AbhikalpArya.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "af70e9c3",
   "metadata": {},
   "source": [
    "# EDA on Zomato Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac54387d",
   "metadata": {},
   "source": [
    "### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f68b31ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "plt.style.use('dark_background')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8f9260",
   "metadata": {},
   "source": [
    "### Understanding the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5ae1a8dc",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"zomato.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cb32a14e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51717 entries, 0 to 51716\n",
      "Data columns (total 17 columns):\n",
      "url                            51717 non-null object\n",
      "address                        51717 non-null object\n",
      "name                           51717 non-null object\n",
      "online_order                   51717 non-null object\n",
      "book_table                     51717 non-null object\n",
      "rate                           43942 non-null object\n",
      "votes                          51717 non-null int64\n",
      "phone                          50509 non-null object\n",
      "location                       51696 non-null object\n",
      "rest_type                      51490 non-null object\n",
      "dish_liked                     23639 non-null object\n",
      "cuisines                       51672 non-null object\n",
      "approx_cost(for two people)    51371 non-null object\n",
      "reviews_list                   51717 non-null object\n",
      "menu_item                      51717 non-null object\n",
      "listed_in(type)                51717 non-null object\n",
      "listed_in(city)                51717 non-null object\n",
      "dtypes: int64(1), object(16)\n",
      "memory usage: 6.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "47baa8d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51717, 17)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e47c4b69",
   "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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>51717.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>283.697527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>803.838853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16832.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              votes\n",
       "count  51717.000000\n",
       "mean     283.697527\n",
       "std      803.838853\n",
       "min        0.000000\n",
       "25%        7.000000\n",
       "50%       41.000000\n",
       "75%      198.000000\n",
       "max    16832.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "14d47e2f",
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "781c6460",
   "metadata": {},
   "source": [
    "### Cleaning the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "feeee375",
   "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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)  \n",
       "0                         800          Buffet  \n",
       "1                         800          Buffet  \n",
       "2                         800          Buffet  \n",
       "3                         300          Buffet  \n",
       "4                         600          Buffet  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Deleting Unnnecessary Columns\n",
    "df=df.drop(['url','dish_liked','phone','address','listed_in(city)','menu_item','reviews_list','dish_liked'],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "68a06104",
   "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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)  \n",
       "0                         800          Buffet  \n",
       "1                         800          Buffet  \n",
       "2                         800          Buffet  \n",
       "3                         300          Buffet  \n",
       "4                         600          Buffet  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removing the Duplicates\n",
    "df.duplicated().sum()\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "667dcf3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 30958 entries, 0 to 51716\n",
      "Data columns (total 10 columns):\n",
      "name                           30958 non-null object\n",
      "online_order                   30958 non-null object\n",
      "book_table                     30958 non-null object\n",
      "rate                           30958 non-null object\n",
      "votes                          30958 non-null int64\n",
      "location                       30958 non-null object\n",
      "rest_type                      30958 non-null object\n",
      "cuisines                       30958 non-null object\n",
      "approx_cost(for two people)    30958 non-null object\n",
      "listed_in(type)                30958 non-null object\n",
      "dtypes: int64(1), object(9)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "#Remove the NaN values from the dataset\n",
    "df.isnull().sum()\n",
    "df.dropna(how='any',inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3198708e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 30958 entries, 0 to 51716\n",
      "Data columns (total 10 columns):\n",
      "name            30958 non-null object\n",
      "online_order    30958 non-null object\n",
      "book_table      30958 non-null object\n",
      "rate            30958 non-null object\n",
      "votes           30958 non-null int64\n",
      "location        30958 non-null object\n",
      "rest_type       30958 non-null object\n",
      "cuisines        30958 non-null object\n",
      "cost            30958 non-null object\n",
      "type            30958 non-null object\n",
      "dtypes: int64(1), object(9)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "#Changing the column names\n",
    "df = df.rename(columns={'approx_cost(for two people)':'cost','listed_in(type)':'type'})\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fa1da274",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rates\n",
    "def rates(value):\n",
    "    if(value == 'NEW' or value == '-'):\n",
    "        return np.nan\n",
    "    else:\n",
    "        value = str(value).split('/')\n",
    "        value = value[0]\n",
    "        return float(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8b960d00",
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'] = df['rate'].apply(rates)\n",
    "df['rate'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "07be7db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1320"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "be2b3126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name            0\n",
       "online_order    0\n",
       "book_table      0\n",
       "rate            0\n",
       "votes           0\n",
       "location        0\n",
       "rest_type       0\n",
       "cuisines        0\n",
       "cost            0\n",
       "type            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'] = pd.to_numeric(df['rate'])\n",
    "df['rate'].fillna(df['rate'].mean(), inplace = True)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d250a48c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cost(value):\n",
    "    value = str(value)\n",
    "    if ',' in value:\n",
    "        value = value.replace(',', '')\n",
    "        return float(value)\n",
    "    else:\n",
    "        return float(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4e163dfe",
   "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>cost</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   cost    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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cost'] = df['cost'].apply(cost)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "39105797",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTM                      2228\n",
       "Indiranagar              1653\n",
       "Whitefield               1630\n",
       "Koramangala 5th Block    1577\n",
       "HSR                      1558\n",
       "Marathahalli             1415\n",
       "JP Nagar                 1221\n",
       "Jayanagar                1154\n",
       "Bellandur                1001\n",
       "Electronic City           937\n",
       "Bannerghatta Road         907\n",
       "Sarjapur Road             838\n",
       "Brigade Road              653\n",
       "Koramangala 7th Block     604\n",
       "Koramangala 6th Block     580\n",
       "Brookefield               548\n",
       "Koramangala 4th Block     548\n",
       "MG Road                   538\n",
       "Kalyan Nagar              530\n",
       "Malleshwaram              519\n",
       "Ulsoor                    513\n",
       "Banashankari              502\n",
       "Koramangala 1st Block     472\n",
       "New BEL Road              472\n",
       "Church Street             440\n",
       "Basavanagudi              431\n",
       "Residency Road            407\n",
       "Frazer Town               401\n",
       "Rajajinagar               390\n",
       "Lavelle Road              386\n",
       "                         ... \n",
       "CV Raman Nagar             63\n",
       "Sadashiv Nagar             57\n",
       "City Market                56\n",
       "RT Nagar                   54\n",
       "Sanjay Nagar               48\n",
       "Sahakara Nagar             47\n",
       "Koramangala 2nd Block      44\n",
       "South Bangalore            44\n",
       "Hosur Road                 39\n",
       "East Bangalore             27\n",
       "Magadi Road                24\n",
       "Jalahalli                  23\n",
       "Sankey Road                20\n",
       "Kanakapura Road            19\n",
       "Old Madras Road            18\n",
       "Koramangala                17\n",
       "Mysore Road                17\n",
       "Langford Town              16\n",
       "Rammurthy Nagar            16\n",
       "KR Puram                   11\n",
       "Hebbal                     10\n",
       "North Bangalore            10\n",
       "Uttarahalli                 9\n",
       "Kengeri                     5\n",
       "West Bangalore              5\n",
       "Central Bangalore           5\n",
       "Yelahanka                   4\n",
       "Rajarajeshwari Nagar        2\n",
       "Nagarbhavi                  1\n",
       "Peenya                      1\n",
       "Name: location, Length: 92, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# location\n",
    "df['location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ae983d47",
   "metadata": {},
   "outputs": [],
   "source": [
    "location = df['location'].value_counts(ascending= False)\n",
    "location_lessthan500 = location[location < 500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a27877f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def location_type(value):\n",
    "    if value in location_lessthan500:\n",
    "        return 'other'\n",
    "    else:\n",
    "        return value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "57b15f45",
   "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>cost</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>other</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         other   \n",
       "\n",
       "             rest_type                        cuisines   cost    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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['location'] = df['location'].apply(location_type)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5de425a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North Indian                                            1602\n",
       "North Indian, Chinese                                   1401\n",
       "South Indian                                             902\n",
       "Bakery, Desserts                                         471\n",
       "Biryani                                                  429\n",
       "South Indian, North Indian, Chinese                      414\n",
       "Cafe                                                     405\n",
       "Desserts                                                 377\n",
       "Fast Food                                                374\n",
       "Ice Cream, Desserts                                      289\n",
       "Chinese                                                  285\n",
       "Bakery                                                   276\n",
       "Mithai, Street Food                                      237\n",
       "Chinese, North Indian                                    217\n",
       "North Indian, Chinese, Biryani                           201\n",
       "Desserts, Ice Cream                                      195\n",
       "Finger Food                                              193\n",
       "Desserts, Beverages                                      182\n",
       "North Indian, South Indian                               180\n",
       "North Indian, South Indian, Chinese                      170\n",
       "South Indian, North Indian                               163\n",
       "Chinese, Momos                                           162\n",
       "Biryani, North Indian                                    154\n",
       "Street Food                                              152\n",
       "North Indian, Biryani                                    148\n",
       "Cafe, Fast Food                                          148\n",
       "Beverages, Fast Food                                     148\n",
       "North Indian, Mughlai                                    143\n",
       "Burger, Fast Food                                        142\n",
       "South Indian, North Indian, Chinese, Street Food         138\n",
       "                                                        ... \n",
       "Kerala, Biryani, North Indian, Chettinad                   1\n",
       "Continental, Thai, North Indian, Chinese                   1\n",
       "Mediterranean, Arabian, Lebanese, Rolls                    1\n",
       "Cafe, Italian, Continental, European                       1\n",
       "Seafood, Chinese, South Indian                             1\n",
       "North Indian, Modern Indian                                1\n",
       "Fast Food, Arabian, Beverages                              1\n",
       "Chinese, Vietnamese, Thai, Malaysian                       1\n",
       "North Indian, Chinese, Mughlai, Rolls                      1\n",
       "Sandwich, Salad, North Indian, Chinese                     1\n",
       "Turkish, Fast Food, Biryani, Chinese                       1\n",
       "Continental, Italian, Mediterranean, Salad                 1\n",
       "North Indian, Street Food, Fast Food                       1\n",
       "Oriya, South Indian, Fast Food                             1\n",
       "North Indian, Lebanese, Rolls                              1\n",
       "North Indian, Chinese, Oriya, Mithai                       1\n",
       "Chinese, Thai, Seafood, Malaysian                          1\n",
       "Biryani, Fast Food, Cafe, Coffee, Rolls, Wraps             1\n",
       "Pizza, Italian, Chinese, Rolls, Biryani                    1\n",
       "North Indian, Continental, Chinese, Seafood                1\n",
       "North Indian, Fast Food, Biryani, Beverages                1\n",
       "North Indian, Chinese, Kebab, Mughlai                      1\n",
       "North Indian, Biryani, Mughlai                             1\n",
       "Biryani, Mughlai, Andhra                                   1\n",
       "Fast Food, Chinese, Lebanese                               1\n",
       "Burger, Desserts                                           1\n",
       "North Indian, Street Food, Pizza                           1\n",
       "Chinese, North Indian, South Indian, Biryani, Andhra       1\n",
       "Mughlai, North Indian, Kebab, Rolls, Chinese               1\n",
       "Chinese, Mangalorean                                       1\n",
       "Name: cuisines, Length: 2487, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cuisines\n",
    "df['cuisines'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a673fb27",
   "metadata": {},
   "outputs": [],
   "source": [
    "cuisines = df['cuisines'].value_counts(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "60b55c2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "cuisines_lessthan100 = cuisines[cuisines < 100]\n",
    "def cuis_type(value):\n",
    "    if value in cuisines_lessthan100:\n",
    "        return 'other'\n",
    "    else:\n",
    "        return value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "689ab03b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "other                                               18965\n",
       "North Indian                                         1602\n",
       "North Indian, Chinese                                1401\n",
       "South Indian                                          902\n",
       "Bakery, Desserts                                      471\n",
       "Biryani                                               429\n",
       "South Indian, North Indian, Chinese                   414\n",
       "Cafe                                                  405\n",
       "Desserts                                              377\n",
       "Fast Food                                             374\n",
       "Ice Cream, Desserts                                   289\n",
       "Chinese                                               285\n",
       "Bakery                                                276\n",
       "Mithai, Street Food                                   237\n",
       "Chinese, North Indian                                 217\n",
       "North Indian, Chinese, Biryani                        201\n",
       "Desserts, Ice Cream                                   195\n",
       "Finger Food                                           193\n",
       "Desserts, Beverages                                   182\n",
       "North Indian, South Indian                            180\n",
       "North Indian, South Indian, Chinese                   170\n",
       "South Indian, North Indian                            163\n",
       "Chinese, Momos                                        162\n",
       "Biryani, North Indian                                 154\n",
       "Street Food                                           152\n",
       "North Indian, Biryani                                 148\n",
       "Cafe, Fast Food                                       148\n",
       "Beverages, Fast Food                                  148\n",
       "North Indian, Mughlai                                 143\n",
       "Burger, Fast Food                                     142\n",
       "South Indian, North Indian, Chinese, Street Food      138\n",
       "Cafe, Continental                                     123\n",
       "Biryani, Kebab                                        123\n",
       "Kerala                                                122\n",
       "Desserts, Bakery                                      122\n",
       "Pizza, Fast Food                                      120\n",
       "Chinese, Thai                                         115\n",
       "South Indian, Chinese                                 115\n",
       "Fast Food, Rolls                                      114\n",
       "Biryani, North Indian, Chinese                        111\n",
       "South Indian, Biryani                                 111\n",
       "Continental                                           109\n",
       "Beverages                                             104\n",
       "Pizza                                                 104\n",
       "Ice Cream                                             102\n",
       "North Indian, Chinese, Fast Food                      100\n",
       "Name: cuisines, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cuisines']= df['cuisines'].apply(cuis_type)\n",
    "df['cuisines'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0390f9b3",
   "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>cost</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>other</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>other</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>other</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>other</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>other</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         other   \n",
       "\n",
       "             rest_type                    cuisines   cost    type  \n",
       "0        Casual Dining                       other  800.0  Buffet  \n",
       "1        Casual Dining                       other  800.0  Buffet  \n",
       "2  Cafe, Casual Dining                       other  800.0  Buffet  \n",
       "3          Quick Bites  South Indian, North Indian  300.0  Buffet  \n",
       "4        Casual Dining                       other  600.0  Buffet  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63512b95",
   "metadata": {},
   "source": [
    "### Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "02c55071",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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]),\n",
       " <a list of 23 Text xticklabel objects>)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"Q.1 Which locations are having maximum restaurants? So which will be a suitable location to open \n",
    "a restaurant?\"\"\"\n",
    "plt.figure(figsize = (8,4))\n",
    "ax = sns.countplot(df['location'])\n",
    "plt.xticks(rotation = 90)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8bfddca5",
   "metadata": {},
   "outputs": [
    {
     "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]),\n",
       " <a list of 23 Text xticklabel objects>)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"Q.2 Visualize online order facility location wise.\"\"\"\n",
    "plt.figure(figsize = (10,6))\n",
    "sns.countplot(x='location', hue='online_order', data = df)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fd93cce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x209c297ea20>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"Q.3 Visualize book_table vs Rate\"\"\"\n",
    "plt.figure(figsize = (10,6))\n",
    "sns.boxplot(x = 'book_table', y = 'rate', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a34b524f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x209c52d95f8>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"Q.4 Visualize online_order vs Rate\"\"\"\n",
    "plt.figure(figsize = (10,6))\n",
    "sns.boxplot(x = 'online_order', y = 'rate', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "aa24b398",
   "metadata": {},
   "outputs": [
    {
     "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]),\n",
       " <a list of 23 Text xticklabel objects>)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"Q.5 Visualizing Book Table facility Location Wise\"\"\"\n",
    "plt.figure(figsize = (10,6))\n",
    "sns.countplot(x='location', hue='book_table', data = df)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "eb276e25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6]), <a list of 7 Text xticklabel objects>)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"Q.6 Visualizing Types of Restaurants vs Rate\"\"\"\n",
    "plt.figure(figsize = (10,6))\n",
    "sns.pointplot(x = 'type', y = 'rate', data = df)\n",
    "plt.xticks()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "09ef7f95",
   "metadata": {},
   "outputs": [
    {
     "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]),\n",
       " <a list of 23 Text xticklabel objects>)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"Q.7 Types of Restaurants location wise\"\"\"\n",
    "plt.figure(figsize = (20,12))\n",
    "sns.countplot(x='location', hue='type', data = df)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "fe1a0804",
   "metadata": {},
   "outputs": [
    {
     "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]),\n",
       " <a list of 46 Text xticklabel objects>)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"Q.8 Visualizing Top Cuisines wrt votes\"\"\"\n",
    "plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'cuisines',y = 'votes', data = df)\n",
    "plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a70e6f0",
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}