EDA - ZOMATO-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a176d14f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyforest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5445ef67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('zomato.csv',encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eb48d807",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Restaurant ID</th>\n",
       "      <th>Restaurant Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Locality Verbose</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>...</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Switch to order menu</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6317637</td>\n",
       "      <td>Le Petit Souffle</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Third Floor, Century City Mall, Kalayaan Avenu...</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City, Mak...</td>\n",
       "      <td>121.027535</td>\n",
       "      <td>14.565443</td>\n",
       "      <td>French, Japanese, Desserts</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6304287</td>\n",
       "      <td>Izakaya Kikufuji</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Little Tokyo, 2277 Chino Roces Avenue, Legaspi...</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City, Ma...</td>\n",
       "      <td>121.014101</td>\n",
       "      <td>14.553708</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6300002</td>\n",
       "      <td>Heat - Edsa Shangri-La</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...</td>\n",
       "      <td>121.056831</td>\n",
       "      <td>14.581404</td>\n",
       "      <td>Seafood, Asian, Filipino, Indian</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6318506</td>\n",
       "      <td>Ooma</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Fashion Hall, SM Megamall, O...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.056475</td>\n",
       "      <td>14.585318</td>\n",
       "      <td>Japanese, Sushi</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6314302</td>\n",
       "      <td>Sambo Kojin</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Atrium, SM Megamall, Ortigas...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.057508</td>\n",
       "      <td>14.584450</td>\n",
       "      <td>Japanese, Korean</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Restaurant ID         Restaurant Name  Country Code              City  \\\n",
       "0        6317637        Le Petit Souffle           162       Makati City   \n",
       "1        6304287        Izakaya Kikufuji           162       Makati City   \n",
       "2        6300002  Heat - Edsa Shangri-La           162  Mandaluyong City   \n",
       "3        6318506                    Ooma           162  Mandaluyong City   \n",
       "4        6314302             Sambo Kojin           162  Mandaluyong City   \n",
       "\n",
       "                                             Address  \\\n",
       "0  Third Floor, Century City Mall, Kalayaan Avenu...   \n",
       "1  Little Tokyo, 2277 Chino Roces Avenue, Legaspi...   \n",
       "2  Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...   \n",
       "3  Third Floor, Mega Fashion Hall, SM Megamall, O...   \n",
       "4  Third Floor, Mega Atrium, SM Megamall, Ortigas...   \n",
       "\n",
       "                                     Locality  \\\n",
       "0   Century City Mall, Poblacion, Makati City   \n",
       "1  Little Tokyo, Legaspi Village, Makati City   \n",
       "2  Edsa Shangri-La, Ortigas, Mandaluyong City   \n",
       "3      SM Megamall, Ortigas, Mandaluyong City   \n",
       "4      SM Megamall, Ortigas, Mandaluyong City   \n",
       "\n",
       "                                    Locality Verbose   Longitude   Latitude  \\\n",
       "0  Century City Mall, Poblacion, Makati City, Mak...  121.027535  14.565443   \n",
       "1  Little Tokyo, Legaspi Village, Makati City, Ma...  121.014101  14.553708   \n",
       "2  Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...  121.056831  14.581404   \n",
       "3  SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.056475  14.585318   \n",
       "4  SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.057508  14.584450   \n",
       "\n",
       "                           Cuisines  ...          Currency Has Table booking  \\\n",
       "0        French, Japanese, Desserts  ...  Botswana Pula(P)               Yes   \n",
       "1                          Japanese  ...  Botswana Pula(P)               Yes   \n",
       "2  Seafood, Asian, Filipino, Indian  ...  Botswana Pula(P)               Yes   \n",
       "3                   Japanese, Sushi  ...  Botswana Pula(P)                No   \n",
       "4                  Japanese, Korean  ...  Botswana Pula(P)               Yes   \n",
       "\n",
       "  Has Online delivery Is delivering now Switch to order menu Price range  \\\n",
       "0                  No                No                   No           3   \n",
       "1                  No                No                   No           3   \n",
       "2                  No                No                   No           4   \n",
       "3                  No                No                   No           4   \n",
       "4                  No                No                   No           4   \n",
       "\n",
       "   Aggregate rating  Rating color Rating text Votes  \n",
       "0               4.8    Dark Green   Excellent   314  \n",
       "1               4.5    Dark Green   Excellent   591  \n",
       "2               4.4         Green   Very Good   270  \n",
       "3               4.9    Dark Green   Excellent   365  \n",
       "4               4.8    Dark Green   Excellent   229  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b3f387b2",
   "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": "11982c47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Botswana Pula(P)\n",
       "1       Botswana Pula(P)\n",
       "2       Botswana Pula(P)\n",
       "3       Botswana Pula(P)\n",
       "4       Botswana Pula(P)\n",
       "              ...       \n",
       "9546    Turkish Lira(TL)\n",
       "9547    Turkish Lira(TL)\n",
       "9548    Turkish Lira(TL)\n",
       "9549    Turkish Lira(TL)\n",
       "9550    Turkish Lira(TL)\n",
       "Name: Currency, Length: 9551, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Currency\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0972fb22",
   "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": 7,
   "id": "cdbf32a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Restaurant ID</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Average Cost for two</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.551000e+03</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.051128e+06</td>\n",
       "      <td>18.365616</td>\n",
       "      <td>64.126574</td>\n",
       "      <td>25.854381</td>\n",
       "      <td>1199.210763</td>\n",
       "      <td>1.804837</td>\n",
       "      <td>2.666370</td>\n",
       "      <td>156.909748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.791521e+06</td>\n",
       "      <td>56.750546</td>\n",
       "      <td>41.467058</td>\n",
       "      <td>11.007935</td>\n",
       "      <td>16121.183073</td>\n",
       "      <td>0.905609</td>\n",
       "      <td>1.516378</td>\n",
       "      <td>430.169145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-157.948486</td>\n",
       "      <td>-41.330428</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.019625e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.081343</td>\n",
       "      <td>28.478713</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.004089e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.191964</td>\n",
       "      <td>28.570469</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.835229e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.282006</td>\n",
       "      <td>28.642758</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>131.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.850065e+07</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>174.832089</td>\n",
       "      <td>55.976980</td>\n",
       "      <td>800000.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>10934.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Restaurant ID  Country Code    Longitude     Latitude  \\\n",
       "count   9.551000e+03   9551.000000  9551.000000  9551.000000   \n",
       "mean    9.051128e+06     18.365616    64.126574    25.854381   \n",
       "std     8.791521e+06     56.750546    41.467058    11.007935   \n",
       "min     5.300000e+01      1.000000  -157.948486   -41.330428   \n",
       "25%     3.019625e+05      1.000000    77.081343    28.478713   \n",
       "50%     6.004089e+06      1.000000    77.191964    28.570469   \n",
       "75%     1.835229e+07      1.000000    77.282006    28.642758   \n",
       "max     1.850065e+07    216.000000   174.832089    55.976980   \n",
       "\n",
       "       Average Cost for two  Price range  Aggregate rating         Votes  \n",
       "count           9551.000000  9551.000000       9551.000000   9551.000000  \n",
       "mean            1199.210763     1.804837          2.666370    156.909748  \n",
       "std            16121.183073     0.905609          1.516378    430.169145  \n",
       "min                0.000000     1.000000          0.000000      0.000000  \n",
       "25%              250.000000     1.000000          2.500000      5.000000  \n",
       "50%              400.000000     2.000000          3.200000     31.000000  \n",
       "75%              700.000000     2.000000          3.700000    131.000000  \n",
       "max           800000.000000     4.000000          4.900000  10934.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e71bc27e",
   "metadata": {},
   "source": [
    "# DATA ANALYSIS\n",
    "1. Missing Values\n",
    "2. Explore about the numerical variables\n",
    "3. Explore about categorical Variables\n",
    "4. Finding relationship btwn features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c4cc10bc",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b1d206af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "66f32714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cuisines']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[features for features in df.columns if df[features].isnull().sum()>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "64cfc9a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9551, 21)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d49c0254",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country=pd.read_excel('Country-code.xlsx')\n",
    "df_country.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "68d2f0b8",
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9bb092f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_df=pd.merge(df,df_country,on='Country Code',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3ad21901",
   "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",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 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",
       "\n",
       "                                             Address  \\\n",
       "0  Third Floor, Century City Mall, Kalayaan Avenu...   \n",
       "1  Little Tokyo, 2277 Chino Roces Avenue, Legaspi...   \n",
       "\n",
       "                                     Locality  \\\n",
       "0   Century City Mall, Poblacion, Makati City   \n",
       "1  Little Tokyo, Legaspi Village, Makati 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",
       "\n",
       "                     Cuisines  ...  Has Table booking Has Online delivery  \\\n",
       "0  French, Japanese, Desserts  ...                Yes                  No   \n",
       "1                    Japanese  ...                Yes                  No   \n",
       "\n",
       "  Is delivering now Switch to order menu Price range Aggregate rating  \\\n",
       "0                No                   No           3              4.8   \n",
       "1                No                   No           3              4.5   \n",
       "\n",
       "   Rating color  Rating text Votes      Country  \n",
       "0    Dark Green    Excellent   314  Phillipines  \n",
       "1    Dark Green    Excellent   591  Phillipines  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "df69a5db",
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e4f49a02",
   "metadata": {},
   "outputs": [],
   "source": [
    "country_name=  final_df.Country.value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c2952132",
   "metadata": {},
   "outputs": [],
   "source": [
    "country_val=final_df.Country.value_counts().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3fb48dcb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x196da49bed0>,\n",
       "  <matplotlib.patches.Wedge at 0x196da4aa5d0>,\n",
       "  <matplotlib.patches.Wedge at 0x196da4b0290>],\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": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(country_val[:3],labels=country_name[:3],autopct='%1.2f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c83efd",
   "metadata": {},
   "source": [
    "Oberseration: Zomato max records or transaction are from India after USA & then United kingdoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2a8594b5",
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f4508da6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ratings=final_df.groupby(['Aggregate rating', 'Rating color', 'Rating text']).size().reset_index().rename(columns={0:'Rating Count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1d493108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Rating Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Not rated</td>\n",
       "      <td>2148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.8</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.9</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.1</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.2</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.3</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.4</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.5</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.6</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.7</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.8</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.9</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.1</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.2</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.3</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.4</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.5</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.6</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.7</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.8</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.9</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.1</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4.2</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.3</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4.4</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4.5</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.6</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.7</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4.9</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Aggregate rating Rating color Rating text  Rating Count\n",
       "0                0.0        White   Not rated          2148\n",
       "1                1.8          Red        Poor             1\n",
       "2                1.9          Red        Poor             2\n",
       "3                2.0          Red        Poor             7\n",
       "4                2.1          Red        Poor            15\n",
       "5                2.2          Red        Poor            27\n",
       "6                2.3          Red        Poor            47\n",
       "7                2.4          Red        Poor            87\n",
       "8                2.5       Orange     Average           110\n",
       "9                2.6       Orange     Average           191\n",
       "10               2.7       Orange     Average           250\n",
       "11               2.8       Orange     Average           315\n",
       "12               2.9       Orange     Average           381\n",
       "13               3.0       Orange     Average           468\n",
       "14               3.1       Orange     Average           519\n",
       "15               3.2       Orange     Average           522\n",
       "16               3.3       Orange     Average           483\n",
       "17               3.4       Orange     Average           498\n",
       "18               3.5       Yellow        Good           480\n",
       "19               3.6       Yellow        Good           458\n",
       "20               3.7       Yellow        Good           427\n",
       "21               3.8       Yellow        Good           400\n",
       "22               3.9       Yellow        Good           335\n",
       "23               4.0        Green   Very Good           266\n",
       "24               4.1        Green   Very Good           274\n",
       "25               4.2        Green   Very Good           221\n",
       "26               4.3        Green   Very Good           174\n",
       "27               4.4        Green   Very Good           144\n",
       "28               4.5   Dark Green   Excellent            95\n",
       "29               4.6   Dark Green   Excellent            78\n",
       "30               4.7   Dark Green   Excellent            42\n",
       "31               4.8   Dark Green   Excellent            25\n",
       "32               4.9   Dark Green   Excellent            61"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eedb25d",
   "metadata": {},
   "source": [
    "  Observation\n",
    "  \n",
    "  1. When rating are btw 4.5 to 4.9 --> Excellent\n",
    "  2. When rating are btw 4.0 to 4.4 --> Very Good\n",
    "  3. When rating are btw 3.5 to 3.9 --> Good\n",
    "  4. When rating are btw 2.5 to 3.4 --> Average\n",
    "  5. When rating are btw 1.8 to 2.4 --> Poor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "63d9eb03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Rating Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Not rated</td>\n",
       "      <td>2148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.8</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.9</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.1</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.2</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.3</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.4</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.5</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.6</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Aggregate rating Rating color Rating text  Rating Count\n",
       "0               0.0        White   Not rated          2148\n",
       "1               1.8          Red        Poor             1\n",
       "2               1.9          Red        Poor             2\n",
       "3               2.0          Red        Poor             7\n",
       "4               2.1          Red        Poor            15\n",
       "5               2.2          Red        Poor            27\n",
       "6               2.3          Red        Poor            47\n",
       "7               2.4          Red        Poor            87\n",
       "8               2.5       Orange     Average           110\n",
       "9               2.6       Orange     Average           191"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "af7fb43e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Aggregate rating', ylabel='Rating Count'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 30,
   "id": "0050bb5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Aggregate rating', ylabel='Rating Count'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Aggregate rating',y='Rating Count',hue='Rating color', data=ratings,palette=['blue','red','orange','yellow','green','green'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c515ed5d",
   "metadata": {},
   "source": [
    "Observation \n",
    "    \n",
    "    1. Not Rated count is very high\n",
    "    2. Max no of rating is btw 2.5 & 3.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "07764196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Rating color', ylabel='count'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Rating color\", data=ratings, palette=['blue','red','orange','yellow','green','green'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "73b3e115",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>2139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United States</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country     0\n",
       "0          Brazil     5\n",
       "1           India  2139\n",
       "2  United Kingdom     1\n",
       "3   United States     3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[final_df['Rating color']=='White'].groupby('Country').size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8db51299",
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "be280bac",
   "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[[\"Country\",\"Currency\"]].groupby(['Country','Currency']).size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "92c1344d",
   "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>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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[[\"Country\",\"Has Online delivery\"]].groupby(['Country','Has Online delivery']).size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ceb3227e",
   "metadata": {},
   "outputs": [],
   "source": [
    "city=final_df.City.value_counts().values\n",
    "city_labels=final_df.City.value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "83200a5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x196e6e48d10>,\n",
       "  <matplotlib.patches.Wedge at 0x196e6e4a3d0>,\n",
       "  <matplotlib.patches.Wedge at 0x196e6e4bc10>,\n",
       "  <matplotlib.patches.Wedge at 0x196e6e5d390>,\n",
       "  <matplotlib.patches.Wedge at 0x196e6d9ce90>],\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": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(city[:5],labels=city_labels[:5],autopct='%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0908ba0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd993ff0",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}