Zomato sales data analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4be0d29d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "061810ed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>address</th>\n",
       "      <th>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>phone</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>dish_liked</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>reviews_list</th>\n",
       "      <th>menu_item</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.zomato.com/bangalore/jalsa-banasha...</td>\n",
       "      <td>942, 21st Main Road, 2nd Stage, Banashankari, ...</td>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>775</td>\n",
       "      <td>080 42297555\\r\\n+91 9743772233</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.zomato.com/bangalore/spice-elephan...</td>\n",
       "      <td>2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...</td>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>787</td>\n",
       "      <td>080 41714161</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://www.zomato.com/SanchurroBangalore?cont...</td>\n",
       "      <td>1112, Next to KIMS Medical College, 17th Cross...</td>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>918</td>\n",
       "      <td>+91 9663487993</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.zomato.com/bangalore/addhuri-udupi...</td>\n",
       "      <td>1st Floor, Annakuteera, 3rd Stage, Banashankar...</td>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7/5</td>\n",
       "      <td>88</td>\n",
       "      <td>+91 9620009302</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Masala Dosa</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300</td>\n",
       "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.zomato.com/bangalore/grand-village...</td>\n",
       "      <td>10, 3rd Floor, Lakshmi Associates, Gandhi Baza...</td>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>166</td>\n",
       "      <td>+91 8026612447\\r\\n+91 9901210005</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Panipuri, Gol Gappe</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  \\\n",
       "0  https://www.zomato.com/bangalore/jalsa-banasha...   \n",
       "1  https://www.zomato.com/bangalore/spice-elephan...   \n",
       "2  https://www.zomato.com/SanchurroBangalore?cont...   \n",
       "3  https://www.zomato.com/bangalore/addhuri-udupi...   \n",
       "4  https://www.zomato.com/bangalore/grand-village...   \n",
       "\n",
       "                                             address                   name  \\\n",
       "0  942, 21st Main Road, 2nd Stage, Banashankari, ...                  Jalsa   \n",
       "1  2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...         Spice Elephant   \n",
       "2  1112, Next to KIMS Medical College, 17th Cross...        San Churro Cafe   \n",
       "3  1st Floor, Annakuteera, 3rd Stage, Banashankar...  Addhuri Udupi Bhojana   \n",
       "4  10, 3rd Floor, Lakshmi Associates, Gandhi Baza...          Grand Village   \n",
       "\n",
       "  online_order book_table   rate  votes                             phone  \\\n",
       "0          Yes        Yes  4.1/5    775    080 42297555\\r\\n+91 9743772233   \n",
       "1          Yes         No  4.1/5    787                      080 41714161   \n",
       "2          Yes         No  3.8/5    918                    +91 9663487993   \n",
       "3           No         No  3.7/5     88                    +91 9620009302   \n",
       "4           No         No  3.8/5    166  +91 8026612447\\r\\n+91 9901210005   \n",
       "\n",
       "       location            rest_type  \\\n",
       "0  Banashankari        Casual Dining   \n",
       "1  Banashankari        Casual Dining   \n",
       "2  Banashankari  Cafe, Casual Dining   \n",
       "3  Banashankari          Quick Bites   \n",
       "4  Basavanagudi        Casual Dining   \n",
       "\n",
       "                                          dish_liked  \\\n",
       "0  Pasta, Lunch Buffet, Masala Papad, Paneer Laja...   \n",
       "1  Momos, Lunch Buffet, Chocolate Nirvana, Thai G...   \n",
       "2  Churros, Cannelloni, Minestrone Soup, Hot Choc...   \n",
       "3                                        Masala Dosa   \n",
       "4                                Panipuri, Gol Gappe   \n",
       "\n",
       "                         cuisines approx_cost(for two people)  \\\n",
       "0  North Indian, Mughlai, Chinese                         800   \n",
       "1     Chinese, North Indian, Thai                         800   \n",
       "2          Cafe, Mexican, Italian                         800   \n",
       "3      South Indian, North Indian                         300   \n",
       "4        North Indian, Rajasthani                         600   \n",
       "\n",
       "                                        reviews_list menu_item  \\\n",
       "0  [('Rated 4.0', 'RATED\\n  A beautiful place to ...        []   \n",
       "1  [('Rated 4.0', 'RATED\\n  Had been here for din...        []   \n",
       "2  [('Rated 3.0', \"RATED\\n  Ambience is not that ...        []   \n",
       "3  [('Rated 4.0', \"RATED\\n  Great food and proper...        []   \n",
       "4  [('Rated 4.0', 'RATED\\n  Very good restaurant ...        []   \n",
       "\n",
       "  listed_in(type) listed_in(city)  \n",
       "0          Buffet    Banashankari  \n",
       "1          Buffet    Banashankari  \n",
       "2          Buffet    Banashankari  \n",
       "3          Buffet    Banashankari  \n",
       "4          Buffet    Banashankari  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(r'C:\\Users\\Triveni\\Downloads\\zomato.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6bee1458",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51717, 17)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93b1c50d",
   "metadata": {},
   "source": [
    "3 columns are representing same information, so just dropping column which are not important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4b1e42aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_col=['url','phone','address', 'listed_in(city)']\n",
    "df.drop(drop_col,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3eb1130",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51717 entries, 0 to 51716\n",
      "Data columns (total 13 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   dish_liked                   23639 non-null  object\n",
      " 8   cuisines                     51672 non-null  object\n",
      " 9   approx_cost(for two people)  51371 non-null  object\n",
      " 10  reviews_list                 51717 non-null  object\n",
      " 11  menu_item                    51717 non-null  object\n",
      " 12  listed_in(type)              51717 non-null  object\n",
      "dtypes: int64(1), object(12)\n",
      "memory usage: 5.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "993a8935",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                               0\n",
       "online_order                       0\n",
       "book_table                         0\n",
       "rate                            7775\n",
       "votes                              0\n",
       "location                          21\n",
       "rest_type                        227\n",
       "dish_liked                     28078\n",
       "cuisines                          45\n",
       "approx_cost(for two people)      346\n",
       "reviews_list                       0\n",
       "menu_item                          0\n",
       "listed_in(type)                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3499fb9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9809"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "71ac3f1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ec5de6e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41908, 13)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "29c19210",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                            8792\n",
       "online_order                       2\n",
       "book_table                         2\n",
       "rate                              64\n",
       "votes                           2328\n",
       "location                          93\n",
       "rest_type                         93\n",
       "dish_liked                      5271\n",
       "cuisines                        2723\n",
       "approx_cost(for two people)       70\n",
       "reviews_list                   22513\n",
       "menu_item                       9098\n",
       "listed_in(type)                    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b35d26a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                               0\n",
       "online_order                       0\n",
       "book_table                         0\n",
       "rate                            5914\n",
       "votes                              0\n",
       "location                          14\n",
       "rest_type                        172\n",
       "dish_liked                     20206\n",
       "cuisines                          36\n",
       "approx_cost(for two people)      253\n",
       "reviews_list                       0\n",
       "menu_item                          0\n",
       "listed_in(type)                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3036aea3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop null values\n",
    "df.dropna(subset=['rate', 'approx_cost(for two people)'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "31af09a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove cuisines missing values\n",
    "df=df[df.cuisines.isna()==False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6c0abb58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35791, 13)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "11ee0be8",
   "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                        121\n",
       "dish_liked                     14360\n",
       "cuisines                           0\n",
       "approx_cost(for two people)        0\n",
       "reviews_list                       0\n",
       "menu_item                          0\n",
       "listed_in(type)                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "34838f12",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'listed_in(type)':'type','approx_cost(for two people)':'cost'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "acbdc320",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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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>dish_liked</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>cost</th>\n",
       "      <th>reviews_list</th>\n",
       "      <th>menu_item</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>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",
       "    </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>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",
       "    </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>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",
       "    </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>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",
       "    </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>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",
       "    </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                                         dish_liked  \\\n",
       "0        Casual Dining  Pasta, Lunch Buffet, Masala Papad, Paneer Laja...   \n",
       "1        Casual Dining  Momos, Lunch Buffet, Chocolate Nirvana, Thai G...   \n",
       "2  Cafe, Casual Dining  Churros, Cannelloni, Minestrone Soup, Hot Choc...   \n",
       "3          Quick Bites                                        Masala Dosa   \n",
       "4        Casual Dining                                Panipuri, Gol Gappe   \n",
       "\n",
       "                         cuisines cost  \\\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    type  \n",
       "0  [('Rated 4.0', 'RATED\\n  A beautiful place to ...        []  Buffet  \n",
       "1  [('Rated 4.0', 'RATED\\n  Had been here for din...        []  Buffet  \n",
       "2  [('Rated 3.0', \"RATED\\n  Ambience is not that ...        []  Buffet  \n",
       "3  [('Rated 4.0', \"RATED\\n  Great food and proper...        []  Buffet  \n",
       "4  [('Rated 4.0', 'RATED\\n  Very good restaurant ...        []  Buffet  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7d787582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quick Bites                   11580\n",
       "Casual Dining                  8573\n",
       "Cafe                           2923\n",
       "Dessert Parlor                 1623\n",
       "Delivery                       1415\n",
       "                              ...  \n",
       "Food Court, Beverage Shop         2\n",
       "Dessert Parlor, Kiosk             2\n",
       "Dessert Parlor, Food Court        1\n",
       "Bakery, Beverage Shop             1\n",
       "Quick Bites, Kiosk                1\n",
       "Name: rest_type, Length: 87, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rest_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "52146422",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filling missing values of rest_type with the most occuring value\n",
    "\n",
    "df.rest_type.fillna(value='Quick Bites',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "68040348",
   "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",
       "dish_liked      14360\n",
       "cuisines            0\n",
       "cost                0\n",
       "reviews_list        0\n",
       "menu_item           0\n",
       "type                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1ce27e2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "5    Onion Rings, Pasta, Kadhai Paneer, Salads, Sal...\n",
       "6                                                  NaN\n",
       "7    Farmhouse Pizza, Chocolate Banana, Virgin Moji...\n",
       "8    Pizza, Mocktails, Coffee, Nachos, Salad, Pasta...\n",
       "9    Waffles, Pasta, Coleslaw Sandwich, Choco Waffl...\n",
       "Name: dish_liked, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dish_liked.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a6eedb5",
   "metadata": {},
   "source": [
    "# 1) Top restaurant chain "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e82558e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Onesta               85\n",
       "Cafe Coffee Day      84\n",
       "Empire Restaurant    69\n",
       "Kanti Sweets         66\n",
       "Just Bake            61\n",
       "Five Star Chicken    61\n",
       "Domino's Pizza       59\n",
       "KFC                  59\n",
       "Smoor                59\n",
       "Pizza Hut            58\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_restaurants=df['name'].value_counts()[:10]\n",
    "top_restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "88e98232",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "ax= top_restaurants.plot(kind='bar')\n",
    "plt.title('Top Restaurants')\n",
    "plt.xlabel(\"Restaurant's Name\")\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16c11967",
   "metadata": {},
   "source": [
    "# How many of the restuarants do not accept online orders?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "10dbbaa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "online_order\n",
       "Yes             26566\n",
       "No              15342\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analysing Online order\n",
    "\n",
    "df[['online_order']].value_counts()                       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5fcd9275",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='online_order', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['online_order'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc18c397",
   "metadata": {},
   "source": [
    "so, 15342 restaurants do not accept online orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3a550944",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location', ylabel='count'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['location'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ab9a721b",
   "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>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BTM</th>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banashankari</th>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banaswadi</th>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bannerghatta Road</th>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basavanagudi</th>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Bangalore</th>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whitefield</th>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilson Garden</th>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yelahanka</th>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yeshwantpur</th>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   max\n",
       "location              \n",
       "BTM                Yes\n",
       "Banashankari       Yes\n",
       "Banaswadi          Yes\n",
       "Bannerghatta Road  Yes\n",
       "Basavanagudi       Yes\n",
       "...                ...\n",
       "West Bangalore     Yes\n",
       "Whitefield         Yes\n",
       "Wilson Garden      Yes\n",
       "Yelahanka          Yes\n",
       "Yeshwantpur        Yes\n",
       "\n",
       "[93 rows x 1 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('location')['online_order'].agg({max})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9576981",
   "metadata": {},
   "source": [
    "Observation:\n",
    "\n",
    "93 restaurants take online orders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b532f8",
   "metadata": {},
   "source": [
    "# What is the ratio b/w restaurants that provide and do not provide table booking ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "592e26c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "book_table\n",
       "No            35843\n",
       "Yes            6065\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Anlysing book table column\n",
    "\n",
    "df[['book_table']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8a7eb588",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='book_table', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['book_table'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f6a0db",
   "metadata": {},
   "source": [
    "6065 restaurants provide table booking"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e754a6f",
   "metadata": {},
   "source": [
    "# Rating distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8404262c",
   "metadata": {
    "scrolled": false
   },
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analysing rate column\n",
    "\n",
    "df['rate'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "96d7c544",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['4.1' '3.8' '3.7' '3.6' '4.6' '4.0' '4.2' '3.9' '3.1' '3.0' '3.2' '3.3'\n",
      " '2.8' '4.4' '4.3' 'NEW' '2.9' '3.5' nan '2.6' '3.8 ' '3.4' '4.5' '2.5'\n",
      " '2.7' '4.7' '2.4' '2.2' '2.3' '3.4 ' '-' '3.6 ' '4.8' '3.9 ' '4.2 '\n",
      " '4.0 ' '4.1 ' '3.7 ' '3.1 ' '2.9 ' '3.3 ' '2.8 ' '3.5 ' '2.7 ' '2.5 '\n",
      " '3.2 ' '2.6 ' '4.5 ' '4.3 ' '4.4 ' '4.9' '2.1' '2.0' '1.8' '4.6 ' '4.9 '\n",
      " '3.0 ' '4.8 ' '2.3 ' '4.7 ' '2.4 ' '2.1 ' '2.2 ' '2.0 ' '1.8 ']\n"
     ]
    }
   ],
   "source": [
    "#Splitting the rate into valuable Information\n",
    "import numpy as np\n",
    "\n",
    "df['rate']=df['rate'].str.replace(r'/5', '')\n",
    "print(df['rate'].unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a583068a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['rate']=df['rate'].replace('NEW', np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7abefb67",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['rate']=df['rate'].replace('nan', np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e781764b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['rate']=df['rate'].replace('-', np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "883b2559",
   "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",
       "         ... \n",
       "51709    3.7 \n",
       "51711    2.5 \n",
       "51712    3.6 \n",
       "51715    4.3 \n",
       "51716    3.4 \n",
       "Name: rate, Length: 35994, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b2ff3363",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4.1', '3.8', '3.7', '3.6', '4.6', '4.0', '4.2', '3.9', '3.1',\n",
       "       '3.0', '3.2', '3.3', '2.8', '4.4', '4.3', nan, '2.9', '3.5', '2.6',\n",
       "       '3.8 ', '3.4', '4.5', '2.5', '2.7', '4.7', '2.4', '2.2', '2.3',\n",
       "       '3.4 ', '3.6 ', '4.8', '3.9 ', '4.2 ', '4.0 ', '4.1 ', '3.7 ',\n",
       "       '3.1 ', '2.9 ', '3.3 ', '2.8 ', '3.5 ', '2.7 ', '2.5 ', '3.2 ',\n",
       "       '2.6 ', '4.5 ', '4.3 ', '4.4 ', '4.9', '2.1', '2.0', '1.8', '4.6 ',\n",
       "       '4.9 ', '3.0 ', '4.8 ', '2.3 ', '4.7 ', '2.4 ', '2.1 ', '2.2 ',\n",
       "       '2.0 ', '1.8 '], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8a27ba3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='rate', ylabel='Density'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rating distribution\n",
    "\n",
    "sns.distplot(df['rate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a347cac",
   "metadata": {},
   "source": [
    "most rating is between 2.5 and 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06f05934",
   "metadata": {},
   "source": [
    "# Is there any difference b/w votes of restaurants accepting and not accepting online orders?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bf4d6878",
   "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></th>\n",
       "      <th>name</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>online_order</th>\n",
       "      <th>votes</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">No</th>\n",
       "      <th>0</th>\n",
       "      <td>3636</td>\n",
       "      <td>3636</td>\n",
       "      <td>4</td>\n",
       "      <td>3622</td>\n",
       "      <td>3604</td>\n",
       "      <td>68</td>\n",
       "      <td>3618</td>\n",
       "      <td>3581</td>\n",
       "      <td>3636</td>\n",
       "      <td>3636</td>\n",
       "      <td>3636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>516</td>\n",
       "      <td>516</td>\n",
       "      <td>509</td>\n",
       "      <td>516</td>\n",
       "      <td>516</td>\n",
       "      <td>0</td>\n",
       "      <td>515</td>\n",
       "      <td>508</td>\n",
       "      <td>516</td>\n",
       "      <td>516</td>\n",
       "      <td>516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>277</td>\n",
       "      <td>277</td>\n",
       "      <td>277</td>\n",
       "      <td>277</td>\n",
       "      <td>277</td>\n",
       "      <td>0</td>\n",
       "      <td>277</td>\n",
       "      <td>274</td>\n",
       "      <td>277</td>\n",
       "      <td>277</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Yes</th>\n",
       "      <th>9080</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9084</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9085</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16345</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16832</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3218 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  book_table  rate  location  rest_type  dish_liked  \\\n",
       "online_order votes                                                            \n",
       "No           0      3636        3636     4      3622       3604          68   \n",
       "             1         1           1     1         1          1           0   \n",
       "             2         3           3     3         3          3           0   \n",
       "             4       516         516   509       516        516           0   \n",
       "             5       277         277   277       277        277           0   \n",
       "...                  ...         ...   ...       ...        ...         ...   \n",
       "Yes          9080      5           5     5         5          5           5   \n",
       "             9084      4           4     4         4          4           4   \n",
       "             9085      2           2     2         2          2           2   \n",
       "             16345     3           3     3         3          3           3   \n",
       "             16832     3           3     3         3          3           3   \n",
       "\n",
       "                    cuisines  approx_cost(for two people)  reviews_list  \\\n",
       "online_order votes                                                        \n",
       "No           0          3618                         3581          3636   \n",
       "             1             1                            1             1   \n",
       "             2             3                            3             3   \n",
       "             4           515                          508           516   \n",
       "             5           277                          274           277   \n",
       "...                      ...                          ...           ...   \n",
       "Yes          9080          5                            5             5   \n",
       "             9084          4                            4             4   \n",
       "             9085          2                            2             2   \n",
       "             16345         3                            3             3   \n",
       "             16832         3                            3             3   \n",
       "\n",
       "                    menu_item  listed_in(type)  \n",
       "online_order votes                              \n",
       "No           0           3636             3636  \n",
       "             1              1                1  \n",
       "             2              3                3  \n",
       "             4            516              516  \n",
       "             5            277              277  \n",
       "...                       ...              ...  \n",
       "Yes          9080           5                5  \n",
       "             9084           4                4  \n",
       "             9085           2                2  \n",
       "             16345          3                3  \n",
       "             16832          3                3  \n",
       "\n",
       "[3218 rows x 11 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# diff b/w votes of restaurants accepting and not accepting online orders\n",
    "df.groupby(['online_order','votes']).agg('count')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c7bfed",
   "metadata": {},
   "source": [
    "# Which are the most common restaurant type in Banglore?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fdeabd7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location             \n",
       "BTM                      3578\n",
       "Koramangala 5th Block    2122\n",
       "HSR                      2085\n",
       "Whitefield               2010\n",
       "Indiranagar              1973\n",
       "                         ... \n",
       "Central Bangalore           5\n",
       "Nagarbhavi                  4\n",
       "Rajarajeshwari Nagar        2\n",
       "Jakkur                      1\n",
       "Peenya                      1\n",
       "Length: 93, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analyzing Location\n",
    "\n",
    "df[['location']].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6bbd897",
   "metadata": {},
   "source": [
    "there are total 93 different locations in Bangalore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dc389055",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting\n",
    "plt.figure(figsize=(20,15))\n",
    "ax =df.location.value_counts()[:10].plot(kind='bar')\n",
    "plt.title('Number of Restaurants in each location', weight='bold')\n",
    "plt.xlabel('location')\n",
    "plt.ylabel('No. of Restaurants')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bed37ff",
   "metadata": {},
   "source": [
    "BTM, Koramangala 5th Block, HSR, Whitefield, Indiranagar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26a58444",
   "metadata": {},
   "source": [
    "# Cost factor ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "78729801",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "400      4558\n",
       "300      4334\n",
       "500      3585\n",
       "600      2838\n",
       "200      2585\n",
       "         ... \n",
       "199         2\n",
       "70          1\n",
       "5,000       1\n",
       "3,700       1\n",
       "80          1\n",
       "Name: cost, Length: 63, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cost.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9ddaf6fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "ax =df.cost.value_counts()[:10].plot(kind='bar')\n",
    "plt.title('Average cost for two person')\n",
    "plt.xlabel('Cost')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9cbf33d",
   "metadata": {},
   "source": [
    "# Which are the foodie areas?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0121fe5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location     cuisines                           \n",
       "BTM          North Indian, Chinese                  250\n",
       "             North Indian                           197\n",
       "             Fast Food                               75\n",
       "             Chinese, North Indian                   65\n",
       "             Biryani, North Indian                   53\n",
       "                                                   ... \n",
       "Yeshwantpur  North Indian, South Indian, Chinese      1\n",
       "             North Indian, Street Food                1\n",
       "             Seafood, North Indian, Mangalorean       1\n",
       "             Street Food                              1\n",
       "             Thai, Chinese, Momos                     1\n",
       "Name: cuisines, Length: 6510, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('location')['cuisines'].value_counts(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7925c19",
   "metadata": {},
   "source": [
    "# BTM is the most foodie area of Banglore"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed5cbfad",
   "metadata": {},
   "source": [
    "# Which are the most common cuisines in each locations?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "631dfe16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "BTM                  [South Indian, North Indian, Chinese, Cafe, He...\n",
       "Banashankari         [North Indian, Mughlai, Chinese, Chinese, Nort...\n",
       "Banaswadi            [Cafe, Continental, Cafe, Fast Food, Italian, ...\n",
       "Bannerghatta Road    [North Indian, Italian, Arabian, Biryani, Chin...\n",
       "Basavanagudi         [North Indian, Rajasthani, North Indian, North...\n",
       "                                           ...                        \n",
       "West Bangalore       [Healthy Food, Chinese, Biryani, North Indian,...\n",
       "Whitefield           [Cafe, Fast Food, Pizza, Burger, Beverages, No...\n",
       "Wilson Garden        [Continental, Chinese, North Indian, South Ind...\n",
       "Yelahanka            [South Indian, Biryani, Kerala, South Indian, ...\n",
       "Yeshwantpur          [North Indian, Chinese, North Indian, Chinese,...\n",
       "Name: cuisines, Length: 92, dtype: object"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('location')['cuisines'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c50c5bf1",
   "metadata": {},
   "source": [
    "North Indian food is at top, followed by chinese and so on"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e2e8488",
   "metadata": {},
   "source": [
    "# Heatmap of restaurant count on each location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86aa0b10",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.heatmap(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ef47848",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "383f2eee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ba5b5eb2",
   "metadata": {},
   "source": [
    "# Which are the most popular cuisines of Bangalore?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "433e717d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North Indian                           1839\n",
       "North Indian, Chinese                  1639\n",
       "South Indian                           1007\n",
       "Bakery, Desserts                        528\n",
       "Biryani                                 480\n",
       "Cafe                                    478\n",
       "South Indian, North Indian, Chinese     474\n",
       "Desserts                                457\n",
       "Fast Food                               427\n",
       "Ice Cream, Desserts                     343\n",
       "Name: cuisines, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_cuisines=df['cuisines'].value_counts()[:10]\n",
    "top_cuisines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a3f8096e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "ax =df.cuisines.value_counts()[:10].plot(kind='bar')\n",
    "plt.title('top 10 cuisines')\n",
    "plt.xlabel('cuisines')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "689353e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06b6dad5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01903ae2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9b53efd6",
   "metadata": {},
   "source": [
    "# Heatmap of North Indian restaurantWhat about South Indian cuisines?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46eeeeec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0e9ad73",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a94a6bc0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6175a50d",
   "metadata": {},
   "source": [
    "# Analysing Restaurant chains"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a43a87a",
   "metadata": {},
   "source": [
    "# a. Which are the most popular casual dining restaurant chains?\n",
    "\n",
    "    i. Where are their outlets located?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a471303",
   "metadata": {},
   "source": [
    "# b. Top quick bites restaurant chains in Banglore\n",
    "    i. Where are their outlets located?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cdc45d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26723429",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "44abb147",
   "metadata": {},
   "source": [
    "# c. Top Cafes of Banglore?\n",
    "    i. Where are their outlets located?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d5163bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "779db651",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94634d00",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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