Project Zomato API-II.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Authorization Key : 33f63ed15365921c3c1b6f01ed0fd123"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # Note - Consider only Indian restaurants in this analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Firstly we will read the zomato.csv file using the pandas and save the Dataframe in df. Now we will extract the Indian Restaurants data using the country code equals to 1 and save the dataframe in df_India. Now we will extract the Delhi_NCR Region and Rest of India and store the dataframe in df_Delhi_NCR and df_ROI respectively\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "from matplotlib import pyplot as plt\n",
    "import operator\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Datasets/zomato.csv\",encoding=\"cp437\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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>624</th>\n",
       "      <td>3400025</td>\n",
       "      <td>Jahanpanah</td>\n",
       "      <td>1</td>\n",
       "      <td>Agra</td>\n",
       "      <td>E 23, Shopping Arcade, Sadar Bazaar, Agra Cant...</td>\n",
       "      <td>Agra Cantt</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>78.011544</td>\n",
       "      <td>27.161661</td>\n",
       "      <td>North Indian, Mughlai</td>\n",
       "      <td>...</td>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>3400341</td>\n",
       "      <td>Rangrezz Restaurant</td>\n",
       "      <td>1</td>\n",
       "      <td>Agra</td>\n",
       "      <td>E-20, Shopping Arcade, Sadar Bazaar, Agra Cant...</td>\n",
       "      <td>Agra Cantt</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>North Indian, Mughlai</td>\n",
       "      <td>...</td>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>3400005</td>\n",
       "      <td>Time2Eat - Mama Chicken</td>\n",
       "      <td>1</td>\n",
       "      <td>Agra</td>\n",
       "      <td>Main Market, Sadar Bazaar, Agra Cantt, Agra</td>\n",
       "      <td>Agra Cantt</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>78.011608</td>\n",
       "      <td>27.160832</td>\n",
       "      <td>North Indian</td>\n",
       "      <td>...</td>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>3400021</td>\n",
       "      <td>Chokho Jeeman Marwari Jain Bhojanalya</td>\n",
       "      <td>1</td>\n",
       "      <td>Agra</td>\n",
       "      <td>1/48, Delhi Gate, Station Road, Raja Mandi, Ci...</td>\n",
       "      <td>Civil Lines</td>\n",
       "      <td>Civil Lines, Agra</td>\n",
       "      <td>77.998092</td>\n",
       "      <td>27.195928</td>\n",
       "      <td>Rajasthani</td>\n",
       "      <td>...</td>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>3400017</td>\n",
       "      <td>Pinch Of Spice</td>\n",
       "      <td>1</td>\n",
       "      <td>Agra</td>\n",
       "      <td>23/453, Opposite Sanjay Cinema, Wazipura Road,...</td>\n",
       "      <td>Civil Lines</td>\n",
       "      <td>Civil Lines, Agra</td>\n",
       "      <td>78.007553</td>\n",
       "      <td>27.201725</td>\n",
       "      <td>North Indian, Chinese, Mughlai</td>\n",
       "      <td>...</td>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>177</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",
       "624        3400025                             Jahanpanah             1  Agra   \n",
       "625        3400341                    Rangrezz Restaurant             1  Agra   \n",
       "626        3400005                Time2Eat - Mama Chicken             1  Agra   \n",
       "627        3400021  Chokho Jeeman Marwari Jain Bhojanalya             1  Agra   \n",
       "628        3400017                         Pinch Of Spice             1  Agra   \n",
       "\n",
       "                                               Address     Locality  \\\n",
       "624  E 23, Shopping Arcade, Sadar Bazaar, Agra Cant...   Agra Cantt   \n",
       "625  E-20, Shopping Arcade, Sadar Bazaar, Agra Cant...   Agra Cantt   \n",
       "626        Main Market, Sadar Bazaar, Agra Cantt, Agra   Agra Cantt   \n",
       "627  1/48, Delhi Gate, Station Road, Raja Mandi, Ci...  Civil Lines   \n",
       "628  23/453, Opposite Sanjay Cinema, Wazipura Road,...  Civil Lines   \n",
       "\n",
       "      Locality Verbose  Longitude   Latitude                        Cuisines  \\\n",
       "624   Agra Cantt, Agra  78.011544  27.161661           North Indian, Mughlai   \n",
       "625   Agra Cantt, Agra   0.000000   0.000000           North Indian, Mughlai   \n",
       "626   Agra Cantt, Agra  78.011608  27.160832                    North Indian   \n",
       "627  Civil Lines, Agra  77.998092  27.195928                      Rajasthani   \n",
       "628  Civil Lines, Agra  78.007553  27.201725  North Indian, Chinese, Mughlai   \n",
       "\n",
       "     ...            Currency Has Table booking Has Online delivery  \\\n",
       "624  ...  Indian Rupees(Rs.)                No                  No   \n",
       "625  ...  Indian Rupees(Rs.)                No                  No   \n",
       "626  ...  Indian Rupees(Rs.)                No                  No   \n",
       "627  ...  Indian Rupees(Rs.)                No                  No   \n",
       "628  ...  Indian Rupees(Rs.)                No                  No   \n",
       "\n",
       "    Is delivering now Switch to order menu Price range  Aggregate rating  \\\n",
       "624                No                   No           3               3.9   \n",
       "625                No                   No           2               3.5   \n",
       "626                No                   No           2               3.6   \n",
       "627                No                   No           2               4.0   \n",
       "628                No                   No           3               4.2   \n",
       "\n",
       "     Rating color Rating text Votes  \n",
       "624        Yellow        Good   140  \n",
       "625        Yellow        Good    71  \n",
       "626        Yellow        Good    94  \n",
       "627         Green   Very Good    87  \n",
       "628         Green   Very Good   177  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_India = df[df[\"Country Code\"]==1]\n",
    "df_India.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
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       "</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>865</th>\n",
       "      <td>301728</td>\n",
       "      <td>Desire Foods</td>\n",
       "      <td>1</td>\n",
       "      <td>Faridabad</td>\n",
       "      <td>G 25/22, Main Road, 40 Feet, Molarband Extensi...</td>\n",
       "      <td>Badarpur Border</td>\n",
       "      <td>Badarpur Border, Faridabad</td>\n",
       "      <td>77.306640</td>\n",
       "      <td>28.490059</td>\n",
       "      <td>Chinese, Fast Food, Bakery</td>\n",
       "      <td>...</td>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>5992</td>\n",
       "      <td>Knight Rock</td>\n",
       "      <td>1</td>\n",
       "      <td>Faridabad</td>\n",
       "      <td>11/6, Delhi Mathura Road, Near Bharat Petrol P...</td>\n",
       "      <td>Badarpur Border</td>\n",
       "      <td>Badarpur Border, Faridabad</td>\n",
       "      <td>77.304776</td>\n",
       "      <td>28.490157</td>\n",
       "      <td>North Indian, Chinese</td>\n",
       "      <td>...</td>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Restaurant ID Restaurant Name  Country Code       City  \\\n",
       "865         301728    Desire Foods             1  Faridabad   \n",
       "866           5992     Knight Rock             1  Faridabad   \n",
       "\n",
       "                                               Address         Locality  \\\n",
       "865  G 25/22, Main Road, 40 Feet, Molarband Extensi...  Badarpur Border   \n",
       "866  11/6, Delhi Mathura Road, Near Bharat Petrol P...  Badarpur Border   \n",
       "\n",
       "               Locality Verbose  Longitude   Latitude  \\\n",
       "865  Badarpur Border, Faridabad  77.306640  28.490059   \n",
       "866  Badarpur Border, Faridabad  77.304776  28.490157   \n",
       "\n",
       "                       Cuisines  ...            Currency Has Table booking  \\\n",
       "865  Chinese, Fast Food, Bakery  ...  Indian Rupees(Rs.)                No   \n",
       "866       North Indian, Chinese  ...  Indian Rupees(Rs.)                No   \n",
       "\n",
       "    Has Online delivery Is delivering now Switch to order menu Price range  \\\n",
       "865                  No                No                   No           1   \n",
       "866                  No                No                   No           2   \n",
       "\n",
       "     Aggregate rating  Rating color Rating text Votes  \n",
       "865               2.9        Orange     Average     4  \n",
       "866               2.8        Orange     Average    16  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Delhi_NCR = df_India[(df_India[\"City\"]==\"New Delhi\")|(df_India[\"City\"]==\"Ghaziabad\")|(df_India[\"City\"]==\"Noida\")|(df_India[\"City\"]==\"Gurgaon\")|(df_India[\"City\"]==\"Faridabad\")]\n",
    "df_Delhi_NCR.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>624</th>\n",
       "      <td>3400025</td>\n",
       "      <td>Jahanpanah</td>\n",
       "      <td>1</td>\n",
       "      <td>Agra</td>\n",
       "      <td>E 23, Shopping Arcade, Sadar Bazaar, Agra Cant...</td>\n",
       "      <td>Agra Cantt</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>78.011544</td>\n",
       "      <td>27.161661</td>\n",
       "      <td>North Indian, Mughlai</td>\n",
       "      <td>...</td>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>3400341</td>\n",
       "      <td>Rangrezz Restaurant</td>\n",
       "      <td>1</td>\n",
       "      <td>Agra</td>\n",
       "      <td>E-20, Shopping Arcade, Sadar Bazaar, Agra Cant...</td>\n",
       "      <td>Agra Cantt</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>North Indian, Mughlai</td>\n",
       "      <td>...</td>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Restaurant ID      Restaurant Name  Country Code  City  \\\n",
       "624        3400025           Jahanpanah             1  Agra   \n",
       "625        3400341  Rangrezz Restaurant             1  Agra   \n",
       "\n",
       "                                               Address    Locality  \\\n",
       "624  E 23, Shopping Arcade, Sadar Bazaar, Agra Cant...  Agra Cantt   \n",
       "625  E-20, Shopping Arcade, Sadar Bazaar, Agra Cant...  Agra Cantt   \n",
       "\n",
       "     Locality Verbose  Longitude   Latitude               Cuisines  ...  \\\n",
       "624  Agra Cantt, Agra  78.011544  27.161661  North Indian, Mughlai  ...   \n",
       "625  Agra Cantt, Agra   0.000000   0.000000  North Indian, Mughlai  ...   \n",
       "\n",
       "               Currency Has Table booking Has Online delivery  \\\n",
       "624  Indian Rupees(Rs.)                No                  No   \n",
       "625  Indian Rupees(Rs.)                No                  No   \n",
       "\n",
       "    Is delivering now Switch to order menu Price range  Aggregate rating  \\\n",
       "624                No                   No           3               3.9   \n",
       "625                No                   No           2               3.5   \n",
       "\n",
       "     Rating color Rating text Votes  \n",
       "624        Yellow        Good   140  \n",
       "625        Yellow        Good    71  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ROI = df_India[(df_India[\"City\"]!=\"New Delhi\")&(df_India[\"City\"]!=\"Ghaziabad\")&(df_India[\"City\"]!=\"Noida\")&(df_India[\"City\"]!=\"Gurgaon\")&(df_India[\"City\"]!=\"Faridabad\")]\n",
    "df_ROI.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 The dataset is highly skewed toward the cities included in Delhi-NCR. So, we will summarise all the other cities in Rest of India while those in New Delhi, Ghaziabad, Noida, Gurgaon, Faridabad to Delhi-NCR. Doing this would make our analysis turn toward Delhi-NCR v Rest of India."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Plot the bar graph of number of restaurants present in Delhi NCR vs Rest of India."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaination and Answer :- In this firstly we will find the length of df_Delhi_NCR and df_ROI which will give number of restaurants present in Delhi NCR and Rest of India and now we will plot a bar graph of this data using the matplotlib. Number of restaurants present in Delhi NCR and Rest of India are:-\n",
    "•\tDelhi-NCR 7947\n",
    "•\tRest of India 705\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Wnbr/QBfBbEA0s8/ifmCCpI1z38NewJ3AFcCH8jyTgYvy69n5PXn6H/PA5LOBg/LZUtsB40gjdZmZWT9pZp/FdZJmATeTxo29hdRMNAc4X9LJOe3svMjZwHmSFpJqFAflfOZLupAUaDqBI/NQiWZm1k+aeiPBiDiONKZv0T3UOZspIp4GDmyQz9dI4+GamdkA8BXcZmZWysHCzMxKOViYmVkpBwszMyvlYGFmZqUcLMzMrJSDhZmZlXKwMDOzUg4WZmZWysHCzMxKOViYmVkpBwszMyvlYGFmZqUcLMzMrJSDhZmZlXKwMDOzUg4WZmZWqmnBQtIOkm4tPB6XdLSkkZLmSlqQn7fI80vSmZIWSrpd0q6FvCbn+RdImtx4rWZm1gxNCxYRcXdE7BIRuwBvAp4EfgNMBy6PiHHA5fk9wHuAcfkxFfg+gKSRpKFZ9yANx3pcLcCYmVn/6K9mqL2Av0fEfcAkYGZOnwkckF9PAs6N5FpghKRRwD7A3IhYERGPAHOBffup3GZmBqzXT+s5CPhFft0SEcsAImKZpK1z+mhgcWGZJTmtUfoqJE0l1UhoaWmhvb2914WdNr6z18uubVqGeXsU9WW/subo6Ojw99IPmh4sJG0AvB84tmzWOmnRTfqqCREzgBkAra2t0dbW1rOCFkyZPqfXy65tpo3v5PR5/XVMMfgtOrhtoItgXbS3t9OX37tV0x/NUO8Bbo6IB/P7B3PzEvl5eU5fAmxTWG4MsLSbdDMz6yf9ESw+yotNUACzgdoZTZOBiwrph+azoiYAj+XmqkuBiZK2yB3bE3OamZn1k6a2L0jaGNgb+GQh+VTgQkmHA/cDB+b0S4D9gIWkM6cOA4iIFZJOAm7I850YESuaWW4zM1tVU4NFRDwJbNkl7Z+ks6O6zhvAkQ3yOQc4pxllNDOzcr6C28zMSjlYmJlZKQcLMzMr5WBhZmalHCzMzKyUg4WZmZVysDAzs1IOFmZmVsrBwszMSjlYmJlZKQcLMzMr5WBhZmalSoOFpAMlDc+vvyzp15J2bX7RzMxssKhSs/hKRKyU9DbSeNgzge83t1hmZjaYVAkWz+Xn/YHvR8RFwAbNK5KZmQ02VYLFPySdBXwYuETShhWXMzOztUSVP/0Pk4Yx3TciHgVGAl9oaqnMzGxQqRIszoqIX0fEAoA8LvYhVTKXNELSLEl3SfqrpDdLGilprqQF+XmLPK8knSlpoaTbi53okibn+RdImtx4jWZm1gxVgsVOxTeS1gXeVDH/M4DfR8RrgTcAfwWmA5dHxDjg8vwe4D3AuPyYSu5ElzQSOA7YA9gdOK4WYMzMrH80DBaSjpW0EthZ0uP5sRJYDlxUlrGkzYA9gbMBIuLZ3Iw1iXRGFfn5gPx6EnBuJNcCIySNIp2BNTciVkTEI8BcYN/efFgzM+ud9RpNiIhTgFMknRIRx/Yi71cBDwE/lvQG4Cbgc0BLbsoiIpZJ2jrPPxpYXFh+SU5rlL4KSVNJNRJaWlpob2/vRZGTaeM7e73s2qZlmLdHUV/2K2uOjo4Ofy/9oGGwqImIYyWNBrYtzh8RV1XIe1fgMxFxnaQzeLHJqR7VW3036V3LOQOYAdDa2hptbW0lxWtsyvQ5vV52bTNtfCenzyvdTYaMRQe3DXQRrIv29nb68nu3akr/BSSdChwE3MmL11wEUBYslgBLIuK6/H4WKVg8KGlUrlWMIjVr1ebfprD8GGBpTm/rkt5eVm4zM1t9qnRwfwDYISL2i4j35cf7yxaKiAeAxZJ2yEl7kQLObKB2RtNkXuz/mA0cms+KmgA8lpurLgUmStoid2xPzGlmZtZPqrQv3AOsDzzTi/w/A/xM0gY5n8NIAepCSYcD9wMH5nkvAfYDFgJP5nmJiBWSTgJuyPOdGBErelEWMzPrpSrB4kngVkmXUwgYEfHZsgUj4lagtc6kverMG8CRDfI5BzinQlnNzKwJqgSL2flhZmZDVJWzoWaWzWNmZmu3KmdDjQNOAXYENqqlR8SrmlguMzMbRKqcDfVj0q03OoF3AucC5zWzUGZmNrhUCRbDIuJyQBFxX0QcD7yrucUyM7PBpEoH99OS1gEWSDoK+AewdckyZma2FqlSszga2Bj4LOlusx/jxYvqzMxsCOi2ZpFvR/7hiPgC0EG+UM7MzIaWbmsWEfEc8CZJ9W7mZ2ZmQ0SVPotbgIsk/RJ4opYYEb9uWqnMzGxQqRIsRgL/ZNUzoAJwsDAzGyKqXMHtfgozsyGuyhXcP6b+YEMfb0qJzMxs0KnSDHVx4fVGpPEtljanOGZmNhhVaYb6VfG9pF8Af2haiczMbNCpclFeV+OAV67ugpiZ2eBVpc9iJanPQvn5AeCYJpfLzMwGkdKaRUQMj4jNCs+v6do01YikRZLmSbpV0o05baSkuZIW5OctcroknSlpoaTbJe1ayGdynn+BJN9qxMysn1Xp4Cb/oY9j1fEsrqq4jndGxMOF99OByyPiVEnT8/tjgPfkdYwD9iDdFn0PSSOB40jDswZwk6TZEfFIxfWbmVkfldYsJB0BXAVcCpyQn4/vwzonAbXR92YCBxTSz43kWmCEpFHAPsDciFiRA8RcYN8+rN/MzHqoSgf354DdgPsi4p3AG4GHKuYfwGWSbpI0Nae1RMQygPxcu935aGBxYdklOa1RupmZ9ZNK41lExNOSkLRhRNwlaYeK+b81IpZK2hqYK+mubuatd7PC6CZ91YVTMJoK0NLSQnt7e8UivtS08Z29XnZt0zLM26OoL/uVNUdHR4e/l35QJVgskTQC+C3pD/8RKl6UFxFL8/NySb8BdgcelDQqIpblZqbltfUA2xQWH5PXswRo65LeXmddM4AZAK2trdHW1tZ1lsqmTJ/T62XXNtPGd3L6vEpdW0PCooPbBroI1kV7ezt9+b1bNVXOhvpARDyah1P9CnA2qX+hW5I2kTS89hqYCNwBzObFwZMmAxfl17OBQ/NZUROAx3Iz1aXARElb5I72iTnNzMz6SZXrLM6LiEMAIuLKWhpwSMmiLcBv8lAY6wE/j4jfS7oBuFDS4cD9wIF5/kuA/YCFwJPkgZYiYoWkk4Ab8nwnRsSK6h/RzMz6qkr7wk7FN3n0vDeVLRQR9wBvqJP+T2CvOukBHNkgr3OAcyqU1czMmqBhM5SkY/PV2ztLejw/VpL6GC5qtJyZma19GgaLiDglIoYDX89Xbteu4t4yIo7txzKamdkAq3KdxcW5gxpJH5P0TUnbNrlcZmY2iFQJFt8HnpT0BuC/gPuAc5taKjMzG1SqBIvO3Pk8CTgjIs4Ahje3WGZmNphUORtqpaRjSafKvj2fDbV+c4tlZmaDSZWaxUeAZ4CPR8QDpPsyfb2ppTIzs0GlyhXcDwC/AjbMSQ8Dv2lmoczMbHCpcovyTwCzgLNy0mjSfaLMzGyIqNIMdSTwVuBxgIhYwIu3FTczsyGgSrB4JiKerb2RtB51bhFuZmZrryrB4kpJXwSGSdob+CXwu+YWy8zMBpMqwWI6aWS8ecAnSXeH/XIzC2VmZoNL6XUWEfE88MP8AEDSW4Grm1guMzMbRBoGi3zx3YdJZz/9PiLukPRe4IvAMNJY3GZmNgR0V7M4mzTM6fXAmZLuA94MTI8InzprZjaEdBcsWoGdI+J5SRuRLsbbPl+kZ2ZmQ0h3HdzP5v4KIuJp4G+9CRSS1pV0i6SL8/vtJF0naYGkCyRtkNM3zO8X5uljC3kcm9PvlrRPT8tgZmZ9012weK2k2/NjXuH9PEm392AdnwP+Wnh/GvCtiBgHPAIcntMPBx6JiO2Bb+X5kLQjcBBpeNd9ge/l/hQzM+sn3TVDva6vmUsaA+wPfA34T0kC3gX8e55lJnA8acyMSfk1pNuLfCfPPwk4PyKeAe6VtBDYHbimr+UzM7NqGgaLiLhvNeT/bdKASbXxL7YEHo2Izvx+CelsK/Lz4rzuTkmP5flHA9cW8iwuY2Zm/aDKeBa9kk+zXR4RN0lqqyXXmTVKpnW3THF9U4GpAC0tLbS3t/e0yC+YNr6zfKYhomWYt0dRX/Yra46Ojg5/L/2gacGCdPPB90vaD9gI2IxU0xghab1cuxgDLM3zLyGdqrsk339qc2BFIb2muMwLImIGMAOgtbU12trael3wKdPn9HrZtc208Z2cPq+Zu8maZdHBbQNdBOuivb2dvvzerZqGHdySLs/Pp/Um44g4NiLGRMRYUgf1HyPiYOAK4EN5tsnARfn17PyePP2PeTjX2cBB+Wyp7YBxpGs/zMysn3R3yDhK0jtItYPz6dIcFBE393KdxwDnSzoZuIV08R/5+bzcgb2CFGCIiPmSLgTuBDqBIyPiuV6u28zMeqG7YPFV0k0ExwDf7DItSGc1VRIR7UB7fn0P6WymrvM8DRzYYPmvkc6oMjOzAdDd2VCzgFmSvhIRJ/VjmczMbJCpctfZkyS9H9gzJ7VHxMXNLZaZmQ0mVcbgPoV0Ffad+fG5nGZmZkNElXMi9wd2qd0nStJMUsf0sc0smJmZDR5VRsoDGFF4vXkzCmJmZoNXlZrFKcAtkq4gnT67J65VmJkNKVU6uH8hqR3YjRQsjvGYFmZmQ0ul+zhExDLSldRmZjYEVe2zMDOzIczBwszMSnUbLCStI+mO/iqMmZkNTt0Gi3xtxW2SXtlP5TEzs0GoSgf3KGC+pOuBJ2qJEfH+ppXKzMwGlSrB4oSml8LMzAa1KtdZXClpW2BcRPxB0sbAus0vmpmZDRZVbiT4CWAWcFZOGg38tpmFMjOzwaXKqbNHksbTfhwgIhYAWzezUGZmNrhUCRbPRMSztTeS1iONlNctSRtJul7SbZLmSzohp28n6TpJCyRdIGmDnL5hfr8wTx9byOvYnH63pH16+iHNzKxvqgSLKyV9ERgmaW/gl8DvKiz3DPCuiHgDsAuwr6QJwGnAtyJiHPAIcHie/3DgkYjYHvhWng9JO5LG494J2Bf4niT3mZiZ9aMqwWI68BAwD/gkcAnw5bKFIunIb9fPj9rY3bNy+kzggPx6Un5Pnr6XJOX08yPimYi4F1hInTG8zcyseaqcDfV8HvDoOtKf/d0RUdoMBZBrADcB2wPfBf4OPBoRnXmWJaQOc/Lz4rzOTkmPAVvm9GsL2RaXMTOzflAaLCTtD/yA9EcvYDtJn4yI/ytbNiKeA3aRNAL4DfC6erPVVtVgWqP0ruWcCkwFaGlpob29vax4DU0b31k+0xDRMszbo6gv+5U1R0dHh7+XflDlorzTgXdGxEIASa8G5gClwaImIh7NY2JMAEZIWi/XLsYAS/NsS4BtgCW5E31zYEUhvaa4THEdM4AZAK2trdHW1la1eC8xZfqcXi+7tpk2vpPT51W6k/2QsOjgtoEugnXR3t5OX37vVk2VPovltUCR3QMsL1tI0styjQJJw4B3A38FrgA+lGebDFyUX8/O78nT/5ibu2YDB+WzpbYDxgHXVyi3mZmtJg0PGSV9ML+cL+kS4EJS88+BwA0V8h4FzMz9FusAF0bExZLuBM6XdDJwC3B2nv9s4DxJC0k1ioMAImK+pAuBO4FO4MjcvGVmZv2ku/aF9xVePwi8I79+CNiiLOOIuB14Y530e6hzNlNEPE0KRPXy+hrwtbJ1mplZczQMFhFxWH8WxMzMBq8qZ0NtB3wGGFuc37coNzMbOqqc5vJbUn/C74Dnm1scMzMbjKoEi6cj4syml8TMzAatKsHiDEnHAZeR7vcEQETc3LRSmZnZoFIlWIwHDiHd06nWDFW7x5OZmQ0BVYLFB4BXFW9TbmZmQ0uVK7hvA0Y0uyBmZjZ4ValZtAB3SbqBVfssfOqsmdkQUSVYHNf0UpiZ2aBWZTyLK/ujIGZmNnhVuYJ7JS+OH7EBacS7JyJis2YWzMzMBo8qNYvhxfeSDsDDmpqZDSlVzoZaRUT8Fl9jYWY2pFRphvpg4e06QCt1hjU1M7O1V5WzoYrjWnQCi4BJTSmNmZkNSlX6LDyuhZnZENfdsKpf7Wa5iIiTmlAeMzMbhLrr4H6izgPgcOCYsowlbSPpCkl/lTRf0udy+khJcyUtyM9b5HRJOlPSQkm3S9q1kNfkPP8CSZN7+VnNzKyXuhtW9fTaa0nDgc8Bhw14ik0AAAsxSURBVAHnA6c3Wq6gE5gWETfn5W+SNBeYAlweEadKmg5MJwWf9wDj8mMP4PvAHpJGkq4ir3Ws3yRpdkQ80tMPa2ZmvdPtqbO5FnAycDspsOwaEcdExPKyjCNiWW3Mi4hYCfwVGE3qHJ+ZZ5sJHJBfTwLOjeRaYISkUcA+wNyIWJEDxFxg355+UDMz673u+iy+DnwQmAGMj4iO3q5E0ljgjcB1QEtELIMUUCRtnWcbDSwuLLYkpzVK77qOqcBUgJaWFtrb23tbXKaN7+z1smublmHeHkV92a+sOTo6Ovy99IPuzoaaRrrL7JeBL0mqpYvUwV3pdh+SNgV+BRwdEY8X8nnJrHXSopv0VRMiZpACG62trdHW1laleHVNmT6n18uubaaN7+T0eVXOsB4aFh3cNtBFsC7a29vpy+/dqumuz6LHV3d3JWl9UqD4WUT8Oic/KGlUrlWMAmpNWkuAbQqLjwGW5vS2LuntfS2bmZlV1+eA0IhSFeJs4K8R8c3CpNlA7YymycBFhfRD81lRE4DHcnPVpcBESVvkM6cm5jQzM+snzWxfeCtp7O55km7NaV8ETgUulHQ4cD9wYJ52CbAfsBB4knTmFRGxQtJJwA15vhMjYkUTy21mZl00LVhExJ+p398AsFed+QM4skFe5wDnrL7SmZlZTzStGcrMzNYeDhZmZlbKwcLMzEo5WJiZWSkHCzMzK+VgYWZmpRwszMyslIOFmZmVcrAwM7NSDhZmZlbKwcLMzEo5WJiZWSkHCzMzK+VgYWZmpRwszMyslIOFmZmVcrAwM7NSzRyD+xxJyyXdUUgbKWmupAX5eYucLklnSloo6XZJuxaWmZznXyBpcr11mZlZczWzZvETYN8uadOByyNiHHB5fg/wHmBcfkwFvg8puADHAXsAuwPH1QKMmZn1n6YFi4i4CljRJXkSMDO/ngkcUEg/N5JrgRGSRgH7AHMjYkVEPALM5aUByMzMmmy9fl5fS0QsA4iIZZK2zumjgcWF+ZbktEbpLyFpKqlWQktLC+3t7b0u5LTxnb1edm3TMszbo6gv+5U1R0dHh7+XftDfwaIR1UmLbtJfmhgxA5gB0NraGm1tbb0uzJTpc3q97Npm2vhOTp83WHaTgbfo4LaBLoJ10d7eTl9+71ZNf58N9WBuXiI/L8/pS4BtCvONAZZ2k25mZv2ov4PFbKB2RtNk4KJC+qH5rKgJwGO5uepSYKKkLXLH9sScZmZm/ahp7QuSfgG0AVtJWkI6q+lU4EJJhwP3Awfm2S8B9gMWAk8ChwFExApJJwE35PlOjIiuneZmZtZkTQsWEfHRBpP2qjNvAEc2yOcc4JzVWDQzM+shX8FtZmalfJqL2RporM/Ye8G08Z0+g7Fg0an7NyVf1yzMzKyUg4WZmZVysDAzs1IOFmZmVsrBwszMSjlYmJlZKQcLMzMr5WBhZmalHCzMzKyUg4WZmZVysDAzs1IOFmZmVsrBwszMSjlYmJlZKQcLMzMrtcYEC0n7Srpb0kJJ0we6PGZmQ8kaESwkrQt8F3gPsCPwUUk7DmypzMyGjjUiWAC7Awsj4p6IeBY4H5g0wGUyMxsyFBEDXYZSkj4E7BsRR+T3hwB7RMRRhXmmAlPz2x2Au/u9oGunrYCHB7oQZt3wPrr6bBsRL6s3YU0Zg1t10laJchExA5jRP8UZOiTdGBGtA10Os0a8j/aPNaUZagmwTeH9GGDpAJXFzGzIWVOCxQ3AOEnbSdoAOAiYPcBlMjMbMtaIZqiI6JR0FHApsC5wTkTMH+BiDRVu2rPBzvtoP1gjOrjNzGxgrSnNUGZmNoAcLMzMrJSDxRpO0nOSbpU0X9Jtkv5TUrffq6Sxku7Ir6dI+k6D+S6RNKJO+hRJz0vauZB2h6Sx+fWmks6S9Pdcrqsk7dGlvHdI+l29/G3NtLq+W0lHS9q4h8u8Pe9rt0oa1mVaRw/zapN0cX79ft9eKHGwWPM9FRG7RMROwN7AfsBxqyPjiNgvIh5tMHkJ8KUG034ErADG5XJNIV04VSzv6/M8R66OstqgsLq+26OBHgUL4GDgG3n9T/VyvS8REbMj4tTVld+azMFiLRIRy0lXsR+lZF1JX5d0g6TbJX2ywaKvkPR7SQsk/U8tUdIiSVs1WOZiYCdJOxQTJb0a2AP4ckQ8n8t1T0TMqZPHNcDoHn5MWzOs8t1K+kJhPzwhp20iaU6uEd8h6SOSPgu8ArhC0hVdM5W0l6RbJM2TdI6kDSUdAXwY+KqknzUqUK4xtEuaJekuST+TpDxt35z2Z+CDhWVeqHlLep+k6/L6/yCpZfVsqjXDGnHqrFUXEffkZqitSffPeiwidpO0IXC1pMvocvU7sAvwRuAZ4G5J/xsRi0tW9TzwP8AXgcmF9J2AWyPiue4WzjeH3As4u+JHszVE1+9W0kRgHOkebwJmS9oTeBmwNCL2z/NtHhGPSfpP4J0R8XCXfDcCfgLsFRF/k3Qu8OmI+LaktwEXR8SskuK9kbSPLgWuBt4q6Ubgh8C7gIXABQ2W/TMwISIiB6j/AqZV3jBrONcs1k6126NMBA6VdCtwHbAl6Ufb1eUR8VhEPA3cCWxbcT0/ByZI2q4HZRuWy/NPYCQwtwfL2uDW6LudmB+3ADcDryXth/OAd0s6TdLbI+Kxkvx3AO6NiL/l9zOBPXtYxusjYkmu9d4KjM3luTciFkS6luCnDZYdA1wqaR7wBVLQGTIcLNYykl4FPAcsJwWNz+R23F0iYruIuKzOYs8UXj9HlxqnpCNzx+Gtkl5RS4+ITuB04JjC7POBN3TTyf5UROxCCkgb4D6LtUmj71bAKYX9cPuIODv/6b+JFDROkfTVkvzr3SOupxrt61UuOPtf4DsRMR74JLDRaijPGsPBYi0i6WXAD0g7dJCueP+0pPXz9NdI2qSn+UbEdws/9K735PoJ8G5SkwIR8XfgRuCEQnvwOEmTuuT5GPBZ4PO18tnaoc53eynwcUmbAkgaLWnrfODxZET8FPgGsGvOYiUwvE7WdwFjJW2f3x8CXLkainwXsF3ubwP4aIP5Ngf+kV9PbjDPWsvBYs03LB/xzwf+AFwGnJCn/YjUrHSz0qmyZ7Ga+6ny+CJnkvpIao4AXg4szFX2H1Lnxo8RcQtwG+leX7YWKX63uTb7c+CavD/MIgWD8cD1uenqS8DJefEZwP917eDOzaSHAb/M+TxPOjjqa1mfJp0YMid3cN/XYNbj87r/xBC8Jbpv92FmZqVcszAzs1IOFmZmVsrBwszMSjlYmJlZKQcLMzMr5WBh1geSXi7pfKU77N6pdKfePSXNytN3kbRfYX7fxdTWSD511qyX8kWHfwFmRsQPctouwPCI+FN+PwVojYijBqygZquBg4VZL0l6F3B8ROzZJX0s6a68u5JuTDeMdOXvKfl1a0QcVbji/pV50aMj4mpJ7wDOyGkB7BkRK5v8ccy65bvOmvXe64GbGk2MiGfz/Y5eqFnkmkbNGcC3IuLPkl5Jui3G64DPA0fmwLEp8HSzPoBZVQ4WZgPn3cCO+RZaAJtJGk66dfY389gMv46IJQNVQLMad3Cb9d580l1Te2sd4M2FmzSOjoiVeWS2I0hNVtdKeu3qKKxZXzhYmPXeH4ENJX2iliBpN1YdD6TRHVQh3fTxhY7v3DmOpFdHxLyIOI10B18HCxtwDhZmvZRvA/8BYO986ux80p1Ji3fYvYLU1HSrpI90yeKzQGseavRO4FM5/eg8zOhtwFPA/zX1g5hV4LOhzMyslGsWZmZWysHCzMxKOViYmVkpBwszMyvlYGFmZqUcLMzMrJSDhZmZlfr/pkLHNNYFigsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delhi-NCR 7947\n",
      "Rest of India 705\n"
     ]
    }
   ],
   "source": [
    "Delhi_NCR = len(df_Delhi_NCR)\n",
    "ROI = len(df_ROI)\n",
    "li = [Delhi_NCR,ROI]\n",
    "label = [\"Delhi-NCR\",\"Rest of India\"]\n",
    "plt.bar(label,li)\n",
    "plt.title(\"Number of Restaurants in Delhi-NCR VS Rest Of India\")\n",
    "plt.xlabel(\"Cities\")\n",
    "plt.ylabel(\"Number of Restaurants\")\n",
    "plt.grid()\n",
    "plt.show()\n",
    "for i in range(2):\n",
    "    print(label[i],li[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Find the cuisines which are not present in restaurant of Delhi NCR but present in rest of India.Check using Zomato API whether this cuisines are actually not served in restaurants of Delhi-NCR or just it due to incomplete dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaination and Answer:- In this we will extract the values of cuisines in the form of list from the df_Delhi_NCR and df_ROI respectively and apply a function named cuisines which will return the list of cuisines in that region. After that we will run a for loop and check the cuisines that are not served in Delhi-NCR. The cuisines which are not present in restaurant of Delhi NCR but present in rest of India are:-\n",
    "•\tGerman\n",
    "•\tMalwani\n",
    "•\tBBQ\n",
    "•\tCajun\n",
    "### Now we will use the zomato-api to check whether these cuisines are actually not served in restaurants of Delhi-NCR or just it due to incomplete dataset. Firstly we will generate a zomato api key from the website and use it as a user-key in headers. Now we will find the city id for Delhi-NCR region by making a get request to  https://developers.zomato.com/api/v2.1/cities and passing headers and params. After obtaining the city id we will make a get request to https://developers.zomato.com/api/v2.1/cuisines by passing the headers as user-key and params as city id which will give all the cuisines served in the Delhi-NCR region. So the cuisines which are not present in restaurant of Delhi NCR but present in rest of India are:-\n",
    "•\tGerman\n",
    "•\tCajun\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def cuisines(li):\n",
    "    li1 = []\n",
    "    for row in li:\n",
    "        cuisines = row.strip().split(\",\")\n",
    "        for cuisine in cuisines:\n",
    "            cuisine = cuisine.strip()\n",
    "            if cuisine not in li1:\n",
    "                li1.append(cuisine)\n",
    "    return li1\n",
    "\n",
    "cuisines_in_DNCR = cuisines(df_Delhi_NCR[\"Cuisines\"].values)\n",
    "cuisines_in_ROI = cuisines(df_ROI[\"Cuisines\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cuisines_not_in_DNCR = []\n",
    "for cuisines in cuisines_in_ROI:\n",
    "    if cuisines not in cuisines_in_DNCR:\n",
    "        cuisines_not_in_DNCR.append(cuisines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cuisines which are not present in restaurant of Delhi NCR but present in Rest of India are German Malwani BBQ Cajun\n"
     ]
    }
   ],
   "source": [
    "print(\"Cuisines which are not present in restaurant of Delhi NCR but present in Rest of India are\",*cuisines_not_in_DNCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "h={\"user-key\":\"33f63ed15365921c3c1b6f01ed0fd123\"}\n",
    "p = {\"q\":\"Delhi NCR\"}\n",
    "response=requests.get(\"https://developers.zomato.com/api/v2.1/cities\",headers=h,params=p)\n",
    "data = response.json()\n",
    "city_id = 0\n",
    "for row in data[\"location_suggestions\"]:\n",
    "    if row[\"name\"]==\"Delhi NCR\":\n",
    "        city_id = row[\"id\"]\n",
    "        break\n",
    "\n",
    "response_2 = requests.get(\"https://developers.zomato.com/api/v2.1/cuisines\",headers = h,params = {\"city_id\":city_id})\n",
    "cuisines_data = response_2.json()[\"cuisines\"]\n",
    "all_cuisines_in_DNCR=[]\n",
    "for row in cuisines_data:\n",
    "    all_cuisines_in_DNCR.append(row[\"cuisine\"][\"cuisine_name\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "discrepancy_in_cuisines=[]\n",
    "for cuisine in cuisines_not_in_DNCR:\n",
    "    if cuisine in all_cuisines_in_DNCR:\n",
    "        discrepancy_in_cuisines.append(cuisine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cuisines that are served in Delhi-NCR are  Malwani BBQ But due to incomplete dataset they were not served in Delhi-NCR\n"
     ]
    }
   ],
   "source": [
    "print(\"Cuisines that are served in Delhi-NCR are \",*discrepancy_in_cuisines,\"But due to incomplete dataset they were not served in Delhi-NCR\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Find the top 10 cuisines served by maximum number of restaurants in Delhi NCR and rest of India."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaination and Answer:- In order to find the top 10 cuisines served by maximum number of restaurants in Delhi NCR and rest of India we will firstly extract the cuisines from df_India and run through a function cuisines which will return a dictionary with cuisine and count as a key-value pair. Now we will a sort the dictionary in reverse order on the basis of count and make it into the list and extract the top 10 from the list. So the top 10 cuisines served by maximum number of restaurants in Delhi NCR and rest of India are:-\n",
    "•\tf-India\n",
    "•\tNorth Indian\n",
    "•\tChinese\n",
    "•\tFast Food\n",
    "•\tMughlai\n",
    "•\tBakery\n",
    "•\tContinental\n",
    "•\tItalian\n",
    "•\tSouth Indian\n",
    "•\tCafe\n",
    "•\tDesserts\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cuisines(li):\n",
    "    d={}\n",
    "    for row in li:\n",
    "        cuisines = row.strip().split(\",\")\n",
    "        for cuisine in cuisines:\n",
    "            cuisine = cuisine.strip()\n",
    "            d[cuisine] = d.get(cuisine,0) + 1\n",
    "    return d\n",
    "\n",
    "cuisines_serve_by_max_restaurant_in_DNCR1 = cuisines(df_Delhi_NCR[\"Cuisines\"].values)\n",
    "cuisines_serve_by_max_restaurant_in_ROI1 = cuisines(df_ROI[\"Cuisines\"].values)\n",
    "cuisines_serve_by_max_restaurant_in_DNCR1andROI1 = cuisines(df_India[\"Cuisines\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 cuisines served by maximum number of restaurants in Delhi-NCR and Rest-Of-India\n",
      "North Indian\n",
      "Chinese\n",
      "Fast Food\n",
      "Mughlai\n",
      "Bakery\n",
      "Continental\n",
      "Italian\n",
      "South Indian\n",
      "Cafe\n",
      "Desserts\n"
     ]
    }
   ],
   "source": [
    "cuisines_serve_by_max_restaurant_in_DNCRandROI = sorted(cuisines_serve_by_max_restaurant_in_DNCR1andROI1.items(),key=operator.itemgetter(1),reverse=True)\n",
    "print(\"Top 10 cuisines served by maximum number of restaurants in Delhi-NCR and Rest-Of-India\")\n",
    "for row in cuisines_serve_by_max_restaurant_in_DNCRandROI[:10]:\n",
    "    print(row[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 cuisines served by maximum number of restaurants in Delhi-NCR\n",
      "North Indian\n",
      "Chinese\n",
      "Fast Food\n",
      "Mughlai\n",
      "Bakery\n",
      "South Indian\n",
      "Continental\n",
      "Desserts\n",
      "Street Food\n",
      "Italian\n"
     ]
    }
   ],
   "source": [
    "cuisines_serve_by_max_restaurant_in_DNCR= sorted(cuisines_serve_by_max_restaurant_in_DNCR1.items(),key=operator.itemgetter(1),reverse=True)\n",
    "print(\"Top 10 cuisines served by maximum number of restaurants in Delhi-NCR\")\n",
    "for row in cuisines_serve_by_max_restaurant_in_DNCR[:10]:\n",
    "    print(row[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 cuisines served by maximum number of restaurants in Rest-of-India\n",
      "North Indian\n",
      "Chinese\n",
      "Continental\n",
      "Italian\n",
      "Cafe\n",
      "Fast Food\n",
      "South Indian\n",
      "Mughlai\n",
      "Desserts\n",
      "Mexican\n"
     ]
    }
   ],
   "source": [
    "cuisines_serve_by_max_restaurant_in_ROI= sorted(cuisines_serve_by_max_restaurant_in_ROI1.items(),key=operator.itemgetter(1),reverse=True)\n",
    "print(\"Top 10 cuisines served by maximum number of restaurants in Rest-of-India\")\n",
    "for row in cuisines_serve_by_max_restaurant_in_ROI[:10]:\n",
    "    print(row[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Write a short detailed analysis of how cuisine served is different from Delhi NCR to Rest of India. Plot the suitable graph to explain your inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaination and Answer:- In this we will compare the two regions on the basis of cuisines and number of restaurants serving the cuisines. As done previously we will extract the top 10 cuisines in Delhi-NCR and ROI separately and plot the two line graphs accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "li_DNCR_cuisines = []\n",
    "li_DNCR_count_cuisines = []\n",
    "for row in cuisines_serve_by_max_restaurant_in_DNCR[:10]:\n",
    "    li_DNCR_cuisines.append(row[0])\n",
    "    li_DNCR_count_cuisines.append(row[1])\n",
    "plt.plot(li_DNCR_cuisines,li_DNCR_count_cuisines)\n",
    "plt.xlabel(\"Cuisines\",fontweight=\"bold\")\n",
    "plt.ylabel(\"Number of Restaurants have these cuisines\",fontweight=\"bold\")\n",
    "plt.title(\"Cuisines In DELHI-NCR\",fontweight=\"bold\")\n",
    "plt.grid()\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "li_ROI_cuisines = []\n",
    "li_ROI_count_cuisines = []\n",
    "for row in cuisines_serve_by_max_restaurant_in_ROI[:10]:\n",
    "    li_ROI_cuisines.append(row[0])\n",
    "    li_ROI_count_cuisines.append(row[1])\n",
    "plt.plot(li_ROI_cuisines,li_ROI_count_cuisines,c=\"r\")\n",
    "plt.xlabel(\"Cuisines\",fontweight=\"bold\")\n",
    "plt.ylabel(\"Number of Restaurants have these cuisines\",fontweight=\"bold\")\n",
    "plt.title(\"Cuisines In Rest-of-India\",fontweight=\"bold\")\n",
    "plt.grid()\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86\n",
      "70\n"
     ]
    }
   ],
   "source": [
    "print(len(cuisines_in_DNCR))\n",
    "print(len(cuisines_in_ROI))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the above two graphs we can see that in Delhi-NCR region there are more than 3500 restaurants selling North Indian cuisine while there are only 350 restaurants which sell North Indian cuisine in the Rest of India which is a lot of difference. Moving on to Chinese cuisine there are about 2500 restaurants selling Chinese cuisine in Delhi-NCR while there are about 250 restaurants which sell Chinese cuisine in the Rest of India. Moreover there are 86 cuisines that are sold in Delhi-NCR region while there are 70 cuisines sold in Rest of India.This makes a huge difference as Delhi-NCR region restaurants sell more cuisines than that of Rest of India"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. User Rating of a restaurant plays a crucial role in selecting a restaurant or ordering the food from the restaurant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Firstly we will make a copy of the df_India and store it in df_2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>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>624</th>\n",
       "      <td>3400025</td>\n",
       "      <td>Jahanpanah</td>\n",
       "      <td>1</td>\n",
       "      <td>Agra</td>\n",
       "      <td>E 23, Shopping Arcade, Sadar Bazaar, Agra Cant...</td>\n",
       "      <td>Agra Cantt</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>78.011544</td>\n",
       "      <td>27.161661</td>\n",
       "      <td>North Indian, Mughlai</td>\n",
       "      <td>...</td>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>3400341</td>\n",
       "      <td>Rangrezz Restaurant</td>\n",
       "      <td>1</td>\n",
       "      <td>Agra</td>\n",
       "      <td>E-20, Shopping Arcade, Sadar Bazaar, Agra Cant...</td>\n",
       "      <td>Agra Cantt</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>North Indian, Mughlai</td>\n",
       "      <td>...</td>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>3400005</td>\n",
       "      <td>Time2Eat - Mama Chicken</td>\n",
       "      <td>1</td>\n",
       "      <td>Agra</td>\n",
       "      <td>Main Market, Sadar Bazaar, Agra Cantt, Agra</td>\n",
       "      <td>Agra Cantt</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>78.011608</td>\n",
       "      <td>27.160832</td>\n",
       "      <td>North Indian</td>\n",
       "      <td>...</td>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Restaurant ID          Restaurant Name  Country Code  City  \\\n",
       "624        3400025               Jahanpanah             1  Agra   \n",
       "625        3400341      Rangrezz Restaurant             1  Agra   \n",
       "626        3400005  Time2Eat - Mama Chicken             1  Agra   \n",
       "\n",
       "                                               Address    Locality  \\\n",
       "624  E 23, Shopping Arcade, Sadar Bazaar, Agra Cant...  Agra Cantt   \n",
       "625  E-20, Shopping Arcade, Sadar Bazaar, Agra Cant...  Agra Cantt   \n",
       "626        Main Market, Sadar Bazaar, Agra Cantt, Agra  Agra Cantt   \n",
       "\n",
       "     Locality Verbose  Longitude   Latitude               Cuisines  ...  \\\n",
       "624  Agra Cantt, Agra  78.011544  27.161661  North Indian, Mughlai  ...   \n",
       "625  Agra Cantt, Agra   0.000000   0.000000  North Indian, Mughlai  ...   \n",
       "626  Agra Cantt, Agra  78.011608  27.160832           North Indian  ...   \n",
       "\n",
       "               Currency Has Table booking Has Online delivery  \\\n",
       "624  Indian Rupees(Rs.)                No                  No   \n",
       "625  Indian Rupees(Rs.)                No                  No   \n",
       "626  Indian Rupees(Rs.)                No                  No   \n",
       "\n",
       "    Is delivering now Switch to order menu Price range  Aggregate rating  \\\n",
       "624                No                   No           3               3.9   \n",
       "625                No                   No           2               3.5   \n",
       "626                No                   No           2               3.6   \n",
       "\n",
       "     Rating color Rating text Votes  \n",
       "624        Yellow        Good   140  \n",
       "625        Yellow        Good    71  \n",
       "626        Yellow        Good    94  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2 = df_India.copy()\n",
    "df_2.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Write a short detail analysis of how the rating is affected by restaurant due following features: Plot a suitable graph to explain your inference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Votes given Restaurant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaination and Answer :- We will now group the df_2 by Aggregate rating and find the mean of votes and sort them according to index in ascending order. We will extract the rating from the index and the votes from the values and plot a line graph between the rating and the votes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rating_and_votes = df_2.groupby(\"Aggregate rating\")[\"Votes\"].mean().sort_index(ascending=True)\n",
    "rating = rating_and_votes.index\n",
    "votes = rating_and_votes.values\n",
    "plt.plot(rating,votes)\n",
    "plt.xlabel(\"Aggregate Rating\",fontweight=\"bold\")\n",
    "plt.ylabel(\"Total Votes\",fontweight=\"bold\")\n",
    "plt.title(\"Aggregate Rating Vs Votes\",fontweight=\"bold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer :-  From the given graph we can see that number of votes have huge impact on the ratings.So with increase in number of votes it results in increase of rating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Restaurant serving more number of cuisines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaination and Answer:- In this we will make a new column in df_2 named Cuisines_count with the help of count_cuisines function which returns the count of the cuisines a restaurant  serves. Now We will group the df_2 by Aggregate rating and find the mean of Cuisines_count and sort them according to index in ascending order. We will extract the rating from the index and the Cuisines_count from the values and plot a line graph between the rating and the votes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Restaurant ID</th>\n",
       "      <th>Restaurant Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Locality Verbose</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>...</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Switch to order menu</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Cuisines_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>3400025</td>\n",
       "      <td>Jahanpanah</td>\n",
       "      <td>1</td>\n",
       "      <td>Agra</td>\n",
       "      <td>E 23, Shopping Arcade, Sadar Bazaar, Agra Cant...</td>\n",
       "      <td>Agra Cantt</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>78.011544</td>\n",
       "      <td>27.161661</td>\n",
       "      <td>North Indian, Mughlai</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>140</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>3400341</td>\n",
       "      <td>Rangrezz Restaurant</td>\n",
       "      <td>1</td>\n",
       "      <td>Agra</td>\n",
       "      <td>E-20, Shopping Arcade, Sadar Bazaar, Agra Cant...</td>\n",
       "      <td>Agra Cantt</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>North Indian, Mughlai</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>71</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>3400005</td>\n",
       "      <td>Time2Eat - Mama Chicken</td>\n",
       "      <td>1</td>\n",
       "      <td>Agra</td>\n",
       "      <td>Main Market, Sadar Bazaar, Agra Cantt, Agra</td>\n",
       "      <td>Agra Cantt</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>78.011608</td>\n",
       "      <td>27.160832</td>\n",
       "      <td>North Indian</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>94</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Restaurant ID          Restaurant Name  Country Code  City  \\\n",
       "624        3400025               Jahanpanah             1  Agra   \n",
       "625        3400341      Rangrezz Restaurant             1  Agra   \n",
       "626        3400005  Time2Eat - Mama Chicken             1  Agra   \n",
       "\n",
       "                                               Address    Locality  \\\n",
       "624  E 23, Shopping Arcade, Sadar Bazaar, Agra Cant...  Agra Cantt   \n",
       "625  E-20, Shopping Arcade, Sadar Bazaar, Agra Cant...  Agra Cantt   \n",
       "626        Main Market, Sadar Bazaar, Agra Cantt, Agra  Agra Cantt   \n",
       "\n",
       "     Locality Verbose  Longitude   Latitude               Cuisines  ...  \\\n",
       "624  Agra Cantt, Agra  78.011544  27.161661  North Indian, Mughlai  ...   \n",
       "625  Agra Cantt, Agra   0.000000   0.000000  North Indian, Mughlai  ...   \n",
       "626  Agra Cantt, Agra  78.011608  27.160832           North Indian  ...   \n",
       "\n",
       "     Has Table booking Has Online delivery Is delivering now  \\\n",
       "624                 No                  No                No   \n",
       "625                 No                  No                No   \n",
       "626                 No                  No                No   \n",
       "\n",
       "    Switch to order menu Price range Aggregate rating  Rating color  \\\n",
       "624                   No           3              3.9        Yellow   \n",
       "625                   No           2              3.5        Yellow   \n",
       "626                   No           2              3.6        Yellow   \n",
       "\n",
       "     Rating text Votes Cuisines_count  \n",
       "624         Good   140              2  \n",
       "625         Good    71              2  \n",
       "626         Good    94              1  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def count_cuisines(s):\n",
    "    if type(s)==float:\n",
    "        return 0\n",
    "    cuisines = s.split(\",\")\n",
    "    return len(cuisines)\n",
    "\n",
    "df_2[\"Cuisines_count\"] = df_2[\"Cuisines\"].apply(count_cuisines)\n",
    "df_2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OKQLDMIwkxxSBYRjGAFPb3MZT68riLUYnpggMwzAGmCfXlPGlx96g/HBTvEUBTBEYhmEMOLurnAI43NgaZ0kcpggMwzAGmLJqF2uzrrm9h5IDgykCwzCMAaas2rUIapv6mvKkfzBFYBiGMcBYi8AwDCOJqWlqo9ZTALXN1iIwDMNIOsqruzyFrEVgGIaRhAS7hcBsBIZhGElJ0FCcmZaSMC2CmGYoMwzDMI6krLqJnIxURg/JShgbgSkCwzCMAaSsupFxw7LJzUyzFoFhGEYyUlbdxLhhOXQElGobWWwYhpF8BFsE+VmJ0yKIZc7iLBFZJSJviMibIvLtCGUyReQxEdkmIitFpCRW8hiGYcSb4BiCccOyKchOTwqvoRbgAlWdC5wMXCIiZ4SV+QRQrapTgJ8DP4yhPIZhGHEl6Do6blhOcrQI1FHvLaZ7k4YVuwL4nTf/OHCheFnsDcMPqso7B+riLYZh+CLoOjpuWDYFWem0dgRobuuIs1QxthGISKqIrAcOAi+q6sqwImOBPQCq2g7UAIURjnOLiKwWkdUVFRWxFNkYZCzfdoiLf76U7RX1PRc2jDgTVATjh+VQkOV8dRLBhTSmikBVO1T1ZGAccJqIzAorEunrP7zVgKouUtX5qjp/xIgRsRDVGKQcqG0Gjhy2bxiJSll1I7kZqQzNSacgOx1IjDATA+I1pKqHgZeBS8I2lQHjAUQkDRgCVA2ETMbxQWOre4gONbTEWRLD6Jmg66iIkB9sESSAwTiWXkMjRGSoN58NXAS8FVbsGeAGb/5q4N+qelSLwDC6o6HV9a8eqk8Mf2zDiIZTBNkAFGQN0haBiBSIyDifxUcDL4nIBuB1nI3gORH5johc7pV5ACgUkW3Al4Gv90Yew2hoCbYITBEYiU9wDAFAvqcIEsFG0OPIYhF5BKgE7gbWADkicoeq/l+0/VR1AzAvwvo7QuabgWt6K7RhBGlocS2CKmsRGAlOTVMbdc3tjBuWA0BBtnv9DpYWwcXAVlzXzUFgGfCZWAplGH4xG4ExWOgaQxDWIhgkNoJ8oBX3df8YcD8RXDwNIx502gisa8hIcLrGELgWQW5GKimSGC0CP0HntgI/BbKBq4ATgQOxFMow/BK0EVSZIjASnNDBZIDnOZSeEDYCPy2CTwMvAXcCzwOZwD2xFMow/NJpLDYbgZHghI4hCFKQnRhhJnpsEajqqyJyDTATKOjJSGwYA0mj1zVU39JOc1sHWempcZbIMCITOoYgSH5mYgSe67FFICLzgG3AamCBiGwWkftiLplh+KChtetryrqHjEQmdAxBkEQJPOena+guoBEXDiIAPIIbHGYYcaehpZ1hXlPbFIGRyISOIQhSkD14bARzgQdDlvcCI2MijWH0ksaWDoqHOy+MynpzITUSk5rGI8cQBBlMLYIyYKE3Pwe4DSiNlUCG4RdVpaG1nfGeIrAWgZGo7AkbQxCkICsxbAR+3Ed/hBs7APAz7/fGmEhjGL2gpT1AQOlsEZjnkJGodIafHn5ki6AgK4361nYCASUlJX6pWPx4Df1WRHYAl+LsBM+p6pKYS2YYPVDvuY6OKsgiPVVsUJmRsISPKg5SkJ2OKtS3tncGoYsHfoPObQOeBp4FVETOi51IhuGPRi/OUG5mGoW5mVRZmAkjQSmrbiIvM40h2Ue+7BMlFLWfoHM/Ar4SYZM5bBtxJeg6mpuRyvDcDOsaMhKWoOtoeCbeRAlF7cdGcAvOOPwCEH/ztmF4BAPO5WamUZiXYV1DRsISyXUUEifwnB9F8AbwuKr+KtbCGEZvqO/sGkqlMDeD0kMNcZbIMI5GVSmvbuKMSUfH6kyUUNR+FEE18H0ROc2bB1BV/ULsxDKMnmn0jMU5GWkMz820nARGQlLb1E5dS3v0FkGcB5X5UQTBbGIfC1mngCkCI64EQ1DnZriuoYbWDos3ZCQc3Y0hAOc+CoOjRfCuYzmwiIwHHgJOwIWmWKSqvwgrMwR4GCj2ZPmJqi4+lvqM5KPLRuC6hsDlJRg79OgHzjDiRXgeglAS3kYgIsOBWmDjMR67HfiKqq4VkXxgjYi8qKqbQ8p8Ftisqu8XkRHA2yLyiKpaG9/okeA4AmcszgRcykpTBEYi0d0YAoCMtBSy0lOoa0ncFkEFcC3whwjbtId9UdV9wD5vvk5EtgBjgVBFoEC+OJ+qPKAK80wyfNLY0kGKQGZaCsO9FkGljSUw+oiq8vLWCgTn55+flU5eZhr5WWnkZqT1egRwd2MIguQnQJiJaC/zpThlsAz3wg4iYcs9IiIluFSXK8M23QU8gwtklw98WFUDEfa/BefGSnFxcW+qNo5jGlrbyc1IQ0QoynOKwAzGRl95fuM+bn90XcRtqSnCTWeV8F+XziDVp0LobgxBkIIECDzXrSJQ1aBt4KW+VCAiecATwBdVtTZs83uA9cAFwGTgRRFZFl5OVRcBiwDmz5/fKyVkHL80tnSQm+lu4eGdNgJrERh946FXd1E8PIeff/hk6prbqG9pp665nfrmdjbvq+X+V3ayo7KBX147j7zMns2s3Y0hCJII6Sr9jCz+Eq675ve4r/fRwJdU9Tkf+6bjlMAjqvpkhCI3AT9QVQW2ichOXE7kVf5PwUhW6lvbycl0HkJ5mWlkpKXYoDKjT7y1v5ZVpVX896UncuqEYRHLnFI8lG89u5mrf/MqD9y4IKpNSlUp62YMQZCC7HRq4tw15CfW0Bdxieuvw3XvZOMikkbF6/d/ANiiqj/rpthu4EKv/ChgOrDDh0yGQWOL6xoClwi80MJMGH3koRW7yExL4UPzx3db5vozS/jtjQsor27iiruWs37P4W7L1jS5FkX0FkEadYNAEYwCDgCnAIuB/wEm+tjvbOB64AIRWe9Nl4rIbSJym1fmu8BZIrIR+BfwNVWt7PVZGElJQ2sHuZldYwYK8zIsJ4FxzNQ0tfHU2nIunzuGoTkZUcsunDaCJz5zFlnpKXz43hX8deO+iOWiuY4GKchKpzZRbQQhHAS+BIzDBZ/LBep62klVX8EZlqOV2Qu824cMhnEUja3tjMrP6lwenpvJIctSZhwjT6wpo6mtgxvOKvFVftqofP7y2bO59fdr+Mwja7nm1HG856QTOGtKITleSzWa62gQZyxOcBsB8H/AD4GtwJPAnThPIsOIKw0tHeQUdd3ChbkZ7Kioj6NExmAlEFAefm0X84qHMmvsEN/7FeVl8sgnT+e7z23mqXXl/HlNGRlpKZwxqZB3TR9BeTAhTZQWQX5WGi3tAVraO8hMi8+oeD+Jae4F7g1Z9YnYiWMY/mloaSc3I6RrKNe6hoxjY/n2SnZUNvDzD8/t9b5Z6an831WzueP9M1ldWs2/3zrIS28f5NvPuiFT+ZlpncHlIlGQ3RWKOjMvwRSBiDyDawl8LcJmVdUrYiaVYfigsbXLfRRgeF4Gja0dNLV2kJ1h8YYM//zu1V0U5mZw6ezRx3yMzLRUzp5SxNlTivjfy2ay61ADL711kOF5md2OIYAjk9MUeSPkB5poLYLLgEe833DMl9+IK8HE9aEtgqJc9xAdamhhXEb3TXHDCKWsupF/v3WAT58/uV+7ZiYU5nLj2T371SRCcppoimAibmSxHw8hwxhQmto6UIWc0BZBcFBZfWtULw3DCOWRlbsB+OjpE+JSfyKEoo42sngXgIjY17+RcDQEk9JkHOk+CpidwPBNc1sHj72+h4tmjIpbsMJESE7jx2uolKO7gnoMOmcYsSQ0TWWQQq9rqNJcSA2fPL9hH1UNrXz8zJK4yZAIoaj9vMz/SpciGAacDrweM4kMwwf1IdnJgliLwOgtD722i0kjcjl7SvchIGJNIiSn8eM+eoSxWEQ+CVwVM4kMwweNrV35ioPkZKSSafGGDJ9sKDvMG3sO8633z4zq1RNrXATdBLURBBGRL4eVfx9wcswkMgwfNERoEVi8IaM33PXvbeRnpvGBU8fFVY6UFCE/M76hqP10Df0E1zUUqjIfjY04huGPYIsgPAxwYV4mVRaK2uiB9XsO88LmA3z54mmd7pvxJN7JafwogptC5juAUi+OkGHEjS4bwZF+38NzM3rsGvrIohUsnDaST58/OWbyGYnNj//xFoW5Gdx8TmJ4xxdkxzfwXLSRxSOBUar6u5B1AswSkZGqenAgBDSMSDS2HO01BM5gvO1g9/GGDje28tqOKoZmR48uaRy/LN9WyfJth/jfy2b6SiwzEORnpSWsjeBB7/fS4ApVVRH5f0Aq8N4YymUYUWnwuobCWwSFuRlRs5RtKnfJ78zFNDlRVX70j7cZMySLj52eOGlvC7LSKT/cFLf6o+UjOB2XkSyc54AzYiOOYfijsbWdtBQhM+3IW7gwL5PmtkDnOINwNpS7JCLmWZScvLj5AG/sOcwXLppKVnrixKMqyEqLq40gmiLIAoZHWF8IWLvaiCsNLR3kZKQe5fYXGmYiEpvKawBrEQxmmts6jmm/joDy0xe2Mqkolw+eEl9PoXAKstPjmpMgmiJYB3xNRK4RkQJvuhr4D2DtwIhnGJFpaGk/yj4AUJQXTGIfWRFsKHOKoK65nZb2Y3uhGPHjQG0zC773T/7ryQ0EAr2LfvPMG+W8faCOL797GmmpfpIzDhz5WWnUt7T3+pz6i2hX41u4bGR/BKq96TEg39sWFREZLyIvicgWEXlTRL7QTbnzvTSWb4rIkt6egJGcNLZ2HGUfAJelDIiYqay6oZWy6iZKCnO8MtY9NNj4y7py6lra+cOqPfzP05tQ9ffibG0P8PMX32Hm6AIunXXsoaZjRUFWOgGFhm66NGNNt4pAVf8JXAK8CjQDTcArwLtV9V8+jt0OfEVVZ+BsCp8VkZmhBURkKPBr4HJVPQm45pjOwkg6GlrbI3p8FOZ23yLY6HULnT99pCtjimDQ8dS6cuYVD+W2hZN5dOVuvvnMm76UwWOr97C7qpGvvmc6KSnxG0XcHflxDjMR1XfKUwb/PJYDq+o+YJ83XyciW4CxwOaQYh8FnlTV3V45c0k1fNHQ0n7EqOIg0eINBRXBwukjePDVUipt4NmgYvPeWt7aX8d3rziJ686YQEcgwH3LdpKaItxxWfdhIppaO/jVv95hQckwzp8+YoCl9kcwS1ltcxtjGPgoqAPiRCsiJcA8YGXYpmlAuoi8jOty+oWqPhRh/1uAWwCKixPH5cuIHw0tHYwZevSI0JyMNLLSUyJ2DW0sq2FCYQ6Ti/IAaxEMNp5aV0ZainDZnDGICP996QzaA8ri5aWkpbjlUGXQ3hFgza5qHlm5m4N1Ldz10VPiGlMoGgndIugPRCQPeAL4oqrWRqj/VOBCIBtYISKvqerW0EKqughYBDB//nzLj2DQ2Bq5RQAuHHV3XUMnFw+lKN+1GsxzaPDQEVCeXr+X86ePZJjX/SfiWgIdAfVaBil8euFklrxTwb+2HODltyuoaWojPVW44cwJnDYxkhNkYhDvUNTRRhanqGqgLwcXkXScEnhEVZ+MUKQMqFTVBqBBRJYCc4GtEcoaRicNYfmKQynMOzrwXFVDK+WHm/j4mRPIyUgjOz01YqvBSExe3V7JwboWPnDK2CPWiwjfvvwkOgLKPUu2s2jpdgLq3IgvmjGKC2eM5NypRZ0v2kQl3qGoo7UI2kTkWuAHwOdU9fneHNgLR/EAsEVVf9ZNsaeBu0QkDTc24XTg572px0hOGlqOzFccSmFuBpVhiiBoH5g9dogrE0FZGInLU2vLyc9K44ITRx61TUT47hWzGFWQRXNbBxfOGMXJ44eSmoBG4e6Id7rKaIqgHbgcKAEuFZEjMjdE6ssP42zgemCjiKz31v03UOztf4+qbhGRvwMbgABwv6pu6vVZGElFIKDOfbSbFsHw3Eze3l93xLrgQLKTOhVBJpU2unhQ0Njazt/f3M8VJ4/pdjRwSorw+QunDrBk/Uci2wjW4bx6FLjNm8CFo1YgqiLwIpT2qJJV9cfAj/0IaxjgEtcD3bYIivJcBFJV7TQObig7TElhDkM874yi3Az21zYPjMBGn/jHm/tpbO3gqnmJNRq4P8lKTyUjLSXxbATAlcAHgV8BjwDro5Q1jAGjIUK+4lCG52bQ0h6gobWjc6zBpvJaTlESlZ8AACAASURBVJkwrLNMUV4mm/bWxF5Yo888ubacccOymR/y/x2PFGTFLxR1t4pAVfcDd4vIJuBNoNFb3zhAshlGRBpajk5TGUphnhtdXFXfSl5mGofqWyg/3MQNZ00IKeNsBKGtBiPxOFjbzPJtlXzm/CkJORCsPymIYyhqPwE3tgOPA3VAnYj8W0TG9rCPYcSMSGkqQ+kaXey8groMxUO7yuRl0h5QapvilwzE6Jln3thLQOGqU47/V05+dnrcbAR+FMFdwDnACm86D9ddZBhxoTNxfXeKIO/ICKQby4KG4oLOMsHgdDa6OLF5cm05c8cNYfKIvHiLEnPiGYrajyJYCHxDVc9R1XOAbwDnx1Qqw4hCl40gctdQMBR1MMzExvIaJhXlHpGbtsjrPqqsM0WQqLy9v47N+2q5at7x3xoAZyOIVyhqP4qgEZgmImneALFpuAB0hhEXGrpJUxmk0ItAWhnSNTTLcxvtLNNDuGoj/jy5rozUFOH9c8fEW5QBwaWrTNyuoT/hEtg34ZTCjbhw1IYRFxpbIqepDJKdkUpORipV9a1U1LWwr6aZOePCFEGUcNVG/OkIKE+v28vCaSM6jf/HO/FMTuMn1tDXcIbiYI7i54Hvx0wiw+iBYNdQtMTjw3PdWILgQLLwFsGwnHREOGoEspEYrNpZxf7aZr7xvhnxFmXAyM9Mo7ktQGt7gIy0gU2c06MiUNVW4A5vMoy409iZuL7727cwzwWe21hegwicNKbgiO1pqSkMy8mwwHMJystvHyQ9VSKGlDheCYairmtuG/BWUGLlazMMH9S3tJOeKlG/mgpzMzhU38KGshomFuVGDDpWZPGGEpYlWyuYP2F4t3ag45F4hpkwRWAMOhq7SUoTSmFuBlVe19CcsG6hrjKZnWMNjMThQG0zb+2v47xpiZlEJlYUxDHwXFRFICIpIjJHRE4YKIEMoydCQ0d0x/A8F0tof23zUfaBIBaBNDFZurUCgPOmFcVZkoElYVsEXj6CpcClAyOOYfSMS1MZ2WMoSFFuJsFUtnPGDY1cJi/TbAQJyNJ3KhmRn8nM0QU9Fz6O6ExXGYdBZX66hh4BLvMyjRlG3GmIEoI6yPDOLFZHG4qDFOVlUNvcTkt7R7/LaBwbHQHllXcqOHdqUdLFgIpni8CPJeYGXBrJGhFp8NapqkZubxtGjGmMkpQmyHBvwNjkEXlRMpl5wekaWhk9ZOAThhtHs7G8hurGNhYmmX0AjkxgP9D4UQSVuPwDhpEQNLR2dOat7Y4ib8DY7G7sAxASnK7eFEGisHRrBSJwzpTksg8A5GWkIUJcRhf7GUdQMgByGIZvoqWpDDKqIBMROHl8ZPsAdLUIzE6QOCzdWsHssUOSZjRxKCkpQl5GfALP+XIfFZHPiMhTInKqiHxdRC7wsc94EXlJRLaIyJsi8oUoZReISIeIXN0b4Y3kpLG1vUcbwciCLP5865lce1pxt2U6I5Ca51BCUNPUxro9hzlvavJ1CwUpiFMo6h4VgYj8P1wo6suBIcAM4Ns+jt0OfEVVZwBnAJ8VkZkRjp8K/BD4Ry/kNpKYhpaOHlsEAPNLhkcddBaMQGrxhhKDV7dV0hFQFk5PXkWQH6fkNH5aBB8H7g1ZfgWY3dNOqrpPVdd683XAFiBSPNnPAU8AB33IYiQ5HQGlqa2jX0ac5mSkkpWe0u8RSLcdrOOae17l1W2V/Xrc452l71SQn5kWtTvveCdeoaj9KIJsYF/I8ligV5KKSAkwD1gZtn4scBVwTw/73yIiq0VkdUVFRW+qNo4zGoO5CHoYWewHEaEwt3/HEqzaWcUHfv0qr5dW8/jasn477vGOqrJ0ayVnTSkkPTV5Ax7kZ6XFJWuenyv+EvBlb/4nuMQ0//ZbgTf+4Angi6paG7b5TuBrqhrVkVtVF6nqfFWdP2JE8jYbjZCAc90kpektRXkZ/WYjeG7DXq67fyVF+ZnMnzCM1aXV/XLcZGB7RT3lh5tYOC15gsxFoiA7nbqWxGwRfA5Y583PBZYBX/RzcC+RzRPAI6r6ZIQi84E/ikgpcDXwaxG50s+xjeSkMylNP7QIwNkJ+mojUFXuX7aD2x9dx9zxQ3jy02dxyawT2F3VyIHa5n6R83hnyVbXjZZsYSXCiVeLwI/76F7gXSKS6y039LALAOKGBT4AbFHVn3Vz7Ikh5R8EnlPVv/g5vpGcdOYr7qeolIV5Gby5N7yh6p+OgPK95zezeHkpl84+gZ996GSy0lNZUDIcgNdLq7hsTnJk2OoLS7dWMGlELuOG5cRblLhSkJVOfUs7qjqgI6v9eA2NE5HHgV3ALhH5k4iM83Hss4HrgQtEZL03XSoit4nIbX2U20hS6jtbBP3TNeTyFrSg2vsxk20dAW5/dC2Ll5fyiXMmcte1p5CV7uSaOaaA7PRU6x7yQXNbByt3Hkpqt9Eg+VlpdAS084NnoPDzWfUYcCZQBgiuC2cMcE60nVT1Fa+8L1T1Rr9ljeQlaCzuaRyBXwpzM2jrUGqb2xmSfXTOgmj8a8tB/rZpP1+75EQ+ff7kI7alp6Ywr3gor5dW9YucxzOrdlbR3BZIarfRIKFhJgYyF4MfG8E04P9UtVhVxwP/D5geW7EMIzINXr7i/moRFPVhdPGaXVVkpKVw8zklEbfPLxnOln21cctDO1hYurWCjLQUzphYGG9R4k68As/5UQQPACNEJFVE0oBC4DexFcswItPpPtpPX0tdg8p67zm0elc1c8YOITMtslJaUDKMgMK63Yf7JOPxztJ3KjitZDjZ/aTcBzOdyWkGOMxEt4pARGpFpBb4DPApoBlo9Oa7DRdhGLGkvrNF0H/GYuj96OLmtg42lddw6oRh3ZaZVzyMFIHV1j3ULXsPN7H1QH1SRhuNRLBFMNCji6M9TVV0RR09NACyGEaPNHrG4v76egwqgspeji7eWF5DW4dGVQR5mWmcNGYIq0wRdMtT68oBki4tZXeUFOYiApvKa7ngxFEDVm+3isCijhqJSENrBxmpKVFjCPWG4TkZiEBlXe9aBGt2OW+gU6IoAoD5JcP4w6rdtLYH+k3m44Hmtg6+/exm/rBqN2dNLmTaKMt7BTAsN4PZY4ew7J0KPn/h1AGrt8f2tYikAJcBE4HgZ5iq6s9jKZhhRKKxtZ3cfhpVDJCWmsKwnIxeJ7FfXVrNxKLcThtDdywoGc7i5aW8ubeGecXRlUaysO1gHbc/uo639tfx6fMn8+WLpyVdNrJonDu1iHuW7KCuuY38rN55sh0rfjpan8BFHg39pxQwRWAMOPUt7eT0k30gSGFu75LYqyprd1dzwYk9h0OY77UYVpdWmyIAHl9Txv/+ZRM5Gak8eNMCzp+e3CElInHu1BHc/dJ2XttRxcUzB6Z7yM8TdTHwAvBnwJK7GnGlsaWjX1sE4OwEvVEEOysbqGpojWofCDKyIIsJhTm8XlrFp86b1BcxBzUNLe3879ObeHJtOWdMGs4vPjKPUQVZ8RYrITmleBg5Gakse6cioRTBX4Bq4CFVHfggGIYRQkNrDFoEeZls2ec/zETQPjDfhyJw5Ybz0tsHBzxsQKJQ3dDKdQ+sZPO+Wr5w4VQ+f+FUUlOS7zr4JSMthTMmFbLsnYELY+7HevUw8GmgKehSKiI1MZbLMCLS2NpBXj+PuCzKzeiVsXjNrmoKstKYPMKfgfO0icOoamhle4WvMF3HFYfqW7j2vtd452A9v71hAV+6eJopAR+cO7WInZUN7KlqHJD6/CiC3+HsAwdwbqSHcK6lhjHgNLS0k9PPA4+K8jKpbW6ntT3gq/zqXdWcOmEYKT5faPO9AHTJNp6gsr6Fj963kp2VDTxww3ze5cOmYjjO9eIuDVSrwI8iqAQ+r6rjVHVicIq1YIYRiYbW9n6PwRJMlF7lYyzB4cZWth2s92UfCDKpKJfhuRm8nkQB6CrqWrh20Wvsqmpg8Y0LOl9shj8mj8hlzJAslr0zMIm4/DxRq4CvishIINglZO6jRlxobOno9xZB56Cy+hZOGBLdgLl2t3uZnzphuO/ji4hLVLMrOVoEB2ubufa+19h7uJnFN57GmZMthlBvERHOnTqCv23aR3tHgLQYZ23zc/QbgGLgf3EZyoKTYQw4Da3t/W8jCFEEPbFmVzWpKcLc8UN6VceCkuHsOtTIweM8Uc2B2mY+sug19tU087ubTQn0hXOnFVHb3M6G8tibZP08UTfTFWrCMOJGe0eA5rZAv3sN9Sbw3OrSak4aU9BrGeaXeOMJdlVz6ezRvRcywWnrCPDk2jLu/Oc71Da18dDNp3XaRoxj4+zJRYjAK+9UckqMx6D4yVD2YEwlMAyfNLYFs5P1d9eQpwh6GF3c1hHgjbLDXHtaca/rmDV2CFnpKbxeWnVcKYL2jgBPrSvnV//exu6qRuaOG8I9153K3PFD4y3aoGcgw034CTGxI8JqVdXJEdYbRsxobOnfNJVBcjNSyUxL6bFFsHlvLc1tgV4ZioOkp6Ywb/yw4yZRTUdAeeaNcn7xz3coPdTIrLEFPHDDfC44cWRSjpWIFQMVbsKPjWAkMMKbioESoMfhbiIyXkReEpEtIvKmiBwVulpEPiYiG7zpVRGZ20v5jSSiIZidrJ+NxSJCUV4mFT3YCIIDyY5FEYDLT7B5b21nus3BSk1TG5f+YhlfeuwNsjPSWHT9qTx7+zlcOGOUKYF+5typI+gIKCu2xzYAtJ+uoc5RMyKSAfwAfwqkHfiKqq4VkXxgjYi8qKqbQ8rsBBaqarWIvBdYBJzeqzMwkoaGznzF/Z/Cz0+YiTW7qhk7NJvRQ7KPqY75JcO9RDXVg9qd8o+rdvP2gTp+/uG5XDF3rO/xFEbv6Qo3Ucm7TzohZvX4SV5/SnACTgYygI/3tJ+q7lPVtd58HbAFGBtW5lVVDTpXvwaM66X8RhIRTFOZ0882AnAG42g2AlVl9a6qY24NAMwrHkqKwGs7Bm96j/aOAA+t2MUZk4Zz1bxxpgRiTFe4idiOJ/DzZb8aeN2bVuAylm3tTSUiUgLMA1ZGKfYJ4G/d7H+LiKwWkdUVFQMzwMJIPIJpKvvbfRR6jkBafriJA7UtfVIE+VnpnDm5kN+8vJ27X9pGIDD4nPFe2HyA8sNN3Hy2jSkdKM6dWkTpoUZ2H4pduAk/iuChkGkxcAdwhd8KRCQPF8r6i6oaMbKXiLwLpwi+Fmm7qi5S1fmqOn/EiMHbpDb6RrBvvb/dR8F5Dh2qb0U18su5r/aBIPdcdyqXzh7Nj//xNjc9+HqvU2TGm8XLdzJ+eDYXzhi47FnJTme4iW2x+wjuURGo6o2qepM3fVJVv6eqB/wcXETScUrgEVV9spsyc4D7gStUdfC2mY2Y09gaG/dRcIPKWjsC1DZHNuSu2VVNTkYqJ56Q36d68rPS+dW18/jelbNYseMQ7/vlK4PGk2hjWQ2vl1Zz41kTLXDcABIMN/FKDOMOdftpJSLPRNlPVTVqq0Cc+8ADwBZV/Vk3ZYqBJ4HrVbVX3U1G8tEQwxZB16CyFoZkH+2mt2ZXNfOKh/bLUH8R4bozJnDy+KHc/uhaPrLoNf7j3dO59bxJCd3nvnj5TnIzUrlmvpnyBpKBCDcR7Ym6LMI6xUUi9dO5eTZwPbBRRNZ76/4b54KKqt6D62YqBH7tuZ21q+p8f6IbyUZni6Cf3UehK97QoYZWJoX1Pta3tLNlXy23v2tKv9Y5a+wQnv3cOXz9yY388O9vsW53NXd/7BTSYxxX5lg4WNvMsxv28rHTJ1AwQOkTjS7OnVbEY6v3sKG8JiajjKMpglBrUCZwI85QnA881dOBVfUVjkxvGanMJ4FP9iilYeBaBJlpKTH5IirM7WoRhLN+92ECCqfGIGRCflY6d107j3njh/K957fwzWfe5P+unJVw/vgPr9xNe0C54aySeIuSlATDTSzbGptwE90qAlXdJSI5wG3Al3EDyx4FfqCqb/W7JIbRA7EIQR0kGHiuIsxzaHtFPd957k0y01KYVxybsAkiwifPncShhlZ+8/J2SgpzuOW8xBm439zWwSOv7eKC6SOZWJQbb3GSkmG5GXzl4mkxi98UzUZwB/A5IAf4LfAjVd0TEykMwwexCEEdZFiu1zUU0iJ4fsM+/vPxN8hMT+X+G+bHvEvkq++ezu5DjXz/r28xflgO702QmETPvrGXQw2t3HyOuYzGk9sviF28oWifV9/C2QLacKGobwhprqqq9i4Or2H0kViEoA6SnprCsJx0DtW30toe4P/9bQuLl5cyr3god3/0FMYMPbbRxL0hJUX46YfmsremiS8+tp4ThmQxL8ZRJ3tCVVm8vJTpo/I5y0JKH7dE62zd7U376EpRaakqjbjREMMWAbixBG/vr+Mji1aweHkpN51dwmO3nDkgSiBIVnoq9318PiMLMvnUQ6sHLGdtd6zcWcXmfbXceHZJwtktjP6jW0WgqiWhqSnDp4EU0jAgtjYCcKOLV5VW8fb+Ou766Dy++f6TyEgbeA+eorxMFt+4gNb2ADc/+Do1TW0DLkOQxct3Miwnnavmje25sDFoSTw/NcPohljaCADOmFTI3PFDefr2c7hszpiY1eOHKSPzuef6U9lZ2cDtj67tdsRzLNlT1ciLmw9w7WnFZKXH7rob8ccUgTFoiHWL4EsXT+Ppz57NlJF5PRceAM6aXMTXLjmRZe9U8s7B+gGvf9HSHaSmCNefOWHA6zYGFlMExqChoaU9JiGoE5nzp7vRbZsGIG9tKAdqm3ls9R6uPnXcMYfdNgYPvhSBiGSIyMkiYp5CRtxoaO2ISQjqRGbSiDyy01PZOMCK4N4lO+gIKJ9e2L+jqY3ExE8+gnnAdlw46gVexrH7Yi6ZYYTQ1hGgtT2QdC2C1BRh5pgC3iyPGLg3JlTWt/Doql1ccfIYigtzBqxeI374aRHcBTTgwkUEgIeBi2IplGGE0xV5NLkUAcDssUN4c2/NgOUvuH/ZTlraA3y2n2MrGYmLH0UwF3gwZHkvLtyEYQwYXWkqk6trCOCkMQU0tHaw81BDzOuqbmjl9ytKuWzOGCaPSAyjuRF7/CiCMmChNz8HF3uoNFYCGUYkgtnJcpKxRTDOmeYGwmC8ePlOGlo7+j3SqpHY+FEEPwLeg+sa+imwAPhhLIUyjHCC+YqTsUUwZUQemWkpMVcEtc1tLH61lPecNIrpfUzAYwwuevy8UtXfisgO4FKcMnhOVZfEXDLDCKHBaxEko40gLTWFGaMLYu459NCrpdQ1t/O5GAY3MxKTHp8qL4vYDpzROLiuoLv8w4YRC7paBMmnCABmjS3g6XV7CQQ0JlnMGlraeeCVnVxw4khmjTUv8WTDT9dQKbAzbKoWkZUiUhIzyQwjhC4bQfJ1DYHzHKpraWd3jILQPbJyF9WNbdx+gdkGkhE/imAZLhz1CuA1b34Vzpvop93tJCLjReQlb9zBmyLyhQhlRER+KSLbRGSDiJxybKdhHO8ke4vgpDGewXhv/3cPNbd1sGjpTs6ZUhST7FdG4uNHERwG/ktVz1HVs3F5h/cCnwfOjbJfO/AVVZ0BnAF8VkRmhpV5LzDVm24BftNL+Y0kobHTRpCcLYJpo/LJSE2JiZ3gsdf3UFnfwuesNZC0+FEE5wFniMhEEZkInA5cAGwDunU0VtV9qrrWm68DtgDhsWyvAB5Sx2vAUBFJjLRMRsKgqpRVNwGQk6Qtgoy0FKafkB8Tz6E/rd7DnHFDOH2SJZ5JVvw8Vc8C1wFXhqx7BJgNbPVTiWdLmAesDNs0FghNf1nmrdsXtv8tuBYDxcXFfqo0jgPaOwL8ddN+7l2ynTf31jJ1ZB6pMTCUDhZmjR3CXzfuQ1X7LUnMtoN1vLm3ljsuC2+sG8mEH0XwSVycoXfh7AMvAfcC6cADPe0sInnAE8AXI3gaRbqbjxpHr6qLgEUA8+fPH/jA7MaA0tTawZ/X7OG+ZTvYU9XEpBG5/PCDs7kyyZOjzBpbwB9W7aasuonxw/snBtDT6/eSInDZXGuIJzN+xhG0Ar/0plBae9pXRNJxSuARVX0yQpEyYHzI8jic/cFIQqobWnloxS5+t6KUqoZW5hUP5X/eN5OLZ4yKicvkYGP22K4Rxv2hCFSVp9fv5ewpRYzMz+rz8YzBi59xBDOBn+O6goJ3i6pq1A5FcW3XB4Atqvqzboo9A9wuIn/E2R5qVHVfN2WN45Q9VY088MpOHnt9D01tHVx44khuXTiZBSXDLE9uCNNG5ZOWImwsr+G9s/v+Bb9292F2VzXy+QttAFmy46dr6B6cq2g+7gt+HLDRx35nA9cDG0Vkvbfuv4FiAFW9B/grbsTyNqARuKk3whuDm817a7l36Xae27APAa44eSy3LpzEtFEW3iASWempTBuVz6a9/TOW8+n15WSmpfCek0b1y/GMwYsfRTAP+AHwHeBmnBdRj3eOqr5CZBtAaBkFPutDBuM4QVVZsf0Q9yzdwdKtFeRmpHLTWSXcfM5Exgy1TFg9MWtsAf/ccrDPBuO2jgDPb9jHRTNGkZ+V3o8SGoMRv754wX779+NaBlcAt8ZEIuO4pCOg/H3Tfu5dup0NZTUU5WXy1fdM57rTJzAkx15Efpk9dgh/Wl3GvprmPinOV7ZVcqihlStOHtOP0hmDFT+K4B2cS+cK4HM4r57XYymUcfzQ3NbB42vKuG/ZDnYdamRiUS7fv2o2HzhlLFnpyTk4rC+c5BmMN5bX9EkRPL2unCHZ6Zw/3VKLGP4UwcW4l/8DQDBMRLgHkWEcweHGVn6/YhcPvlrKoYZW5o4fyn+990QunnlCUo8F6CszRxeQmiK8WV7De0464ZiO0djazgubD3DFyWPJSPOVttw4zomqCEQkFdgE/K+q3g98fUCkMgYt5YebeGDZTv74+m4aWzs4f/oIbls4mdMnDjcPoH4gKz2VKSPy+hRq4sXNB2hs7bBuIaOTqIpAVTtEZBMweYDkMQYpb+2vZdGSHTzzhjMnXT53DJ86bxIzRhfEWbLjj1ljh7Bka8UxG4yfXr+XMUOyOK1keAykMwYjfrqGcoD/FJGL6TIaq6peETuxjMGAqrJyZxX3LNnOy29XkJORysfPLOET505krHkAxYzZYwt4Ym0ZB+taGFXQu4FgVQ2tLN1awSfOnWiD9IxO/CiCM73fU7wJIoSBMJKHjoDy4ub9/GbJDt7Yc5jC3Az+493TuO6MCQzNyYi3eMc9wcQxG8tqGDWzd4rg+Q17aQ8oV8xN7nAdxpH4UQQTYy6FMShobuvgybXl3LdsBzsrG5hQmMP3rpzF1aeOMw+gAWTmmAJEXG6Ci2b2bjDYX9bvZdqoPGaMtkF7Rhd+Yg3tEpE5uNDTz+BcSUtjLJeRQNQ0tvHwyl0sXl5KZX0Ls8cO4e6PnsIls8wDKB7kZKQxeURer0NS76lqZM2uar76nulmuDeOwE+soY8Av8flLtgA/BdQD1wVW9GMeLOvxnkA/WHVbhpaOzhv2ghuO28SZ04utBdJnJk9dggrth/q1T6hhnzDCMVP19C3gX8DF3nLz+OUgXGcsvVAHfcu2cHT68tR4LI5o7n1vMnMHGMeQInCSWMKeGpdORV1LYzIz+yx/LaD9dy/bAenlQzvtxDWxvGDH0UwBvgtXYqgDTCXkOMMVeX10mruXbKdf711kOz0VK47YwKfOGeivTgSkLnjhwJw75LtfON9M6K20PZUNXLd/StJTUnhh1fPGSgRjUGEH0WwEfi4N389cAnwRswkMgaUQEB5ccsB7l2ynbW7DzM8N4MvXTSNj585gWG55gGUqMyfMIxrTxvP/a/s5FBDKz/84JyIo4QP1jVz/QMraWxt57Fbz2RiUW4cpDUSHT+K4CvAc7hIojcAVcB/xFIoI/a0tHfwl3Xl3Lt0BzsqGhg/PJvvXHES15w6nuwM8wBKdESE7181m7FDs/nJC1s5WNfMb647lYKQSKI1jW18/IFVHKxr4eFPnm6D+4xu8aMIVgFTcOMJBHhVVatjKpURM2qb23h05W5++8pODta1cNKYAn517TzeO+sE0lIt7sxgQkS4/YKpjBmazX8+voEP3bOCxTctYPSQbBpa2rnpwVXsqGjgtzcu4JTiYfEW10hg/CiCfcAfcekmw5PPG4OE/TXNLF6+k0dW7qa+pZ1zpxbxsw+dzNlTzANosPOBU8YxMj+L2x5ew1V3v8q915/Kj//xNuv3HObXHzuVc6YWxVtEI8ERlxsmSgGR14FTcaOJtwMP45TC9tiLdzTz58/X1atXx6PqQcm2g3UsWrqDp9aV0xFQ3jdnDLeeN6lzdKpx/LBlXy03LX6d/bXNAPzkmrlcfeq4OEtlJAoiskZV50fc1pMi8A5QDFztTacBqGpPkUt/C1wGHFTVWRG2D8EplWJcy+Qnqrq4J1lMEfhjza4qfvPyDv655QBZ6Sl8aP54PnXuJPMAOs7ZV9PEfz6+gUtmncDHTp8Qb3GMBCKaIvCboSwdyAAycQPL/MQaehC4C3iom+2fBTar6vtFZATwtog8oqqtPmUywggElH+/dZB7lmxn9a5qhuak8/kLp3LDmRMozOvZ19wY/Iweks3vP3F6vMUwBhl+RhavA+bgDMVvA3cAj/S0n6ouFZGSaEWAfHEd1Hk4b6T2nkU2wmltD/CX9eUsWrqDbQfrGTs0m2+9fyYfWjCenAy/ut4wjGTFz1viBOAXOLvAmn6s+y5c7KK9uDzIH1bVQKSCInILcAtAcXFxP4owuKlrbuMPq3bzwCs7OVDbwozRBfziIyfzvtmjzQPIMAzf+FEEY0Nf0CJyEu6lfUcf634PsB4XzG4y8KKILFPV2vCCqroIWATORtDHegc9B2ubWfxqKQ+/tou65nbOmlzIj6+ey7lTi8wDyDCMXuMn+mhARE4EPgR8GDjR29RXRXAT8AN11uptIrLTO/aqPh73uGV7RT33Ld3BeoTnMwAADJZJREFUk2vLaQ8EeO+s0dy6cBJzxg2Nt2iGYQxiulUEIjIV9/L/EDALZyNQXNC53/dD3buBC4FlIjIKmA7s6IfjHnes3e1iAL2w+QAZqSl8aME4PnnOJEosXIBhGP1AtBbB27gX/z7gbtyX+kPA/ar6TE8HFpE/AOcDRSJSBnwT532Eqt4DfBd4UEQ24pTM11S18thP5fgiEFBe3nqQe5bsYNXOKoZkp3P7u6Zww1klFJkHkGEY/UhPXUMBYAkuDPXbvTmwql7bw/a9wLt7c8xkoLU9wLNv7OXepdvZeqCeMUOyuOOymXx4wXhyM80DyDCM/ifam+XzwDU4u8BHgFZcC2GBiCxX1d5lxTCiUt/Szh89D6B9Nc2ceEI+P//wXC6bM4Z08wAyDCOG+AkxMZouQ/HpuG6cDlVNj7pjjDjeRhZX1LXw4Ks7+f2KXdQ2t3PGpOHcunAy508bYR5AhmH0G30aWayq+3DjCH4hIuNwCuGa/hUx+dhZ2cB9y3bw+Joy2joCXHLSCdxy3iTmWZRIwzAGmF51OqtqGfBTbzKOgTf2HOaeJdv5+5v7SU9N4YOnjOOW8yZZwhDDMOKGWR8HAFVlydYK7lmyndd2VFGQlcZnzp/MDWeVMDI/K97iGYaR5JgiiCFtHQGe27CXe5fs4K39dZxQkMX/vG8GHzmtmDzzADIMI0Gwt1EMaGhp57HX9/DAKzspP9zE1JF5/OSauVw+d0zEvLKGYRjxxBRBP1JZ38JDr5byuxW7qGlq47SS4Xz3ypM4f9pIUlLMA8gwjMTEFEE/sOtQA/cv28mfVu+htSPAu2eO4pbzJnPqBPMAMgwj8TFF0Ac2ltVwz9Lt/G3jPtJSUvjAKWP51HmTmDwiL96iGYZh+MYUQS9RVZa9U8m9S7ezfNsh8jPTuOW8ydx8dgkjC8wDyDCMwYcpAp+0dwR4fuM+7l2yg837ahlVkMl/vfdEPnp6MflZcRlkbRiG0S+YIuiBxtZ2/ry6jPuW7aCsuonJI3L50dVzuOLkMWSmpcZbPMMwjD5jiqAbqhpa+d2rpTy0opTqxjbmTxjGN99/EheeaB5AhmEcX5giCGNPVSP3L9vBY6v30NwW4KIZo7ht4STmlwyPt2iGYRgxwRSBx6byGhYt3cHzG/eRInDlyWO5deEkpozMj7dohmEYMSWpFYGq8ur2Q9yzZDvL3qkkLzONT54zkZvOnsgJQ8wDyDCM5CBmikBEfgtcBhxU1VndlDkfuBOXwrJSVRfGSp5Q2jsC/G3Tfu5dup1N5bWMyM/ka5c4D6Ah2eYBZBhGchHLFsGDwF24PMdHISJDgV8Dl6jqbhEZGUNZAGhq7eDxNXu4b9lOdlc1MmlELj/84GyunDfWPIAMw0haYqYIVHWpiJREKfJR4ElV3e2VPxgrWQD+/dYB/uPPG6hqaGVe8VC+8b4ZXDxjlHkAGYaR9MTTRjANSBeRl4F84Beq2l3r4RbgFoDi4uJjqqykMJd544dy68LJLCgZZmkgDcMwPOKpCNKAU4ELgWxghYi8pqpbwwuq6iJgEbicxcdS2aQReTxw44I+iGsYhnF8Ek9FUIYzEDcADSKyFJgLHKUIDMMwjNgRzywpTwPnikiaiOQApwNb4iiPYRhGUhJL99E/AOcDRSJSBnwT5yaKqt6jqltE5O/ABiAA3K+qm2Ilj2EYhhGZWHoNXeujzI+BH8dKBsMwDKNnLIGuYRhGkmOKwDAMI8kxRWAYhpHkmCIwDMNIckT1mMZnxQ0RqQB2HePuRUBlP4ozmEjWc7fzTi7svLtngqqOiLRh0CmCviAiq1V1frzliAfJeu523smFnfexYV1DhmEYSY4pAsMwjCQn2RTBongLEEeS9dztvJMLO+9jIKlsBIZhGMbRJFuLwDAMwwjDFIFhGEaSkzSKQEQuEZG3RWSbiHw93vIMBCLyWxE5KCJJFdVVRMaLyEsiskVE3hSRL8RbpoFARLJEZJWIvOGd97fjLdNAIiKpIrJORJ6LtywDiYiUishGEVkvIquP6RjJYCMQkVRcwpuLcQlxXgeuVdXNcRUsxojIeUA98JCqzoq3PAOFiIwGRqvqWhHJB9YAVybB/y1ArqrWi0g68ArwBVV9Lc6iDQgi8mVgPlCgqpfFW56BQkRKgfmqeswD6ZKlRXAasE1Vd6hqK/BH4Io4yxRzVHUpUBVvOQYaVd2n/7+9cw+xqori8PejMhsLpJBKLUZSBGuwMiKbhuxlmSIWNGoZ2oMykrKUoiIyogyMjJAeoBVqKVpWWlRqqWQPE7Oyh71oILW0KLUxopTVH2tf5vwxLxvnnmtnfXA495y999pr37mz19p7n7O22cfp8x/4hke98tWq8zGnMV0elo7/v6cHSOoNDAdm563LwUhRDEEv4MfM9RYK0DEEIKkaOA1Yl68m5SFNj3wC7ABWmFkh2g08BtyBb3JVNAxYLmmDpBv+i4CiGAI1c68QnlKRkXQk8BIw2cx2561POTCzfWZ2KtAbOFPS/35KUNIIYIeZbchbl5yoNbPTgWHAzWlKeL8oiiHYApyQue4NbMtJl6AMpDnyl4DnzWxJ3vqUGzPbCawGLslZlXJQC4xMc+ULgfMlzc9XpfJhZtvSeQfwMj4Vvl8UxRCsB/pJ6iOpCzAGWJqzTkEnkRZN5wBfmdmjeetTLiT1kNQ9fT4CuBDYnK9WnY+Z3WVmvc2sGv/ffsfMxuWsVlmQ1C09EIGkbsBQYL+fEiyEITCzvcAk4C184XCRmX2Rr1adj6QFwAdAf0lbJF2Xt05loha4GvcMP0nHpXkrVQaOB1ZJ+gx3flaYWaEepSwgxwJrJX0KfAS8bmZv7q+QQjw+GgRBELRMIUYEQRAEQcuEIQiCICg4YQiCIAgKThiCIAiCghOGIAiCoOCEIQgqDkmTJFk6+uetT3uRVCVpmqQJZahrQKprSObec+k7K9zm7UHHCEMQVCL1NMWMqe+MClJE2gNNFXAfMKGjgiQd2kaWAamuIZl7TwJjge87Wn9QLMIQBBWFpJ74C2GL8DAg9Zm0wyXNlbRT0lJJa5IHXJ3Sp0j6NQXfKnnHE1Jag6Q9kp6QtAuokTRY0geSGiV9I2lspq7WZC2W9LukvyR9KemyVKwUC/7clH+apC6SHpG0Nem9WFKPZtpdncq8L2klsDW9Kbwx6dco6V1JJ6f2Lk5F70vlhgA3AQuAkzLy1kp6VdJuSS+kt66RdKWknyRtljSrpG8H/3zBQUoYgqDSuAL/XS4GlgCnSBqQ0m7E3xheDbwHnFMqJGkg8AiwHd/I++JmZFcBPYGpeHTO14DuwINAAzBP0qntkLUej3R5V7qeK6krcHe6/gr3zF9MeaYAy/AImcNwz70lBuP7J9yLj4qWALcCDwMDk4xf0hk8ntJYoKW9FgYDHwJfp3znSDoWD8FhSc4FregTFIC2hp9BUG5GA3/jMXKq8NAg9cA04LyUZ6qZfSdpJHB2ujcknWea2WxJJ9LUMWcZb2a7JA0Hjk7HQ5n084F9LclKU0oD8E61S6ZcNbA8fd5hZgtT/mfTvRszeYe20v6NZnZnKtsTDxo3mKYIujVmtkfSe8Bk4PNMXc3JW2dm0yUZvmlLdWpzV+AZM3tK0j7c4AUFJQxBUDFIOgE4C+/0srGgRuOGoERrcVFaS9tjZrtK1aXzXGBeJk8DvsFJS7IuAsYDb+Pe9MSUv2sL+QXsBUbQZGBaG4lno+Leghu6WfiIYg5wVCu6NUdpY6K96ZxdG4n4MgEQhiCoLOrxjnM6HkAL4DpghKQaYBUwCpghaR1uNEqsTufb0kLrtW3U9T7eSV6CT/UcinfWD7Qhq2RAqnDvujaTthufzukr6Sp8q8hlwCDceKzERxN9aBo9tEapriOBOjx8esmQ/Z7OdZLGAK+2Q16JD4G/gGskbcFHFkGBiTWCoJKox73UmWb2ipm9QpO3Php4GpiPz2mfhXfgADvN7FN87v843EtfWUprriIz+w3v+L/D59/vAf4EGtqQtRyPeV8DXI5HtC3J/AeYga87zMc77+npXh3u2Q8D1rTz+3g8tXFU0iUbXngtPiqpwxeIj2mnTMxsO25gD0ntXJVpX1BAIvpocNAg33HsemAT0BfvKDeZ2RkpfSLwA+5BzwB6AP3M7Of/UNcBk1WJSBqHjyoE3I8vRA9KRjAoGDE1FBxMCLgG6A80Am8At2fSa/FOG9x7vr4DHfeBlFWJnIIvYB8OfAuMDSNQXGJEEARBUHBijSAIgqDghCEIgiAoOGEIgiAICk4YgiAIgoIThiAIgqDg/Au4HxKw4dKxfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a1=df_2.groupby(\"Aggregate rating\")[\"Cuisines_count\"].mean().sort_index()\n",
    "plt.plot(a1.index,a1.values)\n",
    "plt.xlabel(\"Aggregate rating\",fontweight=\"bold\")\n",
    "plt.ylabel(\"Average Number of Cuisines\",fontweight=\"bold\")\n",
    "plt.title(\"Aggregate rating vs Average Number of Cuisines\",fontweight=\"bold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the graph we can see that more the cuisines more is the rating but for some restaurants this is not applied"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Cost of Restaurant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaination and Answer:- We will now group the df_2 by Aggregate rating and find the mean of Average Cost for two and sort them according to index in ascending order. We will extract the rating from the index and the Average Cost for two from the values and plot a line graph between the rating and the votes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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": [
    "final_currency = df_2.groupby(\"Aggregate rating\")[\"Average Cost for two\"].mean().sort_index()\n",
    "rating = final_currency.index\n",
    "average_cost = final_currency.values\n",
    "plt.plot(rating,average_cost)\n",
    "plt.xlabel(\"Aggregate Rating\",fontweight=\"bold\")\n",
    "plt.ylabel(\"Average Cost of two\",fontweight=\"bold\")\n",
    "plt.title(\"Aggregate Rating vs Average Cost of two\",fontweight=\"bold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer:- From the graph we can conclude that higher the average cost of restaurant higher is the rating but not applied to the 0 to 2 ratings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Restaurant serving some specific cuisines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaination and Answer:- In this we will firstly extract the unique values of Rating text using value_counts and then whose rating text is Excellent and Very Good we will extract that cuisines and make a dictionary of these cuisines and their count. Then we will sort the dictionary in the reverse order on the basis of count and make the list of special cuisines. Then using specialcuisines function we will make a new column which stores the special cuisine of that restaurant. Then we will group the df_2 by Special_Cuisine and find the median of Aggregate rating and sort them according to values in descending order. We will extract the rating from the values and the cuisines from the index and plot a bar graph between the cuisines and the rating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average      3678\n",
      "Not rated    2139\n",
      "Good         1847\n",
      "Very Good     692\n",
      "Poor          180\n",
      "Excellent     116\n",
      "Name: Rating text, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_2[\"Rating text\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['North Indian' 'Continental' 'Italian' 'Chinese' 'Cafe' 'Fast Food'\n",
      " 'Asian' 'Desserts' 'Mughlai' 'American' 'European' 'Mexican' 'Bakery'\n",
      " 'Thai']\n"
     ]
    }
   ],
   "source": [
    "dict_cuisines = {}\n",
    "dict_rating = {}\n",
    "dict_count = {}\n",
    "array_of_rating_text = df_2[[\"Cuisines\",\"Aggregate rating\",\"Rating text\"]].values\n",
    "for row in range(len(array_of_rating_text)):\n",
    "    food = array_of_rating_text[row][0].strip().split(\",\")\n",
    "    for foo in food:\n",
    "        foo=foo.strip()\n",
    "    if array_of_rating_text[row][2]==\"Excellent\" or array_of_rating_text[row][2]==\"Very Good\":\n",
    "        for foo in food:\n",
    "            foo=foo.strip()\n",
    "            dict_cuisines[foo] = dict_cuisines.get(foo,0)+1\n",
    "special = np.array(sorted(dict_cuisines.items(),key=operator.itemgetter(1),reverse=True)[:14])\n",
    "special_cuisine = special[:,0]\n",
    "print(special_cuisine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Restaurant ID</th>\n",
       "      <th>Restaurant Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Locality Verbose</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>...</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Switch to order menu</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Cuisines_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>3400025</td>\n",
       "      <td>Jahanpanah</td>\n",
       "      <td>1</td>\n",
       "      <td>Agra</td>\n",
       "      <td>E 23, Shopping Arcade, Sadar Bazaar, Agra Cant...</td>\n",
       "      <td>Agra Cantt</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>78.011544</td>\n",
       "      <td>27.161661</td>\n",
       "      <td>North Indian, Mughlai</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>140</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Restaurant ID Restaurant Name  Country Code  City  \\\n",
       "624        3400025      Jahanpanah             1  Agra   \n",
       "\n",
       "                                               Address    Locality  \\\n",
       "624  E 23, Shopping Arcade, Sadar Bazaar, Agra Cant...  Agra Cantt   \n",
       "\n",
       "     Locality Verbose  Longitude   Latitude               Cuisines  ...  \\\n",
       "624  Agra Cantt, Agra  78.011544  27.161661  North Indian, Mughlai  ...   \n",
       "\n",
       "     Has Table booking Has Online delivery Is delivering now  \\\n",
       "624                 No                  No                No   \n",
       "\n",
       "    Switch to order menu Price range Aggregate rating  Rating color  \\\n",
       "624                   No           3              3.9        Yellow   \n",
       "\n",
       "     Rating text Votes Cuisines_count  \n",
       "624         Good   140              2  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "def specialcuisines(s):\n",
    "    s = s.strip().split(\",\")\n",
    "    li=[]\n",
    "    for foo in s:\n",
    "        li.append(foo.strip())\n",
    "    for cuis in special_cuisine:\n",
    "        if cuis in li:\n",
    "            return cuis\n",
    "df_2[\"Special_Cuisine\"] = df_2[\"Cuisines\"].apply(specialcuisines)\n",
    "data_special_cuisines = df_2.groupby(\"Special_Cuisine\")[\"Aggregate rating\"].median().sort_values(ascending=False)\n",
    "cuisines = data_special_cuisines.index\n",
    "rating = data_special_cuisines.values\n",
    "plt.bar(cuisines,rating)\n",
    "plt.xlabel(\"Special Cuisines\",fontweight=\"bold\")\n",
    "plt.ylabel(\"Rating\",fontweight=\"bold\")\n",
    "plt.title(\"Special Cuisines vs Rating\",fontweight=\"bold\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylim(ymax = 5, ymin = 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer:- Restaurants serving some special cuisines like asian,continental,italian have more ratings than the other restaurants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Find the weighted restaurant rating of each locality and find out the top 10 localities with more weighted restaurant rating?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Weighted Restaurant Rating=Σ (number of votes * rating) / Σ (number of votes) ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaination and Answer:- In this we will make a new column of Weighted Restaurant Rating which will be number of votes * rating. Then we will group df_2 by Locality and find the sum of Weighted Restaurant Rating and store it in a1. After that we will group df_2 by locality and find the sum of Votes and store it in a2. Then we will divide a1 by a2 and sort them on the basis of values in descending order and extract the top 10 localities and then with the help of this data we will plot the bar graph of locality vs Weighted Restaurant Rating.\n",
    "The top 10 localities with more weighted restaurant rating are:-\n",
    "•\tAminabad \t\t\t\t\t4.9\n",
    "•\tHotel Clarks Amer, Malviya Nagar \t4.9\n",
    "•\tFriends Colony \t\t\t\t4.886916367367881\n",
    "•\tPowai \t\t\t\t\t\t4.841868613138686\n",
    "•\tKirlampudi Layout \t\t\t\t4.820161290322581\n",
    "•\tExpress Avenue Mall,  Royapettah \t4.8\n",
    "•\tDeccan Gymkhana \t\t\t\t4.8\n",
    "•\tBanjara Hills \t\t\t\t\t4.718761726078799\n",
    "•\tSector 5, Salt Lake \t\t\t\t4.707022552098202\n",
    "•\tRiverside Mall, Gomti Nagar \t\t4.7\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aminabad 4.9\n",
      "Hotel Clarks Amer, Malviya Nagar 4.9\n",
      "Friends Colony 4.886916367367881\n",
      "Powai 4.841868613138686\n",
      "Kirlampudi Layout 4.820161290322581\n",
      "Express Avenue Mall,  Royapettah 4.8\n",
      "Deccan Gymkhana 4.8\n",
      "Banjara Hills 4.7187617260787995\n",
      "Sector 5, Salt Lake 4.707022552098202\n",
      "Riverside Mall, Gomti Nagar 4.7\n"
     ]
    },
    {
     "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": [
    "df_2[\"Weighted Restaurant Rating\"] = df_2[\"Votes\"]*df_2[\"Aggregate rating\"]\n",
    "a1=df_2.groupby(\"Locality\")[\"Weighted Restaurant Rating\"].sum()\n",
    "a2=df_2.groupby(\"Locality\")[\"Votes\"].sum()\n",
    "weighted_restaurant_rating = a1/a2\n",
    "final_ans = weighted_restaurant_rating.sort_values(ascending=False)[:10]\n",
    "for i in range(10):\n",
    "    print(final_ans.index[i],final_ans.values[i])\n",
    "plt.bar(final_ans.index,final_ans.values)\n",
    "plt.xlabel(\"Locality\",fontweight=\"bold\")\n",
    "plt.ylabel(\"Weighted Restaurant Rating\",fontweight=\"bold\")\n",
    "plt.title(\"Locality vs Weighted Restaurant Rating\",fontweight=\"bold\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Plot the bar graph top 15 restaurants have a maximum number of outlets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaination and Answer:-In this we will group df_3 by Restaurant Name and find the value_counts() and sort it according to descending order and extract top 15. We will extract the restaurants from the index and the count of outlets from the values. So with this data we will plot the bar graph of Restaurants vs Number of Outlets. Top 15 restaurants have a maximum number of outlets are:-\n",
    "•\tCafe Coffee Day \t\t83\n",
    "•\tDomino's Pizza \t\t79\n",
    "•\tSubway \t\t\t63\n",
    "•\tGreen Chick Chop\t51\n",
    "•\tMcDonald's \t\t48\n",
    "•\tKeventers \t\t\t34\n",
    "•\tPizza Hut \t\t\t29\n",
    "•\tGiani \t\t\t\t29\n",
    "•\tBaskin Robbins \t\t28\n",
    "•\tBarbeque Nation \t\t25\n",
    "•\tDunkin' Donuts \t\t22\n",
    "•\tGiani's \t\t\t22\n",
    "•\tBarista \t\t\t22\n",
    "•\tPind Balluchi \t\t20\n",
    "•\tCosta Coffee \t\t20\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>624</th>\n",
       "      <td>3400025</td>\n",
       "      <td>Jahanpanah</td>\n",
       "      <td>1</td>\n",
       "      <td>Agra</td>\n",
       "      <td>E 23, Shopping Arcade, Sadar Bazaar, Agra Cant...</td>\n",
       "      <td>Agra Cantt</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>78.011544</td>\n",
       "      <td>27.161661</td>\n",
       "      <td>North Indian, Mughlai</td>\n",
       "      <td>...</td>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>3400341</td>\n",
       "      <td>Rangrezz Restaurant</td>\n",
       "      <td>1</td>\n",
       "      <td>Agra</td>\n",
       "      <td>E-20, Shopping Arcade, Sadar Bazaar, Agra Cant...</td>\n",
       "      <td>Agra Cantt</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>North Indian, Mughlai</td>\n",
       "      <td>...</td>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Restaurant ID      Restaurant Name  Country Code  City  \\\n",
       "624        3400025           Jahanpanah             1  Agra   \n",
       "625        3400341  Rangrezz Restaurant             1  Agra   \n",
       "\n",
       "                                               Address    Locality  \\\n",
       "624  E 23, Shopping Arcade, Sadar Bazaar, Agra Cant...  Agra Cantt   \n",
       "625  E-20, Shopping Arcade, Sadar Bazaar, Agra Cant...  Agra Cantt   \n",
       "\n",
       "     Locality Verbose  Longitude   Latitude               Cuisines  ...  \\\n",
       "624  Agra Cantt, Agra  78.011544  27.161661  North Indian, Mughlai  ...   \n",
       "625  Agra Cantt, Agra   0.000000   0.000000  North Indian, Mughlai  ...   \n",
       "\n",
       "               Currency Has Table booking Has Online delivery  \\\n",
       "624  Indian Rupees(Rs.)                No                  No   \n",
       "625  Indian Rupees(Rs.)                No                  No   \n",
       "\n",
       "    Is delivering now Switch to order menu Price range  Aggregate rating  \\\n",
       "624                No                   No           3               3.9   \n",
       "625                No                   No           2               3.5   \n",
       "\n",
       "     Rating color Rating text Votes  \n",
       "624        Yellow        Good   140  \n",
       "625        Yellow        Good    71  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_3 = df_India.copy()\n",
    "df_3.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cafe Coffee Day 83\n",
      "Domino's Pizza 79\n",
      "Subway 63\n",
      "Green Chick Chop 51\n",
      "McDonald's 48\n",
      "Keventers 34\n",
      "Giani 29\n",
      "Pizza Hut 29\n",
      "Baskin Robbins 28\n",
      "Barbeque Nation 25\n",
      "Barista 22\n",
      "Giani's 22\n",
      "Dunkin' Donuts 22\n",
      "Costa Coffee 20\n",
      "Pind Balluchi 20\n"
     ]
    },
    {
     "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": [
    "restaurant_with_max_outlets = df_3[\"Restaurant Name\"].value_counts().sort_values(ascending=False)[:15]\n",
    "restaurants = restaurant_with_max_outlets.index\n",
    "count_of_outlets = restaurant_with_max_outlets.values\n",
    "for i in range(15):\n",
    "    print(restaurants[i],count_of_outlets[i])\n",
    "plt.bar(restaurants,count_of_outlets)\n",
    "plt.xlabel(\"Restaurants\",fontweight=\"bold\")\n",
    "plt.ylabel(\"Number of Outlets\",fontweight=\"bold\")\n",
    "plt.title(\"Restaurants vs Number of Outlets\",fontweight=\"bold\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Plot the histogram of aggregate rating of restaurant( drop the unrated restaurant)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaination and Answer:- In this we will make a df_hist in which Rating text is not equal to Not rated. After that we will store the values of Aggregate rating in the data and make the historgram with this data keeping the bins as auto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Average      3678\n",
       "Not rated    2139\n",
       "Good         1847\n",
       "Very Good     692\n",
       "Poor          180\n",
       "Excellent     116\n",
       "Name: Rating text, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_3[\"Rating text\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df_hist = df_3[df_3[\"Rating text\"]!=\"Not rated\"]\n",
    "data = df_hist[\"Aggregate rating\"]\n",
    "plt.hist(data,bins=\"auto\",edgecolor=\"black\")\n",
    "plt.xlabel(\"Aggregate Rating\",fontweight=\"bold\")\n",
    "plt.ylabel(\"Number of Restaurants\",fontweight=\"bold\")\n",
    "plt.title(\"Aggregate Rating vs Number of Restaurants\",fontweight=\"bold\")\n",
    "plt.xticks([0,1,2,3,4,5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Plot the bar graph top 10 restaurants in the data with the highest number of votes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaination and Answer:- In this we will group df_3 by Restaurant Name and find the sum of the votes and sort by the values in descending order and extract the top 10 restaurants. We will extract the restaurants by the index and the votes by the values. Now we will Make a bar graph with the help of this data. Top 10 restaurants in the data with the highest number of votes are :-\n",
    "•\tBarbeque Nation \t\t\t27835\n",
    "•\tAB's - Absolute Barbecues \t13400\n",
    "•\tToit \t\t\t\t\t10934\n",
    "•\tBig Chill \t\t\t\t\t10853\n",
    "•\tTruffles \t\t\t\t\t9682\n",
    "•\tFarzi Cafe \t\t\t\t9189\n",
    "•\tChili's \t\t\t\t\t8156\n",
    "•\tHauz Khas Social \t\t\t7931\n",
    "•\tJoey's Pizza \t\t\t\t7807\n",
    "•\tPeter Cat \t\t\t\t7574\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barbeque Nation 27835\n",
      "AB's - Absolute Barbecues 13400\n",
      "Toit 10934\n",
      "Big Chill 10853\n",
      "Truffles 9682\n",
      "Farzi Cafe 9189\n",
      "Chili's 8156\n",
      "Hauz Khas Social 7931\n",
      "Joey's Pizza 7807\n",
      "Peter Cat 7574\n"
     ]
    },
    {
     "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": [
    "data_restaurants = df_3.groupby(\"Restaurant Name\")[\"Votes\"].sum().sort_values(ascending=False)[:10]\n",
    "restaurants = data_restaurants.index\n",
    "votes = data_restaurants.values\n",
    "for i in range(10):\n",
    "    print(restaurants[i],votes[i])\n",
    "plt.bar(restaurants,votes)\n",
    "plt.xlabel(\"Restaurants\",fontweight=\"bold\")\n",
    "plt.ylabel(\"Votes\",fontweight=\"bold\")\n",
    "plt.title(\"Restaurants VS Votes\",fontweight=\"bold\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.Plot the pie graph of top 10 cuisines present in restaurants in the USA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaination and Answer:- In this firstly we will extract the data of USA from the df by using Country Code as 216 and store it in df_USA and we will drop the na values from that. Now we will extract the cuisines values from the df_USA and with the help of cuisines function we will make a dictionary which stores the cuisines and its count. Now we will sort the dictionary on the basis of count and convert it into list and get the top 10 cuisines. Now with the help of this data we will plot a pie chart. The top 10 cuisines present in restaurants in the USA are:-\n",
    "•\tAmerican \t112\n",
    "•\tSeafood \t59\n",
    "•\tBurger \t\t49\n",
    "•\tSandwich \t49\n",
    "•\tPizza \t\t49\n",
    "•\tSteak \t\t42\n",
    "•\tItalian \t\t38\n",
    "•\tBreakfast \t37\n",
    "•\tMexican \t36\n",
    "•\tSushi \t\t34\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aarya\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "df_USA = df[df[\"Country Code\"]==216]\n",
    "df_USA.head(3)\n",
    "df_USA.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "American 112\n",
      "Seafood 59\n",
      "Burger 49\n",
      "Sandwich 49\n",
      "Pizza 49\n",
      "Steak 42\n",
      "Italian 38\n",
      "Breakfast 37\n",
      "Mexican 36\n",
      "Sushi 34\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def cuisines(li):\n",
    "    d={}\n",
    "    for row in li:\n",
    "        cuisines = row.strip().split(\",\")\n",
    "        for cuisine in cuisines:\n",
    "            cuisine = cuisine.strip()\n",
    "            d[cuisine] = d.get(cuisine,0) + 1\n",
    "    return d\n",
    "usa_cuisines = df_USA[\"Cuisines\"].values\n",
    "top_cuisines = cuisines(usa_cuisines)\n",
    "usa_top_10_cuisines = np.array(sorted(top_cuisines.items(),key=operator.itemgetter(1),reverse=True)[:10])\n",
    "for i in range(10):\n",
    "    print(usa_top_10_cuisines[i][0],usa_top_10_cuisines[i][1])\n",
    "plt.pie(usa_top_10_cuisines[:,1],labels=usa_top_10_cuisines[:,0],autopct=\"%.2f%%\")\n",
    "plt.title(\"Top 10 cuisines present in restaurants in the USA\",fontweight=\"bold\")\n",
    "plt.axis(\"equal\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Plot the bubble graph of a number of Restaurants present in the city of India and keeping the weighted restaurant rating of the city in a bubble."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaination and Answer:- In this we will do exact thing which we have done in 2.2.1 but the only difference is that we will group the data by cities. Now we get the weighted_restaurant_rating. Now we will extract the rating by values and cities by index. Then we will find the number of restaurants in the city with the help of value_counts() and sort on the index in the ascending order and store it’s values in number_of_restaurants_in_city. Now we will plot a bubble graph of the cities and number_of_restaurants_in_city using s as weighted_rating*9."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df_3[\"Weighted Restaurant Rating\"] = df_3[\"Votes\"]*df_3[\"Aggregate rating\"]\n",
    "a1=df_3.groupby(\"City\")[\"Weighted Restaurant Rating\"].sum()\n",
    "a2=df_3.groupby(\"City\")[\"Votes\"].sum()\n",
    "weighted_restaurant_rating = a1/a2\n",
    "final_ans = weighted_restaurant_rating.sort_index()\n",
    "cities = final_ans.index\n",
    "weighted_rating =final_ans.values\n",
    "number_of_restaurants_in_city=df_3[\"City\"].value_counts().sort_index().values\n",
    "plt.scatter(cities,number_of_restaurants_in_city,s=weighted_rating*9)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Cities\",fontweight=\"bold\")\n",
    "plt.ylabel(\"Number of Restaurants\",fontweight=\"bold\")\n",
    "plt.title(\"City VS Number of Restaurants\",fontweight=\"bold\")\n",
    "plt.tick_params(axis='x', which='major', labelsize=7)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}