final_mimp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d4724b1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "84f11ee2",
   "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>url</th>\n",
       "      <th>address</th>\n",
       "      <th>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>phone</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>dish_liked</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>reviews_list</th>\n",
       "      <th>menu_item</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.zomato.com/bangalore/jalsa-banasha...</td>\n",
       "      <td>942, 21st Main Road, 2nd Stage, Banashankari, ...</td>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>775</td>\n",
       "      <td>080 42297555\\r\\n+91 9743772233</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.zomato.com/bangalore/spice-elephan...</td>\n",
       "      <td>2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...</td>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>787</td>\n",
       "      <td>080 41714161</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://www.zomato.com/SanchurroBangalore?cont...</td>\n",
       "      <td>1112, Next to KIMS Medical College, 17th Cross...</td>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>918</td>\n",
       "      <td>+91 9663487993</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.zomato.com/bangalore/addhuri-udupi...</td>\n",
       "      <td>1st Floor, Annakuteera, 3rd Stage, Banashankar...</td>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7/5</td>\n",
       "      <td>88</td>\n",
       "      <td>+91 9620009302</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Masala Dosa</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300</td>\n",
       "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.zomato.com/bangalore/grand-village...</td>\n",
       "      <td>10, 3rd Floor, Lakshmi Associates, Gandhi Baza...</td>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>166</td>\n",
       "      <td>+91 8026612447\\r\\n+91 9901210005</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Panipuri, Gol Gappe</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  \\\n",
       "0  https://www.zomato.com/bangalore/jalsa-banasha...   \n",
       "1  https://www.zomato.com/bangalore/spice-elephan...   \n",
       "2  https://www.zomato.com/SanchurroBangalore?cont...   \n",
       "3  https://www.zomato.com/bangalore/addhuri-udupi...   \n",
       "4  https://www.zomato.com/bangalore/grand-village...   \n",
       "\n",
       "                                             address                   name  \\\n",
       "0  942, 21st Main Road, 2nd Stage, Banashankari, ...                  Jalsa   \n",
       "1  2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...         Spice Elephant   \n",
       "2  1112, Next to KIMS Medical College, 17th Cross...        San Churro Cafe   \n",
       "3  1st Floor, Annakuteera, 3rd Stage, Banashankar...  Addhuri Udupi Bhojana   \n",
       "4  10, 3rd Floor, Lakshmi Associates, Gandhi Baza...          Grand Village   \n",
       "\n",
       "  online_order book_table   rate  votes                             phone  \\\n",
       "0          Yes        Yes  4.1/5    775    080 42297555\\r\\n+91 9743772233   \n",
       "1          Yes         No  4.1/5    787                      080 41714161   \n",
       "2          Yes         No  3.8/5    918                    +91 9663487993   \n",
       "3           No         No  3.7/5     88                    +91 9620009302   \n",
       "4           No         No  3.8/5    166  +91 8026612447\\r\\n+91 9901210005   \n",
       "\n",
       "       location            rest_type  \\\n",
       "0  Banashankari        Casual Dining   \n",
       "1  Banashankari        Casual Dining   \n",
       "2  Banashankari  Cafe, Casual Dining   \n",
       "3  Banashankari          Quick Bites   \n",
       "4  Basavanagudi        Casual Dining   \n",
       "\n",
       "                                          dish_liked  \\\n",
       "0  Pasta, Lunch Buffet, Masala Papad, Paneer Laja...   \n",
       "1  Momos, Lunch Buffet, Chocolate Nirvana, Thai G...   \n",
       "2  Churros, Cannelloni, Minestrone Soup, Hot Choc...   \n",
       "3                                        Masala Dosa   \n",
       "4                                Panipuri, Gol Gappe   \n",
       "\n",
       "                         cuisines approx_cost(for two people)  \\\n",
       "0  North Indian, Mughlai, Chinese                         800   \n",
       "1     Chinese, North Indian, Thai                         800   \n",
       "2          Cafe, Mexican, Italian                         800   \n",
       "3      South Indian, North Indian                         300   \n",
       "4        North Indian, Rajasthani                         600   \n",
       "\n",
       "                                        reviews_list menu_item  \\\n",
       "0  [('Rated 4.0', 'RATED\\n  A beautiful place to ...        []   \n",
       "1  [('Rated 4.0', 'RATED\\n  Had been here for din...        []   \n",
       "2  [('Rated 3.0', \"RATED\\n  Ambience is not that ...        []   \n",
       "3  [('Rated 4.0', \"RATED\\n  Great food and proper...        []   \n",
       "4  [('Rated 4.0', 'RATED\\n  Very good restaurant ...        []   \n",
       "\n",
       "  listed_in(type) listed_in(city)  \n",
       "0          Buffet    Banashankari  \n",
       "1          Buffet    Banashankari  \n",
       "2          Buffet    Banashankari  \n",
       "3          Buffet    Banashankari  \n",
       "4          Buffet    Banashankari  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.read_csv('C:\\\\Users\\HP\\\\Desktop\\\\Mission_Improbable\\\\zomato.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56ee8d3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51717, 17)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b33ad4ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['url', 'address', 'name', 'online_order', 'book_table', 'rate', 'votes',\n",
       "       'phone', 'location', 'rest_type', 'dish_liked', 'cuisines',\n",
       "       'approx_cost(for two people)', 'reviews_list', 'menu_item',\n",
       "       'listed_in(type)', 'listed_in(city)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "62b8e997",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['url','address','phone','menu_item','dish_liked','reviews_list'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "19f804cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51717 entries, 0 to 51716\n",
      "Data columns (total 11 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   name                         51717 non-null  object\n",
      " 1   online_order                 51717 non-null  object\n",
      " 2   book_table                   51717 non-null  object\n",
      " 3   rate                         43942 non-null  object\n",
      " 4   votes                        51717 non-null  int64 \n",
      " 5   location                     51696 non-null  object\n",
      " 6   rest_type                    51490 non-null  object\n",
      " 7   cuisines                     51672 non-null  object\n",
      " 8   approx_cost(for two people)  51371 non-null  object\n",
      " 9   listed_in(type)              51717 non-null  object\n",
      " 10  listed_in(city)              51717 non-null  object\n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 4.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ab2c9b8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51609, 11)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2d917c2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'online_order', 'book_table', 'rate', 'votes', 'location',\n",
       "       'rest_type', 'cuisines', 'approx_cost(for two people)',\n",
       "       'listed_in(type)', 'listed_in(city)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9295826a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                              0\n",
       "online_order                      0\n",
       "book_table                        0\n",
       "rate                           7755\n",
       "votes                             0\n",
       "location                         21\n",
       "rest_type                       227\n",
       "cuisines                         45\n",
       "approx_cost(for two people)     344\n",
       "listed_in(type)                   0\n",
       "listed_in(city)                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "827e7b71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8392"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dfa36035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4.1/5', '3.8/5', '3.7/5', '3.6/5', '4.6/5', '4.0/5', '4.2/5',\n",
       "       '3.9/5', '3.1/5', '3.0/5', '3.2/5', '3.3/5', '2.8/5', '4.4/5',\n",
       "       '4.3/5', 'NEW', '2.9/5', '3.5/5', nan, '2.6/5', '3.8 /5', '3.4/5',\n",
       "       '4.5/5', '2.5/5', '2.7/5', '4.7/5', '2.4/5', '2.2/5', '2.3/5',\n",
       "       '3.4 /5', '-', '3.6 /5', '4.8/5', '3.9 /5', '4.2 /5', '4.0 /5',\n",
       "       '4.1 /5', '3.7 /5', '3.1 /5', '2.9 /5', '3.3 /5', '2.8 /5',\n",
       "       '3.5 /5', '2.7 /5', '2.5 /5', '3.2 /5', '2.6 /5', '4.5 /5',\n",
       "       '4.3 /5', '4.4 /5', '4.9/5', '2.1/5', '2.0/5', '1.8/5', '4.6 /5',\n",
       "       '4.9 /5', '3.0 /5', '4.8 /5', '2.3 /5', '4.7 /5', '2.4 /5',\n",
       "       '2.1 /5', '2.2 /5', '2.0 /5', '1.8 /5'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"rate\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e7827cc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.1\n",
       "1    4.1\n",
       "2    3.8\n",
       "3    3.7\n",
       "4    3.8\n",
       "Name: rate, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def clean_rate (value):\n",
    "    if(value == 'NEW' or value == '-'):\n",
    "        return np.nan\n",
    "    else:\n",
    "        value = str(value).split('/')\n",
    "        value = value[0]\n",
    "        return float(value)\n",
    "    \n",
    "df['rate'] = df['rate'].apply(clean_rate)\n",
    "df['rate'].head()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7e3fec73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10019"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e626678e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['rate'].fillna(df['rate'].mean(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8c455135",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "07cb28f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "015cf92f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.fillna(\"missing\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "23c7dc25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Jalsa', 'Spice Elephant', 'San Churro Cafe', ..., 'Nawabs Empire',\n",
       "       'SeeYa Restaurant',\n",
       "       'Plunge - Sheraton Grand Bengaluru Whitefield Hotel &...'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"name\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "08c154fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['North Indian, Mughlai, Chinese', 'Chinese, North Indian, Thai',\n",
       "       'Cafe, Mexican, Italian', ...,\n",
       "       'North Indian, Street Food, Biryani', 'Chinese, Mughlai',\n",
       "       'North Indian, Chinese, Arabian, Momos'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"cuisines\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b68599bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Banashankari', 'Basavanagudi', 'Mysore Road', 'Jayanagar',\n",
       "       'Kumaraswamy Layout', 'Rajarajeshwari Nagar', 'Vijay Nagar',\n",
       "       'Uttarahalli', 'JP Nagar', 'South Bangalore', 'City Market',\n",
       "       'Nagarbhavi', 'Bannerghatta Road', 'BTM', 'Kanakapura Road',\n",
       "       'Bommanahalli', 'CV Raman Nagar', 'Electronic City', 'HSR',\n",
       "       'Marathahalli', 'Wilson Garden', 'Shanti Nagar',\n",
       "       'Koramangala 5th Block', 'Koramangala 8th Block', 'Richmond Road',\n",
       "       'Koramangala 7th Block', 'Jalahalli', 'Koramangala 4th Block',\n",
       "       'Bellandur', 'Sarjapur Road', 'Whitefield', 'East Bangalore',\n",
       "       'Old Airport Road', 'Indiranagar', 'Koramangala 1st Block',\n",
       "       'Frazer Town', 'RT Nagar', 'MG Road', 'Brigade Road',\n",
       "       'Lavelle Road', 'Church Street', 'Ulsoor', 'Residency Road',\n",
       "       'Shivajinagar', 'Infantry Road', 'St. Marks Road',\n",
       "       'Cunningham Road', 'Race Course Road', 'Commercial Street',\n",
       "       'Vasanth Nagar', 'HBR Layout', 'Domlur', 'Ejipura',\n",
       "       'Jeevan Bhima Nagar', 'Old Madras Road', 'Malleshwaram',\n",
       "       'Seshadripuram', 'Kammanahalli', 'Koramangala 6th Block',\n",
       "       'Majestic', 'Langford Town', 'Central Bangalore', 'Sanjay Nagar',\n",
       "       'Brookefield', 'ITPL Main Road, Whitefield',\n",
       "       'Varthur Main Road, Whitefield', 'KR Puram',\n",
       "       'Koramangala 2nd Block', 'Koramangala 3rd Block', 'Koramangala',\n",
       "       'Hosur Road', 'Rajajinagar', 'Banaswadi', 'North Bangalore',\n",
       "       'Nagawara', 'Hennur', 'Kalyan Nagar', 'New BEL Road', 'Jakkur',\n",
       "       'Rammurthy Nagar', 'Thippasandra', 'Kaggadasapura', 'Hebbal',\n",
       "       'Kengeri', 'Sankey Road', 'Sadashiv Nagar', 'Basaveshwara Nagar',\n",
       "       'Yeshwantpur', 'West Bangalore', 'Magadi Road', 'Yelahanka',\n",
       "       'Sahakara Nagar', 'Peenya'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"location\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b07d0569",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Casual Dining', 'Cafe, Casual Dining', 'Quick Bites',\n",
       "       'Casual Dining, Cafe', 'Cafe', 'Quick Bites, Cafe',\n",
       "       'Cafe, Quick Bites', 'Delivery', 'Mess', 'Dessert Parlor',\n",
       "       'Bakery, Dessert Parlor', 'Pub', 'Bakery', 'Takeaway, Delivery',\n",
       "       'Fine Dining', 'Beverage Shop', 'Sweet Shop', 'Bar',\n",
       "       'Beverage Shop, Quick Bites', 'Confectionery',\n",
       "       'Quick Bites, Beverage Shop', 'Dessert Parlor, Sweet Shop',\n",
       "       'Bakery, Quick Bites', 'Sweet Shop, Quick Bites', 'Kiosk',\n",
       "       'Food Truck', 'Quick Bites, Dessert Parlor',\n",
       "       'Beverage Shop, Dessert Parlor', 'Takeaway', 'Pub, Casual Dining',\n",
       "       'Casual Dining, Bar', 'Dessert Parlor, Beverage Shop',\n",
       "       'Quick Bites, Bakery', 'Dessert Parlor, Quick Bites',\n",
       "       'Microbrewery, Casual Dining', 'Lounge', 'Bar, Casual Dining',\n",
       "       'Food Court', 'Cafe, Bakery', 'Dhaba', 'Quick Bites, Sweet Shop',\n",
       "       'Microbrewery', 'Food Court, Quick Bites', 'Pub, Bar',\n",
       "       'Casual Dining, Pub', 'Lounge, Bar', 'Food Court, Dessert Parlor',\n",
       "       'Casual Dining, Sweet Shop', 'Food Court, Casual Dining',\n",
       "       'Casual Dining, Microbrewery', 'Sweet Shop, Dessert Parlor',\n",
       "       'Bakery, Beverage Shop', 'Lounge, Casual Dining',\n",
       "       'Cafe, Food Court', 'Beverage Shop, Cafe', 'Cafe, Dessert Parlor',\n",
       "       'Dessert Parlor, Cafe', 'Dessert Parlor, Bakery',\n",
       "       'Microbrewery, Pub', 'Bakery, Food Court', 'Club',\n",
       "       'Quick Bites, Food Court', 'Bakery, Cafe', 'Bar, Cafe',\n",
       "       'Pub, Cafe', 'Casual Dining, Irani Cafee', 'Fine Dining, Lounge',\n",
       "       'Bar, Quick Bites', 'Bakery, Kiosk', 'Pub, Microbrewery',\n",
       "       'Microbrewery, Lounge', 'Fine Dining, Microbrewery',\n",
       "       'Fine Dining, Bar', 'Mess, Quick Bites', 'Dessert Parlor, Kiosk',\n",
       "       'Bhojanalya', 'Casual Dining, Quick Bites', 'Pop Up', 'Cafe, Bar',\n",
       "       'Casual Dining, Lounge', 'Bakery, Sweet Shop', 'Microbrewery, Bar',\n",
       "       'Cafe, Lounge', 'Bar, Pub', 'Lounge, Cafe', 'Club, Casual Dining',\n",
       "       'Quick Bites, Mess', 'Quick Bites, Meat Shop',\n",
       "       'Quick Bites, Kiosk', 'Lounge, Microbrewery',\n",
       "       'Food Court, Beverage Shop', 'Dessert Parlor, Food Court',\n",
       "       'Bar, Lounge'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"rest_type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cc86c417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quick Bites                   19010\n",
       "Casual Dining                 10253\n",
       "Cafe                           3682\n",
       "Delivery                       2574\n",
       "Dessert Parlor                 2242\n",
       "                              ...  \n",
       "Dessert Parlor, Kiosk             2\n",
       "Food Court, Beverage Shop         2\n",
       "Dessert Parlor, Food Court        2\n",
       "Quick Bites, Kiosk                1\n",
       "Sweet Shop, Dessert Parlor        1\n",
       "Name: rest_type, Length: 93, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"rest_type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "60f1da7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quick Bites           19010\n",
       "Casual Dining         10253\n",
       "others                 9003\n",
       "Cafe                   3682\n",
       "Delivery               2574\n",
       "Dessert Parlor         2242\n",
       "Takeaway, Delivery     2008\n",
       "Bakery                 1140\n",
       "Casual Dining, Bar     1130\n",
       "Name: rest_type, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restTypeDf=df['rest_type'].value_counts(ascending = True)\n",
    "restTypeLessThan1000 = restTypeDf[restTypeDf<1000]\n",
    "def groupRestType (value):\n",
    "    if(value in restTypeLessThan1000):\n",
    "        return 'others'\n",
    "    else:\n",
    "        return value\n",
    "    \n",
    "df['rest_type']=df['rest_type'].apply(groupRestType)\n",
    "df['rest_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f1c54202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'No'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"online_order\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "736976d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Buffet', 'Cafes', 'Delivery', 'Desserts', 'Dine-out',\n",
       "       'Drinks & nightlife', 'Pubs and bars'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"listed_in(type)\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4e72a071",
   "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>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>listed_in(type)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>775</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>787</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>918</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>others</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>88</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>166</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51712</th>\n",
       "      <td>Best Brews - Four Points by Sheraton Bengaluru...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>27</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>others</td>\n",
       "      <td>Continental</td>\n",
       "      <td>1,500</td>\n",
       "      <td>Pubs and bars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51713</th>\n",
       "      <td>Vinod Bar And Restaurant</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.700142</td>\n",
       "      <td>0</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>others</td>\n",
       "      <td>Finger Food</td>\n",
       "      <td>600</td>\n",
       "      <td>Pubs and bars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51714</th>\n",
       "      <td>Plunge - Sheraton Grand Bengaluru Whitefield H...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.700142</td>\n",
       "      <td>0</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>others</td>\n",
       "      <td>Finger Food</td>\n",
       "      <td>2,000</td>\n",
       "      <td>Pubs and bars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51715</th>\n",
       "      <td>Chime - Sheraton Grand Bengaluru Whitefield Ho...</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>236</td>\n",
       "      <td>ITPL Main Road, Whitefield</td>\n",
       "      <td>others</td>\n",
       "      <td>Finger Food</td>\n",
       "      <td>2,500</td>\n",
       "      <td>Pubs and bars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51716</th>\n",
       "      <td>The Nest - The Den Bengaluru</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>13</td>\n",
       "      <td>ITPL Main Road, Whitefield</td>\n",
       "      <td>others</td>\n",
       "      <td>Finger Food, North Indian, Continental</td>\n",
       "      <td>1,500</td>\n",
       "      <td>Pubs and bars</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51042 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    name online_order  \\\n",
       "0                                                  Jalsa          Yes   \n",
       "1                                         Spice Elephant          Yes   \n",
       "2                                        San Churro Cafe          Yes   \n",
       "3                                  Addhuri Udupi Bhojana           No   \n",
       "4                                          Grand Village           No   \n",
       "...                                                  ...          ...   \n",
       "51712  Best Brews - Four Points by Sheraton Bengaluru...           No   \n",
       "51713                           Vinod Bar And Restaurant           No   \n",
       "51714  Plunge - Sheraton Grand Bengaluru Whitefield H...           No   \n",
       "51715  Chime - Sheraton Grand Bengaluru Whitefield Ho...           No   \n",
       "51716                       The Nest - The Den Bengaluru           No   \n",
       "\n",
       "      book_table      rate  votes                    location      rest_type  \\\n",
       "0            Yes  4.100000    775                Banashankari  Casual Dining   \n",
       "1             No  4.100000    787                Banashankari  Casual Dining   \n",
       "2             No  3.800000    918                Banashankari         others   \n",
       "3             No  3.700000     88                Banashankari    Quick Bites   \n",
       "4             No  3.800000    166                Basavanagudi  Casual Dining   \n",
       "...          ...       ...    ...                         ...            ...   \n",
       "51712         No  3.600000     27                  Whitefield         others   \n",
       "51713         No  3.700142      0                  Whitefield         others   \n",
       "51714         No  3.700142      0                  Whitefield         others   \n",
       "51715        Yes  4.300000    236  ITPL Main Road, Whitefield         others   \n",
       "51716         No  3.400000     13  ITPL Main Road, Whitefield         others   \n",
       "\n",
       "                                     cuisines approx_cost(for two people)  \\\n",
       "0              North Indian, Mughlai, Chinese                         800   \n",
       "1                 Chinese, North Indian, Thai                         800   \n",
       "2                      Cafe, Mexican, Italian                         800   \n",
       "3                  South Indian, North Indian                         300   \n",
       "4                    North Indian, Rajasthani                         600   \n",
       "...                                       ...                         ...   \n",
       "51712                             Continental                       1,500   \n",
       "51713                             Finger Food                         600   \n",
       "51714                             Finger Food                       2,000   \n",
       "51715                             Finger Food                       2,500   \n",
       "51716  Finger Food, North Indian, Continental                       1,500   \n",
       "\n",
       "      listed_in(type)  \n",
       "0              Buffet  \n",
       "1              Buffet  \n",
       "2              Buffet  \n",
       "3              Buffet  \n",
       "4              Buffet  \n",
       "...               ...  \n",
       "51712   Pubs and bars  \n",
       "51713   Pubs and bars  \n",
       "51714   Pubs and bars  \n",
       "51715   Pubs and bars  \n",
       "51716   Pubs and bars  \n",
       "\n",
       "[51042 rows x 10 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['listed_in(city)'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "821ec457",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = { 'votes':'VOTES',\n",
    "                              'cuisines':'CUISINES', 'approx_cost(for two people)': 'costOfTwo'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6c4e2efd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North Indian                                2852\n",
       "North Indian, Chinese                       2351\n",
       "South Indian                                1820\n",
       "Biryani                                      903\n",
       "Bakery, Desserts                             898\n",
       "                                            ... \n",
       "North Indian, Chinese, Oriya, Mithai           1\n",
       "Beverages, Burger                              1\n",
       "North Indian, Mughlai, Lucknowi                1\n",
       "Continental, Thai, North Indian, Chinese       1\n",
       "North Indian, Chinese, Arabian, Momos          1\n",
       "Name: CUISINES, Length: 2704, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CUISINES'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f03d6682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "others                                 26159\n",
       "North Indian                            2852\n",
       "North Indian, Chinese                   2351\n",
       "South Indian                            1820\n",
       "Biryani                                  903\n",
       "                                       ...  \n",
       "South Indian, Chinese, North Indian      105\n",
       "North Indian, Mughlai, Chinese           104\n",
       "South Indian, Fast Food                  104\n",
       "Italian, Pizza                           102\n",
       "North Indian, Chinese, Seafood           102\n",
       "Name: CUISINES, Length: 70, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cuisinesDf=df['CUISINES'].value_counts(ascending = False)\n",
    "cuisinesLessThan100=cuisinesDf[cuisinesDf<100]\n",
    "def groupCuisines (value):\n",
    "    if(value in cuisinesLessThan100):\n",
    "        return 'others'\n",
    "    else:\n",
    "        return value\n",
    "df['CUISINES']=df['CUISINES'].apply(groupCuisines)\n",
    "df['CUISINES'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9f7b573c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    30228\n",
       "No     20814\n",
       "Name: online_order, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"online_order\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "28ec2b59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTM                      5056\n",
       "HSR                      2494\n",
       "Koramangala 5th Block    2479\n",
       "JP Nagar                 2218\n",
       "Whitefield               2105\n",
       "                         ... \n",
       "West Bangalore              6\n",
       "Yelahanka                   5\n",
       "Jakkur                      3\n",
       "Rajarajeshwari Nagar        2\n",
       "Peenya                      1\n",
       "Name: location, Length: 93, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a5af763c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "others                   8007\n",
       "BTM                      5056\n",
       "HSR                      2494\n",
       "Koramangala 5th Block    2479\n",
       "JP Nagar                 2218\n",
       "Whitefield               2105\n",
       "Indiranagar              2026\n",
       "Jayanagar                1916\n",
       "Marathahalli             1805\n",
       "Bannerghatta Road        1609\n",
       "Bellandur                1268\n",
       "Electronic City          1246\n",
       "Koramangala 1st Block    1236\n",
       "Brigade Road             1210\n",
       "Koramangala 7th Block    1174\n",
       "Koramangala 6th Block    1127\n",
       "Sarjapur Road            1047\n",
       "Koramangala 4th Block    1017\n",
       "Ulsoor                   1011\n",
       "Banashankari              902\n",
       "MG Road                   893\n",
       "Kalyan Nagar              841\n",
       "Richmond Road             803\n",
       "Malleshwaram              721\n",
       "Frazer Town               714\n",
       "Basavanagudi              684\n",
       "Residency Road            671\n",
       "Brookefield               656\n",
       "New BEL Road              644\n",
       "Banaswadi                 640\n",
       "Kammanahalli              639\n",
       "Rajajinagar               591\n",
       "Church Street             566\n",
       "Lavelle Road              518\n",
       "Shanti Nagar              508\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the values below 500\n",
    "locationDf=df['location'].value_counts(ascending=False)\n",
    "locationLessThan500=locationDf[locationDf<500]\n",
    "def groupLocation (value):\n",
    "    if (value in locationLessThan500):\n",
    "        return 'others'\n",
    "    else:\n",
    "        return value\n",
    "df['location']=df['location'].apply(groupLocation)\n",
    "df['location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "91a59c8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     44626\n",
       "Yes     6416\n",
       "Name: book_table, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"book_table\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "487e6dee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['800', '300', '600', '700', '550', '500', '450', '650', '400',\n",
       "       '900', '200', '750', '150', '850', '100', '1,200', '350', '250',\n",
       "       '950', '1,000', '1,500', '1,300', '199', '80', '1,100', '160',\n",
       "       '1,600', '230', '130', '50', '190', '1,700', '1,400', '180',\n",
       "       '1,350', '2,200', '2,000', '1,800', '1,900', '330', '2,500',\n",
       "       '2,100', '3,000', '2,800', '3,400', '40', '1,250', '3,500',\n",
       "       '4,000', '2,400', '2,600', '120', '1,450', '469', '70', '3,200',\n",
       "       '60', '560', '240', '360', '6,000', '1,050', '2,300', '4,100',\n",
       "       '5,000', '3,700', '1,650', '2,700', '4,500', '140'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['costOfTwo'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "35efc6da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 800.,  300.,  600.,  700.,  550.,  500.,  450.,  650.,  400.,\n",
       "        900.,  200.,  750.,  150.,  850.,  100., 1200.,  350.,  250.,\n",
       "        950., 1000., 1500., 1300.,  199.,   80., 1100.,  160., 1600.,\n",
       "        230.,  130.,   50.,  190., 1700., 1400.,  180., 1350., 2200.,\n",
       "       2000., 1800., 1900.,  330., 2500., 2100., 3000., 2800., 3400.,\n",
       "         40., 1250., 3500., 4000., 2400., 2600.,  120., 1450.,  469.,\n",
       "         70., 3200.,   60.,  560.,  240.,  360., 6000., 1050., 2300.,\n",
       "       4100., 5000., 3700., 1650., 2700., 4500.,  140.])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting costOfTwo to float from string \n",
    "def costFloat(value):\n",
    "    value=str(value)\n",
    "    if ','in value:\n",
    "        value=value.replace(',','')\n",
    "        return float(value)\n",
    "    else: \n",
    "        return float(value)\n",
    "df['costOfTwo']=df['costOfTwo'].apply(costFloat)\n",
    "df['costOfTwo'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c3916368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.700142    9852\n",
       "3.900000    3953\n",
       "3.800000    3816\n",
       "3.700000    3801\n",
       "3.600000    3282\n",
       "4.000000    3142\n",
       "4.100000    2922\n",
       "3.500000    2761\n",
       "3.400000    2443\n",
       "3.300000    2272\n",
       "4.200000    2151\n",
       "3.200000    1839\n",
       "4.300000    1677\n",
       "3.100000    1550\n",
       "4.400000    1136\n",
       "3.000000     990\n",
       "2.900000     800\n",
       "4.500000     653\n",
       "2.800000     580\n",
       "2.700000     303\n",
       "4.600000     299\n",
       "2.600000     249\n",
       "4.700000     167\n",
       "2.500000     100\n",
       "2.400000      66\n",
       "4.800000      66\n",
       "4.900000      55\n",
       "2.300000      51\n",
       "2.200000      26\n",
       "2.100000      24\n",
       "2.000000      11\n",
       "1.800000       5\n",
       "Name: rate, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"rate\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c15b8ff8",
   "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>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>VOTES</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>CUISINES</th>\n",
       "      <th>costOfTwo</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>775</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>787</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>others</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>918</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>others</td>\n",
       "      <td>others</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>88</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>166</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>others</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51712</th>\n",
       "      <td>Best Brews - Four Points by Sheraton Bengaluru...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>27</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>others</td>\n",
       "      <td>Continental</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>Pubs and bars</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51713</th>\n",
       "      <td>Vinod Bar And Restaurant</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.700142</td>\n",
       "      <td>0</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>others</td>\n",
       "      <td>Finger Food</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Pubs and bars</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51714</th>\n",
       "      <td>Plunge - Sheraton Grand Bengaluru Whitefield H...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.700142</td>\n",
       "      <td>0</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>others</td>\n",
       "      <td>Finger Food</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>Pubs and bars</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51715</th>\n",
       "      <td>Chime - Sheraton Grand Bengaluru Whitefield Ho...</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>236</td>\n",
       "      <td>others</td>\n",
       "      <td>others</td>\n",
       "      <td>Finger Food</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>Pubs and bars</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51716</th>\n",
       "      <td>The Nest - The Den Bengaluru</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>13</td>\n",
       "      <td>others</td>\n",
       "      <td>others</td>\n",
       "      <td>others</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>Pubs and bars</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51042 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    name online_order  \\\n",
       "0                                                  Jalsa          Yes   \n",
       "1                                         Spice Elephant          Yes   \n",
       "2                                        San Churro Cafe          Yes   \n",
       "3                                  Addhuri Udupi Bhojana           No   \n",
       "4                                          Grand Village           No   \n",
       "...                                                  ...          ...   \n",
       "51712  Best Brews - Four Points by Sheraton Bengaluru...           No   \n",
       "51713                           Vinod Bar And Restaurant           No   \n",
       "51714  Plunge - Sheraton Grand Bengaluru Whitefield H...           No   \n",
       "51715  Chime - Sheraton Grand Bengaluru Whitefield Ho...           No   \n",
       "51716                       The Nest - The Den Bengaluru           No   \n",
       "\n",
       "      book_table      rate  VOTES      location      rest_type  \\\n",
       "0            Yes  4.100000    775  Banashankari  Casual Dining   \n",
       "1             No  4.100000    787  Banashankari  Casual Dining   \n",
       "2             No  3.800000    918  Banashankari         others   \n",
       "3             No  3.700000     88  Banashankari    Quick Bites   \n",
       "4             No  3.800000    166  Basavanagudi  Casual Dining   \n",
       "...          ...       ...    ...           ...            ...   \n",
       "51712         No  3.600000     27    Whitefield         others   \n",
       "51713         No  3.700142      0    Whitefield         others   \n",
       "51714         No  3.700142      0    Whitefield         others   \n",
       "51715        Yes  4.300000    236        others         others   \n",
       "51716         No  3.400000     13        others         others   \n",
       "\n",
       "                             CUISINES  costOfTwo listed_in(type)  \\\n",
       "0      North Indian, Mughlai, Chinese      800.0          Buffet   \n",
       "1                              others      800.0          Buffet   \n",
       "2                              others      800.0          Buffet   \n",
       "3          South Indian, North Indian      300.0          Buffet   \n",
       "4                              others      600.0          Buffet   \n",
       "...                               ...        ...             ...   \n",
       "51712                     Continental     1500.0   Pubs and bars   \n",
       "51713                     Finger Food      600.0   Pubs and bars   \n",
       "51714                     Finger Food     2000.0   Pubs and bars   \n",
       "51715                     Finger Food     2500.0   Pubs and bars   \n",
       "51716                          others     1500.0   Pubs and bars   \n",
       "\n",
       "      listed_in(city)  \n",
       "0        Banashankari  \n",
       "1        Banashankari  \n",
       "2        Banashankari  \n",
       "3        Banashankari  \n",
       "4        Banashankari  \n",
       "...               ...  \n",
       "51712      Whitefield  \n",
       "51713      Whitefield  \n",
       "51714      Whitefield  \n",
       "51715      Whitefield  \n",
       "51716      Whitefield  \n",
       "\n",
       "[51042 rows x 11 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42ae3450",
   "metadata": {},
   "source": [
    "## Visualisations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9ffe55e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Q2) Visualize online order facility location-wise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "79cd98f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "        34]),\n",
       " [Text(0, 0, 'Banashankari'),\n",
       "  Text(1, 0, 'Basavanagudi'),\n",
       "  Text(2, 0, 'others'),\n",
       "  Text(3, 0, 'Jayanagar'),\n",
       "  Text(4, 0, 'JP Nagar'),\n",
       "  Text(5, 0, 'Bannerghatta Road'),\n",
       "  Text(6, 0, 'BTM'),\n",
       "  Text(7, 0, 'Electronic City'),\n",
       "  Text(8, 0, 'HSR'),\n",
       "  Text(9, 0, 'Marathahalli'),\n",
       "  Text(10, 0, 'Shanti Nagar'),\n",
       "  Text(11, 0, 'Koramangala 5th Block'),\n",
       "  Text(12, 0, 'Richmond Road'),\n",
       "  Text(13, 0, 'Koramangala 7th Block'),\n",
       "  Text(14, 0, 'Koramangala 4th Block'),\n",
       "  Text(15, 0, 'Bellandur'),\n",
       "  Text(16, 0, 'Sarjapur Road'),\n",
       "  Text(17, 0, 'Whitefield'),\n",
       "  Text(18, 0, 'Indiranagar'),\n",
       "  Text(19, 0, 'Koramangala 1st Block'),\n",
       "  Text(20, 0, 'Frazer Town'),\n",
       "  Text(21, 0, 'MG Road'),\n",
       "  Text(22, 0, 'Brigade Road'),\n",
       "  Text(23, 0, 'Lavelle Road'),\n",
       "  Text(24, 0, 'Church Street'),\n",
       "  Text(25, 0, 'Ulsoor'),\n",
       "  Text(26, 0, 'Residency Road'),\n",
       "  Text(27, 0, 'Malleshwaram'),\n",
       "  Text(28, 0, 'Kammanahalli'),\n",
       "  Text(29, 0, 'Koramangala 6th Block'),\n",
       "  Text(30, 0, 'Brookefield'),\n",
       "  Text(31, 0, 'Rajajinagar'),\n",
       "  Text(32, 0, 'Banaswadi'),\n",
       "  Text(33, 0, 'Kalyan Nagar'),\n",
       "  Text(34, 0, 'New BEL Road')])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Question-1\n",
    "plt.figure(figsize = (16,6))\n",
    "zd = sns.countplot(df.location)\n",
    "plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8e80dde4",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">VOTES</th>\n",
       "      <th colspan=\"2\" halign=\"left\">costOfTwo</th>\n",
       "      <th colspan=\"2\" halign=\"left\">rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>online_order</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BTM</th>\n",
       "      <td>107109</td>\n",
       "      <td>466559</td>\n",
       "      <td>665220</td>\n",
       "      <td>1337290</td>\n",
       "      <td>6318.705970</td>\n",
       "      <td>11891.161851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banashankari</th>\n",
       "      <td>34628</td>\n",
       "      <td>127746</td>\n",
       "      <td>146280</td>\n",
       "      <td>234350</td>\n",
       "      <td>1431.717591</td>\n",
       "      <td>1868.404823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banaswadi</th>\n",
       "      <td>5970</td>\n",
       "      <td>28875</td>\n",
       "      <td>111900</td>\n",
       "      <td>141300</td>\n",
       "      <td>1076.918584</td>\n",
       "      <td>1198.606526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bannerghatta Road</th>\n",
       "      <td>28628</td>\n",
       "      <td>186361</td>\n",
       "      <td>278980</td>\n",
       "      <td>435350</td>\n",
       "      <td>2414.840998</td>\n",
       "      <td>3305.613619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basavanagudi</th>\n",
       "      <td>32270</td>\n",
       "      <td>62649</td>\n",
       "      <td>96760</td>\n",
       "      <td>150150</td>\n",
       "      <td>885.708512</td>\n",
       "      <td>1627.904114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bellandur</th>\n",
       "      <td>38362</td>\n",
       "      <td>166946</td>\n",
       "      <td>262120</td>\n",
       "      <td>410450</td>\n",
       "      <td>1822.526386</td>\n",
       "      <td>2701.212200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brigade Road</th>\n",
       "      <td>248950</td>\n",
       "      <td>177732</td>\n",
       "      <td>372800</td>\n",
       "      <td>412640</td>\n",
       "      <td>2025.415747</td>\n",
       "      <td>2456.007235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brookefield</th>\n",
       "      <td>26600</td>\n",
       "      <td>92362</td>\n",
       "      <td>93260</td>\n",
       "      <td>195140</td>\n",
       "      <td>843.011349</td>\n",
       "      <td>1529.704681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Church Street</th>\n",
       "      <td>188677</td>\n",
       "      <td>401629</td>\n",
       "      <td>198900</td>\n",
       "      <td>273700</td>\n",
       "      <td>889.701986</td>\n",
       "      <td>1362.501277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic City</th>\n",
       "      <td>28201</td>\n",
       "      <td>82573</td>\n",
       "      <td>345590</td>\n",
       "      <td>274650</td>\n",
       "      <td>2408.744261</td>\n",
       "      <td>2028.614470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frazer Town</th>\n",
       "      <td>17187</td>\n",
       "      <td>80481</td>\n",
       "      <td>110950</td>\n",
       "      <td>193700</td>\n",
       "      <td>1042.312909</td>\n",
       "      <td>1550.108512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSR</th>\n",
       "      <td>91226</td>\n",
       "      <td>407096</td>\n",
       "      <td>268210</td>\n",
       "      <td>921170</td>\n",
       "      <td>2120.335465</td>\n",
       "      <td>7064.135749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiranagar</th>\n",
       "      <td>429377</td>\n",
       "      <td>736532</td>\n",
       "      <td>515160</td>\n",
       "      <td>805960</td>\n",
       "      <td>2645.122414</td>\n",
       "      <td>5087.210356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP Nagar</th>\n",
       "      <td>184322</td>\n",
       "      <td>402200</td>\n",
       "      <td>493880</td>\n",
       "      <td>668100</td>\n",
       "      <td>3311.949793</td>\n",
       "      <td>4856.322272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jayanagar</th>\n",
       "      <td>86726</td>\n",
       "      <td>400318</td>\n",
       "      <td>246300</td>\n",
       "      <td>667150</td>\n",
       "      <td>2052.421279</td>\n",
       "      <td>5167.818726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kalyan Nagar</th>\n",
       "      <td>50598</td>\n",
       "      <td>117394</td>\n",
       "      <td>186840</td>\n",
       "      <td>304600</td>\n",
       "      <td>1301.614895</td>\n",
       "      <td>1867.606242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kammanahalli</th>\n",
       "      <td>11794</td>\n",
       "      <td>93456</td>\n",
       "      <td>115810</td>\n",
       "      <td>175750</td>\n",
       "      <td>954.514895</td>\n",
       "      <td>1394.105958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 1st Block</th>\n",
       "      <td>52629</td>\n",
       "      <td>199052</td>\n",
       "      <td>165900</td>\n",
       "      <td>367250</td>\n",
       "      <td>1405.032912</td>\n",
       "      <td>3165.921705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 4th Block</th>\n",
       "      <td>311035</td>\n",
       "      <td>374121</td>\n",
       "      <td>335350</td>\n",
       "      <td>372850</td>\n",
       "      <td>1752.315037</td>\n",
       "      <td>2194.509930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 5th Block</th>\n",
       "      <td>1088003</td>\n",
       "      <td>1126080</td>\n",
       "      <td>656050</td>\n",
       "      <td>989000</td>\n",
       "      <td>3432.513619</td>\n",
       "      <td>6443.712484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 6th Block</th>\n",
       "      <td>184959</td>\n",
       "      <td>278544</td>\n",
       "      <td>295050</td>\n",
       "      <td>403850</td>\n",
       "      <td>1619.805249</td>\n",
       "      <td>2636.305391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 7th Block</th>\n",
       "      <td>149038</td>\n",
       "      <td>339187</td>\n",
       "      <td>195050</td>\n",
       "      <td>500550</td>\n",
       "      <td>1460.010781</td>\n",
       "      <td>3043.606384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lavelle Road</th>\n",
       "      <td>223961</td>\n",
       "      <td>271816</td>\n",
       "      <td>438400</td>\n",
       "      <td>238650</td>\n",
       "      <td>1266.004681</td>\n",
       "      <td>860.101277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MG Road</th>\n",
       "      <td>195297</td>\n",
       "      <td>232969</td>\n",
       "      <td>633200</td>\n",
       "      <td>399300</td>\n",
       "      <td>1962.911916</td>\n",
       "      <td>1459.702412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malleshwaram</th>\n",
       "      <td>78433</td>\n",
       "      <td>160534</td>\n",
       "      <td>184050</td>\n",
       "      <td>214540</td>\n",
       "      <td>1139.908795</td>\n",
       "      <td>1566.602553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marathahalli</th>\n",
       "      <td>170209</td>\n",
       "      <td>264026</td>\n",
       "      <td>391950</td>\n",
       "      <td>533100</td>\n",
       "      <td>2519.636175</td>\n",
       "      <td>3941.820286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New BEL Road</th>\n",
       "      <td>56689</td>\n",
       "      <td>118998</td>\n",
       "      <td>118250</td>\n",
       "      <td>193550</td>\n",
       "      <td>933.713902</td>\n",
       "      <td>1444.905674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajajinagar</th>\n",
       "      <td>30113</td>\n",
       "      <td>55161</td>\n",
       "      <td>124850</td>\n",
       "      <td>132750</td>\n",
       "      <td>1030.514754</td>\n",
       "      <td>1117.203688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residency Road</th>\n",
       "      <td>177627</td>\n",
       "      <td>112886</td>\n",
       "      <td>518300</td>\n",
       "      <td>128600</td>\n",
       "      <td>1651.007377</td>\n",
       "      <td>929.602553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richmond Road</th>\n",
       "      <td>75119</td>\n",
       "      <td>43783</td>\n",
       "      <td>427200</td>\n",
       "      <td>180350</td>\n",
       "      <td>2099.825393</td>\n",
       "      <td>941.202979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sarjapur Road</th>\n",
       "      <td>60526</td>\n",
       "      <td>338073</td>\n",
       "      <td>195400</td>\n",
       "      <td>402350</td>\n",
       "      <td>1191.617591</td>\n",
       "      <td>2675.110072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanti Nagar</th>\n",
       "      <td>24552</td>\n",
       "      <td>30746</td>\n",
       "      <td>127850</td>\n",
       "      <td>101150</td>\n",
       "      <td>1033.516456</td>\n",
       "      <td>780.603688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ulsoor</th>\n",
       "      <td>45724</td>\n",
       "      <td>134508</td>\n",
       "      <td>431000</td>\n",
       "      <td>345600</td>\n",
       "      <td>1470.713193</td>\n",
       "      <td>2320.510640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whitefield</th>\n",
       "      <td>180346</td>\n",
       "      <td>285388</td>\n",
       "      <td>632960</td>\n",
       "      <td>630900</td>\n",
       "      <td>3575.153056</td>\n",
       "      <td>4095.423123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>others</th>\n",
       "      <td>503319</td>\n",
       "      <td>906857</td>\n",
       "      <td>2101909</td>\n",
       "      <td>2150806</td>\n",
       "      <td>13350.682291</td>\n",
       "      <td>15913.767951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         VOTES          costOfTwo                   rate  \\\n",
       "online_order                No      Yes        No      Yes            No   \n",
       "location                                                                   \n",
       "BTM                     107109   466559    665220  1337290   6318.705970   \n",
       "Banashankari             34628   127746    146280   234350   1431.717591   \n",
       "Banaswadi                 5970    28875    111900   141300   1076.918584   \n",
       "Bannerghatta Road        28628   186361    278980   435350   2414.840998   \n",
       "Basavanagudi             32270    62649     96760   150150    885.708512   \n",
       "Bellandur                38362   166946    262120   410450   1822.526386   \n",
       "Brigade Road            248950   177732    372800   412640   2025.415747   \n",
       "Brookefield              26600    92362     93260   195140    843.011349   \n",
       "Church Street           188677   401629    198900   273700    889.701986   \n",
       "Electronic City          28201    82573    345590   274650   2408.744261   \n",
       "Frazer Town              17187    80481    110950   193700   1042.312909   \n",
       "HSR                      91226   407096    268210   921170   2120.335465   \n",
       "Indiranagar             429377   736532    515160   805960   2645.122414   \n",
       "JP Nagar                184322   402200    493880   668100   3311.949793   \n",
       "Jayanagar                86726   400318    246300   667150   2052.421279   \n",
       "Kalyan Nagar             50598   117394    186840   304600   1301.614895   \n",
       "Kammanahalli             11794    93456    115810   175750    954.514895   \n",
       "Koramangala 1st Block    52629   199052    165900   367250   1405.032912   \n",
       "Koramangala 4th Block   311035   374121    335350   372850   1752.315037   \n",
       "Koramangala 5th Block  1088003  1126080    656050   989000   3432.513619   \n",
       "Koramangala 6th Block   184959   278544    295050   403850   1619.805249   \n",
       "Koramangala 7th Block   149038   339187    195050   500550   1460.010781   \n",
       "Lavelle Road            223961   271816    438400   238650   1266.004681   \n",
       "MG Road                 195297   232969    633200   399300   1962.911916   \n",
       "Malleshwaram             78433   160534    184050   214540   1139.908795   \n",
       "Marathahalli            170209   264026    391950   533100   2519.636175   \n",
       "New BEL Road             56689   118998    118250   193550    933.713902   \n",
       "Rajajinagar              30113    55161    124850   132750   1030.514754   \n",
       "Residency Road          177627   112886    518300   128600   1651.007377   \n",
       "Richmond Road            75119    43783    427200   180350   2099.825393   \n",
       "Sarjapur Road            60526   338073    195400   402350   1191.617591   \n",
       "Shanti Nagar             24552    30746    127850   101150   1033.516456   \n",
       "Ulsoor                   45724   134508    431000   345600   1470.713193   \n",
       "Whitefield              180346   285388    632960   630900   3575.153056   \n",
       "others                  503319   906857   2101909  2150806  13350.682291   \n",
       "\n",
       "                                     \n",
       "online_order                    Yes  \n",
       "location                             \n",
       "BTM                    11891.161851  \n",
       "Banashankari            1868.404823  \n",
       "Banaswadi               1198.606526  \n",
       "Bannerghatta Road       3305.613619  \n",
       "Basavanagudi            1627.904114  \n",
       "Bellandur               2701.212200  \n",
       "Brigade Road            2456.007235  \n",
       "Brookefield             1529.704681  \n",
       "Church Street           1362.501277  \n",
       "Electronic City         2028.614470  \n",
       "Frazer Town             1550.108512  \n",
       "HSR                     7064.135749  \n",
       "Indiranagar             5087.210356  \n",
       "JP Nagar                4856.322272  \n",
       "Jayanagar               5167.818726  \n",
       "Kalyan Nagar            1867.606242  \n",
       "Kammanahalli            1394.105958  \n",
       "Koramangala 1st Block   3165.921705  \n",
       "Koramangala 4th Block   2194.509930  \n",
       "Koramangala 5th Block   6443.712484  \n",
       "Koramangala 6th Block   2636.305391  \n",
       "Koramangala 7th Block   3043.606384  \n",
       "Lavelle Road             860.101277  \n",
       "MG Road                 1459.702412  \n",
       "Malleshwaram            1566.602553  \n",
       "Marathahalli            3941.820286  \n",
       "New BEL Road            1444.905674  \n",
       "Rajajinagar             1117.203688  \n",
       "Residency Road           929.602553  \n",
       "Richmond Road            941.202979  \n",
       "Sarjapur Road           2675.110072  \n",
       "Shanti Nagar             780.603688  \n",
       "Ulsoor                  2320.510640  \n",
       "Whitefield              4095.423123  \n",
       "others                 15913.767951  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, values=None, index=['location'], columns=['online_order'], fill_value=0, aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbcf10a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(kind='bar',figsize=(15,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "569d9e9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='book_table', ylabel='rate'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Question-3\n",
    "plt.figure(figsize=(6,6))\n",
    "sns.boxplot(x='book_table', y='rate', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "43b6c238",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='online_order', ylabel='rate'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Question-4\n",
    "plt.figure(figsize = (6,6))\n",
    "sns.boxplot(x='online_order', y='rate', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c6314b85",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">VOTES</th>\n",
       "      <th colspan=\"2\" halign=\"left\">costOfTwo</th>\n",
       "      <th colspan=\"2\" halign=\"left\">rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>book_table</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BTM</th>\n",
       "      <td>387231</td>\n",
       "      <td>186437</td>\n",
       "      <td>1854310</td>\n",
       "      <td>148200</td>\n",
       "      <td>17521.165977</td>\n",
       "      <td>688.701844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banashankari</th>\n",
       "      <td>115032</td>\n",
       "      <td>47342</td>\n",
       "      <td>330330</td>\n",
       "      <td>50300</td>\n",
       "      <td>3043.420995</td>\n",
       "      <td>256.701419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banaswadi</th>\n",
       "      <td>31645</td>\n",
       "      <td>3200</td>\n",
       "      <td>246400</td>\n",
       "      <td>6800</td>\n",
       "      <td>2245.525109</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bannerghatta Road</th>\n",
       "      <td>104219</td>\n",
       "      <td>110770</td>\n",
       "      <td>624130</td>\n",
       "      <td>90200</td>\n",
       "      <td>5331.654475</td>\n",
       "      <td>388.800142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basavanagudi</th>\n",
       "      <td>88824</td>\n",
       "      <td>6095</td>\n",
       "      <td>225710</td>\n",
       "      <td>21200</td>\n",
       "      <td>2450.812626</td>\n",
       "      <td>62.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bellandur</th>\n",
       "      <td>108317</td>\n",
       "      <td>96991</td>\n",
       "      <td>540370</td>\n",
       "      <td>132200</td>\n",
       "      <td>4125.738586</td>\n",
       "      <td>398.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brigade Road</th>\n",
       "      <td>198590</td>\n",
       "      <td>228092</td>\n",
       "      <td>557840</td>\n",
       "      <td>227600</td>\n",
       "      <td>3753.422272</td>\n",
       "      <td>728.000709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brookefield</th>\n",
       "      <td>81247</td>\n",
       "      <td>37715</td>\n",
       "      <td>232600</td>\n",
       "      <td>55800</td>\n",
       "      <td>2085.315747</td>\n",
       "      <td>287.400284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Church Street</th>\n",
       "      <td>342820</td>\n",
       "      <td>247486</td>\n",
       "      <td>266300</td>\n",
       "      <td>206300</td>\n",
       "      <td>1496.803263</td>\n",
       "      <td>755.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic City</th>\n",
       "      <td>69868</td>\n",
       "      <td>40906</td>\n",
       "      <td>502090</td>\n",
       "      <td>118150</td>\n",
       "      <td>4059.358447</td>\n",
       "      <td>378.000284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frazer Town</th>\n",
       "      <td>81818</td>\n",
       "      <td>15850</td>\n",
       "      <td>298000</td>\n",
       "      <td>6650</td>\n",
       "      <td>2558.121421</td>\n",
       "      <td>34.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSR</th>\n",
       "      <td>315456</td>\n",
       "      <td>182866</td>\n",
       "      <td>993630</td>\n",
       "      <td>195750</td>\n",
       "      <td>8316.669937</td>\n",
       "      <td>867.801277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiranagar</th>\n",
       "      <td>590485</td>\n",
       "      <td>575424</td>\n",
       "      <td>782670</td>\n",
       "      <td>538450</td>\n",
       "      <td>5886.729933</td>\n",
       "      <td>1845.602837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP Nagar</th>\n",
       "      <td>223769</td>\n",
       "      <td>362753</td>\n",
       "      <td>841080</td>\n",
       "      <td>320900</td>\n",
       "      <td>6887.468235</td>\n",
       "      <td>1280.803830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jayanagar</th>\n",
       "      <td>280290</td>\n",
       "      <td>206754</td>\n",
       "      <td>638500</td>\n",
       "      <td>274950</td>\n",
       "      <td>6108.438586</td>\n",
       "      <td>1111.801419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kalyan Nagar</th>\n",
       "      <td>75591</td>\n",
       "      <td>92401</td>\n",
       "      <td>327490</td>\n",
       "      <td>163950</td>\n",
       "      <td>2556.720286</td>\n",
       "      <td>612.500851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kammanahalli</th>\n",
       "      <td>65840</td>\n",
       "      <td>39410</td>\n",
       "      <td>248910</td>\n",
       "      <td>42650</td>\n",
       "      <td>2145.920854</td>\n",
       "      <td>202.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 1st Block</th>\n",
       "      <td>149198</td>\n",
       "      <td>102483</td>\n",
       "      <td>482950</td>\n",
       "      <td>50200</td>\n",
       "      <td>4354.554616</td>\n",
       "      <td>216.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 4th Block</th>\n",
       "      <td>177515</td>\n",
       "      <td>507641</td>\n",
       "      <td>352600</td>\n",
       "      <td>355600</td>\n",
       "      <td>2598.623265</td>\n",
       "      <td>1348.201702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 5th Block</th>\n",
       "      <td>1019647</td>\n",
       "      <td>1194436</td>\n",
       "      <td>847250</td>\n",
       "      <td>797800</td>\n",
       "      <td>6923.822981</td>\n",
       "      <td>2952.403121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 6th Block</th>\n",
       "      <td>367436</td>\n",
       "      <td>96067</td>\n",
       "      <td>564400</td>\n",
       "      <td>134500</td>\n",
       "      <td>3795.110640</td>\n",
       "      <td>461.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 7th Block</th>\n",
       "      <td>285870</td>\n",
       "      <td>202355</td>\n",
       "      <td>498800</td>\n",
       "      <td>196800</td>\n",
       "      <td>3810.117165</td>\n",
       "      <td>693.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lavelle Road</th>\n",
       "      <td>143823</td>\n",
       "      <td>351954</td>\n",
       "      <td>278150</td>\n",
       "      <td>398900</td>\n",
       "      <td>1145.005958</td>\n",
       "      <td>981.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MG Road</th>\n",
       "      <td>93891</td>\n",
       "      <td>334375</td>\n",
       "      <td>427000</td>\n",
       "      <td>605500</td>\n",
       "      <td>2005.014328</td>\n",
       "      <td>1417.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malleshwaram</th>\n",
       "      <td>124495</td>\n",
       "      <td>114472</td>\n",
       "      <td>267140</td>\n",
       "      <td>131450</td>\n",
       "      <td>2331.811349</td>\n",
       "      <td>374.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marathahalli</th>\n",
       "      <td>164522</td>\n",
       "      <td>269713</td>\n",
       "      <td>718850</td>\n",
       "      <td>206200</td>\n",
       "      <td>5789.856461</td>\n",
       "      <td>671.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New BEL Road</th>\n",
       "      <td>101659</td>\n",
       "      <td>74028</td>\n",
       "      <td>253900</td>\n",
       "      <td>57900</td>\n",
       "      <td>2147.719577</td>\n",
       "      <td>230.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajajinagar</th>\n",
       "      <td>48296</td>\n",
       "      <td>36978</td>\n",
       "      <td>210600</td>\n",
       "      <td>47000</td>\n",
       "      <td>1977.618442</td>\n",
       "      <td>170.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residency Road</th>\n",
       "      <td>163784</td>\n",
       "      <td>126729</td>\n",
       "      <td>346800</td>\n",
       "      <td>300100</td>\n",
       "      <td>1955.209930</td>\n",
       "      <td>625.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richmond Road</th>\n",
       "      <td>55900</td>\n",
       "      <td>63002</td>\n",
       "      <td>411650</td>\n",
       "      <td>195900</td>\n",
       "      <td>2550.428372</td>\n",
       "      <td>490.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sarjapur Road</th>\n",
       "      <td>89598</td>\n",
       "      <td>309001</td>\n",
       "      <td>389750</td>\n",
       "      <td>208000</td>\n",
       "      <td>3212.227521</td>\n",
       "      <td>654.500142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanti Nagar</th>\n",
       "      <td>31350</td>\n",
       "      <td>23948</td>\n",
       "      <td>168200</td>\n",
       "      <td>60800</td>\n",
       "      <td>1579.920144</td>\n",
       "      <td>234.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ulsoor</th>\n",
       "      <td>100772</td>\n",
       "      <td>79460</td>\n",
       "      <td>460550</td>\n",
       "      <td>316050</td>\n",
       "      <td>3072.323833</td>\n",
       "      <td>718.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whitefield</th>\n",
       "      <td>220594</td>\n",
       "      <td>245140</td>\n",
       "      <td>926660</td>\n",
       "      <td>337200</td>\n",
       "      <td>6634.476038</td>\n",
       "      <td>1036.100142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>others</th>\n",
       "      <td>666734</td>\n",
       "      <td>743442</td>\n",
       "      <td>3096365</td>\n",
       "      <td>1156350</td>\n",
       "      <td>25950.350243</td>\n",
       "      <td>3314.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         VOTES          costOfTwo                   rate  \\\n",
       "book_table                  No      Yes        No      Yes            No   \n",
       "location                                                                   \n",
       "BTM                     387231   186437   1854310   148200  17521.165977   \n",
       "Banashankari            115032    47342    330330    50300   3043.420995   \n",
       "Banaswadi                31645     3200    246400     6800   2245.525109   \n",
       "Bannerghatta Road       104219   110770    624130    90200   5331.654475   \n",
       "Basavanagudi             88824     6095    225710    21200   2450.812626   \n",
       "Bellandur               108317    96991    540370   132200   4125.738586   \n",
       "Brigade Road            198590   228092    557840   227600   3753.422272   \n",
       "Brookefield              81247    37715    232600    55800   2085.315747   \n",
       "Church Street           342820   247486    266300   206300   1496.803263   \n",
       "Electronic City          69868    40906    502090   118150   4059.358447   \n",
       "Frazer Town              81818    15850    298000     6650   2558.121421   \n",
       "HSR                     315456   182866    993630   195750   8316.669937   \n",
       "Indiranagar             590485   575424    782670   538450   5886.729933   \n",
       "JP Nagar                223769   362753    841080   320900   6887.468235   \n",
       "Jayanagar               280290   206754    638500   274950   6108.438586   \n",
       "Kalyan Nagar             75591    92401    327490   163950   2556.720286   \n",
       "Kammanahalli             65840    39410    248910    42650   2145.920854   \n",
       "Koramangala 1st Block   149198   102483    482950    50200   4354.554616   \n",
       "Koramangala 4th Block   177515   507641    352600   355600   2598.623265   \n",
       "Koramangala 5th Block  1019647  1194436    847250   797800   6923.822981   \n",
       "Koramangala 6th Block   367436    96067    564400   134500   3795.110640   \n",
       "Koramangala 7th Block   285870   202355    498800   196800   3810.117165   \n",
       "Lavelle Road            143823   351954    278150   398900   1145.005958   \n",
       "MG Road                  93891   334375    427000   605500   2005.014328   \n",
       "Malleshwaram            124495   114472    267140   131450   2331.811349   \n",
       "Marathahalli            164522   269713    718850   206200   5789.856461   \n",
       "New BEL Road            101659    74028    253900    57900   2147.719577   \n",
       "Rajajinagar              48296    36978    210600    47000   1977.618442   \n",
       "Residency Road          163784   126729    346800   300100   1955.209930   \n",
       "Richmond Road            55900    63002    411650   195900   2550.428372   \n",
       "Sarjapur Road            89598   309001    389750   208000   3212.227521   \n",
       "Shanti Nagar             31350    23948    168200    60800   1579.920144   \n",
       "Ulsoor                  100772    79460    460550   316050   3072.323833   \n",
       "Whitefield              220594   245140    926660   337200   6634.476038   \n",
       "others                  666734   743442   3096365  1156350  25950.350243   \n",
       "\n",
       "                                    \n",
       "book_table                     Yes  \n",
       "location                            \n",
       "BTM                     688.701844  \n",
       "Banashankari            256.701419  \n",
       "Banaswadi                30.000000  \n",
       "Bannerghatta Road       388.800142  \n",
       "Basavanagudi             62.800000  \n",
       "Bellandur               398.000000  \n",
       "Brigade Road            728.000709  \n",
       "Brookefield             287.400284  \n",
       "Church Street           755.400000  \n",
       "Electronic City         378.000284  \n",
       "Frazer Town              34.300000  \n",
       "HSR                     867.801277  \n",
       "Indiranagar            1845.602837  \n",
       "JP Nagar               1280.803830  \n",
       "Jayanagar              1111.801419  \n",
       "Kalyan Nagar            612.500851  \n",
       "Kammanahalli            202.700000  \n",
       "Koramangala 1st Block   216.400000  \n",
       "Koramangala 4th Block  1348.201702  \n",
       "Koramangala 5th Block  2952.403121  \n",
       "Koramangala 6th Block   461.000000  \n",
       "Koramangala 7th Block   693.500000  \n",
       "Lavelle Road            981.100000  \n",
       "MG Road                1417.600000  \n",
       "Malleshwaram            374.700000  \n",
       "Marathahalli            671.600000  \n",
       "New BEL Road            230.900000  \n",
       "Rajajinagar             170.100000  \n",
       "Residency Road          625.400000  \n",
       "Richmond Road           490.600000  \n",
       "Sarjapur Road           654.500142  \n",
       "Shanti Nagar            234.200000  \n",
       "Ulsoor                  718.900000  \n",
       "Whitefield             1036.100142  \n",
       "others                 3314.100000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Question-5\n",
    "z_df2=pd.pivot_table(df, values=None, index=['location'], columns=['book_table'], fill_value=0, aggfunc=np.sum)\n",
    "z_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "534cb6d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAJVCAYAAACvcAbHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAACWn0lEQVR4nOzdd5hV1dXH8e+iqGBBVEiMDYxRERiRomhsREVjQTRqRCL2EjVqbFGT2DsGfS0JMQmGILHGFqzBBtiQMqAIRoO9AipqhNDW+8c+d7gzTGPOPnfOML/P8/DM3DLrbGbuveesXdY2d0dERERERETyo0VjN0BEREREREQqU6ImIiIiIiKSM0rUREREREREckaJmoiIiIiISM4oURMREREREckZJWoiIiIiIiI506iJmpmNMLPPzOy1ej7/MDN73cxmmNnfs26fiIiIiIhIY7DG3EfNzHYFvgH+5u7d6njuD4B7gB+5+xdm1tHdPytFO0VEREREREqpUUfU3H0c8HnxfWb2fTN73Mwmm9l4M9s6eegE4FZ3/yL5WSVpIiIiIiKySsrjGrXbgF+4ey/gHOD3yf1bAlua2fNm9pKZ7dNoLRQREREREclQq8ZuQDEzWwvYCbjXzAp3r558bQX8ANgd2BgYb2bd3P3LEjdTREREREQkU7lK1AgjfF+6e49qHvsAeMndFwNvm9kbhMTtlRK2T0REREREJHO5mvro7l8RkrBDASzYNnn4QaBfcv8GhKmQsxujnSIiIiIiIllq7PL8dwIvAluZ2QdmdhwwGDjOzKYBM4ADk6c/Acwzs9eBZ4Bz3X1eY7RbREREREQkS41anl9ERERERERWlKupjyIiIiIiIqJETUREREREJHcarerjBhts4J06dWqsw4uIiIiIiDSqyZMnz3X3DtU91miJWqdOnZg0aVJjHV5ERERERKRRmdm7NT2mqY8iIiIiIiI5o0RNREREREQkZ5SoiYiIiIiI5EyjrVGrzuLFi/nggw9YuHBhYzdFIlhjjTXYeOONad26dWM3RURERESkSclVovbBBx+w9tpr06lTJ8yssZsjKbg78+bN44MPPqBz586N3RwRERERkSYlV1MfFy5cyPrrr68kbRVgZqy//voaHRURERERaYBcJWqAkrRViP6WIiIiIiINk7tETUREREREpLlbJRM1M+Pss8+uuH399ddzySWXZHrMtdZaK0qc3Xffvd4bgV911VX1el5NbTv66KO577776t02EREREREpjVUyUVt99dW5//77mTt3bmM3JVP1TdRERERERKRpWSUTtVatWnHiiSdyww03rPDYu+++yx577EFZWRl77LEH7733HhBGl04//XR22mknNt9880ojTUOHDqVPnz6UlZVx8cUX13jcs88+m549e7LHHnswZ84cAMrLy+nbty9lZWUcdNBBfPHFF7XeX7Bs2TKOOuoofvOb31R7rPPPP58FCxbQo0cPBg8eDMDAgQPp1asXXbt25bbbbquzbcUmT57MbrvtRq9evdh77735+OOPa/x/ioiIiIhItlbJRA3g1FNPZfTo0cyfP7/S/aeddhpDhgxh+vTpDB48mNNPP73isY8//pgJEyYwZswYzj//fACefPJJ3nzzTSZOnEh5eTmTJ09m3LhxKxzvv//9Lz179mTKlCnstttuXHrppQAMGTKEa6+9lunTp9O9e/c67wdYsmQJgwcPZsstt+SKK66o9v93zTXX0KZNG8rLyxk9ejQAI0aMYPLkyUyaNImbbrqJefPm1dq2gsWLF/OLX/yC++67j8mTJ3Psscfy61//eqV+3yIiIiIiEs8qm6its846DBkyhJtuuqnS/S+++CJHHHEEAEceeSQTJkyoeGzgwIG0aNGCbbbZhk8//RQIidqTTz7JdtttR8+ePZk1axZvvvnmCsdr0aIFP/3pTwH42c9+xoQJE5g/fz5ffvklu+22GwBHHXUU48aNq/H+gpNOOolu3bqtdLJ00003se2229K3b1/ef//9inZW17Zib7zxBq+99hp77bUXPXr04IorruCDDz5YqWOLiIiIiEg8udrwOrYzzzyTnj17cswxx9T4nOIS8quvvnrF9+5e8fWCCy7gpJNOWqljpylNv9NOO/HMM89w9tlns8Yaa9TrZ5599lnGjh3Liy++SNu2bdl9991r3MOsatvcna5du/Liiy82uM0iIiIiIhLPKjuiBrDeeutx2GGH8Ze//KXivp122om77roLgNGjR7PzzjvXGmPvvfdmxIgRfPPNNwB8+OGHfPbZZwDssccefPjhh0BYU1ZY1/b3v/+dnXfemXbt2tG+fXvGjx8PwKhRo9htt91qvL/guOOOY9999+XQQw9lyZIlNbatdevWLF68GID58+fTvn172rZty6xZs3jppZcqnldd24pttdVWzJkzpyJRW7x4MTNmzKj19yIiIiIiItlZpUfUIBTRuOWWWypu33TTTRx77LEMHTqUDh06cPvtt9f68/3792fmzJnsuOOOQCh1f8cdd7DBBhvw1ltvsd566wGw5pprMmPGDHr16kW7du24++67ARg5ciQnn3wy3377LZtvvnnF8Wq6v+Css85i/vz5HHnkkYwePZoWLVbMqU888UTKysro2bMnI0aMYPjw4ZSVlbHVVlvRt2/fiufV1LaC1VZbjfvuu4/TTz+d+fPns2TJEs4880y6du1a31+ziIiIiIhEZIUpfqXWu3dvr7pf2MyZM+nSpUujtGdlvfbaa4wYMYJhw4Y1dlNyrSn9TUVERERESsnMJrt77+oeW6WnPmapW7duStJERERERCQTq/zUx1XBDjvswP/+979K940aNYru3bs3UotERERERCRLStSagJdffrmxmyAiIiIiIjXoPjIMoLx61KvRYmrqo4iIiIiISM4oURMREREREckZJWoiIiIiIiI5o0RNREREREQkZ3JdTKTT+Y9EjffONfvV+ZwFCxawzz778PTTT7PFFlvw+OOPs9VWW1U8fuaZZ/K9732P8847jwcffJCLLrqIRYsW0bp1ay6//HIGDhzIqaeeyvPPP8+iRYt4++23K37+N7/5DWPGjOG5556jXbt2ALRt25YXXniBTz/9lOOOO47333+fxYsX06lTJx599NFa29qpUyd69erFP/7xDwDuu+8+xowZw1//+tcaf2bMmDG88sorXHrppXX+LkREREREpHHkOlFrDCNGjODggw+mZcuWHH744dx1111cfPHFACxbtoz77ruP559/nmnTpnHOOefwr3/9i86dO/P222+z1157sfnmm3PrrbcC8M4777D//vtTXl5eEX/MmDEMHTqUQw45pNJxL7roIvbaay/OOOMMAKZPn16v9k6aNIkZM2bQtWvXej1/v/3247e//S2/+tWvaNu2bb1+RkRERERESktTH6sYPXo0Bx54IACDBg3irrvuqnhs3LhxdOrUic0224zrr7+eCy+8kM6dOwPQuXNnLrjgAoYOHdqg43788cdsvPHGFbfLysrq9XPnnHMOV1111Qr3f/755wwcOJCysjL69u1bkfiZGbvvvjtjxoxpUDtFRERERCR7StSKLFq0iNmzZ9OpUycgJEstWrRg2rRpANx1110MGjQIgBkzZtCrV69KP9+7d29mzJhR53HOPfdcevToQY8ePRg8eDAAp556Kscddxz9+vXjyiuv5KOPPqpXmw877DCmTJnCW2+9Ven+iy++mO22247p06dz1VVXMWTIkErtHD9+fL3ii4iIiIhI6dWZqJnZGmY20cymmdkMM1thcZMFN5nZW2Y23cx6ZtPcbM2dO5d111230n2FUbUlS5bw0EMPceihhwLg7phZpedWd191hg4dSnl5OeXl5YwePRqAvffem9mzZ3PCCScwa9YstttuO+bMmVNnrJYtW3Luuedy9dVXV7p/woQJHHnkkQD86Ec/Yt68ecyfPx+Ajh071jsRFBERERGR0qvPiNr/gB+5+7ZAD2AfM+tb5Tk/Bn6Q/DsR+EPMRpZKmzZtWLhwYaX7Bg0axD333MPYsWMpKyujY8eOAHTt2pVJkyZVeu6UKVPYZpttGnz89dZbjyOOOIJRo0bRp08fxo0bV6+fO/LIIxk3bhzvvfdexX3uvsLzCknkwoULadOmTYPbKSIiIiIi2aozUfPgm+Rm6+Rf1SzgQOBvyXNfAtY1sw3jNjV77du3Z+nSpZWSte9///usv/76nH/++RXTHiGsDbv66qt55513gFA45KqrruLss89u0LGffvppvv32WwC+/vpr/vOf/7DpppsCsPXWW9f6s61bt+aXv/wlN954Y8V9u+66a8Vo3bPPPssGG2zAOuusA8C///1vunXr1qB2ioiIiIhI9upV9dHMWgKTgS2AW9395SpP2Qh4v+j2B8l9H6dpXH3K6cfWv39/JkyYwJ577llx36BBg7jgggs46KCDKu7r0aMH1157LQcccACLFy+mdevWXHfddfTo0aPOY5x77rlcccUVFbcnTpzI5MmTOe2002jVqhXLli3j+OOPp0+fPsydO7fa0bGqjjvuuEoxL7nkEo455hjKyspo27YtI0eOrHjsmWeeWWGqpIiIiIiI5IfVJwmoeLLZusADwC/c/bWi+x8Brnb3Ccntp4Dz3H1ylZ8/kTA1kk033bTXu+++Wyn+zJkz6dKlS8P+J5FMnTqVYcOGMWrUqEZtR8GYMWOYPXs2p59+epR4n376KUcccQRPPfVUlHh1ycPfVEREREQkS91Hdgfg1aNeXamfM7PJ7t67usdWah81d//SzJ4F9gFeK3roA2CTotsbAytUq3D324DbAHr37l3/DLGEtttuO/r168fSpUtp2bJlYzeH/fffP2q89957j9/97ndRY4qIiIiISFz1qfrYIRlJw8zaAHsCs6o87WFgSFL9sS8w391TTXtsTMcee2wukrQs9OnTp17TM0VEREREpPHUZ0RtQ2Bksk6tBXCPu48xs5MB3H048CiwL/AW8C1wTEbtFRERERERWeXVmai5+3Rgu2ruH170vQOnxm2aiIiIiIhI81SffdRERERERESkhJSoiYiIiIiI5MxKVX0suUvaRY43v86nLFiwgH322Yenn36aLbbYgscff5ytttqq4vEzzzyT733ve5x33nk8+OCDXHTRRSxatIjWrVtz+eWXM3DgQE499VSef/55Fi1axNtvv13x87/5zW8YM2YMzz33HO3ahf9b27ZteeGFF/j000857rjjeP/991m8eDGdOnXi0UcfrbGdRxxxBLvssgs///nPAXj55Zc54YQTmDJlCq1a1e/Puueee3LvvffSvn37ej1fRERERERKI9+JWiMYMWIEBx98MC1btuTwww/nrrvu4uKLLwZg2bJl3HfffTz//PNMmzaNc845h3/961907tyZt99+m7322ovNN9+cW2+9FYB33nmH/fffn/Ly8or4Y8aMYejQoRxyyCGVjnvRRRex1157ccYZZwAwffr0Wtt5ww03sOOOO3LIIYew/vrrc9ppp/H73/++3kkawJFHHsnvf/97fv3rX9f7Z0REREREJHua+ljF6NGjOfDAAwEYNGgQd911V8Vj48aNo1OnTmy22WZcf/31XHjhhXTu3BmAzp07c8EFFzB06NAGHffjjz9m4403rrhdVlZW6/O/853vcM4553DeeecxfPhwysrK2HHHHTn33HPp06cPZWVl/PGPf6yIveuuu9KjRw+6devG+PHjARgwYAB33nlng9orIiIiIiLZUaJWZNGiRcyePZtOnToBIVlq0aIF06ZNA+Cuu+5i0KBBAMyYMYNevXpV+vnevXszY8aMOo9z7rnn0qNHD3r06MHgwYMBOPXUUznuuOPo168fV155JR99tMJ+4Ss4+eSTef311xk6dCjXXXcdf/nLX2jXrh2vvPIKr7zyCn/60594++23+fvf/87ee+9NeXk506ZNq9hHrX379vzvf/9j3rx59f0ViYiIiIhICWjqY5G5c+ey7rrrVrqvMKrWtWtXHnroIS677DIA3B0zq/Tc6u6rTnVTH/fee29mz57N448/zmOPPcZ2223Ha6+9RocOHWqM06JFC0466SQmTZrE+uuvz5NPPsn06dO57777AJg/fz5vvvkmffr04dhjj2Xx4sUMHDiw0obXHTt25KOPPmL99devs90iIiIiIlIaGlEr0qZNGxYuXFjpvkGDBnHPPfcwduxYysrK6NixIwBdu3Zl0qRJlZ47ZcoUttlmmwYff7311uOII45g1KhR9OnTh3HjxtX5My1atKBFi/BndHduvvlmysvLKS8v5+2336Z///7suuuujBs3jo022ogjjzySv/3tbxU/v3DhQtq0adPgNouIiIiISHxK1Iq0b9+epUuXVkrWvv/977P++utz/vnnV0x7BDjnnHO4+uqreeedd4BQOOSqq67i7LPPbtCxn376ab799lsAvv76a/7zn/+w6aabArD11lvXK8bee+/NH/7wBxYvXgzAv//9b/773//y7rvv0rFjR0444QSOO+44pkyZAoTE7pNPPqmY6ikiIiIiIvmQ76mP9SinH1v//v2ZMGECe+65Z8V9gwYN4oILLuCggw6quK9Hjx5ce+21HHDAASxevJjWrVtz3XXXVZpWWJNzzz2XK664ouL2xIkTmTx5MqeddhqtWrVi2bJlHH/88fTp04e5c+fi7vVq+/HHH88777xDz549cXc6dOjAgw8+yLPPPsvQoUNp3bo1a621VsWI2uTJk+nbt+9KVYoUEREREZHsWX2TgNh69+7tVacOzpw5ky5dujRKewqmTp3KsGHDGDVqVKO2o2DMmDHMnj2b008/PXrsM844gwEDBrDHHntEj12Qh7+piIiIiEiWuo/sDsCrR726Uj9nZpPdvXd1j2kopYrtttuOfv36sXTpUlq2bNnYzWH//ffPLHa3bt0yTdJERERERKRhlKhV49hjj23sJpTECSec0NhNEBERERGRaqiYiIiIiIiISM4oURMREREREckZJWoiIiIiIiI5o0RNREREREQkZ3JdTKRQ5jKW+pTLXLBgAfvssw9PP/10lKqPzz77LKuttho77bRTxX233XYbw4YNA2CdddZh2LBh7LzzzgCMHz+ek08+mVmzZrHFFlvQpk0b3nvvPdq1a0e7du3YYIMNGDt2bOp2Ffz1r3/l2GOPpby8nLKyMiBUgxwzZkytG2Hvueee3HvvvbRv3z5aW0REREREJNCIWhUjRozg4IMPjlaa/9lnn+WFF16ouD1mzBj++Mc/MmHCBGbNmsXw4cM54ogj+OSTTwAYPXo055xzDkuXLuWNN96gvLycAQMGMHToUMrLy6MmaQUbb7wxV1555Ur9zJFHHsnvf//76G0RERERERElaisYPXo0Bx54YMXt6667ju7du7Ptttty/vnnA1BeXk7fvn0pKyvjoIMO4osvvgDgpptuYptttqGsrIzDDz+cd955h+HDh3PDDTfQo0cPxo8fz7XXXsvQoUPZYIMNAOjZsydHHXUUt956K3/+85+55557uOyyyxg8ePAKbZs4cSIHH3wwAA899BBt2rRh0aJFLFy4kM0337zWttVm//33Z8aMGbzxxhsrPHbnnXfSvXt3unXrxq9+9auK+wcMGMCdd95Z31+riIiIiIisBCVqRRYtWsTs2bMrpvw99thjPPjgg7z88stMmzaN8847D4AhQ4Zw7bXXMn36dLp3786ll14KwDXXXMPUqVOZPn06w4cPp1OnTpx88sn88pe/pLy8nF122YUZM2bQq1evSsft3bs3M2bM4Pjjj68YPRs9evQK7evZsydTp04FwhTJbt268corr/Dyyy+zww471Nq22rRo0YLzzjuPq666qtL9H330Eb/61a94+umnKS8v55VXXuHBBx8EoH379vzvf/9j3rx59f8Fi4iIiIhIvShRKzJ37lzWXXfdittjx47lmGOOoW3btgCst956zJ8/ny+//JLddtsNgKOOOopx48YBUFZWxuDBg7njjjto1ar+y//cHTOr83mtWrViiy22YObMmUycOJGzzjqLcePGMX78eHbZZZda21aXI444gpdeeom333674r5XXnmF3XffnQ4dOtCqVSsGDx5cKV7Hjh356KOP6v3/FBERERGR+lGiVqRNmzYsXLiw4nZ9E6iCRx55hFNPPZXJkyfTq1cvlixZssJzttlmGyZPnlzpvilTprDNNtvU6xi77LILjz32GK1bt2bPPfdkwoQJTJgwgV133bXe7axOq1atOPvss7n22msr7nP3Wn9m4cKFtGnTJtVxRURERERkRUrUirRv356lS5dWJGv9+/dnxIgRfPvttwB8/vnntGvXjvbt2zN+/HgARo0axW677cayZct4//336devH9dddx1ffvkl33zzDWuvvTZff/11xTHOO+88fvWrX1VMGSwvL+evf/0rp5xySr3auOuuu3LjjTey44470qFDB+bNm8esWbPo2rVrjW0DuOWWW7jllltqjX300UczduxY5syZA8AOO+zAc889x9y5c1m6dCl33nlnRTx355NPPqm1MqSIiIiIiDRMrsvz16ecfmz9+/dnwoQJ7Lnnnuyzzz6Ul5fTu3dvVlttNfbdd1+uuuoqRo4cycknn8y3337L5ptvzu23387SpUv52c9+xvz583F3fvnLX7LuuutywAEHcMghh/DQQw9x8803M2DAAD788EN22mknzIy1116bO+64gw033LBe7dthhx349NNPK0bQysrK6NixY8XIX3VtA5g1axY//OEPa4292mqrcfrpp3PGGWcAsOGGG3L11VfTr18/3J199923otDK5MmT6du370pN8RQRERERkfqxuqa3ZaV3794+adKkSvfNnDmTLl26NEp7CqZOncqwYcMYNWpUo7Yjtv3335/777+f1VZbLUq8M844gwEDBrDHHnvU+rw8/E1FRERERLJU2P95ZQeazGyyu/eu7jENh1Sx3Xbb0a9fP5YuXRptL7U8GDNmTNR43bp1qzNJExERERGRhlGiVo1jjz22sZuQeyeccEJjN0FEREREZJWlYiIiIiIiIiI5o0RNREREREQkZ5SoiYiIiIiI5IwSNRERERERkZzJdTGRmVvHLeveZdbMOp+zYMEC9tlnH55++ukoVR+fffZZVlttNXbaaaeK+2677TaGDRsGwDrrrMOwYcPYeeedARg/fjwnn3wys2bNYosttqBNmza89957tGvXjnbt2rHBBhswduzY1O0CmDFjBgcddBDTpk2jTZs2AOy3334ceeSRHH744fWKMWbMGF555RUuvfTSKG0SERERERGNqK1gxIgRHHzwwdFK8z/77LO88MILFbfHjBnDH//4RyZMmMCsWbMYPnw4RxxxBJ988gkAo0eP5pxzzmHp0qW88cYblJeXM2DAAIYOHUp5eXm0JA2ga9euHHzwwVx55ZUAPPjggyxevLjeSRqExO7hhx/m22+/jdYuEREREZHmTolaFaNHj+bAAw+suH3dddfRvXt3tt12W84//3wAysvL6du3L2VlZRx00EF88cUXANx0001ss802lJWVcfjhh/POO+8wfPhwbrjhBnr06MH48eO59tprGTp0KBtssAEAPXv25KijjuLWW2/lz3/+M/fccw+XXXYZgwcPXqFtEydO5OCDDwbgoYceok2bNixatIiFCxey+eab19q2mlx00UXce++9lJeXc/7553PrrbcyefJkdtttN3r16sXee+/Nxx9/XO3/D8DM2H333aPv0yYiIiIi0pzleupjqS1atIjZs2fTqVMnAB577DEefPBBXn75Zdq2bcvnn38OwJAhQ7j55pvZbbfduOiii7j00ku58cYbueaaa3j77bdZffXV+fLLL1l33XU5+eSTWWuttTjnnHOAMN2wV69elY7bu3dvRo4cyeWXX86ECRPYf//9OeSQQ1ZoX8+ePZk6dSoQpkh269aNV155hSVLlrDDDjvU2raatG3bluuvv55dd92Vs846i06dOrHbbrvx0EMP0aFDB+6++25+/etfM2LEiBX+f8XtHz9+PIcddlhDf/UiIiIiIlJEiVqRuXPnsu6661bcHjt2LMcccwxt27YFYL311mP+/Pl8+eWX7LbbbgAcddRRHHrooQCUlZUxePBgBg4cyMCBA+t9XHfHzOp8XqtWrdhiiy2YOXMmEydO5KyzzmLcuHEsXbqUXXbZpda21eaAAw5g3XXX5ZRTTuGNN97gtddeY6+99gJg6dKlbLjhhrX+/zp27MhHH31U7/+viIiIiIjUTlMfi7Rp04aFCxdW3K5vAlXwyCOPcOqppzJ58mR69erFkiVLVnjONttsw+TJkyvdN2XKFLbZZpt6HWOXXXbhscceo3Xr1uy5555MmDCBCRMmsOuuu9a7ndVp0aIFLVq0wN3p2rUr5eXllJeX8+qrr/Lkk0/W+v9buHBhRTESERERERFJT4lakfbt27N06dKKZK1///6MGDGiolDG559/Trt27Wjfvj3jx48HYNSoUey2224sW7aM999/n379+nHdddfx5Zdf8s0337D22mvz9ddfVxzjvPPO41e/+hXz5s0Dwpqyv/71r5xyyin1auOuu+7KjTfeyI477kiHDh2YN28es2bNomvXrjW2DeCWW27hlltuqTP+VlttxZw5c3jxxRcBWLx4MTNmzKjx/wfw73//m27dutWr/SIiIiIiUrdcT32sTzn92Pr378+ECRPYc8892WeffSgvL6d3796sttpq7Lvvvlx11VWMHDmSk08+mW+//ZbNN9+c22+/naVLl/Kzn/2M+fPn4+788pe/ZN111+WAAw7gkEMO4aGHHuLmm29mwIABfPjhh+y0006YGWuvvTZ33HFHxfTCuuywww58+umnFSNoZWVldOzYsWLkr7q2AcyaNYsf/vCHdcZfbbXVuO+++zj99NOZP38+S5Ys4cwzz2TLLbes9v8H8Mwzz3D11Vc34LctIiIiIiLVMXdvlAP37t3bJ02aVOm+mTNn0qVL3L3TVtbUqVMZNmwYo0aNatR2xLb//vtz//33s9pqq0WN++mnn3LEEUfw1FNPVft4Hv6mIiIiIiJZ6j6yOwCvHvXqSv2cmU12997VPZbrEbXGsN1229GvXz+WLl0abS+1PMiqfP57773H7373u0xii4iIiIg0V0rUqnHsscc2dhOajD59+jR2E0REREREVjkqJiIiIiIiIpIzStRERERERERyRomaiIiIiIhIzihRExERERERyZlcFxO59eSno8Y7dfiP6nzOggUL2GeffXj66adXqurjVVddxYUXXtjgth199NH861//Yvbs2ay++urMnTuX3r17884779T4M3PmzOHII4/k8ccfb/BxRUREREQkfzSiVsWIESM4+OCDV0jSli5dWuvPXXXVVamP3bJlS0aMGFHv53fo0IENN9yQ559/PvWxRUREREQkP5SoVTF69GgOPPBAAJ599ln69evHEUccQffuYRO7gQMH0qtXL7p27cptt90GwPnnn8+CBQvo0aMHgwcPBuCOO+5g++23p0ePHpx00kl1JnoAZ555JjfccANLliypdL+7c+6559KtWze6d+/O3XffXfHYwIEDGT16dJT/u4iIiIiI5IMStSKLFi1i9uzZdOrUqeK+iRMncuWVV/L6668DYcRt8uTJTJo0iZtuuol58+ZxzTXX0KZNG8rLyxk9ejQzZ87k7rvv5vnnn6e8vJyWLVvWK5nadNNN2XnnnRk1alSl+++//37Ky8uZNm0aY8eO5dxzz+Xjjz8GoHfv3owfPz7eL0FERERERBpdrteoldrcuXNZd911K923/fbb07lz54rbN910Ew888AAA77//Pm+++Sbrr79+pZ956qmnmDx5csVm0AsWLKBjx471asOFF17IgAED2G+//SrumzBhAoMGDaJly5Z85zvfYbfdduOVV15hwIABdOzYkY8++qgh/10REREREckpJWpF2rRpw8KFCyvdt+aaa1Z8/+yzzzJ27FhefPFF2rZty+67777C8yFMVTzqqKO4+uqrV7oNW2yxBT169OCee+6pFK8mCxcupE2bNit9HBERERERyS9NfSzSvn17li5dWm3yBTB//nzat29P27ZtmTVrFi+99FLFY61bt2bx4sUA7LHHHtx333189tlnAHz++ee8++67AAwZMoSJEyfW2o5f//rXXH/99RW3d911V+6++26WLl3KnDlzGDduHNtvvz0A//73v+nWrVvD/9MiIiIiIpI7uR5Rq085/dj69+/PhAkT2HPPPVd4bJ999mH48OGUlZWx1VZb0bdv34rHTjzxRMrKyujZsyejR4/miiuuoH///ixbtozWrVtz6623stlmmzF9+nQ23HDDWtvQtWtXevbsyZQpUwA46KCDePHFF9l2220xM6677jq++93vAvDMM89UmiYpIiIiIiJNn9U2rS5LvXv39kmTJlW6b+bMmXTp0qVR2lMwdepUhg0btkJBjxi++uorjjvuOO69995oMXfddVceeugh2rdvHy1mTHn4m4qIiIiIZKn7yFAh/tWjXl2pnzOzye7eu7rHNPWxiu22245+/frVq5z+ylpnnXWiJmlz5szhrLPOym2SJiIiIiIiDZPrqY+N5dhjj23sJtRLhw4dGDhwYGM3Q0REREREIqtzRM3MNjGzZ8xsppnNMLMzqnnO7mY238zKk38XZdNcERERERGRVV99RtSWAGe7+xQzWxuYbGb/cvfXqzxvvLvvH7+JIiIiIiIizUudI2ru/rG7T0m+/xqYCWyUdcNERERERESaq5UqJmJmnYDtgJereXhHM5tmZo+ZWdcYjRMREREREWmO6l1MxMzWAv4BnOnuX1V5eAqwmbt/Y2b7Ag8CP6gmxonAiQCbbrppncf83U/jzqQ8++4xdT5nwYIF7LPPPjz99NO0bNmy3rGvuuoqLrzwwga167bbbuOpp57i7rvvBkIZ/+22246xY8fSuXPnesU455xz2HffffnRj0q/95yIiIiIiMRVrxE1M2tNSNJGu/v9VR9396/c/Zvk+0eB1ma2QTXPu83de7t77w4dOqRsejZGjBjBwQcfvEKSVle5/quuuqrBxzzhhBP44IMPGDt2LAAXXXQRxx57bL2TNIBf/OIXXHPNNQ1ug4iIiIiI5Ed9qj4a8BdgprsPq+E5302eh5ltn8SdF7OhpTJ69GgOPPBAAJ599ln69evHEUccQffuYRO7gQMH0qtXL7p27cptt90GwPnnn8+CBQvo0aMHgwcPBuCOO+5g++23p0ePHpx00km1Jnpmxh/+8AfOPPNMJk2axFNPPcW5555bbYylS5dy9NFH061bN7p3784NN9wAwGabbca8efP45JNPsvz1iIiIiIhICdRn6uMPgSOBV82sPLnvQmBTAHcfDhwC/NzMlgALgMPd3eM3N1uLFi1i9uzZdOrUqeK+iRMn8tprr1WMbo0YMYL11luPBQsW0KdPH37yk59wzTXXcMstt1BeXg7AzJkzufvuu3n++edp3bo1p5xyCqNHj2bIkCE1HrusrIy9996bPfbYgwcffJD//Oc/1cbo2rUrH374Ia+99hoAX375ZUWMnj178vzzz/OTn/wk+u9GRERERERKp85Ezd0nAFbHc24BbonVqMYyd+5c1l133Ur3bb/99pWmIN5000088MADALz//vu8+eabrL/++pV+5qmnnmLy5Mn06dMHCOveOnbsWOfxTz31VB577DH69evHLbfcUm2MAw44gNmzZ/OLX/yC/fbbj/79+1f8fMeOHfnoo48a9H8XEREREZH8qHcxkeagTZs2LFy4sNJ9a665ZsX3zz77LGPHjuXFF1+kbdu27L777is8H8DdOeqoo7j66qtX6vgtWrSgRYsWdcaYNm0aTzzxBLfeeiv33HMPI0aMAGDhwoW0adNmpY4pIiIiIiL5s1Ll+Vd17du3Z+nSpdUmXwDz58+nffv2tG3bllmzZvHSSy9VPNa6dWsWL14MwB577MF9993HZ599BsDnn3/Ou+++C8CQIUOYOHFinW2pKcbcuXNZtmwZP/nJT7j88suZMmVKxc/8+9//plu3bg37z4uIiIiISG7kekStPuX0Y+vfvz8TJkxgzz33XOGxffbZh+HDh1NWVsZWW21F3759Kx478cQTKSsro2fPnowePZorrriC/v37s2zZMlq3bs2tt97KZpttxvTp09lwww3rbMc222xTbYw2bdpwzDHHsGzZMoCKEbfFixfz1ltv0bt370i/CRERERERaSzWWDU/evfu7ZMmTap038yZM+nSpUujtKdg6tSpDBs2jFGjRkWP/dVXX3Hcccdx7733Ro/9wAMPMGXKFC6//PLosdPIw99URERERCRL3UeGCvGvHvXqSv2cmU1292pHWjT1sYrtttuOfv361blvWkOss846mSRpAEuWLOHss8/OJLaIiIiIiJRWrqc+NpZjjz22sZuw0g499NDGboKIiIiIiESSuxG1Jrj9mtRAf0sRERERkYbJVaK2xhprMG/ePF3grwLcnXnz5rHGGms0dlNERERERJqcXE193Hjjjfnggw+YM2dOYzdFIlhjjTXYeOONG7sZIiIiIiJNTq4StdatW9O5c+fGboaIiIiIiEijytXURxEREREREVGiJiIiIiIikjtK1ERERERERHJGiZqIiIiIiEjOKFETERERERHJGSVqIiIiIiIiOaNETUREREREJGeUqImIiIiIiOSMEjUREREREZGcUaImIiIiIiKSM0rUREREREREckaJmoiIiIiISM4oURMREREREckZJWoiIiIiIiI5o0RNREREREQkZ5SoiYiIiIiI5IwSNRERERERkZxRoiYiIiIiIpIzStRERERERERyRomaiIiIiIhIzihRExERERERyRklaiIiIiIiIjmjRE1ERERERCRnlKiJiIiIiIjkjBI1ERERERGRnFGiJiIiIiIikjNK1ERERERERHJGiZqIiIiIiEjOKFETERERERHJGSVqIiIiIiIiOaNETUREREREJGeUqImIiIiIiOSMEjUREREREZGcUaImIiIiIiKSM0rUREREREREckaJmoiIiIiISM4oURMREREREckZJWoiIiIiIiI5o0RNREREREQkZ5SoiYiIiIiI5IwSNRERERERkZxRoiYiIiIiIpIzStRERERERERyRomaiIiIiIhIzihRExERERERyRklaiIiIiIiIjmjRE1ERERERCRnlKiJiIiIiIjkTJ2JmpltYmbPmNlMM5thZmdU8xwzs5vM7C0zm25mPbNproiIiIiIyKqvVT2eswQ4292nmNnawGQz+5e7v170nB8DP0j+7QD8IfkqIiIiIiIiK6nOETV3/9jdpyTffw3MBDaq8rQDgb958BKwrpltGL21IiIiIiIizcBKrVEzs07AdsDLVR7aCHi/6PYHrJjMiYiIiIiISD3UO1Ezs7WAfwBnuvtXVR+u5ke8mhgnmtkkM5s0Z86clWupiIiIiIhIM1GvRM3MWhOStNHufn81T/kA2KTo9sbAR1Wf5O63uXtvd+/doUOHhrRXRERERERklVefqo8G/AWY6e7Danjaw8CQpPpjX2C+u38csZ0iIiIiIiLNRn2qPv4QOBJ41czKk/suBDYFcPfhwKPAvsBbwLfAMdFbKiIiIiIi0kzUmai5+wSqX4NW/BwHTo3VKBERERERkeZspao+ioiIiIiISPaUqImIiIiIiOSMEjUREREREZGcUaImIiIiIiKSM0rUREREREREckaJmoiIiIiISM4oURMREREREckZJWoiIiIiIiI5o0RNREREREQkZ5SoiYiIiIiI5IwSNRERERERkZxRoiYiIiIiIpIzStRERERERERyRomaiIiIiIhIzuQ+Ues+sjvdR3Zv7GaIiIiIiIiUTO4TNRERERERkeZGiZqIiIiIiEjOKFETERERERHJGSVqIiIiIiIiOaNETUREREREJGeUqImIiIiIiOSMEjUREREREZGcUaImIiIiIiKSM0rUREREREREckaJmoiIiIiISM4oURMREREREckZJWoiIiIiIiI5o0RNREREREQkZ5SoiYiIiIiI5IwSNRERERERkZxRoiYiIiIiIpIzStRERERERERyRomaiIiIiIhIzihRExERERERyRklaiIiIiIiIjmjRE1ERERERCRnlKiJiIiIiIjkjBI1ERERERGRnFGiJiIiIiIikjNK1ERERERERHJGiZqIiIiIiEjOKFETERERERHJGSVqIiIiIiIiOaNETUREREREJGeUqImIiIiIiOSMEjUREREREZGcUaImIiIiIiKSM0rUREREREREckaJmoiIiIiISM4oURMREREREckZJWoiIiIiIiI5o0RNREREREQkZ5SoiYiIiIiI5IwSNRERERERkZxRoiYiIiIiIpIzStRERERERERyRomaiIiIiIhIztSZqJnZCDP7zMxeq+Hx3c1svpmVJ/8uit9MERERERGR5qNVPZ7zV+AW4G+1PGe8u+8fpUUiIiIiIiLNXJ0jau4+Dvi8BG0RERERERER4q1R29HMppnZY2bWNVJMERERERGRZqk+Ux/rMgXYzN2/MbN9gQeBH1T3RDM7ETgRYNNNN41waBERERERkVVP6hE1d//K3b9Jvn8UaG1mG9Tw3Nvcvbe79+7QoUPaQ4uIiIiIiKySUidqZvZdM7Pk++2TmPPSxhUREREREWmu6pz6aGZ3ArsDG5jZB8DFQGsAdx8OHAL83MyWAAuAw93dM2uxiIiIiIjIKq7ORM3dB9Xx+C2E8v0iIiIiIiISQayqjyIiIiIiIhKJEjUREREREZGcUaImIiIiIiKSM0rUREREREREckaJmoiIiIiISM4oURMREREREckZJWoiIiIiIiI5o0RNREREREQkZ5SoiYiIiIiI5IwSNRERERERkZxRoiYiIiIiIpIzStRERERERERyRomaiIiIiIhIzihRExERERERyRklaiIiIiIiIjmjRE1ERERERCRnlKiJiIiIiIjkjBI1ERERERGRnFGiJiIiIiIikjNK1ERERERERHJGiZqIiIiIiEjOKFETERERERHJGSVqIiIiIiIiOaNETUREREREJGeUqImIiIiIiOSMEjUREREREZGcUaImIiIiIiKSM0rUREREREREckaJmoiIiIiISM4oURMREREREckZJWoiIiIiIiI5o0RNREREREQkZ5SoiYiIiIiI5IwSNRERERERkZxRoiYiIiIiIpIzStRERERERERyRomaiIhIU3FJu/BPRERWeUrUREREREREckaJmoiIiIiISM4oURMREREREckZJWoiIiIiIiI5o0RNREREREQkgplbd2Hm1l2ixFKiJiIiIiIikjNK1ERERERERHJGiZqIiIiIiEjOKFETERERERHJGSVqIiIiIiIiOaNETUREREREJGeUqImIiIiIiOSMEjUREREREZGcUaImIiIiIiKSM0rUREREREREckaJmoiIiIiISM4oURMREREREckZJWoiIiIiIiI5o0RNREREREQkZ5SoiYiIiIiI5IwSNRERERERkZypM1EzsxFm9pmZvVbD42ZmN5nZW2Y23cx6xm+miIiIiIhI81GfEbW/AvvU8viPgR8k/04E/pC+WSIiIiIiIs1Xq7qe4O7jzKxTLU85EPibuzvwkpmta2YbuvvHsRopIiLSnHU6/xEA3lmjkRsiIiIlE2ON2kbA+0W3P0juExERERERkQaIkahZNfd5tU80O9HMJpnZpDlz5kQ4tIiIiIiIyKonRqL2AbBJ0e2NgY+qe6K73+buvd29d4cOHSIcWkSaq+4ju9N9ZPfGboaIiIhIJmIkag8DQ5Lqj32B+VqfJiIiIiIi0nB1FhMxszuB3YENzOwD4GKgNYC7DwceBfYF3gK+BY7JqrEiIiIiIiLNQX2qPg6q43EHTo3WIhERERERkWYuxtRHERERERERiUiJmoiIiIiISM4oURMREREREckZJWoiIiIiIiI5o0RNREREREQkZ5SoiYiIiIjIKq37yO50H9m9sZuxUpSoiYiIiIiI5IwSNRERERERkZxRoiYiIiIiIpIzStRERERERERyRomaiIiIiIhIzihRExERERERyRklaiIiIiIiIjmjRE1ERERERCRnlKiJiIiIiIjkjBI1ERERERGRnFGiJiIi0sR0H9md7iO7N3YzREQkQ0rUREQagS60RUREpDZK1ERERERERHJGiZqIiIiIiEjOKFETERERERHJGSVqIiIiIiIiOaNETUREpImauXUXZm7dpbGbISIiGVCiJiIiIiIikjNK1EREqqHy+SIiItKYlKiJiIiIiIjkjBI1ERERERGRnFGiJiIiIiIikjNK1ERERERERHJGiZqIiIiIiEjOtGrsBoiIiIiIiDRJl7QLXztvGj20RtRERERERERyRomaiIiIiIhIzihRExEREWkmuo/sTveR3Ru7GSJSD0rUREREREREckaJmoiIiIiISM4oURMREREREckZJWoiIiIiIiI5o0RNREREREQkZ5SoiYiIiIiI5Eyrxm6AiIiIiIhIU9Lp/EcAeGeN7I6hETUREREREZGcUaImIiIiIiKSM0rUREREREREckaJmojIKqj7yO50H9m9sZshIiIiDaRETUREREREJGeUqDVx6jUXEREREVn1KFETERERERHJGSVqIiIiIitp5tZdmLl1l8ZuhoiswpSoiYiIiIiI5IwSNRERoNP5j9Dp/EcauxkiIiIigBI1ERERERGR3GnV2A0QEcmVS9qFr503bdx2iIiISLOmETUREREREZGcUaImqzTtMycisnL0uSkikg9K1ERERERERHJGiZqIiIiIiEjOKFGTRqUpNpJ32tRWREREGkO9EjUz28fM3jCzt8zs/Goe393M5ptZefLvovhNFRERERERaR7qLM9vZi2BW4G9gA+AV8zsYXd/vcpTx7v7/hm0UUREREREpFmpz4ja9sBb7j7b3RcBdwEHZtssERERERGR5qs+idpGwPtFtz9I7qtqRzObZmaPmVnXKK0TERFpSi5pt3zTdBERkRTqnPoIWDX3eZXbU4DN3P0bM9sXeBD4wQqBzE4ETgTYdNNNV66lIhJVoYjLq0e92sgtEZHmRJ89IiL1U58RtQ+ATYpubwx8VPwEd//K3b9Jvn8UaG1mG1QN5O63uXtvd+/doUOHFM0WERERERFZddUnUXsF+IGZdTaz1YDDgYeLn2Bm3zUzS77fPok7L3ZjRUREYtDWICIiknd1Tn109yVmdhrwBNASGOHuM8zs5OTx4cAhwM/NbAmwADjc3atOjxQREREREZF6qM8atcJ0xker3De86PtbgFviNk1ERERERKR5qteG1yIiIiIiIlnRlPQVKVETERERERHJGSVqIiIiIiIiOaNETUREREREJGeUqImIiIiIiORMvao+ioiIiAgVxQ7uaeR2iMiqTyNqIiIiIiIiOaNETURERESkBFSCXlaGEjUREREREZGcUaImIiIiq5yZW3dh5tZdGrsZIiINlrtErdP5j9Dp/EcauxkiIiIiIk2KplauWnKXqImIiIiIiDR3StRERERERERyRomaiIhIM6YlByIi+aQNr0VESumSduFr500btx1SEoViFl1mzWzkloiISFOjRE1ERCSywmL+exq5Hc1KoRPkkvmN2w4RkUg09VEkJzT9SEREpHGpaqLkiUbUREREROqiacsiq4SmNCW92Y+oqedERGTl6bNTREQkW80+URMRaUwzt+5S0bsnIiIiUqBETUSaBK3hExERWfWpA3M5rVETEREREWnKtIZylaQRNRHJhNYwiYhIU6VRHckDJWoiIiIiIiI5o0RNREREMqP1pSIiDaM1aiIiq4DChfA71+xX6f6mtF+MSAyFKdf3NHI7RETSUqImklOFi41Xj3q1kVsiIiKlktlnv4pNiDQ5mvooIiIiIiKSM0rURERERCQKVfwViUdTHzOm6Wuy0jQ9RUSfnSIiEkcTvq7SiJqIiIiIiEjOKFETSUFTPEREREQkC5r6KLVqstOPqgxzq0S5NLaK8vlrNHJDREREpElQoiYiIiIiIquUVaGDVImaSM5pNFBWShNeNC0ipdNUzy1NdqZPiTXVv69UpkRNVimrQu+JiMgqSZ0IIiIrRcVERKRJm7l1l4qeQxFpOvTeleZMr3+pj/yOqKnnTUREREREmqn8JmoiItVRJ47kkKZdi4hIbJr6KCIiIiIikjMaURMRkQZTZTGRfNNob+0qfj/X7NfILWkauo/sroqbJaQRNRERERERkZxpMomaquOIiEhDdTr/kYqecxERkaZAUx9FRESkydLUPhFZVTWZETVZtWnEVEQkX/S53Lj0+xcRjaiJiIiINBIVs8gJbf0iOaQRNZFmTr22IiIiIvnT7EbUVpmeK/X8iIiIrDp0XheRKppdotbUNfVF06tMoiwiTZMuhkWi0nldJDtK1KRx6GJJRCRf9LlcrabeQSqSV0ry66ZEbRVRWGPUZdbMVHH0phGRWuliXkQkN9SRsGpToiayEpTI1ky/GxEREVlp6gCskRI1ERGpk3ptRaRWutgWiU7l+UWaiU7nP1JxsS2SN3p9ioiIVKZETURERESi0h6dVVzSbvmo4ypAf9vS0NTHRKxiHNI86fUj0jTpvSsiWdK0cUmj+SZqmkstIpJ7SqREmreaClXps0Gag+abqImk0ZQT/abcdln16fUpItXRZ4M0Q/Vao2Zm+5jZG2b2lpmdX83jZmY3JY9PN7Oe8ZvaNGhBvIiIiIisCnRd27jqHFEzs5bArcBewAfAK2b2sLu/XvS0HwM/SP7tAPwh+SqrCE0xEBEREWmmNKK50m49+WkATh3+owbHqM/Ux+2Bt9x9NoCZ3QUcCBQnagcCf3N3B14ys3XNbEN3/7jBLatBjP+0iGRIH+YiIiIiqdUnUdsIeL/o9gesOFpW3XM2AqInak1VqUakoiWyjXSxrUR81dPkXvuK3yw19d9PU2+/NB69dqS5agqvfQuDYLU8wexQYG93Pz65fSSwvbv/oug5jwBXu/uE5PZTwHnuPrlKrBOBE5ObWwFvrERbNwDmrsTzV5biK35e4zfltiu+4it+043flNuu+Iqv+E03flNue0Pib+buHap7oD4jah8AmxTd3hj4qAHPwd1vA26rxzFXYGaT3L13Q35W8RW/Kcdvym1XfMVX/KYbvym3XfEVX/Gbbvym3PbY8etT9fEV4Adm1tnMVgMOBx6u8pyHgSFJ9ce+wPws1qeJiIiIiIg0B3WOqLn7EjM7DXgCaAmMcPcZZnZy8vhw4FFgX+At4FvgmOyaLCIiIiIismqr14bX7v4oIRkrvm940fcOnBq3aSto0JRJxVf8VSB+U2674iu+4jfd+E257Yqv+IrfdOM35bZHjV9nMREREREREREprfqsURMREREREZESUqImIlIiZtbCzHbKKLaZ2SZ1PzPfxxARkfzI8rwlddPUR2kUZmbAxu7+fp1PbmbM7FB3v7eu+1LEPw0Y7e5fxIgnK8fMXnT3HTOKPdnde2URu5THyJKZdXL3d6rc18fdX4kUv1c1e4ge4O7/zHv8ptz2LJnZWbU97u7DStUWaXrMbL3aHnf3z1PGP7iO+PeniZ8cI7PzltSuXsVEVjVm9jN3v6OmD9+0H7pm9iN3f7qmN0+aN42Z/ROoMbt29wENjV1K7u5m9iAQ9YLPzL6m9t/POhGO0bO2x919SspDXABUTcqqu6+hvgu8YmZTgBHAEx6xx8bMOgAnAJ0o+oxx92NjHaOG427m7u9GiNOS8DvZM0KzqvOkmf0EuD/m7z3xUsykoxGPkaX7k+TgQwAz2w24BegeKf6fzOwod381iT8IOBOIlYxkGb9Jtt3Mbqb2z/3T08QH1k6+bgX0YfkWRQcA41LGrsTM1gWGsOLnZ9r/Q2ZK8PsvHKclcI27nxsjXlHcrK8bJifxDdgU+CL5fl3gPaBzyvgHJF87AjsBTye3+wHPAqkTNbI9b2X2t01iZ5oo13DM9sAm7j49baxcJmpmdlNtj0d406+ZfF271mc13G6EN8oB1TzmpHvTXJ98PZhwwX1HcnsQ8E6KuBXMbIK771zNh5cRcqzUyU4i+gWfu68NYGaXAZ8AowjtHky8v/fvkq9rAL2BackxyoCXgZ0bEtTMfkzY5mKjKu+BdYAlDW5tFe7+GzP7LdCfsJXGLWZ2D/AXd/9PhEM8BIwHxgJLI8SrxMx2BDYCxrn7Z2ZWBpwP7AKknpbn7kvN7Fsza+fu89PGq8ZZhM+gJWa2kLjvq37ASWb2LvDfothlEWJnfgwzO8/dr6vpwi/SBd9JwINmdgDQE7iK8L6L5RDgPjMbTPgsGEJ4rzWF+E217ZOSrz8EtgHuTm4fSrhITsXdLwUwsyeBnu7+dXL7EuJ1oBU8CrwEvAosixU04/dWpr//guSzuZeZWcxkIevrBnfvnMQfDjycVFIvnPNTdwi6+zFJvDHANoV9jM1sQ+DWtPETWZ63MvvbJrJOlAEws2eBAYTcqhyYY2bPuXutI/J1xs3j1EczWwS8BtwDfET4hVZw95ERjtESON3db0gbqzGY2Th337Wu+/LMzF4HtgSyuOB72d13qOu+lMe4C7iyqHe4G3COux/dwHjbAj2Ay4CLih76Gngm9lTF5HjHAPsAzwB9gX+5+3kp45a7e4/0Law29lBgf8KH4BbAGOAUwsX2H919YaTj3EPy+yC8NoF892pDGFWs7v4YI42lOEZhGpyZHVXDMVJ/9ifH2RH4I7AQ2M/d58SIWxR/S+BB4H1goLsvaCrxm3jbnwH6u/vi5HZr4El37xcp/ixgW3f/X3J7dWCau28dI34Sc4q71zpro4FxM39vZf37T2L+DvgBIUEu/myOMb0v0+uG6qaNm9kkd+8dKf5r7t6t6HYLYHrxfXmW5d82iV9touzuZ0eKP9XdtzOz4wmjaReb2fS017S5HFEDNiT0xPyUMJJwN/CPmBeqSfY+AIieqJVoPnsHM9vc3Wcnx+wMdIgQt5TDxD+OFKc6S5Ne27sIPSmDiD+6s3UhSQNw99fMrEdDg7n7NGCamf2d8N7c1N3fSN/MyszsdOAoYC7wZ+Bcd1+cfKi/CaRK1IAxZrZv4cMwsv2A7dx9YTK14COgzN3fjHycR5J/mUja/gPCqCwA7p56ClUhWTKzjsWxY8ryGJ6sVYqVkBWzFaeNtwXmA38xs9TTxs3s1Srx1wNaAi8n8VOdrLOM35TbXsX3CCMghXPUWsl9sYwCJprZA4T/z0HA3yLGBxhlZicQOqH+V7gz7Xk3y/dWkax//xBeO/OAHxXdl3amUkHW1w1zzew3hJlQDvyM8H+J5VkzewK4M4l/OKETNoqszltFsvzbAvRx95MrArs/ZmaXR4oN0CoZxTwM+HW0oLECxeTu84DhwHAz24jwZplhZr9y91ERD/WCmd1CSASLs/e0a4xKMZ/9l4Q35ezkdifClJ4YSjJMTC1zwiM4Avi/5J8Dzyf3xTTTzP5M5Q/dmRHi7kOY4roa0DlJ/i5LeyFZZAPg4KojIO6+zMz2jxD/DOBCM/sfsJi4UyQWFEbN3P0LM3sjgyQt04uZpLftDGBjwshgX+BFKp+cGhp7AGFq7veAz4DNCK/Jrmljl+IY1SRTlaR8D1xf91NSifHeaaz4Tbntxa4BpiYjOxCWIVwSK7i7X2lmj7N8evsx7j41VvzEImAo4UKv8F5wYPM0QTN+bxVk+vuH5dP8MpL1dcMg4GLggeT2uOS+KNz9NAu1EXZJ7rrN3R+o7WfqK8vzVkHGf1vIPlG+FHgCmODur5jZ5oTO71RyOfWxwELRhkHAXoTk4Xfu/nrE+NX1NLi7R3nhJfPZf1I0n31t4F533ydS/NWBwpSLWYXpGLGUYJi40MtqhB6azsAb7h7tojJLZrYG8HOgMN10HPCHtNPvzGwy4cPvWXffLrkv9fB5UfxR7n5kXfflkZl9SeXOjl2Lb8dKZs3sB8DVhPUWxb2HqS6WktivEjpwXnL3Hma2NXCpu/80QuxphNfO2GQKRj9gkLufmDZ2KY5hobBHjdz9uQjH6Ax8XHifmlkb4DtepRJkivh9gRlVPve3cfeX8x6/Kbe96BjfBQpT1V52909ixS46RqXRZHd/L2Ls/wA7uPvcWDGTuJm/t5LjZPr7T867xxE6hor/BpkWq2rusjxvFR1jY+BmwlpHByYAZ7j7B5Hir0dIlIuv2S6NMUvMMlxOlcsRNTO7lNADN5MwBH2Bu0crplAQc950DTYl9I4VLCKMfMXyA8Ko3RrAtskUkpjTMDIdJnb3SlXWksQ8yqhgKT7Mkwu9G4g/fXaJu883s7qf2TCVEuHkAyZa9U0z+xuhmMh4d58VK27iwCq3f1fts9K7nfCBfgOheMYxVFkrm8LCZOomZra6u88ys60ixV7s7vMs7HvTwt2fMbNrI8XO/BixLhbrcC+hMlrB0uS+PpHi/4FQpKTgv9Xcl9f4TbntBf8DPiZ87m9pZlvGmp5VzWjypsAsIo5YAzOAbyPGA0r23oIMf/+JUYTf+d6E9dyDiTOTJfPrBgsVkc+rJn6swYG+hESnC2FGTkvgv5Fms2R53iq4Hfg7YekThBGv2wmDNaklCdkZMWJVEzuz5VS5TNSA3wKzgW2Tf1clF63RK5iZ2X6s+Ka5LFL4zOazm9nFwO6EHv9HCeu9JsSKn8h6mLgSd59iZrEulrL8MK+63qKSCK/P18zsCKBlMrJzOvBCypiY2QXAhUAbM/uqcDehA+G2tPGL/JUwNejmZOi/nFCh8f/SBq56sWFhsXo34EN3/yxt/CJt3P0pM7NkiuglZjaekLyl9YGFEtwPAv8ysy8Ia+1i+NLM1iL0FI42s8+IWDE062OU4L0F0MrdKzrQ3H2Rma0WIW5BpaplyZTimOfaLOM35baXYnrW5UnMSqPJkWIXLAXKkxk/xWvUUhUyKsV7qxTT44At3P1QMzvQ3UdaWNP9RKTYmV03JEYTltrsD5xMWCses5DRLYR1afcSKlIPIRTdiiHL81ZBB3e/vej2X83szLRBSzTtFzJaTpXLqY9WQ1Wxgqpra1IcZzhhQXk/QlGFQ4CJ7n5cjPjJMXqxfD77uFjz2ZMP3W2Bqe6+rZl9B/izu1e3JUBDj5HZMHESv7joSgtCr+r67r53hNhTkxPpdHcvSy7on4jRc5X169PM2hLWJxTKVj8BXJF2SmVR/Kvd/YIYsWo5RkvCCEU/wglpgUeojJa8Z2929xlm1o5wEbCUsAj5HHe/M+0xkuM8T5jnfx9hq40PCXu8RO1BTKYjtQMeL04eUsRbk1DJsFBauh1hc/NoHSxZHqMUn/1m9i/Ca+jh5PaBhCkre6SNncS7n7B30R+Su04B+rn7wLzHb8ptT+JnOj3Lkgp9yfTf7ZJEc6K7bx8jfnKMTKoylui9VYrpcRPdfXszG0d4/XxCuG6LMS09s+uGJP5kd+9lRUsZLJRvr3Va6krEL7w+i+O/4O471fWzK3mcqOetorhjCR29hfP4IMI60FSfzVa6ab+ZLKfK64jan9w95t4tNdkpeTNOd/dLLZQGjVVdBgB3n2xm75OM2JnZph5nPvuC5CSxxMzWIUzDSP1BVSzLYeJE8f4kSwhV9v4RKfbi5OuXFsrmf0KkaafFJ7Tk5PcDdx9rYa1L6veUu38L/NrMrnL3/9b5A/VkZlsnUxHvtWo27U7b61N0nKcI+628SJgC2SfiaNcuRdNxjwH+7e4DLayLeIzlH/BpnUnoxDmd0Iv+I0LvZ2pWuapqoWpolB6zKq+XTAqiZHmMWJ1wdTiZMBJY2F/ofSDm+syTgZuA3yS3xwLR1ghmHL8ptx2yn55VGE0eT0Yj1mkTslriluK9VYrpcbdZqD74W0KhtrWovJ1NGpldN1SJ/3Eym+sjwuhjLN8mswPKzew6whTUNev4mXrJ8rxV5FjCqGBh+uDzyX2pFCdiyXVaJhW1PaPlVHlN1KKUma+Hwv4t35rZ9wjT+mJVNMx6PvukZBj6T4RCK98AEyPErZD0DlS3MWaU3iVPNhHNSHUf5r+NeQALJZRPJIzmfJ/wgTscSNv7sxNhhHctYFML+52d5O6npGsxZwMnUP26Life9JTphDVv3Qjlz780sxc9zn5Jxb13e5FsNuvun1jENX2+fBP2bwgJYUxTCBtzF1dT/Ti56DvB3Ru8QaytuEk9hL/BJOBsT7bzSKNEx8hsrYWHTd37Jhfc5klhi1iSTonDY8YsVfym3PZE1tOzDiSMJp/J8tHkWEslgGwLGSXxs1zHlPn0OHf/c/Ltc0TunCb764YrkpkgZxP+BusQKnjHciRhdtJpSdxNgJ9Eip3ZeasgGcSINQ1xBWZ2ABlW1E5mtl0FfM/df2xm2wA7uvtfUsXN6dTH2cA5NT3u8Ta/+y3hzbIHYfd2J0wfjPLGtBJUYEuO0wlYx92nR45bXGBiDcIbfomn3xC5VPOFM2Vm5cD2hMpWheqMr3qVIikNiPsyYRruw0VxK21k2RQkF8LHEN7L33X31SPEfIaQaH5I2B9m6yRJawW8FmN6ZXKc6l6jhWQk1cbayfTNB9z9ieR2f8KWDPcA/+cpNle1UIjpI8KCbCNcFH8XeAP4ubvv3tDYJT7GJKpZa+HuqfemSS6Uiqd0P0c4Wc9PGzuJn3XlssziN+W2V3OsrKZnfYflhWcmRpwtUIg/geWFjA4gKWTk7jHWx2b63qpynKx+/9XtUzsfmOzu5bGO01QlI2pbJjff8GTz8QhxMztvFR0j68+frCtqP0YofvJrD0uSWhGWJ6W6JmwRo3EZaEdYbHlANf9i7sdynbt/6e7/IOwFtDVxK7Ys9rBuo6I6GtAjVnAzG2Bm1xN6T2qdf94Q7j656N/z7n4Wy8vupnE94WL7bcKo5p+Sf98Ar0WIj5m1M7MbzGxS8u/65AItpv8Vn4CSN2WsKWzvV7kr9aabZvYzM1thipeZnWCheEkUZnaamd1NWEw+EBhBvM3NTyK83m8HzvTlpZ/3IO4G1bMJr8fCa/Mr4FPCCfBPKWP3LpzsANz9SWBXd38JSJvM7uPuf3T3r939K3e/DdjX3e8G2qeMXcpj4O5vAS3dfamHBeaxppWMAL4mbEp6GOFve3utP7Fybif0xn8P2Aj4ZxOK35TbDoCZbWtmpwHdgXcjJwmHEWauHEp47bxsZofEip9o4+5PEZKzd939EuIW48jyvZXp7z/RmzCFdqPk34mEwmp/MrO0nciZXjeY2cZm9oCZzTGzT83sH0lyEiv+7oR9u24Ffg/828x2re1nVkKW562CrD8flsTqkKvBBu5+D7AMwEO1+tTXbnmd+viul2ZPjPvNbKC7L3b3/yVD3o8Qr1R5YT57FtXRriH06o1O7jrdzHbyiEUiqsxJbkH4vXw3bdzCfGEzu9zdiz9E/mlhgXAMIwhJ32HJ7SMJb/iDI8UHeM7MClUU9yIsbP5nhLjvJ9MfPekdO504lafOZvkoQrG7CaNTf49wDIA2wDBCD2fs9Rv/JvTiVb3/CeJV/oJQKGCF16a772pmM1LG/tzMfkXYegTgp8AXFgqwLEsZe1lyMXlfcrv4IjLW9IlSHCOztRbA9929eDrQpcnoeCyZVC4rUfym3HbM7AzC9O7CrJs7zOw2d7850iF+TdGaWwvl1sey/L0Qw0IzawG8mSQ8HwIdI8bPch1T1r9/gPWBnu7+TXLMiwm//10Jy0CuSxE76+uGTMvPEzrA+3uy/srMtiSs245xTZvleasg68+fTCpqF/mvma1Pch60MM04fWLo7rn7RxgqLMVxTiDMpW5JWDA6nfAijxV/TUKC04pQiOB0QlXDGLGnAy2KbrcEpkf+/bxNGFl4m9BL8ySwc8T4M4HNi253BmZGil1en/tSHqNF8hq6l3CiOIFkOnHKuBsQEvBPCWsb74jxuqnt9ZHBa2dbwsjXacC2EePeTChGUO2/iMeZSVhwXLi9KfB68v3UCH/fm4GphFHHWwjrclcjTEFKE3tzQmfBXELZ538SyjO3ifXeLdExNiNMt16HMA1sWNrfTVHsF4vbSZhm82LE185YwgVYy+Tfz4CnmkL8ptz2JP50YM2i22vG/GwDXq1yu0XV+yIcow9hbdTGhIv4fwB9I8bP8r2V6e8/iTkTWK3o9uok1w0RPpvL63NfjuOv8LuO9fvP8rxVdIysPx/aAlcCrxCWMVwJrBExfk9CAZT5ydd/A2Vp4+Z1RK266VkbAPM8+W3E4O5/SnqWHiQkaie5e8zs+qeETX/fJJsKbOsChVL5saf14e7RCqvU4JfAs8maREj+BpFiLzCznd19AoCZ/ZDlxWOicPdlLJ8aF0XSO3Wjuw+OFbNIazNb06tUkjSztQkftlGY2emE6ShZ9KpOKvr+UuLsa1ads4EJZvYfwjqszsApFkrTp3ovu/tc4Bc1PPxWytizCVPEqzMhTewSH6NQoW4h4e8c08+BkcmUJiN8hh4dMX5x5TIn9NjGnCGSZfym3HYIf8/iqUZLk/tiedzMnmB5ddmfEqrNRpF8/h/m7ueSTSGjrN9bWf/+IYxIvWRmDyW3DwDuTD6bX08ZO+vrhrlm9jMql5+PuTftJDP7C2E/OAiJTuoiH5DteatIJlUfCzypqJ38i87DXsC7AVsRXvdR1gjmtZhIX+Aawgn0csKLbgNC79UQd388ZfzixahGSAxfJfQU4O7D0sQvOs5lhD3UOhEuMMcTErfyCLEHEX5HzxD+D7sCF7j7XbX+4Modo7rh/vmEHsQoC6jNbHXC2kCAWe7+v9qevxJxexAuqIsvxo7yCAVXzOwedz/MathA1FMuTE0uBA7wyHP7zewcwlqun7v7O8l9nQjz2Z9196GRjjOdUOnov8ntNQkjFtE2qk/iTvVkQXAWil6bRnhtxtrHrgNwHqH6a3FVtxh7/K0BHFdN7GgnuxId44fAJYTe/4oORY9U+S45xjpJzK/qeq40Dcm5/SjggeSugcBId4+29tzMfkIYhTXCsoYHY3Ygm9nTwB4xY1aJn9l7qxS//+Q4vVn+N5jg7pPq+JH6xu1BRtcNSfxNCYnIjizvqDjd42zZVDhnnUq47jRCoaTfx7iWyPK8lTUzu9Hdz7QaCtl5xAJ2ybKVTlR+b/0tVcycJmqTgAsJb5bbgB+7+0sWNk+8M+3FWTKnuUYeuWy8hX0bTiBUv9vI3VtGirshYZqEAS8Dq3vEvVLM7BHCB8ozyV27Ay8RCipc5u6javjRlTlG9Bd1lfiFksPfAj9199G1Pb+eMb/n7h9ZDRuIpv0bmNkfCUPoD1N5d/vUHQhmdjJwAWFqjSfxr3H3P9T6gyt3jFcJ6zgWJrfXAF7xlJWPqjnOFHdfYT+4iPG7sWKJ7NSvTTN7krAu8BzCovijgDnu/qsIse8lbAFyBKFs+GDCtKAz0sYu8TFmEUbcJ1PUQ+8pNtW26qvFVUj7/jKzm6m9mu3peY3flNtezbF6svxCdRxhr8XU+1Emo13tk5GFQnW9o4FfunuXtPGLjvM74AeEKfXFn/+xql1Hf29ViZ/J77+a43Sk8mdzlGQniR39uqGG47QHTnH3KzOK3x84191Tr4HL8rxVdIzrgCsIo5iPE5ZQnOnud6SM27NotGsFHm/D61GErZrKWf7e8rSfb3md+tjKQ0UZzOwyD1Vl8LB5YurgsROxmpjZbwi9PmsRRuvOIYyqpY27I6Eizjh3f9jMygjzzHch7HMRyzKgi7t/mhz3O8AfCJUfx7F8eL1BanpRAw2+GE4+YE8l/H4eIsx5PpXwu5/G8uIraYwhJFJXuHvMjXILPkr+tWD5puBRelTcfTgw3DLYQ8rM/uruRxPWVbxsZsW9qqn2ESm1pDNnd0Ki9iihauUEUrw2i6zv7n8xszOSE8RzZhblREFYK3ComR3o7iPN7O/ELbJSqmPMd/doU8oSa9f9lFSi9Oo3Uvym3HYAzGwjYEPCmpwpyYX8mYRk6nspYx8O/JFQLOBNwojUKMJal9jT1NcjTIcrHqlwlk8lTyuL91amv/8qx4m+P23W1w1mtglhP7bvEUYb7yR0cg1h+TTINPF/RNjD9XuEpTxXEc5VRliHFUOW562C/u5+npkdBHxAKLryDGGdfhpDCbOJ9o2ZWFajN7BN7NHwvCZqxRVkqs4PjjnFYEvCG7ETlUd0Yg3lHkyo8vgIYQj6pbTTp8xsKGGLgnLgV2Y2hlBt8CrizvUH6FRI0hKfAVu6++dmFmNvjixe1KMIGzK+SBjFPI+w/mpgjCmnidXM7Chgp+qmh0bo+Xzd3e8tvsPMDq3pyQ3hScWsyMqS2MPM7FmW96oe4+5TYxzAKm+23NbMCtPWLBw6yqatECoZbktYnH5M0knx5zp+pr4K752PzWw/QlIeq0RzIfaXyYjgJ4TPt5gyO0bSGw/wTPJZdz9QMR3a3ac0NHbWHXTunsU65JLEb8ptB7BQGe7XhLUyq5vZ/xE6L/9GnIp3vwF6uftbyWv0ReBwd3+gjp9bae4efV0aZPveKsHvv9jlQF+q7E+bMmbW1w1/I1wD/oNQtfglYAah0MQntf1gPf2OsC78RUKn4kvAb939/yLELsjyvFXQOvm6L2H23OcxBmeADZPRtAFmdhdV1k2mee1X8RqhMvrHkeIB+Z36uJQw5G+ESmLfFh4iVGhpXdPPruRxphF6IapOAYiy+DI5xtqEC9adCSVfP3X3nVPEe51QmnZhMmz+EeHN/maUBlc+1u8JvVWFpOEnhF6Oc4Ex7p5q75VkCtXp7h7tRW1FG04nU1XmEqr3xRw52pnQi3oYYXpiMfeUa3Wqm9KX9TS/GJIpNYOoYfF4xA/DzJnZRHff3sIGmf0I+2695u4N7rUtir0/YWR9E0IVrXWAS9296mupIbGPJ1wMlBFGNtcCLkpGUqPI8hgWNjSviafpREum4P6UcEH2T8Ln2K7Af4DLC1PaUsTfgNAL/wWhzPdQwiyH/wBne9i7Kpfxm3Lbk/ivEyp5fm5hHdBbLN/jKbWqn79mNsvdt67tZ1IcK5M1oBm/tzL9/Vc51iR3751cv23n7ssKn9cpYmZ63WBm09x926LbnybxY63Jr/r6/I+7fz9G7KKYmZ23io5xDWEGzgJge0LBvDGecjNtC3sdHke4Dq86up/qtZ/EL6x9W5uwV/JEKneCpFoDl8tErVTMbLK7x+7tKY7fjXAy2o0wevQ+oZjIRSliVmqzmZW7e4+0ba3hWEZIzioW7QL/iDUClpw4ehDxRV3NB1ZmCY6ZHefu0ab0mdmPCT1JhxHmghesQxh5bPCJqBSS0a5XqD5RS/1hWEpJJ8WFwOGECpDfEMooZ9LbLdkzs3sIvcJrEjbmfo2QsO0M9HD3/VPGf5JwEbA2YZrN7Un8XYDB7r57XuM35bYn8at+7r/m7t3SxKwS/wPCCFHBWcW3PVIBsuRYma8BjS3r33+VY40lXMxfTSgy9xlhTfROKWJmet2QJJW7s/zc+EzxbXf/vNofrH/82YTZYQXXF9+OMMunZJIBiK/cfamZtQXWiTTqiJn91t0vjxGrStxq174VeMo1cM09UbuE8CZ/gMqJQqo3TVH8wpTHCYRiCunLdJp9SVgfVrBr8e20mXsp1fTiTvOiLhqNhcojsrGnxmGhmuEvCT1jJ1rYQHErdx/TwHjbEhLXy4DiZP5r4Bl3/yJlkwvHORUY7e5fJrfbA4Pc/fcp4071DKswNhYLlTHX8ZSVv8ystg4aj3ECsVD16yesOJ37sgixh9TysHuE4kJVjrcfK44qNPj/Ubh4NLNWwAfu/t2ixyr1eDcw/jR33zbp4HrX3Tcteix1h1qW8Zty25MYn7F8I14IHSwVtz19MZSSFSArfI6a2XR3LzOz1sATMTu6MnhvZfr7r3KsNQlbCxghiW1HOJ+lKTSU6XWDmb1DWNJTUydmqoqbZnZ7LQ+nmuVTovNWrRuKx0o0LWwkfwRh/97LktHf77r7xEjxr/Uqa+Cqu29l5XWNWqkclXw9t+g+J2zompq771f43szam9kmaS/2gAOr3P5dyng1srBNws1AF8J87ZbAf2MlO2l7GWqIGaWiZj2NIEybLfTkfUCYJtqgRM3dpwHTzOzv7r44OUF3Az6MlaQlTnD3W4uO+4WZnQCkStRWNRYWx1eUsDazXd19XO0/VavqKp+tSZiSsT5h7UVaDxG20JhMUedTJH2quc8I+xhtRMriQpWCmg0nbE7aj7A28BDCyHsaiwDcfYmZfVTlsaXVPH9lLU3iu5lVnUa5rJrn5yl+U247VD6HQ6S9owpiJmL1kOk604zeW5n+/ot55QqSUdY+Zn3d4O6dMo6f5UyPUpy3DqBy/YnihDZmIZ1bCZ83PyJ0iH9NmMZf3bmtIfYCqiZlP67mvpXSrBM1z3hDZwsFFQYQfs/lwBwze87day0TXZsskpta3ELoGbuXMHVzCLBFrOBZJ4Il8H13/6mFPe1w9wVJj3GDJCfQm919hoXNeF8kXOCsZ2bnuHvq6lCJFmZmhSmsyZz8GBteZ1lNqaTM7FrCeqbXqVyRtMGJmrtXdKpYWLt6BmFD27uI1+GysbvvEylWJe5esdlp8jofTPibv0S8ymIFOyWjCdPd/VILJcvTnqw3NrObCBcBhe9Jbm+UMjbA5mb2cBKv8H0hfoxzTZbxm3LbMy9WUmK3JbMcfktYA71W8n0s0d9bpfz9J6Mv1wIdCa+f6LNlZLkSnbdeq3pYYA5hj7y3Ix0DYAd372lmhT2Tv7CwzUYqZvZzQlG/71vYR7ZgbcJeeak060QNKtaRRd8rKdHO3b+ysPj+dne/uMofMfc8VLlq6e5LgdvNLPWLrkh1ieAPIsbP2iILe+QVEp7vk24UYxd3Pzn5/hjC/jMDzey7wGNEKOObeAK4J0kMnbAnSqpN5AE82VJjFTGQMI016qiUma1HWN8ymNAb3DPyaOkLZtbd3V+NGLNCMm3waMK6vZeBQ9z9jQwOVaj2+62ZfY9QrjztBX1xr3/VBeUxyscXz3a4vspjVW/nLX5Tbvsqxd0L1WWfI9LsniqyeG+V0nXAAe4+s7Eb0lyU4Ly1VjX3bQb82swucfe7qnm8IRYnHdOFa7YOxBnR/zvhGu1q4BrCkiQIiWbqitfNOlGzbPdKAmhlYVPqwwila5uab5PehnILGxF+TBjyjibjRDBrFxMSnE3MbDSh6MrRKeItKvp+L5Jqm+7+SYqBuur8CjgJ+DmhN/JJ4pWeX1XMJpQKjpaoWSiJfTBwG9Dds9kiYWfgaDN7m9D2Qm9zWdrAFtY2ngE8BezjKTd2r8MYM1uXUB1wCuHEmuo1mnWvf9azHbKM35Tbvqoxs/8QRqnHE/ZKfT3yIaK/t0rsUyVpK0rWX/V196jXUKU4b9U0tThJEMdSef1jGjcRalJ0NLMrCdN+U49Wu/t8YL6ZvUTY8+1+wrl3pJn9yd1vThO/uRcTeZXleyVta8leSe5+QKT4hxJeBBPc/RQz2xwY6u4/iRE/a2a2GfApYVrcLwmLdn/vKUspF8UfB+xJOEl8QkgEj/aUi/qL4m8G/MDdxyYjX608bpn+9Qhvxr7J15eAtRs6VG+hCubvgA8JVaG2TpK0VoTS8JmUg44p6a26xt2rrlloUszsH4TPhqeoXGiowYvizWxZEmsJK87HjzJ1J3nNryBGUpW0/zPClJTq2p86GazhuKsTtmWZn0V8kbqY2caEfUUnJLfPYvkowN9jnROT2KsDOxAqYv4Q2BqY5u4HxTpGlWNl+t4yszPd/caI8f6PsFfVg1T+bE69jsnMTiMUJok5WlS4VqiRxytg96K77xgjVlHMzM9bdRx/qkcsUmZmWxMqzxrwVMykP5kxt2NhHaWFwjcvpj03NusRNWCBhz04lljYmf4zIk418LBp8b1Ft2cTKrKllgzZ/ooVp21GqwxVdHG3EMhiMfWRhHVppxESwU2I9/s5gbAB5HrA9wkbMw4nvEFj+SfwY3d/JDlmF8Lfu6FliU8i9Ph8FzjTl5ek3YOwaXoqZnaPux+WdFCs0EMT40LbQ0ndXsVr4Jqoh1lxj7xU3L1FzHg1HONdADPrSNHnQiQlnR5lZjtRVL3SzGJOS5dVkJltCfwB+I6HCp9lwAB3vyJl6KHA6KLbJxFGGNoSzo2DU8YvtpRQUGQpYVrWp4Rrk2hK/N46C7gxYrx1CBUZ+xfdF6vgxHeBV8xsCqFY2BORzmOTCW00wt60XyTfrwu8R7zP1ifN7CfA/bHOv6U4b9XEzH5E+F3FijfK3Y8kbH9R9b4oh6ByYaqlVF/pc+WCNu1rqXQs472SLKONK5PYTxL22jqHsMboKGCOpywDWuUYPwQuoajyHYCnLCVbCmZWTtgw8eVCb4wVbWoZ6Rj7AecR9j7bmjBldrC7l8c6RkxmtqG7f5zlqEtynN8R1hreS1HFqBg9nlI7MxtAGJX9HuHibjPCHkypN+ouJTMbRehgKaeomEuaEc2i2D909+fruk+aHjN7jrAW8Y9Fn/up9/SyFffZmloUf7y775ImfpVjfQu8StinbaynKDtfQ/zM3ls1HO99d98ki9hZsLDOoD9hnXhv4B7gL+7+nwixhwMPu/ujye0fA3u6+9lpYyfxviYsT1nC8i0MMh/1SquGzuP1gI+AIe4+a8WfatBxqr6PWwKvuvs2keKfRbgWfyC5ayDw17Qjys16RM3dT0m+HW5mjxNhr6QqRhEy970p2rgyUuz13f0vZnZGMv//ueQkFdNfCCNdk4lTvhqo8U1ZIdIUqv+5+6LC2q5k+mDUXgl3f8RCCf1/Ear7DHT3N2MeIyZ3/zj5muXaIggfsPMIJXArDk+8EruZs7An3tWsOGKd906KywlTccd62IupHzCokdvUEL0Jm7xn0ZN4M1B1M9vq7muQZFTnXFbs4Ioy2yHL+E257Ym27j6xypreJRHiVh2dLp6ZsX6E+MUGEdaangIcn6zbHufuT0WKn+V7qzpRjmNm57n7dWZ2c3UxYyWa7u5m9glhOcYSoD1wn5n9y93PSxm+jy8vGIa7P2Zm0TZgdve1Y8Uqsf2r3HZgnlfeiqHBzOwCwqBMGzP7iuWjXIsII+NRuPswC9Xed06OcYyrmEh6Fn+vpGJbuPuhZnagu480s78TKu7FUNhr5eNkZOcjwvS+mOa7+2ORY8KKb8osPGdmhTfmXoST3j9jBK7mRLEOofjEL5IpJJn0TMZiGZc3jjUi3chuJxSLuYGw39AxRJjCUAKL3X2embUwsxbu/oyFrQaamtcI05A+jhXQzHYk7HnYIen5LFiHMAU7lnsJ06z/RMQOrhLFb8ptB5hrofpuoarbIcR5DX1tZlu6+79h+ZqiZL1L1OIK7v4Q8FAS+8fAmYSZG20iHSKL99bXVJ+QFTaPjqHQyR2jQmu1zOx0wojIXMLa+XM97GnaAniT8HdIY66Z/YZQcMKBnxE6NaMws12ruz/iNW0msu48dvergavN7Gp3vyDjY00hFOmJplknapbBXklVZLlx5RUW9to6m9AbvA7hAz2mZyxU/Lmfyot2U70ISzCiA3A+Ydrpq4T1BI8Sr7JV1RNFZpt7ZiTT8sYZrhMppTbu/lSy1u5d4BIzG09I3hrMzAYS9iJ81d1jddoU+9LM1iJ8ho02s8+IM6JQahsAr5vZRCp/9gxIEXM1QgGIVoQR8IKvCNW/Ylni7n+IGK+U8Zty2wFOJfSQb21mHwJvEy6G07qYUC3xSpZfhPUi9NKfESF+BQuFjHoAbxEqPw4hbIWRNu4/Cdc3axP5vVWKkRx3/2fyNcvqrRsAB1e9RvFQyyBGB/MgwmupMDVuHHFnPBQX8VqDsPxjMpVntzQ7ZrZ1Mn3yXjNbYeZE2mvarDX3NWpvAGUeea+kovjHE3Y9LyP00K8FXOTuwyPEznydhYUqhFV52mkqVXrfCqMUhYW2uZ9PXczC9gVbJjffcPfFtT2/njFXJxRV6UTl6UGXpY2dxH/e3X8YI1YN8TNZJ1JKZvY8oerafcDThEqc17j7Vili/p6wXvUFwtSpf7p7tGkvyTHWZPnahMGESq2jY65zqWbtauF9G21aqJntVt39HqHMu5lt5suLrrQA1nL3ryLELVR2O52wPvABKl8Ip6rslmX8ptz2Go63JtDC41b57UYYUSms95wBXOfuVTfrTXucPsAUD1vWxIxb7XuqIM17y8x+5O5PJ9939qLKx2Z2sMepyHiju59ZlHAWc+BzwjnnpRTH6AvMKLxuLGzwvI27p06UG4OZbUJ4jTY4GTSzCe6+czWjpk3mes3MbnP3E7O6ps1ac0/UHgMO9Wz2M8pU1UWRNd3X3JRo/VvhWLsTNn98h/ChtQlwVNppBsl6yflUWRvo7r9LE7cofmbljZP4r7h7H6u84L7c3XvEiF8KycXSTEJVrssJI9ZDU14EvAZs66EyZltgvLv3itHeomNkUl66yjFmUc3a1ZjJYJaSKegnE9o+mZDMDnP3oSnjvs3yDqeqUieyWcZvym1P4p9V2+PuPixN/FKzKlUZgWhVGc3sWq9SdKy6+1YyZsW1R9XrkFjXJWbWy90n15JwbgBc7ikKQ5jZVMJmzoWpsy2ASbGuq5LZJuew4t82k0TBzAyY7hGLqEnpNcupj0VrjL4lbOYcba+kKseJPjJSwnUWhePtx4pVK6OM7CTxdybsdXa7mW1Ain3IEoXpCacmX0clXwcT/t4x/Q7o7+5vQMWH8J2EKTFpbOzu+6RtXC2yLG8M2a0TKQkLlaAO87AX3DeE9WkxLCr0krv7t8lJNLasyksXy2rtaoWkZ/tmoAthymJL4L+Rem+3cfevzGwwYUr0rwgJW6pEzd07A5jZGu6+sPgxCxWAU8kyflNueyLTqXdmVutWHSmn5FY9VrVVGQlVhWPYi/CaL/bjau5bGVbD99XdbhB3n5x8rXHkz8wWpTxMpW1lkimPMa+TC2s0/0wGazSt8vr5FoQptNMixV6hjH119+Vdlp0gWWmWiRrL1xhNJvJeSVU8xPKRkVjTK0u1zqJQSrYtoZjCn5P4EyPGv5hQgWorwtTQ1QiLbBs8La9oStMPq0zvOz+ZzhYtyQRaF5K05Nj/tlAFMq0XzKy7u78aIdYKPPtiH1mtEykJz24vuC4WNsQsXLh8v+i2xxjtdfffmNlvWV5e+hYzi1ZeOpHJ2tUqbiFsm3Iv4TNiCGHLhxhaJ+/TgcAtHooFxPw7v8CKFSSruy+P8Ztk2909i30+i+0IvE/oiHuZbAsLZVKV0cx+TiiqtXnyuVOwNuFvkIbX8H11t1OxWiryerKOLYXZFgqKFNZRnkIoFBZL1ms0i9fPLwHu9HjLYSpt8ZIksFFnhGStBJ0gmWiWiZpXsxjVzNoDm3jc8vzRR0Z8eSn+Be5+XfFjZnYooTJRLDu5e5mZTXf3Sy3sjxWzxPpBwHYki7Pd/aNkTngMa5rZzu4+ASp6UdaMFLtgkpn9hcqjdg0uLFI0bbMVcIyZzSZcCEe7kE+OszFhtOKHyfEmAGe4+wcx4nvY2H3PLNaJlNBUQuW1mHvBbU3ki5bquGdaXhpgh+Rr7+LDEnnBuru/ZWYtk1HI2y2UKY/hj4TpytOAcRb2FYyxRu27wEaESrPbsfxifh1Ch1du4zfltifxb6rt8QizZL5LGIkaBBwBPEK4CJ6RMm51oldlTPwdeIyQ5JxfdP/Xnn6N4ObJqKMVfU9yO9ZmzgVZVuQ9GbgJ+A3hM+0p4MRIsQH+aWankNEaTQ/VxVdj+bnmjTp+pE62Yml7CL/vqKXtS6TUW1NE0dzXqD0LDCBcGJcDc4Dn3L3W+e4rEf824OYsRkaqm/cday54UbyX3X0HM3sJOJhQRvY1d4/Ss21mE919+0K7kwv7F2MkJGbWizD1q11y15fAsTF7/ZOpraeyfM+MccDvvYHFaayGjagLPN6G1P8inLQLCebPCBt17xUp/lLCNLILiub6N6n1k2Z2ezV3u6fYrN5qLmEN4aT9H+DXnmK/JFuxvPSDXlRe2t2/39DYpWRm44A9Cf+HTwgXrUe7+7YZHMuAlu6eqjqmmR0FHE24GHiF5RePXwEjUyb5tcX/mrCpaoPjZxm7jvgxfzc1qq5zNsWxVickbEOBy9z95khxi6sy9iDMXolV8bS643Wk8ojUeyliZVaopJpjTXb3Xmb2amHtlUXedDwrFtZqVuUeqRCTme1L6Ij6D8uT5JNiTFW3EpS2z1rS8Xq6J3vKNhXNPVGb6mFT2OMJo2kXJ6NHsUYuXieU4n6bSCMjFnay3xc4DLi76KF1CD0F2ze8xSsc67eEkZc9gFsJJ5E/uftFkeKfQ5jOtBehl+9Y4O+xTnzJMdYhvM7nx4pZJf5qhKmbTryqj5lWnrJqCntUd1+K+NOBxwmjpT9198+tqLCIrMjCurhuhEIgDa6OaWaXEaY5rpDUm1kXj7Qlg2W/dnUzQnXA1oTCJe0InSBvxTpGleMd4+7VJecNifUTd/9HjFiljt+U217lOGt6pM1yi2KuDuxHSNI6EZZNjHD3DyPFL0myY2YHAMOA7xHeY5sBM929a60/mBOWQUXeotgdgBNYcQ1TgzvoSslCoaf9C5+TFtaKP+LuW0eKX2nfYcj/Hm1Q+k6Q2Jp7ovYqYS3HSEJP9iuRE7VqR0jSjIyY2baEF9plQHHC9DXwjGdU7S05Sa0RO+GxsBl1f0IS+4S7/yti7KwvJncnm6qPWVeeGgv8lbDeAsKFxzHuvkek+IUR0sMIU1SGEBL83I+omdl57n6drbipORCv0FAtxz/J3f8YIU603vJqYle7dtXdj4t1jFIzs/fcfdMM4v7N3YfEjpvE3pmwT9KraT83zWwHwsX6V2bWhjA1ridhj9Gr0n7uJyO998eaXl3LcXYE/kLYcmHT5Hx5krufkjLuSEJHymPAXR65JH+VY60JLPBQyGJLwjS2x2J0AibxpxGmKY9NOqr7AYPcvcFT/CysG7sQ+IKQBP6JkEz9BzjO3aNtUm0rVuRtRyhB3+CKvEWxXyDsXVe1om2UzgULa2N/DhQ2pn6WsKVArL/tOHfftei2EWaJVbsR9krGvoawbrjSvsN5T3IAzOyXhORyKsv3N64Qc8Q3C809UTsU+C0wwd1PMbPNCSW4fxL5ONEvmsysdaw3dy3HmEYYtbvb4xUiqOlYGwDzPNILshQXk2Y2GTjCq1R99JQl12sY8YrZgbApoVjDjoRk5AXCGrVYUysrRs/MrCshIdzU3deNET9LZnaAu/+zpqlUMadQZaEUveWF12LR17UIF+H96/zh+h9jf8JFWNW92hpc9dEqF1Co9BCwpbuv3tDYSfyqhamM8PnzNKTvtbVkqnjy/QmEadcPEDq6/unu16SIPYOwdcQSC1P2vyWMWOyR3H9wyrbPJ6z1/A/h8+Bed5+TJmYNx3mZ8Fn/sEfcw9HMlrF8rWqme0kl55VdCGtLXyIUiPjW3QdHij/J3Xsn5/ftkoSw4rXVwJgTCAUZ1iGMgJ8J/JPw/7jC3Xeo+afzI+bMkhri/5kwS6BwHjkSWOrux0eK/wfCZ+Y9hNfpoYR1as9DujXWlvG+w1kys+sJ1dK7ENYmv0D4nbzokfdwzEKzLCZS4O73EqqKFW7PJpTTj8LMBhBKuFe6aKJK9ZwG6mRm1VY+ihC7YADwU+Ce5ER1N3BP2kQzmdp3DWGDyssJa6U2AFqY2RB3fzxds4HsC6FAdlUfM6s8lUyxuyrjXrCKk467z0h6/gdmeLxoPKkalveErBZXAH2p0lse+RgLkq/fmtn3CGtXYxcMuJGwLvbVWJ03wHeAvQm9/sWM9FXvADYm9Db/meV7hvUmnANiKP5sORHYy93nJBchLxE+UxuqhS9fo9e7aPR7gpmVp4hbMJtQIW5Pwjnl0iQhuZOQ5EcrOOTu71vlnS9Sl0F39xZpY6wE87B9x3GENe7XRfobFHyZdK6MA0ab2WeEokNprOXutwGY2cnJtRXAvyxUiE3NSrDhNTDGzPZ190dTxKhNH6+8zvbpJGGOZQ3gU6AwjXYOsB5wAOm34JlN+Axqcomau58DFUtVehOStmOBP5nZl55i771SaNaJmoX9W45jxelxseYjX052F01ZVj4CKqZoXgdcl0xt+C1wLen3a7uFME2iHaG3+cfu/pKZbU04ccdI1EpxMTnZIlZ9LJJZ5SkPpec7mNlq7p52z5majjHZzLpRpROhKajhIqBCE5jmsdjd55lZCzNr4e7PmNm1kY8xxszWJRRTmEKydjXyMd4nFC6KOeVjDOGCsrzqAxYKS6XVGzgD+DVwrruXW6jOG2taTQsL1YlbEC7m5wC4+3/NLO2F9mu2fJ3eNDPr7e6TklkCMWZuuLsvA54Enkw6tH5MOB9eD3SIcAyA9y1U+PXkoux0QudoU2LJFM7BhOsTiLtH6oGE8+Mvk2O0I/22NcuKvq9aQXUZcRTOs9fX8PgGhAJiaS66zwAutLAf2yLij5guNbPvF2YoJbO4ou2n5tluvZPpvsMl0oYw6tsu+fcRkMk2SDE160SN8MafRehlvYzwoRXzQz3Li6Y27v6UmVmSUF1iZuMJyVs0ZtaJULjkp4QPlBjlvVu5+5NJ/MsKPWDuPsvi7QFc3cXkn2MFT5xMmH50Oiyv+pgmYDLiNczdD0/fvBq9AzyfTNUqLj0/LEZwC/vj7U44YT5KuCArTI3Ju8JFwMGEEtl3JLcHEX5veVfoLR9PvN7yStz98uTbf5jZGDJYu0r4nHnUzJ6j8kVBg1+jtU17dvcjGhq3KMYy4AYLlcVuMLNPiXuObUfoCDJCIvJdd/8k+Xun/eA8Hvg/M/sNoWLoi2b2PiFhjjEtq1L7kmn7DwMPW1gTF8vJwP8RtgL4gJAYnhoxfimcCVwAPJDMSNgceCZG4OT88pC770lIoGLNHNjalu8JWdgfkuR2lFk+XoINr909043TgXMJ+1DOJvxuNiN0skeR8eDDw2S773BmkuncXQm1HF4mzKAY5hnVdIituSdqW7j7oWZ2oIf9J/4OPBExfhZTDAoWWlJy28xOI1Q+6hgpNlAx3781YXroocnU0BiKe9gWVHksSg961heTye9+crL2IUqCA6UZ8SL0In1E6JkvnJhijlwcAmwLTHX3Y8zsO8RPkjNRuAgws8u98gLsf1ooGZ93A4CFhJ7hnxF6D6NuBmxmkwgj+n9PTnRZTIW5EviGcLGxWgbxM+OhYMahFooZpd6frShupxoeWkbYkzJN7PnA0RYqzG5OuDb4wN0/TRO3yE9rOXbVc0AaFmstV2Px5Xulrpncnk3oDIwRe6mZfWtm7SJ3rnSJGKtWluGG1xZ6igcDnd39cjPbBNjQ3SemiVvUxqeS9m9FSNRmRV7zldngQxNeDgCwKbA6YZ/hDwmdOF82ZoNWRnMvJlLYx2scYR3QJ4SCE7H2tFiTkIi0YPkUg9HuPi9C7MwqHxUdY2t3nxUrXlHcpYSRHCMMRX9beIiQUKVe52Vm1S1+n09Y8/JZ2vjJMUYT9gqLVlEviftHQsW1rEa8Di1aQ1DjfSniF95XkwnTcr8mTGNrEuWfAcxsJrBfoXPCzDoDj7p7yS5IVoZVv0dbYRRjIRH2aCs61haEXuCfEgod3A48GXOaoiUFD2LFk+bBzN4kbIdzN/APd/+ycVu08iyjypVF8e8hLMn4F5XPL01iCpuFwiWFZR8HkCz7cPfUs4ksFONYBvzI3bskU42fdPc+aWMn8VsStnjoROUS97HO7VOTZTaFQk+tCdW0fxQhdo0JctrYpZAk4V0J69N2IlRx/ZxQUCTqTLTYmvuI2m3JG/G3hIvitahc8j4VX76PyzIze4SIVQ3d/ZXk22+IOHRe5RizLIMS9+4ec759TY4jVDUsTBnZnbDgfstkuuWomn5wJWwIzDCziVQ+4aVdx1TdiFdMF1BURKeW+xpqUjLt9E+EqVrfEPYtaUp+CTybTFGBcGI9qfGaU7vapuxY0R5tyde0x3oL+LWFfRb3J6wLWWZmI4D/8zhVtMaaWf/CFGmR+nD3H5jZ9oQy4r+2sJfpXe5+Rx0/mic3EkZEHgZw92lmlrq8epFHkn9NVZbLPnbwsLXMVAB3/yJZ6xjLPwkdZ68Sb+1escJ60i8trBP/hHDuiiHzughZSq69XzOzLwmd9vMJ56/tibxkKLZmnai5e2E61nNEmkcNpalqmCzyPpcVNx9M3XNSdIxqS9zHip+xZUCXwtSdZPrdH4AdCFNRYyRqUaeUFbh7JnFt+WbpG5nZTUUPrUOkKblJr9XVSU/2cDN7HFjH3WsqjZ5L7v540oNY2Cg09hSVknH3pYQCETE3ki8jnKj3Bf5BSAJ3JhQH6hHhEKcC55nZ/wgXH9HLoMuqKZmmNtHMriJMSx/J8rWmTYJnULmyKHZTnsIG2S77WJx0bBX2MO1A3IRqY4+0zU4Nshx8KEldhCxYqKK9E/BDwvnkeeBFQiejionkmYVNnH/CisPQaSsglaKq4b3AcMKoRbQP8SpKUeI+K52qrK/4jLBX0udmFmX/ueJFzRZxHzgze4bqN1tOm4R/RJiqNoDK1Sm/JowgpebubmYPEkpx4+7vxIjbSHqx/LNhWzPD3ZtCQZRqeYSNtKFin6cvCdOzzi9KYF82sx/GOEaWi/qTjrSbCetqViNU1PuvksCmz8zWIazXOxz4PmGfuQbvD9ZIMq1cmeUUNjP7EfCSu39b55Mb7kxCB/LphI7wHwGxNpW/ifCa6WhmVxI6p38TKTbAY1nOFMhq8CGReV2EDHUi7Av5S3f/uJHbstKadaIGPEQY/pxM3AXxpahquMTd/1D301IpRYn7rIxPiogUpvP9BBiXrBv8Mk3gEoyYnlP0/RqEtqce8XL3aYSRlQcIF6ZLoWJqXKrNfqt4ycz6FE3PbXLMbBThQq+c5R0hTtOoXJm1GgsLecqNkYslo3adqNyJFqOj6BbChfy9hJL6Q4AtIsQFsk8Es4zflNuemAY8CFzm7i9GillqWVeuzHIK29GEmRTzCJVnxwMTPGJ1varLPsysFWG97MsRYo9OOqL2IPxOBrp7zErgLwEPJAlPtJkCZvYzd7/DzM6q5uHCHnMPp/w7nMmKCfJRKeKVjLtX93tpMpp7oraxu++TQdzMqhqa2XrJt/80s1MIvT/F5atj7rJeihL3WTmVkOD8kPBh+DfC4nInnJzSyHTE1JMyxEWet1CmPJYnCRvPfpPcbpPct1Ok+P2Ak83sHZYXjfGMp3zE1hvYJmaBjKau+CKgug6nWAvik/gjgDJgBss/T9Nu2FrB3d8ys5ZJZ8XtZhZjw+uCTBPBjOM35bYDbN7U37PuPpdQfCwrmU1hc/chAEnH7iHArcD3iHCtmYyWnkpIYB8mFEM5ldCxOY0w/bqhsdcruvkZ4Txe8VjE66rfEdbOvxr5dbpm8rWmmQidgZ8Tisg0SCnqIkj1mnui9oKZdXf32HNUtzWzr0iqGibfk9xOuwHwZMIFS+FK6dyix5yIw91emv2SMpF8CN6X/Ist0xHTKieNFoQpeN9NHXi5Ndy9kKTh7t+YWdu0Qc1sUw8VMH+cNlYOvEb4nTe5aRIZynqPoWJ93T3NxrW1+TaZUlZuZtcR/sZr1vEzKyXjRDDT+E257cAGZnYeKxbAirZ2OyvJGtIaL949XlXGzKawmdnPgF2A7oT9+G4hjKrFMAr4grC26HjCtc9qhFGv8pSxi6+rNk2OY4Sq2u8RbybRm4QKyFE7E9z9j8nMmK/c/YbqnmNmqZb0lKIuglSvuSdqOxP2jnmbMCoVpeffM6xq6O4lm3poZocCj7v714Q3aE8L+0tNLVUbVpaZTXD3nW3FcuUxixFkvQ9c8UljCaHcdI2b9TbAf82sp7tPATCzXqz4/2iIB4Ge7v6umf3D3X8SIWZj2QB43UJFz+IR67QVPZusrIrc1OBFM9vG3V/PIPaRhCl3pxHWZm5CGH2PJetEMMv4TbntEEZV7iZUczuZMDVrTsT4WZpUouOcSXZT2G4kbAUyHHgm8hrlzd29O4CZ/ZmQCG6aXJ+kUriuslBA7WF3fzS5/WPC7JNYPiZUE36MyueV1LMRPOyRN4AwpbW6x9MWFSlFXQSpRnPfR22z6u5PpgPkmpVmn7DCXhw7ExYfXw9c6O47xIjfVFkJ9oHLkoU9+O4iFBeBsM3AT6uZcrmycae6+3ZVv2+KzGy36u4vLiDTXJnZGoSOg6qjFsdGPMauhFLWnxCxE60UkvPKZ0BrQiLYDvi9h20Nch2/Kbc9iT/Z3XsVzl3Jfc+5e7XvZ4nPzLoCuxI6wn8AvOHuR0aIO8Xde9Z0O4bC66fKfdH2dDSzaqeXxuoESwqgtCN0VhRvGTQlQuwVfjdSGs06USsws45UvuCIuoFxFizsy1btPmGEhdSpy8/b8s0TryYkgH9vChfgybSO6e6ees+oxmKh6lcnKk8xiFbIwsJGmFsRLoBnuXvqSpjFJ84sTqKSD2Z2LzALOAK4jLCeZqa7nxHxGG8BZ1Flv6E0nWhm9iq1Ty3LfRIotTOzl9y9r5k9Qajg9xFwn7t/v5GbVicz+ye1vz5Tjeab2cO1PR5jtkCyjuyHwG6EKZAbEKpAph6xK+oghcqdpNFmyySvm/GE7Rwc+Bmwq7vvnTZ2KVioGF2Vp5meWLQU43RCJ0uWdRGkGs06UUuGiX9HWOz6GWHu7Ux379qoDauH5EP9eF9xn7DjgXExkpRkXdqHhKH/wvS4ie6+bdrYWTOz0cAFTSHprspqqDgYa41Csh7tLGAzdz/BQrnmrdx9TMq4tY00xpp2mqlqpsxWPEQT+T9kragDpzDi3hp4IuZaBTN7Ovbah5pmUBSknUmRdSKYZfym3PYqx9mfcKG9CaG65DrAJe7+zxjxs1TTKH5B2tF8M5sDvE8olPEyVSo9xpgtYGbTgQnJv3Hu/kHamKWUJCUXE0YEIey5emmsZCRZ53UOK3bC5nadV7I0qLguQjH3CNs6SO2ae6I2jTA/e2xy4dEPGOTuJzZy0+pkZq8W5msnt40w6tUt1qhXckG/TxL3TTPbEOjuGe0BEpOZPQ30IWzQXTwFIPdrjMxsJhlWHDSzuwnr4IYkr5c2wIvu3iOL48mqxcwmuvv2ZjYOOIUwPXFizBO2mf2esJD/n1Tuvc3tPo4lSAQzi9+U216PY5/p7jdmFb+pSIpN7AUMIlRUfQS4091nZHCsNd39v3U/s3lJrjmHE86/Feu80i47qHKM/VhxWnravYGlETX3YiKL3X2embUwsxbu/oyZXdvYjaqnzPYJK/CwaeX9ZtbRzDZN7p4VI3YJlLLwQWxZVxz8vrv/1MwGAbj7giTRF6mP28ysPWEj2IeBtYDfRj5GG0KC1r/ovijl+S2jvbyyXtucZfym3PZ6OItQ5KJJsIw2pPZQZfNx4HEzW52QsD1roXLxzWliF5jZjsBfCJ8Jm5rZtsBJ7n5KjPhZMbMb3f3MmqafRuzgzXT/26QYSlvCFjl/JmyRMDFlzD7A++7+SXJ7COF6813CaLWmPmasuSdqX5rZWoTh7dFm9hkRNhYuBXc/xcx+QliwG3ufMKDaqaGbEhK13E8NLZ7GYWYbAPOyGqGKpegksTbZVhxclIyieXLc7xN3w3dZBZnZxu7+gbsX9lIcR7IdiJkdEPNY7p7lPj3a8LoRYpcifk2HzTB2FjLbkDpJ0PYjJGmdCOv4Yo5S3wjsTejAwd2nWSgMlHeFNf3XZxHcSrf/7U7JdPTp7n6pmf2O9H/fP5JUvkz+ltcAvwB6ALcRkkHJUHNP1A4krLv6JWFBfDvC4vhcq1Is4x8ZHupywgaJlaaGZni81JILgWuAzwntH0VY0NzCzIa4e6rNqDOWyUmiGhcTelY3Sdby/RA4ukTHlqbrKTPb26uU3DazYwija9HWAVnGlSVdG143RuxSxK9OrjvoqpHJhtRmNhLoBjxGWHf1WoS2rsDd368yQSP3pdwLUw+rdPC2BzZx9+kRDlGq/W8L2+x8a2HT8Xmk3wOuZVEi+VPgNnf/B2F/3fKUsaUemnWiVjSHepmFKoq5H3UBcPdlZjbNlm8wnJWmODX0FuBCQtL9NPBjd3/JzLYmLKLOc6L2IfAdd3+++M6kF+vDWAdx93+Z2RRCEm7AGe4+N1Z8WWX9EviXme3r7m8CmNkFhOqPscufjyKM3u9NUWXJSLG14XUjxc4qvtVeBKhN2vglltWG1EcS1mtvCZxelEzFLJT0voWKxZ68x04n3vs2c2b2LDCAcG1cDsyxsL3DWWnieun2vx1jZusCQ4EphPfEn1LGbGlmrdx9CbAHUFzDoVnnEKXSLH/JTXzUpWBDYEYyPS6rYhlNcWpoq0Kxk2Tu/UsA7j6rCSzDupGQZFb1bfJYqullZla1XH5hDdymSdKfeq8VWXW5+6Nm9j/gMTMbSKgw24dQvvqLyIfbwt0PNbMD3X2kmf0deCJSbG143TixM4vv7munbll+nEkGG1K7e4u0MerhZOD/gI2AD4AngVNLcNxY2rn7V2Z2PHC7u19soZJlFGZ2KjDa3b9MbrcnFLD7fYz47n558u0/khoGa7j7/JRh7wSeM7O5hBG78QBmtgVh717JWLOs+mhmk1g+6nIbVUZdYlRMzJqVYEPepDDJAqAFy6eGjnb3ebGOEZvVspdX1dt5Y2aveQ3bKliVKp8NjF/dHisF7jkuESz5YWY7Aw8CLwCHufvCDI6ReWXJrFgT3jS6KbddJC0L20j0B0YCv3b3V6xo8/QI8cu9SnVli1ClO+uCH8ngxobAk4WZaBa2GlhLHbzZa66JWsWbxcxmunuXosdSv2lKJTnp/cDdx1oopd/S3b/O6FhNpSBHbXt5reHurRurbXUxs7fcvdr1GrU9JlIKRdPLDFgdWExYfxJ9j7mkR/sfQHfgrySVJd39jxFiV7en13xgEnBFnjuiZNVlJdiQOitmdlEtD3vRSE+umdmhhAq2z7v7z81sc2Cou0cZcU9G57YtXEdZ2DJhuqfcuzdZyrCnu3+eLJW4i+UFP7q4uwp+NGHNcuojsKzo+wVVHst1IlJgZicQ5gqvR9gceSPC/hx7RIjdZKeGunvLxm5DCq+Y2QnuXmlOuZkdR1iMnIqZnefu1yXfH+ru9xY9dpW7VzftUgQozfQyK01lyccICebfk9uHE5LN+YSkMO0U40wTwSzjN+W2rwJ2pJYNqXOuuj3T1iQUBFqfcB2Re8k58d6i27OJOy36CeAeC2X0nTBVNMb1lAp+rMKa64hakx11KUjefNsDLxdGAGNMj0viNPmpoU2RmX2HULZ3EcsTs96EMtYHFaY1pIjfZKeFSvNgZm8ANVaWdPfvRzjG8+7+w+ruizTF+DpqTgR3dve0iWBm8Zty25s6K+GG1Fkys7WBMwhJ2j3A79z9s8ZtVf0k0/n+QCjq1c3MyoAB7n5FpPgtgJMIHepGWMP356SwTpq4rwE93H2Jmc0CTnT3cYXHalpSIU1DsxxRa+KjLgX/c/dFhQIZZtaKeKOBTbkgR5Pl7p8CO1nYBqHwwfqIuz8d6RBWw/fV3RZpDKWoLLmWme3g7i8n8bcnTK2EOMWS/r+9u4/RrCzvOP79rRpW1JUaa2O7qVRs6wvbxW02IFJbhaZdG4FQIm1oGxHsS9LW0IBpbWqrxmhL2tQ0TRURl1qi0RaLGiSiItgu8rKwL620GrLQmmgJICngQuju1T/OGXYYdheYOWfmuc98P8lk55xnuJ5r2M3Cb+77XPdrFwTB3fOC4K/NeP2We29aLcOB1GNKd1bYH9A9z34ZsKmGHzI0to/Qjc7/MEBV7eoHGQ0S1KpqP10QHPrQawd+TNiqDGoTcV2SdwLPTvLzdA/cD3WOUfNbQ1tWVdcChxv8sejSh/j8YNfSsqvlmSx5HnBpuom2Af4XOK8fnvT+AeqPHQTHrN9y783L+AdSjyLJRcAZdDtwNlTVgyvc0mIdWVU3LfiB9JL/XCb5VFW9+RBbf1nqsJKqel+SL3Ng4Mfce6yhe1ZNDVuVWx+noF9CP5duQlHo9j5fMsSwjylsDdUT+fuqVizTZMnn0/038P6B624GLqULII8FQeDfgV+qqk/Nav2We29dHn8g9SdrpAOpx5BkP/AIXaiZ//8ggw8aGlOSL9Ad2/HpqtqU5Ezg3KrassS6L66q7/QD4J6guoPNpYMyqEmSZsJyTJbsVy1+mW7F4rFdJVX1nqXWXvA+owTB5ajfcu+t6sPO3FCOZsNOy/opjxcDJwLfA/YAZ48VpPrnEn+lqi4fo76mwa2PjUryWuDPgJfQ/T7O/WU+8+cMSdLBLMdkSeBKuuc2ttOtAgxqYRCc20Y1VBAcs37LvbeuludAah1GP+XxlH4b9Bq6Rz/OojuPbNGSrKM7+PtHgM8C19Ct3F0A7AAMajokg1q7Pkr34P12up84S5Ke3Pqq+sUR648aBEeu33Lv0qIsCFJXAl/qry8AdrL0IPVxuhW6G+i2+l5IN835tKrascTamji3PjYqyY1VdfxK9yFJLUlyMfA3VbV7pPqjjsMes37LvUuLleRKDgSpk4EfoAtSbx8iSM0/9qPf7ngP8KNV9cBSa2v6XFFr17X9pKUrmPeTyaq6deVakqSZdxLwliR76P7unNs2vqTJa/NsS7JhrCA4cv2We5cW66XzgtQlDB+kHp37pKr2JdljSNNT5Ypao5IcbHx7VdUblr0ZSWrE2JPXknwDeBndIILBg+CY9VvuXVqsJLdW1aZDXQ9Qf27iMjx+6rKDYvSkDGqSpFUnyYuAtXPXVfVfA9UdOwiOVr/l3qXFMkhplhnUGrVcI6YlaUqSnAr8JfDDwN10k3Nvr6pXDfw+owTB5ajfcu+SNCWOg23XlcBpdAdMPjTvQ5J0aO8FTgC+WVU/Rjc84F+HKp7k1CTfotvedx1wJ90hxjNfv+XeJWmKHCbSrrFHTEvSFD1aVfcmWZNkTVVdm+TPB6w/FwS/VFWvTvJ64Fcbqd9y75I0Oa6otWtbkg0r3YQkNeb+JM8FrgcuT/JBup0JQ3m0qu4FHguCwHGN1G+5d0maHFfU2jX2iGlJmqLTgIeB84GzgecDQz7buzAI3s2wQXDM+i33LkmT4zCRRjk9S5IWL8k6Hj+I6b6B6j6HLgiGA0Hw8n4laabrt9y7JE2RQa1xTs+SpKcuyW/RraDtBfZzYDfCSwd+n1GC4HLUb7l3SZoSg1qjlmvEtCRNST918DVVdc9I9UcNgmPWb7l3SZoig1qjkuwE3sCC6VlV9Zsr3JokzawkVwNnVNX3R6o/dhAcrX7LvUvSFDlMpF1jj5iWpCn6I7qpuTfSDWICoKp+f6D6dwCjhMBlqN9y75I0OQa1djk9S5Kevg8DXwF2022/G9rYQXDM+i33LkmT49bHRvXTs/bSnYXn9CxJegqSbKuqE0esfxPwLywIglV12azXb7l3SZoig9oEJHkhcG/5mylJh5XkfcBdwOd4/KrOUOP5xw6Co9VvuXdJmiKDWmOSnAB8ALgPeC/wceCFdCtrv1FVV69ge5I005LsOcjtIScbjh0ER6vfcu+SNEUGtcYkuQV4J91Wx4uBLVX19SQvBz5RVa9e0QYlaRVbhiA4Wv2We5ekKTKoNSbJjqo6rv/89qp6xbzXbjOoSdLhJTkWeCWwdu5eVf39ynUkSdITOfWxPfOnlO1d8JqpW5IOI8mfAj9HF9SuArbQDbgYLKiNHQTHrN9y75I0Na6oNSbJPuAhIMCzOXAmTYC1VfWslepNkmZdkt3ARuC2qtqY5IeAS6rqTQPVP2gQrKozZ71+y71L0hStWekG9PRU1TOqal1VPa+qntl/PndtSJOkw9tbVfuB/0uyDrgbGPIZqTOBk4HvVtU5dKHwiEbqt9y7JE2OWx8lSavJLUmOAj4CbAceBG4asP7eqtqfZKwgOGb9lnuXpMkxqEmSVoUkAd5fVfcDH0pyNbCuqnYN+DZjB8Ex67fcuyRNjs+oSZJWjSTbq+qnR6odYH1V/Xd/fTQDBsEx67fcuyRNlUFNkrRqJPlbYGtV3TxS/dGC4Nj1W+5dkqbIYSKSpNXk9cANSe5IsivJ7iRDrup8PcnmAestZ/2We5ekyXFFTZK0aiR5ycHuV9VdA9X/BvATwF0cOEqlquqnZr1+y71L0hQZ1CRJq0qSjcDP9Jdfq6qdA9YeOwiOVr/l3iVpitz6KElaNZK8HbgceFH/8Q9Jfm+o+n3oOAp4U/9x1JBBZMz6LfcuSVNkUJMkrSbnAsdX1buq6l3ACcDbhio+dhAcs37LvUvSFLn1UZK0aiTZDWyuqof767XAzVW1YaD6u4DXVNVD/fVzgBsGfM5rtPot9y5JU+SB15Kk1eRjwI1JPtNfnw58dMD6AfbNu97X32uhfsu9S9LkGNQkSZOXZH1Vfbuq/irJV4GT6ELCOcD6Ad9q7CA4Zv2We5ekyXHroyRp8pL8J/ALVXXngvtvBf64qo5ZYv31VfXt/vNNHAiC1wPrq+pzs1q/5d4lacoMapKkyUvyRuCDwBur6lv9vT8Ezga2zAWJJdQfOwiOVr/l3iVpypz6KEmavKq6Cvht4AtJjk3y13Qj4l+31JDWOx+4JsmPz93og+D5wM/OeP2We5ekyfIZNUnSqlBVX07yFuCrwDbg5LnpjwPUvirJI3RB8HTgPGAzXRD83izXb7l3SZoytz5KkiYvyQNA0T0bdQTwKAemDlZVrRvofU4C/pkuCL55qCC4HPVb7l2SpsigJknSEo0dBMes33LvkjRlBjVJkiRJmjEOE5EkSZKkGWNQkyRJkqQZY1CTJEmSpBljUJMkNSXJgwPXOz3JK+ddvyfJKUO+hyRJT5fDRCRJTUnyYFU9d8B6W4HPV9U/DlVTkqSlckVNktSkdC5K8m9Jdic5a95r7+jv7Uzygf7e25Lc3N/7pyRHJjkROBW4KMmOJMck2ZrkzP6fOTnJbX2tS5Mc0d+/M8m7k9zav/bylfh3IEmaLoOaJKlVZwDHARuBU+jC1ouTbAFOB46vqo3AX/Rff0VVbe7v3Q6cW1XbgM8CF1bVcVV1x1zxJGuBrcBZVbUBeCbwO/Pe/56q2gT8HXDBeN+mJGk1MqhJklp1EvCJqtpXVf8DXAdspgttH6uq7wNU1X391x+b5GtJdgNnA696kvo/Ceypqm/215cBr5v3+hX9r9uBo5f6zUiSNJ9BTZLUqhzm/sEewN4K/G6/OvZuYO0i6895pP91H91qmyRJgzGoSZJadT1wVpJnJPlButWum4AvAm9NciRAkhf0X/884DtJnkW3ojbngf61hf4DODrJy/rrX6dbtZMkaXQGNUlSqz4D7AJ2Al8B3lFV362qq+meO7slyQ4OPD/2J8CNwDV0IWzOJ4EL+6Ehx8zdrKqHgXOAT/fbJfcDHxr3W5IkqeN4fkmSJEmaMa6oSZIkSdKMMahJkiRJ0owxqEmSJEnSjDGoSZIkSdKMMahJkiRJ0owxqEmSJEnSjDGoSZIkSdKMMahJkiRJ0oz5f63hTeY3Wd8FAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "z_df2.plot(kind='bar', figsize = (15,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a6c4c157",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='rest_type', ylabel='rate'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Question-6\n",
    "plt.figure(figsize=(14,8))\n",
    "sns.boxplot(x='rest_type', y='rate', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "004e8723",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"9\" halign=\"left\">VOTES</th>\n",
       "      <th colspan=\"3\" halign=\"left\">costOfTwo</th>\n",
       "      <th colspan=\"9\" halign=\"left\">rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rest_type</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Cafe</th>\n",
       "      <th>Casual Dining</th>\n",
       "      <th>Casual Dining, Bar</th>\n",
       "      <th>Delivery</th>\n",
       "      <th>Dessert Parlor</th>\n",
       "      <th>Quick Bites</th>\n",
       "      <th>Takeaway, Delivery</th>\n",
       "      <th>others</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>...</th>\n",
       "      <th>others</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Cafe</th>\n",
       "      <th>Casual Dining</th>\n",
       "      <th>Casual Dining, Bar</th>\n",
       "      <th>Delivery</th>\n",
       "      <th>Dessert Parlor</th>\n",
       "      <th>Quick Bites</th>\n",
       "      <th>Takeaway, Delivery</th>\n",
       "      <th>others</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BTM</th>\n",
       "      <td>1525</td>\n",
       "      <td>62551</td>\n",
       "      <td>224719</td>\n",
       "      <td>21217</td>\n",
       "      <td>33078</td>\n",
       "      <td>9139</td>\n",
       "      <td>158276</td>\n",
       "      <td>16042</td>\n",
       "      <td>47121</td>\n",
       "      <td>28100</td>\n",
       "      <td>...</td>\n",
       "      <td>199800</td>\n",
       "      <td>282.604540</td>\n",
       "      <td>1058.206242</td>\n",
       "      <td>2576.010214</td>\n",
       "      <td>93.800142</td>\n",
       "      <td>908.209221</td>\n",
       "      <td>419.603972</td>\n",
       "      <td>9688.808666</td>\n",
       "      <td>1176.410356</td>\n",
       "      <td>2006.214470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banashankari</th>\n",
       "      <td>530</td>\n",
       "      <td>14054</td>\n",
       "      <td>61109</td>\n",
       "      <td>0</td>\n",
       "      <td>2845</td>\n",
       "      <td>7739</td>\n",
       "      <td>38401</td>\n",
       "      <td>463</td>\n",
       "      <td>37233</td>\n",
       "      <td>6350</td>\n",
       "      <td>...</td>\n",
       "      <td>65400</td>\n",
       "      <td>73.900426</td>\n",
       "      <td>327.000284</td>\n",
       "      <td>552.001844</td>\n",
       "      <td>3.700142</td>\n",
       "      <td>103.200709</td>\n",
       "      <td>174.100567</td>\n",
       "      <td>1542.613902</td>\n",
       "      <td>63.401702</td>\n",
       "      <td>460.202837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banaswadi</th>\n",
       "      <td>358</td>\n",
       "      <td>1389</td>\n",
       "      <td>14018</td>\n",
       "      <td>582</td>\n",
       "      <td>882</td>\n",
       "      <td>679</td>\n",
       "      <td>11321</td>\n",
       "      <td>930</td>\n",
       "      <td>4686</td>\n",
       "      <td>9900</td>\n",
       "      <td>...</td>\n",
       "      <td>36050</td>\n",
       "      <td>97.301419</td>\n",
       "      <td>161.800851</td>\n",
       "      <td>366.901277</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>48.100993</td>\n",
       "      <td>43.800567</td>\n",
       "      <td>1151.316881</td>\n",
       "      <td>76.800567</td>\n",
       "      <td>298.502553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bannerghatta Road</th>\n",
       "      <td>614</td>\n",
       "      <td>3609</td>\n",
       "      <td>79849</td>\n",
       "      <td>17312</td>\n",
       "      <td>3703</td>\n",
       "      <td>4398</td>\n",
       "      <td>40980</td>\n",
       "      <td>2955</td>\n",
       "      <td>61569</td>\n",
       "      <td>19500</td>\n",
       "      <td>...</td>\n",
       "      <td>140250</td>\n",
       "      <td>193.305674</td>\n",
       "      <td>296.702553</td>\n",
       "      <td>1092.405533</td>\n",
       "      <td>43.400000</td>\n",
       "      <td>244.002979</td>\n",
       "      <td>356.004681</td>\n",
       "      <td>2243.923975</td>\n",
       "      <td>218.802979</td>\n",
       "      <td>1031.906242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basavanagudi</th>\n",
       "      <td>1240</td>\n",
       "      <td>9408</td>\n",
       "      <td>23422</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>2093</td>\n",
       "      <td>51025</td>\n",
       "      <td>36</td>\n",
       "      <td>7607</td>\n",
       "      <td>11750</td>\n",
       "      <td>...</td>\n",
       "      <td>36050</td>\n",
       "      <td>130.801986</td>\n",
       "      <td>158.900000</td>\n",
       "      <td>367.100567</td>\n",
       "      <td>27.400142</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>194.301277</td>\n",
       "      <td>1378.308370</td>\n",
       "      <td>13.500142</td>\n",
       "      <td>243.300142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bellandur</th>\n",
       "      <td>327</td>\n",
       "      <td>4623</td>\n",
       "      <td>90958</td>\n",
       "      <td>24801</td>\n",
       "      <td>5045</td>\n",
       "      <td>1839</td>\n",
       "      <td>24461</td>\n",
       "      <td>1351</td>\n",
       "      <td>51903</td>\n",
       "      <td>6050</td>\n",
       "      <td>...</td>\n",
       "      <td>163020</td>\n",
       "      <td>57.400142</td>\n",
       "      <td>202.700000</td>\n",
       "      <td>1023.403547</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>221.604114</td>\n",
       "      <td>116.100993</td>\n",
       "      <td>1564.217307</td>\n",
       "      <td>233.605533</td>\n",
       "      <td>990.706951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brigade Road</th>\n",
       "      <td>482</td>\n",
       "      <td>13954</td>\n",
       "      <td>72879</td>\n",
       "      <td>20435</td>\n",
       "      <td>0</td>\n",
       "      <td>24656</td>\n",
       "      <td>12358</td>\n",
       "      <td>0</td>\n",
       "      <td>281918</td>\n",
       "      <td>8900</td>\n",
       "      <td>...</td>\n",
       "      <td>314000</td>\n",
       "      <td>107.401419</td>\n",
       "      <td>398.300709</td>\n",
       "      <td>1071.801419</td>\n",
       "      <td>108.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>339.002128</td>\n",
       "      <td>1106.908937</td>\n",
       "      <td>37.001419</td>\n",
       "      <td>1312.406951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brookefield</th>\n",
       "      <td>2098</td>\n",
       "      <td>13825</td>\n",
       "      <td>44030</td>\n",
       "      <td>7072</td>\n",
       "      <td>39</td>\n",
       "      <td>1165</td>\n",
       "      <td>31248</td>\n",
       "      <td>619</td>\n",
       "      <td>18866</td>\n",
       "      <td>7950</td>\n",
       "      <td>...</td>\n",
       "      <td>48900</td>\n",
       "      <td>80.600567</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>473.401135</td>\n",
       "      <td>47.100000</td>\n",
       "      <td>20.800426</td>\n",
       "      <td>129.700709</td>\n",
       "      <td>1110.410356</td>\n",
       "      <td>32.700851</td>\n",
       "      <td>346.001986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Church Street</th>\n",
       "      <td>0</td>\n",
       "      <td>145589</td>\n",
       "      <td>172806</td>\n",
       "      <td>75963</td>\n",
       "      <td>0</td>\n",
       "      <td>3436</td>\n",
       "      <td>13324</td>\n",
       "      <td>0</td>\n",
       "      <td>179188</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>378.200000</td>\n",
       "      <td>590.500000</td>\n",
       "      <td>300.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>166.600000</td>\n",
       "      <td>337.201419</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>478.901844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic City</th>\n",
       "      <td>794</td>\n",
       "      <td>3149</td>\n",
       "      <td>47147</td>\n",
       "      <td>1172</td>\n",
       "      <td>1931</td>\n",
       "      <td>771</td>\n",
       "      <td>29295</td>\n",
       "      <td>1329</td>\n",
       "      <td>25186</td>\n",
       "      <td>14000</td>\n",
       "      <td>...</td>\n",
       "      <td>131650</td>\n",
       "      <td>136.902837</td>\n",
       "      <td>149.801277</td>\n",
       "      <td>988.506667</td>\n",
       "      <td>65.300000</td>\n",
       "      <td>223.105958</td>\n",
       "      <td>81.500284</td>\n",
       "      <td>1996.232912</td>\n",
       "      <td>208.005391</td>\n",
       "      <td>588.003405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frazer Town</th>\n",
       "      <td>2540</td>\n",
       "      <td>4011</td>\n",
       "      <td>63419</td>\n",
       "      <td>0</td>\n",
       "      <td>1439</td>\n",
       "      <td>2043</td>\n",
       "      <td>14424</td>\n",
       "      <td>1648</td>\n",
       "      <td>8144</td>\n",
       "      <td>10650</td>\n",
       "      <td>...</td>\n",
       "      <td>26600</td>\n",
       "      <td>126.300709</td>\n",
       "      <td>125.600709</td>\n",
       "      <td>555.600851</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>220.905249</td>\n",
       "      <td>159.301135</td>\n",
       "      <td>1068.411065</td>\n",
       "      <td>134.000993</td>\n",
       "      <td>202.300709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSR</th>\n",
       "      <td>2230</td>\n",
       "      <td>43836</td>\n",
       "      <td>179526</td>\n",
       "      <td>43402</td>\n",
       "      <td>10708</td>\n",
       "      <td>18587</td>\n",
       "      <td>89577</td>\n",
       "      <td>12866</td>\n",
       "      <td>97590</td>\n",
       "      <td>12210</td>\n",
       "      <td>...</td>\n",
       "      <td>154900</td>\n",
       "      <td>122.301277</td>\n",
       "      <td>538.201702</td>\n",
       "      <td>1986.303830</td>\n",
       "      <td>95.500000</td>\n",
       "      <td>660.109788</td>\n",
       "      <td>487.102979</td>\n",
       "      <td>3362.634472</td>\n",
       "      <td>859.811349</td>\n",
       "      <td>1072.505816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiranagar</th>\n",
       "      <td>3267</td>\n",
       "      <td>62919</td>\n",
       "      <td>429678</td>\n",
       "      <td>69448</td>\n",
       "      <td>18385</td>\n",
       "      <td>37321</td>\n",
       "      <td>68306</td>\n",
       "      <td>7678</td>\n",
       "      <td>468907</td>\n",
       "      <td>10300</td>\n",
       "      <td>...</td>\n",
       "      <td>391430</td>\n",
       "      <td>103.900851</td>\n",
       "      <td>728.201702</td>\n",
       "      <td>1880.103830</td>\n",
       "      <td>208.800426</td>\n",
       "      <td>600.207377</td>\n",
       "      <td>463.400284</td>\n",
       "      <td>1734.810498</td>\n",
       "      <td>303.103688</td>\n",
       "      <td>1709.804114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP Nagar</th>\n",
       "      <td>2200</td>\n",
       "      <td>28296</td>\n",
       "      <td>239000</td>\n",
       "      <td>39704</td>\n",
       "      <td>7373</td>\n",
       "      <td>9641</td>\n",
       "      <td>42801</td>\n",
       "      <td>3490</td>\n",
       "      <td>214017</td>\n",
       "      <td>25650</td>\n",
       "      <td>...</td>\n",
       "      <td>242750</td>\n",
       "      <td>236.704540</td>\n",
       "      <td>577.202695</td>\n",
       "      <td>2028.210072</td>\n",
       "      <td>210.000709</td>\n",
       "      <td>449.608795</td>\n",
       "      <td>420.703547</td>\n",
       "      <td>2525.429649</td>\n",
       "      <td>442.505958</td>\n",
       "      <td>1277.906100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jayanagar</th>\n",
       "      <td>259</td>\n",
       "      <td>65353</td>\n",
       "      <td>249184</td>\n",
       "      <td>16036</td>\n",
       "      <td>588</td>\n",
       "      <td>24493</td>\n",
       "      <td>82786</td>\n",
       "      <td>280</td>\n",
       "      <td>48065</td>\n",
       "      <td>21050</td>\n",
       "      <td>...</td>\n",
       "      <td>127750</td>\n",
       "      <td>215.906809</td>\n",
       "      <td>791.701560</td>\n",
       "      <td>1843.805249</td>\n",
       "      <td>73.100142</td>\n",
       "      <td>53.200567</td>\n",
       "      <td>520.900000</td>\n",
       "      <td>2683.117874</td>\n",
       "      <td>47.500993</td>\n",
       "      <td>991.006809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kalyan Nagar</th>\n",
       "      <td>70</td>\n",
       "      <td>13286</td>\n",
       "      <td>70783</td>\n",
       "      <td>5822</td>\n",
       "      <td>1022</td>\n",
       "      <td>8331</td>\n",
       "      <td>17489</td>\n",
       "      <td>174</td>\n",
       "      <td>51015</td>\n",
       "      <td>6400</td>\n",
       "      <td>...</td>\n",
       "      <td>111700</td>\n",
       "      <td>60.401419</td>\n",
       "      <td>286.201135</td>\n",
       "      <td>914.201277</td>\n",
       "      <td>37.600000</td>\n",
       "      <td>108.201135</td>\n",
       "      <td>241.302270</td>\n",
       "      <td>895.409363</td>\n",
       "      <td>60.701560</td>\n",
       "      <td>565.202979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kammanahalli</th>\n",
       "      <td>0</td>\n",
       "      <td>11443</td>\n",
       "      <td>43318</td>\n",
       "      <td>3833</td>\n",
       "      <td>861</td>\n",
       "      <td>12</td>\n",
       "      <td>23061</td>\n",
       "      <td>1475</td>\n",
       "      <td>21247</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>30650</td>\n",
       "      <td>7.400284</td>\n",
       "      <td>186.101277</td>\n",
       "      <td>536.300851</td>\n",
       "      <td>39.300000</td>\n",
       "      <td>85.001277</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1030.712200</td>\n",
       "      <td>152.302412</td>\n",
       "      <td>304.502553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 1st Block</th>\n",
       "      <td>995</td>\n",
       "      <td>91595</td>\n",
       "      <td>47346</td>\n",
       "      <td>24393</td>\n",
       "      <td>15575</td>\n",
       "      <td>8312</td>\n",
       "      <td>38379</td>\n",
       "      <td>7738</td>\n",
       "      <td>17348</td>\n",
       "      <td>16100</td>\n",
       "      <td>...</td>\n",
       "      <td>32850</td>\n",
       "      <td>151.501844</td>\n",
       "      <td>376.102270</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>217.703405</td>\n",
       "      <td>388.302412</td>\n",
       "      <td>135.601702</td>\n",
       "      <td>2236.631919</td>\n",
       "      <td>362.906384</td>\n",
       "      <td>305.304681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 4th Block</th>\n",
       "      <td>2332</td>\n",
       "      <td>120402</td>\n",
       "      <td>115166</td>\n",
       "      <td>18553</td>\n",
       "      <td>13210</td>\n",
       "      <td>9090</td>\n",
       "      <td>22618</td>\n",
       "      <td>1988</td>\n",
       "      <td>381797</td>\n",
       "      <td>13500</td>\n",
       "      <td>...</td>\n",
       "      <td>238450</td>\n",
       "      <td>129.101560</td>\n",
       "      <td>556.400000</td>\n",
       "      <td>697.600567</td>\n",
       "      <td>180.300000</td>\n",
       "      <td>449.108228</td>\n",
       "      <td>220.701419</td>\n",
       "      <td>623.205816</td>\n",
       "      <td>113.001702</td>\n",
       "      <td>977.405674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 5th Block</th>\n",
       "      <td>1325</td>\n",
       "      <td>285225</td>\n",
       "      <td>642656</td>\n",
       "      <td>249678</td>\n",
       "      <td>1903</td>\n",
       "      <td>45104</td>\n",
       "      <td>79530</td>\n",
       "      <td>4206</td>\n",
       "      <td>904456</td>\n",
       "      <td>11200</td>\n",
       "      <td>...</td>\n",
       "      <td>518700</td>\n",
       "      <td>101.600142</td>\n",
       "      <td>1443.700000</td>\n",
       "      <td>2242.500567</td>\n",
       "      <td>371.700000</td>\n",
       "      <td>186.803830</td>\n",
       "      <td>706.100709</td>\n",
       "      <td>2156.314470</td>\n",
       "      <td>173.900851</td>\n",
       "      <td>2493.605533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 6th Block</th>\n",
       "      <td>106</td>\n",
       "      <td>63196</td>\n",
       "      <td>123236</td>\n",
       "      <td>0</td>\n",
       "      <td>11023</td>\n",
       "      <td>10440</td>\n",
       "      <td>109924</td>\n",
       "      <td>1545</td>\n",
       "      <td>144033</td>\n",
       "      <td>3600</td>\n",
       "      <td>...</td>\n",
       "      <td>228750</td>\n",
       "      <td>48.200000</td>\n",
       "      <td>423.000993</td>\n",
       "      <td>836.700567</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.103263</td>\n",
       "      <td>195.000142</td>\n",
       "      <td>1492.104681</td>\n",
       "      <td>102.500284</td>\n",
       "      <td>858.500709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 7th Block</th>\n",
       "      <td>513</td>\n",
       "      <td>20963</td>\n",
       "      <td>142618</td>\n",
       "      <td>67374</td>\n",
       "      <td>3182</td>\n",
       "      <td>22226</td>\n",
       "      <td>41539</td>\n",
       "      <td>142</td>\n",
       "      <td>189668</td>\n",
       "      <td>9300</td>\n",
       "      <td>...</td>\n",
       "      <td>133500</td>\n",
       "      <td>92.300993</td>\n",
       "      <td>451.703263</td>\n",
       "      <td>1096.302412</td>\n",
       "      <td>252.100000</td>\n",
       "      <td>350.402979</td>\n",
       "      <td>323.700000</td>\n",
       "      <td>954.201986</td>\n",
       "      <td>109.902270</td>\n",
       "      <td>873.003263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lavelle Road</th>\n",
       "      <td>280</td>\n",
       "      <td>1607</td>\n",
       "      <td>92017</td>\n",
       "      <td>71972</td>\n",
       "      <td>0</td>\n",
       "      <td>1900</td>\n",
       "      <td>10321</td>\n",
       "      <td>0</td>\n",
       "      <td>317680</td>\n",
       "      <td>6000</td>\n",
       "      <td>...</td>\n",
       "      <td>365100</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>336.000000</td>\n",
       "      <td>460.700000</td>\n",
       "      <td>37.001419</td>\n",
       "      <td>49.200567</td>\n",
       "      <td>180.102128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>973.801844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MG Road</th>\n",
       "      <td>36</td>\n",
       "      <td>61636</td>\n",
       "      <td>132950</td>\n",
       "      <td>8630</td>\n",
       "      <td>58</td>\n",
       "      <td>2018</td>\n",
       "      <td>6700</td>\n",
       "      <td>49</td>\n",
       "      <td>216189</td>\n",
       "      <td>1200</td>\n",
       "      <td>...</td>\n",
       "      <td>582150</td>\n",
       "      <td>11.400000</td>\n",
       "      <td>393.900000</td>\n",
       "      <td>636.502837</td>\n",
       "      <td>124.300000</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>174.401135</td>\n",
       "      <td>597.807235</td>\n",
       "      <td>42.300709</td>\n",
       "      <td>1422.702412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malleshwaram</th>\n",
       "      <td>592</td>\n",
       "      <td>8359</td>\n",
       "      <td>58251</td>\n",
       "      <td>42468</td>\n",
       "      <td>0</td>\n",
       "      <td>3533</td>\n",
       "      <td>48760</td>\n",
       "      <td>58</td>\n",
       "      <td>76946</td>\n",
       "      <td>4500</td>\n",
       "      <td>...</td>\n",
       "      <td>151750</td>\n",
       "      <td>57.601135</td>\n",
       "      <td>221.800851</td>\n",
       "      <td>451.300142</td>\n",
       "      <td>62.200000</td>\n",
       "      <td>25.900993</td>\n",
       "      <td>207.901135</td>\n",
       "      <td>854.704965</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>802.902128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marathahalli</th>\n",
       "      <td>789</td>\n",
       "      <td>2941</td>\n",
       "      <td>187863</td>\n",
       "      <td>78192</td>\n",
       "      <td>8286</td>\n",
       "      <td>8185</td>\n",
       "      <td>51576</td>\n",
       "      <td>2546</td>\n",
       "      <td>93857</td>\n",
       "      <td>19750</td>\n",
       "      <td>...</td>\n",
       "      <td>177200</td>\n",
       "      <td>174.203121</td>\n",
       "      <td>232.700993</td>\n",
       "      <td>1410.603830</td>\n",
       "      <td>177.900000</td>\n",
       "      <td>387.007093</td>\n",
       "      <td>245.202270</td>\n",
       "      <td>2627.428372</td>\n",
       "      <td>241.202128</td>\n",
       "      <td>965.208654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New BEL Road</th>\n",
       "      <td>72</td>\n",
       "      <td>24194</td>\n",
       "      <td>35354</td>\n",
       "      <td>614</td>\n",
       "      <td>336</td>\n",
       "      <td>4450</td>\n",
       "      <td>22527</td>\n",
       "      <td>2327</td>\n",
       "      <td>85813</td>\n",
       "      <td>3950</td>\n",
       "      <td>...</td>\n",
       "      <td>69550</td>\n",
       "      <td>31.100709</td>\n",
       "      <td>273.100426</td>\n",
       "      <td>418.400567</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>77.601702</td>\n",
       "      <td>118.300567</td>\n",
       "      <td>1033.212767</td>\n",
       "      <td>57.600851</td>\n",
       "      <td>361.301986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajajinagar</th>\n",
       "      <td>2387</td>\n",
       "      <td>50</td>\n",
       "      <td>34141</td>\n",
       "      <td>17493</td>\n",
       "      <td>1104</td>\n",
       "      <td>1361</td>\n",
       "      <td>16844</td>\n",
       "      <td>2511</td>\n",
       "      <td>9383</td>\n",
       "      <td>5800</td>\n",
       "      <td>...</td>\n",
       "      <td>31650</td>\n",
       "      <td>60.200993</td>\n",
       "      <td>20.500142</td>\n",
       "      <td>414.000284</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>46.800284</td>\n",
       "      <td>108.800709</td>\n",
       "      <td>1159.312626</td>\n",
       "      <td>89.901277</td>\n",
       "      <td>182.702128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residency Road</th>\n",
       "      <td>0</td>\n",
       "      <td>3171</td>\n",
       "      <td>87212</td>\n",
       "      <td>135</td>\n",
       "      <td>588</td>\n",
       "      <td>25175</td>\n",
       "      <td>9872</td>\n",
       "      <td>1652</td>\n",
       "      <td>162708</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>381350</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>209.301419</td>\n",
       "      <td>601.502553</td>\n",
       "      <td>33.300000</td>\n",
       "      <td>54.200851</td>\n",
       "      <td>251.901419</td>\n",
       "      <td>411.402270</td>\n",
       "      <td>78.600709</td>\n",
       "      <td>940.400709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richmond Road</th>\n",
       "      <td>140</td>\n",
       "      <td>2791</td>\n",
       "      <td>76128</td>\n",
       "      <td>2319</td>\n",
       "      <td>779</td>\n",
       "      <td>1643</td>\n",
       "      <td>18472</td>\n",
       "      <td>80</td>\n",
       "      <td>16550</td>\n",
       "      <td>2000</td>\n",
       "      <td>...</td>\n",
       "      <td>237850</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>171.601419</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>55.900000</td>\n",
       "      <td>27.300142</td>\n",
       "      <td>118.002128</td>\n",
       "      <td>1154.019861</td>\n",
       "      <td>59.201560</td>\n",
       "      <td>619.003263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sarjapur Road</th>\n",
       "      <td>2388</td>\n",
       "      <td>8334</td>\n",
       "      <td>54514</td>\n",
       "      <td>55380</td>\n",
       "      <td>9088</td>\n",
       "      <td>3411</td>\n",
       "      <td>20627</td>\n",
       "      <td>542</td>\n",
       "      <td>244315</td>\n",
       "      <td>22900</td>\n",
       "      <td>...</td>\n",
       "      <td>171400</td>\n",
       "      <td>191.501419</td>\n",
       "      <td>184.900851</td>\n",
       "      <td>728.802553</td>\n",
       "      <td>69.400000</td>\n",
       "      <td>351.503830</td>\n",
       "      <td>180.900851</td>\n",
       "      <td>1332.413051</td>\n",
       "      <td>110.200993</td>\n",
       "      <td>717.104114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanti Nagar</th>\n",
       "      <td>113</td>\n",
       "      <td>3579</td>\n",
       "      <td>32488</td>\n",
       "      <td>0</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>4792</td>\n",
       "      <td>0</td>\n",
       "      <td>14062</td>\n",
       "      <td>1250</td>\n",
       "      <td>...</td>\n",
       "      <td>32200</td>\n",
       "      <td>20.800000</td>\n",
       "      <td>168.601135</td>\n",
       "      <td>501.100142</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>48.900709</td>\n",
       "      <td>37.001419</td>\n",
       "      <td>717.512484</td>\n",
       "      <td>74.002837</td>\n",
       "      <td>246.201419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ulsoor</th>\n",
       "      <td>658</td>\n",
       "      <td>31893</td>\n",
       "      <td>61908</td>\n",
       "      <td>34376</td>\n",
       "      <td>266</td>\n",
       "      <td>765</td>\n",
       "      <td>13116</td>\n",
       "      <td>0</td>\n",
       "      <td>37250</td>\n",
       "      <td>13500</td>\n",
       "      <td>...</td>\n",
       "      <td>251300</td>\n",
       "      <td>133.200567</td>\n",
       "      <td>514.101702</td>\n",
       "      <td>686.600000</td>\n",
       "      <td>191.600000</td>\n",
       "      <td>93.602837</td>\n",
       "      <td>91.800851</td>\n",
       "      <td>1318.311065</td>\n",
       "      <td>55.502128</td>\n",
       "      <td>706.504681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whitefield</th>\n",
       "      <td>1189</td>\n",
       "      <td>8129</td>\n",
       "      <td>171004</td>\n",
       "      <td>29904</td>\n",
       "      <td>14853</td>\n",
       "      <td>6310</td>\n",
       "      <td>32810</td>\n",
       "      <td>1364</td>\n",
       "      <td>200171</td>\n",
       "      <td>25650</td>\n",
       "      <td>...</td>\n",
       "      <td>393100</td>\n",
       "      <td>211.604398</td>\n",
       "      <td>241.900993</td>\n",
       "      <td>1675.106100</td>\n",
       "      <td>137.700000</td>\n",
       "      <td>496.006951</td>\n",
       "      <td>301.204398</td>\n",
       "      <td>2540.036742</td>\n",
       "      <td>361.207519</td>\n",
       "      <td>1705.809079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>others</th>\n",
       "      <td>3135</td>\n",
       "      <td>196008</td>\n",
       "      <td>330215</td>\n",
       "      <td>161357</td>\n",
       "      <td>55211</td>\n",
       "      <td>11950</td>\n",
       "      <td>175589</td>\n",
       "      <td>9588</td>\n",
       "      <td>467123</td>\n",
       "      <td>62270</td>\n",
       "      <td>...</td>\n",
       "      <td>1125180</td>\n",
       "      <td>601.810072</td>\n",
       "      <td>1722.405816</td>\n",
       "      <td>5545.911774</td>\n",
       "      <td>684.600993</td>\n",
       "      <td>2023.622698</td>\n",
       "      <td>837.609079</td>\n",
       "      <td>11392.740159</td>\n",
       "      <td>1078.015747</td>\n",
       "      <td>5377.733905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       VOTES                                           \\\n",
       "rest_type             Bakery    Cafe Casual Dining Casual Dining, Bar   \n",
       "location                                                                \n",
       "BTM                     1525   62551        224719              21217   \n",
       "Banashankari             530   14054         61109                  0   \n",
       "Banaswadi                358    1389         14018                582   \n",
       "Bannerghatta Road        614    3609         79849              17312   \n",
       "Basavanagudi            1240    9408         23422                 88   \n",
       "Bellandur                327    4623         90958              24801   \n",
       "Brigade Road             482   13954         72879              20435   \n",
       "Brookefield             2098   13825         44030               7072   \n",
       "Church Street              0  145589        172806              75963   \n",
       "Electronic City          794    3149         47147               1172   \n",
       "Frazer Town             2540    4011         63419                  0   \n",
       "HSR                     2230   43836        179526              43402   \n",
       "Indiranagar             3267   62919        429678              69448   \n",
       "JP Nagar                2200   28296        239000              39704   \n",
       "Jayanagar                259   65353        249184              16036   \n",
       "Kalyan Nagar              70   13286         70783               5822   \n",
       "Kammanahalli               0   11443         43318               3833   \n",
       "Koramangala 1st Block    995   91595         47346              24393   \n",
       "Koramangala 4th Block   2332  120402        115166              18553   \n",
       "Koramangala 5th Block   1325  285225        642656             249678   \n",
       "Koramangala 6th Block    106   63196        123236                  0   \n",
       "Koramangala 7th Block    513   20963        142618              67374   \n",
       "Lavelle Road             280    1607         92017              71972   \n",
       "MG Road                   36   61636        132950               8630   \n",
       "Malleshwaram             592    8359         58251              42468   \n",
       "Marathahalli             789    2941        187863              78192   \n",
       "New BEL Road              72   24194         35354                614   \n",
       "Rajajinagar             2387      50         34141              17493   \n",
       "Residency Road             0    3171         87212                135   \n",
       "Richmond Road            140    2791         76128               2319   \n",
       "Sarjapur Road           2388    8334         54514              55380   \n",
       "Shanti Nagar             113    3579         32488                  0   \n",
       "Ulsoor                   658   31893         61908              34376   \n",
       "Whitefield              1189    8129        171004              29904   \n",
       "others                  3135  196008        330215             161357   \n",
       "\n",
       "                                                                              \\\n",
       "rest_type             Delivery Dessert Parlor Quick Bites Takeaway, Delivery   \n",
       "location                                                                       \n",
       "BTM                      33078           9139      158276              16042   \n",
       "Banashankari              2845           7739       38401                463   \n",
       "Banaswadi                  882            679       11321                930   \n",
       "Bannerghatta Road         3703           4398       40980               2955   \n",
       "Basavanagudi                 0           2093       51025                 36   \n",
       "Bellandur                 5045           1839       24461               1351   \n",
       "Brigade Road                 0          24656       12358                  0   \n",
       "Brookefield                 39           1165       31248                619   \n",
       "Church Street                0           3436       13324                  0   \n",
       "Electronic City           1931            771       29295               1329   \n",
       "Frazer Town               1439           2043       14424               1648   \n",
       "HSR                      10708          18587       89577              12866   \n",
       "Indiranagar              18385          37321       68306               7678   \n",
       "JP Nagar                  7373           9641       42801               3490   \n",
       "Jayanagar                  588          24493       82786                280   \n",
       "Kalyan Nagar              1022           8331       17489                174   \n",
       "Kammanahalli               861             12       23061               1475   \n",
       "Koramangala 1st Block    15575           8312       38379               7738   \n",
       "Koramangala 4th Block    13210           9090       22618               1988   \n",
       "Koramangala 5th Block     1903          45104       79530               4206   \n",
       "Koramangala 6th Block    11023          10440      109924               1545   \n",
       "Koramangala 7th Block     3182          22226       41539                142   \n",
       "Lavelle Road                 0           1900       10321                  0   \n",
       "MG Road                     58           2018        6700                 49   \n",
       "Malleshwaram                 0           3533       48760                 58   \n",
       "Marathahalli              8286           8185       51576               2546   \n",
       "New BEL Road               336           4450       22527               2327   \n",
       "Rajajinagar               1104           1361       16844               2511   \n",
       "Residency Road             588          25175        9872               1652   \n",
       "Richmond Road              779           1643       18472                 80   \n",
       "Sarjapur Road             9088           3411       20627                542   \n",
       "Shanti Nagar               264              0        4792                  0   \n",
       "Ulsoor                     266            765       13116                  0   \n",
       "Whitefield               14853           6310       32810               1364   \n",
       "others                   55211          11950      175589               9588   \n",
       "\n",
       "                              costOfTwo  ...                 rate  \\\n",
       "rest_type              others    Bakery  ...   others      Bakery   \n",
       "location                                 ...                        \n",
       "BTM                     47121     28100  ...   199800  282.604540   \n",
       "Banashankari            37233      6350  ...    65400   73.900426   \n",
       "Banaswadi                4686      9900  ...    36050   97.301419   \n",
       "Bannerghatta Road       61569     19500  ...   140250  193.305674   \n",
       "Basavanagudi             7607     11750  ...    36050  130.801986   \n",
       "Bellandur               51903      6050  ...   163020   57.400142   \n",
       "Brigade Road           281918      8900  ...   314000  107.401419   \n",
       "Brookefield             18866      7950  ...    48900   80.600567   \n",
       "Church Street          179188         0  ...   120000    0.000000   \n",
       "Electronic City         25186     14000  ...   131650  136.902837   \n",
       "Frazer Town              8144     10650  ...    26600  126.300709   \n",
       "HSR                     97590     12210  ...   154900  122.301277   \n",
       "Indiranagar            468907     10300  ...   391430  103.900851   \n",
       "JP Nagar               214017     25650  ...   242750  236.704540   \n",
       "Jayanagar               48065     21050  ...   127750  215.906809   \n",
       "Kalyan Nagar            51015      6400  ...   111700   60.401419   \n",
       "Kammanahalli            21247      1000  ...    30650    7.400284   \n",
       "Koramangala 1st Block   17348     16100  ...    32850  151.501844   \n",
       "Koramangala 4th Block  381797     13500  ...   238450  129.101560   \n",
       "Koramangala 5th Block  904456     11200  ...   518700  101.600142   \n",
       "Koramangala 6th Block  144033      3600  ...   228750   48.200000   \n",
       "Koramangala 7th Block  189668      9300  ...   133500   92.300993   \n",
       "Lavelle Road           317680      6000  ...   365100   70.000000   \n",
       "MG Road                216189      1200  ...   582150   11.400000   \n",
       "Malleshwaram            76946      4500  ...   151750   57.601135   \n",
       "Marathahalli            93857     19750  ...   177200  174.203121   \n",
       "New BEL Road            85813      3950  ...    69550   31.100709   \n",
       "Rajajinagar              9383      5800  ...    31650   60.200993   \n",
       "Residency Road         162708         0  ...   381350    0.000000   \n",
       "Richmond Road           16550      2000  ...   237850   34.000000   \n",
       "Sarjapur Road          244315     22900  ...   171400  191.501419   \n",
       "Shanti Nagar            14062      1250  ...    32200   20.800000   \n",
       "Ulsoor                  37250     13500  ...   251300  133.200567   \n",
       "Whitefield             200171     25650  ...   393100  211.604398   \n",
       "others                 467123     62270  ...  1125180  601.810072   \n",
       "\n",
       "                                                                     \\\n",
       "rest_type                     Cafe Casual Dining Casual Dining, Bar   \n",
       "location                                                              \n",
       "BTM                    1058.206242   2576.010214          93.800142   \n",
       "Banashankari            327.000284    552.001844           3.700142   \n",
       "Banaswadi               161.800851    366.901277          31.000000   \n",
       "Bannerghatta Road       296.702553   1092.405533          43.400000   \n",
       "Basavanagudi            158.900000    367.100567          27.400142   \n",
       "Bellandur               202.700000   1023.403547         114.000000   \n",
       "Brigade Road            398.300709   1071.801419         108.600000   \n",
       "Brookefield             132.000000    473.401135          47.100000   \n",
       "Church Street           378.200000    590.500000         300.800000   \n",
       "Electronic City         149.801277    988.506667          65.300000   \n",
       "Frazer Town             125.600709    555.600851           0.000000   \n",
       "HSR                     538.201702   1986.303830          95.500000   \n",
       "Indiranagar             728.201702   1880.103830         208.800426   \n",
       "JP Nagar                577.202695   2028.210072         210.000709   \n",
       "Jayanagar               791.701560   1843.805249          73.100142   \n",
       "Kalyan Nagar            286.201135    914.201277          37.600000   \n",
       "Kammanahalli            186.101277    536.300851          39.300000   \n",
       "Koramangala 1st Block   376.102270    396.900000         217.703405   \n",
       "Koramangala 4th Block   556.400000    697.600567         180.300000   \n",
       "Koramangala 5th Block  1443.700000   2242.500567         371.700000   \n",
       "Koramangala 6th Block   423.000993    836.700567           0.000000   \n",
       "Koramangala 7th Block   451.703263   1096.302412         252.100000   \n",
       "Lavelle Road             19.300000    336.000000         460.700000   \n",
       "MG Road                 393.900000    636.502837         124.300000   \n",
       "Malleshwaram            221.800851    451.300142          62.200000   \n",
       "Marathahalli            232.700993   1410.603830         177.900000   \n",
       "New BEL Road            273.100426    418.400567           8.000000   \n",
       "Rajajinagar              20.500142    414.000284          65.500000   \n",
       "Residency Road          209.301419    601.502553          33.300000   \n",
       "Richmond Road           171.601419    802.000000          55.900000   \n",
       "Sarjapur Road           184.900851    728.802553          69.400000   \n",
       "Shanti Nagar            168.601135    501.100142           0.000000   \n",
       "Ulsoor                  514.101702    686.600000         191.600000   \n",
       "Whitefield              241.900993   1675.106100         137.700000   \n",
       "others                 1722.405816   5545.911774         684.600993   \n",
       "\n",
       "                                                                 \\\n",
       "rest_type                 Delivery Dessert Parlor   Quick Bites   \n",
       "location                                                          \n",
       "BTM                     908.209221     419.603972   9688.808666   \n",
       "Banashankari            103.200709     174.100567   1542.613902   \n",
       "Banaswadi                48.100993      43.800567   1151.316881   \n",
       "Bannerghatta Road       244.002979     356.004681   2243.923975   \n",
       "Basavanagudi              0.000000     194.301277   1378.308370   \n",
       "Bellandur               221.604114     116.100993   1564.217307   \n",
       "Brigade Road              0.000000     339.002128   1106.908937   \n",
       "Brookefield              20.800426     129.700709   1110.410356   \n",
       "Church Street             0.000000     166.600000    337.201419   \n",
       "Electronic City         223.105958      81.500284   1996.232912   \n",
       "Frazer Town             220.905249     159.301135   1068.411065   \n",
       "HSR                     660.109788     487.102979   3362.634472   \n",
       "Indiranagar             600.207377     463.400284   1734.810498   \n",
       "JP Nagar                449.608795     420.703547   2525.429649   \n",
       "Jayanagar                53.200567     520.900000   2683.117874   \n",
       "Kalyan Nagar            108.201135     241.302270    895.409363   \n",
       "Kammanahalli             85.001277       7.000000   1030.712200   \n",
       "Koramangala 1st Block   388.302412     135.601702   2236.631919   \n",
       "Koramangala 4th Block   449.108228     220.701419    623.205816   \n",
       "Koramangala 5th Block   186.803830     706.100709   2156.314470   \n",
       "Koramangala 6th Block   300.103263     195.000142   1492.104681   \n",
       "Koramangala 7th Block   350.402979     323.700000    954.201986   \n",
       "Lavelle Road             37.001419      49.200567    180.102128   \n",
       "MG Road                  19.300000     174.401135    597.807235   \n",
       "Malleshwaram             25.900993     207.901135    854.704965   \n",
       "Marathahalli            387.007093     245.202270   2627.428372   \n",
       "New BEL Road             77.601702     118.300567   1033.212767   \n",
       "Rajajinagar              46.800284     108.800709   1159.312626   \n",
       "Residency Road           54.200851     251.901419    411.402270   \n",
       "Richmond Road            27.300142     118.002128   1154.019861   \n",
       "Sarjapur Road           351.503830     180.900851   1332.413051   \n",
       "Shanti Nagar             48.900709      37.001419    717.512484   \n",
       "Ulsoor                   93.602837      91.800851   1318.311065   \n",
       "Whitefield              496.006951     301.204398   2540.036742   \n",
       "others                 2023.622698     837.609079  11392.740159   \n",
       "\n",
       "                                                       \n",
       "rest_type             Takeaway, Delivery       others  \n",
       "location                                               \n",
       "BTM                          1176.410356  2006.214470  \n",
       "Banashankari                   63.401702   460.202837  \n",
       "Banaswadi                      76.800567   298.502553  \n",
       "Bannerghatta Road             218.802979  1031.906242  \n",
       "Basavanagudi                   13.500142   243.300142  \n",
       "Bellandur                     233.605533   990.706951  \n",
       "Brigade Road                   37.001419  1312.406951  \n",
       "Brookefield                    32.700851   346.001986  \n",
       "Church Street                   0.000000   478.901844  \n",
       "Electronic City               208.005391   588.003405  \n",
       "Frazer Town                   134.000993   202.300709  \n",
       "HSR                           859.811349  1072.505816  \n",
       "Indiranagar                   303.103688  1709.804114  \n",
       "JP Nagar                      442.505958  1277.906100  \n",
       "Jayanagar                      47.500993   991.006809  \n",
       "Kalyan Nagar                   60.701560   565.202979  \n",
       "Kammanahalli                  152.302412   304.502553  \n",
       "Koramangala 1st Block         362.906384   305.304681  \n",
       "Koramangala 4th Block         113.001702   977.405674  \n",
       "Koramangala 5th Block         173.900851  2493.605533  \n",
       "Koramangala 6th Block         102.500284   858.500709  \n",
       "Koramangala 7th Block         109.902270   873.003263  \n",
       "Lavelle Road                    0.000000   973.801844  \n",
       "MG Road                        42.300709  1422.702412  \n",
       "Malleshwaram                   22.200000   802.902128  \n",
       "Marathahalli                  241.202128   965.208654  \n",
       "New BEL Road                   57.600851   361.301986  \n",
       "Rajajinagar                    89.901277   182.702128  \n",
       "Residency Road                 78.600709   940.400709  \n",
       "Richmond Road                  59.201560   619.003263  \n",
       "Sarjapur Road                 110.200993   717.104114  \n",
       "Shanti Nagar                   74.002837   246.201419  \n",
       "Ulsoor                         55.502128   706.504681  \n",
       "Whitefield                    361.207519  1705.809079  \n",
       "others                       1078.015747  5377.733905  \n",
       "\n",
       "[35 rows x 27 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Question-7\n",
    "df3 = pd.pivot_table(df, values=None, index=['location'], columns=['rest_type'], fill_value=0, aggfunc=np.sum)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "78809132",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2592x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.plot(kind = 'bar', figsize = (36,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c574b54c",
   "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>VOTES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUISINES</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>others</th>\n",
       "      <td>11542182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Indian</th>\n",
       "      <td>516310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Indian, Chinese</th>\n",
       "      <td>258225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Indian</th>\n",
       "      <td>161975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Indian, Mughlai</th>\n",
       "      <td>103706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          VOTES\n",
       "CUISINES                       \n",
       "others                 11542182\n",
       "North Indian             516310\n",
       "North Indian, Chinese    258225\n",
       "South Indian             161975\n",
       "North Indian, Mughlai    103706"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# QUESTION-8\n",
    "df4 = df[['VOTES','CUISINES']]\n",
    "df4.drop_duplicates()\n",
    "df5 = df4.groupby(['CUISINES'])['VOTES'].sum()\n",
    "df5 = df5.to_frame()\n",
    "df5 = df5.sort_values('VOTES', ascending = False)\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b8bedb9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "        51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "        68, 69]),\n",
       " [Text(0, 0, 'others'),\n",
       "  Text(1, 0, 'North Indian'),\n",
       "  Text(2, 0, 'North Indian, Chinese'),\n",
       "  Text(3, 0, 'South Indian'),\n",
       "  Text(4, 0, 'North Indian, Mughlai'),\n",
       "  Text(5, 0, 'Chinese'),\n",
       "  Text(6, 0, 'Cafe'),\n",
       "  Text(7, 0, 'North Indian, South Indian'),\n",
       "  Text(8, 0, 'Desserts'),\n",
       "  Text(9, 0, 'Chinese, Momos'),\n",
       "  Text(10, 0, 'Ice Cream, Desserts'),\n",
       "  Text(11, 0, 'Cafe, Continental'),\n",
       "  Text(12, 0, 'Biryani, North Indian, Chinese'),\n",
       "  Text(13, 0, 'Chinese, Thai, Momos'),\n",
       "  Text(14, 0, 'South Indian, North Indian, Chinese'),\n",
       "  Text(15, 0, 'Desserts, Beverages'),\n",
       "  Text(16, 0, 'Biryani'),\n",
       "  Text(17, 0, 'Finger Food'),\n",
       "  Text(18, 0, 'Bakery, Desserts'),\n",
       "  Text(19, 0, 'Burger, Fast Food'),\n",
       "  Text(20, 0, 'Continental'),\n",
       "  Text(21, 0, 'Cafe, Desserts'),\n",
       "  Text(22, 0, 'Andhra, Biryani'),\n",
       "  Text(23, 0, 'North Indian, Continental'),\n",
       "  Text(24, 0, 'Pizza, Fast Food'),\n",
       "  Text(25, 0, 'Chinese, Thai'),\n",
       "  Text(26, 0, 'North Indian, Chinese, Biryani'),\n",
       "  Text(27, 0, 'South Indian, North Indian, Chinese, Street Food'),\n",
       "  Text(28, 0, 'Mithai, Street Food'),\n",
       "  Text(29, 0, 'North Indian, South Indian, Chinese'),\n",
       "  Text(30, 0, 'Desserts, Ice Cream'),\n",
       "  Text(31, 0, 'Biryani, North Indian'),\n",
       "  Text(32, 0, 'North Indian, Street Food'),\n",
       "  Text(33, 0, 'Fast Food'),\n",
       "  Text(34, 0, 'North Indian, Chinese, Seafood'),\n",
       "  Text(35, 0, 'Chinese, North Indian'),\n",
       "  Text(36, 0, 'North Indian, Biryani'),\n",
       "  Text(37, 0, 'Biryani, Kebab'),\n",
       "  Text(38, 0, 'Italian, Pizza'),\n",
       "  Text(39, 0, 'North Indian, Chinese, Fast Food'),\n",
       "  Text(40, 0, 'North Indian, Chinese, Continental'),\n",
       "  Text(41, 0, 'Desserts, Bakery'),\n",
       "  Text(42, 0, 'Arabian'),\n",
       "  Text(43, 0, 'North Indian, Chinese, South Indian'),\n",
       "  Text(44, 0, 'Pizza'),\n",
       "  Text(45, 0, 'South Indian, North Indian'),\n",
       "  Text(46, 0, 'North Indian, Mughlai, Chinese'),\n",
       "  Text(47, 0, 'Beverages, Fast Food'),\n",
       "  Text(48, 0, 'South Indian, Biryani'),\n",
       "  Text(49, 0, 'Fast Food, Rolls'),\n",
       "  Text(50, 0, 'Bakery'),\n",
       "  Text(51, 0, 'Andhra'),\n",
       "  Text(52, 0, 'Street Food'),\n",
       "  Text(53, 0, 'Kerala'),\n",
       "  Text(54, 0, 'Cafe, Bakery'),\n",
       "  Text(55, 0, 'Biryani, Fast Food'),\n",
       "  Text(56, 0, 'South Indian, Fast Food'),\n",
       "  Text(57, 0, 'North Indian, Fast Food'),\n",
       "  Text(58, 0, 'Bakery, Fast Food'),\n",
       "  Text(59, 0, 'Fast Food, Burger'),\n",
       "  Text(60, 0, 'Cafe, Fast Food'),\n",
       "  Text(61, 0, 'South Indian, Chinese'),\n",
       "  Text(62, 0, 'Beverages, Desserts'),\n",
       "  Text(63, 0, 'Ice Cream'),\n",
       "  Text(64, 0, 'Beverages'),\n",
       "  Text(65, 0, 'Biryani, South Indian'),\n",
       "  Text(66, 0, 'Kerala, South Indian'),\n",
       "  Text(67, 0, 'Fast Food, Beverages'),\n",
       "  Text(68, 0, 'South Indian, Chinese, North Indian'),\n",
       "  Text(69, 0, 'Mithai')])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,8))\n",
    "sns.barplot(df5.index , df5['VOTES'])\n",
    "plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34564f8a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}