Zomato_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c27bfca0",
   "metadata": {},
   "source": [
    "# <centre>Zomato Analysis</centre>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1522df1a",
   "metadata": {},
   "source": [
    "Zomato is a popular food delivery and restaurant discovery service based in India. The company was founded in 2008 and has since expanded to operate in over 24 countries around the world. On the Zomato platform, users can search for and discover restaurants in their area, view menus, photos, and user reviews, and place orders for delivery or pickup. Zomato also offers a subscription service called \"Zomato Gold,\" which gives members access to exclusive discounts and deals at participating restaurants. In addition to its food delivery and restaurant discovery services, Zomato also provides point-of-sale systems, tabletop ordering, and white-label apps for restaurants.\n",
    "\n",
    "In India, Zomato is a popular food delivery and restaurant discovery service. The company allows users to search for restaurants in their area, view menus, photos, and user reviews, and place orders for delivery or pickup. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a97f5642",
   "metadata": {},
   "source": [
    "## Business Requirement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e91fa1e0",
   "metadata": {},
   "source": [
    "<ul>\n",
    "    <li>Analyse the demographic divident.</li>\n",
    "    <li>Analyse the ratings,order types</li>\n",
    "\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0fdadb0",
   "metadata": {},
   "source": [
    "## Importing library's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "6ea44ec9",
   "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>Restaurant ID</th>\n",
       "      <th>Restaurant Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Locality Verbose</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>...</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Switch to order menu</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6317637</td>\n",
       "      <td>Le Petit Souffle</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Third Floor, Century City Mall, Kalayaan Avenu...</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City, Mak...</td>\n",
       "      <td>121.027535</td>\n",
       "      <td>14.565443</td>\n",
       "      <td>French, Japanese, Desserts</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6304287</td>\n",
       "      <td>Izakaya Kikufuji</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Little Tokyo, 2277 Chino Roces Avenue, Legaspi...</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City, Ma...</td>\n",
       "      <td>121.014101</td>\n",
       "      <td>14.553708</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6300002</td>\n",
       "      <td>Heat - Edsa Shangri-La</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...</td>\n",
       "      <td>121.056831</td>\n",
       "      <td>14.581404</td>\n",
       "      <td>Seafood, Asian, Filipino, Indian</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6318506</td>\n",
       "      <td>Ooma</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Fashion Hall, SM Megamall, O...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.056475</td>\n",
       "      <td>14.585318</td>\n",
       "      <td>Japanese, Sushi</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6314302</td>\n",
       "      <td>Sambo Kojin</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Atrium, SM Megamall, Ortigas...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.057508</td>\n",
       "      <td>14.584450</td>\n",
       "      <td>Japanese, Korean</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Restaurant ID         Restaurant Name  Country Code              City  \\\n",
       "0        6317637        Le Petit Souffle           162       Makati City   \n",
       "1        6304287        Izakaya Kikufuji           162       Makati City   \n",
       "2        6300002  Heat - Edsa Shangri-La           162  Mandaluyong City   \n",
       "3        6318506                    Ooma           162  Mandaluyong City   \n",
       "4        6314302             Sambo Kojin           162  Mandaluyong City   \n",
       "\n",
       "                                             Address  \\\n",
       "0  Third Floor, Century City Mall, Kalayaan Avenu...   \n",
       "1  Little Tokyo, 2277 Chino Roces Avenue, Legaspi...   \n",
       "2  Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...   \n",
       "3  Third Floor, Mega Fashion Hall, SM Megamall, O...   \n",
       "4  Third Floor, Mega Atrium, SM Megamall, Ortigas...   \n",
       "\n",
       "                                     Locality  \\\n",
       "0   Century City Mall, Poblacion, Makati City   \n",
       "1  Little Tokyo, Legaspi Village, Makati City   \n",
       "2  Edsa Shangri-La, Ortigas, Mandaluyong City   \n",
       "3      SM Megamall, Ortigas, Mandaluyong City   \n",
       "4      SM Megamall, Ortigas, Mandaluyong City   \n",
       "\n",
       "                                    Locality Verbose   Longitude   Latitude  \\\n",
       "0  Century City Mall, Poblacion, Makati City, Mak...  121.027535  14.565443   \n",
       "1  Little Tokyo, Legaspi Village, Makati City, Ma...  121.014101  14.553708   \n",
       "2  Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...  121.056831  14.581404   \n",
       "3  SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.056475  14.585318   \n",
       "4  SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.057508  14.584450   \n",
       "\n",
       "                           Cuisines  ...          Currency Has Table booking  \\\n",
       "0        French, Japanese, Desserts  ...  Botswana Pula(P)               Yes   \n",
       "1                          Japanese  ...  Botswana Pula(P)               Yes   \n",
       "2  Seafood, Asian, Filipino, Indian  ...  Botswana Pula(P)               Yes   \n",
       "3                   Japanese, Sushi  ...  Botswana Pula(P)                No   \n",
       "4                  Japanese, Korean  ...  Botswana Pula(P)               Yes   \n",
       "\n",
       "  Has Online delivery Is delivering now Switch to order menu Price range  \\\n",
       "0                  No                No                   No           3   \n",
       "1                  No                No                   No           3   \n",
       "2                  No                No                   No           4   \n",
       "3                  No                No                   No           4   \n",
       "4                  No                No                   No           4   \n",
       "\n",
       "   Aggregate rating  Rating color Rating text Votes  \n",
       "0               4.8    Dark Green   Excellent   314  \n",
       "1               4.5    Dark Green   Excellent   591  \n",
       "2               4.4         Green   Very Good   270  \n",
       "3               4.9    Dark Green   Excellent   365  \n",
       "4               4.8    Dark Green   Excellent   229  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize']=(12,6)\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "df=pd.read_csv('D:/Data Analysis/Zomato_k/files/datasets/zomato.csv',encoding='latin-1')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "431f9ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9551, 21)"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "4e639b97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200571"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "23b98c2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',\n",
       "       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',\n",
       "       'Average Cost for two', 'Currency', 'Has Table booking',\n",
       "       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
       "       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',\n",
       "       'Votes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86cb14c6",
   "metadata": {},
   "source": [
    "## Checking for Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "5f91d13f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Restaurant ID           0\n",
       "Restaurant Name         0\n",
       "Country Code            0\n",
       "City                    0\n",
       "Address                 0\n",
       "Locality                0\n",
       "Locality Verbose        0\n",
       "Longitude               0\n",
       "Latitude                0\n",
       "Cuisines                9\n",
       "Average Cost for two    0\n",
       "Currency                0\n",
       "Has Table booking       0\n",
       "Has Online delivery     0\n",
       "Is delivering now       0\n",
       "Switch to order menu    0\n",
       "Price range             0\n",
       "Aggregate rating        0\n",
       "Rating color            0\n",
       "Rating text             0\n",
       "Votes                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "a350a507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cuisines']"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[features for features in df.columns if df[features].isnull().sum()>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "0e636092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(),yticklabels=False,cbar='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "fa35eeb1",
   "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>Country Code</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>94</td>\n",
       "      <td>Indonesia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country Code    Country\n",
       "0             1      India\n",
       "1            14  Australia\n",
       "2            30     Brazil\n",
       "3            37     Canada\n",
       "4            94  Indonesia"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country=pd.read_excel(\"D:/Data Analysis/Zomato_k/files/datasets/Country-Code.xlsx\")\n",
    "df_country.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "5a858285",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 2)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "c6400998",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country Code', 'Country'], dtype='object')"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "7a843dd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',\n",
       "       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',\n",
       "       'Average Cost for two', 'Currency', 'Has Table booking',\n",
       "       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
       "       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',\n",
       "       'Votes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "685c58f9",
   "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>Restaurant ID</th>\n",
       "      <th>Restaurant Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Locality Verbose</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>...</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Switch to order menu</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6317637</td>\n",
       "      <td>Le Petit Souffle</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Third Floor, Century City Mall, Kalayaan Avenu...</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City, Mak...</td>\n",
       "      <td>121.027535</td>\n",
       "      <td>14.565443</td>\n",
       "      <td>French, Japanese, Desserts</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>314</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6304287</td>\n",
       "      <td>Izakaya Kikufuji</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Little Tokyo, 2277 Chino Roces Avenue, Legaspi...</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City, Ma...</td>\n",
       "      <td>121.014101</td>\n",
       "      <td>14.553708</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>591</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6300002</td>\n",
       "      <td>Heat - Edsa Shangri-La</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...</td>\n",
       "      <td>121.056831</td>\n",
       "      <td>14.581404</td>\n",
       "      <td>Seafood, Asian, Filipino, Indian</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>270</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6318506</td>\n",
       "      <td>Ooma</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Fashion Hall, SM Megamall, O...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.056475</td>\n",
       "      <td>14.585318</td>\n",
       "      <td>Japanese, Sushi</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>365</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6314302</td>\n",
       "      <td>Sambo Kojin</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Atrium, SM Megamall, Ortigas...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.057508</td>\n",
       "      <td>14.584450</td>\n",
       "      <td>Japanese, Korean</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>229</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Restaurant ID         Restaurant Name  Country Code              City  \\\n",
       "0        6317637        Le Petit Souffle           162       Makati City   \n",
       "1        6304287        Izakaya Kikufuji           162       Makati City   \n",
       "2        6300002  Heat - Edsa Shangri-La           162  Mandaluyong City   \n",
       "3        6318506                    Ooma           162  Mandaluyong City   \n",
       "4        6314302             Sambo Kojin           162  Mandaluyong City   \n",
       "\n",
       "                                             Address  \\\n",
       "0  Third Floor, Century City Mall, Kalayaan Avenu...   \n",
       "1  Little Tokyo, 2277 Chino Roces Avenue, Legaspi...   \n",
       "2  Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...   \n",
       "3  Third Floor, Mega Fashion Hall, SM Megamall, O...   \n",
       "4  Third Floor, Mega Atrium, SM Megamall, Ortigas...   \n",
       "\n",
       "                                     Locality  \\\n",
       "0   Century City Mall, Poblacion, Makati City   \n",
       "1  Little Tokyo, Legaspi Village, Makati City   \n",
       "2  Edsa Shangri-La, Ortigas, Mandaluyong City   \n",
       "3      SM Megamall, Ortigas, Mandaluyong City   \n",
       "4      SM Megamall, Ortigas, Mandaluyong City   \n",
       "\n",
       "                                    Locality Verbose   Longitude   Latitude  \\\n",
       "0  Century City Mall, Poblacion, Makati City, Mak...  121.027535  14.565443   \n",
       "1  Little Tokyo, Legaspi Village, Makati City, Ma...  121.014101  14.553708   \n",
       "2  Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...  121.056831  14.581404   \n",
       "3  SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.056475  14.585318   \n",
       "4  SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.057508  14.584450   \n",
       "\n",
       "                           Cuisines  ...  Has Table booking  \\\n",
       "0        French, Japanese, Desserts  ...                Yes   \n",
       "1                          Japanese  ...                Yes   \n",
       "2  Seafood, Asian, Filipino, Indian  ...                Yes   \n",
       "3                   Japanese, Sushi  ...                 No   \n",
       "4                  Japanese, Korean  ...                Yes   \n",
       "\n",
       "  Has Online delivery Is delivering now Switch to order menu Price range  \\\n",
       "0                  No                No                   No           3   \n",
       "1                  No                No                   No           3   \n",
       "2                  No                No                   No           4   \n",
       "3                  No                No                   No           4   \n",
       "4                  No                No                   No           4   \n",
       "\n",
       "  Aggregate rating  Rating color  Rating text Votes      Country  \n",
       "0              4.8    Dark Green    Excellent   314  Phillipines  \n",
       "1              4.5    Dark Green    Excellent   591  Phillipines  \n",
       "2              4.4         Green    Very Good   270  Phillipines  \n",
       "3              4.9    Dark Green    Excellent   365  Phillipines  \n",
       "4              4.8    Dark Green    Excellent   229  Phillipines  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df=pd.merge(df,df_country,on='Country Code',how='left')\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "1b91beb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Restaurant ID             int64\n",
       "Restaurant Name          object\n",
       "Country Code              int64\n",
       "City                     object\n",
       "Address                  object\n",
       "Locality                 object\n",
       "Locality Verbose         object\n",
       "Longitude               float64\n",
       "Latitude                float64\n",
       "Cuisines                 object\n",
       "Average Cost for two      int64\n",
       "Currency                 object\n",
       "Has Table booking        object\n",
       "Has Online delivery      object\n",
       "Is delivering now        object\n",
       "Switch to order menu     object\n",
       "Price range               int64\n",
       "Aggregate rating        float64\n",
       "Rating color             object\n",
       "Rating text              object\n",
       "Votes                     int64\n",
       "Country                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "c2ead17e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',\n",
       "       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',\n",
       "       'Average Cost for two', 'Currency', 'Has Table booking',\n",
       "       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
       "       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',\n",
       "       'Votes', 'Country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "62a0a1de",
   "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>Restaurant Name</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Locality Verbose</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Switch to order menu</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9551</td>\n",
       "      <td>9551</td>\n",
       "      <td>9551</td>\n",
       "      <td>9551</td>\n",
       "      <td>9551</td>\n",
       "      <td>9542</td>\n",
       "      <td>9551</td>\n",
       "      <td>9551</td>\n",
       "      <td>9551</td>\n",
       "      <td>9551</td>\n",
       "      <td>9551</td>\n",
       "      <td>9551</td>\n",
       "      <td>9551</td>\n",
       "      <td>9551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7446</td>\n",
       "      <td>141</td>\n",
       "      <td>8918</td>\n",
       "      <td>1208</td>\n",
       "      <td>1265</td>\n",
       "      <td>1825</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Cafe Coffee Day</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>Dilli Haat, INA, New Delhi</td>\n",
       "      <td>Connaught Place</td>\n",
       "      <td>Connaught Place, New Delhi</td>\n",
       "      <td>North Indian</td>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>83</td>\n",
       "      <td>5473</td>\n",
       "      <td>11</td>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>936</td>\n",
       "      <td>8652</td>\n",
       "      <td>8393</td>\n",
       "      <td>7100</td>\n",
       "      <td>9517</td>\n",
       "      <td>9551</td>\n",
       "      <td>3737</td>\n",
       "      <td>3737</td>\n",
       "      <td>8652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Restaurant Name       City                     Address  \\\n",
       "count              9551       9551                        9551   \n",
       "unique             7446        141                        8918   \n",
       "top     Cafe Coffee Day  New Delhi  Dilli Haat, INA, New Delhi   \n",
       "freq                 83       5473                          11   \n",
       "\n",
       "               Locality            Locality Verbose      Cuisines  \\\n",
       "count              9551                        9551          9542   \n",
       "unique             1208                        1265          1825   \n",
       "top     Connaught Place  Connaught Place, New Delhi  North Indian   \n",
       "freq                122                         122           936   \n",
       "\n",
       "                  Currency Has Table booking Has Online delivery  \\\n",
       "count                 9551              9551                9551   \n",
       "unique                  12                 2                   2   \n",
       "top     Indian Rupees(Rs.)                No                  No   \n",
       "freq                  8652              8393                7100   \n",
       "\n",
       "       Is delivering now Switch to order menu Rating color Rating text Country  \n",
       "count               9551                 9551         9551        9551    9551  \n",
       "unique                 2                    1            6           6      15  \n",
       "top                   No                   No       Orange     Average   India  \n",
       "freq                9517                 9551         3737        3737    8652  "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# separate the object column\n",
    "cat_col=[]\n",
    "\n",
    "for col in final_df.columns:\n",
    "    if final_df[col].dtype=='object':\n",
    "        cat_col.append(col)\n",
    "        \n",
    "final_df[cat_col].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ee0a2b",
   "metadata": {},
   "source": [
    "The above data depicts the following\n",
    "<ul>\n",
    "    <li>The top restrant among is Cafe Coffee day.</li>\n",
    "    <li>Most prefered loction is New Delhi, locality-Connaught place</li>\n",
    "    <li>Most prefered Cusines are North indian</li>\n",
    "\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "74bb2aea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['India', 'United States', 'United Kingdom', 'Brazil', 'UAE',\n",
       "       'South Africa', 'New Zealand', 'Turkey', 'Australia', 'Phillipines',\n",
       "       'Indonesia', 'Singapore', 'Qatar', 'Sri Lanka', 'Canada'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_names=final_df.Country.value_counts().index\n",
    "country_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "b45f3bf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8652,  434,   80,   60,   60,   60,   40,   34,   24,   22,   21,\n",
       "         20,   20,   20,    4], dtype=int64)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_val=final_df.Country.value_counts().values\n",
    "country_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "3673c7a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1a97da4a9a0>,\n",
       "  <matplotlib.patches.Wedge at 0x1a97ac76100>,\n",
       "  <matplotlib.patches.Wedge at 0x1a97ac76820>],\n",
       " [Text(-1.0829742700952103, 0.19278674827836725, 'India'),\n",
       "  Text(1.077281715838356, -0.22240527134123297, 'United States'),\n",
       "  Text(1.0995865153823035, -0.03015783794312073, 'United Kingdom')],\n",
       " [Text(-0.590713238233751, 0.10515640815183668, '94.4%'),\n",
       "  Text(0.5876082086391032, -0.12131196618612707, '4.7%'),\n",
       "  Text(0.5997744629358018, -0.01644972978715676, '0.9%')])"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Top 3 countries that use Zomato\n",
    "plt.pie(x=country_val[:3],labels=country_names[:3],autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4290a9ab",
   "metadata": {},
   "source": [
    "Maximum transcation records are from India,USA and Uk respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "c8de9b0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',\n",
       "       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',\n",
       "       'Average Cost for two', 'Currency', 'Has Table booking',\n",
       "       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
       "       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',\n",
       "       'Votes', 'Country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "a769a5ad",
   "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>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Rating_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Not rated</td>\n",
       "      <td>2148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.8</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.9</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.1</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.2</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.3</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.4</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.5</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.6</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.7</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.8</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.9</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.1</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.2</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.3</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.4</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.5</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.6</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.7</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.8</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.9</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.1</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4.2</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.3</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4.4</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4.5</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.6</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.7</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4.9</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Aggregate rating Rating color Rating text  Rating_count\n",
       "0                0.0        White   Not rated          2148\n",
       "1                1.8          Red        Poor             1\n",
       "2                1.9          Red        Poor             2\n",
       "3                2.0          Red        Poor             7\n",
       "4                2.1          Red        Poor            15\n",
       "5                2.2          Red        Poor            27\n",
       "6                2.3          Red        Poor            47\n",
       "7                2.4          Red        Poor            87\n",
       "8                2.5       Orange     Average           110\n",
       "9                2.6       Orange     Average           191\n",
       "10               2.7       Orange     Average           250\n",
       "11               2.8       Orange     Average           315\n",
       "12               2.9       Orange     Average           381\n",
       "13               3.0       Orange     Average           468\n",
       "14               3.1       Orange     Average           519\n",
       "15               3.2       Orange     Average           522\n",
       "16               3.3       Orange     Average           483\n",
       "17               3.4       Orange     Average           498\n",
       "18               3.5       Yellow        Good           480\n",
       "19               3.6       Yellow        Good           458\n",
       "20               3.7       Yellow        Good           427\n",
       "21               3.8       Yellow        Good           400\n",
       "22               3.9       Yellow        Good           335\n",
       "23               4.0        Green   Very Good           266\n",
       "24               4.1        Green   Very Good           274\n",
       "25               4.2        Green   Very Good           221\n",
       "26               4.3        Green   Very Good           174\n",
       "27               4.4        Green   Very Good           144\n",
       "28               4.5   Dark Green   Excellent            95\n",
       "29               4.6   Dark Green   Excellent            78\n",
       "30               4.7   Dark Green   Excellent            42\n",
       "31               4.8   Dark Green   Excellent            25\n",
       "32               4.9   Dark Green   Excellent            61"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings=final_df.groupby(['Aggregate rating','Rating color','Rating text']).size().reset_index().rename(columns={0:'Rating_count'})\n",
    "ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d7faa6e",
   "metadata": {},
   "source": [
    "## Observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "139451d5",
   "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>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Rating_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.2</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4.9</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4.4</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.9</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Aggregate rating Rating color Rating text  Rating_count\n",
       "5                2.2          Red        Poor            27\n",
       "32               4.9   Dark Green   Excellent            61\n",
       "27               4.4        Green   Very Good           144\n",
       "13               3.0       Orange     Average           468\n",
       "2                1.9          Red        Poor             2"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "0096b84a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Aggregate rating', ylabel='Rating_count'>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Aggregate rating',y='Rating_count',data=ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "b4304d92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Aggregate rating', ylabel='Rating_count'>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Aggregate rating',y='Rating_count',hue='Rating color',data=ratings,palette=['white','red','orange','yellow','green','green'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31017cc3",
   "metadata": {},
   "source": [
    "Observation\n",
    "<ul>\n",
    "    <li>Not rated count is very high </li>\n",
    "    <li>Max no of ratings are between 2.4 and 3.4</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "f1f36c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rating color', ylabel='count'>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## count Plot on frequency\n",
    "sns.countplot(x='Rating color',data=ratings,palette=['blue','red','orange','yellow','green','green'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "dc0a88b4",
   "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>Country</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>2139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United States</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country     0\n",
       "0          Brazil     5\n",
       "1           India  2139\n",
       "2  United Kingdom     1\n",
       "3   United States     3"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[final_df['Rating color']=='White'].groupby(['Country']).size().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13c0bdcc",
   "metadata": {},
   "source": [
    "Maximum number of 0 ratings are from Inida followed by Brazil,US & UK."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "db195e39",
   "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>Country</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Currency count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Dollar($)</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brazilian Real(R$)</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Canada</td>\n",
       "      <td>Dollar($)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>India</td>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>8652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Indonesian Rupiah(IDR)</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>NewZealand($)</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Phillipines</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>Qatari Rial(QR)</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>Dollar($)</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>Rand(R)</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Sri Lankan Rupee(LKR)</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>UAE</td>\n",
       "      <td>Emirati Diram(AED)</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Pounds(£)</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>United States</td>\n",
       "      <td>Dollar($)</td>\n",
       "      <td>434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country                Currency  Currency count\n",
       "0        Australia               Dollar($)              24\n",
       "1           Brazil      Brazilian Real(R$)              60\n",
       "2           Canada               Dollar($)               4\n",
       "3            India      Indian Rupees(Rs.)            8652\n",
       "4        Indonesia  Indonesian Rupiah(IDR)              21\n",
       "5      New Zealand           NewZealand($)              40\n",
       "6      Phillipines        Botswana Pula(P)              22\n",
       "7            Qatar         Qatari Rial(QR)              20\n",
       "8        Singapore               Dollar($)              20\n",
       "9     South Africa                 Rand(R)              60\n",
       "10       Sri Lanka   Sri Lankan Rupee(LKR)              20\n",
       "11          Turkey        Turkish Lira(TL)              34\n",
       "12             UAE      Emirati Diram(AED)              60\n",
       "13  United Kingdom              Pounds(£)              80\n",
       "14   United States               Dollar($)             434"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[['Country','Currency']].groupby(['Country','Currency']).size().reset_index().rename(columns={0:'Currency count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "ada7fca9",
   "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>Country</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>India</td>\n",
       "      <td>2423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UAE</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country     0\n",
       "0   India  2423\n",
       "1     UAE    28"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[final_df['Has Online delivery']=='Yes'].groupby('Country').size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "d2780b26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Country</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>Australia</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Canada</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>India</td>\n",
       "      <td>6229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>No</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>No</td>\n",
       "      <td>Phillipines</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>No</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>No</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>No</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>No</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>No</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>No</td>\n",
       "      <td>UAE</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>No</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Yes</td>\n",
       "      <td>India</td>\n",
       "      <td>2423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Yes</td>\n",
       "      <td>UAE</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Has Online delivery         Country  count\n",
       "0                   No       Australia     24\n",
       "1                   No          Brazil     60\n",
       "2                   No          Canada      4\n",
       "3                   No           India   6229\n",
       "4                   No       Indonesia     21\n",
       "5                   No     New Zealand     40\n",
       "6                   No     Phillipines     22\n",
       "7                   No           Qatar     20\n",
       "8                   No       Singapore     20\n",
       "9                   No    South Africa     60\n",
       "10                  No       Sri Lanka     20\n",
       "11                  No          Turkey     34\n",
       "12                  No             UAE     32\n",
       "13                  No  United Kingdom     80\n",
       "14                  No   United States    434\n",
       "15                 Yes           India   2423\n",
       "16                 Yes             UAE     28"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.groupby(['Has Online delivery','Country']).size().reset_index().rename(columns={0:'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "848bc93d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5473, 1118, 1080,  251,   25,   21,   21,   21,   21,   21,   20,\n",
       "         20,   20,   20,   20,   20,   20,   20,   20,   20,   20,   20,\n",
       "         20,   20,   20,   20,   20,   20,   20,   20,   20,   20,   20,\n",
       "         20,   20,   20,   20,   20,   20,   20,   20,   20,   20,   20,\n",
       "         20,   20,   20,   20,   20,   20,   20,   20,   20,   20,   20,\n",
       "         20,   20,   20,   20,   20,   20,   20,   20,   20,   20,   20,\n",
       "         20,   20,   20,   20,   20,   20,   20,   20,   20,   20,   20,\n",
       "         18,   18,   16,   14,   11,    6,    4,    4,    3,    3,    2,\n",
       "          2,    2,    2,    2,    2,    2,    2,    1,    1,    1,    1,\n",
       "          1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
       "          1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
       "          1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
       "          1,    1,    1,    1,    1,    1,    1,    1,    1], dtype=int64)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_counts=final_df.City.value_counts().values\n",
    "city_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "ff3362b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['New Delhi', 'Gurgaon', 'Noida', 'Faridabad', 'Ghaziabad',\n",
       "       'Bhubaneshwar', 'Amritsar', 'Ahmedabad', 'Lucknow', 'Guwahati',\n",
       "       ...\n",
       "       'Ojo Caliente', 'Montville', 'Monroe', 'Miller', 'Middleton Beach',\n",
       "       'Panchkula', 'Mc Millan', 'Mayfield', 'Macedon', 'Vineland Station'],\n",
       "      dtype='object', length=141)"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_names=final_df.City.value_counts().index\n",
    "city_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "0c078053",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1a97e713d30>,\n",
       "  <matplotlib.patches.Wedge at 0x1a97e722490>,\n",
       "  <matplotlib.patches.Wedge at 0x1a97e722bb0>,\n",
       "  <matplotlib.patches.Wedge at 0x1a97e72d340>],\n",
       " [Text(-0.6207499702746028, 0.9081131396495042, 'New Delhi'),\n",
       "  Text(0.07888824382658387, -1.0971675555656746, 'Gurgaon'),\n",
       "  Text(0.8905335390443715, -0.6457166683903298, 'Noida'),\n",
       "  Text(1.0945551812130687, -0.10931127700117002, 'Faridabad')],\n",
       " [Text(-0.338590892877056, 0.4953344398088204, '69.09%'),\n",
       "  Text(0.043029951178136655, -0.5984550303085497, '14.11%'),\n",
       "  Text(0.4857455667514753, -0.35220909184927074, '13.63%'),\n",
       "  Text(0.597030098843492, -0.05962433290972909, '3.17%')])"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "plt.pie(city_counts[:4],labels=city_names[:4],autopct='%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "d309afe0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North Indian                      936\n",
       "North Indian, Chinese             511\n",
       "Chinese                           354\n",
       "Fast Food                         354\n",
       "North Indian, Mughlai             334\n",
       "Cafe                              299\n",
       "Bakery                            218\n",
       "North Indian, Mughlai, Chinese    197\n",
       "Bakery, Desserts                  170\n",
       "Street Food                       149\n",
       "Name: Cuisines, dtype: int64"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cusines_final=final_df.Cuisines.value_counts()[:10]\n",
    "cusines_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "8baca6b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([936, 511, 354, ...,   1,   1,   1], dtype=int64)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cusines_count=final_df.Cuisines.value_counts().values\n",
    "cusines_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "097219c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['North Indian', 'North Indian, Chinese', 'Chinese', 'Fast Food',\n",
       "       'North Indian, Mughlai', 'Cafe', 'Bakery',\n",
       "       'North Indian, Mughlai, Chinese', 'Bakery, Desserts', 'Street Food',\n",
       "       ...\n",
       "       'Cafe, Pizza, Burger',\n",
       "       'Healthy Food, Continental, Juices, Beverages, Italian, Salad, Lebanese',\n",
       "       'Goan, American, Portuguese', 'South Indian, Desserts, Beverages',\n",
       "       'Healthy Food, North Indian, Italian, Salad', 'Bengali, Fast Food',\n",
       "       'North Indian, Rajasthani, Asian',\n",
       "       'Chinese, Thai, Malaysian, Indonesian',\n",
       "       'Bakery, Desserts, North Indian, Bengali, South Indian',\n",
       "       'Italian, World Cuisine'],\n",
       "      dtype='object', length=1825)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cusines_names=final_df.Cuisines.value_counts().index\n",
    "cusines_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "97e43089",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1a97e76f7c0>,\n",
       "  <matplotlib.patches.Wedge at 0x1a97e76fee0>,\n",
       "  <matplotlib.patches.Wedge at 0x1a97e8dd640>,\n",
       "  <matplotlib.patches.Wedge at 0x1a97e8ddd60>,\n",
       "  <matplotlib.patches.Wedge at 0x1a97e8ec4c0>,\n",
       "  <matplotlib.patches.Wedge at 0x1a97e8ecbe0>,\n",
       "  <matplotlib.patches.Wedge at 0x1a97e8f7340>,\n",
       "  <matplotlib.patches.Wedge at 0x1a97e8f7a60>,\n",
       "  <matplotlib.patches.Wedge at 0x1a97e9041c0>,\n",
       "  <matplotlib.patches.Wedge at 0x1a97e9048e0>],\n",
       " [Text(0.7383739846958008, 0.8153550507137645, 'New Delhi'),\n",
       "  Text(-0.5794679314239953, 0.9349956772366362, 'Gurgaon'),\n",
       "  Text(-1.067309479615702, 0.26617752482593154, 'Noida'),\n",
       "  Text(-1.0185984499802057, -0.4152796620326146, 'Faridabad'),\n",
       "  Text(-0.5935788454809928, -0.9261015895664211, 'Ghaziabad'),\n",
       "  Text(-0.005887079599915552, -1.0999842463843672, 'Bhubaneshwar'),\n",
       "  Text(0.4842062514572988, -0.9876964645323336, 'Amritsar'),\n",
       "  Text(0.808736477166136, -0.7456174022251013, 'Ahmedabad'),\n",
       "  Text(1.0055375294202338, -0.44597564611473206, 'Lucknow'),\n",
       "  Text(1.090298995560443, -0.14576728123927227, 'Guwahati')],\n",
       " [Text(0.4027494461977095, 0.4447391185711442, '26.58%'),\n",
       "  Text(-0.316073417140361, 0.5099976421290743, '14.51%'),\n",
       "  Text(-0.5821688070631101, 0.14518774081414446, '10.05%'),\n",
       "  Text(-0.5555991545346576, -0.22651617929051704, '10.05%'),\n",
       "  Text(-0.32377027935326874, -0.5051463215816842, '9.48%'),\n",
       "  Text(-0.003211134327226664, -0.5999914071187457, '8.49%'),\n",
       "  Text(0.26411250079489024, -0.5387435261085456, '6.19%'),\n",
       "  Text(0.441128987545165, -0.40670040121369155, '5.59%'),\n",
       "  Text(0.5484750160474001, -0.24325944333530836, '4.83%'),\n",
       "  Text(0.5947085430329688, -0.07950942613051214, '4.23%')])"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "plt.pie(cusines_count[:10],labels=city_names[:10],autopct='%1.2f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e627b272",
   "metadata": {},
   "source": [
    "## Findings "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c65821b5",
   "metadata": {},
   "source": [
    "By analyzing the data we come to the below findings:\n",
    "<ul>\n",
    "    <li>Maximum transcation records are from India,USA and Uk respectively</li>\n",
    "    <li>The top restrant among is Cafe Coffee day in India and the most prefered loction is New Delhi with locality in Connaught place, the prefered Cusines are North indian.</li>\n",
    "    <li>The most responses are from Delhi,Gurgaon,Noida.</li>\n",
    "    <li>India accounts the top postion for giving 0 rating, followed by Brazil and USA.</li>\n",
    "\n",
    "</ul>"
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}