zomato EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da6693a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6e08881c",
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9546</th>\n",
       "      <td>5915730</td>\n",
       "      <td>NamlÛ± Gurme</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Kemankeô Karamustafa Paôa Mahallesi, RÛ±htÛ±...</td>\n",
       "      <td>Karakí_y</td>\n",
       "      <td>Karakí_y, ÛÁstanbul</td>\n",
       "      <td>28.977392</td>\n",
       "      <td>41.022793</td>\n",
       "      <td>Turkish</td>\n",
       "      <td>...</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9547</th>\n",
       "      <td>5908749</td>\n",
       "      <td>Ceviz AÛôacÛ±</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Koôuyolu Mahallesi, Muhittin íìstí_ndaÛô Cadd...</td>\n",
       "      <td>Koôuyolu</td>\n",
       "      <td>Koôuyolu, ÛÁstanbul</td>\n",
       "      <td>29.041297</td>\n",
       "      <td>41.009847</td>\n",
       "      <td>World Cuisine, Patisserie, Cafe</td>\n",
       "      <td>...</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>1034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9548</th>\n",
       "      <td>5915807</td>\n",
       "      <td>Huqqa</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...</td>\n",
       "      <td>Kuruí_eôme</td>\n",
       "      <td>Kuruí_eôme, ÛÁstanbul</td>\n",
       "      <td>29.034640</td>\n",
       "      <td>41.055817</td>\n",
       "      <td>Italian, World Cuisine</td>\n",
       "      <td>...</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9549</th>\n",
       "      <td>5916112</td>\n",
       "      <td>Aôôk Kahve</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...</td>\n",
       "      <td>Kuruí_eôme</td>\n",
       "      <td>Kuruí_eôme, ÛÁstanbul</td>\n",
       "      <td>29.036019</td>\n",
       "      <td>41.057979</td>\n",
       "      <td>Restaurant Cafe</td>\n",
       "      <td>...</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9550</th>\n",
       "      <td>5927402</td>\n",
       "      <td>Walter's Coffee Roastery</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>CafeaÛôa Mahallesi, BademaltÛ± Sokak, No 21/B,...</td>\n",
       "      <td>Moda</td>\n",
       "      <td>Moda, ÛÁstanbul</td>\n",
       "      <td>29.026016</td>\n",
       "      <td>40.984776</td>\n",
       "      <td>Cafe</td>\n",
       "      <td>...</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9551 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",
       "9546        5915730              NamlÛ± Gurme           208         ÛÁstanbul   \n",
       "9547        5908749             Ceviz AÛôacÛ±           208         ÛÁstanbul   \n",
       "9548        5915807                     Huqqa           208         ÛÁstanbul   \n",
       "9549        5916112              Aôôk Kahve           208         ÛÁstanbul   \n",
       "9550        5927402  Walter's Coffee Roastery           208         ÛÁstanbul   \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",
       "9546  Kemankeô Karamustafa Paôa Mahallesi, RÛ±htÛ±...   \n",
       "9547  Koôuyolu Mahallesi, Muhittin íìstí_ndaÛô Cadd...   \n",
       "9548  Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...   \n",
       "9549  Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...   \n",
       "9550  CafeaÛôa Mahallesi, BademaltÛ± Sokak, No 21/B,...   \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",
       "9546                                    Karakí_y   \n",
       "9547                                   Koôuyolu   \n",
       "9548                                 Kuruí_eôme   \n",
       "9549                                 Kuruí_eôme   \n",
       "9550                                        Moda   \n",
       "\n",
       "                                       Locality Verbose   Longitude  \\\n",
       "0     Century City Mall, Poblacion, Makati City, Mak...  121.027535   \n",
       "1     Little Tokyo, Legaspi Village, Makati City, Ma...  121.014101   \n",
       "2     Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...  121.056831   \n",
       "3     SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.056475   \n",
       "4     SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.057508   \n",
       "...                                                 ...         ...   \n",
       "9546                                Karakí_y, ÛÁstanbul   28.977392   \n",
       "9547                               Koôuyolu, ÛÁstanbul   29.041297   \n",
       "9548                             Kuruí_eôme, ÛÁstanbul   29.034640   \n",
       "9549                             Kuruí_eôme, ÛÁstanbul   29.036019   \n",
       "9550                                    Moda, ÛÁstanbul   29.026016   \n",
       "\n",
       "       Latitude                          Cuisines  ...          Currency  \\\n",
       "0     14.565443        French, Japanese, Desserts  ...  Botswana Pula(P)   \n",
       "1     14.553708                          Japanese  ...  Botswana Pula(P)   \n",
       "2     14.581404  Seafood, Asian, Filipino, Indian  ...  Botswana Pula(P)   \n",
       "3     14.585318                   Japanese, Sushi  ...  Botswana Pula(P)   \n",
       "4     14.584450                  Japanese, Korean  ...  Botswana Pula(P)   \n",
       "...         ...                               ...  ...               ...   \n",
       "9546  41.022793                           Turkish  ...  Turkish Lira(TL)   \n",
       "9547  41.009847   World Cuisine, Patisserie, Cafe  ...  Turkish Lira(TL)   \n",
       "9548  41.055817            Italian, World Cuisine  ...  Turkish Lira(TL)   \n",
       "9549  41.057979                   Restaurant Cafe  ...  Turkish Lira(TL)   \n",
       "9550  40.984776                              Cafe  ...  Turkish Lira(TL)   \n",
       "\n",
       "     Has Table booking Has Online delivery Is delivering now  \\\n",
       "0                  Yes                  No                No   \n",
       "1                  Yes                  No                No   \n",
       "2                  Yes                  No                No   \n",
       "3                   No                  No                No   \n",
       "4                  Yes                  No                No   \n",
       "...                ...                 ...               ...   \n",
       "9546                No                  No                No   \n",
       "9547                No                  No                No   \n",
       "9548                No                  No                No   \n",
       "9549                No                  No                No   \n",
       "9550                No                  No                No   \n",
       "\n",
       "     Switch to order menu Price range  Aggregate rating  Rating color  \\\n",
       "0                      No           3               4.8    Dark Green   \n",
       "1                      No           3               4.5    Dark Green   \n",
       "2                      No           4               4.4         Green   \n",
       "3                      No           4               4.9    Dark Green   \n",
       "4                      No           4               4.8    Dark Green   \n",
       "...                   ...         ...               ...           ...   \n",
       "9546                   No           3               4.1         Green   \n",
       "9547                   No           3               4.2         Green   \n",
       "9548                   No           4               3.7        Yellow   \n",
       "9549                   No           4               4.0         Green   \n",
       "9550                   No           2               4.0         Green   \n",
       "\n",
       "     Rating text Votes  \n",
       "0      Excellent   314  \n",
       "1      Excellent   591  \n",
       "2      Very Good   270  \n",
       "3      Excellent   365  \n",
       "4      Excellent   229  \n",
       "...          ...   ...  \n",
       "9546   Very Good   788  \n",
       "9547   Very Good  1034  \n",
       "9548        Good   661  \n",
       "9549   Very Good   901  \n",
       "9550   Very Good   591  \n",
       "\n",
       "[9551 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('zomato.csv',encoding='latin-1')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa811d3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": "183f7493",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7bf4daee",
   "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": 6,
   "id": "3bd02f55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9551, 21)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d06bd8d",
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f31a7f6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cuisines']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[features for features in df.columns if df[features].isnull().sum()>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2724baf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "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.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "92cea595",
   "metadata": {
    "scrolled": true
   },
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_excel('Country-Code.xlsx')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "54c1a089",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 2)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3ecbd5bb",
   "metadata": {
    "scrolled": true
   },
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dffinal = pd.merge(df,df1,on ='Country Code',how='left')\n",
    "dffinal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4c31b29b",
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dffinal.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "344f39ce",
   "metadata": {
    "scrolled": true
   },
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dffinal.Country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea1b56bf",
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_names = dffinal.Country.value_counts().index\n",
    "country_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "77c5585d",
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_values = dffinal.Country.value_counts().values\n",
    "country_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3ea8d27a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x21ee6956c40>,\n",
       "  <matplotlib.patches.Wedge at 0x21ee696b160>,\n",
       "  <matplotlib.patches.Wedge at 0x21ee696b640>],\n",
       " [Text(-1.0829742700952103, 0.19278674827836725, 'India'),\n",
       "  Text(1.077281715838356, -0.22240527134123297, 'United States'),\n",
       "  Text(1.0995865153823035, -0.03015783794312073, 'United Kingdom')])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(country_values[:3],labels=country_names[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bc518656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x21ee69bb580>,\n",
       "  <matplotlib.patches.Wedge at 0x21ee69bbca0>,\n",
       "  <matplotlib.patches.Wedge at 0x21ee69c9400>],\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, '94.39%'),\n",
       "  Text(0.5876082086391032, -0.12131196618612707, '4.73%'),\n",
       "  Text(0.5997744629358018, -0.01644972978715676, '0.87%')])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(country_values[:3],labels=country_names[:3],autopct='%1.2f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5613875d",
   "metadata": {},
   "source": [
    "observations : The maximum transactions from India followed by USA and UK."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d3fc1861",
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dffinal.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "28af848d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = dffinal.groupby(['Aggregate rating', 'Rating color', 'Rating text']).size().reset_index().rename(columns={0:'Rating Count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f1dd30e5",
   "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",
       "  </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"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e5d6c80a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Aggregate rating', ylabel='Rating Count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize']=(12,6) \n",
    "sns.barplot(x='Aggregate rating',y='Rating Count',data=ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4434956d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Aggregate rating', ylabel='Rating Count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zZ09WrVoFQKdOnWr6lZWV8d5779XMdtclpcTEiRMZPXp0Cx9Ry3LpiCRJkprksMMO49133+WWW24BoLq6mgsvvJDPf/7zfOhDH9qg7/Lly9l5553p2LEjTzzxBAsXLmww9oc//GG23XZb/vjHPwJw++2317w2evRorr/+etasWQPASy+9xDvvvNOSh9YiLLQlSZLUJBHBvffey1133cXee+/NPvvsQ+fOnfnOd77zgb5jx46lsrKSwYMHM3nyZPbbb79G4990002MGzeOYcOGkVKiW7duAJx11ln06dOHgQMHUl5eztlnn83atWtb/PiaKxqalm/PBg8enCorKxl0UeEvrNnfP7WVM5IkSWo58+bNo3fv3q2dRq5WrlzJNttsA8CVV17J66+/zo9//ONWy6euzzwiZqeUBtfV3zXakiRJapN+/etf893vfpe1a9ey5557MmnSpNZOqSQW2pIkSWqTTjrpJE466aTWTqPJXKMtSZIk5cBCW5IkScqBhbYkSZKUAwttSZIkKQcW2pIkSSpZSomRI0fy8MMP17TdeeedHHnkkR/oO3XqVI466igAJk2axPnnn7/J8mxNXnVEkiRpM/D7gz7RovE+Me33Db4eEdxwww2ceOKJHHLIIVRXV/P1r3+dRx55pEXzaM+c0ZYkSVKTlJeXc/TRR/O9732Pyy+/nM9+9rNcccUVDBkyhAEDBnD//fc3uP/ChQs57LDD6NevH4cddhiLFi2iurqavfbai5QSy5Yto0OHDkybNg2Aj3/847z88sub4tBahIW2JEmSmuyb3/wmt912Gw8//DCrVq3i0EMPZdasWTzxxBNcdNFFvPPOO/Xue/7553PqqacyZ84cxo4dyxe/+EXKysrYZ599eP7553nyyScZNGgQf/jDH1i9ejWLFy/m3/7t3zbh0TWPS0ckSZLUZF27duWkk05im2224c477+TBBx/kqquuAmDVqlUsWrSo3n1nzpzJPffcA8DnPvc5Lr74YqAwcz1t2jReffVVLrnkEn72s5/xiU98giFDhuR/QC3IGW1JkiQ1S4cOHejQoQMpJaZMmUJVVRVVVVUsWrSI3r17Z44TEUCh0P7DH/7AU089xac+9SmWLVvG1KlTOeigg/I6hFxYaEuSJKlFjB49mokTJ5JSAuDpp59usP/w4cO5/fbbAZg8eTIjR44E4IADDmDGjBl06NCBzp07U1FRwU9/+lM+/vGP53sALcxCW5IkSS1iwoQJrFmzhn79+lFeXs6ECRMa7H/NNdfwy1/+kn79+nHrrbfy4x//GIBOnTqxxx57cOCBBwKFGe4VK1aw//77534MLSnW/8WxuRk8eHCqrKxk0EW3ADD7+6e2ckaSJEktZ968eSUty1Dz1fWZR8TslNLguvo7oy1JkiTlwEJbkiRJyoGFtiRJkpQDC21JkiQpBxbakiRJUg4stCVJkqQcWGhLkiSpyd544w3GjBnDXnvtxaBBgxg2bBj33ntva6fVJmzV2glIkiSp+a698MEWjXf+D45utE9KieOOO47TTjuN2267DYCFCxfywAMPbNBv7dq1bLXVlld2OqMtSZKkJvnd737H1ltvzRe+8IWatj333JMLLriASZMmceKJJ3L00UczatQo3nnnHc444wyGDBnCgAEDuP/++wGorq7moosuYsiQIfTr14+f/vSnAEydOpWDDz6YE044gf3224+xY8fS3m60uOX9aSFJkqQWMXfuXAYOHFjv6zNnzmTOnDl0796dSy+9lEMPPZRf/OIXLFu2jKFDh3L44YczefJkunXrxqxZs1i9ejUjRoxg1KhRADz99NPMnTuXj3zkI4wYMYLp06czcuTITXV4zeaMtiRJklrEeeedR//+/RkyZAgARxxxBN27dwfg0Ucf5corr6SiooKDDz6YVatWsWjRIh599FFuueUWKioqOOCAA3jrrbeYP38+AEOHDqVHjx506NCBiooKFixY0FqH1iTOaEuSJKlJ+vbty5QpU2q2r7vuOt58800GDx4MQNeuXWteSykxZcoU9t133w1ipJSYOHEio0eP3qB96tSpdOrUqWa7rKyMtWvX5nEYuXFGW5IkSU1y6KGHsmrVKq6//vqatnfffbfOvqNHj2bixIk166yffvrpmvbrr7+eNWvWAPDSSy/xzjvv5Jz5pmGhLUmSpCaJCO677z5+//vf06tXL4YOHcppp53G9773vQ/0nTBhAmvWrKFfv36Ul5czYcIEAM466yz69OnDwIEDKS8v5+yzz253M9f1ifZ29mZWgwcPTpWVlQy66BYAZn//1FbOSJIkqeXMmzeP3r17t3YaW5S6PvOImJ1SGlxXf2e0JUmSpBxYaEuSJEk5sNCWJEmScmChLUmSJOXAQluSJEnKgYW2JEmSlAMLbUmSJDVJWVkZFRUV9O3bl/79+/PDH/6QdevWlRRjwYIFlJeXN9pv/vz5HHXUUXzsYx9j0KBBHHLIIUybNq2pqW8S3oJdkiRpM3DFZ09o0Xhf/9Xdjfbp0qULVVVVACxZsoQxY8awfPlyLr/88kzvUV1dnanfqlWr+Pd//3euuuoqjjnmGACee+45KisrOeiggzbou3btWrbaqm2UuM5oS5Ikqdl23nlnbrzxRq699lpSSixYsICPf/zjDBw4kIEDBzJjxgwApk6dyiGHHMKYMWPYf//9N4jxyiuvMGDAAGbNmrVB++TJkxk2bFhNkQ1QXl7O5z//eQAuu+wyxo0bx6hRozj11FNZunQpn/nMZxgyZAhDhgxh+vTpALzzzjucccYZDBkyhAEDBnD//fcDMGnSJD796U9z5JFHsvfee3PxxRe3yGfSNsp9SZIktXt77bUX69atY8mSJey888489thjdO7cmfnz53PKKadQWVkJwFNPPcVzzz1Hr169WLBgAQAvvvgiJ598Mr/85S+pqKjYIO7cuXMZOHBgg+89e/ZsnnzySbp06cKYMWP4yle+wsiRI1m0aBGjR49m3rx5XHHFFRx66KH84he/YNmyZQwdOpTDDz8cgKqqKp5++mk6derEvvvuywUXXMAee+zRrM/DQluSJEktJqUEwJo1azj//POpqqqirKyMl156qabP0KFD6dWrV8320qVLOfbYY5kyZQp9+/Zt9D2OP/545s+fzz777MM999wDwDHHHEOXLl0A+O1vf8vzzz9f0//tt99mxYoVPProozzwwANcddVVQGFJyqJFiwA47LDD6NatGwB9+vRh4cKFFtqSJElqG1555RXKysrYeeedufzyy9lll1145plnWLduHZ07d67p17Vr1w3269atG3vssQfTp0+vs9Du27fvBic+3nvvvVRWVvLVr361zpjr1q1j5syZNYX3eiklpkyZwr777rtB+5/+9Cc6depUs11WVsbatWtLPPoPym2NdkT8IiKWRMRztdoui4jXIqKq+PhUrdcuiYiXI+LFiBhdq31QRDxbfO2aiIi8cpYkSVLTLF26lC984Qucf/75RATLly9nt912o0OHDtx6660Nnvi49dZbc99993HLLbdw2223feD1MWPGMH36dB544IGatnfffbfeeKNGjeLaa6+t2V5/wubo0aOZOHFizaz7008/XephliTPkyEnAUfW0f6jlFJF8fEbgIjoA5wM9C3u85OIKCv2vx4YB+xdfNQVU5IkSZvYe++9V3N5v8MPP5xRo0bxzW9+E4Bzzz2Xm2++mQMPPJCXXnrpA7PYG+vatSsPPfQQP/rRj2pOUlyvS5cuPPTQQ9xwww3stddeDBs2jG9/+9v813/9V52xrrnmGiorK+nXrx99+vThhhtuAGDChAmsWbOGfv36UV5ezoQJE1rgU6hfrK/ocwke0RN4KKVUXty+DFiZUrpqo36XAKSUvlvc/j/gMmAB8ERKab9i+ynAwSmlsxt778GDB6fKykoGXXQLALO/f2qLHJMkSVJbMG/ePHr37t3aaWxR6vrMI2J2SmlwXf1b4/J+50fEnOLSkg8X23YH/lqrz+Ji2+7F5xu3S5IkSW3api60rwc+BlQArwM/KLbXte46NdBep4gYFxGVEVG5dOnSZqYqSZIkNd0mLbRTSm+klKpTSuuAnwFDiy8tBmpfP6UH8Ldie4862uuLf2NKaXBKafBOO+3UsslLkiRJJdikhXZE7FZr83hg/RVJHgBOjohOEdGLwkmPT6WUXgdWRMSBxauNnApsuDpekiRpC5XnuXbaUFM+69yuox0R/wscDOwYEYuBbwIHR0QFheUfC4CzAVJKcyPiTuB5YC1wXkpp/TVgzqFwBZMuwMPFhyRJ0hatc+fOvPXWW+ywww549eN8pZR46623NrgWeBa5FdoppVPqaL6pgf5XAFfU0V4JlLdgapIkSe1ejx49WLx4MZ6Xtml07tyZHj16NN6xFu8MKUmS1A517Nhxg9uYq+1pjcv7SZIkSZs9C21JkiQpBxbakiRJUg4stCVJkqQcWGhLkiRJObDQliRJknJgoS1JkiTlwEJbkiRJyoGFtiRJkpQDC21JkiQpBxbakiRJUg4stCVJkqQcWGhLkiRJObDQliRJknJgoS1JkiTlwEJbkiRJyoGFtiRJkpSDRgvtiPheljZJkiRJ/5JlRvuIOto+2dKJSJIkSZuTrep7ISLOAc4F9oqIObVe2haYnndikiRJUntWb6EN3AY8DHwXGF+rfUVK6R+5ZiVJkiS1c/UW2iml5cBy4JSIKAN2KfbfJiK2SSkt2kQ5SpIkSe1OQzPaAETE+cBlwBvAumJzAvrll5YkSZLUvjVaaANfBvZNKb2Vcy6SJEnSZiPLVUf+SmEJiSRJkqSMssxovwJMjYhfA6vXN6aUfphbVpIkSVI7l6XQXlR8bF18SJIkSWpEo4V2SunyTZGIJEmStDnJctWRJyhcZWQDKaVDc8lIkiRJ2gxkWTry1VrPOwOfAdbmk44kSZK0eciydGT2Rk3TI+L3OeUjSZIkbRayLB3pXmuzAzAI2DW3jCRJkqTNQJalI7MprNEOCktGXgXOzDMpSZIkqb3LsnSk16ZIRJIkSdqcZFk60hE4Bzio2DQV+GlKaU2OeUmSJEntWpalI9cDHYGfFLc/V2w7K6+kJEmSpPYuS6E9JKXUv9b27yLimbwSkiRJkjYHHTL0qY6Ij63fiIi9gOr8UpIkSZLavywz2hcBT0TEKxSuPLIncHquWUmSJEntXJarjjweEXsD+1IotF9IKa3OPTNJkiSpHau30I6IzwKRUrq1WFjPKbb/R0S8k1K6bVMlKUmSJLU3Da3RvhC4r472O4qvSZIkSapHQ4V2WUppxcaNKaW3KVzuT5IkSVI9Giq0O0ZE140bI2JbYOv8UpIkSZLav4YK7ZuAuyOi5/qG4vPbi69JkiRJqke9J0OmlK6KiJXA7yNiGyAB7wBXppSu31QJSpIkSe1Rg5f3SyndANxQLLSjrjXbkiRJkj4oyw1rSCmtzDsRSZIkaXOS5RbskiRJkkpkoS1JkiTloNGlIxHx6TqalwPPppSWtHxKkiRJUvuXZY32mcAw4Ini9sHAH4F9IuJbKaVbc8pNkiRJareyFNrrgN4ppTcAImIX4HrgAGAaYKEtSZIkbSTLGu2e64vsoiXAPimlfwBr8klLkiRJat+yzGj/ISIeAu4qbn8GmFa8PfuyvBKTJEmS2rMshfZ5FIrrEUAAtwBTUkoJOCTH3CRJkqR2q9FCu1hQ3118SJIkScqg0TXaEfHpiJgfEcsj4u2IWBERb2+K5CRJkqT2KsvSkf8Bjk4pzcs7GUmSJGlzkeWqI29YZEuSJEmlyTKjXRkRdwD3AavXN6aU7skrKUmSJKm9y1Jobwe8C4yq1ZYAC21JkiSpHlmuOnL6pkhEkiRJ2pzUu0Y7Ii4ufp0YEdds/GgscET8IiKWRMRztdq6R8RjxauYPBYRH6712iUR8XJEvBgRo2u1D4qIZ4uvXRMR0fTDlSRJkjaNhk6GXH8CZCUwu45HYyYBR27UNh54PKW0N/B4cZuI6AOcDPQt7vOTiCgr7nM9MA7Yu/jYOKYkSZLU5tS7dCSl9GDx6bsppbtqvxYRJzYWOKU0LSJ6btR8LHBw8fnNwFTga8X221NKq4FXI+JlYGhELAC2SynNLL7vLcBxwMONvb8kSZLUmrJc3u+SjG1Z7JJSeh2g+HXnYvvuwF9r9VtcbNu9+Hzj9jpFxLiIqIyIyqVLlzYxRUmSJKn56p3RjohPAp8Cdt9oTfZ2wNoWzqOuddepgfY6pZRuBG4EGDx4cL39JEmSpLw1dNWRv1FYn30MG67JXgF8pYnv90ZE7JZSej0idgOWFNsXA3vU6tej+P6Li883bpckSZLatIbWaD8DPBMRt6WU1rTQ+z0AnAZcWfx6f6322yLih8BHKJz0+FRKqToiVkTEgcCfgFOBiS2UiyRJkpSbLDes6RkR3wX6AJ3XN6aU9mpop4j4XwonPu4YEYuBb1IosO+MiDOBRcCJxVhzI+JO4HkKy1LOSylVF0OdQ+EKJl0onATpiZCSJElq87IU2r+kUCT/CDgEOJ26105vIKV0Sj0vHVZP/yuAK+porwTKM+QpSZIktRlZrjrSJaX0OBAppYUppcuAQ/NNS5IkSWrfssxor4qIDsD8iDgfeI1/XZZPkiRJUh2yzGh/GfgQ8EVgEPA5CiclSpIkSapHozPaKaVZxacrgdMjYivgJApXAZEkSZJUh3pntCNiu4i4JCKujYhRUXA+8DLw/zZdipIkSVL709CM9q3AP4GZwFnARcDWwHEppar8U5MkSZLar4YK7b1SSvsDRMTPgTeBj6aUVmySzCRJkqR2rKGTIWvuBlm8ecyrFtmSJElSNg3NaPePiLeLzwPoUtwOIKWUtss9O0mSJKmdqrfQTimVbcpEJEmSpM1JlutoS5IkSSqRhbYkSZKUAwttSZIkKQcW2pIkSVIOLLQlSZKkHFhoS5IkSTmw0JYkSZJyYKEtSZIk5cBCW5IkScqBhbYkSZKUAwttSZIkKQcW2pIkSVIOLLQlSZKkHFhoS5IkSTmw0JYkSZJyYKEtSZIk5cBCW5IkScqBhbYkSZKUAwttSZIkKQcW2pIkSVIOLLQlSZKkHFhoS5IkSTmw0JYkSZJyYKEtSZIk5cBCW5IkScqBhbYkSZKUAwttSZIkKQcW2pIkSVIOLLQlSZKkHFhoS5IkSTmw0JYkSZJyYKEtSZIk5cBCW5IkScqBhbYkSZKUAwttSZIkKQcW2pIkSVIOLLQlSZKkHFhoS5IkSTmw0JYkSZJyYKEtSZIk5cBCW5IkScqBhbYkSZKUAwttSZIkKQcW2pIkSVIOLLQlSZKkHFhoS5IkSTmw0JYkSZJyYKEtSZIk5cBCW5IkScqBhbYkSZKUAwttSZIkKQcW2pIkSVIOLLQlSZKkHLRKoR0RCyLi2YioiojKYlv3iHgsIuYXv364Vv9LIuLliHgxIka3Rs6S1FaMmDiCERNHtHYakqRGbNWK731ISunNWtvjgcdTSldGxPji9tciog9wMtAX+Ajw24jYJ6VUvelTlqTS1C6Ip18wvRUzkSRtam1p6cixwM3F5zcDx9Vqvz2ltDql9CrwMjB006cnSZIkZddahXYCHo2I2RExrti2S0rpdYDi152L7bsDf6217+Ji2wdExLiIqIyIyqVLl+aUuiRJktS41lo6MiKl9LeI2Bl4LCJeaKBv1NGW6uqYUroRuBFg8ODBdfaRJOXL5TKSVNAqM9oppb8Vvy4B7qWwFOSNiNgNoPh1SbH7YmCPWrv3AP626bKVJEmSSrfJZ7QjoivQIaW0ovh8FPAt4AHgNODK4tf7i7s8ANwWET+kcDLk3sBTmzpvSdpctacZ6N8f9Ima55+Y9vtWzESSGtcaS0d2Ae6NiPXvf1tK6ZGImAXcGRFnAouAEwFSSnMj4k7geWAtcJ5XHJEkSVJbt8kL7ZTSK0D/OtrfAg6rZ58rgCtyTk2S2tXsriSpbWtLl/eTJEmSNhutecMaSZJalWu+JeXJGW1JkiQpBxbakiRJUg4stCVJkqQcWGhLktSCfn/QJzZY+y1py2WhLUmSJOXAQluSJEnKgYW2JEmSlAOvoy1JUhvmtb6l9ssZbUmSJCkHFtqSJElSDiy0JbVrIyaOYMTEEa2dhiRJH2ChLUmSJOXAQluSJEnKgYW2JEmSlAMLbUmSJCkHFtqSJElSDrxhjSRJWxBvgCNtOs5oS5IkSTmw0JYkSZJy4NIRSZtM7RvLTL9geitmIklS/pzRliRJknJgoS1JkiTlwEJbkiRJyoGFtiRJkpQDC21JkiQpBxbakiSpzbr2wge59sIHWzsNqUm8vJ8kSWoRtQvi839wdCtmIrUNzmhLkiRJObDQliRJknLg0hFJ9fJOjpIkNZ0z2pIkSVIOLLQlSZKkHFhoS5IkSTlwjbYkSdpieAlCbUrOaEuSJEk5sNCWJEmScmChLUmSJOXAQlvajIyYOGKDa19LkqTWY6EtSZIk5cBCW5IkScqBl/eTJElqIi8XqIY4oy1JkiTlwEJbkiRJyoGFtiRJkpQD12hLkiS1Ea753rw4oy1JkiTlwEJbkiRJyoGFtiRJkpQDC21JkiQpB54MKbWSERNH1DyffsH0VsxEkiTlwRltSZIkKQfOaEuSJG0BrvjsCTXPv/6ru1sxky2HhbbUgEXf2r/m+Ue/8WwrZiJJktobl45IkiRJObDQliRJUpNc8dkTNliSog1ZaEuSJEk5sNCWJEmSMiplFt9CW5IkScqBVx3RZsWrhEiSpLbCQluSJEltwuZ2rW+XjkiSJEk5aDcz2hFxJPBjoAz4eUrpylZOSS3ApR6SJCkvrT1D3i5mtCOiDLgO+CTQBzglIvq0blaSJElS/dpFoQ0MBV5OKb2SUnofuB04tpVz2iIt+tb+G8xCS5IkqW7tZenI7sBfa20vBg5opVzaDZdlSJIktZ5IKbV2Do2KiBOB0Smls4rbnwOGppQu2KjfOGBccXNf4MUM4XcE3mzBdNt6vDxitvV4ecTc0uLlEbOtx8sjZluPl0fMLS1eHjHberw8Ym5p8fKI2dbj5RGzteLtmVLaqa4X2suM9mJgj1rbPYC/bdwppXQjcGMpgSOiMqU0uHnptZ94ecRs6/HyiLmlxcsjZluPl0fMth4vj5hbWrw8Yrb1eHnE3NLi5RGzrcfLI2ZbjNde1mjPAvaOiF4RsTVwMvBAK+ckSZIk1atdzGinlNZGxPnA/1G4vN8vUkpzWzktSZIkqV7totAGSCn9BvhNDqFLWmqyGcTLI2Zbj5dHzC0tXh4x23q8PGK29Xh5xNzS4uURs63HyyPmlhYvj5htPV4eMdtcvHZxMqQkSZLU3rSXNdqSJElSu7JFFNoRcWREvBgRL0fE+Dpej4i4pvj6nIgY2Ei8X0TEkoh4rp7Xu0XEgxHxTETMjYjTM+TYWMwPR8S9xfyeiojyBmLtERFPRMS84vt/qQWOOUvM/SJiZkSsjoivtkC8scXc5kTEjIjo38x4xxZjVUVEZUSMbG6OtfoOiYjqiDihgT5Zcjw4IpYXc6yKiG80N79izKpin98395gj4qJa+T1XPO7uzYiXebxkjFfKWOlc7LP+vS+vo0+pYyVLzFLGSpZ4pYyVLPFKHSuNxqzVN8tYyZJjKWMlU35Zx0rG/DKPkxJiljJWssTLPFZq7VMWEU9HxEN1vFbSWMkQL/M4KSFm5rGSMV5JY6WxeLX6NDpOMuaXeZyUkmPWsZIxx5LGSoZ4JddgGWKWPFZqpJQ26weFkyf/AuwFbA08A/TZqM+ngIeBAA4E/tRIzIOAgcBz9bx+KfC94vOdgH8AWzcz5veBbxaf7wc83kCs3YCBxefbAi+1wDFnibkzMAS4AvhqC8QbDny4+PyTDeWYMd42/Gu5VD/ghebmWOtn7HcUziE4oZk5Hgw8lPFnO0u87YHngY+u/x61xDHX6n808Ltm5ph5vGSMV8pYCWCb4vOOwJ+AA5s5VrLELGWsZIlXyljJEq/UsdJozBLHSpYcSxkrWeJlHitZjzfrOCkhx1LGSpZ4mcdKrX3+E7itrs++1LGSIV7mcVJCzMxjJWO8ksZKY/FKGScZ88s8TkqImXmsZD3mWv0aHSsZ8iu5BssQs+Sxsv6xJcxoZ7l9+7HALangj8D2EbFbfQFTStMofOPq7QJsGxFBYRD+A1jbUJIZYvYBHi/2fQHoGRG71BPr9ZTSn4vPVwDzKNxds7ZSj7nRmCmlJSmlWcCaBo6jlHgzUkr/LG7+kcL105sTb2UqjhKgK4XvU7NyLLoAmAIsaaF4mWSMNwa4J6W0qNivpXM8BfjfZsbLPF4yxitlrKSU0sriZsfiY+Ofi1LHSqMxSxwrWeKVMlayxCt1rGT5HCH7WMkaL5OM8TKPlSbk1+A4KSFmKWMlS7zMYwUgInoA/w78vJ4uJY2VxuKVMk5KiJl5rGSMV9JYyfAZQsZxUkK8kmSIWdLvlRJzbHSsZIhXcg2WIWZJY6W2LaHQruv27Rv/Ys7SpxTXAr0p3FTnWeBLKaV1zYgHhZn4TwNExFBgTxr5B6LYtycwgMJsRm1NPuYGYjZJxnhnUpgpaVa8iDg+Il4Afg2c0dwcI2J34HjghqyxGssRGFb8L6+HI6JvM+PtA3w4IqZGxOyIOLWFciQiPgQcSeEXQnPiNWm8NBCvpLFS/O/CKgq/1B5LKTV7rGSIWZIS4zU6VrLEK3WsNBaz1LGS8Zgzj5UM8UoaK1m/J6WMkwwxSxorGeKV+nvlauBioL73LHWsNBavKUqJmeX3SqPxShwrDcZrwu+URvOj9N8pjcUs9fdKlhxLGSuNxWvK75TGYjapBoMto9COOto2/oszS59SjAaqgI8AFcC1EbFdM+IBXEnhB7uKwl+7T9P4X2jbUPiB/XJK6e2NX65jl0aPuZGYJcsSLyIOofAP4teaGy+ldG9KaT/gOOC/WyDHq4GvpZSqs8TKEO/PFG7l2h+YCNzXzHhbAYMo/KU+GpgQEfs0M+Z6RwPTU0oN/U9Mlnglj5dG4pU0VlJK1SmlCgr/aA6tY+1dyWMlQ8ySZI2XdaxkiVfqWMkQ82pKGCsZ4pU0VjLEK2mslPA9zjxOMsQsaaxkiJd5rETEUcCSlNLsBg4h81jJGK8kpcTMMlayxss6VjLGu5qM4yRjvJLGScaYmcdKid/nRsdKxngljZOMMUuuwWqkEtfttLcHMAz4v1rblwCXbNTnp8AptbZfBHZrJG5P6l9P/Wvg47W2fwcMzZBrvTE36hfAAmC7Bvp0pHCDn/+s5/WmHHODMWv1u4wM6+myxKOw5u0vwD4tEW+j/q8COzYnZjHGguJjJYWZo+NaMMcFDeWYIb/xwGW1tm8CTmyh7/O9wJjmfl9KHS+lfIZZxspG/b+58c9uU8ZKYzFrvZZprGSJV8pYyZpfrT6NjpUMn2NJY6UJOTY4VjLkV/JYyfg9zjROMubYpN8tWT7DxsYK8F0KM9QLgL8D7wK/2qhP5rGSJV6tvpnGSdaYWcdKKTlu9HNe589hxs8w8zhpYn4NjpOMOWYeKyV+nxsdKxnzK/V3SkmfI6X+XsnSqT0/KPzl9QrQi3+dDNl3oz7/zoYncDyVIW5P6i+0r1//QwjsArzW0A92xpjbU1zMD/wHhXVwDf0Q3AJc3UCfko45S8xafS8j2wlejeX4UeBlYHiG98wS79/410krA4vfl2iJYy72n0TjJ3g1luOutXIcCiyqL8eM8XpTWFe2FfAh4DmgvLnHDHSjsO6tawt8XzKPl4zxShkrOwHbF593Af4AHLVRn1LHSqMxa/XNMlay5FjKWMkSr9SxkvmYM46VLDmWMlayxMs8VrIeb9ZxUkKOpYyVLPEyj5WN9juYuk8YK/l3aUPxShknJeSYeaxkjFfSWMl6zFnGScb8Mo+TEmKW9HslyzGXMlYy5NekGqyRmE0aKyml9nNnyKZK9dy+PSK+UHz9Bgpn9n6KwuB7F2jwUjAR8b8Uvhk7RsRiCjMFHWvF+29gUkQ8S+EfnK+llN5sZszewC0RUU3hbN8zGwg3Avgc8GzxvzmgcBbuR5t6zFliRsSuQCWwHbAuIr5M4YoQdS09yJLjN4AdgJ9EBMDalNLgZhzzZ4BTI2IN8B5wUiqOmmbELEWWeCcA50TE2mKOJzeQY6PxUkrzIuIRYA6FtWc/TynVeQnJEnKEwhrCR1NK77TAMZcyXrLEK2Ws7AbcHBFlFJbS3ZlSeqg5/z5kiVniWMmSYyljJUu8UsdKlpilyBKvlLHSaLwSx0rW4806TrLGLGWsZIlXylipUzPHSoPxShwnWXMsZaxkiVfqWGksXrM1Y5xkitmE3yuN5QiljZXG4pVcg2WI2eSx4p0hJUmSpBxsCSdDSpIkSZuchbYkSZKUAwttSZIkKQcW2pIkSVIOLLQlSZKkHFhoS9ImEoVbNaeI2K+1c6lPRFyaY+zjIqJPre1vRcTheb2fJLU2L+8nSZtIRNxJ4frGj6eULmuhmFullLLdCjhbvJUppW2asX9Zquf20RExicLNIO5uanxJak+c0ZakTSAitqFww50zgZNrtXeIiJ9ExNyIeCgifhMRJxRf+1REvBART0bENRHxULH9soi4MSIepXAThZ0iYkpEzCo+RhT77RQRj0XEnyPipxGxMCJ2LL52X0TMLr7vuGLblUCXiKiKiMnFts9GxFPFtp8Wb4Cy8bEtiIhvRMSTwIkR8R/FPJ4p5vWhiBgOHAN8vxjrYxExqdaxLoiIy4u5Prt+1r+hY5Ckts5CW5I2jeOAR1JKLwH/iIiBxfZPAz2B/YGzgGEAEdEZ+CnwyZTSSAq31K5tEHBsSmkM8GPgRymlIRTuVPfzYp9vAr9LKQ0E7qV4B82iM1JKg4DBwBcjYoeU0njgvZRSRUppbET0Bk4CRqSUKoBqYGw9x7cqpTQypXQ7cE9KaUhKqT8wDzgzpTQDeAC4qBj/L3XEeLOY6/XAVzMcgyS1aZv9LdglqY04Bbi6+Pz24vafgZHAXSmldcDfI+KJYp/9gFdSSq8Wt/8XGFcr3gMppfeKzw8H+hRvKQ2wXURsW4x9PEBK6ZGI+Get/b8YEccXn+8B7A28tVHOh1Eo6GcVY3cBltRzfHfUel4eEd8Gtge2Af6vnn02dk/x62wKf4DQyDFIUptmoS1JOYuIHYBDKRSgCSgDUkRcDER9uzUS9p1azzsAw2oV3uvft84YEXEwheJ8WErp3YiYCnSuJ4ebU0qXNJLLxvlMAo5LKT0TEZ8HDs6wP8Dq4tdq/vX7qbHPQZLaLJeOSFL+TgBuSSntmVLqmVLaA3iVwmztk8Bnimu1d+FfRekLwF4R0bO4fVID8R8Fzl+/EREVxadPAv+v2DYK+HCxvRvwz2KRvR9wYK1YayKiY/H548AJEbFzMUb3iNgzw/FuC7xejFN7qcmK4mulqO8YJKnNs9CWpPydQmF9cW1TgDHFr4uB5yisyf4TsLw4O30u8EjxJMM3gOX1xP8iMDgi5kTE88AXiu2XA6Mi4s/AJ4HXKRS7jwBbRcQc4L+BP9aKdSMwJyImp5SeB/4LeLTY9zEKV01pzITicTxG4Q+G9W4HLoqIpyPiYxniNHQMktTmeXk/SWplEbFNSmllcYnJUxROPvx7rfYArgPmp5R+VELcTkB1SmltRAwDri+e1NhubA7HIGnL5RptSWp9D0XE9sDWwH+nlP5ebP+PiDit2P40hRnvUnwUuDMiOgDvA//RQvluSpvDMUjaQjmjLUmSJOXANdqSJElSDiy0JUmSpBxYaEuSJEk5sNCWJEmScmChLUmSJOXAQluSJEnKwf8HAFcvUy/t9KgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Aggregate rating',y='Rating Count',hue = 'Rating color',data=ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2bd0bef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Aggregate rating', ylabel='Rating Count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Aggregate rating',y='Rating Count',hue = 'Rating color',data=ratings,palette=['blue','red','orange','yellow','green','green'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a19537a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rating color', ylabel='count'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## counts of rating color(frequency of color)\n",
    "sns.countplot(x='Rating color',data=ratings,palette=['blue','red','orange','yellow','green','green'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e15cb1ae",
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dffinal.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e156803d",
   "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>Rating Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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",
       "    <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>4</th>\n",
       "      <td>1.8</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Aggregate rating         Country  Rating Count\n",
       "0               0.0          Brazil             5\n",
       "1               0.0           India          2139\n",
       "2               0.0  United Kingdom             1\n",
       "3               0.0   United States             3\n",
       "4               1.8           India             1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_rating = dffinal.groupby(['Aggregate rating','Country']).size().reset_index().rename(columns={0:'Rating Count'})\n",
    "country_rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fe00d3ed",
   "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>0</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     0\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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "currency_to_country = dffinal.groupby(['Country','Currency']).size().reset_index()\n",
    "currency_to_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "13f40f16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>No</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>No</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Canada</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>India</td>\n",
       "      <td>No</td>\n",
       "      <td>6229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>India</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>No</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>No</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Phillipines</td>\n",
       "      <td>No</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>No</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>No</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>No</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>No</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>UAE</td>\n",
       "      <td>No</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>UAE</td>\n",
       "      <td>Yes</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>No</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country Has Online delivery     0\n",
       "0        Australia                  No    24\n",
       "1           Brazil                  No    60\n",
       "2           Canada                  No     4\n",
       "3            India                  No  6229\n",
       "4            India                 Yes  2423\n",
       "5        Indonesia                  No    21\n",
       "6      New Zealand                  No    40\n",
       "7      Phillipines                  No    22\n",
       "8            Qatar                  No    20\n",
       "9        Singapore                  No    20\n",
       "10    South Africa                  No    60\n",
       "11       Sri Lanka                  No    20\n",
       "12          Turkey                  No    34\n",
       "13             UAE                  No    32\n",
       "14             UAE                 Yes    28\n",
       "15  United Kingdom                  No    80\n",
       "16   United States                  No   434"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "online_delivery = dffinal.groupby(['Country','Has Online delivery']).size().reset_index()\n",
    "online_delivery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "563d8d0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>City</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Armidale</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Balingup</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Beechworth</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Dicky Beach</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>East Ballina</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>United States</td>\n",
       "      <td>Valdosta</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>United States</td>\n",
       "      <td>Vernonia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>United States</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>United States</td>\n",
       "      <td>Weirton</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>United States</td>\n",
       "      <td>Winchester Bay</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country            City   0\n",
       "0        Australia        Armidale   1\n",
       "1        Australia        Balingup   1\n",
       "2        Australia      Beechworth   1\n",
       "3        Australia     Dicky Beach   1\n",
       "4        Australia    East Ballina   1\n",
       "..             ...             ...  ..\n",
       "136  United States        Valdosta  20\n",
       "137  United States        Vernonia   1\n",
       "138  United States        Waterloo  20\n",
       "139  United States         Weirton   1\n",
       "140  United States  Winchester Bay   1\n",
       "\n",
       "[141 rows x 3 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities = dffinal.groupby(['Country','City']).size().reset_index()\n",
    "cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e23a20e5",
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coun_val = dffinal.City.value_counts().values\n",
    "coun_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0dd9b856",
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "citi_val = dffinal.City.value_counts().index\n",
    "citi_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "13e9f3d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x21ee89a4700>,\n",
       "  <matplotlib.patches.Wedge at 0x21ee89a4be0>,\n",
       "  <matplotlib.patches.Wedge at 0x21ee89b2100>,\n",
       "  <matplotlib.patches.Wedge at 0x21ee89b25e0>,\n",
       "  <matplotlib.patches.Wedge at 0x21ee89b2ac0>],\n",
       " [Text(-0.6145352824185932, 0.9123301960708633, 'New Delhi'),\n",
       "  Text(0.0623675251198054, -1.0982305276263407, 'Gurgaon'),\n",
       "  Text(0.8789045225625368, -0.6614581167535246, 'Noida'),\n",
       "  Text(1.0922218418223437, -0.13058119407559224, 'Faridabad'),\n",
       "  Text(1.099946280005612, -0.010871113182029924, 'Ghaziabad')])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(coun_val[:5],labels=citi_val[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cc2fbccd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x21ee89e6760>,\n",
       "  <matplotlib.patches.Wedge at 0x21ee89e6f70>,\n",
       "  <matplotlib.patches.Wedge at 0x21ee89f36d0>,\n",
       "  <matplotlib.patches.Wedge at 0x21ee89f3d90>,\n",
       "  <matplotlib.patches.Wedge at 0x21ee89fe4f0>],\n",
       " [Text(-0.6145352824185932, 0.9123301960708633, 'New Delhi'),\n",
       "  Text(0.0623675251198054, -1.0982305276263407, 'Gurgaon'),\n",
       "  Text(0.8789045225625368, -0.6614581167535246, 'Noida'),\n",
       "  Text(1.0922218418223437, -0.13058119407559224, 'Faridabad'),\n",
       "  Text(1.099946280005612, -0.010871113182029924, 'Ghaziabad')],\n",
       " [Text(-0.3352010631374145, 0.497634652402289, '68.87%'),\n",
       "  Text(0.0340186500653484, -0.5990348332507311, '14.07%'),\n",
       "  Text(0.47940246685229276, -0.36079533641101336, '13.59%'),\n",
       "  Text(0.5957573682667329, -0.07122610585941394, '3.16%'),\n",
       "  Text(0.5999706981848791, -0.005929698099289049, '0.31%')])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(coun_val[:5],labels=citi_val[:5],autopct='%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7dadef91",
   "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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dffinal.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0cb98b9b",
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_cuisines = dffinal.Cuisines.value_counts()\n",
    "top_cuisines.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cce0bf54",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6521feb",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}