZWUpODg43/2Nj49XzZo1r5ru5+enKVOmqG7dujp+/Li6du0qSZo5c6YGDx6sRo0a6f3339fUqVOvmtfLy0vffPONevTooXr16snDw0MDBw68bh/27dun6dOna+LEifL399d///tfSdLmzZv1xBNP5HudCjoGswYAAAAAoJDw9fXV8ePH80yLi4vTwYMHJUkJCQmqVq2a1q9fr5YtW+qDDz5QsWLFVKZMGW3dulU///yzJCkyMlKSlJycrK1btyojI0OSlJ6ersqVK6t58+YKCAiQxWKRJBUtWlRHjhzJd39NJtM1p+/Zs0eJiYmSpC1btqhatWoqXry4Hn/8cYWHh9vu5+XlddW8NWrU0J49e7Rz505JUlhYmAYNGqQvv/wyX307cuTINUOswo6gCAAAAACAQuLMmTO207RynTt3zvb/rKwseXh4yMvLS1OnTlWjRo104MABhYSE5Jkvd57s7Ow882dnZ8vDw0Mmk0lhYWEaMWLEHfXX399fsbGxV02/ss9FixaVm5ubjh8/Ln9//zz3dXNz05YtWyRdCriWLFly3XoXL160nSJ35eN0JW9vb505c+aW16WwICgCAAAAAMBBnH05++PHj8vd3V1eXl55wpYr5YYkmZmZKl68uLp166bFixffcp3Y2FhFRERo4sSJOnr0qEqXLi0fHx/t379fYWFhmjx5su1oo+sZPHiwKlSooBUrVtxSzZMnT2rPnj15+lqvXj0lJSXlCY+8vLxUrVo1Pfzww9q9e7defvllrVmzRpK0d+9eBQQEaMWKFbbT2XKXfd999+Wp9+ijj2r9+vW31LfChDGKAAAAAAAoRKKjo9W8efMb3ufEiROaNWuWbbDom4U6V0pLS9PIkSMVHR2txMRExcTEqEKFCpIuhTeHDh265nzjxo1TQkKCduzYocDAQLVs2VIXLly45brBwcHq37+/EhIStHXrVnXp0uWq+5w7d059+/ZVeHi4kpKSlJ2drenTp0uSRo8erS+//FIWi0VZWVm2eX766Sc999xztsGsJalZs2aKiYm55b4VJsbd2iwWyy3f1xyz3TDHbHd5n2k0Go1Go9FoNBqtoDS+R9m/zZs3z+V98Pf3d1k/fHx8jEWLFrn8MbjT1qBBg7viubRHu9Z63Chv4YgiAAAAAAAKEavVKrPZnO/L1dvDyZMn1b17d6fXtTdfX1+NGjXK1d1wCcYoAgAAAACgkAkNDXV1Fwq0lStXuroLLsMRRQAAAAAAAJBEUAQAAAAAAIAcBEUAAAAAAACQxBhFAAAAAAA4TNbP/ey6PPdOc296H29vb61YsUKtWrVSdna2XevfidDQULVo0UJ///23ihYtqo0bN2rEiBE6ePDgNe8/a9YsTZgwQWlpaTdc5s8//6wlS5bkuz+dOnXS559/ruzsbEVERGjkyJH5Xsb69evVrFmzG95nz549atSokf78888801u0aKHz589rw4YNkqSQkBCdOnVK48eP1+jRo7V27VrFxsaqefPmmj59ui5cuKCmTZvq7NmzV9Xw9PTUypUr1apVK2VlZeV7PS7HEUUAAAAAABQi/fr109KlS++qkCjXsGHD1KBBA9WoUUNWq1WrVq2Sp6fnVfdzc3PTa6+9dsOQ6E5NmjRJHTt21GOPPabZs2ff1jJuFhLdyFNPPaXHH3/8mreFhIQoNjZWkhQcHKyxY8fK39//miGRJF24cEGxsbHq0aPHbfcnF0ERAAAAAACFSHBwsCIiIiRdOmrFbDYrPDxcaWlpWrBgge1+o0aNUlxcnJKTkzVjxgzbdLPZrAkTJshisSg1NVWNGjXSkiVLtGPHDo0ZMyZPnU2bNslqtWr69Olyc8tfxDBp0iRlZGSoQ4cOkqSTJ0/qP//5jxISEtS0aVOZzWYFBATYbvvkk0+UkJCgDRs26IEHHrhqeR9//LFCQ0Pl5uamsWPHauvWrUpMTNS4ceOuWf/8+fOqVKmSJGnv3r3X7WdISIjmzJkjs9ms3bt3a/DgwbbbTp48KUkymUyaMmWK0tLSFB0dreXLl6tr1662+w0ePFhbtmxRUlKSatSooapVq+qNN97QO++8I6vVqubNm+epGRoaqq5du6p///7q3r27xowZY3vu3n//fcXFxSkxMVEfffSRbZ5ly5YpODj4uutxqwiKAAAAAAAoJDw9PVW9enXt27fPNs3f319Dhw5V7dq1Vb16ddtRMJMnT1ZQUJAee+wxFS1aVJ06dbLNc/78eQUGBmr69OmKiIjQoEGDVLduXb3yyisqU6aMatasqR49eqhZs2by9/dXVlbWbYUU8fHxqlmzpiSpRIkS2rRpkxo0aKD169fnuV+JEiW0ceNGNWjQQGvXrtVrr72W5/YvvvhC999/v/r27atSpUrpueeeU506dVS/fn198sknV9U1mUxKTU3V3LlzVbVq1Zv2s2bNmmrXrp2CgoIUEhIiD4+8I/k8//zzqlatmmrXrq2XX35ZTZs2zXN7ZmamAgICNG3aNL3//vvat2+fpk+frokTJ8rf31///e9/r1l3zpw5ioyM1LBhw/TSSy+pTZs28vPzU1BQkBo0aKCAgAA98cQTkqSUlBQFBgbedF1uhqAIAAAAAIBCwtfXV8ePH88zLS4uTgcPHpRhGEpISFC1atUkSS1bttTGjRuVlJSkVq1aqU6dOrZ5IiMjJUnJycnaunWrMjIydP78eaWnp6ty5cpq3bq1AgICZLFYZLVa1bp1a1WvXj3f/TWZTLb/X7x48bpjDZ07d04///yzJGnLli22dZAuHRlVsmRJDRw4UJJ04sQJnT17VnPmzNFzzz2nf/7556rlDR48WImJiRo4cKB++ukn+fr6qlGjRgoPD79m/eXLl+v8+fP6888/deTIEZUrVy7P7c2bN1d4eLgMw9Dhw4dlNpvz3L506dJr9j2/2rZtq7Zt28pqtdpCNj8/P0lSdna2zp8/rxIlStz28iUGswYAAAAAoNA4c+aMvL2980w7d+6c7f9ZWVny8PCQl5eXpk6dqkaNGunAgQMKCQnJM1/uPNnZ2Xnmz87OloeHh0wmk8LCwjRixIg76q+/v79tLJ6zZ89ed1ylCxcuXLUOuSwWiwICAlS6dGkdO3ZMWVlZCgoKUuvWrdWtWze99dZbat26dZ7ltWvXTl988YXWrFmjMWPGaPny5YqLi9PChQuvWf9aj2F+5M5/O/NezmQyaezYsZo5c+Y1b/fy8rruOEa3iiOKAAAAAAAoJI4fPy53d3d5eXnd8H65oVBmZqaKFy+ubt265atObGysunXrpvvvv1+SVLp0aVWpUkWSFBYWdkunQA0ePFgVKlTQihUr8lX7SitWrNBnn32m5cuXq0SJEipevLhKliypX3/9Ve+8847q169/1TxWq1UvvfSSTCaTwsPDtXPnTvXq1UvLly+/rT6sX79eXbt2lclk0gMPPKCnnnrqpvOcPHlSPj4++aoTFRWlfv36qXjx4pKkBx980PYclClTRpmZmbp48WK++385jigCAAAAAMBBbuVy9vYWHR2t5s2b247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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.plot(kind= 'bar',figsize=(20,10))\n",
    "plt.savefig('kind.png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d82c2d19",
   "metadata": {},
   "source": [
    "# Number of votes Location wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9f2245d2",
   "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>location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Koramangala 5th Block</th>\n",
       "      <td>2214083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiranagar</th>\n",
       "      <td>1165909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 4th Block</th>\n",
       "      <td>685156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Church Street</th>\n",
       "      <td>590306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP Nagar</th>\n",
       "      <td>586522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         votes\n",
       "location                      \n",
       "Koramangala 5th Block  2214083\n",
       "Indiranagar            1165909\n",
       "Koramangala 4th Block   685156\n",
       "Church Street           590306\n",
       "JP Nagar                586522"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4= df[['location','votes']]\n",
    "df4.drop_duplicates()\n",
    "df5=df4.groupby(['location'])['votes'].sum()\n",
    "df5 =df5.to_frame()\n",
    "df5=df5.sort_values('votes',ascending =False)\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "caa688bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vivek\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "        34, 35, 36, 37, 38, 39, 40, 41]),\n",
       " [Text(0, 0, 'Koramangala 5th Block'),\n",
       "  Text(1, 0, 'Indiranagar'),\n",
       "  Text(2, 0, 'Koramangala 4th Block'),\n",
       "  Text(3, 0, 'Church Street'),\n",
       "  Text(4, 0, 'JP Nagar'),\n",
       "  Text(5, 0, 'BTM'),\n",
       "  Text(6, 0, 'Others'),\n",
       "  Text(7, 0, 'HSR'),\n",
       "  Text(8, 0, 'Lavelle Road'),\n",
       "  Text(9, 0, 'Koramangala 7th Block'),\n",
       "  Text(10, 0, 'Jayanagar'),\n",
       "  Text(11, 0, 'Whitefield'),\n",
       "  Text(12, 0, 'Koramangala 6th Block'),\n",
       "  Text(13, 0, 'Marathahalli'),\n",
       "  Text(14, 0, 'MG Road'),\n",
       "  Text(15, 0, 'Brigade Road'),\n",
       "  Text(16, 0, 'Sarjapur Road'),\n",
       "  Text(17, 0, 'Residency Road'),\n",
       "  Text(18, 0, 'Cunningham Road'),\n",
       "  Text(19, 0, 'St. Marks Road'),\n",
       "  Text(20, 0, 'Koramangala 1st Block'),\n",
       "  Text(21, 0, 'Malleshwaram'),\n",
       "  Text(22, 0, 'Bannerghatta Road'),\n",
       "  Text(23, 0, 'Bellandur'),\n",
       "  Text(24, 0, 'Ulsoor'),\n",
       "  Text(25, 0, 'New BEL Road'),\n",
       "  Text(26, 0, 'Kalyan Nagar'),\n",
       "  Text(27, 0, 'Banashankari'),\n",
       "  Text(28, 0, 'Old Airport Road'),\n",
       "  Text(29, 0, 'Brookefield'),\n",
       "  Text(30, 0, 'Richmond Road'),\n",
       "  Text(31, 0, 'Electronic City'),\n",
       "  Text(32, 0, 'Kammanahalli'),\n",
       "  Text(33, 0, 'Frazer Town'),\n",
       "  Text(34, 0, 'Domlur'),\n",
       "  Text(35, 0, 'Basavanagudi'),\n",
       "  Text(36, 0, 'Rajajinagar'),\n",
       "  Text(37, 0, 'Shanti Nagar'),\n",
       "  Text(38, 0, 'Banaswadi'),\n",
       "  Text(39, 0, 'Commercial Street'),\n",
       "  Text(40, 0, 'Ejipura'),\n",
       "  Text(41, 0, 'Shivajinagar')])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(df5.index,df5['votes'])\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36264276",
   "metadata": {},
   "source": [
    "# Top Cuisine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9eb82508",
   "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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = df[['cuisines','votes']]\n",
    "df6.drop_duplicates()\n",
    "df7 = df6.groupby(['cuisines'])['votes'].sum()\n",
    "df7 = df7.to_frame()\n",
    "df7 = df7.sort_values('votes',ascending =False)\n",
    "df7.head()\n",
    "                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "be1b119c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df7 = df7.iloc[1:, :]\n",
    "#df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0fe523a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vivek\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "        51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "        68, 69]),\n",
       " [Text(0, 0, 'Others'),\n",
       "  Text(1, 0, 'North Indian'),\n",
       "  Text(2, 0, 'North Indian, Chinese'),\n",
       "  Text(3, 0, 'South Indian'),\n",
       "  Text(4, 0, 'North Indian, Mughlai'),\n",
       "  Text(5, 0, 'Chinese'),\n",
       "  Text(6, 0, 'Cafe'),\n",
       "  Text(7, 0, 'North Indian, South Indian'),\n",
       "  Text(8, 0, 'Desserts'),\n",
       "  Text(9, 0, 'Chinese, Momos'),\n",
       "  Text(10, 0, 'Ice Cream, Desserts'),\n",
       "  Text(11, 0, 'Cafe, Continental'),\n",
       "  Text(12, 0, 'Biryani, North Indian, Chinese'),\n",
       "  Text(13, 0, 'Chinese, Thai, Momos'),\n",
       "  Text(14, 0, 'South Indian, North Indian, Chinese'),\n",
       "  Text(15, 0, 'Desserts, Beverages'),\n",
       "  Text(16, 0, 'Biryani'),\n",
       "  Text(17, 0, 'Finger Food'),\n",
       "  Text(18, 0, 'Bakery, Desserts'),\n",
       "  Text(19, 0, 'Burger, Fast Food'),\n",
       "  Text(20, 0, 'Continental'),\n",
       "  Text(21, 0, 'Cafe, Desserts'),\n",
       "  Text(22, 0, 'Andhra, Biryani'),\n",
       "  Text(23, 0, 'North Indian, Continental'),\n",
       "  Text(24, 0, 'Pizza, Fast Food'),\n",
       "  Text(25, 0, 'Chinese, Thai'),\n",
       "  Text(26, 0, 'North Indian, Chinese, Biryani'),\n",
       "  Text(27, 0, 'South Indian, North Indian, Chinese, Street Food'),\n",
       "  Text(28, 0, 'Mithai, Street Food'),\n",
       "  Text(29, 0, 'North Indian, South Indian, Chinese'),\n",
       "  Text(30, 0, 'Desserts, Ice Cream'),\n",
       "  Text(31, 0, 'Biryani, North Indian'),\n",
       "  Text(32, 0, 'North Indian, Street Food'),\n",
       "  Text(33, 0, 'Fast Food'),\n",
       "  Text(34, 0, 'North Indian, Chinese, Seafood'),\n",
       "  Text(35, 0, 'Chinese, North Indian'),\n",
       "  Text(36, 0, 'North Indian, Biryani'),\n",
       "  Text(37, 0, 'Biryani, Kebab'),\n",
       "  Text(38, 0, 'Italian, Pizza'),\n",
       "  Text(39, 0, 'North Indian, Chinese, Fast Food'),\n",
       "  Text(40, 0, 'North Indian, Chinese, Continental'),\n",
       "  Text(41, 0, 'Desserts, Bakery'),\n",
       "  Text(42, 0, 'Arabian'),\n",
       "  Text(43, 0, 'North Indian, Chinese, South Indian'),\n",
       "  Text(44, 0, 'Pizza'),\n",
       "  Text(45, 0, 'South Indian, North Indian'),\n",
       "  Text(46, 0, 'North Indian, Mughlai, Chinese'),\n",
       "  Text(47, 0, 'Beverages, Fast Food'),\n",
       "  Text(48, 0, 'South Indian, Biryani'),\n",
       "  Text(49, 0, 'Fast Food, Rolls'),\n",
       "  Text(50, 0, 'Bakery'),\n",
       "  Text(51, 0, 'Andhra'),\n",
       "  Text(52, 0, 'Street Food'),\n",
       "  Text(53, 0, 'Kerala'),\n",
       "  Text(54, 0, 'Cafe, Bakery'),\n",
       "  Text(55, 0, 'Biryani, Fast Food'),\n",
       "  Text(56, 0, 'South Indian, Fast Food'),\n",
       "  Text(57, 0, 'North Indian, Fast Food'),\n",
       "  Text(58, 0, 'Bakery, Fast Food'),\n",
       "  Text(59, 0, 'Fast Food, Burger'),\n",
       "  Text(60, 0, 'Cafe, Fast Food'),\n",
       "  Text(61, 0, 'South Indian, Chinese'),\n",
       "  Text(62, 0, 'Beverages, Desserts'),\n",
       "  Text(63, 0, 'Ice Cream'),\n",
       "  Text(64, 0, 'Beverages'),\n",
       "  Text(65, 0, 'Biryani, South Indian'),\n",
       "  Text(66, 0, 'Kerala, South Indian'),\n",
       "  Text(67, 0, 'Fast Food, Beverages'),\n",
       "  Text(68, 0, 'South Indian, Chinese, North Indian'),\n",
       "  Text(69, 0, 'Mithai')])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.barplot(df7.index,df7['votes'])\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fed6641",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}