Zomato_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cfc831be",
   "metadata": {},
   "source": [
    "# ZOMATO dataset Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "933f8c07",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f0f71505",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('zomato.csv',encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5707301c",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85f21ee9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows =  9551\n"
     ]
    }
   ],
   "source": [
    "row = df.shape[0]\n",
    "print(\"Number of rows = \",row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3d1ad639",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns =  21\n"
     ]
    }
   ],
   "source": [
    "col = df.shape[1]\n",
    "print(\"Number of columns = \",col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "49444efd",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c1c5de63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9551"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "860dd90b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9551 entries, 0 to 9550\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Restaurant ID         9551 non-null   int64  \n",
      " 1   Restaurant Name       9551 non-null   object \n",
      " 2   Country Code          9551 non-null   int64  \n",
      " 3   City                  9551 non-null   object \n",
      " 4   Address               9551 non-null   object \n",
      " 5   Locality              9551 non-null   object \n",
      " 6   Locality Verbose      9551 non-null   object \n",
      " 7   Longitude             9551 non-null   float64\n",
      " 8   Latitude              9551 non-null   float64\n",
      " 9   Cuisines              9542 non-null   object \n",
      " 10  Average Cost for two  9551 non-null   int64  \n",
      " 11  Currency              9551 non-null   object \n",
      " 12  Has Table booking     9551 non-null   object \n",
      " 13  Has Online delivery   9551 non-null   object \n",
      " 14  Is delivering now     9551 non-null   object \n",
      " 15  Switch to order menu  9551 non-null   object \n",
      " 16  Price range           9551 non-null   int64  \n",
      " 17  Aggregate rating      9551 non-null   float64\n",
      " 18  Rating color          9551 non-null   object \n",
      " 19  Rating text           9551 non-null   object \n",
      " 20  Votes                 9551 non-null   int64  \n",
      "dtypes: float64(3), int64(5), object(13)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "894356ab",
   "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>Country Code</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Average Cost for two</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.551000e+03</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.051128e+06</td>\n",
       "      <td>18.365616</td>\n",
       "      <td>64.126574</td>\n",
       "      <td>25.854381</td>\n",
       "      <td>1199.210763</td>\n",
       "      <td>1.804837</td>\n",
       "      <td>2.666370</td>\n",
       "      <td>156.909748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.791521e+06</td>\n",
       "      <td>56.750546</td>\n",
       "      <td>41.467058</td>\n",
       "      <td>11.007935</td>\n",
       "      <td>16121.183073</td>\n",
       "      <td>0.905609</td>\n",
       "      <td>1.516378</td>\n",
       "      <td>430.169145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-157.948486</td>\n",
       "      <td>-41.330428</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.019625e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.081343</td>\n",
       "      <td>28.478713</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.004089e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.191964</td>\n",
       "      <td>28.570469</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.835229e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.282006</td>\n",
       "      <td>28.642758</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>131.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.850065e+07</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>174.832089</td>\n",
       "      <td>55.976980</td>\n",
       "      <td>800000.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>10934.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Restaurant ID  Country Code    Longitude     Latitude  \\\n",
       "count   9.551000e+03   9551.000000  9551.000000  9551.000000   \n",
       "mean    9.051128e+06     18.365616    64.126574    25.854381   \n",
       "std     8.791521e+06     56.750546    41.467058    11.007935   \n",
       "min     5.300000e+01      1.000000  -157.948486   -41.330428   \n",
       "25%     3.019625e+05      1.000000    77.081343    28.478713   \n",
       "50%     6.004089e+06      1.000000    77.191964    28.570469   \n",
       "75%     1.835229e+07      1.000000    77.282006    28.642758   \n",
       "max     1.850065e+07    216.000000   174.832089    55.976980   \n",
       "\n",
       "       Average Cost for two  Price range  Aggregate rating         Votes  \n",
       "count           9551.000000  9551.000000       9551.000000   9551.000000  \n",
       "mean            1199.210763     1.804837          2.666370    156.909748  \n",
       "std            16121.183073     0.905609          1.516378    430.169145  \n",
       "min                0.000000     1.000000          0.000000      0.000000  \n",
       "25%              250.000000     1.000000          2.500000      5.000000  \n",
       "50%              400.000000     2.000000          3.200000     31.000000  \n",
       "75%              700.000000     2.000000          3.700000    131.000000  \n",
       "max           800000.000000     4.000000          4.900000  10934.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dd51cea3",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f5353665",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cuisines']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[features for features in df.columns if df[features].isnull().sum()>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2537348e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(16,9))\n",
    "sns.heatmap(df.isnull(),yticklabels=False,linewidths=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "064e908d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_country = pd.read_excel('Country-Code.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bd905323",
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "10d612bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 2)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0dd25c6d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "final_df = pd.merge(df,df_country,on='Country Code',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "681181ce",
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "61773305",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "India             8652\n",
       "United States      434\n",
       "United Kingdom      80\n",
       "Brazil              60\n",
       "UAE                 60\n",
       "South Africa        60\n",
       "New Zealand         40\n",
       "Turkey              34\n",
       "Australia           24\n",
       "Phillipines         22\n",
       "Indonesia           21\n",
       "Singapore           20\n",
       "Qatar               20\n",
       "Sri Lanka           20\n",
       "Canada               4\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_df = final_df.Country.value_counts()\n",
    "counts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6801d85d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Above details shows that zomato will make maximum business from India "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8ee892e0",
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_name = final_df.Country.value_counts().index\n",
    "country_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0181a5c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "country_val = final_df.Country.value_counts().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a98c2199",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(16,9))\n",
    "sns.countplot(final_df.Country)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e61761ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##PieChart->> for top 3 countries\n",
    "plt.pie(country_val[:3],labels = country_name[:3],autopct='%.2f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "76f1a841",
   "metadata": {},
   "source": [
    "#OBSERVATION\n",
    "1-> Zomato maximum record or transaction are from India \n",
    "2-> After that there is USA and then UK\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5dd81524",
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "33a096db",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_agg = final_df.groupby(['Aggregate rating','Rating color', 'Rating text']).size().reset_index().rename({0:'Rating Count'},axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e3008fb2",
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg"
   ]
  },
  {
   "cell_type": "raw",
   "id": "75fb0c8f",
   "metadata": {},
   "source": [
    "##Observation\n",
    "1-> When Rating is between 1.8 and 2.4 --->Poor\n",
    "2-> When Rating is between 2.5 and 3.0 --->Average\n",
    "3-> When Rating is between 2.5 and 3.4 --->good\n",
    "4-> When Rating is between 4.0 and 4.5 --->Very good\n",
    "5-> When Rating is between 4.9 and 4.5 --->Excellent\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bddf99b0",
   "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>Rating Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rating text</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>3737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Excellent</th>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Good</th>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not rated</th>\n",
       "      <td>2148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poor</th>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very Good</th>\n",
       "      <td>1079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Rating Count\n",
       "Rating text              \n",
       "Average              3737\n",
       "Excellent             301\n",
       "Good                 2100\n",
       "Not rated            2148\n",
       "Poor                  186\n",
       "Very Good            1079"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivot = pd.pivot_table(data=df_agg,index='Rating text',values='Rating Count',aggfunc= np.sum)\n",
    "df_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "34dbe434",
   "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>Rating text</th>\n",
       "      <th>Rating Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Average</td>\n",
       "      <td>3737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Excellent</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Good</td>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Not rated</td>\n",
       "      <td>2148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poor</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Very Good</td>\n",
       "      <td>1079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Rating text  Rating Count\n",
       "0     Average          3737\n",
       "1   Excellent           301\n",
       "2        Good          2100\n",
       "3   Not rated          2148\n",
       "4        Poor           186\n",
       "5   Very Good          1079"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot = df_pivot.reset_index()\n",
    "pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a4c41bf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<BarContainer object of 6 artists>\n"
     ]
    },
    {
     "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": [
    "x= pivot['Rating text']\n",
    "y = pivot['Rating Count']\n",
    "fig,ax = plt.subplots(figsize=(10,8))\n",
    "bar_pathes = ax.bar(x,y,width=0.5)\n",
    "ax.set_xlabel('X-axis',fontdict={'fontsize':'xx-large'})\n",
    "ax.set_ylabel('Y-axis',fontdict={'fontsize':'xx-large'})\n",
    "for patch in bar_pathes:\n",
    "    ht = patch.get_height()\n",
    "    wd = patch.get_x() + patch.get_width()/2\n",
    "    ax.text(wd,ht,s = ht,ha='center',va='bottom')\n",
    "print(bar_pathes)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d0b292f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Aggregate rating', ylabel='Rating Count'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
    "sns.barplot(x='Aggregate rating',y='Rating Count',data=df_agg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "acd3dc0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Dark Green', 'Green', 'Yellow', 'Orange', 'White', 'Red']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1 = list(df['Rating color'].unique())\n",
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "76fe82ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Aggregate rating', ylabel='Rating Count'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
    "sns.barplot(x='Aggregate rating',y='Rating Count',data=df_agg,hue='Rating color',palette=['blue','Red','Orange', 'Yellow','Green', 'Green'],)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "18d6a8c4",
   "metadata": {},
   "source": [
    "##Observation\n",
    "1-> Not Rated count is very high \n",
    "2-> Maximum number of ratings are from 2.5 to 4.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fe5530ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rating color', ylabel='count'>"
      ]
     },
     "execution_count": 33,
     "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(x='Rating color',data = df_agg,palette =['blue','Red','Orange', 'Yellow','Green', 'Green'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e8adde24",
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ae98c4e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The countries having 0 Rating)\n",
    "list1 = list(final_df[final_df['Aggregate rating'] == 0]['Country'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "75047018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Brazil', 'United States', 'India', 'United Kingdom']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1870f660",
   "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>Country</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>India</td>\n",
       "      <td>2139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Aggregate rating         Country     0\n",
       "2               0.0  United Kingdom     1\n",
       "3               0.0   United States     3\n",
       "0               0.0          Brazil     5\n",
       "1               0.0           India  2139"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[final_df['Aggregate rating'] == 0].groupby(['Aggregate rating','Country']).size().reset_index().sort_values(by=0)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "1fb39e19",
   "metadata": {},
   "source": [
    "#Observation\n",
    "1-> Maximum number of rating are from India\n",
    "2-> Minimum number of rating are from UK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c864cbce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Find out which currency is used by which country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "37fd4db7",
   "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>Currency</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brazilian Real(R$)</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dollar($)</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dollar($)</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dollar($)</td>\n",
       "      <td>Singapore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dollar($)</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Emirati Diram(AED)</td>\n",
       "      <td>UAE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Indonesian Rupiah(IDR)</td>\n",
       "      <td>Indonesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NewZealand($)</td>\n",
       "      <td>New Zealand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pounds(£)</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Qatari Rial(QR)</td>\n",
       "      <td>Qatar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Rand(R)</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sri Lankan Rupee(LKR)</td>\n",
       "      <td>Sri Lanka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Currency         Country\n",
       "0         Botswana Pula(P)     Phillipines\n",
       "1       Brazilian Real(R$)          Brazil\n",
       "2                Dollar($)       Australia\n",
       "3                Dollar($)          Canada\n",
       "4                Dollar($)       Singapore\n",
       "5                Dollar($)   United States\n",
       "6       Emirati Diram(AED)             UAE\n",
       "7       Indian Rupees(Rs.)           India\n",
       "8   Indonesian Rupiah(IDR)       Indonesia\n",
       "9            NewZealand($)     New Zealand\n",
       "10              Pounds(£)  United Kingdom\n",
       "11         Qatari Rial(QR)           Qatar\n",
       "12                 Rand(R)    South Africa\n",
       "13   Sri Lankan Rupee(LKR)       Sri Lanka\n",
       "14        Turkish Lira(TL)          Turkey"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.groupby(['Currency','Country']).size().reset_index()[['Currency','Country']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "25698d43",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Which country have online delivery option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ce195366",
   "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>Has Online delivery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UAE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Has Online delivery\n",
       "0                 UAE\n",
       "1               India"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(final_df[final_df['Has Online delivery'] == 'Yes']['Country'].unique(),columns=['Has Online delivery'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e127aa8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "India    2423\n",
       "UAE        28\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[final_df['Has Online delivery'] == 'Yes']['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ad541c42",
   "metadata": {},
   "source": [
    "#Observation\n",
    "1->Online delivery is in India and UAE \n",
    "2->India has maximun online delivery option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "71d88b80",
   "metadata": {},
   "outputs": [],
   "source": [
    " #Create pie chart for cities distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7c2e5bbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(final_df.City.value_counts().values[:5],labels = final_df.City.value_counts().index[:5],autopct='%.2f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e66e6ba6",
   "metadata": {},
   "source": [
    "#Observation \n",
    "1-> MAximum number of sale is from New Delhi City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7c86188",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}