kolkata_zomato_eda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7fcf07b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all necessary library\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "from collections import OrderedDict\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a916eca0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the CSV(DataSet)\n",
    "df = pd.read_csv(\"./dataset/Zomato_Kolkata_Restaurants.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8e89722f",
   "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>Name</th>\n",
       "      <th>URL</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Area</th>\n",
       "      <th>Timing</th>\n",
       "      <th>Full_Address</th>\n",
       "      <th>PhoneNumber</th>\n",
       "      <th>IsHomeDelivery</th>\n",
       "      <th>isTakeaway</th>\n",
       "      <th>isIndoorSeating</th>\n",
       "      <th>isVegOnly</th>\n",
       "      <th>Dinner Ratings</th>\n",
       "      <th>Dinner Reviews</th>\n",
       "      <th>Delivery Ratings</th>\n",
       "      <th>Delivery Reviews</th>\n",
       "      <th>KnownFor</th>\n",
       "      <th>PopularDishes</th>\n",
       "      <th>PeopleKnownFor</th>\n",
       "      <th>AverageCost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jyoti Vihar</td>\n",
       "      <td>https://www.zomato.com/kolkata/jyoti-vihar-cam...</td>\n",
       "      <td>South Indian</td>\n",
       "      <td>Camac Street Area, Kolkata</td>\n",
       "      <td>Opens tomorrow at 10am</td>\n",
       "      <td>3A/1, Ho Chi Minh Sarani, Camac Street Area, K...</td>\n",
       "      <td>+919748084091</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>930</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5498</td>\n",
       "      <td>South Indian Delicacies</td>\n",
       "      <td>Milk Upma, Jyoti Vihar, Vada Sambar, Filtered ...</td>\n",
       "      <td>South Indian Food, Good Crowd, Excellent Place...</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WOW! Momo</td>\n",
       "      <td>https://www.zomato.com/kolkata/wow-momo-park-s...</td>\n",
       "      <td>Momos, Fast Food, Tibetan</td>\n",
       "      <td>Park Street Area, Kolkata</td>\n",
       "      <td>9am – 3am (Today)</td>\n",
       "      <td>57, Park Street, Beside Vodafone Store, Park S...</td>\n",
       "      <td>+917604053802</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>469</td>\n",
       "      <td>4.1</td>\n",
       "      <td>11100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tandoori Paneer Momos, Momo Platter, Chocolate...</td>\n",
       "      <td>Comfortable Seating Area, Staff Service, Famil...</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Zam Zam</td>\n",
       "      <td>https://www.zomato.com/kolkata/zam-zam-park-ci...</td>\n",
       "      <td>Biryani, Rolls</td>\n",
       "      <td>Park Circus Area, Kolkata</td>\n",
       "      <td>Opens at 12noon</td>\n",
       "      <td>28/A, Syed Amir Ali Avenue, Park Circus Area, ...</td>\n",
       "      <td>+919748710818</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1901</td>\n",
       "      <td>4.3</td>\n",
       "      <td>83100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Beef Malai, Beef Bhuna, Hariyali Chicken, Sahi...</td>\n",
       "      <td>Ordering Process, Gastronomical Experience, Co...</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Daily Bhoj</td>\n",
       "      <td>https://www.zomato.com/kolkata/daily-bhoj-topsia</td>\n",
       "      <td>Bengali</td>\n",
       "      <td>Topsia, Kolkata</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139/1G, Tljala Road, Topsia, Kolkata</td>\n",
       "      <td>+919330778809</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Great Portions, Worth the Money, Timely Servic...</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Behnam</td>\n",
       "      <td>https://www.zomato.com/kolkata/behnam-topsia</td>\n",
       "      <td>Biryani, North Indian, Mughlai, Rolls, Kebab, ...</td>\n",
       "      <td>Topsia, Kolkata</td>\n",
       "      <td>Opens at 11am</td>\n",
       "      <td>139/1G, Tiljala Road, Near OYO Townhouse 229, ...</td>\n",
       "      <td>+919330778809</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Worth the Money, Timely Service, Great Portion...</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dilnawaz Biryani Centre</td>\n",
       "      <td>https://www.zomato.com/kolkata/dilnawaz-biryan...</td>\n",
       "      <td>Biryani, Mughlai</td>\n",
       "      <td>Tollygunge, Kolkata</td>\n",
       "      <td>Opens at 12noon</td>\n",
       "      <td>28, Deshpran Shasmal Road, Ward 81 BR X, Near ...</td>\n",
       "      <td>+918282836227</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>506</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Comfort Food, Cute Little Place, Inexpensive, ...</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Aminia</td>\n",
       "      <td>https://www.zomato.com/kolkata/aminia-new-mark...</td>\n",
       "      <td>Biryani, Mughlai, North Indian, Rolls, Kebab</td>\n",
       "      <td>New Market Area, Kolkata</td>\n",
       "      <td>Opens at 11:32am</td>\n",
       "      <td>6A, S.N. Banerjee Road, New Market Area, Kolkata</td>\n",
       "      <td>+913322651318</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3271</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1112</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rice Pudding, Mutton Galouti Kabab, Bhuna Chic...</td>\n",
       "      <td>Gastronomical Experience, Good for Large Group...</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Haka</td>\n",
       "      <td>https://www.zomato.com/kolkata/haka-ballygunge</td>\n",
       "      <td>Chinese, Momos, Desserts, Beverages</td>\n",
       "      <td>Ballygunge, Kolkata</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3A, Uniworth House, Gurusaday Road, Ballygunge...</td>\n",
       "      <td>+919330627506</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>712</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chicken Suimai, Crispy Chilli Baby Corn, Dumpl...</td>\n",
       "      <td>Cute Packaging, Proper Packaging, Food Present...</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Arsalan</td>\n",
       "      <td>https://www.zomato.com/kolkata/arsalan-park-st...</td>\n",
       "      <td>Biryani, North Indian, Kebab, Bengali</td>\n",
       "      <td>Park Street Area, Kolkata</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119A, Muzaffar Ahmed Street, Park Street Area,...</td>\n",
       "      <td>+919007007935</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1367</td>\n",
       "      <td>4.2</td>\n",
       "      <td>9638</td>\n",
       "      <td>Biryani and Tandoor!</td>\n",
       "      <td>Chicken Boneless Masala, Mutton Haleem, Specia...</td>\n",
       "      <td>Cost Worthy, Gastronomical Experience, Ample S...</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Domino's Pizza</td>\n",
       "      <td>https://www.zomato.com/kolkata/dominos-pizza-2...</td>\n",
       "      <td>Pizza, Fast Food, Beverages, Desserts</td>\n",
       "      <td>Park Street Area, Kolkata</td>\n",
       "      <td>10am – 2:57am (Today)</td>\n",
       "      <td>31, Ward 63, Chowringhee Road, Shakespeare Sar...</td>\n",
       "      <td>+913340070755</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>133</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Student Crowd, Good Music, Elaborate Menu, Cus...</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name                                                URL  \\\n",
       "0              Jyoti Vihar  https://www.zomato.com/kolkata/jyoti-vihar-cam...   \n",
       "1                WOW! Momo  https://www.zomato.com/kolkata/wow-momo-park-s...   \n",
       "2                  Zam Zam  https://www.zomato.com/kolkata/zam-zam-park-ci...   \n",
       "3               Daily Bhoj   https://www.zomato.com/kolkata/daily-bhoj-topsia   \n",
       "4                   Behnam       https://www.zomato.com/kolkata/behnam-topsia   \n",
       "5  Dilnawaz Biryani Centre  https://www.zomato.com/kolkata/dilnawaz-biryan...   \n",
       "6                   Aminia  https://www.zomato.com/kolkata/aminia-new-mark...   \n",
       "7                     Haka     https://www.zomato.com/kolkata/haka-ballygunge   \n",
       "8                  Arsalan  https://www.zomato.com/kolkata/arsalan-park-st...   \n",
       "9           Domino's Pizza  https://www.zomato.com/kolkata/dominos-pizza-2...   \n",
       "\n",
       "                                            Cuisines  \\\n",
       "0                                       South Indian   \n",
       "1                          Momos, Fast Food, Tibetan   \n",
       "2                                     Biryani, Rolls   \n",
       "3                                            Bengali   \n",
       "4  Biryani, North Indian, Mughlai, Rolls, Kebab, ...   \n",
       "5                                   Biryani, Mughlai   \n",
       "6       Biryani, Mughlai, North Indian, Rolls, Kebab   \n",
       "7                Chinese, Momos, Desserts, Beverages   \n",
       "8              Biryani, North Indian, Kebab, Bengali   \n",
       "9              Pizza, Fast Food, Beverages, Desserts   \n",
       "\n",
       "                         Area                  Timing  \\\n",
       "0  Camac Street Area, Kolkata  Opens tomorrow at 10am   \n",
       "1   Park Street Area, Kolkata       9am – 3am (Today)   \n",
       "2   Park Circus Area, Kolkata         Opens at 12noon   \n",
       "3             Topsia, Kolkata                     NaN   \n",
       "4             Topsia, Kolkata           Opens at 11am   \n",
       "5         Tollygunge, Kolkata         Opens at 12noon   \n",
       "6    New Market Area, Kolkata        Opens at 11:32am   \n",
       "7         Ballygunge, Kolkata                     NaN   \n",
       "8   Park Street Area, Kolkata                     NaN   \n",
       "9   Park Street Area, Kolkata   10am – 2:57am (Today)   \n",
       "\n",
       "                                        Full_Address    PhoneNumber  \\\n",
       "0  3A/1, Ho Chi Minh Sarani, Camac Street Area, K...  +919748084091   \n",
       "1  57, Park Street, Beside Vodafone Store, Park S...  +917604053802   \n",
       "2  28/A, Syed Amir Ali Avenue, Park Circus Area, ...  +919748710818   \n",
       "3               139/1G, Tljala Road, Topsia, Kolkata  +919330778809   \n",
       "4  139/1G, Tiljala Road, Near OYO Townhouse 229, ...  +919330778809   \n",
       "5  28, Deshpran Shasmal Road, Ward 81 BR X, Near ...  +918282836227   \n",
       "6   6A, S.N. Banerjee Road, New Market Area, Kolkata  +913322651318   \n",
       "7  3A, Uniworth House, Gurusaday Road, Ballygunge...  +919330627506   \n",
       "8  119A, Muzaffar Ahmed Street, Park Street Area,...  +919007007935   \n",
       "9  31, Ward 63, Chowringhee Road, Shakespeare Sar...  +913340070755   \n",
       "\n",
       "   IsHomeDelivery  isTakeaway  isIndoorSeating  isVegOnly Dinner Ratings  \\\n",
       "0               1           1                1          1            4.4   \n",
       "1               1           1                1          0            4.0   \n",
       "2               1           1                1          0            4.4   \n",
       "3               1           0                0          0              -   \n",
       "4               1           1                0          0              -   \n",
       "5               1           1                0          0            4.0   \n",
       "6               1           1                1          0            3.5   \n",
       "7               1           0                0          0              -   \n",
       "8               1           1                1          0            4.1   \n",
       "9               1           1                1          0            3.7   \n",
       "\n",
       "   Dinner Reviews Delivery Ratings  Delivery Reviews                 KnownFor  \\\n",
       "0             930              4.1              5498  South Indian Delicacies   \n",
       "1             469              4.1             11100                      NaN   \n",
       "2            1901              4.3             83100                      NaN   \n",
       "3               0              3.9              2260                      NaN   \n",
       "4               0              2.9               151                      NaN   \n",
       "5             506              4.0             42500                      NaN   \n",
       "6            3271              3.9              1112                      NaN   \n",
       "7               0              4.4               712                      NaN   \n",
       "8            1367              4.2              9638     Biryani and Tandoor!   \n",
       "9             133              4.1              3931                      NaN   \n",
       "\n",
       "                                       PopularDishes  \\\n",
       "0  Milk Upma, Jyoti Vihar, Vada Sambar, Filtered ...   \n",
       "1  Tandoori Paneer Momos, Momo Platter, Chocolate...   \n",
       "2  Beef Malai, Beef Bhuna, Hariyali Chicken, Sahi...   \n",
       "3                                                NaN   \n",
       "4                                                NaN   \n",
       "5                                                NaN   \n",
       "6  Rice Pudding, Mutton Galouti Kabab, Bhuna Chic...   \n",
       "7  Chicken Suimai, Crispy Chilli Baby Corn, Dumpl...   \n",
       "8  Chicken Boneless Masala, Mutton Haleem, Specia...   \n",
       "9                                                NaN   \n",
       "\n",
       "                                      PeopleKnownFor  AverageCost  \n",
       "0  South Indian Food, Good Crowd, Excellent Place...          300  \n",
       "1  Comfortable Seating Area, Staff Service, Famil...          350  \n",
       "2  Ordering Process, Gastronomical Experience, Co...          500  \n",
       "3  Great Portions, Worth the Money, Timely Servic...          150  \n",
       "4  Worth the Money, Timely Service, Great Portion...          200  \n",
       "5  Comfort Food, Cute Little Place, Inexpensive, ...          200  \n",
       "6  Gastronomical Experience, Good for Large Group...          700  \n",
       "7  Cute Packaging, Proper Packaging, Food Present...          300  \n",
       "8  Cost Worthy, Gastronomical Experience, Ample S...          600  \n",
       "9  Student Crowd, Good Music, Elaborate Menu, Cus...          400  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Understanding the data\n",
    "# Read first 10 row of the dataset\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b19070a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4923 entries, 0 to 4922\n",
      "Data columns (total 19 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   Name              4923 non-null   object\n",
      " 1   URL               4923 non-null   object\n",
      " 2   Cuisines          4923 non-null   object\n",
      " 3   Area              4923 non-null   object\n",
      " 4   Timing            3281 non-null   object\n",
      " 5   Full_Address      4923 non-null   object\n",
      " 6   PhoneNumber       4923 non-null   object\n",
      " 7   IsHomeDelivery    4923 non-null   int64 \n",
      " 8   isTakeaway        4923 non-null   int64 \n",
      " 9   isIndoorSeating   4923 non-null   int64 \n",
      " 10  isVegOnly         4923 non-null   int64 \n",
      " 11  Dinner Ratings    4923 non-null   object\n",
      " 12  Dinner Reviews    4923 non-null   int64 \n",
      " 13  Delivery Ratings  4923 non-null   object\n",
      " 14  Delivery Reviews  4923 non-null   int64 \n",
      " 15  KnownFor          199 non-null    object\n",
      " 16  PopularDishes     1004 non-null   object\n",
      " 17  PeopleKnownFor    2039 non-null   object\n",
      " 18  AverageCost       4923 non-null   int64 \n",
      "dtypes: int64(7), object(12)\n",
      "memory usage: 730.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Get some information about the dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a30372f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4923, 19)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape of the dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "df48e425",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'URL', 'Cuisines', 'Area', 'Timing', 'Full_Address',\n",
       "       'PhoneNumber', 'IsHomeDelivery', 'isTakeaway', 'isIndoorSeating',\n",
       "       'isVegOnly', 'Dinner Ratings', 'Dinner Reviews', 'Delivery Ratings',\n",
       "       'Delivery Reviews', 'KnownFor', 'PopularDishes', 'PeopleKnownFor',\n",
       "       'AverageCost'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print all columns of the dataset\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "91938693",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                0\n",
       "URL                 0\n",
       "Cuisines            0\n",
       "Area                0\n",
       "Timing              0\n",
       "Full_Address        0\n",
       "PhoneNumber         0\n",
       "IsHomeDelivery      0\n",
       "isTakeaway          0\n",
       "isIndoorSeating     0\n",
       "isVegOnly           0\n",
       "Dinner Ratings      0\n",
       "Dinner Reviews      0\n",
       "Delivery Ratings    0\n",
       "Delivery Reviews    0\n",
       "KnownFor            0\n",
       "PopularDishes       0\n",
       "PeopleKnownFor      0\n",
       "AverageCost         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see the duplicate value in dataset\n",
    "duplicate_df = df[df.duplicated()]\n",
    "duplicate_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "819e4eba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                   0\n",
       "URL                    0\n",
       "Cuisines               0\n",
       "Area                   0\n",
       "Timing              1642\n",
       "Full_Address           0\n",
       "PhoneNumber            0\n",
       "IsHomeDelivery         0\n",
       "isTakeaway             0\n",
       "isIndoorSeating        0\n",
       "isVegOnly              0\n",
       "Dinner Ratings         0\n",
       "Dinner Reviews         0\n",
       "Delivery Ratings       0\n",
       "Delivery Reviews       0\n",
       "KnownFor            4724\n",
       "PopularDishes       3919\n",
       "PeopleKnownFor      2884\n",
       "AverageCost            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check the NULL value in dataset\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dcc4a453",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the unused columns\n",
    "new_df = df.drop(['URL','Timing','KnownFor','Full_Address','PhoneNumber'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0dd82edf",
   "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>Name</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Area</th>\n",
       "      <th>IsHomeDelivery</th>\n",
       "      <th>isTakeaway</th>\n",
       "      <th>isIndoorSeating</th>\n",
       "      <th>isVegOnly</th>\n",
       "      <th>Dinner Ratings</th>\n",
       "      <th>Dinner Reviews</th>\n",
       "      <th>Delivery Ratings</th>\n",
       "      <th>Delivery Reviews</th>\n",
       "      <th>PopularDishes</th>\n",
       "      <th>PeopleKnownFor</th>\n",
       "      <th>AverageCost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jyoti Vihar</td>\n",
       "      <td>South Indian</td>\n",
       "      <td>Camac Street Area, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>930</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5498</td>\n",
       "      <td>Milk Upma, Jyoti Vihar, Vada Sambar, Filtered ...</td>\n",
       "      <td>South Indian Food, Good Crowd, Excellent Place...</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WOW! Momo</td>\n",
       "      <td>Momos, Fast Food, Tibetan</td>\n",
       "      <td>Park Street Area, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>469</td>\n",
       "      <td>4.1</td>\n",
       "      <td>11100</td>\n",
       "      <td>Tandoori Paneer Momos, Momo Platter, Chocolate...</td>\n",
       "      <td>Comfortable Seating Area, Staff Service, Famil...</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Zam Zam</td>\n",
       "      <td>Biryani, Rolls</td>\n",
       "      <td>Park Circus Area, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1901</td>\n",
       "      <td>4.3</td>\n",
       "      <td>83100</td>\n",
       "      <td>Beef Malai, Beef Bhuna, Hariyali Chicken, Sahi...</td>\n",
       "      <td>Ordering Process, Gastronomical Experience, Co...</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Daily Bhoj</td>\n",
       "      <td>Bengali</td>\n",
       "      <td>Topsia, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Great Portions, Worth the Money, Timely Servic...</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Behnam</td>\n",
       "      <td>Biryani, North Indian, Mughlai, Rolls, Kebab, ...</td>\n",
       "      <td>Topsia, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Worth the Money, Timely Service, Great Portion...</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name                                           Cuisines  \\\n",
       "0  Jyoti Vihar                                       South Indian   \n",
       "1    WOW! Momo                          Momos, Fast Food, Tibetan   \n",
       "2      Zam Zam                                     Biryani, Rolls   \n",
       "3   Daily Bhoj                                            Bengali   \n",
       "4       Behnam  Biryani, North Indian, Mughlai, Rolls, Kebab, ...   \n",
       "\n",
       "                         Area  IsHomeDelivery  isTakeaway  isIndoorSeating  \\\n",
       "0  Camac Street Area, Kolkata               1           1                1   \n",
       "1   Park Street Area, Kolkata               1           1                1   \n",
       "2   Park Circus Area, Kolkata               1           1                1   \n",
       "3             Topsia, Kolkata               1           0                0   \n",
       "4             Topsia, Kolkata               1           1                0   \n",
       "\n",
       "   isVegOnly Dinner Ratings  Dinner Reviews Delivery Ratings  \\\n",
       "0          1            4.4             930              4.1   \n",
       "1          0            4.0             469              4.1   \n",
       "2          0            4.4            1901              4.3   \n",
       "3          0              -               0              3.9   \n",
       "4          0              -               0              2.9   \n",
       "\n",
       "   Delivery Reviews                                      PopularDishes  \\\n",
       "0              5498  Milk Upma, Jyoti Vihar, Vada Sambar, Filtered ...   \n",
       "1             11100  Tandoori Paneer Momos, Momo Platter, Chocolate...   \n",
       "2             83100  Beef Malai, Beef Bhuna, Hariyali Chicken, Sahi...   \n",
       "3              2260                                                NaN   \n",
       "4               151                                                NaN   \n",
       "\n",
       "                                      PeopleKnownFor  AverageCost  \n",
       "0  South Indian Food, Good Crowd, Excellent Place...          300  \n",
       "1  Comfortable Seating Area, Staff Service, Famil...          350  \n",
       "2  Ordering Process, Gastronomical Experience, Co...          500  \n",
       "3  Great Portions, Worth the Money, Timely Servic...          150  \n",
       "4  Worth the Money, Timely Service, Great Portion...          200  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "36bebd95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                   0\n",
       "Cuisines               0\n",
       "Area                   0\n",
       "IsHomeDelivery         0\n",
       "isTakeaway             0\n",
       "isIndoorSeating        0\n",
       "isVegOnly              0\n",
       "Dinner Ratings         0\n",
       "Dinner Reviews         0\n",
       "Delivery Ratings       0\n",
       "Delivery Reviews       0\n",
       "PopularDishes       3919\n",
       "PeopleKnownFor      2884\n",
       "AverageCost            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check missing data again\n",
    "new_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "729191a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling the missing data in columns \n",
    "new_df['PopularDishes'] = new_df['PopularDishes'].fillna('No Data')\n",
    "new_df['PeopleKnownFor'] = new_df['PeopleKnownFor'].fillna('No Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "166243cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4923 entries, 0 to 4922\n",
      "Data columns (total 14 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   Name              4923 non-null   object\n",
      " 1   Cuisines          4923 non-null   object\n",
      " 2   Area              4923 non-null   object\n",
      " 3   IsHomeDelivery    4923 non-null   int64 \n",
      " 4   isTakeaway        4923 non-null   int64 \n",
      " 5   isIndoorSeating   4923 non-null   int64 \n",
      " 6   isVegOnly         4923 non-null   int64 \n",
      " 7   Dinner Ratings    4923 non-null   object\n",
      " 8   Dinner Reviews    4923 non-null   int64 \n",
      " 9   Delivery Ratings  4923 non-null   object\n",
      " 10  Delivery Reviews  4923 non-null   int64 \n",
      " 11  PopularDishes     4923 non-null   object\n",
      " 12  PeopleKnownFor    4923 non-null   object\n",
      " 13  AverageCost       4923 non-null   int64 \n",
      "dtypes: int64(7), object(7)\n",
      "memory usage: 538.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check once again the data type of the new dataset\n",
    "new_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6639b614",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace empty rating from '-' to 0\n",
    "new_df['Dinner Ratings'] = new_df['Dinner Ratings'].replace('-','0')\n",
    "new_df['Delivery Ratings'] = new_df['Delivery Ratings'].replace('-','0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5cad7a24",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Need to convert \"Dinner Ratings\" and \"Delivery Ratings\" dtype\n",
    "new_df['Dinner Ratings'] = new_df['Dinner Ratings'].astype('float64')\n",
    "new_df['Delivery Ratings'] = new_df['Delivery Ratings'].astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9d00e1bc",
   "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>Name</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Area</th>\n",
       "      <th>IsHomeDelivery</th>\n",
       "      <th>isTakeaway</th>\n",
       "      <th>isIndoorSeating</th>\n",
       "      <th>isVegOnly</th>\n",
       "      <th>Dinner Ratings</th>\n",
       "      <th>Dinner Reviews</th>\n",
       "      <th>Delivery Ratings</th>\n",
       "      <th>Delivery Reviews</th>\n",
       "      <th>PopularDishes</th>\n",
       "      <th>PeopleKnownFor</th>\n",
       "      <th>AverageCost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jyoti Vihar</td>\n",
       "      <td>South Indian</td>\n",
       "      <td>Camac Street Area, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>930</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5498</td>\n",
       "      <td>Milk Upma, Jyoti Vihar, Vada Sambar, Filtered ...</td>\n",
       "      <td>South Indian Food, Good Crowd, Excellent Place...</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WOW! Momo</td>\n",
       "      <td>Momos, Fast Food, Tibetan</td>\n",
       "      <td>Park Street Area, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>469</td>\n",
       "      <td>4.1</td>\n",
       "      <td>11100</td>\n",
       "      <td>Tandoori Paneer Momos, Momo Platter, Chocolate...</td>\n",
       "      <td>Comfortable Seating Area, Staff Service, Famil...</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Zam Zam</td>\n",
       "      <td>Biryani, Rolls</td>\n",
       "      <td>Park Circus Area, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1901</td>\n",
       "      <td>4.3</td>\n",
       "      <td>83100</td>\n",
       "      <td>Beef Malai, Beef Bhuna, Hariyali Chicken, Sahi...</td>\n",
       "      <td>Ordering Process, Gastronomical Experience, Co...</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Daily Bhoj</td>\n",
       "      <td>Bengali</td>\n",
       "      <td>Topsia, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2260</td>\n",
       "      <td>No Data</td>\n",
       "      <td>Great Portions, Worth the Money, Timely Servic...</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Behnam</td>\n",
       "      <td>Biryani, North Indian, Mughlai, Rolls, Kebab, ...</td>\n",
       "      <td>Topsia, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>151</td>\n",
       "      <td>No Data</td>\n",
       "      <td>Worth the Money, Timely Service, Great Portion...</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name                                           Cuisines  \\\n",
       "0  Jyoti Vihar                                       South Indian   \n",
       "1    WOW! Momo                          Momos, Fast Food, Tibetan   \n",
       "2      Zam Zam                                     Biryani, Rolls   \n",
       "3   Daily Bhoj                                            Bengali   \n",
       "4       Behnam  Biryani, North Indian, Mughlai, Rolls, Kebab, ...   \n",
       "\n",
       "                         Area  IsHomeDelivery  isTakeaway  isIndoorSeating  \\\n",
       "0  Camac Street Area, Kolkata               1           1                1   \n",
       "1   Park Street Area, Kolkata               1           1                1   \n",
       "2   Park Circus Area, Kolkata               1           1                1   \n",
       "3             Topsia, Kolkata               1           0                0   \n",
       "4             Topsia, Kolkata               1           1                0   \n",
       "\n",
       "   isVegOnly  Dinner Ratings  Dinner Reviews  Delivery Ratings  \\\n",
       "0          1             4.4             930               4.1   \n",
       "1          0             4.0             469               4.1   \n",
       "2          0             4.4            1901               4.3   \n",
       "3          0             0.0               0               3.9   \n",
       "4          0             0.0               0               2.9   \n",
       "\n",
       "   Delivery Reviews                                      PopularDishes  \\\n",
       "0              5498  Milk Upma, Jyoti Vihar, Vada Sambar, Filtered ...   \n",
       "1             11100  Tandoori Paneer Momos, Momo Platter, Chocolate...   \n",
       "2             83100  Beef Malai, Beef Bhuna, Hariyali Chicken, Sahi...   \n",
       "3              2260                                            No Data   \n",
       "4               151                                            No Data   \n",
       "\n",
       "                                      PeopleKnownFor  AverageCost  \n",
       "0  South Indian Food, Good Crowd, Excellent Place...          300  \n",
       "1  Comfortable Seating Area, Staff Service, Famil...          350  \n",
       "2  Ordering Process, Gastronomical Experience, Co...          500  \n",
       "3  Great Portions, Worth the Money, Timely Servic...          150  \n",
       "4  Worth the Money, Timely Service, Great Portion...          200  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check dataset again\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b0e784cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Top 20 area has highest number of restaurants\n",
    "new_df['Area'].value_counts().head(20).plot(kind='bar')\n",
    "plt.title(\"Number of Restaurants based on Area\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "147e66e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                                                  Barbeque Nation\n",
       "Cuisines                  BBQ, Chinese, Kebab, North Indian, Desserts\n",
       "Area                                               Wellesley, Kolkata\n",
       "IsHomeDelivery                                                      1\n",
       "isTakeaway                                                          1\n",
       "isIndoorSeating                                                     1\n",
       "isVegOnly                                                           0\n",
       "Dinner Ratings                                                    4.9\n",
       "Dinner Reviews                                                   5619\n",
       "Delivery Ratings                                                  3.9\n",
       "Delivery Reviews                                                 1507\n",
       "PopularDishes       Mangalorean Fish Curry, Sheer Korma, Chicken C...\n",
       "PeopleKnownFor      Cost Suits the Pocket, Live Grill, Complimenta...\n",
       "AverageCost                                                      1800\n",
       "Name: 56, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top restaurants based on Dinner Ratings\n",
    "column_id_by_dinner = new_df['Dinner Ratings'].idxmax()\n",
    "new_df.iloc[column_id_by_dinner]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e17e721e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                Kwality Wall’s Frozen Dessert and Ice Cream Shop\n",
       "Cuisines                                         Ice Cream, Desserts\n",
       "Area                                               Kestopur, Kolkata\n",
       "IsHomeDelivery                                                     1\n",
       "isTakeaway                                                         0\n",
       "isIndoorSeating                                                    0\n",
       "isVegOnly                                                          0\n",
       "Dinner Ratings                                                   0.0\n",
       "Dinner Reviews                                                     0\n",
       "Delivery Ratings                                                 4.6\n",
       "Delivery Reviews                                                 224\n",
       "PopularDishes                                                No Data\n",
       "PeopleKnownFor                                               No Data\n",
       "AverageCost                                                      100\n",
       "Name: 552, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top restaurants based on Delivery Ratings\n",
    "column_id_by_delivery = new_df['Delivery Ratings'].idxmax()\n",
    "new_df.iloc[column_id_by_delivery]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ea0086c4",
   "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>Name</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Area</th>\n",
       "      <th>IsHomeDelivery</th>\n",
       "      <th>isTakeaway</th>\n",
       "      <th>isIndoorSeating</th>\n",
       "      <th>isVegOnly</th>\n",
       "      <th>Dinner Ratings</th>\n",
       "      <th>Dinner Reviews</th>\n",
       "      <th>Delivery Ratings</th>\n",
       "      <th>Delivery Reviews</th>\n",
       "      <th>PopularDishes</th>\n",
       "      <th>PeopleKnownFor</th>\n",
       "      <th>AverageCost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Natural Ice Cream</td>\n",
       "      <td>Ice Cream, Desserts</td>\n",
       "      <td>Park Street Area, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2419</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9632</td>\n",
       "      <td>Chiku Shake, Chocobite, Choco Bite, Sitafal Ic...</td>\n",
       "      <td>Bank for the Buck, Totally Worth it, Pretty De...</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name             Cuisines                       Area  \\\n",
       "72  Natural Ice Cream  Ice Cream, Desserts  Park Street Area, Kolkata   \n",
       "\n",
       "    IsHomeDelivery  isTakeaway  isIndoorSeating  isVegOnly  Dinner Ratings  \\\n",
       "72               1           1                1          1             4.9   \n",
       "\n",
       "    Dinner Reviews  Delivery Ratings  Delivery Reviews  \\\n",
       "72            2419               4.5              9632   \n",
       "\n",
       "                                        PopularDishes  \\\n",
       "72  Chiku Shake, Chocobite, Choco Bite, Sitafal Ic...   \n",
       "\n",
       "                                       PeopleKnownFor  AverageCost  \n",
       "72  Bank for the Buck, Totally Worth it, Pretty De...          300  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top restaurants based on Dinner Ratings and Delivery Ratings\n",
    "new_df.loc[(new_df['Dinner Ratings'] >= 4.5) & (new_df['Delivery Ratings'] >= 4.5)].sort_values(by=['Dinner Ratings','Delivery Ratings'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f51e376c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                                 Bengali\n",
       "1                                                 Biryani\n",
       "2                                                 Chinese\n",
       "3                                            North Indian\n",
       "4                                        Bakery, Desserts\n",
       "                              ...                        \n",
       "2005                     Chinese, Fast Food, Momos, Rolls\n",
       "2006    North Indian, Bengali, Chinese, Mughlai, Fast ...\n",
       "2007     Bengali, Chinese, North Indian, Rolls, Fast Food\n",
       "2008                   Chinese, Tibetan, Momos, Fast Food\n",
       "2009                       Chinese, Bengali, South Indian\n",
       "Name: cusine, Length: 2010, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a new Dataframe having total count from each cuisine by separating those from the single string \n",
    "restaurant_count=pd.DataFrame(new_df['Cuisines'].value_counts().sort_values(ascending=False))\n",
    "restaurant_count.rename(columns = {'cusine':'Count'},inplace=True)\n",
    "restaurant_count.index.rename('cusine',inplace=True)\n",
    "restaurant_count.reset_index(inplace=True)\n",
    "restaurant_count['cusine']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6c998dab",
   "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>cusine</th>\n",
       "      <th>Cuisines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bengali</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Biryani</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chinese</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North Indian</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bakery, Desserts</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>Chinese, Fast Food, Momos, Rolls</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>North Indian, Bengali, Chinese, Mughlai, Fast ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>Bengali, Chinese, North Indian, Rolls, Fast Food</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>Chinese, Tibetan, Momos, Fast Food</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>Chinese, Bengali, South Indian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2010 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 cusine  Cuisines\n",
       "0                                               Bengali       133\n",
       "1                                               Biryani       125\n",
       "2                                               Chinese       118\n",
       "3                                          North Indian        79\n",
       "4                                      Bakery, Desserts        78\n",
       "...                                                 ...       ...\n",
       "2005                   Chinese, Fast Food, Momos, Rolls         1\n",
       "2006  North Indian, Bengali, Chinese, Mughlai, Fast ...         1\n",
       "2007   Bengali, Chinese, North Indian, Rolls, Fast Food         1\n",
       "2008                 Chinese, Tibetan, Momos, Fast Food         1\n",
       "2009                     Chinese, Bengali, South Indian         1\n",
       "\n",
       "[2010 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurant_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dea12dd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spliting cuisines by coma and creating the dataframe\n",
    "split_data = restaurant_count['cusine'].str.split(\",\")\n",
    "data = split_data.to_list()\n",
    "names = ['cusine1','cusine2','cusine3','cusine4','cusine5','cusine6','cusine7','cusine8']\n",
    "restaurant_count = pd.DataFrame(data, columns=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4563e8b4",
   "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>cusine1</th>\n",
       "      <th>cusine2</th>\n",
       "      <th>cusine3</th>\n",
       "      <th>cusine4</th>\n",
       "      <th>cusine5</th>\n",
       "      <th>cusine6</th>\n",
       "      <th>cusine7</th>\n",
       "      <th>cusine8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bengali</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Biryani</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chinese</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North Indian</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bakery</td>\n",
       "      <td>Desserts</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>Chinese</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>Momos</td>\n",
       "      <td>Rolls</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>North Indian</td>\n",
       "      <td>Bengali</td>\n",
       "      <td>Chinese</td>\n",
       "      <td>Mughlai</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>Rolls</td>\n",
       "      <td>Momos</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>Bengali</td>\n",
       "      <td>Chinese</td>\n",
       "      <td>North Indian</td>\n",
       "      <td>Rolls</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>Chinese</td>\n",
       "      <td>Tibetan</td>\n",
       "      <td>Momos</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>Chinese</td>\n",
       "      <td>Bengali</td>\n",
       "      <td>South Indian</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2010 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           cusine1     cusine2        cusine3     cusine4     cusine5 cusine6  \\\n",
       "0          Bengali        None           None        None        None    None   \n",
       "1          Biryani        None           None        None        None    None   \n",
       "2          Chinese        None           None        None        None    None   \n",
       "3     North Indian        None           None        None        None    None   \n",
       "4           Bakery    Desserts           None        None        None    None   \n",
       "...            ...         ...            ...         ...         ...     ...   \n",
       "2005       Chinese   Fast Food          Momos       Rolls        None    None   \n",
       "2006  North Indian     Bengali        Chinese     Mughlai   Fast Food   Rolls   \n",
       "2007       Bengali     Chinese   North Indian       Rolls   Fast Food    None   \n",
       "2008       Chinese     Tibetan          Momos   Fast Food        None    None   \n",
       "2009       Chinese     Bengali   South Indian        None        None    None   \n",
       "\n",
       "     cusine7 cusine8  \n",
       "0       None    None  \n",
       "1       None    None  \n",
       "2       None    None  \n",
       "3       None    None  \n",
       "4       None    None  \n",
       "...      ...     ...  \n",
       "2005    None    None  \n",
       "2006   Momos    None  \n",
       "2007    None    None  \n",
       "2008    None    None  \n",
       "2009    None    None  \n",
       "\n",
       "[2010 rows x 8 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurant_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c8a1fa99",
   "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>Cuisines</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chinese</td>\n",
       "      <td>369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>North Indian</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fast Food</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Biryani</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bengali</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Cuisines  Count\n",
       "0       Chinese    369\n",
       "1  North Indian    360\n",
       "2     Fast Food    183\n",
       "3       Biryani    120\n",
       "4       Bengali     97"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the most popular cuisine \n",
    "unique_cuisines = restaurant_count['cusine1'].value_counts()\n",
    "unique_cuisines\n",
    "for col in names[:1]:\n",
    "#     unique_cuisines = unique_cuisines.append(restaurant_count[col].value_counts().sort_values(ascending=False))\n",
    "    unique_cuisines = pd.DataFrame(unique_cuisines)\n",
    "    pd.concat([unique_cuisines],ignore_index=True)\n",
    "# unique_cuisines = pd.DataFrame(unique_cuisines)\n",
    "unique_cuisines.rename(columns = {'cusine1':'Count'},inplace=True)\n",
    "unique_cuisines.index.rename('Cuisines',inplace=True)\n",
    "unique_cuisines.reset_index(inplace=True)\n",
    "unique_cuisines.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2f5a20b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Popular cuisine\n",
    "popular_cuisines = unique_cuisines[unique_cuisines['Count']>50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2c09710c",
   "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>Cuisines</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chinese</td>\n",
       "      <td>369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>North Indian</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fast Food</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Biryani</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bengali</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pizza</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cafe</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Continental</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>South Indian</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Cuisines  Count\n",
       "0       Chinese    369\n",
       "1  North Indian    360\n",
       "2     Fast Food    183\n",
       "3       Biryani    120\n",
       "4       Bengali     97\n",
       "5         Pizza     86\n",
       "6          Cafe     78\n",
       "7   Continental     58\n",
       "8  South Indian     57"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popular_cuisines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d6a9ba32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Chinese",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Chinese",
         "offsetgroup": "Chinese",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Chinese"
         ],
         "xaxis": "x",
         "y": [
          369
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "North Indian",
         "marker": {
          "color": "#EF553B",
          "pattern": {
           "shape": ""
          }
         },
         "name": "North Indian",
         "offsetgroup": "North Indian",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "North Indian"
         ],
         "xaxis": "x",
         "y": [
          360
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Fast Food",
         "marker": {
          "color": "#00cc96",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Fast Food",
         "offsetgroup": "Fast Food",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Fast Food"
         ],
         "xaxis": "x",
         "y": [
          183
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Biryani",
         "marker": {
          "color": "#ab63fa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Biryani",
         "offsetgroup": "Biryani",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Biryani"
         ],
         "xaxis": "x",
         "y": [
          120
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Bengali",
         "marker": {
          "color": "#FFA15A",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Bengali",
         "offsetgroup": "Bengali",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Bengali"
         ],
         "xaxis": "x",
         "y": [
          97
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Pizza",
         "marker": {
          "color": "#19d3f3",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Pizza",
         "offsetgroup": "Pizza",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Pizza"
         ],
         "xaxis": "x",
         "y": [
          86
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Cafe",
         "marker": {
          "color": "#FF6692",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Cafe",
         "offsetgroup": "Cafe",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Cafe"
         ],
         "xaxis": "x",
         "y": [
          78
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Continental",
         "marker": {
          "color": "#B6E880",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Continental",
         "offsetgroup": "Continental",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Continental"
         ],
         "xaxis": "x",
         "y": [
          58
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "South Indian",
         "marker": {
          "color": "#FF97FF",
          "pattern": {
           "shape": ""
          }
         },
         "name": "South Indian",
         "offsetgroup": "South Indian",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "South Indian"
         ],
         "xaxis": "x",
         "y": [
          57
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Burger",
         "marker": {
          "color": "#FECB52",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Burger",
         "offsetgroup": "Burger",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Burger"
         ],
         "xaxis": "x",
         "y": [
          47
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Sandwich",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Sandwich",
         "offsetgroup": "Sandwich",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Sandwich"
         ],
         "xaxis": "x",
         "y": [
          44
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Street Food",
         "marker": {
          "color": "#EF553B",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Street Food",
         "offsetgroup": "Street Food",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Street Food"
         ],
         "xaxis": "x",
         "y": [
          40
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Beverages",
         "marker": {
          "color": "#00cc96",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Beverages",
         "offsetgroup": "Beverages",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Beverages"
         ],
         "xaxis": "x",
         "y": [
          36
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Bakery",
         "marker": {
          "color": "#ab63fa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Bakery",
         "offsetgroup": "Bakery",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Bakery"
         ],
         "xaxis": "x",
         "y": [
          35
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Italian",
         "marker": {
          "color": "#FFA15A",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Italian",
         "offsetgroup": "Italian",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Italian"
         ],
         "xaxis": "x",
         "y": [
          33
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Rolls",
         "marker": {
          "color": "#19d3f3",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Rolls",
         "offsetgroup": "Rolls",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Rolls"
         ],
         "xaxis": "x",
         "y": [
          33
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Mishti",
         "marker": {
          "color": "#FF6692",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Mishti",
         "offsetgroup": "Mishti",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Mishti"
         ],
         "xaxis": "x",
         "y": [
          27
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Asian",
         "marker": {
          "color": "#B6E880",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Asian",
         "offsetgroup": "Asian",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Asian"
         ],
         "xaxis": "x",
         "y": [
          24
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Kebab",
         "marker": {
          "color": "#FF97FF",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Kebab",
         "offsetgroup": "Kebab",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Kebab"
         ],
         "xaxis": "x",
         "y": [
          24
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Mughlai",
         "marker": {
          "color": "#FECB52",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Mughlai",
         "offsetgroup": "Mughlai",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Mughlai"
         ],
         "xaxis": "x",
         "y": [
          23
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "barmode": "relative",
        "height": 500,
        "legend": {
         "title": {
          "text": "Cuisines"
         },
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "showlegend": false,
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "width": 1000,
        "xaxis": {
         "anchor": "y",
         "categoryarray": [
          "Chinese",
          "North Indian",
          "Fast Food",
          "Biryani",
          "Bengali",
          "Pizza",
          "Cafe",
          "Continental",
          "South Indian",
          "Burger",
          "Sandwich",
          "Street Food",
          "Beverages",
          "Bakery",
          "Italian",
          "Rolls",
          "Mishti",
          "Asian",
          "Kebab",
          "Mughlai"
         ],
         "categoryorder": "array",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Cuisines"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Count"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot showcasing total number of restaurants for each cuisine\n",
    "fig = px.bar(unique_cuisines.head(20),x='Cuisines',y='Count',color='Cuisines',height=500,width=1000)\n",
    "fig.update_layout(showlegend=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "aabbd98c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cuisine count based on dataset\n",
    "data = new_df['Cuisines'].to_list()\n",
    "namess = ['cuisine']\n",
    "restaurant_counts = pd.DataFrame(data, columns=namess)\n",
    "unique_cuisiness = restaurant_counts['cuisine'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3e1aff4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_cuisiness = pd.DataFrame(unique_cuisiness)\n",
    "unique_cuisiness.rename(columns = {'cuisine':'Count'},inplace=True)\n",
    "unique_cuisiness.index.rename('Cuisines',inplace=True)\n",
    "unique_cuisiness.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "adca6899",
   "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>Cuisines</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bengali</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Biryani</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chinese</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North Indian</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bakery, Desserts</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>Bengali, Chinese, Biryani, North Indian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>North Indian, Chinese, Continental, Beverages,...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>Biryani, North Indian, Rolls, Mughlai</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>Sandwich, Fast Food, Tea, Beverages</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>Chinese, Bengali, South Indian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2010 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Cuisines  Count\n",
       "0                                               Bengali    133\n",
       "1                                               Biryani    125\n",
       "2                                               Chinese    118\n",
       "3                                          North Indian     79\n",
       "4                                      Bakery, Desserts     78\n",
       "...                                                 ...    ...\n",
       "2005            Bengali, Chinese, Biryani, North Indian      1\n",
       "2006  North Indian, Chinese, Continental, Beverages,...      1\n",
       "2007              Biryani, North Indian, Rolls, Mughlai      1\n",
       "2008                Sandwich, Fast Food, Tea, Beverages      1\n",
       "2009                     Chinese, Bengali, South Indian      1\n",
       "\n",
       "[2010 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_cuisiness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bca3f9fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Bengali",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Bengali",
         "offsetgroup": "Bengali",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Bengali"
         ],
         "xaxis": "x",
         "y": [
          133
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Biryani",
         "marker": {
          "color": "#EF553B",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Biryani",
         "offsetgroup": "Biryani",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Biryani"
         ],
         "xaxis": "x",
         "y": [
          125
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Chinese",
         "marker": {
          "color": "#00cc96",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Chinese",
         "offsetgroup": "Chinese",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Chinese"
         ],
         "xaxis": "x",
         "y": [
          118
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "North Indian",
         "marker": {
          "color": "#ab63fa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "North Indian",
         "offsetgroup": "North Indian",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "North Indian"
         ],
         "xaxis": "x",
         "y": [
          79
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Bakery, Desserts",
         "marker": {
          "color": "#FFA15A",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Bakery, Desserts",
         "offsetgroup": "Bakery, Desserts",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Bakery, Desserts"
         ],
         "xaxis": "x",
         "y": [
          78
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "North Indian, Chinese",
         "marker": {
          "color": "#19d3f3",
          "pattern": {
           "shape": ""
          }
         },
         "name": "North Indian, Chinese",
         "offsetgroup": "North Indian, Chinese",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "North Indian, Chinese"
         ],
         "xaxis": "x",
         "y": [
          76
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Fast Food",
         "marker": {
          "color": "#FF6692",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Fast Food",
         "offsetgroup": "Fast Food",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Fast Food"
         ],
         "xaxis": "x",
         "y": [
          72
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Ice Cream, Desserts",
         "marker": {
          "color": "#B6E880",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Ice Cream, Desserts",
         "offsetgroup": "Ice Cream, Desserts",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Ice Cream, Desserts"
         ],
         "xaxis": "x",
         "y": [
          72
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Biryani, North Indian",
         "marker": {
          "color": "#FF97FF",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Biryani, North Indian",
         "offsetgroup": "Biryani, North Indian",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Biryani, North Indian"
         ],
         "xaxis": "x",
         "y": [
          69
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "South Indian",
         "marker": {
          "color": "#FECB52",
          "pattern": {
           "shape": ""
          }
         },
         "name": "South Indian",
         "offsetgroup": "South Indian",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "South Indian"
         ],
         "xaxis": "x",
         "y": [
          68
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Chinese, Fast Food",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Chinese, Fast Food",
         "offsetgroup": "Chinese, Fast Food",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Chinese, Fast Food"
         ],
         "xaxis": "x",
         "y": [
          54
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Chinese, North Indian",
         "marker": {
          "color": "#EF553B",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Chinese, North Indian",
         "offsetgroup": "Chinese, North Indian",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Chinese, North Indian"
         ],
         "xaxis": "x",
         "y": [
          43
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Ice Cream, Desserts, Beverages",
         "marker": {
          "color": "#00cc96",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Ice Cream, Desserts, Beverages",
         "offsetgroup": "Ice Cream, Desserts, Beverages",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Ice Cream, Desserts, Beverages"
         ],
         "xaxis": "x",
         "y": [
          40
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Bakery, Desserts, Fast Food",
         "marker": {
          "color": "#ab63fa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Bakery, Desserts, Fast Food",
         "offsetgroup": "Bakery, Desserts, Fast Food",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Bakery, Desserts, Fast Food"
         ],
         "xaxis": "x",
         "y": [
          38
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Bakery",
         "marker": {
          "color": "#FFA15A",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Bakery",
         "offsetgroup": "Bakery",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Bakery"
         ],
         "xaxis": "x",
         "y": [
          32
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Mishti",
         "marker": {
          "color": "#19d3f3",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Mishti",
         "offsetgroup": "Mishti",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Mishti"
         ],
         "xaxis": "x",
         "y": [
          31
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Street Food",
         "marker": {
          "color": "#FF6692",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Street Food",
         "offsetgroup": "Street Food",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Street Food"
         ],
         "xaxis": "x",
         "y": [
          29
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Bengali, North Indian",
         "marker": {
          "color": "#B6E880",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Bengali, North Indian",
         "offsetgroup": "Bengali, North Indian",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Bengali, North Indian"
         ],
         "xaxis": "x",
         "y": [
          29
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Bengali, Chinese",
         "marker": {
          "color": "#FF97FF",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Bengali, Chinese",
         "offsetgroup": "Bengali, Chinese",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Bengali, Chinese"
         ],
         "xaxis": "x",
         "y": [
          25
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Cuisines=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "Chinese, Momos",
         "marker": {
          "color": "#FECB52",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Chinese, Momos",
         "offsetgroup": "Chinese, Momos",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Chinese, Momos"
         ],
         "xaxis": "x",
         "y": [
          23
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "barmode": "relative",
        "height": 500,
        "legend": {
         "title": {
          "text": "Cuisines"
         },
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "showlegend": false,
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "width": 1000,
        "xaxis": {
         "anchor": "y",
         "categoryarray": [
          "Bengali",
          "Biryani",
          "Chinese",
          "North Indian",
          "Bakery, Desserts",
          "North Indian, Chinese",
          "Fast Food",
          "Ice Cream, Desserts",
          "Biryani, North Indian",
          "South Indian",
          "Chinese, Fast Food",
          "Chinese, North Indian",
          "Ice Cream, Desserts, Beverages",
          "Bakery, Desserts, Fast Food",
          "Bakery",
          "Mishti",
          "Street Food",
          "Bengali, North Indian",
          "Bengali, Chinese",
          "Chinese, Momos"
         ],
         "categoryorder": "array",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Cuisines"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Count"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot showcasing total number of restaurants for cuisine as per dataset\n",
    "fig = px.bar(unique_cuisiness.head(20),x='Cuisines',y='Count',color='Cuisines',height=500,width=1000)\n",
    "fig.update_layout(showlegend=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "160aed83",
   "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>IsHomeDelivery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IsHomeDelivery\n",
       "1            4918\n",
       "0               5"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Provide home delivery \n",
    "new_df['IsHomeDelivery'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8aaaa79b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='IsHomeDelivery'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df['IsHomeDelivery'].value_counts().plot(kind='pie',autopct='%0.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "59e6a33f",
   "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>isVegOnly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   isVegOnly\n",
       "0       4583\n",
       "1        340"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Provide veg only food\n",
    "new_df['isVegOnly'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cc4a9d34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='isVegOnly'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df['isVegOnly'].value_counts().plot(kind='pie',autopct='%0.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6e3252a1",
   "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>isIndoorSeating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   isIndoorSeating\n",
       "0             2964\n",
       "1             1959"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Provide indoor seating\n",
    "new_df['isIndoorSeating'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8d805bb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='isIndoorSeating'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df['isIndoorSeating'].value_counts().plot(kind='pie',autopct='%0.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6dd0fd1d",
   "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>isTakeaway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   isTakeaway\n",
       "1        3375\n",
       "0        1548"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Is take away \n",
    "new_df['isTakeaway'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "25527ae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='isTakeaway'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df['isTakeaway'].value_counts().plot(kind='pie',autopct='%0.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9c192434",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Top 10 area having highest delivery rating\n",
    "top_10_area = new_df.groupby('Area')['Delivery Ratings'].mean().sort_values(ascending=[False]).head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5fc40905",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Area\n",
       "ITC Sonar, Science City Area, Kolkata               4.400000\n",
       "Silver Spring Arcade, Science City Area, Kolkata    4.225000\n",
       "Forum Mall, Elgin, Kolkata                          4.225000\n",
       "Howard Johnson, Chinar Park, Kolkata                4.200000\n",
       "GT Road, Howrah                                     4.200000\n",
       "Upohar Town Centre, Garia, Kolkata                  4.200000\n",
       "Swimming Pool Chaat, Sector 1, Kolkata              4.133333\n",
       "DLF IT Park, Rajarhat, Kolkata                      4.100000\n",
       "Mani Square Mall, Kankurgachi, Kolkata              4.100000\n",
       "Fort Knox, Camac Street Area, Kolkata               4.066667\n",
       "Name: Delivery Ratings, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0403d774",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAMFCAYAAABavd3kAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAADD4ElEQVR4nOzdd1QU198G8GeX3osCVgQUsaFi74AVe+8FbDHGQtQY9WeU2Fus0cQkFuwlBhM1sSAq9l7Q2BXEGAVRKWJDuO8fhn1dFnQX0Zldns85ew57Z4XnOgP73Zk79yqEEAJEREREMqSUOgARERFRTlioEBERkWyxUCEiIiLZYqFCREREssVChYiIiGSLhQoRERHJFgsVIiIiki0WKkRERCRbLFSIiIhItlioEBmIoKAguLm5SR0jz2TXH4VCgW+//VaSPPokNDQUCoUCMTExUkch+mAsVChf+eGHH6BQKFCzZk2poxgEPz8/KBQKKBQKKJVK2NrawsvLC71790Z4eLjU8WTjwIEDqv8nhUIBIyMjODs7o1OnTrhy5Uquv+/06dPx+++/511QIhlScK0fyk/q1q2Lf//9FzExMbhx4wZKlSoldaQ8k5aWhoyMDJiZmX2yn+nn54dbt25hxowZAIDU1FTcvHkTYWFhuH37Nrp06YK1a9fCxMRE5+8dFBSEAwcOqJ0VePHiBYyNjWFsbJxXXfgkDhw4AH9/fwwfPhzVq1dHWloaoqKisHTpUlhZWeHSpUsoVKiQzt/X2toanTp1QmhoqFp7eno60tLSYGZmBoVCkUe9IJKGfv22E32A6OhoHD16FGFhYRg0aBDWrVuHkJCQ9/67169fIyMjA6ampp8gZe7lphjIC3Z2dujVq5da28yZMzF8+HD88MMPcHNzw6xZs/LkZ5mbm+fJ99FFamoqrKys8uR71a9fH506dVI99/LywuDBg7F69Wp8/fXXefIzAMDIyAhGRkZ59v2IpMRLP5RvrFu3Dg4ODmjZsiU6deqEdevWabwmJiYGCoUC3333HRYsWICSJUvCzMwMly9fBgBcvXoVnTp1gqOjI8zNzVGtWjVs27ZN7Xs8fvwYX331Fby9vWFtbQ1bW1s0b94cFy5ceG/GxMREGBkZYdGiRaq2hIQEKJVKFChQAG+fAB08eLDap/DsxnRs3LgRVatWhY2NDWxtbeHt7Y2FCxdq/Mwvv/wSxYsXh5mZGUqVKoVZs2YhIyPjvXlzktmHcuXKYfHixUhKSlLbvnbtWlStWhUWFhZwdHREt27dcPfu3fd+37fHqGzZsgUKhQKRkZEar/vpp5+gUChw6dIlVZs2+y5zbEdkZCS++OILODs7o1ixYti/fz8UCgW2bt2q8bPWr18PhUKBY8eOafNfo6Z+/foAgFu3bqm1f/fdd6hTpw4KFCgACwsLVK1aFVu2bNH4v0hNTcWqVatUl5SCgoLU+vH22Sg3Nze0atUKhw8fRo0aNWBubg4PDw+sXr1aI1dUVBR8fX1hYWGBYsWKYerUqVi5cqXG9zx9+jSaNWuGggULwsLCAu7u7ujXr5/O/w9E78IzKpRvrFu3Dh06dICpqSm6d++OH3/8EadOnUL16tU1Xrty5Uq8ePECn332GczMzODo6Ii///4bdevWRdGiRTF27FhYWVlh8+bNaNeuHX777Te0b98eAHD79m38/vvv6Ny5M9zd3REXF4effvoJvr6+uHz5MooUKZJjRnt7e1SoUAEHDx7E8OHDAQCHDx+GQqHA48ePcfnyZZQvXx4AcOjQIdUbXXbCw8PRvXt3NGrUSHVG48qVKzhy5AiCg4MBAM+ePYOvry/u3buHQYMGwdXVFUePHsW4ceNw//59LFiwIFf/18CbYqV79+6YMGECDh8+jJYtWwIApk2bhgkTJqBLly4YMGAAHj58iO+//x4NGjTAuXPnYG9vr9X3b9myJaytrbF582b4+vqqbdu0aRPKly+PChUqAIDW+y7TF198AScnJ0ycOBGpqanw8/ND8eLFsW7dOo3Xrlu3DiVLlkTt2rV1/j/KfNN3cHBQa1+4cCHatGmDnj174tWrV9i4cSM6d+6MHTt2qP4f16xZgwEDBqBGjRr47LPPAAAlS5Z858+7efMmOnXqhP79+yMwMBArVqxAUFAQqlatqjqu7t27B39/fygUCowbNw5WVlZYtmyZxiXF+Ph4NG3aFE5OThg7dizs7e0RExODsLAwnf8fiN5JEOUDp0+fFgBEeHi4EEKIjIwMUaxYMREcHKz2uujoaAFA2Nraivj4eLVtjRo1Et7e3uLFixeqtoyMDFGnTh3h6empanvx4oVIT0/X+L5mZmZi8uTJ7806ZMgQ4eLiono+cuRI0aBBA+Hs7Cx+/PFHIYQQjx49EgqFQixcuFD1usDAQFGiRAnV8+DgYGFraytev36d48+aMmWKsLKyEtevX1drHzt2rDAyMhKxsbHvzOrr6yvKly+f4/atW7cKAKqcMTExwsjISEybNk3tdRcvXhTGxsZq7Vn7I4QQAERISIjqeffu3YWzs7NaH+/fvy+USqXa/7W2+27lypUCgKhXr57G/9u4ceOEmZmZSExMVLXFx8cLY2NjtUzZ2b9/vwAgVqxYIR4+fCj+/fdfsWvXLlGqVCmhUCjEyZMn1V7/7NkzteevXr0SFSpUEA0bNlRrt7KyEoGBgRo/L7Mf0dHRqrYSJUoIAOLgwYNq+c3MzMSoUaNUbcOGDRMKhUKcO3dO1fbo0SPh6Oio9j0z9+2pU6fe2XeiD8VLP5QvrFu3Di4uLvD39wfw5rR5165dsXHjRqSnp2u8vmPHjnByclI9f/z4Mfbt24cuXbogJSUFCQkJSEhIwKNHj9CsWTPcuHED9+7dAwCYmZlBqXzzq5Weno5Hjx7B2toaXl5eOHv27Huz1q9fH3Fxcbh27RqAN2dOGjRogPr16+PQoUMA3pxlEUK884yKvb09UlNT33n3za+//or69evDwcFB1aeEhAQ0btwY6enpOHjw4Hvzvou1tTUAICUlBQAQFhaGjIwMdOnSRe3nFSpUCJ6enti/f79O379r166Ij4/HgQMHVG1btmxBRkYGunbtCkC3fZdp4MCBGmM8+vTpg5cvX6pdgtm0aRNev36tMUYnJ/369YOTkxOKFCmCgIAAJCUlYc2aNRpn9SwsLFRfP3nyBElJSahfv75Wx8+7lCtXTu2YcXJygpeXF27fvq1q27VrF2rXro3KlSur2hwdHdGzZ0+175V55mvHjh1IS0v7oFxE78JChQxeeno6Nm7cCH9/f0RHR+PmzZu4efMmatasibi4OERERGj8G3d3d7XnN2/ehBACEyZMgJOTk9ojc0BufHw8ACAjIwPz58+Hp6cnzMzMULBgQTg5OSEqKkptrMaDBw/UHs+fPwfw/+MWDh06hNTUVJw7dw7169dHgwYNVIXKoUOHYGtri0qVKuXY7y+++AKlS5dG8+bNUaxYMfTr1w+7du1Se82NGzewa9cujT41btxYrU+59fTpUwCAjY2N6ucJIeDp6anxM69cuaLzzwsICICdnR02bdqkatu0aRMqV66M0qVLA9Bt32XKuv8BoEyZMqhevbra2KZ169ahVq1aWt89NnHiRISHh2Pr1q3o06cPkpKSVEXt23bs2IFatWrB3Nwcjo6OcHJywo8//qgx1kdXrq6uGm0ODg548uSJ6vmdO3ey7U/WNl9fX3Ts2BGTJk1CwYIF0bZtW6xcuRIvX778oIxEWXGMChm8ffv24f79+9i4cSM2btyosX3dunVo2rSpWtvbn2gBqAaWfvXVV2jWrFm2PyfzD/n06dMxYcIE9OvXD1OmTIGjoyOUSiW+/PJLtQGqhQsXVvv3K1euRFBQEIoUKQJ3d3ccPHgQbm5uEEKgdu3acHJyQnBwMO7cuYNDhw6hTp062b7JZXJ2dsb58+exe/du7Ny5Ezt37sTKlSvRp08frFq1StWvJk2a5HjHSeabfW5lDmbN/L/JyMiAQqHAzp07s70rJfMMjLbMzMzQrl07bN26FT/88APi4uJw5MgRTJ8+XfUaXfZdpqz7P1OfPn0QHByMf/75By9fvsTx48exePFirfN6e3urisB27drh2bNnGDhwIOrVq4fixYsDeFOEtmnTBg0aNMAPP/yAwoULw8TEBCtXrsT69eu1/lnZyelOIJGLWSoUCgW2bNmC48ePY/v27di9ezf69euHuXPn4vjx4zrvS6KcsFAhg7du3To4OztjyZIlGtvCwsKwdetWLF26NMc3JwDw8PAA8OYW4Mw3mpxs2bIF/v7+WL58uVp7YmIiChYsqHqe9ZJM5mBG4M1ZlYMHD8Ld3R2VK1eGjY0NKlWqBDs7O+zatQtnz57FpEmT3pkDAExNTdG6dWu0bt0aGRkZ+OKLL/DTTz9hwoQJKFWqFEqWLImnT5++t0+5kZ6ejvXr18PS0hL16tUD8GawpxAC7u7uH1wEZeratStWrVqFiIgIXLlyBUII1WUfQLd99z7dunXDyJEjsWHDBjx//hwmJiZqP0tXM2fOxNatWzFt2jQsXboUAPDbb7/B3Nwcu3fvVhvAunLlSo1//zHmSClRogRu3ryp0Z5dGwDUqlULtWrVwrRp07B+/Xr07NkTGzduxIABA/I8G+VPvPRDBu358+cICwtDq1at0KlTJ43H0KFDkZKSonGbalbOzs7w8/PDTz/9hPv372tsf/jwoeprIyMjjU+ov/76q8Y4iMaNG6s93j7DUr9+fcTExGDTpk2qS0FKpRJ16tTBvHnzkJaW9s7xKQDw6NEjtedKpRIVK1YEANXp+S5duuDYsWPYvXu3xr9PTEzE69ev3/kzcpKeno7hw4fjypUrGD58OGxtbQEAHTp0gJGRESZNmqTxfySE0MisjcaNG8PR0RGbNm3Cpk2bUKNGDbVLN7rsu/cpWLAgmjdvjrVr12LdunUICAhQKz51VbJkSXTs2BGhoaF48OABgDfHj0KhUBs7FRMTk+0MtFZWVkhMTMz1z89Os2bNcOzYMZw/f17V9vjxY43b+Z88eaKxDzPHtfDyD+UlnlEhg7Zt2zakpKSgTZs22W6vVasWnJycsG7duvd+Ml6yZAnq1asHb29vDBw4EB4eHoiLi8OxY8fwzz//qOZJadWqFSZPnoy+ffuiTp06uHjxItatW6f6ZK+NzCLk2rVrapcxGjRogJ07d8LMzCzb26rfNmDAADx+/BgNGzZEsWLFcOfOHXz//feoXLkyypYtCwAYPXo0tm3bhlatWqluU01NTcXFixexZcsWxMTEvPeNOCkpCWvXrgXw5nbnzJlpb926hW7dumHKlCmq15YsWRJTp07FuHHjEBMTg3bt2sHGxgbR0dHYunUrPvvsM3z11Vda/z8Bb86UdOjQARs3bkRqaiq+++47jddou++00adPH9WkbW/3LbdGjx6NzZs3Y8GCBZg5cyZatmyJefPmISAgAD169EB8fDyWLFmCUqVKISoqSu3fVq1aFXv37sW8efNUlww/dHmIr7/+GmvXrkWTJk0wbNgw1e3Jrq6uePz4seoszqpVq/DDDz+gffv2KFmyJFJSUvDLL7/A1tYWLVq0+KAMRGqkudmI6NNo3bq1MDc3F6mpqTm+JigoSJiYmIiEhATV7clz5szJ9rW3bt0Sffr0EYUKFRImJiaiaNGiolWrVmLLli2q17x48UKMGjVKFC5cWFhYWIi6deuKY8eOCV9fX+Hr66t1dmdnZwFAxMXFqdoOHz4sAIj69etrvD7r7bxbtmwRTZs2Fc7OzsLU1FS4urqKQYMGifv376v9u5SUFDFu3DhRqlQpYWpqKgoWLCjq1KkjvvvuO/Hq1at3ZvT19RUAVA9ra2vh6ekpevXqJfbs2ZPjv/vtt99EvXr1hJWVlbCyshJlypQRQ4YMEdeuXcuxP0Jo3p6cKTw8XAAQCoVC3L17N9ufqc2+y7yt91233L58+VI4ODgIOzs78fz58xxf97bM25N//fXXbLf7+fkJW1tb1a3Py5cvF56ensLMzEyUKVNGrFy5UoSEhIisf7KvXr0qGjRoICwsLAQA1a3KOd2e3LJlS42fnd1xee7cOVG/fn1hZmYmihUrJmbMmCEWLVokAIgHDx4IIYQ4e/as6N69u3B1dRVmZmbC2dlZtGrVSpw+fVqr/xMibXGtHyIiHbx+/RpFihRB69atNcYhGbIvv/wSP/30E54+fcrp+emT4hgVIiId/P7773j48CH69OkjdZSPJvNW+UyPHj3CmjVrUK9ePRYp9MnxjAoRkRZOnDiBqKgoTJkyBQULFvzgydfkrHLlyvDz80PZsmURFxeH5cuX499//0VERAQaNGggdTzKZziYlohICz/++CPWrl2LypUrIzQ0VOo4H1WLFi2wZcsW/Pzzz1AoFKhSpQqWL1/OIoUkwTMqREREJFsco0JERESyxUKFiIiIZEuvx6hkZGTg33//hY2NzUeZSpqIiIjynhACKSkpKFKkyDvXLAP0vFD5999/VQt5ERERkX65e/cuihUr9s7X6HWhkrl0/N27d1VriRAREZG8JScno3jx4qr38XfR60Il83KPra0tCxUiIiI9o82wDQ6mJSIiItlioUJERESyxUKFiIiIZIuFChEREckWCxUiIiKSLRYqREREJFssVIiIiEi2WKgQERGRbLFQISIiItlioUJERESyxUKFiIiIZIuFChEREckWCxUiIiKSLRYqREREJFssVIiIiEi2jKUO8DFUHb1a6gjvdGZOH6kjEBER6QWeUSEiIiLZYqFCREREssVChYiIiGSLhQoRERHJFgsVIiIiki0WKkRERCRbLFSIiIhItlioEBERkWyxUCEiIiLZYqFCREREssVChYiIiGSLhQoRERHJFgsVIiIiki2DXD3ZUMRO9pY6Qo5cJ17U6nV1v6/7kZN8mCPDjkgdgYiI3oFnVIiIiEi2eEaFSAuRDXyljvBOvgcjtXrd4lHbP3KS3Bs6t7VWr5vWq9NHTvJhxq/dInUEIoPCMypEREQkWyxUiIiISLZYqBAREZFssVAhIiIi2eJgWiIiCVyZtk/qCDkqO76h1BGIVHhGhYiIiGSLhQoRERHJFi/9EBFRrnz77bdSR3gnbfNt/rXGxw3ygbp0Pil1BEnxjAoRERHJFgsVIiIiki3ZXPqZOXMmxo0bh+DgYCxYsEDqOERERHql0pbdUkfI0YVOzXL9b2VxRuXUqVP46aefULFiRamjEBERkYxIXqg8ffoUPXv2xC+//AIHBwep4xAREZGMSF6oDBkyBC1btkTjxo2ljkJEREQyI+kYlY0bN+Ls2bM4deqUVq9/+fIlXr58qXqenJz8saIRERGRDEh2RuXu3bsIDg7GunXrYG5urtW/mTFjBuzs7FSP4sWLf+SUREREJCXJCpUzZ84gPj4eVapUgbGxMYyNjREZGYlFixbB2NgY6enpGv9m3LhxSEpKUj3u3r0rQXIiIiL6VCS79NOoUSNcvHhRra1v374oU6YMxowZAyMjI41/Y2ZmBjMzs08VkYiIiCQmWaFiY2ODChUqqLVZWVmhQIECGu1ERESUP0l+1w8RERFRTmQzMy0AHDhwQOoIREREJCM8o0JERESyxUKFiIiIZIuFChEREckWCxUiIiKSLRYqREREJFssVIiIiEi2WKgQERGRbLFQISIiItlioUJERESyxUKFiIiIZIuFChEREckWCxUiIiKSLRYqREREJFssVIiIiEi2WKgQERGRbLFQISIiItlioUJERESyxUKFiIiIZIuFChEREckWCxUiIiKSLRYqREREJFssVIiIiEi2WKgQERGRbLFQISIiItlioUJERESyxUKFiIiIZIuFChEREckWCxUiIiKSLRYqREREJFssVIiIiEi2WKgQERGRbLFQISIiItlioUJERESyxUKFiIiIZIuFChEREckWCxUiIiKSLRYqREREJFssVIiIiEi2WKgQERGRbLFQISIiItlioUJERESyxUKFiIiIZIuFChEREckWCxUiIiKSLRYqREREJFssVIiIiEi2WKgQERGRbLFQISIiItlioUJERESyxUKFiIiIZIuFChEREckWCxUiIiKSLRYqREREJFssVIiIiEi2WKgQERGRbLFQISIiItlioUJERESyxUKFiIiIZIuFChEREckWCxUiIiKSLRYqREREJFssVIiIiEi2WKgQERGRbLFQISIiItlioUJERESyxUKFiIiIZIuFChEREckWCxUiIiKSLRYqREREJFuSFio//vgjKlasCFtbW9ja2qJ27drYuXOnlJGIiIhIRiQtVIoVK4aZM2fizJkzOH36NBo2bIi2bdvi77//ljIWERERyYSxlD+8devWas+nTZuGH3/8EcePH0f58uUlSkVERERyIWmh8rb09HT8+uuvSE1NRe3ataWOQ0RERDKgc6Fy9+5dKBQKFCtWDABw8uRJrF+/HuXKlcNnn32mc4CLFy+idu3aePHiBaytrbF161aUK1cu29e+fPkSL1++VD1PTk7W+ecRERGR/tB5jEqPHj2wf/9+AMCDBw/QpEkTnDx5EuPHj8fkyZN1DuDl5YXz58/jxIkTGDx4MAIDA3H58uVsXztjxgzY2dmpHsWLF9f55xEREZH+0LlQuXTpEmrUqAEA2Lx5MypUqICjR49i3bp1CA0N1TmAqakpSpUqhapVq2LGjBmoVKkSFi5cmO1rx40bh6SkJNXj7t27Ov88IiIi0h86X/pJS0uDmZkZAGDv3r1o06YNAKBMmTK4f//+BwfKyMhQu7zzNjMzM9XPJiIiIsOnc6FSvnx5LF26FC1btkR4eDimTJkCAPj3339RoEABnb7XuHHj0Lx5c7i6uiIlJQXr16/HgQMHsHv3bl1jERERkQHSuVCZNWsW2rdvjzlz5iAwMBCVKlUCAGzbtk11SUhb8fHx6NOnD+7fvw87OztUrFgRu3fvRpMmTXSNRURERAZI50LFz88PCQkJSE5OhoODg6r9s88+g6WlpU7fa/ny5br+eCIiIspHcjWPipGRkVqRAgBubm55kYeIiIhIRedCxcfHBwqFQqNdoVDA3NwcpUqVQlBQEPz9/fMkIBEREeVfOt+eHBAQgNu3b8PKygr+/v7w9/eHtbU1bt26herVq+P+/fto3Lgx/vjjj4+Rl4iIiPIRnc+oJCQkYNSoUZgwYYJa+9SpU3Hnzh3s2bMHISEhmDJlCtq2bZtnQYmIiCj/0fmMyubNm9G9e3eN9m7dumHz5s0AgO7du+PatWsfno6IiIjyNZ0LFXNzcxw9elSj/ejRozA3NwfwZtK2zK+JiIiIckvnSz/Dhg3D559/jjNnzqB69eoAgFOnTmHZsmX43//+BwDYvXs3KleunKdBiYiIKP/RuVD55ptv4O7ujsWLF2PNmjUA3iws+Msvv6BHjx4AgM8//xyDBw/O26RERESU7+RqHpWePXuiZ8+eOW63sLDIdSAiIiKiTLkqVADg1atXiI+PR0ZGhlq7q6vrB4ciIiIiAnJRqNy4cQP9+vXTGFArhIBCoUB6enqehSMiIqL8TedCJSgoCMbGxtixYwcKFy6c7Sy1RERERHlB50Ll/PnzOHPmDMqUKfMx8hARERGp6DyPSrly5ZCQkPAxshARERGp0blQmTVrFr7++mscOHAAjx49QnJystqDiIiIKK/ofOmncePGAIBGjRqptXMwLREREeU1nQuV/fv3f4wcRERERBp0LlR8fX0/Rg4iIiIiDVoVKlFRUahQoQKUSiWioqLe+dqKFSvmSTAiIiIirQqVypUr48GDB3B2dkblypWhUCgghNB4HceoEBERUV7SqlCJjo6Gk5OT6msiIiKiT0GrQqVEiRKqr+/cuYM6derA2Fj9n75+/RpHjx5Vey0RERHRh9B5HhV/f388fvxYoz0pKQn+/v55EoqIiIgIyEWhkjlfSlaPHj2ClZVVnoQiIiIiAnS4PblDhw4A3gyYDQoKgpmZmWpbeno6oqKiUKdOnbxPSERERPmW1oWKnZ0dgDdnVGxsbGBhYaHaZmpqilq1amHgwIF5n5CIiIjyLa0LlZUrVwIA3Nzc8NVXX/EyDxEREX10Os9MGxIS8jFyEBEREWnQuVABgC1btmDz5s2IjY3Fq1ev1LadPXs2T4IRERER6XzXz6JFi9C3b1+4uLjg3LlzqFGjBgoUKIDbt2+jefPmHyMjERER5VM6Fyo//PADfv75Z3z//fcwNTXF119/jfDwcAwfPhxJSUkfIyMRERHlUzoXKrGxsarbkC0sLJCSkgIA6N27NzZs2JC36YiIiChf07lQKVSokGpmWldXVxw/fhzAmzWAsluokIiIiCi3dC5UGjZsiG3btgEA+vbtixEjRqBJkybo2rUr2rdvn+cBiYiIKP/S+a6fn3/+GRkZGQCAIUOGoECBAjh69CjatGmDQYMG5XlAIiIiyr90LlSUSiWUyv8/EdOtWzd069YNAHDv3j0ULVo079IRERFRvqbzpZ/sPHjwAMOGDYOnp2defDsiIiIiADoUKk+ePEH37t1RsGBBFClSBIsWLUJGRgYmTpwIDw8PnDp1SjXNPhEREVFe0PrSz9ixY3H06FEEBQVh9+7dGDFiBHbt2gWlUol9+/ahVq1aHzMnERER5UNan1HZuXMnVq5cie+++w7bt2+HEAKVK1fGjh07WKQQERHRR6F1ofLvv/+ibNmyAN6soGxubo5evXp9tGBEREREWhcqQggYG///lSIjIyNYWFh8lFBEREREgA5jVIQQaNSokapYef78OVq3bg1TU1O113H1ZCIiIsorWhcqISEhas/btm2b52GIiIiI3pbrQoWIiIjoY8uTCd+IiIiIPgYWKkRERCRbLFSIiIhItlioEBERkWzpXKjcvn37Y+QgIiIi0qBzoVKqVCn4+/tj7dq1ePHixcfIRERERAQgF4XK2bNnUbFiRYwcORKFChXCoEGDcPLkyY+RjYiIiPI5nQuVypUrY+HChfj333+xYsUK3L9/H/Xq1UOFChUwb948PHz48GPkJCIionwo14NpjY2N0aFDB/z666+YNWsWbt68ia+++grFixdHnz59cP/+/bzMSURERPlQrguV06dP44svvkDhwoUxb948fPXVV7h16xbCw8Px77//cop9IiIi+mBaT6Gfad68eVi5ciWuXbuGFi1aYPXq1WjRogWUyjc1j7u7O0JDQ+Hm5pbXWYmIiCif0blQ+fHHH9GvXz8EBQWhcOHC2b7G2dkZy5cv/+BwRERElL/pdOnn9evX6NmzJ3r37p1jkQIApqamCAwM/OBwRERElL/pVKgYGxtj7ty5eP369cfKQ0RERKSi82Dahg0bIjIy8mNkISIiIlKj8xiV5s2bY+zYsbh48SKqVq0KKysrte1t2rTJs3BERESUv+lcqHzxxRcA3tz9k5VCoUB6evqHpyIiIiJCLgqVjIyMj5GDiIiISEOuJ3wDwEUJiYiI6KPSuVBJT0/HlClTULRoUVhbW+P27dsAgAkTJnDuFCIiIspTOhcq06ZNQ2hoKGbPng1TU1NVe4UKFbBs2bI8DUdERET5m86FyurVq/Hzzz+jZ8+eMDIyUrVXqlQJV69ezdNwRERElL/pXKjcu3cPpUqV0mjPyMhAWlpanoQiIiIiAnJRqJQrVw6HDh3SaN+yZQt8fHzyJBQRERERkIvbkydOnIjAwEDcu3cPGRkZCAsLw7Vr17B69Wrs2LFDp+81Y8YMhIWF4erVq7CwsECdOnUwa9YseHl56RqLiIiIDJDOZ1Tatm2L7du3Y+/evbCyssLEiRNx5coVbN++HU2aNNHpe0VGRmLIkCE4fvw4wsPDkZaWhqZNmyI1NVXXWERERGSAdD6jAgD169dHeHj4B//wXbt2qT0PDQ2Fs7Mzzpw5gwYNGnzw9yciIiL9pvMZlQEDBuDAgQMfIQqQlJQEAHB0dPwo35+IiIj0i86FysOHDxEQEIDixYtj9OjROH/+fJ4EycjIwJdffom6deuiQoUK2b7m5cuXSE5OVnsQERGR4dK5UPnjjz9w//59TJgwAadOnULVqlVRvnx5TJ8+HTExMbkOMmTIEFy6dAkbN27M8TUzZsyAnZ2d6lG8ePFc/zwiIiKSv1yt9ePg4IDPPvsMBw4cwJ07dxAUFIQ1a9ZkO7+KNoYOHYodO3Zg//79KFasWI6vGzduHJKSklSPu3fv5urnERERkX7I1WDaTGlpaTh9+jROnDiBmJgYuLi46PTvhRAYNmwYtm7digMHDsDd3f2drzczM4OZmdmHRCYiIiI9kqszKvv378fAgQPh4uKCoKAg2NraYseOHfjnn390+j5DhgzB2rVrsX79etjY2ODBgwd48OABnj9/nptYREREZGB0PqNStGhRPH78GAEBAfj555/RunXrXJ/l+PHHHwEAfn5+au0rV65EUFBQrr4nERERGQ6dC5Vvv/0WnTt3hr29/Qf/cCHEB38PIiIiMlw6FyoDBw78GDmIiIiINGhVqHTo0AGhoaGwtbVFhw4d3vnasLCwPAlGREREpFWhYmdnB4VCofqaiIiI6FPQqlBZuXJltl8TERERfUy5uj2ZiIiI6FPQ6oyKj4+P6tLP+5w9e/aDAhERERFl0qpQadeu3UeOQURERKRJq0IlJCTkY+cgIiIi0pCrMSqJiYlYtmwZxo0bh8ePHwN4c8nn3r17eRqOiIiI8jedJ3yLiopC48aNYWdnh5iYGAwcOBCOjo4ICwtDbGwsVq9e/TFyEhERUT6k8xmVkSNHIigoCDdu3IC5ubmqvUWLFjh48GCehiMiIqL8TedC5dSpUxg0aJBGe9GiRfHgwYM8CUVEREQE5KJQMTMzQ3Jyskb79evX4eTklCehiIiIiIBcFCpt2rTB5MmTkZaWBgBQKBSIjY3FmDFj0LFjxzwPSERERPmXzoXK3Llz8fTpUzg7O+P58+fw9fVFqVKlYGNjg2nTpn2MjERERJRP6XzXj52dHcLDw3H48GFERUXh6dOnqFKlCho3bvwx8hEREVE+pnOhkqlevXqoV69eXmYhIiIiUqNToZKRkYHQ0FCEhYUhJiYGCoUC7u7u6NSpE3r37q31ekBERERE2tB6jIoQAm3atMGAAQNw7949eHt7o3z58rhz5w6CgoLQvn37j5mTiIiI8iGtz6iEhobi4MGDiIiIgL+/v9q2ffv2oV27dli9ejX69OmT5yGJiIgof9L6jMqGDRvwv//9T6NIAYCGDRti7NixWLduXZ6GIyIiovxN60IlKioKAQEBOW5v3rw5Lly4kCehiIiIiAAdCpXHjx/DxcUlx+0uLi548uRJnoQiIiIiAnQoVNLT02FsnPOQFiMjI7x+/TpPQhEREREBOgymFUIgKCgIZmZm2W5/+fJlnoUiIiIiAnQoVAIDA9/7Gt7xQ0RERHlJ60Jl5cqVHzMHERERkQadFyUkIiIi+lRYqBAREZFssVAhIiIi2WKhQkRERLLFQoWIiIhki4UKERERyRYLFSIiIpItFipEREQkWyxUiIiISLZYqBAREZFssVAhIiIi2WKhQkRERLLFQoWIiIhki4UKERERyRYLFSIiIpItFipEREQkWyxUiIiISLZYqBAREZFssVAhIiIi2WKhQkRERLLFQoWIiIhki4UKERERyRYLFSIiIpItFipEREQkWyxUiIiISLZYqBAREZFssVAhIiIi2WKhQkRERLLFQoWIiIhki4UKERERyRYLFSIiIpItFipEREQkWyxUiIiISLZYqBAREZFssVAhIiIi2WKhQkRERLLFQoWIiIhki4UKERERyRYLFSIiIpItFipEREQkWyxUiIiISLZYqBAREZFsSVqoHDx4EK1bt0aRIkWgUCjw+++/SxmHiIiIZEbSQiU1NRWVKlXCkiVLpIxBREREMmUs5Q9v3rw5mjdvLmUEIiIikjGOUSEiIiLZkvSMiq5evnyJly9fqp4nJydLmIaIiIg+Nr06ozJjxgzY2dmpHsWLF5c6EhEREX1EelWojBs3DklJSarH3bt3pY5EREREH5FeXfoxMzODmZmZ1DGIiIjoE5G0UHn69Clu3rypeh4dHY3z58/D0dERrq6uEiYjIiIiOZC0UDl9+jT8/f1Vz0eOHAkACAwMRGhoqESpiIiISC4kLVT8/PwghJAyAhEREcmYXg2mJSIiovyFhQoRERHJFgsVIiIiki0WKkRERCRbLFSIiIhItlioEBERkWyxUCEiIiLZYqFCREREssVChYiIiGSLhQoRERHJFgsVIiIiki0WKkRERCRbLFSIiIhItlioEBERkWyxUCEiIiLZYqFCREREssVChYiIiGSLhQoRERHJFgsVIiIiki0WKkRERCRbLFSIiIhItlioEBERkWyxUCEiIiLZYqFCREREssVChYiIiGSLhQoRERHJFgsVIiIiki0WKkRERCRbLFSIiIhItlioEBERkWyxUCEiIiLZYqFCREREssVChYiIiGSLhQoRERHJFgsVIiIiki0WKkRERCRbLFSIiIhItlioEBERkWyxUCEiIiLZYqFCREREssVChYiIiGSLhQoRERHJFgsVIiIiki0WKkRERCRbLFSIiIhItlioEBERkWyxUCEiIiLZYqFCREREssVChYiIiGSLhQoRERHJFgsVIiIiki0WKkRERCRbLFSIiIhItlioEBERkWyxUCEiIiLZYqFCREREssVChYiIiGSLhQoRERHJFgsVIiIiki0WKkRERCRbLFSIiIhItlioEBERkWyxUCEiIiLZYqFCREREssVChYiIiGSLhQoRERHJFgsVIiIiki0WKkRERCRbLFSIiIhItmRRqCxZsgRubm4wNzdHzZo1cfLkSakjERERkQxIXqhs2rQJI0eOREhICM6ePYtKlSqhWbNmiI+PlzoaERERSUzyQmXevHkYOHAg+vbti3LlymHp0qWwtLTEihUrpI5GREREEpO0UHn16hXOnDmDxo0bq9qUSiUaN26MY8eOSZiMiIiI5MBYyh+ekJCA9PR0uLi4qLW7uLjg6tWrGq9/+fIlXr58qXqelJQEAEhOTlZ7XfrL5x8hbd7JmjcnKS/SP3KS3NO2D6+fv/7IST6Mtv1IfW0Y/Xj+8tlHTpJ72vbhRVraR07yYbTtx9MXqR85Se5p24e3/x7Lkbb9ePZMvn9rAe37kf5Mf46pzOdCiPf/YyGhe/fuCQDi6NGjau2jR48WNWrU0Hh9SEiIAMAHH3zwwQcffBjA4+7du++tFSQ9o1KwYEEYGRkhLi5OrT0uLg6FChXSeP24ceMwcuRI1fOMjAw8fvwYBQoUgEKh+CgZk5OTUbx4cdy9exe2trYf5Wd8CobQD0PoA2AY/TCEPgDsh5wYQh8Aw+jHp+iDEAIpKSkoUqTIe18raaFiamqKqlWrIiIiAu3atQPwpviIiIjA0KFDNV5vZmYGMzMztTZ7e/tPkBSwtbXV24PubYbQD0PoA2AY/TCEPgDsh5wYQh8Aw+jHx+6DnZ2dVq+TtFABgJEjRyIwMBDVqlVDjRo1sGDBAqSmpqJv375SRyMiIiKJSV6odO3aFQ8fPsTEiRPx4MEDVK5cGbt27dIYYEtERET5j+SFCgAMHTo020s9cmBmZoaQkBCNS076xhD6YQh9AAyjH4bQB4D9kBND6ANgGP2QWx8UQmhzbxARERHRpyf5zLREREREOWGhQkRERLLFQoWIiIhki4UKERERyZYs7vohepfLly8jNjYWr169Umtv06aNRInyr1evXiE+Ph4ZGRlq7a6urhIlIn2WmpqKyMjIbH+/hw8fLlGq/EnO+4J3/WTjn3/+wbZt27LdYfPmzZMole70vR+3b99G+/btcfHiRSgUCtXiVZnLJaSny3shsbfp+764ceMG+vXrh6NHj6q1CyGgUCj0al+cPn0amzdvznZfhIWFSZRKd/p+TJ07dw4tWrTAs2fPkJqaCkdHRyQkJMDS0hLOzs64ffu21BG1pu/HlOz3xYcvLWhY9u7dKywtLUWFChWEsbGxqFy5srC3txd2dnbC399f6nhaM4R+tGrVSrRt21Y8fPhQWFtbi8uXL4tDhw6JGjVqiIMHD0odT2uGsC/q1KkjGjRoIP766y9x7tw5cf78ebWHvtiwYYMwMTERrVq1EqampqJVq1aidOnSws7OTgQFBUkdT2uGcEz5+vqKgQMHivT0dGFtbS1u3bolYmNjRYMGDcRvv/0mdTytGcIxJfd9wUIli+rVq4uJEycKIYRqh6WkpIg2bdqIH374QeJ02jOEfhQoUEBcuHBBCCGEra2tuHr1qhBCiIiICFG5cmUpo+nEEPaFpaWluHLlitQxPpi3t7dYvHixEOL/90VGRoYYOHCgah/pA0M4puzs7FS/03Z2duLy5ctCCCGOHz8uvLy8pIymE0M4puS+L1ioZGFtbS1u3rwphBDC3t5eXLp0SQghxPnz50WJEiUkTKYbQ+iHvb29uH37thBCCA8PD7Fv3z4hhBA3b94UFhYWUkbTiSHsi2rVqolDhw5JHeODWVpaiujoaCGEEI6OjiIqKkoIIcTly5dFoUKFJEymG0M4pgoWLCiuX78uhBDC09NT7Nq1SwghxJUrV4SlpaWU0XRiCMeU3PcF7/rJwsrKSnWNsXDhwrh165ZqW0JCglSxdGYI/ahQoQIuXLgAAKhZsyZmz56NI0eOYPLkyfDw8JA4nfb0dV8kJyerHrNmzcLXX3+NAwcO4NGjR2rbkpOTpY6qNQcHB6SkpAAAihYtikuXLgEAEhMT8ezZMymj6URfj6m3+fj44NSpUwAAX19fTJw4EevWrcOXX36JChUqSJxOe4ZwTMl9X/Cunyxq1aqFw4cPo2zZsmjRogVGjRqFixcvIiwsDLVq1ZI6ntYMoR/ffPMNUlNTAQCTJ09Gq1atUL9+fRQoUAAbN26UOJ329HVf2NvbqwYuA28GzjZq1EjtNULPBtM2aNAA4eHh8Pb2RufOnREcHIx9+/YhPDxco29ypq/H1NumT5+ueoOfNm0a+vTpg8GDB8PT0xPLly+XOJ32DOGYkvu+4F0/Wdy+fRtPnz5FxYoVkZqailGjRuHo0aPw9PTEvHnzUKJECakjasVQ+pHV48eP4eDgoPYGKnf6ui8iIyO1fq2vr+9HTJJ3Hj9+jBcvXqBIkSLIyMjA7NmzVfvim2++gYODg9QRtaKvx5QhMpRjSs5YqJBs9evXDwsXLoSNjY1ae2pqKoYNG4YVK1ZIlIyIPlTDhg0RFhYGe3t7tfbk5GS0a9cO+/btkyZYPiT3fcExKll4eHjg0aNHGu2JiYl6NS7CEPqxatUqPH/+XKP9+fPnWL16tQSJcscQ9kWmZ8+e4erVq4iKilJ76AsjIyPEx8drtD969AhGRkYSJModQzimDhw4oDHnCAC8ePEChw4dkiBR7hjCMSX3fcExKlnExMRke7395cuXuHfvngSJckef+5GcnAzx5o40pKSkwNzcXLUtPT0df/31F5ydnSVMqBt93heZHj58iL59+2Lnzp3ZbteXMSo5nUB++fIlTE1NP3Ga3NPnY+rtwvby5ct48OCB6nl6ejp27dqFokWLShEtV/T5mNKXfcFC5T/btm1Tfb17927Y2dmpnqenpyMiIgJubm4SJNONIfQjcxCnQqFA6dKlNbYrFApMmjRJgmS6MYR9kenLL79EYmIiTpw4AT8/P2zduhVxcXGYOnUq5s6dK3W891q0aBGAN8fOsmXLYG1trdqWnp6OgwcPokyZMlLF05ohHFOVK1dW/X43bNhQY7uFhQW+//57CZLpxhCOKX3ZFxyj8h+l8s1VsLenas9kYmICNzc3zJ07F61atZIintYMoR+RkZEQQqBhw4b47bff4OjoqNpmamqKEiVKoEiRIhIm1I4h7ItMhQsXxh9//IEaNWrA1tYWp0+fRunSpbFt2zbMnj0bhw8fljriO7m7uwMA7ty5g2LFiqmdkjc1NYWbmxsmT56MmjVrShVRK4ZwTN25cwdCCHh4eODkyZNwcnJSbTM1NYWzs7NeXDIxhGNKX/YFC5Us3N3dcerUKRQsWFDqKB/EEPpx584dFC9eXPXHWV8Zwr6wtbVFVFQU3NzcUKJECaxfvx5169ZFdHQ0ypcvrzfzRfj7+yMsLEzv78QwhGPKUBjKMSVnLFRI9p49e5btYl8VK1aUKFH+U716dUydOhXNmjVDmzZtYG9vjxkzZmDRokXYsmWL2oRjRLrg6ujyIdd9wUIlG3Je7loX+t4PQxnACej/vli7di1ev36NoKAgnDlzBgEBAXj8+DFMTU0RGhqKrl27Sh1Ra/q+6nAmfT+muDq6fMh+X3yiqfr1xtmzZ0WhQoWEra2tMDIyEk5OTkKhUAgrKyvh7u4udTytGUI/evToIerWrStOnTolrKysxJ49e8SaNWuEl5eX2LFjh9TxtGYI+yKr1NRUcebMGfHw4UOpo+jEEFYdFsIwjimuji4fct8XLFSykPty19oyhH4UKlRInDhxQgghhI2Njbh27ZoQQog//vhD1K1bV8poOtH3ffHq1Svh4eGhWlFVnxnCqsNC6P8xJQRXR5cTue8LFipZyH25a20ZQj9sbGxUq5K6urqKw4cPCyGEuH37tl6tnmwI+6JIkSIGUagYwqrDQhjGMcXV0eVD7vtCv2+n+AhMTExUd5k4OzsjNjYWAGBnZ4e7d+9KGU0nhtAPLy8vXLt2DQBQqVIl/PTTT7h37x6WLl2KwoULS5xOe4awL4YMGYJZs2bh9evXUkf5IIaw6jBgGMcUV0eXD7nvC074lkXmcteenp6q5a4TEhKwZs0aWSx3rS1D6EdwcDDu378PAAgJCUFAQADWrVunGsCpLwxhX5w6dQoRERHYs2cPvL29YWVlpbY9LCxMomS6MYRVhwHDOKa4Orp8yH1f8K6fLE6fPo2UlBT4+/sjPj4effr0Ua2EuXz5clSuXFnqiFoxlH68LXOdGVdXV72aP8IQ9kXfvn3fuX3lypWfKMmHMZRVhw3hmMpO5uroAPRmhXRDOaayktO+YKGig+fPn8PCwkLqGB9MX/qxYcMGdO/ePdtto0ePxpw5cz5xorynL/vCUKSlpcHExCTbbQkJCXpVAOdEX46pOXPmYPTo0RrtGRkZ6NmzJzZs2CBBKt0ZwjEl933BMSpZ5DT/QGpqKlq0aPGJ0+SeIfRj8ODB2c6hMmLECKxdu1aCRLljCPtixYoViI6OljrGB+vWrVu2i8jFxcXBz8/v0wfKJUM4pubMmYPly5ertaWnp6Nr1644f/68NKFywRCOKbnvCxYqWfz5558ICQlRa3v69CkCAgL0aiChIfRj3bp16N69u9o6MsOGDcPmzZuxf/9+CZPpxhD2xYwZM1CqVCm4urqid+/eWLZsGW7evCl1LJ3FxsZiwIABam3379+Hn5+f7BeQe5shHFN//vknvvrqK2zZsgUA8Pr1a3Tu3Bl///23Xv1+G8IxJft9IeUtR3J08+ZNUbhwYTF//nwhhBDJycmidu3aon79+uLp06fShtOBofRj3bp1wsHBQZw+fVoMHjxYFClSRDWfir4wlH3xzz//iLVr14rPPvtMeHl5CaVSKYoWLSp69uwpdTStxcfHizJlyogRI0YIIYS4d++eKF26tOjcubNIT0+XOJ32DOWYioiIEDY2NuKPP/4Qbdq0EeXKlRMPHjyQOpZODOWYkvO+YKGSjQsXLghHR0excOFCUatWLeHr66tXv/yZDKUfS5YsEWZmZqJYsWLixo0bUsfJFUPZF0K8mZV2165dIjAwUBgbGwsjIyOpI+kkNjZWuLq6ihEjRghPT0/RtWtX8fr1a6lj6cxQjqmtW7cKY2Nj4e3trXczHWcylGNKrvuCg2lzcOzYMTRp0gQ1a9bEjh079GJwWnb0rR8jR47Mtv3XX39FlSpVULJkSVWbPqyh8TZ92xdv27NnDw4cOIADBw7g3LlzKFu2LHx9feHn54cGDRro3cqx169fR/369dGkSROsWbNG8rsackvfjqkOHTpk2378+HGUKlVKbeCpvtzynknfjil92hcsVPBmToLsDqo7d+7A2dlZ7Zf/7NmznzKaTgyhH/7+/lq9TqFQYN++fR85Te4Zwr54m1KphJOTE0aNGoXPPvsM9vb2UkfSmoODQ7b74tmzZzAzM4ORkZGq7fHjx58ymk4M4Zh6323ub5PzLe+GcEzp077ghG8A2rVrJ3WEPGEI/ZDFwK08YAj74m3z5s3DwYMHMXv2bCxcuFB1NsXPzw+lS5eWOt47LViwQOoIecIQjimp3/DyiiEcU/q0L3hGhYh0cvHiRURGRmLfvn3YsWMHnJ2d8c8//0gdi4gMFM+okKzkdN00O1JfN81vhBA4d+4cDhw4gP379+Pw4cPIyMiAk5OT1NHeKTk5WevX2trafsQklNPlq+zI9fIVYBjHlD7tCxYqyPl6Y3bker0RMIx+2NnZSR0hTxjCvnhb69atceTIESQnJ6NSpUrw8/PDwIED0aBBA9mPV7G3t3/vvhBCQKFQID09/ROl0p0hHFOGcPkKMIxjSp/2BQsVGMb1RsAw+qFP103fxRD2xdvKlCmDQYMGoX79+npXTBrKuCdDOKayTlKnrwzhmNKnfcExKiR7Dx8+xLVr1wAAXl5esr/UQETaO3PmDK5cuQIAKF++PHx8fCROlH/JdV/wjEo20tPT8fvvv6vtsDZt2qjdcqYP9L0fqampGDZsGFavXo2MjAwAgJGREfr06YPvv/8elpaWEifUnr7vCwCIjIzEd999p+pDuXLlMHr0aNSvX1/iZLpJTEzE8uXL1fZFv3799O5Mkb4fU/Hx8ejWrRsOHDigunyYmJgIf39/bNy4Ua8+kOj7MSX7fSHRRHOydePGDeHp6SksLS2Fj4+P8PHxEZaWlsLLy0vcvHlT6nhaM4R+fPbZZ8LDw0P89ddfIikpSSQlJYk///xTlCxZUnz++edSx9OaIeyLNWvWCGNjY9GlSxexcOFCsXDhQtGlSxdhYmIi1q1bJ3U8rZ06dUo4OjqKokWLivbt24v27duLYsWKiQIFCogzZ85IHU9rhnBMdenSRVSrVk1cvnxZ1fb333+LatWqiW7dukmYTDeGcEzJfV+wUMmiefPmIiAgQDx69EjVlpCQIAICAkSLFi0kTKYbQ+hHgQIFxP79+zXa9+3bJwoWLPjpA+WSIeyLMmXKiHnz5mm0z507V5QpU0aCRLlTr149ERQUJNLS0lRtaWlpIjAwUNSvX1/CZLoxhGPK1tZWnDx5UqP9xIkTws7O7tMHyiVDOKbkvi9YqGRhaWkpoqKiNNrPnz8vrKysJEiUO4bQDwsLC7UKP9OlS5eEpaWlBIlyxxD2hampabbrLN24cUOYmZlJkCh3zM3NxZUrVzTa//77b2FhYSFBotwxhGPK2tpanDt3TqP97NmzwsbG5tMHyiVDOKbkvi+U0l54kh8zMzOkpKRotD99+hSmpqYSJModQ+hH7dq1ERISghcvXqjanj9/jkmTJqF27doSJtONIeyL4sWLIyIiQqN97969KF68uASJcsfW1haxsbEa7Xfv3oWNjY0EiXLHEI6phg0bIjg4GP/++6+q7d69exgxYgQaNWokYTLdGMIxJft9IXWlJDe9e/cW5cuXF8ePHxcZGRkiIyNDHDt2TFSoUEEEBgZKHU9rhtCPixcviiJFiogCBQqIhg0bioYNG4oCBQqIIkWKiEuXLkkdT2uGsC9++OEHYWpqKj7//HOxevVqsXr1ajFo0CBhZmYmli5dKnU8rQ0bNkwUK1ZMbNy4UcTGxorY2FixYcMGUaxYMREcHCx1PK0ZwjEVGxsrKleuLExMTISHh4fw8PAQJiYmwsfHR9y9e1fqeFozhGNK7vuChUoWT548EW3atBEKhUKYmpoKU1NToVQqRbt27URiYqLU8bSmz/1ITk5WfZ2amip+/vlnMXLkSDFy5Ejxyy+/iGfPnokDBw5ImFA3+rwv3hYWFibq1q0rHB0dhaOjo6hbt674/fffpY6lk5cvX4rhw4er9oFSqRRmZmbiyy+/FC9evJA6ntb0+Zi6d++e6uuMjAyxZ88esWjRIrFo0SIRHh4uhBBiw4YNUsXTmT4fU/qyL1io/Ofvv/9We37jxg2xbds2sW3bNtW1+dmzZ0sRTSeG0A9fX993/oIfOHBAWFtbf8JEuWMI+8JQREZGqj1PTU0VUVFRIioqSqSmpgohhBg6dKgU0XRiCMdU+fLlxZMnT3LcvnHjRmFiYvLpAuWSIRxT+rIvWKj8p1ixYuLOnTs5bv/uu+9kscPexxD6UaFCBdGmTRuRnp6use3gwYPCyspK9n8AhDCMfWEo7Ozssh0smGnYsGGyGDT4PoZwTPn5+YlatWqp3szftmnTJmFsbCz7YksIwzim9GVfsFD5T7du3YSnp6eIj4/X2Pbdd98JY2NjWZwCex9D6Me9e/eEh4eH6N27t1r7wYMHhbW1tfjiiy8kSqYbQ9gX9vb2wsHB4b0PuRs1apRwcXHJ9s6l4cOHCysrK724nGgIx1RKSoqoWrWqaNKkiXj16pWqffPmzcLExETMnDlTwnTaM4RjSl/2BQuV/6SlpYmAgADh4+MjkpKSVO3z5s0TxsbGejOplaH04+bNm6Jw4cJi+PDhQgghDh06JKytrcWgQYMkTqY9Q9gXoaGhqsfKlSuFubm5mD17tlp7aGio1DG10rdvX1GiRAm16/LBwcHC0tJS7Nu3T8Jk2jOEY0oIIeLj40WZMmVEp06dREZGhvj111+FiYmJmDZtmtTRdGIIx5Q+7AsWKm959uyZqFOnjqhfv754/vy5mD9/vjAyMhJr1qyROppODKUfFy5cEA4ODiIwMFDY2tqKgQMHSh1JZ4ayLzJZW1uLW7duSR0jV9LT00X79u1F2bJlRUJCghgxYoSwsLAQe/fulTqaTgzlmIqNjRWurq6iUaNGwtTUVEyZMkXqSDozlGNK7vuCixJmkZSUBF9fX6SlpeH69etYsWIFevfuLXUsnelzP5KTk1VfHzlyBO3bt0e7du3w008/qS2tbmtrK0U8nenzvsjKxsYGFy5cgIeHh9RRcuXVq1do2bIlLly4gNTUVPzxxx9o3Lix1LF0ps/HVFRUlOrrq1evok+fPmjbti3Gjx+v9rqKFSt+6mi5os/HlL7sCxYq/9m2bZvq6/v37yM4OBitW7fW+OVv06bNp46mE0Poh1KpVCtIMg/RzDYhBBQKBdLT0yXJpy1D2BdZ6WuhsmjRItXXKSkpmDJlCpo1a6YxmdXw4cM/dTSdGMIxlfn7nfl7/Pbv99tfy/332xCOKX3ZFyxU/qNUvn+SXjnssPcxhH5ERkZq9TpfX9+PnOTDGMK+yEpfCxV3d/f3vkahUOD27dufIE3uGcIxdefOHa1eV6JEiY+c5MMYwjGlL/uChQoR5WjkyJFqz5csWYJevXppLF8/b968TxmLiPIRFipElCN/f//3vkahUGDfvn2fIA0R5UcsVIiIiEi2uHoyERERyRYLFSIiIpItFipEREQkWyxUSC/169cPa9askToGEX0EDRs2xJQpU/Ds2TOpo+R7ctgXLFR04O7ujv79++Pff/+VOsoHMYR+3L59GxMmTEDlypWljvJBDGFfGAqlUomGDRvizJkzUkf5IIZwTLm6uiIiIgJlypSROsoHMYRjSg77gnf96ODbb79FTEwMIiMjER0dLXWcXDOUfgDA5cuXUa5cOalj5Joh7IvY2FgULVoURkZGUkf5IKGhoYiJicGuXbtw/PhxqePkmiEcU5mSk5P1ZqmM7BjKMQVIuy9YqBDRB1EqlfD09MSMGTPQoUMHqeOQnli9ejW6du0KMzMztfZXr15h48aN6NOnj0TJSG5YqJCs3bhxA/v370d8fDwyMjLUtk2cOFGiVLq5dOkSKlSokO2233//He3atfu0gfJYZGQkbt++jV27dmHTpk1SxyE9YWRkhPv378PZ2Vmt/dGjR3B2dpb1MgCGKDU1FZGRkYiNjcWrV6/Utkm9XhELlWz8888/2LZtW7Y7TJ+mCtf3fvzyyy8YPHgwChYsiEKFCqktVKhQKHD27FkJ02mvaNGiOHz4sMbaIL/99hv69OmD1NRUiZLlT6dPn8bmzZuz/b0ICwuTKFXeuHv3LkJCQrBixQqpo7yXUqlEXFwcnJyc1NovXLgAf39/PH78WKJk79ehQweEhobC1tb2vWcR9eGYOnfuHFq0aIFnz54hNTUVjo6OSEhIgKWlJZydnSVfr8hY0p8uQxEREWjTpg08PDxw9epVVKhQATExMRBCoEqVKlLH05oh9GPq1KmYNm0axowZI3WUDzJgwAA0btwYR44cQaFChQAAmzZtQr9+/RAaGiptuHwm85JCs2bNsGfPHjRt2hTXr19HXFwc2rdvL3W8D/b48WOsWrVK1oWKj48PFAoFFAoFGjVqBGPj/38bSk9PR3R0NAICAiRM+H52dnaqD05Z173SRyNGjEDr1q2xdOlS2NnZ4fjx4zAxMUGvXr0QHBwsdTyeUcmqRo0aaN68OSZNmqRaKdbZ2Rk9e/ZEQEAABg8eLHVErRhCP2xtbXH+/Hm9W6k3O8OGDcP+/ftx8OBB7Nq1CwMGDMCaNWvQsWNHqaPlKPMNRRv6cnarYsWKGDRoEIYMGaL6vXB3d8egQYNQuHBhTJo0SeqI77Rt27Z3br99+zZGjRol68smmf/HkyZNwqhRo2Btba3aZmpqCjc3N3Ts2BGmpqZSRcx37O3tceLECXh5ecHe3h7Hjh1D2bJlceLECQQGBuLq1avSBhSkxtraWty8eVMIIYS9vb24dOmSEEKI8+fPixIlSkiYTDeG0I9+/fqJH3/8UeoYeaZHjx7C09NTWFpait9//13qOO/17bffqh5jx44Vtra2olatWmLEiBFixIgRonbt2sLW1laMHTtW6qhas7S0FNHR0UIIIRwdHUVUVJQQQojLly+LQoUKSZhMOwqFQiiVSqFQKHJ8KJVKqWNqJTQ0VDx//lzqGCSEKFiwoLh+/boQQghPT0+xa9cuIYQQV65cEZaWllJGE0IIwUs/WVhZWamuWxcuXBi3bt1C+fLlAQAJCQlSRtOJIfSjVKlSmDBhAo4fPw5vb2+YmJiobZd6gNe7ZPfJt0OHDjh06BC6d+8OhUKhek2bNm0+dTythISEqL4eMGAAhg8fjilTpmi85u7du586Wq45ODggJSUFwJuxQ5cuXYK3tzcSExP1YnKxwoUL44cffkDbtm2z3X7+/HlUrVr1E6fKncDAQKkj5Im4uDh89dVXiIiIQHx8PESWixRyPruVycfHB6dOnYKnpyd8fX0xceJEJCQkYM2aNTneCPAp8dJPFu3atUPLli0xcOBAfPXVV/jjjz8QFBSEsLAwODg4YO/evVJH1Ioh9CPr4NO3KRQKyQd4vYtSqd1cigqFQi/+kNnZ2eH06dPw9PRUa79x4waqVauGpKQkiZLppkePHqhWrRpGjhyJKVOm4Pvvv0fbtm0RHh6OKlWqyH7gY5s2bVC5cmVMnjw52+0XLlyAj4+Pxh1ycpSeno758+fnOLBZzoNp39a8eXPExsZi6NChKFy4sMbl0pyKSjk5ffo0UlJS4O/vj/j4ePTp0wdHjx6Fp6cnVqxYgUqVKkkbUOIzOrJz69YtceHCBSGEEE+fPhWDBg0S3t7eokOHDiImJkbidNozlH6QPLi4uIiVK1dqtK9cuVI4Ozt/+kC59OjRI3Hv3j0hhBDp6elixowZonXr1mLkyJHi8ePHEqd7v4MHD4qdO3fmuP3p06fiwIEDnzBR7k2YMEEULlxYfPfdd8Lc3FxMmTJF9O/fXxQoUEAsXLhQ6nhas7a2FufOnZM6hkHjGRWijygtLQ0BAQFYunSpxtkIfTJz5kxMmjQJAwcORI0aNQAAJ06cwIoVKzBhwgSMHTtW4oSkb0qWLIlFixahZcuWsLGxwfnz51Vtx48fx/r166WOqJVy5cph3bp18PHxkTrKB3n9+jUOHDiAW7duoUePHrCxscG///4LW1tbtQHPUmChko3ExERs2bIFt27dwujRo+Ho6IizZ8/CxcUFRYsWlTqe1vSxH5mn5K2srDBy5Mh3vlYf5oIBACcnJ9VpVH22efNmLFy4EFeuXAEAlC1bFsHBwejSpYvEyd7t7am/k5OT3/lafZ6uXd9YWVnhypUrcHV1ReHChfHnn3+iSpUquH37Nnx8fPTmcuKePXswd+5c/PTTT3Bzc5M6Tq7cuXMHAQEBiI2NxcuXL3H9+nV4eHggODgYL1++xNKlSyXNx8G0WURFRaFx48aws7NDTEwMBg4cCEdHR4SFhSE2NharV6+WOqJW9LUf586dQ1pamurrnGh726wc9OrVC8uXL8fMmTOljvJBunTpIvuiJDsODg6qGVDt7e2zPXaEEHozXshQFCtWDPfv34erqytKliyJPXv2oEqVKjh16pTGtPpy4+DgoHYcpaamomTJkrC0tNQY9K8PY22Cg4NRrVo1XLhwAQUKFFC1t2/fHgMHDpQw2RssVLIYOXIkgoKCMHv2bNjY2KjaW7RogR49ekiYTDf62o/9+/dn+7U+e/36NVasWIG9e/eiatWqsLKyUtuuL2eG9NW+ffvg6OgIwHCOKUPQvn17REREoGbNmhg2bJiqoI+NjcWIESOkjvdOCxYskDpCnjp06BCOHj2qMXeNm5sb7t27J1Gq/8dCJYtTp07hp59+0mgvWrQoHjx4IEGi3DGUfhiCS5cuqWYDvn79uto2fTkzpM93aPj6+gJ4UzBGRkaiX79+KFasmMSp6O0zjF27doWrqyuOHTsGT09PtG7dWsJk72cot1ZnysjIyPZs4j///KP2QVcqLFSyMDMzy/Y69vXr1zXWpJAzQ+hH+/bts30jVygUMDc3R6lSpdCjRw94eXlJkE57hvApftKkSVi2bBlGjRqFb775BuPHj0dMTAx+//13vVkc0tjYGHPmzNH7VXnT0tIwaNAgTJgw4Z238Oub2rVro3bt2lLH0Nlff/0FIyMjNGvWTK19z549SE9PR/PmzSVKpr2mTZtiwYIF+PnnnwG8+Rv79OlThISEoEWLFhKn42BaDQMGDMCjR4+wefNmODo6IioqCkZGRmjXrh0aNGigN6f8DKEfQUFB+P3332Fvb6+axOrs2bNITExE06ZNceHCBcTExCAiIgJ169aVOK1hM5Q7NNq2bYsOHTro/SdiOzs7nD9/Xu8LFUNYHb1ixYqYOXOmxhv6rl27MGbMGFy4cEGiZNq7e/cuAgICIIRQzY1048YNFCxYEAcPHtRY4fpTY6GSRVJSEjp16qSaAKdIkSJ48OABateujb/++ktjfIFcGUI/xo4di+TkZCxevFg1gVpGRgaCg4NhY2ODadOm4fPPP8fff/+Nw4cPS5z23fR9xV5DuUNj6dKlmDRpEnr27JnteCG5zhKcVWBgICpXriz7sRzvYiiro1tYWODKlSsad/zExMSgfPnyerM6+uvXr7Fp0yZcuHABT58+RZUqVdCzZ09YWFhIHY2FSk6OHDmitsMaN24sdaRc0ed+ODk54ciRIyhdurRa+/Xr11GnTh0kJCTg4sWLqF+/PhITE6UJqYX3rdi7cuVKqSO+l5eXF1avXo2aNWuiXr16aNWqFcaOHYtNmzZh2LBhiI+PlzqiVt41Y7A+3fUzdepUzJ07F40aNcq24JLz8hKZSpQogS+++ELvV0cvVKgQ1q9fj4YNG6q17927Fz169JD970ZaWhrKlCmDHTt2oGzZslLHyZ4088zJ06tXr4SRkZG4ePGi1FE+iKH0w97eXvzxxx8a7X/88Yewt7cXQghx/fp11ddy5e3tLRYvXiyEeDOL5a1bt0RGRoYYOHCgmDhxosTptDNmzBgxbdo0IYQQGzduFMbGxqJUqVLC1NRUjBkzRuJ0+Y+bm1uOD3d3d6njacXGxkbcunVL6hgf7LPPPhPe3t6qRWCFEOLGjRuiYsWKon///hIm016RIkXE5cuXpY6RIxYqWbi7u4vz589LHeODGUI/hg0bJgoWLCjmzZsnDh06JA4dOiTmzZsnChYsKIYPHy6EEOKXX34RdevWlTjpu+n7ir3ZOXr0qJg7d67Ytm2b1FFITxnK6uiJiYmiVq1awtjYWFUsGhsbC39/f/HkyROp42ll2rRpIjAwUKSlpUkdJVu89JPF8uXLERYWhjVr1qjmXtBHhtCP9PR0zJw5E4sXL0ZcXBwAwMXFBcOGDcOYMWNgZGSE2NhYKJVKWd9uWqxYMezcuRPe3t6oWLEixo0bh+7du+PYsWMICAjQm/EdhiI1NRWRkZHZjhfSh0smWWX+CdeHW90XLVqk+jo1NRXz5s1Dy5Yt9W519KyEEAgPD8eFCxdgYWGBihUrokGDBlLH0lrmnDbW1tbw9vbWuJQo9Tg6FipZ+Pj44ObNm0hLS0OJEiU0dpi+DPAylH5kyrzVWh+nONf3FXsz3bp1CwsWLFBNoV++fHkEBwfDw8ND4mTaO3fuHFq0aIFnz54hNTUVjo6OSEhIgKWlJZydnWW9IndWq1evxpw5c3Djxg0AQOnSpTF69Gj07t1b4mQ50/YuJbmvjm5o+vbt+87tUo+j4zwqWbRr107qCHnCUPqRSR8LlEyLFy/GixcvAADjx4+HiYkJjh49io4dO+Kbb76ROJ12du/ejTZt2qBy5cqqW8GPHDmCn376Cdu3b0eTJk0kTqidESNGoHXr1li6dCns7Oxw/PhxmJiYoFevXggODpY6ntbmzZuHCRMmYOjQoar9cfjwYXz++edISEiQ7d1A0dHRAN6cgYiNjYWzs7Ms7ir5UPp+lk7qQuR9eEZFB+np6TAyMpI6xgeTcz98fHy0PoWtD2eFYmJiEB4ejlevXsHX1xcVKlSQOlKu+Pj4oFmzZhrrFY0dOxZ79uzRi30BAPb29jhx4gS8vLxgb2+PY8eOoWzZsjhx4gQCAwNx9epVqSNqxd3dHZMmTdKYvG7VqlX49ttvVQWBXGVkZMDc3Bx///233i/WaUhn6d6WnJyMdevWYfny5Th9+rSkWXhGRQvXr1/H8uXLsXr1aty/f1/qOLmmD/0wpDNB+/fvR6tWrfD8+XMAb2ZGXbFiBXr16iVxMt1duXIFmzdv1mjv16+fXkwemMnExER1i7KzszNiY2NRtmxZ2NnZ4e7duxKn0979+/dRp04djfY6derI9nf7bUqlEp6ennj06JHeFyqGcpYu0/79+7FixQqEhYXBzs4O7du3lzoSb0/OSWpqqlixYoWoV6+eMDIyEjVr1hSzZ8+WOpbODKUf+qhu3bqibdu24t9//xWPHz8WX3zxhShcuLDUsXKlWLFiYvPmzRrtmzZtEsWLF5cgUe40adJErFu3TgghxIABA0SNGjXE2rVrRbNmzUSNGjUkTqe98uXLq24Xf9uUKVNEhQoVJEiku23btol69erp/TQKdnZ24urVq6qvM2/zPX78uPDy8pIymtb++ecfMXXqVFGyZElRoEABoVQqxcaNG0VGRobU0YQQvD1Zw7Fjx0T//v2Fra2tqFChgjAyMhIHDx6UOpbO9LkfJ06cEK9fv85x+4sXL8SmTZs+YaLcsbOzE3///bfqeWpqqjAyMhIJCQkSpsqdSZMmCXt7ezFz5kxx8OBBcfDgQTFjxgxhb28vJk+eLHU8rZ06dUrs27dPCCFEXFycaNasmbCxsRFVqlTRq9v5t2zZIoyMjESzZs3E5MmTxeTJk0WzZs2EsbGxCAsLkzqeVuzt7YWpqalQKpXC3NxcODg4qD30RcGCBcX169eFEEJ4enqKXbt2CSGEuHLlirC0tJQy2ntt2bJFNG/eXFhZWYlOnTqJ33//Xbx8+VIYGxur/e2SGi/9/Gfu3LlYsWIFkpKS0L17dxw8eBCVKlWCiYkJChQoIHU8rRlCP2rXro379++r1pewtbXF+fPnVXeXJCYmonv37ujSpYuUMd8rOTkZBQsWVD23tLSEhYUFkpKS9GZfZJowYQJsbGwwd+5cjBs3DgBQpEgRfPvtt3oxWDBTtWrVVF87Oztj165dEqbJvY4dO+LEiROYP38+fv/9dwBA2bJlcfLkSfj4+EgbTkv6dMnwXXx8fHDq1Cl4enrC19cXEydOREJCAtasWSP7MWldu3bFmDFjsGnTJlmskpwTDqb9j7GxMcaMGYPJkyerDTQ1MTHBhQsXUK5cOQnTac8Q+qFUKvHgwQNVoWJjY4MLFy6oCpW4uDgULlxYYxEzuVEqlVi1ahXs7OxUbd27d8eCBQvg4uKiatOX9WUypaSkAIDqD9u9e/dQtGhRKSMRSSZzPTV/f3/Ex8ejT58+OHr0KDw9PbFixQpUqlRJ6og5GjRoEDZt2oTy5cujd+/e6Nq1KxwcHOT3fiH1KR25mD59uvD09BTFixcXX3/9teq6qdxOgb2PIfRDoVCIuLg41fPMaeczPXjwQCiVSimi6UShULz3oQ/9yMn9+/fF0KFDhYWFhdRR3uvmzZuib9++qufFixdXu8xQsGBB1TgDfaBUKtV+RzIlJCTo5TH1/PlzkZSUpPagT+PZs2ciNDRUNGjQQJiZmYk2bdrIbgmWnFfoymfGjRuH69evY82aNXjw4AFq1qyJSpUqQQiBJ0+eSB1Pa4bSD0OQkZHx3ofcF8F78uQJunfvjoIFC6JIkSJYtGgRMjIyMHHiRHh4eODUqVOyn4MBAL7//nu1s1hPnjzBuHHjMH/+fMyfPx/Vq1fH/PnzJUyoG5HDifCXL1/C1NT0E6fJndTUVAwdOhTOzs6wsrKCg4OD2oM+DQsLCwQGBiIyMhIXL15E+fLl4eLigrp166JHjx6ymJCSl35ykJKSgvXr12PFihU4c+YMatSogU6dOmHkyJFSR9OJPvZDqVRi3759qqn/69Spg82bN6umyU9ISECTJk1k/yZvCAYNGoRdu3ahc+fO2L17Ny5fvoxmzZpBqVTim2++Qa1ataSOqBVvb28sX74cNWrUAKB5OTEyMhIDBgxQzfIqV5lT0I8YMQJTpkyBtbW1alt6ejoOHjyImJgYnDt3TqqIWhsyZAj279+PKVOmoHfv3liyZAnu3buHn376CTNnzkTPnj2ljqiVnOZ+UigUMDc3R6lSpRAUFAR/f38J0uVORkYG/vzzTyxfvhw7d+7Ey5cvpQ0k6fkcPREVFSWCg4OFk5OT1FE+iL70I/OSSE6XSvT9kok+KV68uIiIiBBCCBEdHS0UCoUYN26cxKl0Z21tLe7evat6/uWXX6rdfRUTEyPMzc2liKaTzEXvFAqFKF68uNqqyaVLlxZNmzYVx48flzqmVooXLy72798vhHizkvKNGzeEEEKsXr1aNG/eXMJkuhk7dqyws7MT9erVEyNHjhQjR44U9evXF3Z2diI4OFg0adJEKJVK8fvvv0sdNVeyu8T4qfGMig7S0tI0Fs7SR3Lvx507d7R6XYkSJT5yEjI2Nsbdu3dRuHBhAG/uXDp9+rR8Btlpyc7ODuHh4aozKlmdPHkSjRs3Vq0pJXf+/v4ICwvT60sk1tbWuHz5MlxdXVGsWDGEhYWhRo0aiI6Ohre3N54+fSp1RK0MHDgQrq6umDBhglr71KlTcefOHfzyyy8ICQnBn3/+KfkMr/qKtyfrQM5v7rqQez9YgMiHEALGxv//Z8LIyEgv12YpX7489u7dm2Ohsnv3btnfSvq2/fv3Sx3hg3l4eCA6Ohqurq4oU6YMNm/ejBo1amD79u2wt7eXOp7WNm/ejDNnzmi0d+vWDVWrVsUvv/yC7t27Y968eRKkMwwcTEv0EWWOG0hMTJQ6Sq4IIdCoUSNUqVIFVapUwfPnz9G6dWvV88yH3PXt2xfTpk3Dn3/+qbFt+/btmDlz5ntXkJWTjh07YtasWRrts2fPRufOnSVIpLu+ffviwoULAN6sGbVkyRKYm5tjxIgRGD16tMTptGdubo6jR49qtB89ehTm5uYA/n9tI8odXvoh+sjMzc1x5coVrZe4l5NJkyZp9bqQkJCPnOTDde/eHZs2bUKZMmXg5eUFALh27RquXbuGjh07ZruWkVw5OTlh37598Pb2Vmu/ePEiGjdujLi4OImS5d6dO3dw5swZlCpVChUrVpQ6jtamTp2K6dOnY+DAgahevToA4NSpU1i2bBn+97//Yfz48Zg/fz7++usvhIeHS5xWP7FQIfrIqlWrhlmzZqFRo0ZSR8n3Nm7ciI0bN+L69esAAE9PT3Tv3h3dunWTOJluLCwscP78eVXBlenq1avw8fFRLYRJn8a6deuwePFiXLt2DQDg5eWFYcOGoUePHgCA58+fq+4CkqPMqQayzpidmJiIKlWqSL4CNAuVLHx9fdG/f3907txZL6/FZzKUfhiCXbt2Ydy4cZgyZQqqVq0KKysrte22trYSJSN9VaNGDbRq1QoTJ05Ua//222+xffv2bMdMyMGiRYvw2WefwdzcXHWrdU6sra1Rvnx51KxZ8xOl093r168xffp09OvXTzV9gj7KOht4pri4OLi6ukp+ezILlSy+/PJLrF+/Hi9fvkSXLl3Qv39/vZkr4m2G0g9DoFT+/1Cwt+dbEEJAoVBwPhjS2fbt29GhQwf06NEDDRs2BABERERgw4YN+PXXX9GuXTtpA+bA3d0dp0+fRoECBd57KfTly5eIj4/HiBEjMGfOnE+UUHfW1ta4dOkS3NzcpI6is23btgEA2rVrp7HcR3p6OiIiIhAeHq46UyQVFirZeP36NbZt24ZVq1Zh586dKFWqFPr164fevXurzW4pd4bSj+w0bNgQ/v7+GDVqFCwtLaWO806RkZHv3O7r6/uJkpAh+fPPPzF9+nScP38eFhYWqFixIkJCQgzqeAoPD0ePHj3w8OFDqaPkqG3btujQoQMCAwOljqKzzA9RCoVCY7ZjExMTuLm5Ye7cuWjVqpUU8VRYqLxHfHw8fv75Z0ybNg3p6elo0aIFhg8frvoUoy8MpR+ZgoKCEBMTg9u3byM2NlbqOET0ETx//hw///wzgoODpY6So6VLl2LSpEno2bNntpd29WHRUXd3d5w6dUpttXc5YaHyDidPnsTKlSuxceNG2NraIigoCPfu3cP69evxxRdf4LvvvpM6olYMpR/ZSU5O1psxHs+ePUNsbCxevXql1i7nOxwaNmyIsLAwvZrXIr9ITEzEli1bcPv2bXz11VdwdHTE2bNn4eLiojerWaempiIyMjLb34vhw4dLlEo3b1/azUofL+2+ePFCfoN+P+1EuPIXFxcnvvvuO1G+fHlhamoqOnbsKHbu3CkyMjJUrzl06JCwsrKSMOX7GUo/DEF8fLxo2bKlUCqV2T7kLOtK1iQPFy5cEE5OTqJUqVLC2NhYtbr4+PHjRe/evSVOp52zZ8+KQoUKCVtbW2FkZCScnJyEQqEQVlZWwt3dXep4+Up6erqYPHmyKFKkiDAyMlIdT998841YtmyZxOmE4My0WRQrVgwlS5ZEv379EBQUBCcnJ43XVKxYUXW/vFzpaz/edyfA2/TlE9eXX36JxMREnDhxAn5+fti6dSvi4uIwdepUzJ07V+p49J9+/frB398fvXv3ljrKe40cORJBQUGYPXs2bGxsVO0tWrRQ3RIrdyNGjEDr1q2xdOlS2NnZ4fjx4zAxMUGvXr1kfanHEE2dOhWrVq3C7NmzMXDgQFV7hQoVsGDBAvTv31/CdLz0o+HQoUOoX7++1DE+mL72Q9tJ0RQKheT39murcOHC+OOPP1CjRg3Y2tri9OnTKF26NLZt24bZs2fj8OHDUkfMUdaVrHMi58tX2vLz80NMTAzs7e1x/vx5qeO8k52dHc6ePYuSJUuqrQR9584deHl54cWLF1JHfC97e3ucOHECXl5esLe3x7Fjx1C2bFmcOHECgYGBuHr1qtQRtTJ58uR3bs96C7kclSpVCj/99BMaNWqkdjxdvXoVtWvXxpMnTyTNxzMqWYSEhGR7TT45ORnt2rXDvn37pAmmI33tR3R0tNQR8lxqaqpqfgIHBwc8fPgQpUuXhre3N86ePStxuvdr1KiRxh0BwP/fKaCP1+Gzc+DAAQDA5cuXpQ2iBTMzs2wXULx+/Xq2Z0/lyMTERDW+w9nZGbGxsShbtizs7Oxw9+5didNpb+vWrWrP09LSEB0dDWNjY5QsWVIvCpV79+6hVKlSGu0ZGRlIS0uTIJE6FipZREZGagzqAt4MMDp06JAEiXLHUPphCLy8vHDt2jW4ubmhUqVK+Omnn+Dm5oalS5eqViWWsxMnTujNm19e0IeVodu0aYPJkyerpv1XKBSIjY3FmDFj0LFjR4nTacfHxwenTp2Cp6cnfH19MXHiRCQkJGDNmjV6tUDkuXPnNNqSk5MRFBSE9u3bS5BId+XKlcOhQ4c0FoTdsmULfHx8JEr1/1io/CcqKgrAm0m4Ll++jAcPHqi2paenY9euXXoxkl7f+zFy5EitX6svq5EGBwfj/v37AN6c6QoICMC6detgamqK0NBQacNpwdXVVWPGSn22Zs0aLF26FNHR0Th27BhKlCiBBQsWwN3dHW3btpU6nlbmzp2LTp06wdnZGc+fP4evry8ePHiA2rVrY9q0aVLH08r06dORkpICAJg2bRr69OmDwYMHw9PTE8uXL5c43YextbXFpEmT0Lp1a70Y8zRx4kQEBgbi3r17yMjIQFhYGK5du4bVq1djx44dUsfjGJVMSqVSNWtodv8lFhYW+P7779GvX79PHU0n+t4Pf39/rV6nUChke/nqfZ49e4arV6/C1dVVtvMWZMppam199eOPP2LixIn48ssvMW3aNFy6dAkeHh4IDQ3FqlWrsH//fqkj6uTIkSO4cOECnj59iipVqqBx48ZSR6L/HD58GK1bt5Z8fIe2Dh06hMmTJ6sdTxMnTkTTpk2ljsZCJdOdO3cghICHhwdOnjypdqrb1NQUzs7OMDIykjChdgylHyQP/v7+2Lp1q8HMo1KuXDlMnz4d7dq1Uxs0eOnSJfj5+SEhIUHqiPne2bNnMXHiRFl8ktdG1jsVhRC4f/8+1qxZA19fX6xfv16iZIaDl37+k3ltLiMjQ+IkH8ZQ+mFI0tPTERoaioiICMTHx2vsGzmfGfrrr78QERGhmkJ73LhxaguUGRkZYcqUKfKbICoH0dHR2V5zNzMzQ2pqqgSJdHPs2DE8evRIbUrz1atXIyQkBKmpqWjXrh2+//57mJmZSZjy/Xbv3o3w8HCYmppiwIABqjtMxo4di+3bt6NZs2ZSR9Ta/Pnz1Z4rlUo4OTkhMDAQ48aNkyiV7uQ8gSALFbxZmKl58+YwMTFRLdKUEzlPh2wo/Xjb6dOnsXnz5mxnrgwLC5MolW6Cg4MRGhqKli1bokKFCmoLE8rdqlWr8Oeff6reGBcvXozy5curVuS+evUqihQpghEjRkgZU2vu7u44f/68xqDBXbt2oWzZshKl0t7kyZPh5+en2h8XL15E//79ERQUhLJly2LOnDkoUqQIvv32W2mDvsPy5csxcOBAODo64smTJ1i2bBnmzZuHYcOGoWvXrrh06ZJe7ItM77pT8fnz558wSe5FRUWhcePGsLOzQ0xMDAYMGABHR0eEhYUhNjYWq1evljagBJPMyc7bs28qFIocH/o0i6g+9yPThg0bhImJiWjVqpUwNTUVrVq1EqVLlxZ2dnYiKChI6nhaK1CggPjzzz+ljpErdevWFdu2bVM9t7a2Vs1aKYQQa9asEbVq1ZIiWq788ssvomjRomLjxo3CyspKbNiwQUydOlX1tdwVKlRInDp1SvX8f//7n6hbt67q+ebNm0XZsmWliKY1b29vMXv2bCGEEFu2bBEKhULUrl1b3L17V+JkuTNs2LBs258+fSr8/Pw+cZrcadSokRg9erQQQv13/MiRI6JEiRISJnuDhQrJlre3t1i8eLEQ4v9/eTIyMsTAgQPFxIkTJU6nvcKFC4tr165JHSNXChUqJKKjo1XPCxYsqPb82rVrwtbW9tMH+wBr164VpUqVUhXuRYsWlcU04dowMzMTsbGxqud169YVU6dOVT2Pjo4W1tbWUkTTmqWlpeoYysjIECYmJuLw4cPShvoAHh4eGn+PUlJSRL169US9evUkSqUbW1tbcfPmTSGEeqESExMjzMzMpIwmhBAi59WUiCR269YttGzZEsCbgcCpqalQKBQYMWIEfv75Z4nTaW/UqFFYuHBhtndhyV1iYqLamJSHDx/Czc1N9TwjI0Ntu5y9fv0aq1evRuPGjXHjxg08ffoUDx48wD///CP5FOHacnFxUV1qePXqFc6ePYtatWqptqekpMDExESqeFp5/vw5LC0tAby5e8/MzEwv5hPKyZ49e/DLL79gwYIFAN7sg6ZNm0KhUGDXrl3ShtOS3CcQ5BiV/+zbtw9Dhw7F8ePHNVbjTUpKQp06dfDjjz+iQYMGEiXUjqH0A3gzi2vmPAtFixbFpUuX4O3tjcTERDx79kzidNo7fPgw9u/fj507d6J8+fIabyRyHmtTrFgxXLp0CV5eXtluj4qKQrFixT5xqtwxNjbG559/jitXrgAALC0tVW+Y+qJFixYYO3YsZs2ahd9//x2WlpZqS2VERUWhZMmSEibUzrJly2BtbQ3gTQEZGhqqcau+vqzlVbJkSezatQv+/v5QKpXYsGEDzMzM8Oeff8LKykrqeFqR+wSCvD35P23atIG/v3+OgwIXLVqE/fv3a0yXLDeG0g8A6NGjB6pVq4aRI0diypQp+P7779G2bVuEh4ejSpUqsn6Df1vfvn3fuX3lypWfKInugoODsXfvXpw5c0bjzp7nz5+jWrVqaNy4MRYuXChRQt34+fnhyy+/RLt27aSOkisJCQno0KEDDh8+DGtra6xatUpt9tNGjRqhVq1asp70zc3N7b0DyvVpLa9Mx44dQ5MmTVCzZk3s2LFDNeBcHyQlJaFTp044ffo0UlJSUKRIEdUEgn/99ZfkBRcLlf+UKFHinSP/r169iqZNmyI2NvYTJ9ONofQDAB4/fowXL16gSJEiyMjIwOzZs3H06FF4enrim2++gYODg9QRDV5cXBwqV64MU1NTDB06FKVLlwYAXLt2DYsXL8br169x7tw5uLi4SJxUO5s3b8a4ceMwYsQIVK1aVeMPsL4srpiUlARra2uNOZEeP34Ma2trmJqaSpQsf/Dx8cm22Lpz5w6cnZ3VihR9WM8rk1wnEGSh8h9zc3NcunQp24WZAODmzZvw9vaW/e1mhtIPko/o6GgMHjwY4eHhqnE2CoUCTZo0wQ8//AAPDw+JE2ovcxG8txna4or08U2aNEnr14aEhHzEJB8uLS0NFhYWOH/+vGzXWOIYlf9kjoHI6Q0+KipKLwZ8GUo/9F2VKlUQEREBBweHHD99ZZL7Jy53d3fs2rULjx8/xs2bNwG8WRbe0dFR4mS6M8TVuenTk3vxoQsTExO4urrKukhnofKfFi1aYMKECQgICMj2WnxISIjabJByZQj90HaKfzn/YrVt21Y1O6i+jofIytHRETVq1JA6xge5c+cO6tSpA2Nj9T99r1+/xtGjRzUmgiPS1qtXr7KdedrV1VWiRNobP348/ve//2HNmjWy/ADCSz//iYuLQ5UqVWBkZIShQ4eq7nK4evUqlixZgvT0dNV0wnJmCP1QKpUoUaIEAgMD37nEuL6sdEvyYWRkhPv372sssvjo0SM4OzvLuvglebp+/Tr69++Po0ePqrXr0+VEHx8f3Lx5E2lpaShRooTG2C2pz/ryjMp/XFxccPToUQwePBjjxo1TuxbfrFkzLFmyRNZv7pkMoR8nT57E8uXLsXDhQri7u6Nfv37o2bOn3g+e1edPXIYi880jq0ePHkl+ZwPpp759+8LY2Bg7duxA4cKF9WqJjExt27aVdW6eUcnGkydPcPPmTQgh4OnpqbdvkPrejxcvXmDLli1YuXIljh8/jtatW6N///5o0qSJ1NF0YgifuPRdhw4dAAB//PEHAgIC1BbtS09PR1RUFLy8vPRmgq53OXjwICpVqgQ7Ozupo+QLVlZWOHPmDMqUKSN1FIPFMyrZcHBwQPXq1aWO8cH0vR/m5ubo1asXevXqhejoaPTv3x8BAQF4+PChLK+j5sQQPnHpu8w3bSEEbGxs1G4fNTU1Ra1atTBw4ECp4uUpPz8/ODg44H//+x9GjRoldZxcUSqV8PPzw5w5c1C1alWp47xTuXLlkJCQIHWMD+Lh4YFTp06hQIECau2JiYmoUqWK5HPasFAhWfvnn38QGhqK0NBQPHv2DKNHj9aYcVfuzp8/z09cEsucVM/NzQ1fffWVQV/miY6Oxu3bt7Fz506po+TaihUrEBMTgyFDhuD48eNSx3mnWbNm4euvv8b06dPh7e2tMfO0Pvy9iomJyfbM7suXL/HPP/9IkEgdL/2Q7Lx69Qpbt27F8uXLcejQITRv3hz9+vVD8+bNtb4jSE6qV6+O+fPno169elJHIaI8ljk3T9YzpfpwaXfbtm0A3tyZuGrVKrXLhenp6YiIiEB4eDiuXbsmVUQALFRIhgoUKAAbGxsEBgaid+/eGndoZJLzJ5W3F/g6ffo0vvnmG73+xGUo4uLi8NVXXyEiIgLx8fEaC0XK+U3lbXI/Va+N/fv3w9/fP9ttS5YswZAhQz5xotyJjIx853ZfX99PlER3bxdZWX8XTExM4Obmhrlz50o+pQULFZKdt2cPzW48hz58UlEqlWrZs7vbRB/6YWiaN2+O2NhYDB06NNvxQvpyy7tSqcSDBw80ivi4uDi4urrqxYrWDg4O2Lt3r8YYlIULF2LChAnZruZLH4e7uztOnTqlsTCkXHCMSjbWrFmDpUuXIjo6GseOHUOJEiWwYMECuLu7680fMkB/+7F//36pI3wwQ+iDITp8+DAOHTqEypUrSx0lVzJP1QPA7t27sz1V7+bmJkEy3c2ZMwfNmzfHwYMHVeO35s6di8mTJ+PPP/+UOJ3unj17htjYWLx69UqtXR/Wj5L9jM2C1Pzwww+iYMGCYurUqcLCwkLcunVLCCHEypUrhZ+fn8TptGco/SDKS2XLlhVnz56VOkauKRQKoVAohFKpVH2d+TA1NRWlS5cW27dvlzqm1mbNmiWKFi0qoqOjxcyZM4Wtra04fPiw1LF0Eh8fL1q2bCmUSmW2Dzk7evSoxvGyatUq4ebmJpycnMTAgQPFixcvJEr3/1ioZFG2bFmxdetWIYQQ1tbWqjf4ixcvigIFCkiYTDeG0g99dv36ddGtWzeRlJSksS0xMVF0795dtV/o09i9e7do2rSpiI6OljrKB3FzcxMPHz6UOkae+Prrr0WBAgWEvb29OHbsmNRxdNajRw9Rt25dcerUKWFlZSX27Nkj1qxZI7y8vMSOHTukjvdOAQEBYubMmarnUVFRwtjYWAwYMEDMnTtXFCpUSISEhEgX8D+89JNFdHR0ttO2m5mZITU1VYJEuWMo/dBnc+bMQfHixbMdLGtnZ4fixYtjzpw5+PHHHyVIlz917doVz549Q8mSJWFpaakxsPnx48cSJdON7E/V52DRokUabUWLFoWlpSUaNGiAkydP4uTJkwCA4cOHf+p4ubJv3z788ccfqFatmmr5jyZNmsDW1hYzZsxAy5YtpY6Yo/Pnz2PKlCmq5xs3bkTNmjXxyy+/AACKFy+OkJAQfPvttxIlfIOFShbu7u44f/68xuJku3btQtmyZSVKpTtD6Yc+i4yMxNq1a3Pc3qVLF/To0eMTJqIFCxZIHSHPpKamIjIyMttxEXJ9k58/f3627UZGRjhy5AiOHDkC4M0gern2IavU1FTVoGYHBwc8fPgQpUuXhre3t+Rr5LzPkydP1JZUiYyMRPPmzVXPq1evjrt370oRTQ0LlSxGjhyJIUOG4MWLFxBC4OTJk9iwYQNmzJiBZcuWSR1Pa4bSD30WGxub463VAFCwYEFZ/BHITwIDA6WOkCfOnTuHFi1a4NmzZ0hNTYWjoyMSEhJgaWkJZ2dn2b7J6+uZoHfx8vLCtWvX4ObmhkqVKuGnn36Cm5sbli5disKFC0sd751cXFwQHR2N4sWL49WrVzh79iwmTZqk2p6SkqJx1lESUl97kqO1a9eKUqVKqQapFS1aVCxbtkzqWDozlH7oKxcXFxEREZHj9r179woXF5dPmIiEEOLmzZti/Pjxolu3biIuLk4IIcRff/0lLl26JHEy7fn6+oqBAweK9PR01Ri02NhY0aBBA/Hbb79JHe+9Xr16JTw8PMTly5eljvLB1qxZI1auXCmEEOL06dOiYMGCQqlUCnNzc7Fx40Zpw73H559/LmrXri0OHjwoRo4cKQoUKCBevnyp2r527VpRrVo1CRO+wXlU3uHZs2d4+vTpOz8V6wN97ceLFy/w/fffY//+/dmuOiz306pdunRBWloatm7dmu32tm3bwtTUFL/++usnTpZ/ZZ7arlu3Lg4ePIgrV67Aw8MDM2fOxOnTp7FlyxapI2rF3t4eJ06cgJeXF+zt7XHs2DGULVsWJ06cQGBgIK5evSp1xPcqWrQo9u7da3CXop89e4arV6/C1dUVaWlpsj6rkpCQgA4dOuDw4cOwtrbGqlWr0L59e9X2Ro0aoVatWpg2bZqEKXnpR0N0dDRev34NT09PWFpawtLSEgBw48YN1Ux9+sAQ+tG/f3/s2bMHnTp1Qo0aNfRuMb9x48ahdu3a6NSpE77++mt4eXkBAK5evYrZs2dj9+7dGisq08c1duxYTJ06FSNHjoSNjY2qvWHDhli8eLGEyXRjYmKimhjR2dkZsbGxKFu2LOzs7PTmcuKQIUMwa9YsLFu2DMbG+vdWNHLkSMybN0+j3dLSElWqVMH9+/fh5+cn+fTz71KwYEEcPHgQSUlJsLa21lii5Ndff4W1tbVE6f6f/h0dH1lQUBD69esHT09PtfYTJ05g2bJlOHDggDTBdGQI/dixYwf++usv1K1bV+ooueLj44MtW7agX79+GmdVChQogM2bN6NKlSoSpcufLl68iPXr12u0Ozs769UKuD4+Pjh16hQ8PT3h6+uLiRMnIiEhAWvWrEGFChWkjqeVU6dOISIiAnv27IG3t7fGQpFhYWESJdPOypUrUaBAAYwfP15j24MHD+Dn5wcnJycJkunu7YkD3yaXVepZqGRx7ty5bN8Ya9WqhaFDh0qQKHcMoR9FixZV+9Srj1q1aoU7d+5g165duHnzJoQQKF26NJo2bao6y0Wfjr29Pe7fvw93d3e19nPnzqFo0aISpdLd9OnTkZKSAgCYNm0a+vTpg8GDB8PT0xMrVqyQOJ127O3t0bFjR6lj5Nq2bdsQEBAAR0dHDB48WNWeWaQ4Ojpi165dEiY0HCxUslAoFKo/AG9LSkrSqzVZDKEfc+fOxZgxY7B06VKN26z1iYWFhdp1X5JOt27dMGbMGPz6669QKBTIyMjAkSNH8NVXX6FPnz5Sx9NatWrVVF87Ozvr5RviypUrpY7wQerXr4/NmzejY8eOcHBwQLdu3fDgwQP4+/vDzs4Oe/bskcVlE0PAwbRZtG7dGhYWFtiwYYPqel16ejq6du2K1NRU7Ny5U+KE2jGEfjx8+BBdunTBwYMH9XpyLpKPV69eYciQIQgNDUV6ejqMjY2Rnp6OHj16IDQ0VOMaPdH7rF+/Hv3798ePP/6I2bNnw8rKCnv37s3xcgrpjoVKFpcvX0aDBg1gb2+P+vXrAwAOHTqE5ORk7Nu3T2+u/xpCPxo3bozY2Fj0798fLi4uGoNpDWVODPr07t69i4sXL+Lp06fw8fHRGMulD7Zs2YLNmzdnO+Gb3O+Iy2QIfQCAH374AcOGDUOVKlVYpHwELFSy8e+//2Lx4sW4cOECLCwsULFiRQwdOlQ2A4u0pe/9sLS0xLFjx1CpUiWpo5ABSE5OhrW1tepumUwZGRl4+vRptksdyNWiRYswfvx4BAUF4eeff0bfvn1x69YtnDp1CkOGDJH8dlJt6HsffHx81D48Xb58GcWLF9cYV6cPBdeMGTPg4uKCfv36qbWvWLECDx8+xJgxYyRK9gYLFZKtKlWq4IcffkCtWrWkjkJ6buvWrRgzZgzOnz+vMYg5NTUVVapUwXfffYfWrVtLlFA3ZcqUQUhICLp37w4bGxtcuHABHh4emDhxIh4/fqwXt1rrex/ensH1XUJCQj5ykg/n5uaG9evXo06dOmrtJ06cQLdu3SSfUZiFSjYSExNx8uTJbCcZ06cBd/rejz179mDSpEmYNm0avL29Ncao6NMnYJJW06ZN0aVLFwwYMCDb7StWrMCmTZuwe/fuT5wsdywtLXHlyhWUKFECzs7OCA8PR6VKlXDjxg3UqlULjx49kjriexlCHwyFubk5rly5onE33O3bt1GuXDm8ePFComRv8K6fLLZv346ePXuqTgW/fWpPoVDoxRs8YBj9CAgIAPBmdsS3CSGgUCj05u6ld1EqlfDz88OcOXNQtWpVqeMYrEuXLuGHH37IcXuDBg3wzTfffMJEH6ZQoUJ4/PgxSpQoAVdXVxw/fhyVKlVCdHQ09OWzpyH0wVAUL14cR44c0ShUjhw5giJFikiU6v+xUMli1KhR6NevH6ZPn67X81wYQj/2798vdYSPbsWKFYiJicGQIUNw/PhxqeMYrCdPnuD169c5bk9LS8OTJ08+YaIP07BhQ2zbtg0+Pj7o27cvRowYgS1btuD06dPo0KGD1PG0Ygh9MBQDBw7El19+ibS0NDRs2BAAEBERga+//hqjRo2SOB0v/WiwsrLCxYsX4eHhIXWUD2Io/SDKC2XLlsX48ePRq1evbLevWbMG06ZN04s1coA3A4AzMjJUU89v3LgRR48ehaenJwYNGgRTU1OJE76fIfTBUAghMHbsWCxatEh195W5uTnGjBmDiRMnSpyOhYqGDh06oFu3bujSpYvUUT6IIfTj4MGD79zeoEGDT5SE9N348eOxdu1anDx5Ei4uLmrbHjx4gJo1a6JXr16yv9PEUBw/fhzbt2/Hq1ev0KhRI9VlXpLW06dPceXKFVhYWMDT0xNmZmZSRwLAQkXD8uXLMXnyZPTt2zfbAZxt2rSRKJluDKEfWW8jBaA21kZfxqikpqZi5syZiIiIyHZg8+3btyVKln+kpKSgdu3aiI2NRa9evdQWiFy3bh2KFy+O48eP69WSDfo6WH7Lli3o2rUrLCwsYGJiguTkZMyaNQtfffWV1NFIplioZJHdm2MmfRrAaQj9SEpKUnuelpaGc+fOYcKECZg2bZrGIFu56t69OyIjI9G7d28ULlxYY+K64OBgiZLlL0lJSRg3bhw2bdqkGo9ib2+Pbt26Ydq0aXBwcJA4ofbeN1hezrM2V61aFdWrV8eSJUtgZGSEGTNmYM6cObLOnFurV69G3bp1UbJkSamjvNfp06dznHxP6gUiWaiQ3omMjMTIkSNx5swZqaNoxd7eHn/++afergJtaIQQSEhIgBACTk5OGoWjPihdujRatGihl4Plra2tcf78eZQqVQrAm2UNrKyscO/ePTg7O0ucLm8plUqYmJjgs88+w/fffy91nBxt3LgRffr0QbNmzbBnzx40bdoU169fR1xcHNq3by/5ukw5f+wmye8dzyuG0o9MLi4uuHbtmtQxtObg4KA3swHnBwqFAk5OTnB2dtbLIgUA7t27h+HDh+tdkQIAz549U5sDydTUFObm5nj69KmEqT6OjIwMXL16FWXLlpU6yjtNnz4d8+fPx/bt22FqaoqFCxfi6tWr6NKlC1xdXaWOxzMqWaWnp2P69OlYunQp4uLicP36dXh4eGDChAlwc3ND//79pY6oFUPoR1RUlNpzIQTu37+PmTNn4vXr1zh8+LBEyXSzdu1a/PHHH1i1apVevrGQ/OjzYHmlUompU6eqrSw8ZswYjB49GgULFlS1DR8+XIp4+ZKVlRX+/vtvuLm5oUCBAjhw4AC8vb1x5coVNGzYEPfv35c0H+dRyWLatGlYtWoVZs+ejYEDB6raK1SogAULFujFGzxgGP2oXLkyFAqFxuRPtWrVwooVKyRKpbu5c+fi1q1bcHFxgZubm8bAZn1YC4Skt23bNtXXLVu2xOjRo3H58mW9Gyzv6uqKX375Ra2tUKFCWLNmjeq5QqGQdaGSnJys9Wv1YQZtBwcHpKSkAACKFi2KS5cuwdvbG4mJiXj27JnE6VioaFi9ejV+/vlnNGrUCJ9//rmqvVKlSnozxwJgGP3Iur6EUqmEk5MTzM3NJUqUO+3atZM6AhmA7I6jyZMna7TJfbB8TEyM1BE+mL29/XsvG+rTDNoNGjRAeHg4vL290blzZwQHB2Pfvn0IDw+XxU0LLFSyuHfvnmqQ19syMjKQlpYmQaLc0fd+pKWloV+/fli6dCk8PT2ljvNB9GFRMpK/rLcgk3QMbdbsxYsXq8Yyjh8/HiYmJjh69Cg6duwoi6UlWKhkUa5cORw6dAglSpRQa9+yZQt8fHwkSqU7fe+HiYmJxhgVfXfmzBlcuXIFAFC+fHm92A+GKCIiIsc5bfTpkiJJx9fXV+oIeertwf5KpRJjx46VMI0mFipZTJw4EYGBgbh37x4yMjIQFhaGa9euYfXq1dixY4fU8bRmCP3o1asXli9fjpkzZ0od5YPEx8ejW7duOHDgAOzt7QG8mazL398fGzduhJOTk7QB85FJkyZh8uTJqFatWrZz2sjdvn37MHToUBw/flxj7ENSUhLq1KmDH3/8kbM2f2S6fIiqWLHiR0ySt+Lj47Mt4KXuA+/6ycahQ4cwefJkXLhwAU+fPkWVKlUwceJENG3aVOpoOtH3fgwbNgyrV6+Gp6cnqlatCisrK7Xt8+bNkyiZbrp27Yrbt29j9erVqtsUL1++jMDAQJQqVQobNmyQOGH+UbhwYcyePRu9e/eWOkqutGnTBv7+/hgxYkS22xctWoT9+/dj69atnzhZ/qJUKrMd6J+VvoxROXPmDAIDA3HlyhWNPsmhDyxUSLb8/f1z3KZQKLBv375PmCb37OzssHfvXlSvXl2t/eTJk2jatCkSExOlCZYPFShQACdPntSLmUKzU6JECezatSvHeTmuXr2Kpk2bIjY29hMn097q1avRtWtX2awjkxt37tzR+rVZL7/LUaVKlVCyZEmMGTMGLi4uGmcape4DL/2QbBnKgLWMjAyN20eBN+NwOEDy0xowYADWr1+PCRMmSB0lV+Li4rI9ljIZGxvj4cOHnzCR7vr27YuAgAC9noVW6jfuvHb79m389ttv2d6AIQcsVPBmINH169dRsGBBODg4vPO6tZzXozCUfmTnn3/+AQAUK1ZM4iS6a9iwIYKDg7FhwwYUKVIEwJu7skaMGCGLW//ykxcvXuDnn3/G3r17UbFiRY03fblfTsyc4yKnN5SoqCgULlz4E6fSjaGexL98+XK26+TIeU6bTI0aNcKFCxdYqMjZ/PnzVaumzp8/X+8G2GUylH5kysjIwNSpUzF37lzV9No2NjYYNWoUxo8f/86FF+Vk8eLFaNOmDdzc3FC8eHEAwN27d1GhQgWsXbtW4nT5S1RUFCpXrgwAuHTpkto2ffh9adGiBSZMmICAgACN+YSeP3+OkJAQtGrVSqJ02tOH/2tt3b59G+3bt8fFixfVxq1k9lHq8R3aWLZsGQIDA3Hp0iVUqFBBdhMIcowKyda4ceOwfPlyTJo0SbWg3+HDh/Htt99i4MCBmDZtmsQJtSeEwN69e1WT7ZUtWxaNGzeWOBXpm7i4OFSpUgVGRkYYOnQovLy8ALwZm7JkyRKkp6fj7NmzcHFxkThpzpRKJSpUqABj43d/TtaXGZtbt24NIyMjLFu2DO7u7jh58iQePXqEUaNG4bvvvkP9+vWljvhe27dvR+/evbOdcZeDaWXor7/+gpGREZo1a6bWvmfPHqSnp6N58+YSJdONIfSjSJEiWLp0qUY1/8cff+CLL77AvXv3JEr24RITE1W3KtOns2/fPtStW1fvB3IOHjwYu3fvVvv03qxZMyxZsgTu7u4SJ3w3pVKJUaNGqa31kx19mSixYMGC2LdvHypWrAg7OzucPHkSXl5e2LdvH0aNGoVz585JHfG93Nzc0KpVK0yYMEGeRa4gNd7e3uLPP//UaN+5c6eoWLGiBIlyxxD6YWZmJq5du6bRfvXqVWFubi5BotyZOXOm2Lhxo+p5586dhVKpFEWKFBHnz5+XMFn+Y2VlJczMzES9evXEN998I8LDw8WzZ8+kjpUrjx8/FidPnhQnTpwQjx8/ljqO1hQKhYiLi5M6Rp6xt7cXt2/fFkII4eHhIfbt2yeEEOLmzZvCwsJCymhas7a2Fjdv3pQ6Ro704yL/J3Tjxg2UK1dOo71MmTK4efOmBIlyxxD6UalSJSxevFijffHixahUqZIEiXJn6dKlqrEp4eHhCA8Px86dO9G8eXOMHj1a4nT5y5MnTxAREYHmzZvj5MmTaN++Pezt7VG3bl1ZTBWuCwcHB1SvXh01atSAg4OD1HG0ZkjjU4A3C71euHABAFCzZk3Mnj0bR44cweTJk+Hh4SFxOu106NBB1ndZcjBtFnZ2drh9+zbc3NzU2m/evKkx4ZicGUI/Zs+ejZYtW2Lv3r2oXbs2AODYsWO4e/cu/vrrL4nTae/BgweqQmXHjh3o0qULmjZtCjc3N9SsWVPidPmLiYkJ6tati7p16+J///sf/v77b8yZMwfr1q3D8ePHMXXqVKkjGjxhYKMNvvnmG6SmpgJ4s0hkq1atUL9+fRQoUACbNm2SOJ12SpcujXHjxuHw4cPZrsYt9UrWHKOSxaBBg3Ds2DFs3bpVNSnUzZs30bFjR1SvXh3Lli2TOKF2DKUf//77L5YsWaI2CPWLL75Q3earD4oUKYItW7agTp068PLywtSpU9G5c2dcu3YN1atX12nJePow169fx4EDB3DgwAFERkbi5cuXqF+/Pvz8/ODn56dXZ+r01Z07d+Dq6mpwZ1be9vjx4/dOESEn7xrXpFAocPv27U+YJpsMLFTUJSUlISAgAKdPn1bN2fHPP/+gfv36CAsL05sBkPrcj9u3b8Pd3V1vfsnfZ+jQodixYwc8PT1x7tw5xMTEwNraGhs3bsTs2bP15u4GQ6BUKuHk5ITg4GC0atUK3t7eBnOc6YuRI0dq9Tq5z2lDnw4LlWwIIRAeHo4LFy7AwsICFStW1MtFvvS1H0ZGRrh//75q5squXbti0aJF8hyNroW0tDQsXLgQd+/eRVBQkGrV5Mx5bwYMGCBxwvzjyy+/xMGDB3H58mVUqVJFdSalXr16sLS0lDpevvCupTEy6cMSGf369dPqdVyR+8OxUCHZUSqVePDggapQsbGxwYULF/RmYBrJX2JiIg4dOoTIyEhERkbi77//ho+PD44cOSJ1tBxt27ZN69dKPUFXfqBUKlGiRAn4+Pi8c9yNviwQ+c8//2Dbtm3Zzq4r9dktDqb9z7Fjx/Do0SO1WR1Xr16NkJAQpKamol27dvj+++9lP/+CofTD0Ny4cQP79+/Pdgn1iRMnSpQq/0pPT0daWhpevnyJFy9e4OXLl7h27ZrUsd6pXbt2Wr1ODhN05QeDBw/Ghg0bEB0djb59+6JXr15wdHSUOlauREREoE2bNvDw8MDVq1dRoUIFxMTEQAiBKlWqSB2P86hkCggIEDNnzlQ9j4qKEsbGxmLAgAFi7ty5olChQiIkJES6gFoyhH4olUoRHx+vem5tba2ap0Af/fzzz8LIyEi4uLiISpUqicqVK6sePj4+UsfLV4YNGya8vb2FkZGRKFiwoOjQoYNYuHChuHDhgsjIyJA6HumZFy9eiPXr14vGjRsLS0tL0blzZ7Fr1y69O5aqV68uJk6cKIR48/f21q1bIiUlRbRp00b88MMPEqcTgpd+/lO4cGFs374d1apVAwCMHz8ekZGROHz4MADg119/RUhICC5fvixlzPcyhH4olUo0b95cddZn+/btaNiwocZt1WFhYVLE01mJEiXwxRdfYMyYMVJHyfc6d+4MX19f+Pn5oUKFClLHIQNy584dhIaGYvXq1Xj9+jX+/vvv986+Kxc2NjY4f/48SpYsCQcHBxw+fBjly5fHhQsX0LZtW8TExEiaj5d+/vPkyRO1wZqRkZFq08xXr14dd+/elSKaTgyhH4GBgWrPe/XqJVGSvPHkyRN07txZ6hiEN4W6oYiMjMR3332HK1euAADKlSuH0aNH68XaMoZIqVSqFiXUt0tvVlZWqnEphQsXxq1bt1C+fHkAQEJCgpTRALBQUXFxcUF0dDSKFy+OV69e4ezZs5g0aZJqe0pKisYkOHJkCP1YuXKl1BHyVOfOnbFnzx58/vnnUkchALdu3cKCBQvU3uCDg4NV8w3pg7Vr16Jv377o0KGDajKuI0eOoFGjRggNDUWPHj0kTpg/vHz5EmFhYVixYgUOHz6MVq1aYfHixQgICNCb1d0BoFatWjh8+DDKli2LFi1aYNSoUbh48SLCwsJQq1YtqeOxUMnUokULjB07FrNmzcLvv/8OS0tLtU8mUVFRevGHzFD6YUhKlSqFCRMm4Pjx47Kc9TE/2b17N9q0aYPKlSurVuQ+cuQIypcvj+3bt6NJkyYSJ9TOtGnTMHv2bIwYMULVNnz4cMybNw9TpkyRdaHSp08fLPm/9u48rMb0/wP4+5w2FO0SQkgibdb4UdlStjA0llHW7IYY+5J1BmEoY6tJYytkN7aoZEtR2ZM2S4yRoqL1+f0Rz9fpVGLUfc7T53VdXVfnfg7e50Lnc+7nc9+3tzdq1qwJAIiJiUGLFi1k/gNUcZMmTcL+/fthYGCA0aNHY9++fdDR0WEd65usX78emZmZAAAPDw9kZmYiICAARkZGzFf8ALQ8mffvv/9i4MCBCA8Ph5qaGnbt2oUBAwbw17t164YOHTpg5cqVDFN+mVBeh5DI+q6PVYmlpSXs7e3x66+/SozPnTsXZ8+elZvN91RUVHD37l00bdpUYjw+Ph6mpqb48OEDo2RfVnyfpFq1aiE6Olruth8Qi8Vo0KABLC0ty9w0UF566WQZzah8pKOjg7CwMGRkZEBNTQ0KCgoS1w8cOCAXjVFCeR1CkpiYyDoC+ej+/fsIDAyUGh89ejQ2btxY+YG+kYGBAYKDg6UKlfPnz/PnSsmq4p+N5fWz8siRI+V+V+M3b95g9+7dcHFxQa1atSSuZWRkwN/fv8RrlY0KlWLU1dVLHJe39fFCeR2EfE+6urqIjo6GkZGRxHh0dDT/CV8euLu7Y9q0aYiOjkbHjh0BFN3C8vPzw++//844XdXg5+fHOsJ/5uXlhdjYWEydOlXqmrq6Oi5duoS3b99iwYIFDNL9DxUqhFSwgoIC+Pn5ITg4uMQN32R9q3AhWLZsGWbNmoVx48Zh/PjxSEhIkHiD/+2338p9Bo0smDhxIurUqQNPT09+hsjExAQBAQHo378/43Rfdu/ePbx48QJA0YzKgwcP+B6JT8zMzFhEq1IOHToET0/PUq+7ublh1qxZzAsV6lEhpIJNmTIFfn5+6N27N/T19aWmizds2MAoWdXxqS9CV1cXGzduhKenJ54/fw6g6HTr2bNnY9q0aXI/lS8PPl/GW9yncdpdt3LUrFkTd+/eRYMGDUq8npKSAlNTU+YnvFOhQkgF09HRgb+/PxwdHVlHqbKKnx8FFC3VB8CvPpFHUVFR/DLrli1b8gdeyrLk5ORyPa9hw4YVnIRoaGjg9OnTpS5BvnbtGnr16oX09PTKDVYM3fohpIIpKytLNT2Syld8tkSeC5R//vkHP/74I0JCQqChoQGg6KBFOzs77N+/H7q6umwDluFLBUh6ejpOnTpFhUolsLS0xJEjR0otVA4fPiwTxa/87EgjAz6tppF3Qnkd8sLd3R2///673K5uEIpmzZpBS0urzC95MXXqVLx79w53795FWloa0tLScOfOHbx9+1bu9+VJTk7GTz/9xDpGlTBlyhR4enrCy8tL4lZbQUEBNm/ejA0bNmDy5MkMExahWz9fQSwWQ1NTE/Pnz4e7uzvrON9MKK9Dlg0cOFDi8YULF6ClpYWWLVtKbWxF+yxUPLFYjI0bN5a6Gu6T4sc3yCp1dXWcP38ebdu2lRiPiIhAz549mU/V/xcxMTGwsrKSqx6Vv/76C1u3bkViYiKuXr2Khg0bYuPGjTA0NJT55uYFCxZg9erVqFmzJr+XTUJCAjIzMzF79mypPYdYoFs/XyExMREJCQn4+++/WUf5T4TyOmRZ8TfEzzfdI2z8+OOPcrUEuSyFhYUl7uSqpKQktaqMVKw//vgDixcvxs8//4yVK1fyBZaGhgY2btwo84XKypUr0b9/f+zZswfx8fHgOA42NjYYNmwY2rVrxzoeAJpRkVBQUIDLly/DzMyMv+9LCJF/xXdDlXf9+/dHeno69u3bh7p16wIAnj17huHDh0NTUxOHDx9mnPDbyduMSosWLbBq1So4OTmhZs2aiImJQePGjXHnzh3Y2trKxKF+8o5mVD6joKCAnj174v79+4IoVNLT03Hw4EE8fvwYs2fPhpaWFm7evAk9PT3Uq1ePdbwq59WrV3j48CEAwNjYWKYbHoVGaJ/HvLy80K9fPzRq1IjfifbJkycwNTXF7t27Gacr26ZNm8q8/uzZs0pK8n0kJiaW2HCqoqKCrKwsBomEhwqVYkxNTZGQkFDm+SzyIDY2Ft27d4e6ujqSkpIwbtw4aGlpISgoCCkpKfD392cdscrIysrC1KlT4e/vz0/LKygoYOTIkdi8eTNq1KjBOKHwCe12iIGBAW7evIng4GB+ebKJiQm6d+/OONmXlWffoNL29ZBFhoaGiI6OllqldPr0aZiYmDBKJSxUqBSzYsUKzJo1C8uXL0fr1q2hqqoqcZ31mQflNXPmTLi6umLNmjUSyzAdHR1l+mRVIZo5cyZCQ0Nx/Phx/sTe8PBwTJs2De7u7vjjjz8YJyTyJCAgAMeOHUNubi66detW4vbnskxoZ1/NnDkTkydPxocPH8BxHCIiIrBv3z6sXr0aO3fuZB1PGDgiQSQS8V9isZj/+vRYXtSqVYuLj4/nOI7j1NTUuMePH3Mcx3FJSUmciooKy2hVjra2Nnfx4kWp8QsXLnA6OjqVH4jIrS1btnAikYhr1qwZZ25uzonFYm7WrFmsY1V5u3fv5po2bcq/d9SrV4/buXMn61iCQTMqxVy8eJF1hO9CRUWlxG2P4+LiqDeikmVnZ0NPT09qvHbt2sjOzmaQiMgrLy8vLFmyBEuWLAEA7N69G25ubli7di3jZF+nsLAQfn5+CAoKQlJSEkQiEQwNDfHDDz/gp59+kpujDPLz87F3717Y29tj+PDhyM7ORmZmpmCatmUFrfoRqLFjx+L169cIDAyElpYWYmNjoaCgACcnJ3Tp0kWujrSXd926dYO2tjb8/f1RrVo1AMD79+/h4uKCtLQ0nD9/nnHCqiEvLw9ubm5YtGiR3PagVa9eHffv30ejRo0AFL3hV69eHUlJSdDX12cbrpw4jkPfvn1x6tQpmJubo3nz5uA4Dvfv38ft27fRr18/HDlyhHXMcqtRowbu378v2J10u3btCjs7O7i7uzPrp6NCpRTZ2dlISUlBbm6uxLi8nOiZkZGBH374AZGRkXj37h3q1q2LFy9ewNraGqdOnZLqvSEV586dO7C3t0dOTg7Mzc0BFC3BrFatGs6cOYOWLVsyTlh1qKurIzo6Wm4LFbFYjJcvX0rMin6+JFYe/Pnnn5g+fTqOHj0KOzs7iWsXLlyAk5MTvLy8MHLkSEYJv46trS1+/vlnODk5sY5SIVxdXZGUlISEhASkpKQwyUCFSjGvXr3CqFGjSt0MTV7W9n8SHh6O2NhYZGZmwsrKSi5WBQhRdnY29uzZgwcPHgAoWqExfPhwVK9enXGyqsXFxQUWFhaYMWMG6yjfRCwWY/z48RKfbL29vTFixAiJTQbXr1/PIl659OzZE127dsXcuXNLvL5q1SqEhobizJkzlZzs2wQGBmLevHmYMWNGiQsw5OXD7Ze8ffuW2WISKlSKGT58OJKTk7Fx40bY2tri8OHDePnyJVasWAFPT0/07t2bdURCyDf69P+4W7duJb6pyPo5Oba2tl/s3xCJRLhw4UIlJfp6derUwenTp2FhYVHi9Vu3bsHBwQEvXryo3GDfSCyWPjJPJBKB4ziIRCK5+3BbXHZ2NvMtFKhQKUZfXx9Hjx5Fu3btUKtWLURGRqJZs2Y4duwY1qxZg/DwcNYRS/WljZQ+J+s/kIXg2LFjX3yOoqIi6tSpA1NTUygrK1dCqqqtrFs+IpEICQkJlZimalJWVkZycnKpPTXPnz+HoaEhcnJyKjnZt0lOTi7zujz0rnTr1g3+/v5SG4FGRERgxIgRiIuLY5SsCK36KSYrK4vv2NbU1MSrV6/QrFkztGrVCjdv3mScrmzFN1J69eoVsrOzJY6Br1GjBmrXrk2FSiX4mnvWderUQUBAADp37lxxgYjg9vCQRwUFBVBULP2tR0FBAfn5+ZWY6L+Rh0LkS6pVqwYzMzNs2bIFzs7OKCwsxLJly7Bq1SpMmjSJdTwqVIozNjbGw4cP0ahRI5ibm2Pbtm1o1KgRtm7dKvNd9Z//EN67dy+2bNkCHx8fGBsbAwAePnyIcePGwc3NjVXEKqU8u6FyHMffWpw+fbrMF8OE/Fccx8HV1RUqKiolXpeXmZTi7t27V+ICjH79+jFKVH4nT56Et7c3Ro8ejaNHjyIpKQnJyck4ceIEevbsyToe3fopbvfu3cjPz4erqyuioqLQq1cvpKWlQVlZGX5+fnB2dmYdsVyaNGmCgwcPSp1BERUVhR9++IE+WcqYpKQkNG/eHB8+fGAdRfCePn2KY8eOlfimIstNqEIxatSocj3vzz//rOAk30dCQgIGDBiA27dv870pAPheInnqUZk3bx5+++03KCoqIiQkBB07dmQdCQDNqEgZMWIE/33r1q2RnJyMBw8eoEGDBtDR0WGY7OukpqaWOH1aUFCAly9fMkhEytKoUSP6e6kEwcHB6NevHxo3bowHDx7A1NQUSUlJ4DgOVlZWrONVCfJSgJTX9OnTYWhoiODgYBgaGiIiIgKvX7+Gu7s71q1bxzpeubx58wZjx45FcHAwtm3bhtDQUPTs2RNr1qyRiVs/tIW+QPXp04eztLTkoqKi+LHIyEjOysqK69u3L8NkhLDTtm1bbvHixRzH/e9oiXfv3nH9+vXjtmzZwjgdkUfa2tpcTEwMx3FFR5c8ePCA4ziOCw4O5iwsLFhGK7e6detynTp14hISEvix/fv3c1paWpyjoyPDZEVoRqWYgoIC+Pn5ITg4GP/8849Un4EsL/v7nK+vL1xcXNCmTRsoKSkBKNru2d7eng7KIlXW/fv3sW/fPgBFK67ev38PNTU1LFu2DP3798fEiRMZJyyf2NjYEsdFIhGqVauGBg0alNoDQr6vgoIC/uBXHR0dPH/+HMbGxmjYsCEePnzIOF35TJgwAQsWLJBYau3s7IxOnTqV+1ZdRaJCpZjp06fDz88PvXv3hqmpqdycOVGcrq4uTp06hbi4OH6TsebNm6NZs2aMkxHCjqqqKt+Xoq+vj8ePH/M7A//7778so30VCwuLMn82KSkpwdnZGdu2beOPbSAVw9TUFDExMTA0NET79u2xZs0aKCsrY/v27XKzW/CiRYv477nPemzq16+Pc+fOsYr1P6yndGSNtrY2d/LkSdYxCCEVoH///tz27ds5juM4d3d3rmnTptyKFSs4Kysrrlu3bozTld+RI0c4Y2NjbufOnVxsbCwXGxvL7dy5kzMxMeH279/P7d69m6tfvz7n7u7OOqrgnT59mjt06BDHcRz36NEjztjYmBOJRJyOjg4XHBzMOF357dq1izM1NeVUVFQ4FRUVrlWrVpy/vz/rWBzHcRyt+immbt26CAkJEcTMA61uYEdTU7Pcs3FpaWkVnIZ8kpCQgMzMTJiZmSErKwvu7u64cuUKjIyMsH79ernZE6Ndu3ZYvnw57O3tJcbPnDmDRYsWISIiAkeOHIG7uzseP37MKGXVlZaW9lU/A1hbv349Fi1ahClTpqBTp04Aio5f8fb2xooVK5gfOUGFSjGenp5ISEiAl5eX3PwjK8mXVjfIS6+NvNq1axf//evXr7FixQrY29vD2toaAHD16lX+TYX1DwEif6pXr45bt26hefPmEuMPHjyApaUl3r9/j6SkJLRo0QLZ2dmMUhJ5YWhoCA8PD6mDIHft2oWlS5cy386CCpViBgwYgIsXL0JLSwstW7bkG1E/CQoKYpTs67Rr1w4ODg7w8PDgT1etXbs2hg8fjl69eslN06AQDBo0CHZ2dpgyZYrEuJeXF86fPy9XR9oLRW5ubonN8g0aNGCU6OtYWlrC3Nwc27dv549eyMvLw7hx4xATE4Nbt27h8uXLGDFiBPM3GaGzs7Mr80OtPHworFatGu7cuYOmTZtKjD969AitWrVivr8TNdMWo6GhgQEDBrCO8Z8JZXWDEJw5cwa//fab1HivXr1KPUGWVIy4uDiMGTMGV65ckRjn5OwAOW9vb/Tr1w/169fnT+e9ffs2CgoKcOLECQBFt7lkYg8MgSt+uGJeXh6io6Nx584duLi4sAn1lZo2bYrAwEDMnz9fYjwgIABGRkaMUv0PFSrFCGUzIqGsbhACbW1tHD16FO7u7hLjR48ehba2NqNUVdOoUaOgqKiIEydOQF9fX25v73bs2BGJiYnYs2cPf2Dc4MGDMWzYMH6p7E8//cQyYpVR/Iy1T5YuXYrMzMxKTvNtPDw84OzsjLCwML5H5fLlywgODkZgYCDjdHTrp1SvXr3i18AbGxtDV1eXcaKv4+TkhN69e2PcuHGYNWsWjh49CldXVwQFBUFTUxPnz59nHbHK8PPzw9ixY+Hg4ID27dsDAK5fv47Tp09jx44dcHV1ZRuwClFVVUVUVJRUbwch31t8fDzatWsnN83yUVFR2LBhA+7fvw8AMDExgbu7u9QxLCzQjEoxWVlZmDp1Kvz9/fn71woKChg5ciQ2b96MGjVqME5YPuvXr+ereQ8PD2RmZvLTeLTip3K5urrCxMQEmzZt4nucTExMEB4ezhcupHK0aNFCMDOKjx49wsWLF0vstVm8eDGjVOSTq1evytUeNq1bt8bu3btZxygRzagU4+bmhvPnz8PLy0timda0adPQo0cP/PHHH4wTEnmSl5cHNzc3LFq0CIaGhqzjVElv377lv4+MjMTChQuxatUqtGrVSqpZvlatWpUd75vs2LEDEydOhI6ODurUqSNxC0skEtEp3JVo4MCBEo85jkNqaioiIyOxaNEiLFmyhFGyr1NYWIj4+PgSC98uXbowSlWECpVidHR0cPDgQdja2kqMX7x4EUOGDMGrV6/YBPtKN27cQGFhodQn9uvXr0NBQQFt2rRhlKzqUVdXR3R0NBUqjIjFYok38k+Ns5+Tt2bahg0bYtKkSZgzZw7rKFVe8S3mxWIxdHV10bVrV/Ts2ZNRqq9z7do1DBs2DMnJySheEsjC/wu69VNMdnY29PT0pMZr164tV/sRTJ48Gb/88otUofLs2TP89ttvuH79OqNkVY+TkxOOHDlC+6UwcvHiRdYRvrs3b95g8ODBrGMQCGMBxoQJE9CmTRucPHlSJpvMaUalmG7dukFbWxv+/v78/cX379/DxcUFaWlpctOEqqamhtjYWKmzJhITE2FmZoZ3794xSlb1rFixAp6enujWrRtat24NVVVVievTpk1jlIzIqzFjxqBt27aYMGEC6yhEAFRVVRETEyO1j4qsoBmVYn7//XfY29ujfv36MDc3BwDExMSgWrVqOHPmDON05aeiooKXL19KFSqpqalQVKS/9srk4+MDDQ0NREVFISoqSuKaSCSiQqWSpaenw8fHh1/d0LJlS4wePRrq6uqMk5Vf06ZNsWjRIly7dq3EXhv6N1V5hHBcRvv27REfHy+zhQrNqJQgOzsbe/bs4U8dNjExwfDhw1G9enXGycpv6NChSE1NxdGjR/kfwOnp6XByckLt2rVlYm08IZUtMjIS9vb2qF69Otq1awegqJ/r/fv3OHv2LKysrBgnLJ+y+p1EIhESEhIqMU3Vtn79+jKPyNDS0uKfK6sbwB0+fBgLFy7E7NmzSyx8P20qyAoVKgL19OlT2NjY4PXr1/w6+OjoaOjp6eHcuXMwMDBgnJCQyte5c2c0bdoUO3bs4GcW8/PzMXbsWCQkJCAsLIxxQiJvhHBEhlgslhoTiUQy02ROhcpHcXFxSE9P5z9lAUUH+61YsQJZWVlwcnKS2l5Y1mVlZWHPnj2IiYlB9erVYWZmhqFDh0pVy6Ti0UnWsqG0w/zu3buHNm3ayFXDPJENampqiI6OlrptEh8fDwsLC7nYnTY5ObnM66xPFadmhY/mzJmDVq1a8YVKYmIi+vbti86dO8PMzAyrV69GjRo18PPPP7MNWg55eXlo3rw5Tpw4gfHjx7OOU+V96SRrUnlq1aqFlJQUqULlyZMn/NbzsmrmzJlYvnw5VFVVMXPmzDKfS8Vv5ZHnIzLu3buHFi1alFmIrF27FrNnz67EVNKoUPkoMjISv/zyC/94z549aNasGd9Aa2Zmhs2bN8tFoaKkpMT8tEvyP/PmzcOsWbP4k6wPHTokcZI1qTzOzs4YM2YM1q1bh44dOwIoOtNk9uzZGDp0KON0Zbt16xby8vL470sja0tLhc7DwwNjx45FSEhIiUdkyDJ7e3tcvny51FPD161bhwULFjAvVMARjuM4rlq1alxKSgr/uGvXrtzChQv5x/Hx8Zy6ujqDZN9m5cqVnIuLC5eXl8c6SpWnpqbGxcfHcxzHcRoaGtydO3c4juO46OhormHDhgyTVT05OTnctGnTOGVlZU4sFnNisZhTUVHhfv75Z+7Dhw+s4xE5de3aNW7YsGGcpaUlZ2lpyQ0bNoy7du0a61hf9OOPP3JGRkbcP//8I3Vt3bp1nKKiIrdv3z4GySTRjMpHWlpaSE1NhYGBAQoLCxEZGSkxvZqbmyu1Y58su3HjBoKDg3H27Fm0atVKau+OT2fOkIpHJ1nLDmVlZfz+++9YvXo1Hj9+DABo0qSJ3JzhRWRT+/btsWfPHtYxvtpff/2Fvn37wt7eHiEhIfwREhs2bMDcuXOxa9cu/Pjjj4xT0q0fnq2tLZYvX44tW7bgwIEDKCwslNhG/969e2jUqBGzfF9LQ0MDgwYNYh2DAOjQoQPCw8NhYmICR0dHuLu74/bt2wgKCkKHDh1Yx6uSatSoAQ0NDf57efPhwwds3ry51EMJ6ayfipefn4+CggKoqKjwYy9fvsTWrVuRlZWFfv364f/+7/8YJvwyRUVFBAUFoXv37ujTpw/Onj2LrVu3Yvbs2fDz88OwYcNYRwRAq354SUlJ6NGjBx4/fgwFBQVs2rQJEydO5K87OTnB0NAQGzZsYJiSyKOEhARkZmbCzMwMWVlZcHd3x5UrV/iTrFl31Fcl+fn58PDwwKZNm/jVGGpqapg6dSqWLFkiNyvihg8fjrNnz+KHH36Anp6eVF+KvByEJ89GjRoFZWVlbNu2DQDw7t07tGzZEh8+fIC+vj7u3buHo0ePwtHRkXHSL8vIyICNjQ3y8vIQFxcHX19f/PTTT6xj8ahQ+Ux+fj7u3r0LXV1d1K1bV+JaTEwM6tevL/Nd3GV5+/Yt9uzZAx8fH0RGRrKOQ0ilmzhxIoKCgrBs2TKJzbmWLl0KJycnuTkdXV1dHadOneJPeCeVr1mzZvDy8uIPHvT29saqVatw7949qKurY86cOYiIiJDps6aOHTvGf5+amorp06ejb9++UkVKv379KjuaBCpUqoCLFy/C19cXQUFBUFdXx4ABA+Dt7c06VpWxePFi2NnZwdramj8/irChrq6O/fv3w8HBQWL81KlTGDp0KDIyMhgl+zotWrTA/v37me8YWpWpqqrizp07/C7BAwcORP369bFp0yYARe0Ctra2+Oeff1jGLFNJG70VJwsbvlGPikA9e/YMfn5++PPPP5Geno43b95g7969GDJkCC1frGRXr17F+vXrkZ+fj7Zt28LGxga2trbo1KmTXB3LIAQqKiol9poZGhpCWVm58gN9I09PT8yZMwdbt26lW4eMVKtWDe/fv+cfX7t2DWvXrpW4LuubvRXvbZJVXy6niFw5dOgQHB0dYWxsjOjoaHh6euL58+cQi8Vo1aoVFSkMnDt3Dunp6QgODoajoyMiIyMxcOBAaGhoyHyzndBMmTIFy5cvR05ODj+Wk5ODlStXSm2BLsvatGmDDx8+oHHjxqhZsya0tLQkvkjFs7CwwF9//QUAuHTpEl6+fImuXbvy1x8/fizVQkC+Dc2oCIyzszPmzJmDgIAAmd9psypRVFREp06doKurCy0tLdSsWRNHjhzhD74klePWrVsIDg6WOh09NzcX3bp1w8CBA/nnyvIS/qFDh+LZs2dYtWpVic20pOItXrwYDg4OCAwMRGpqKlxdXaGvr89fP3z4MPUQfSdUqAjMmDFj4O3tjZCQEPz0009wdnaGpqYm61hV2vbt2xESEoLQ0FDk5OSgc+fOsLW1xcKFC6nHoJKVtGxfHg/ovHLlCq5evcoXW6Ty2djYICoqCmfPnkWdOnUwePBgiesWFhYSZ8eRb0fNtB89evQIixcvxrZt2/hNbz7JyMjAxIkTsWLFCjRu3JhRwvJ7//49AgMD4evri+vXr8Pe3h4nT55EdHQ0TE1NWcercsRiMXR1deHu7o5JkyZBTU2NdSQi56ysrLBlyxbah4dUCdSj8tHatWthYGAgVaQARSsFDAwMJBqlZFn16tXh4uKC0NBQ3L59Gy1btoSenh46deqEYcOGyfSUthAFBQVh+PDh2L9/P3R1ddGxY0fMnz8fZ8+epdN6yTf59ddf4e7ujpCQELx+/Rpv376V+CJESGhG5SNjY2Ps3r0bbdu2LfF6VFQUhg0bhocPH1Zysu+jsLAQJ0+ehI+PD/7++2+JZkJSeTIyMnDp0iUcOHAA+/btg1gspgMkK4GmpmaJfRzq6upo1qwZZs2ahR49ejBI9m0+LSst/po4jpOJ5aREvjRu3Bg3btyQ2icsPT0dVlZWSEhIYJSsCPWofJSSkoLatWuXel1HRwdPnjypxETfl1gsRt++fdG3b1+ZXtcvVK9fv0ZoaChCQkIQEhKCu3fvQlNTE507d2YdrUrYuHFjiePp6emIiopCnz59cPDgQfTt27dyg30jWd5EjMifpKSkEovbnJwcPHv2jEEiSVSofKSuro7Hjx+XuidBfHx8ibeF5FFZBRn5/lq1aoX79+9DU1MTXbp0wbhx42BjY0ONtJXIxcWlzOsWFhZYvXq13BQqNjY2rCMQAfh8Z9ozZ85AXV2df1xQUIDg4GCZOOOObv18NGTIEOTl5eHw4cMlXu/fvz+UlZVx4MCBSk5G5J23tzdsbGyokVmGxcXFoUOHDkhLS2Mdpdw+fPiA2NjYEg8lZL3lOZEPZe1Mq6SkhEaNGsHT0xN9+vSpxFTSqFD56NatW7C2tkafPn3wyy+/wNjYGADw4MEDrFmzBidPnsSVK1dgZWXFOCmRZ5/+u9G+F7Ll9u3b6NGjB168eME6SrmcPn0aI0eOxL///it1jXpUKp6Wlhbi4uKgo6NTav/TJ/JQ/BoaGiIyMlJmz7KjWz8fWVpa4uDBgxg9erTUrIq2tjYCAwOpSCHfzN/fH2vXrsWjR48AFB1oNnv2bJk6obQq8/HxgYWFBesY5TZ16lQMHjwYixcvhp6eHus4Vc6GDRv4DTVL63+SF3l5eWjcuDHS0tJktlChGZWPli1bhlmzZkEkEuH06dOIj48Hx3Fo1qwZevbsiRo1arCOSOTU+vXrsWjRIkyZMoXfqTI8PBze3t5YsWIFZsyYwTih8M2cObPE8YyMDNy8eRNxcXEICwtD69atKznZt6lVqxZu3bqFJk2asI5CBEBXVxdXrlyBkZER6yglokLlIwUFBaSmpgqm0bS06UiRSIRq1aqhadOmcHV1xahRoxikq1oMDQ3h4eGBkSNHSozv2rULS5cuRWJiIqNkVYednV2J47Vq1YKxsTEmTpzIn4IrD0aPHo1OnTphzJgxrKMQFG3/EB8fX2K/UJcuXRilKr8ZM2ZARUUFv/76K+soJaJbPx8JrV5bvHgxVq5cCQcHB34b54iICJw+fRqTJ09GYmIiJk6ciPz8fIwbN45xWmFLTU1Fx44dpcY7duyI1NRUBomqHqEt5/Xy8sLgwYNx6dIltGrVCkpKShLXp02bxihZ1XPt2jUMGzYMycnJUu8j8tIvlJ+fD19fX5w/fx6tW7eGqqqqxPX169czSlaECpXPCKnBMTw8HCtWrMCECRMkxrdt24azZ8/i0KFDMDMzw6ZNm6hQqWBNmzZFYGAg5s+fLzEeEBAgs1OtRLbt27cPZ8+eRbVq1RASEiLxs0skElGhUokmTJiANm3a4OTJk9DX15fL95E7d+7wPZhxcXES12Th9dCtn4/EYjHU1dW/+JciDx3cAKCmpobo6Gg0bdpUYjw+Ph4WFhbIzMzE48ePYWZmhqysLEYpq4ZDhw7B2dkZ3bt353tULl++jODgYAQGBmLAgAGMExJ5U6dOHUybNg1z584tc4kpqXiqqqqIiYmR+llLvh+aUfmMh4eHxIY38kxLSwvHjx+XatQ8fvw4tLS0AABZWVl85zqpOIMGDcL169exYcMGHDlyBABgYmKCiIgIWFpasg1H5FJubi6cnZ2pSJEB7du3R3x8vGAKladPnwIA6tevzzjJ/1Ch8pkff/xRMM20ixYtwsSJE3Hx4kW+R+XGjRs4deoUtm7dCgA4d+4c7XBZSVq3bo3du3ezjkEEwsXFBQEBAVK3E0nlmzp1Ktzd3fHixYsS+4XkYQfqwsJCrFixAp6ensjMzAQA1KxZE+7u7liwYAHzgphu/XwktFU/QNHtBS8vL/4gRWNjY0ydOrXExk7y/ZX3FFuhHM1AKs+0adPg7+8Pc3NzmJmZSb05sm5+rEpKehMXiURydUDkvHnz4OPjAw8PD4ktFJYuXYpx48Zh5cqVTPNRofKRWCzGixcvBFWoELbEYnGZPU/y9IOMyJbSllsDRW+SFy5cqMQ0VVtycnKZ10s7P06W1K1bF1u3bpU6euHo0aOYNGkS84MJ6dbPR8XXvguBvK/tl3efL4nlOA6Ojo7YuXMn6tWrxzAVEQKhLbeWZ/JQiHxJWloamjdvLjXevHlzmVhAQjMqHw0cOLBczwsKCqrgJN+HENb2C03NmjURExODxo0bs45CCPnO7t27h5SUFOTm5kqMy8MBke3bt0f79u2xadMmifGpU6fixo0buHbtGqNkRWhG5SOhrPb5RAhr+wkh/zNw4ED4+fmhVq1aX/xgJS8fqIQgISEBAwYMwO3bt/neFOB/+4/Iw4fCNWvWoHfv3jh//jysra0BAFevXsWTJ09w6tQpxumoUOH9+eefrCN8V48ePcLBgwcFs2SOkKru832ehPbBSp5Nnz4dhoaGCA4OhqGhISIiIvD69Wu4u7tj3bp1rOOVi42NDeLi4uDt7Y0HDx4AKCqMJ02ahLp16zJOR7d+BKtr16745Zdf0KtXL9ZRyEc1a9ZEbGysXJ0pQwgpm46ODi5cuAAzMzOoq6sjIiICxsbGuHDhAtzd3XHr1i3WEUuVkJAAQ0NDmZ9xpxkVgRLC2n55V3x6/sOHD5gwYYLUORo0TU++lq+vL+zs7KjolQEFBQX8xpk6Ojp4/vw5jI2N0bBhQ35rCFllZGQksS2Hs7MzNm3aBD09PcbJJNGMikAJYW2/vCvvydRCu+1IKp6RkRESEhJQr1492NjYwMbGBra2tnSrl4HOnTvD3d0dTk5OGDZsGN68eYOFCxdi+/btiIqKwp07d1hHLFXxbTlkteGfChWBEsLafkJI6Z49e4aQkBCEhYUhNDQUjx49gr6+PmxtbWkX5Ep05swZZGVlYeDAgYiPj0efPn0QFxcHbW1tBAQEoGvXrqwjlooKFUIIIRUuOzsbly5dwr59+7Bnzx5wHIf8/HzWsaq0tLQ0aGpqynzvh4KCAl68eAFdXV0AsttHR4WKgBw7dgwODg5QUlLCsWPHynyuPKztJ4SU7OzZswgJCUFISAhu3boFExMT/vZPly5doKmpyToikQNisRgODg5QUVEBUHRobdeuXWWuj44KFQH5fBqvrEOkqEeFEPkmFouhq6sLd3d3jB8/HhoaGqwjVTmjR48u1/N8fX0rOMm3k5c+OipUCCFEzmzcuBFhYWEICwuDiooKP5tia2uLZs2asY5XJYjFYjRs2BCWlpZSu39/7vDhw5WYSpioUBGgvLw89OrVC1u3boWRkRHrOISQCnT79m2EhobiwoULOHHiBGrXro2nT5+yjiV4kydPxr59+9CwYUOMGjUKI0aMgJaWFutYglT6/QEit5SUlBAbG8s6BiGkAnEch5s3b+LcuXM4c+YMLl68iMLCQr4xklQsb29vpKam4pdffsHx48dhYGCAIUOG4MyZM2XOsJCvRzMqAjVjxgyoqKjg119/ZR2FEPKd9e3bF5cvX8bbt29hbm4OW1tb2NjYoEuXLtSvwkhycjL8/Pzg7++P/Px83L17F2pqaqxjCQLtTCtQ+fn58PX1xfnz59G6dWupLu7169czSkYI+a+aN28ONzc3dO7cmc79kRFisZjfVJMWK3xfNKMiUHZ2dqVeE4lEuHDhQiWmIYQQ4cnJyUFQUBB8fX0RHh6OPn36YNSoUejVq1eZKy/J16FChRBC5MTVq1fx+vVr9OnThx/z9/fHkiVLkJWVBScnJ2zevJnfF4NUnEmTJmH//v0wMDDA6NGjMXz4cOjo6LCOJUhUqFQBT548AQAYGBgwTkII+S8cHBxga2uLOXPmACha8WNlZQVXV1eYmJhg7dq1cHNzw9KlS9kGrQLEYjEaNGgAS0vLMnegZb1ZmhDQ3JRA5efnY9GiRVBXV0ejRo3QqFEjqKurY+HChcjLy2MdjxDyDaKjo9GtWzf+8f79+9G+fXvs2LEDM2fOxKZNmxAYGMgwYdUxcuRI2NnZQUNDA+rq6qV+kf+OmmkFaurUqQgKCsKaNWtgbW0NoGjaeOnSpXj9+jX++OMPxgkJIV/rzZs30NPT4x+HhobCwcGBf9y2bVt+BpVULD8/P9YRqgwqVARq79692L9/v8QPMTMzMxgYGGDo0KFUqBAih/T09JCYmAgDAwPk5ubi5s2b8PDw4K+/e/cOSkpKDBMS8v3RrR+BUlFRQaNGjaTGDQ0NoaysXPmBCCH/maOjI+bOnYtLly5h3rx5qFGjBjp37sxfj42NRZMmTRgmJOT7o0JFoKZMmYLly5cjJyeHH8vJycHKlSsxZcoUhskIId9q+fLlUFRUhI2NDXbs2IEdO3ZIfPDw9fVFz549GSYk5PujVT8CNWDAAAQHB0NFRQXm5uYAgJiYGOTm5ko04wHUlU6IvMnIyICamhoUFBQkxtPS0qCmpkazpkRQqEdFoDQ0NDBo0CCJMVqeTIgwlLaahA7FI0JEMyqEEEIIkVk0o1JFhIaGIisrC9bW1tDU1GQdhxBCCCkXKlQE5rfffkNmZiaWL18OoOgoeAcHB5w9exYAULt2bQQHB6Nly5YsYxJCCCHlQqt+BCYgIACmpqb844MHDyIsLAyXLl3Cv//+izZt2kjsu0AIIYTIMupRERhNTU1cuXIFJiYmAIBRo0ahoKAA/v7+AIBr165h8ODBtHslIYQQuUAzKgKTn58vcXLq1atX0bFjR/5x3bp18e+//7KIRgghhHw1KlQEpkmTJggLCwMApKSkIC4uDl26dOGvP336FNra2qziEUIIIV+FmmkFZvLkyZgyZQouXbqEa9euwdraGi1atOCvX7hwAZaWlgwTEkIIIeVHhYrAjBs3DgoKCjh+/Di6dOmCJUuWSFx//vw5Ro8ezSgdIYQQ8nWomZYQQgghMot6VAghhBAis6hQIYQQQojMokKFEEIIITKLChVCCCGEyCwqVAQoLy8PioqKuHPnDusohBBCyH9ChYoAKSkpoUGDBigoKGAdhRBCCPlPqFARqAULFmD+/PlIS0tjHYUQQgj5ZrSPikBZWloiPj4eeXl5aNiwIVRVVSWu37x5k1EyQgghpPxoZ1qBcnJyYh2BEEII+c9oRoUQQgghMot6VAQsPT0dO3fuxLx58/helZs3b+LZs2eMkxFCCCHlQzMqAhUbG4vu3btDXV0dSUlJePjwIRo3boyFCxciJSUF/v7+rCMSQgghX0QzKgI1c+ZMuLq64tGjR6hWrRo/7ujoiLCwMIbJCCGEkPKjQkWgbty4ATc3N6nxevXq4cWLFwwSEUIIIV+PChWBUlFRwdu3b6XG4+LioKuryyARIYQQ8vWoUBGofv36YdmyZcjLywMAiEQipKSkYM6cORg0aBDjdIQQQkj5UDOtQGVkZOCHH35AZGQk3r17h7p16+LFixewtrbGqVOnpDaAI4QQQmQRFSoCFx4ejtjYWGRmZsLKygrdu3dnHYkQQggpNypUBOrJkycwMDBgHYMQQgj5T6hHRaAaNWoEGxsb7NixA2/evGEdhxBCCPkmVKgIVGRkJNq1a4dly5ZBX18fTk5OOHjwIHJyclhHI4QQQsqNbv0IHMdxCAkJwd69e3Ho0CEUFhZi4MCB8PX1ZR2NEEII+SIqVKqQmzdvYsyYMYiNjUVBQQHrOIQQQsgX0a0fgXv69CnWrFkDCwsLtGvXDmpqavD29mYdixBCCCkXRdYBSMXYtm0b9u7di8uXL6N58+YYPnw4jh49ioYNG7KORgghhJQb3foRKAMDAwwdOhTDhw+Hubk56ziEEELIN6FCRaA4joNIJGIdgxBCCPlP6NaPgMTGxpb7uWZmZhWYhBBCCPk+aEZFQMRiMUQiEUr7K/10TSQS0aofQgghcoFmVAQkMTGRdQRCCCHku6IZFUIIIYTILJpREZBjx47BwcEBSkpKOHbsWJnP7devXyWlIoQQQr4dzagIiFgsxosXL1C7dm2IxaXv5Uc9KoQQQuQFFSqEEEIIkVm0hT4hhBBCZBYVKgJz9epVnDhxQmLM398fhoaGqF27NsaPH4+cnBxG6QghhJCvQ4WKwCxbtgx3797lH9++fRtjxoxB9+7dMXfuXBw/fhyrV69mmJAQQggpP+pRERh9fX0cP34cbdq0AQAsWLAAoaGhCA8PBwAcOHAAS5Yswb1791jGJIQQQsqFZlQE5s2bN9DT0+Mfh4aGwsHBgX/ctm1bPHnyhEU0Qggh5KtRoSIwenp6/A61ubm5uHnzJjp06MBff/fuHZSUlFjFI4QQQr4KFSoC4+joiLlz5+LSpUuYN28eatSogc6dO/PXY2Nj0aRJE4YJCSGEkPKjnWkFZvny5Rg4cCBsbGygpqaGXbt2QVlZmb/u6+uLnj17MkxICCGElB810wpURkYG1NTUoKCgIDGelpYGNTU1ieKFEEIIkVVUqBBCCCFEZlGPCiGEEEJkFhUqhBBCCJFZVKgQQgghRGZRoUIIIYQQmUWFCiGkwly9ehUKCgro3bs36yiEEDlFq34IIRVm7NixUFNTg4+PDx4+fIi6deuW+DyO41BQUABFRdraiRAiiWZUCCEVIjMzEwEBAZg4cSJ69+4NPz8//lpISAhEIhH+/vtvtG7dGioqKggPD0dhYSFWr14NQ0NDVK9eHebm5jh48CD/6woKCjBmzBj+urGxMX7//XeJPzckJATt2rWDqqoqNDQ00KlTJyQnJ1fWyyaEfGf08YUQUiECAwPRvHlzGBsbY8SIEfj5558xb948iEQi/jlz587FunXr0LhxY2hqamL16tXYvXs3tm7dCiMjI4SFhWHEiBHQ1dWFjY0NCgsLUb9+fRw4cADa2tq4cuUKxo8fD319fQwZMgT5+flwcnLCuHHjsG/fPuTm5iIiIkLizySEyBe69UMIqRCdOnXCkCFDMH36dOTn50NfXx8HDhyAra0tQkJCYGdnhyNHjqB///4AgJycHGhpaeH8+fOwtrbmf5+xY8ciOzsbe/fuLfHPmTJlCl68eIGDBw8iLS0N2traCAkJgY2NTaW8TkJIxaIZFULId/fw4UNERETg8OHDAABFRUU4OzvDx8cHtra2/PPatGnDfx8fH4/s7Gz06NFD4vfKzc2FpaUl/9jb2xu+vr5ISUnB+/fvkZubCwsLCwCAlpYWXF1dYW9vjx49eqB79+4YMmQI9PX1K+7FEkIqFBUqhJDvzsfHB/n5+RLNsxzHQUVFBV5eXvyYqqoq/31mZiYA4OTJk6hXr57E76eiogIA2L9/P2bNmgVPT09YW1ujZs2aWLt2La5fv84/988//8S0adNw+vRpBAQEYOHChTh37hw6dOhQIa+VEFKxqFAhhHxX+fn58Pf3h6enp9RJ3U5OTti3bx+aN28u9etatGgBFRUVpKSklHrb5vLly+jYsSMmTZrEjz1+/FjqeZaWlrC0tMS8efNgbW2NvXv3UqFCiJyiQoUQ8l2dOHECb968wZgxY6Curi5xbdCgQfDx8cHatWulfl3NmjUxa9YszJgxA4WFhfi///s/ZGRk4PLly6hVqxZcXFxgZGQEf39/nDlzBoaGhvjrr79w48YNGBoaAgASExOxfft29OvXD3Xr1sXDhw/x6NEjjBw5slJeOyHk+6NChRDyXfn4+KB79+5SRQpQVKisWbMGsbGxJf7a5cuXQ1dXF6tXr0ZCQgI0NDRgZWWF+fPnAwDc3Nxw69YtODs7QyQSYejQoZg0aRL+/vtvAECNGjXw4MED7Nq1C69fv4a+vj4mT54MNze3invBhJAKRat+CCGEECKzaMM3QgghhMgsKlQIIYQQIrOoUCGEEEKIzKJChRBCCCEyiwoVQgghhMgsKlQIIYQQIrOoUCGEEEKIzKJChRBCCCEyiwoVQgghhMgsKlQIIYQQIrOoUCGEEEKIzKJChRBCCCEy6/8BUBkegQA3M/gAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=top_10_area.index, y=top_10_area.values)\n",
    "plt.title(\"Area-wise Delivery Ratings\")\n",
    "plt.xlabel(\"Areas\")\n",
    "plt.ylabel(\"Delivery Ratings\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3af32c2e",
   "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>Name</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Area</th>\n",
       "      <th>IsHomeDelivery</th>\n",
       "      <th>isTakeaway</th>\n",
       "      <th>isIndoorSeating</th>\n",
       "      <th>isVegOnly</th>\n",
       "      <th>Dinner Ratings</th>\n",
       "      <th>Dinner Reviews</th>\n",
       "      <th>Delivery Ratings</th>\n",
       "      <th>Delivery Reviews</th>\n",
       "      <th>PopularDishes</th>\n",
       "      <th>PeopleKnownFor</th>\n",
       "      <th>AverageCost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Natural Ice Cream</td>\n",
       "      <td>Ice Cream, Desserts</td>\n",
       "      <td>Park Street Area, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2419</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9632</td>\n",
       "      <td>Chiku Shake, Chocobite, Choco Bite, Sitafal Ic...</td>\n",
       "      <td>Bank for the Buck, Totally Worth it, Pretty De...</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>Asia Kitchen By Mainland China</td>\n",
       "      <td>Chinese, Momos, Thai, Asian, Seafood, Sushi, B...</td>\n",
       "      <td>Acropolis Mall, Kasba, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>2417</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1288</td>\n",
       "      <td>King Prawn Tempura, Chilli Teriyaki Chicken, L...</td>\n",
       "      <td>Live Kitchen, Service Time, Soothing Ambience,...</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>Pa Pa Ya</td>\n",
       "      <td>Asian, Chinese, Japanese, Thai</td>\n",
       "      <td>Park Street Area, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1975</td>\n",
       "      <td>4.1</td>\n",
       "      <td>707</td>\n",
       "      <td>Rock Shrimp Tempura Roll, Sushi Sashimi, Lamb ...</td>\n",
       "      <td>Magnificent View, Sophisticated, Worth the Pri...</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Oh! Calcutta</td>\n",
       "      <td>Bengali, Seafood</td>\n",
       "      <td>Forum Mall, Elgin, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1997</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1291</td>\n",
       "      <td>Smoked Hilsa, Railway Mutton, Kacha Lonka Murg...</td>\n",
       "      <td>Intimate Setting, Sophisticated, Worth the Pri...</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>Chittoda's Suruchee Restaurant</td>\n",
       "      <td>Chinese, Biryani, North Indian</td>\n",
       "      <td>Esplanade, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1117</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3505</td>\n",
       "      <td>Chitto Babur Dokan, Butter Toast, Fish Diamond...</td>\n",
       "      <td>Service and Quality of Food, Menu Options, Che...</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Name  \\\n",
       "72                 Natural Ice Cream   \n",
       "3386  Asia Kitchen By Mainland China   \n",
       "264                         Pa Pa Ya   \n",
       "110                     Oh! Calcutta   \n",
       "894   Chittoda's Suruchee Restaurant   \n",
       "\n",
       "                                               Cuisines  \\\n",
       "72                                  Ice Cream, Desserts   \n",
       "3386  Chinese, Momos, Thai, Asian, Seafood, Sushi, B...   \n",
       "264                      Asian, Chinese, Japanese, Thai   \n",
       "110                                    Bengali, Seafood   \n",
       "894                      Chinese, Biryani, North Indian   \n",
       "\n",
       "                                Area  IsHomeDelivery  isTakeaway  \\\n",
       "72         Park Street Area, Kolkata               1           1   \n",
       "3386  Acropolis Mall, Kasba, Kolkata               1           1   \n",
       "264        Park Street Area, Kolkata               1           0   \n",
       "110       Forum Mall, Elgin, Kolkata               1           1   \n",
       "894               Esplanade, Kolkata               1           1   \n",
       "\n",
       "      isIndoorSeating  isVegOnly  Dinner Ratings  Dinner Reviews  \\\n",
       "72                  1          1             4.9            2419   \n",
       "3386                1          0             4.8            2417   \n",
       "264                 1          0             4.8            1975   \n",
       "110                 1          0             4.7            1997   \n",
       "894                 1          0             4.7            1117   \n",
       "\n",
       "      Delivery Ratings  Delivery Reviews  \\\n",
       "72                 4.5              9632   \n",
       "3386               4.3              1288   \n",
       "264                4.1               707   \n",
       "110                4.4              1291   \n",
       "894                4.4              3505   \n",
       "\n",
       "                                          PopularDishes  \\\n",
       "72    Chiku Shake, Chocobite, Choco Bite, Sitafal Ic...   \n",
       "3386  King Prawn Tempura, Chilli Teriyaki Chicken, L...   \n",
       "264   Rock Shrimp Tempura Roll, Sushi Sashimi, Lamb ...   \n",
       "110   Smoked Hilsa, Railway Mutton, Kacha Lonka Murg...   \n",
       "894   Chitto Babur Dokan, Butter Toast, Fish Diamond...   \n",
       "\n",
       "                                         PeopleKnownFor  AverageCost  \n",
       "72    Bank for the Buck, Totally Worth it, Pretty De...          300  \n",
       "3386  Live Kitchen, Service Time, Soothing Ambience,...         2000  \n",
       "264   Magnificent View, Sophisticated, Worth the Pri...         2500  \n",
       "110   Intimate Setting, Sophisticated, Worth the Pri...         2000  \n",
       "894   Service and Quality of Food, Menu Options, Che...          300  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 restaurant in Dine-In category\n",
    "new_df.loc[(df['isIndoorSeating']==1) & (new_df['Dinner Ratings'] >= 4.5) & (new_df['Delivery Ratings'] >= 4)].sort_values(by=['Dinner Ratings','Delivery Ratings'], ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "68226165",
   "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>Name</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Area</th>\n",
       "      <th>IsHomeDelivery</th>\n",
       "      <th>isTakeaway</th>\n",
       "      <th>isIndoorSeating</th>\n",
       "      <th>isVegOnly</th>\n",
       "      <th>Dinner Ratings</th>\n",
       "      <th>Dinner Reviews</th>\n",
       "      <th>Delivery Ratings</th>\n",
       "      <th>Delivery Reviews</th>\n",
       "      <th>PopularDishes</th>\n",
       "      <th>PeopleKnownFor</th>\n",
       "      <th>AverageCost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Natural Ice Cream</td>\n",
       "      <td>Ice Cream, Desserts</td>\n",
       "      <td>Park Street Area, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2419</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9632</td>\n",
       "      <td>Chiku Shake, Chocobite, Choco Bite, Sitafal Ic...</td>\n",
       "      <td>Bank for the Buck, Totally Worth it, Pretty De...</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>Asia Kitchen By Mainland China</td>\n",
       "      <td>Chinese, Momos, Thai, Asian, Seafood, Sushi, B...</td>\n",
       "      <td>Acropolis Mall, Kasba, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>2417</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1288</td>\n",
       "      <td>King Prawn Tempura, Chilli Teriyaki Chicken, L...</td>\n",
       "      <td>Live Kitchen, Service Time, Soothing Ambience,...</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Oh! Calcutta</td>\n",
       "      <td>Bengali, Seafood</td>\n",
       "      <td>Forum Mall, Elgin, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1997</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1291</td>\n",
       "      <td>Smoked Hilsa, Railway Mutton, Kacha Lonka Murg...</td>\n",
       "      <td>Intimate Setting, Sophisticated, Worth the Pri...</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>Chittoda's Suruchee Restaurant</td>\n",
       "      <td>Chinese, Biryani, North Indian</td>\n",
       "      <td>Esplanade, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1117</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3505</td>\n",
       "      <td>Chitto Babur Dokan, Butter Toast, Fish Diamond...</td>\n",
       "      <td>Service and Quality of Food, Menu Options, Che...</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3214</th>\n",
       "      <td>Balaram Mullick &amp; Radharaman Mullick Sweets</td>\n",
       "      <td>Mishti, Desserts, Street Food</td>\n",
       "      <td>Bhawanipur, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1379</td>\n",
       "      <td>4.3</td>\n",
       "      <td>10200</td>\n",
       "      <td>Samosa Kachori, Bhapa Doi, Mango Souffle, Raso...</td>\n",
       "      <td>Spacious Seating Area, More Variety, Favorite ...</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Name  \\\n",
       "72                              Natural Ice Cream   \n",
       "3386               Asia Kitchen By Mainland China   \n",
       "110                                  Oh! Calcutta   \n",
       "894                Chittoda's Suruchee Restaurant   \n",
       "3214  Balaram Mullick & Radharaman Mullick Sweets   \n",
       "\n",
       "                                               Cuisines  \\\n",
       "72                                  Ice Cream, Desserts   \n",
       "3386  Chinese, Momos, Thai, Asian, Seafood, Sushi, B...   \n",
       "110                                    Bengali, Seafood   \n",
       "894                      Chinese, Biryani, North Indian   \n",
       "3214                      Mishti, Desserts, Street Food   \n",
       "\n",
       "                                Area  IsHomeDelivery  isTakeaway  \\\n",
       "72         Park Street Area, Kolkata               1           1   \n",
       "3386  Acropolis Mall, Kasba, Kolkata               1           1   \n",
       "110       Forum Mall, Elgin, Kolkata               1           1   \n",
       "894               Esplanade, Kolkata               1           1   \n",
       "3214             Bhawanipur, Kolkata               1           1   \n",
       "\n",
       "      isIndoorSeating  isVegOnly  Dinner Ratings  Dinner Reviews  \\\n",
       "72                  1          1             4.9            2419   \n",
       "3386                1          0             4.8            2417   \n",
       "110                 1          0             4.7            1997   \n",
       "894                 1          0             4.7            1117   \n",
       "3214                1          0             4.7            1379   \n",
       "\n",
       "      Delivery Ratings  Delivery Reviews  \\\n",
       "72                 4.5              9632   \n",
       "3386               4.3              1288   \n",
       "110                4.4              1291   \n",
       "894                4.4              3505   \n",
       "3214               4.3             10200   \n",
       "\n",
       "                                          PopularDishes  \\\n",
       "72    Chiku Shake, Chocobite, Choco Bite, Sitafal Ic...   \n",
       "3386  King Prawn Tempura, Chilli Teriyaki Chicken, L...   \n",
       "110   Smoked Hilsa, Railway Mutton, Kacha Lonka Murg...   \n",
       "894   Chitto Babur Dokan, Butter Toast, Fish Diamond...   \n",
       "3214  Samosa Kachori, Bhapa Doi, Mango Souffle, Raso...   \n",
       "\n",
       "                                         PeopleKnownFor  AverageCost  \n",
       "72    Bank for the Buck, Totally Worth it, Pretty De...          300  \n",
       "3386  Live Kitchen, Service Time, Soothing Ambience,...         2000  \n",
       "110   Intimate Setting, Sophisticated, Worth the Pri...         2000  \n",
       "894   Service and Quality of Food, Menu Options, Che...          300  \n",
       "3214  Spacious Seating Area, More Variety, Favorite ...          100  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 restaurant in Takeaway category\n",
    "new_df.loc[(df['isTakeaway']==1) & (new_df['Dinner Ratings'] >= 4.5) & (new_df['Delivery Ratings'] >= 4)].sort_values(by=['Dinner Ratings','Delivery Ratings'], ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d657bd80",
   "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>Name</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Area</th>\n",
       "      <th>IsHomeDelivery</th>\n",
       "      <th>isTakeaway</th>\n",
       "      <th>isIndoorSeating</th>\n",
       "      <th>isVegOnly</th>\n",
       "      <th>Dinner Ratings</th>\n",
       "      <th>Dinner Reviews</th>\n",
       "      <th>Delivery Ratings</th>\n",
       "      <th>Delivery Reviews</th>\n",
       "      <th>PopularDishes</th>\n",
       "      <th>PeopleKnownFor</th>\n",
       "      <th>AverageCost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Natural Ice Cream</td>\n",
       "      <td>Ice Cream, Desserts</td>\n",
       "      <td>Park Street Area, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2419</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9632</td>\n",
       "      <td>Chiku Shake, Chocobite, Choco Bite, Sitafal Ic...</td>\n",
       "      <td>Bank for the Buck, Totally Worth it, Pretty De...</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3675</th>\n",
       "      <td>The Chutney Co.</td>\n",
       "      <td>South Indian</td>\n",
       "      <td>Dalhousie BBD Bagh, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>846</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1821</td>\n",
       "      <td>Molagapodi Idli, Rawa Onion Dosa, Peanut Chutn...</td>\n",
       "      <td>Comfortable Seating Area, Family Place, Great ...</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>Chennai Square</td>\n",
       "      <td>South Indian, Beverages</td>\n",
       "      <td>Broadway Mall, Sector 3, Salt Lake, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1096</td>\n",
       "      <td>4.2</td>\n",
       "      <td>7286</td>\n",
       "      <td>Coconut Dosa, Ghee Idli, Rawa Idli, Onion Chil...</td>\n",
       "      <td>Serving Style, Safe Delivery, Hygienic Packagi...</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>Piccadilly Square</td>\n",
       "      <td>Cafe, Italian, Salad, Desserts, Pizza, Greek, ...</td>\n",
       "      <td>Elgin, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1491</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2191</td>\n",
       "      <td>Celestial Chocolate, Pita Sandwich, Savoury Cr...</td>\n",
       "      <td>Outside Seating, Good Food Good Quality, Good ...</td>\n",
       "      <td>850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Name                                           Cuisines  \\\n",
       "72    Natural Ice Cream                                Ice Cream, Desserts   \n",
       "3675    The Chutney Co.                                       South Indian   \n",
       "1075     Chennai Square                            South Indian, Beverages   \n",
       "200   Piccadilly Square  Cafe, Italian, Salad, Desserts, Pizza, Greek, ...   \n",
       "\n",
       "                                             Area  IsHomeDelivery  isTakeaway  \\\n",
       "72                      Park Street Area, Kolkata               1           1   \n",
       "3675                  Dalhousie BBD Bagh, Kolkata               1           1   \n",
       "1075  Broadway Mall, Sector 3, Salt Lake, Kolkata               1           1   \n",
       "200                                Elgin, Kolkata               1           1   \n",
       "\n",
       "      isIndoorSeating  isVegOnly  Dinner Ratings  Dinner Reviews  \\\n",
       "72                  1          1             4.9            2419   \n",
       "3675                1          1             4.5             846   \n",
       "1075                1          1             4.5            1096   \n",
       "200                 1          1             4.5            1491   \n",
       "\n",
       "      Delivery Ratings  Delivery Reviews  \\\n",
       "72                 4.5              9632   \n",
       "3675               4.3              1821   \n",
       "1075               4.2              7286   \n",
       "200                4.1              2191   \n",
       "\n",
       "                                          PopularDishes  \\\n",
       "72    Chiku Shake, Chocobite, Choco Bite, Sitafal Ic...   \n",
       "3675  Molagapodi Idli, Rawa Onion Dosa, Peanut Chutn...   \n",
       "1075  Coconut Dosa, Ghee Idli, Rawa Idli, Onion Chil...   \n",
       "200   Celestial Chocolate, Pita Sandwich, Savoury Cr...   \n",
       "\n",
       "                                         PeopleKnownFor  AverageCost  \n",
       "72    Bank for the Buck, Totally Worth it, Pretty De...          300  \n",
       "3675  Comfortable Seating Area, Family Place, Great ...          400  \n",
       "1075  Serving Style, Safe Delivery, Hygienic Packagi...          400  \n",
       "200   Outside Seating, Good Food Good Quality, Good ...          850  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 restaurant in Only Veg category\n",
    "new_df.loc[(df['isVegOnly']==1) & (new_df['Dinner Ratings'] >= 4.5) & (new_df['Delivery Ratings'] >= 4)].sort_values(by=['Dinner Ratings','Delivery Ratings'], ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f0b5e5fb",
   "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>Name</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Area</th>\n",
       "      <th>IsHomeDelivery</th>\n",
       "      <th>isTakeaway</th>\n",
       "      <th>isIndoorSeating</th>\n",
       "      <th>isVegOnly</th>\n",
       "      <th>Dinner Ratings</th>\n",
       "      <th>Dinner Reviews</th>\n",
       "      <th>Delivery Ratings</th>\n",
       "      <th>Delivery Reviews</th>\n",
       "      <th>PopularDishes</th>\n",
       "      <th>PeopleKnownFor</th>\n",
       "      <th>AverageCost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2103</th>\n",
       "      <td>Kasturi Restaurant</td>\n",
       "      <td>Bengali</td>\n",
       "      <td>Gariahat, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>658</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4983</td>\n",
       "      <td>Kochu Patar Chingri, Dhakai Mangsho, Bhetki Pa...</td>\n",
       "      <td>Comfortable Seating Area, Good for Large Group...</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4775</th>\n",
       "      <td>Mahal Restaurant</td>\n",
       "      <td>Bengali</td>\n",
       "      <td>College Street, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>160</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5073</td>\n",
       "      <td>Katla Kalia, Chingri Malaikari, Aloo Posto, Ch...</td>\n",
       "      <td>Economical, Cute Little Place, Good Crowd, Rea...</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name Cuisines                     Area  IsHomeDelivery  \\\n",
       "2103  Kasturi Restaurant  Bengali        Gariahat, Kolkata               1   \n",
       "4775    Mahal Restaurant  Bengali  College Street, Kolkata               1   \n",
       "\n",
       "      isTakeaway  isIndoorSeating  isVegOnly  Dinner Ratings  Dinner Reviews  \\\n",
       "2103           1                1          0             4.1             658   \n",
       "4775           1                1          0             4.0             160   \n",
       "\n",
       "      Delivery Ratings  Delivery Reviews  \\\n",
       "2103               4.1              4983   \n",
       "4775               4.1              5073   \n",
       "\n",
       "                                          PopularDishes  \\\n",
       "2103  Kochu Patar Chingri, Dhakai Mangsho, Bhetki Pa...   \n",
       "4775  Katla Kalia, Chingri Malaikari, Aloo Posto, Ch...   \n",
       "\n",
       "                                         PeopleKnownFor  AverageCost  \n",
       "2103  Comfortable Seating Area, Good for Large Group...          800  \n",
       "4775  Economical, Cute Little Place, Good Crowd, Rea...          400  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The highest rating restaurant for any particular cuisines like Bengali\n",
    "new_df.loc[(df['Cuisines']=='Bengali') & (new_df['Dinner Ratings'] >= 4) & (new_df['Delivery Ratings'] >= 4)].sort_values(by=['Dinner Ratings','Delivery Ratings'], ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b982267b",
   "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>Name</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Area</th>\n",
       "      <th>IsHomeDelivery</th>\n",
       "      <th>isTakeaway</th>\n",
       "      <th>isIndoorSeating</th>\n",
       "      <th>isVegOnly</th>\n",
       "      <th>Dinner Ratings</th>\n",
       "      <th>Dinner Reviews</th>\n",
       "      <th>Delivery Ratings</th>\n",
       "      <th>Delivery Reviews</th>\n",
       "      <th>PopularDishes</th>\n",
       "      <th>PeopleKnownFor</th>\n",
       "      <th>AverageCost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1788</th>\n",
       "      <td>WTF!- Where's The Food</td>\n",
       "      <td>Chinese</td>\n",
       "      <td>Nagerbazar, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>556</td>\n",
       "      <td>4.0</td>\n",
       "      <td>622</td>\n",
       "      <td>Schezwan Rice, Grilled Fish, Sides, Garlic Chi...</td>\n",
       "      <td>Music and Ambience, Behaviour of the Staff, Ch...</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>Chufang The Kitchen</td>\n",
       "      <td>Chinese</td>\n",
       "      <td>New Town, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>250</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4897</td>\n",
       "      <td>Butter Noodles, Mushroom Pepper, Thick Soup, T...</td>\n",
       "      <td>Gastronomical Experience, Cozy Ambience, Chine...</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>Beijing</td>\n",
       "      <td>Chinese</td>\n",
       "      <td>Tangra, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2171</td>\n",
       "      <td>4.0</td>\n",
       "      <td>223</td>\n",
       "      <td>Peking Chicken, Honey Prawn, Chilli Garlic Ric...</td>\n",
       "      <td>Family Favorite, Valet Service, Comfortable Se...</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Name Cuisines                 Area  IsHomeDelivery  \\\n",
       "1788  WTF!- Where's The Food  Chinese  Nagerbazar, Kolkata               1   \n",
       "1366     Chufang The Kitchen  Chinese    New Town, Kolkata               1   \n",
       "302                  Beijing  Chinese      Tangra, Kolkata               1   \n",
       "\n",
       "      isTakeaway  isIndoorSeating  isVegOnly  Dinner Ratings  Dinner Reviews  \\\n",
       "1788           1                1          0             4.2             556   \n",
       "1366           1                1          0             4.1             250   \n",
       "302            1                1          0             4.1            2171   \n",
       "\n",
       "      Delivery Ratings  Delivery Reviews  \\\n",
       "1788               4.0               622   \n",
       "1366               4.1              4897   \n",
       "302                4.0               223   \n",
       "\n",
       "                                          PopularDishes  \\\n",
       "1788  Schezwan Rice, Grilled Fish, Sides, Garlic Chi...   \n",
       "1366  Butter Noodles, Mushroom Pepper, Thick Soup, T...   \n",
       "302   Peking Chicken, Honey Prawn, Chilli Garlic Ric...   \n",
       "\n",
       "                                         PeopleKnownFor  AverageCost  \n",
       "1788  Music and Ambience, Behaviour of the Staff, Ch...          500  \n",
       "1366  Gastronomical Experience, Cozy Ambience, Chine...          650  \n",
       "302   Family Favorite, Valet Service, Comfortable Se...          800  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The highest rating restaurant for any particular cuisines like Chinese\n",
    "new_df.loc[(df['Cuisines']=='Chinese') & (new_df['Dinner Ratings'] >= 4) & (new_df['Delivery Ratings'] >= 4)].sort_values(by=['Dinner Ratings','Delivery Ratings'], ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4e832fc2",
   "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>Area</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Dinner Ratings</th>\n",
       "      <th>Delivery Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3083</th>\n",
       "      <td>Park Street Area, Kolkata</td>\n",
       "      <td>Ice Cream, Desserts</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4147</th>\n",
       "      <td>Wellesley, Kolkata</td>\n",
       "      <td>BBQ, Chinese, Kebab, North Indian, Desserts</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3026</th>\n",
       "      <td>Park Street Area, Kolkata</td>\n",
       "      <td>Asian, Chinese, Japanese, Thai</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Acropolis Mall, Kasba, Kolkata</td>\n",
       "      <td>Chinese, Momos, Thai, Asian, Seafood, Sushi, B...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>Chinar Park, Kolkata</td>\n",
       "      <td>Chinese, Thai, Vietnamese, Malaysian, Asian, N...</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1347</th>\n",
       "      <td>Fort Knox, Camac Street Area, Kolkata</td>\n",
       "      <td>Asian, Sushi, Japanese, Chinese, Thai, Momos, ...</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3607</th>\n",
       "      <td>Sector 5, Salt Lake, Kolkata</td>\n",
       "      <td>Chinese, Momos, Tibetan, Asian</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>Kidderpore, Kolkata</td>\n",
       "      <td>Biryani, Mughlai, North Indian, Kebab, Chinese</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>Esplanade, Kolkata</td>\n",
       "      <td>Chinese, Biryani, North Indian</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>Bhawanipur, Kolkata</td>\n",
       "      <td>Burger, Pizza, Pasta, Continental, Italian, Be...</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Area  \\\n",
       "3083              Park Street Area, Kolkata   \n",
       "4147                     Wellesley, Kolkata   \n",
       "3026              Park Street Area, Kolkata   \n",
       "7            Acropolis Mall, Kasba, Kolkata   \n",
       "874                    Chinar Park, Kolkata   \n",
       "1347  Fort Knox, Camac Street Area, Kolkata   \n",
       "3607           Sector 5, Salt Lake, Kolkata   \n",
       "2250                    Kidderpore, Kolkata   \n",
       "1333                     Esplanade, Kolkata   \n",
       "693                     Bhawanipur, Kolkata   \n",
       "\n",
       "                                               Cuisines  Dinner Ratings  \\\n",
       "3083                                Ice Cream, Desserts             4.9   \n",
       "4147        BBQ, Chinese, Kebab, North Indian, Desserts             4.9   \n",
       "3026                     Asian, Chinese, Japanese, Thai             4.8   \n",
       "7     Chinese, Momos, Thai, Asian, Seafood, Sushi, B...             4.8   \n",
       "874   Chinese, Thai, Vietnamese, Malaysian, Asian, N...             4.7   \n",
       "1347  Asian, Sushi, Japanese, Chinese, Thai, Momos, ...             4.7   \n",
       "3607                     Chinese, Momos, Tibetan, Asian             4.7   \n",
       "2250     Biryani, Mughlai, North Indian, Kebab, Chinese             4.7   \n",
       "1333                     Chinese, Biryani, North Indian             4.7   \n",
       "693   Burger, Pizza, Pasta, Continental, Italian, Be...             4.7   \n",
       "\n",
       "      Delivery Ratings  \n",
       "3083               4.5  \n",
       "4147               3.9  \n",
       "3026               4.1  \n",
       "7                  4.3  \n",
       "874                4.1  \n",
       "1347               4.1  \n",
       "3607               4.2  \n",
       "2250               4.2  \n",
       "1333               4.4  \n",
       "693                3.9  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cuisines are the most famous in different localities\n",
    "cuisine_rating = new_df.groupby(['Area','Cuisines'])[['Dinner Ratings','Delivery Ratings']].mean().reset_index()\n",
    "top10_area_cuisine = cuisine_rating.sort_values('Dinner Ratings', ascending=False).head(10)\n",
    "top10_area_cuisine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f87de390",
   "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>Name</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Area</th>\n",
       "      <th>IsHomeDelivery</th>\n",
       "      <th>isTakeaway</th>\n",
       "      <th>isIndoorSeating</th>\n",
       "      <th>isVegOnly</th>\n",
       "      <th>Dinner Ratings</th>\n",
       "      <th>Dinner Reviews</th>\n",
       "      <th>Delivery Ratings</th>\n",
       "      <th>Delivery Reviews</th>\n",
       "      <th>PopularDishes</th>\n",
       "      <th>PeopleKnownFor</th>\n",
       "      <th>AverageCost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jyoti Vihar</td>\n",
       "      <td>South Indian</td>\n",
       "      <td>Camac Street Area, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>930</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5498</td>\n",
       "      <td>Milk Upma, Jyoti Vihar, Vada Sambar, Filtered ...</td>\n",
       "      <td>South Indian Food, Good Crowd, Excellent Place...</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WOW! Momo</td>\n",
       "      <td>Momos, Fast Food, Tibetan</td>\n",
       "      <td>Park Street Area, Kolkata</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>469</td>\n",
       "      <td>4.1</td>\n",
       "      <td>11100</td>\n",
       "      <td>Tandoori Paneer Momos, Momo Platter, Chocolate...</td>\n",
       "      <td>Comfortable Seating Area, Staff Service, Famil...</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name                   Cuisines                        Area  \\\n",
       "0  Jyoti Vihar               South Indian  Camac Street Area, Kolkata   \n",
       "1    WOW! Momo  Momos, Fast Food, Tibetan   Park Street Area, Kolkata   \n",
       "\n",
       "   IsHomeDelivery  isTakeaway  isIndoorSeating  isVegOnly  Dinner Ratings  \\\n",
       "0               1           1                1          1             4.4   \n",
       "1               1           1                1          0             4.0   \n",
       "\n",
       "   Dinner Reviews  Delivery Ratings  Delivery Reviews  \\\n",
       "0             930               4.1              5498   \n",
       "1             469               4.1             11100   \n",
       "\n",
       "                                       PopularDishes  \\\n",
       "0  Milk Upma, Jyoti Vihar, Vada Sambar, Filtered ...   \n",
       "1  Tandoori Paneer Momos, Momo Platter, Chocolate...   \n",
       "\n",
       "                                      PeopleKnownFor  AverageCost  \n",
       "0  South Indian Food, Good Crowd, Excellent Place...          300  \n",
       "1  Comfortable Seating Area, Staff Service, Famil...          350  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b222dc52",
   "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>Area</th>\n",
       "      <th>Name</th>\n",
       "      <th>Dinner Ratings</th>\n",
       "      <th>Delivery Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3620</th>\n",
       "      <td>Park Street Area, Kolkata</td>\n",
       "      <td>Natural Ice Cream</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4860</th>\n",
       "      <td>Wellesley, Kolkata</td>\n",
       "      <td>Barbeque Nation</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3629</th>\n",
       "      <td>Park Street Area, Kolkata</td>\n",
       "      <td>Pa Pa Ya</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acropolis Mall, Kasba, Kolkata</td>\n",
       "      <td>Asia Kitchen By Mainland China</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2654</th>\n",
       "      <td>Kidderpore, Kolkata</td>\n",
       "      <td>India Restaurant</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1605</th>\n",
       "      <td>Esplanade, Kolkata</td>\n",
       "      <td>Chittoda's Suruchee Restaurant</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>Bhawanipur, Kolkata</td>\n",
       "      <td>My Big Fat Belly</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4253</th>\n",
       "      <td>Sector 5, Salt Lake, Kolkata</td>\n",
       "      <td>Momo I Am</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1617</th>\n",
       "      <td>Fort Knox, Camac Street Area, Kolkata</td>\n",
       "      <td>The Fatty Bao</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>Chinar Park, Kolkata</td>\n",
       "      <td>Friends Of Pho - Asian Fine Dining Club</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Area  \\\n",
       "3620              Park Street Area, Kolkata   \n",
       "4860                     Wellesley, Kolkata   \n",
       "3629              Park Street Area, Kolkata   \n",
       "1            Acropolis Mall, Kasba, Kolkata   \n",
       "2654                    Kidderpore, Kolkata   \n",
       "1605                     Esplanade, Kolkata   \n",
       "881                     Bhawanipur, Kolkata   \n",
       "4253           Sector 5, Salt Lake, Kolkata   \n",
       "1617  Fort Knox, Camac Street Area, Kolkata   \n",
       "1049                   Chinar Park, Kolkata   \n",
       "\n",
       "                                         Name  Dinner Ratings  \\\n",
       "3620                        Natural Ice Cream             4.9   \n",
       "4860                          Barbeque Nation             4.9   \n",
       "3629                                 Pa Pa Ya             4.8   \n",
       "1              Asia Kitchen By Mainland China             4.8   \n",
       "2654                         India Restaurant             4.7   \n",
       "1605           Chittoda's Suruchee Restaurant             4.7   \n",
       "881                          My Big Fat Belly             4.7   \n",
       "4253                                Momo I Am             4.7   \n",
       "1617                            The Fatty Bao             4.7   \n",
       "1049  Friends Of Pho - Asian Fine Dining Club             4.7   \n",
       "\n",
       "      Delivery Ratings  \n",
       "3620               4.5  \n",
       "4860               3.9  \n",
       "3629               4.1  \n",
       "1                  4.3  \n",
       "2654               4.2  \n",
       "1605               4.4  \n",
       "881                3.9  \n",
       "4253               4.2  \n",
       "1617               4.1  \n",
       "1049               4.1  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Restaurant are the most famous in different localities\n",
    "restaurant_rating = new_df.groupby(['Area','Name'])[['Dinner Ratings','Delivery Ratings']].mean().reset_index()\n",
    "top10_area_res = restaurant_rating.sort_values('Dinner Ratings', ascending=False).head(10)\n",
    "top10_area_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5e0fa6c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}