zomatoEDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "853166b6-c6c8-4c86-984e-2ad8555da993",
   "metadata": {},
   "source": [
    "# Zomato Dataset Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "96932587-2278-43f9-a571-31628ee1c4e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1f72c10-64dd-447c-a29d-af503c4275b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Restaurant ID</th>\n",
       "      <th>Restaurant Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Locality Verbose</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>...</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Switch to order menu</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6317637</td>\n",
       "      <td>Le Petit Souffle</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Third Floor, Century City Mall, Kalayaan Avenu...</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City, Mak...</td>\n",
       "      <td>121.027535</td>\n",
       "      <td>14.565443</td>\n",
       "      <td>French, Japanese, Desserts</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6304287</td>\n",
       "      <td>Izakaya Kikufuji</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Little Tokyo, 2277 Chino Roces Avenue, Legaspi...</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City, Ma...</td>\n",
       "      <td>121.014101</td>\n",
       "      <td>14.553708</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6300002</td>\n",
       "      <td>Heat - Edsa Shangri-La</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...</td>\n",
       "      <td>121.056831</td>\n",
       "      <td>14.581404</td>\n",
       "      <td>Seafood, Asian, Filipino, Indian</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6318506</td>\n",
       "      <td>Ooma</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Fashion Hall, SM Megamall, O...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.056475</td>\n",
       "      <td>14.585318</td>\n",
       "      <td>Japanese, Sushi</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6314302</td>\n",
       "      <td>Sambo Kojin</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Atrium, SM Megamall, Ortigas...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.057508</td>\n",
       "      <td>14.584450</td>\n",
       "      <td>Japanese, Korean</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Restaurant ID         Restaurant Name  Country Code              City  \\\n",
       "0        6317637        Le Petit Souffle           162       Makati City   \n",
       "1        6304287        Izakaya Kikufuji           162       Makati City   \n",
       "2        6300002  Heat - Edsa Shangri-La           162  Mandaluyong City   \n",
       "3        6318506                    Ooma           162  Mandaluyong City   \n",
       "4        6314302             Sambo Kojin           162  Mandaluyong City   \n",
       "\n",
       "                                             Address  \\\n",
       "0  Third Floor, Century City Mall, Kalayaan Avenu...   \n",
       "1  Little Tokyo, 2277 Chino Roces Avenue, Legaspi...   \n",
       "2  Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...   \n",
       "3  Third Floor, Mega Fashion Hall, SM Megamall, O...   \n",
       "4  Third Floor, Mega Atrium, SM Megamall, Ortigas...   \n",
       "\n",
       "                                     Locality  \\\n",
       "0   Century City Mall, Poblacion, Makati City   \n",
       "1  Little Tokyo, Legaspi Village, Makati City   \n",
       "2  Edsa Shangri-La, Ortigas, Mandaluyong City   \n",
       "3      SM Megamall, Ortigas, Mandaluyong City   \n",
       "4      SM Megamall, Ortigas, Mandaluyong City   \n",
       "\n",
       "                                    Locality Verbose   Longitude   Latitude  \\\n",
       "0  Century City Mall, Poblacion, Makati City, Mak...  121.027535  14.565443   \n",
       "1  Little Tokyo, Legaspi Village, Makati City, Ma...  121.014101  14.553708   \n",
       "2  Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...  121.056831  14.581404   \n",
       "3  SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.056475  14.585318   \n",
       "4  SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.057508  14.584450   \n",
       "\n",
       "                           Cuisines  ...          Currency Has Table booking  \\\n",
       "0        French, Japanese, Desserts  ...  Botswana Pula(P)               Yes   \n",
       "1                          Japanese  ...  Botswana Pula(P)               Yes   \n",
       "2  Seafood, Asian, Filipino, Indian  ...  Botswana Pula(P)               Yes   \n",
       "3                   Japanese, Sushi  ...  Botswana Pula(P)                No   \n",
       "4                  Japanese, Korean  ...  Botswana Pula(P)               Yes   \n",
       "\n",
       "  Has Online delivery Is delivering now Switch to order menu Price range  \\\n",
       "0                  No                No                   No           3   \n",
       "1                  No                No                   No           3   \n",
       "2                  No                No                   No           4   \n",
       "3                  No                No                   No           4   \n",
       "4                  No                No                   No           4   \n",
       "\n",
       "   Aggregate rating  Rating color Rating text Votes  \n",
       "0               4.8    Dark Green   Excellent   314  \n",
       "1               4.5    Dark Green   Excellent   591  \n",
       "2               4.4         Green   Very Good   270  \n",
       "3               4.9    Dark Green   Excellent   365  \n",
       "4               4.8    Dark Green   Excellent   229  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('zomato.csv', encoding='latin-1')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7f9b665-27e8-4c87-93b1-16ab89767d78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',\n",
       "       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',\n",
       "       'Average Cost for two', 'Currency', 'Has Table booking',\n",
       "       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
       "       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',\n",
       "       'Votes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "65ba3f4d-8d9c-425c-a097-63295f3490bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9551 entries, 0 to 9550\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Restaurant ID         9551 non-null   int64  \n",
      " 1   Restaurant Name       9551 non-null   object \n",
      " 2   Country Code          9551 non-null   int64  \n",
      " 3   City                  9551 non-null   object \n",
      " 4   Address               9551 non-null   object \n",
      " 5   Locality              9551 non-null   object \n",
      " 6   Locality Verbose      9551 non-null   object \n",
      " 7   Longitude             9551 non-null   float64\n",
      " 8   Latitude              9551 non-null   float64\n",
      " 9   Cuisines              9542 non-null   object \n",
      " 10  Average Cost for two  9551 non-null   int64  \n",
      " 11  Currency              9551 non-null   object \n",
      " 12  Has Table booking     9551 non-null   object \n",
      " 13  Has Online delivery   9551 non-null   object \n",
      " 14  Is delivering now     9551 non-null   object \n",
      " 15  Switch to order menu  9551 non-null   object \n",
      " 16  Price range           9551 non-null   int64  \n",
      " 17  Aggregate rating      9551 non-null   float64\n",
      " 18  Rating color          9551 non-null   object \n",
      " 19  Rating text           9551 non-null   object \n",
      " 20  Votes                 9551 non-null   int64  \n",
      "dtypes: float64(3), int64(5), object(13)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5b9a0306-deb1-4968-8a4c-422e820dc084",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Restaurant ID</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Average Cost for two</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.551000e+03</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.051128e+06</td>\n",
       "      <td>18.365616</td>\n",
       "      <td>64.126574</td>\n",
       "      <td>25.854381</td>\n",
       "      <td>1199.210763</td>\n",
       "      <td>1.804837</td>\n",
       "      <td>2.666370</td>\n",
       "      <td>156.909748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.791521e+06</td>\n",
       "      <td>56.750546</td>\n",
       "      <td>41.467058</td>\n",
       "      <td>11.007935</td>\n",
       "      <td>16121.183073</td>\n",
       "      <td>0.905609</td>\n",
       "      <td>1.516378</td>\n",
       "      <td>430.169145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-157.948486</td>\n",
       "      <td>-41.330428</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.019625e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.081343</td>\n",
       "      <td>28.478713</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.004089e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.191964</td>\n",
       "      <td>28.570469</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.835229e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.282006</td>\n",
       "      <td>28.642758</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>131.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.850065e+07</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>174.832089</td>\n",
       "      <td>55.976980</td>\n",
       "      <td>800000.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>10934.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Restaurant ID  Country Code    Longitude     Latitude  \\\n",
       "count   9.551000e+03   9551.000000  9551.000000  9551.000000   \n",
       "mean    9.051128e+06     18.365616    64.126574    25.854381   \n",
       "std     8.791521e+06     56.750546    41.467058    11.007935   \n",
       "min     5.300000e+01      1.000000  -157.948486   -41.330428   \n",
       "25%     3.019625e+05      1.000000    77.081343    28.478713   \n",
       "50%     6.004089e+06      1.000000    77.191964    28.570469   \n",
       "75%     1.835229e+07      1.000000    77.282006    28.642758   \n",
       "max     1.850065e+07    216.000000   174.832089    55.976980   \n",
       "\n",
       "       Average Cost for two  Price range  Aggregate rating         Votes  \n",
       "count           9551.000000  9551.000000       9551.000000   9551.000000  \n",
       "mean            1199.210763     1.804837          2.666370    156.909748  \n",
       "std            16121.183073     0.905609          1.516378    430.169145  \n",
       "min                0.000000     1.000000          0.000000      0.000000  \n",
       "25%              250.000000     1.000000          2.500000      5.000000  \n",
       "50%              400.000000     2.000000          3.200000     31.000000  \n",
       "75%              700.000000     2.000000          3.700000    131.000000  \n",
       "max           800000.000000     4.000000          4.900000  10934.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf13c554-8bfa-4d9f-9357-a195c2d9b284",
   "metadata": {},
   "source": [
    "## Things to do in Data Analyis :\n",
    "1. Missing Values\n",
    "2. Explore about the Numerical Values\n",
    "3. Explore about Categorical Variables\n",
    "4. Finding relationship between features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84c4e522-e9ac-44be-a697-cbdb2f4785f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9551, 21)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e0e757b3-9ad8-4b50-9f09-f3c64f3bd9a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Restaurant ID           0\n",
       "Restaurant Name         0\n",
       "Country Code            0\n",
       "City                    0\n",
       "Address                 0\n",
       "Locality                0\n",
       "Locality Verbose        0\n",
       "Longitude               0\n",
       "Latitude                0\n",
       "Cuisines                9\n",
       "Average Cost for two    0\n",
       "Currency                0\n",
       "Has Table booking       0\n",
       "Has Online delivery     0\n",
       "Is delivering now       0\n",
       "Switch to order menu    0\n",
       "Price range             0\n",
       "Aggregate rating        0\n",
       "Rating color            0\n",
       "Rating text             0\n",
       "Votes                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e8386cb7-1062-46b4-a000-cc57a6a7ee17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cuisines']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#features is a temporary variable which will iterates through df.columns and then if that features is null or the sum is greater than 0 \n",
    "[features for features in df.columns if df[features].isnull().sum()>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "046e0ad6-2724-4094-992e-b915dbc8227a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#finding through HEATMAP\n",
    "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2e31a034-da99-4f54-94d9-1cd31bf4f8ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>94</td>\n",
       "      <td>Indonesia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country Code    Country\n",
       "0             1      India\n",
       "1            14  Australia\n",
       "2            30     Brazil\n",
       "3            37     Canada\n",
       "4            94  Indonesia"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country=pd.read_excel('Country-Code.xlsx')\n",
    "df_country.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "03d5f272-da55-4a69-962e-fffe7123df37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',\n",
       "       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',\n",
       "       'Average Cost for two', 'Currency', 'Has Table booking',\n",
       "       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
       "       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',\n",
       "       'Votes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b1653f70-52c5-4112-8e89-c1edaca90d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Restaurant ID</th>\n",
       "      <th>Restaurant Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Locality Verbose</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>...</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Switch to order menu</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6317637</td>\n",
       "      <td>Le Petit Souffle</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Third Floor, Century City Mall, Kalayaan Avenu...</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City, Mak...</td>\n",
       "      <td>121.027535</td>\n",
       "      <td>14.565443</td>\n",
       "      <td>French, Japanese, Desserts</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>314</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6304287</td>\n",
       "      <td>Izakaya Kikufuji</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Little Tokyo, 2277 Chino Roces Avenue, Legaspi...</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City, Ma...</td>\n",
       "      <td>121.014101</td>\n",
       "      <td>14.553708</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>591</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6300002</td>\n",
       "      <td>Heat - Edsa Shangri-La</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...</td>\n",
       "      <td>121.056831</td>\n",
       "      <td>14.581404</td>\n",
       "      <td>Seafood, Asian, Filipino, Indian</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>270</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Restaurant ID         Restaurant Name  Country Code              City  \\\n",
       "0        6317637        Le Petit Souffle           162       Makati City   \n",
       "1        6304287        Izakaya Kikufuji           162       Makati City   \n",
       "2        6300002  Heat - Edsa Shangri-La           162  Mandaluyong City   \n",
       "\n",
       "                                             Address  \\\n",
       "0  Third Floor, Century City Mall, Kalayaan Avenu...   \n",
       "1  Little Tokyo, 2277 Chino Roces Avenue, Legaspi...   \n",
       "2  Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...   \n",
       "\n",
       "                                     Locality  \\\n",
       "0   Century City Mall, Poblacion, Makati City   \n",
       "1  Little Tokyo, Legaspi Village, Makati City   \n",
       "2  Edsa Shangri-La, Ortigas, Mandaluyong City   \n",
       "\n",
       "                                    Locality Verbose   Longitude   Latitude  \\\n",
       "0  Century City Mall, Poblacion, Makati City, Mak...  121.027535  14.565443   \n",
       "1  Little Tokyo, Legaspi Village, Makati City, Ma...  121.014101  14.553708   \n",
       "2  Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...  121.056831  14.581404   \n",
       "\n",
       "                           Cuisines  ...  Has Table booking  \\\n",
       "0        French, Japanese, Desserts  ...                Yes   \n",
       "1                          Japanese  ...                Yes   \n",
       "2  Seafood, Asian, Filipino, Indian  ...                Yes   \n",
       "\n",
       "  Has Online delivery Is delivering now Switch to order menu Price range  \\\n",
       "0                  No                No                   No           3   \n",
       "1                  No                No                   No           3   \n",
       "2                  No                No                   No           4   \n",
       "\n",
       "  Aggregate rating  Rating color  Rating text Votes      Country  \n",
       "0              4.8    Dark Green    Excellent   314  Phillipines  \n",
       "1              4.5    Dark Green    Excellent   591  Phillipines  \n",
       "2              4.4         Green    Very Good   270  Phillipines  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#combining two datasets through pandas\n",
    "final_df=pd.merge(df,df_country,on='Country Code', how='left') \n",
    "final_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b54ed3a7-dd3e-42fd-b1d4-55ac46a9e88e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Restaurant ID             int64\n",
       "Restaurant Name          object\n",
       "Country Code              int64\n",
       "City                     object\n",
       "Address                  object\n",
       "Locality                 object\n",
       "Locality Verbose         object\n",
       "Longitude               float64\n",
       "Latitude                float64\n",
       "Cuisines                 object\n",
       "Average Cost for two      int64\n",
       "Currency                 object\n",
       "Has Table booking        object\n",
       "Has Online delivery      object\n",
       "Is delivering now        object\n",
       "Switch to order menu     object\n",
       "Price range               int64\n",
       "Aggregate rating        float64\n",
       "Rating color             object\n",
       "Rating text              object\n",
       "Votes                     int64\n",
       "Country                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## to check data types\n",
    "final_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "88e55021-2964-499d-a02d-5bd9bc0c1586",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',\n",
       "       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',\n",
       "       'Average Cost for two', 'Currency', 'Has Table booking',\n",
       "       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
       "       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',\n",
       "       'Votes', 'Country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "babd496d-a4db-406f-815f-794362c7b41a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "India             8652\n",
       "United States      434\n",
       "United Kingdom      80\n",
       "Brazil              60\n",
       "UAE                 60\n",
       "South Africa        60\n",
       "New Zealand         40\n",
       "Turkey              34\n",
       "Australia           24\n",
       "Phillipines         22\n",
       "Indonesia           21\n",
       "Singapore           20\n",
       "Qatar               20\n",
       "Sri Lanka           20\n",
       "Canada               4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.Country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8cc9036d-3556-4bd3-9d03-356cefe9b8df",
   "metadata": {},
   "outputs": [],
   "source": [
    "country_names=final_df.Country.value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "97d00a43-00dc-4236-8b06-c8e8bd917f84",
   "metadata": {},
   "outputs": [],
   "source": [
    "country_value=final_df.Country.value_counts().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "449a4f14-c190-49b0-8d02-bd65c957fe9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1e7bf573950>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7cdc8b8c0>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7bf5c80e0>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7bf572c00>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7bf5c8770>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7bf5c8b60>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7bf5c8fb0>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7bf5c9370>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7bf5c97c0>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7bf5c9c10>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7bf5c9e50>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7bf5ca480>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7bf5ca780>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7bf5cacf0>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7bf5cb170>],\n",
       " [Text(-1.052256163793291, 0.3205572737577906, 'India'),\n",
       "  Text(0.9911329812843455, -0.477132490415823, 'United States'),\n",
       "  Text(1.0572858296119743, -0.3035567072257165, 'United Kingdom'),\n",
       "  Text(1.070138816916019, -0.2545641619112621, 'Brazil'),\n",
       "  Text(1.0793506814479759, -0.21213699926648824, 'UAE'),\n",
       "  Text(1.086881147244973, -0.16937937230799818, 'South Africa'),\n",
       "  Text(1.0918635911832035, -0.1335436192729486, 'New Zealand'),\n",
       "  Text(1.0947903814016446, -0.10692998078388304, 'Turkey'),\n",
       "  Text(1.096631023945382, -0.08602556201794338, 'Australia'),\n",
       "  Text(1.0978070729776455, -0.06942355882735218, 'Phillipines'),\n",
       "  Text(1.0986791544015209, -0.05388984768543213, 'Indonesia'),\n",
       "  Text(1.0993059848742366, -0.039068550263413035, 'Singapore'),\n",
       "  Text(1.0997248508282123, -0.02460187941736628, 'Qatar'),\n",
       "  Text(1.0999533462179636, -0.010130949802716446, 'Sri Lanka'),\n",
       "  Text(1.0999990477553414, -0.0014473898376707638, 'Canada')])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VUerQ7dOnDw8++CBPPPEEUVFRxMfH8+KLLxa7T3JyMr169cJisdCyZUsWLFhQ7Psnn172+Xz897//pWHDhgQFBdG8eXPefvvt0pZYoh3p+aw9kFOuxxRC1Gyz1smcXXFuyjTE/PLLL3nkkUdYuXIl//zzD6NGjaJnz570798fVVUZPnw4tWrVYuXKleTl5TF69OgzHk9VVerWrcvMmTOJjo7m77//5s477yQhIYHrr7++LKX6SQOVEKK8/bTpME8PSkKnUwJdiqjkyhS6bdu25YUXXgCgadOmvPfeeyxatIj+/fuzcOFCduzYwe+//07t2rUBGD9+PFdcccVpj2c0GnnppZf8Xzds2JB//vmHGTNmlEvo+lSNeZvSynwcIYQ40ZF8Fyv2ZtGjSUygSxGVXJmu6bZt27bY1wkJCWRkFG0esH37dhITE/2BC9C9e/ezHnPy5MlcdNFFxMbGYrVa+fjjjzl4sHxGpyv3ZZFdKCtQCSHK3+wNcopZnF2ZQtdoNBb7WlEUVLX0i4BPnz6dxx57jP/+97/Mnz+fDRs2cOutt+J2l09Q/r4lvVyOI4QQJ/t1Szour2z5J87sgrUNJyUlkZKSQlpaGgkJCQCsWLHijI9Zvnw5PXr04N577/XftmdP+axvqmka87fJRvVCiAvD5vTyx/YMrmiTEOhSRCV2waYM9evXj2bNmjFy5Eg2btzIsmXLeOaZZ874mKZNm7JmzRp+//13du3axXPPPcfq1avLpZ4NKbmk5TnL5VhCCFGSBfLGXpzFBQtdnU7HrFmzcDgcdOnShdtvv51XXnnljI+56667GD58ODfccANdu3YlKyur2Ki3LH6TU8tCiAts2e7MQJcgKjlF02rGFu6XTlrC3qOFgS5DCFHN/frQJSQlhAW6DFFJ1YgNDw5m2SVwhRAVYlny0UCXICqxGhG6i3dmBLoEIUQNsSxZTjGL05PQFUKIcrRqXzZOj0wdEiWr9qHr9PhYsTcr0GUIIWoIl1dl1b7sQJchKqlqH7r/7MnC6Sn9gh1CCHG+5LquOJ1qH7p/yj9+IUQFk+u64nSqfeiuP5gb6BKEEDXMjnQbGTZZjEecqlqHrsensi0tP9BlCCFqoGW7ZLQrTlWtQ3dHmg23V67nCiEqnlzXFSWp1qG74VBuoEsQQtRQy/fIrAlxqmoduptScgNdghCihjpqc3EkX67riuKqdehulJGuECKAtktPiThJtQ3dQpeX3RkFgS5DCFGD7Ui3BboEUclU29DdnJqHWiP2TxJCVFY7ZKQrTlJtQ3ejXM8VQgSYjHTFyapt6G46lBfoEoQQNdyeowV4fDJtUfyr2obuBhnpCiECzOPTpLdEFFMtQzezwEVqriPQZQghBDvS5bqu+Fe1DF1p0xdCVBY70uS6rvhXtQzdQzkyyhVCVA7bpZlKnKBahm6qhK4QopKQaUPiRNUydA/L9VwhRCWRYXORXegOdBmikqiWoStNVEKIymTXETnFLIpI6AohxAUmGx+I46pd6KqqJv/AhRCVSmaBnF4WRapd6B6xOfH4ZNFlIUTlkVngCnQJopKodqErTVRCiMom0yahK4pUu9BNzZVTy0KIykVGuuK46he6MkdXCFHJyDVdcVy1C105vSyEqGyyZKQrjpHQFUKICyxTFscQx1S70M2QhgUhRCXj9qrkOTyBLkNUAtUudB0eX6BLEEKIU0gzlYBqGLpurxroEoQQ4hQybUiAhK4QQlQI6WAWUA1D1+WV08tCiMonu1BGuqIahq6MdIUQlZGrBvxuUhSF2bNnB7qMSq3ahW5N+IcthKh6vGr5rAmfnp7OAw88QKNGjTCbzSQmJjJkyBAWLVpU4v2XLFmCoijk5uaWy/OXVoMGDXjrrbcCWkNlUK1CV1W1cvuHLYQQ5clXxt9NR48e5aabbqJu3bpMnjyZ7OxsOnfuzPjx4+nbty/33XdfiY/r0aMHaWlphIeHn/bYEogVp1qFrtsno1whROVU1tC95ppr+Pnnn4mMjGTr1q3Mnz+fwYMHEx4eziOPPMKKFSsAeOONN2jTpg0hISEkJiYyevRorFYriqIAMGXKFCIiIvj9999JSkrCarVy5MgR8vLy/M+1evVq+vfvT0xMDOHh4fTu3Zt169YVqyc5OZlevXphsVho2bIlCxYsOKXmMWPG0KxZM4KDg0lNTeWXX37B46nZ85WrVei6PBK6QojKqSxn4XJzc1m2bBn5+fk88sgjJCUl0aVLF5566imuuuoqACIiIlAUhX/++YfQ0FBUVeXSSy9l3rx5hIaGFju9bLfbef311/nqq6/4888/8fl8zJ071/99m83GyJEj+euvv1i+fDnp6el07twZi8VC8+bNefPNNxk+fDgmk4mVK1dSr149rrvuOgBuvfVWoqOjue+++wgODmbKlCls27aNyMhIVqxYwZtvvgnAp59+SkREhP+0+MlvFu69914KCgpK/ZpVVoZAF1CeXD7pXBZCVE4+tfSDAqvVSlBQEA6Hg/DwcG677TZ+++03MjMzSUhIYNiwYTz//PMA/Pnnn/zvf/+jd+/eGAwGvvjiC1588cVix/N4PCxcuJCZM2cSERFBaGgoycnJ/u9feumlxe57ww038MYbb/DWW28RFBTEf//7X7xeL7///ju1a9cmPj7eP4IdN24cDRo04IYbbuCtt96iR48eAAQHB9O9e3dmzJgBwMSJE5k/fz5dunQBQKfT8c4779CwYUP27t3LvffeyxNPPMH7779f6tetMlI0Tas2F0FTsu1cMnFxoMsQ1USwQeOFBrsIMSrYfVFoRh2a3odiVPCqHryKDwUf+DR8qg7Fp2H0edH7PBg0LzrNh0HzoVe96DQVNFABVVPQVAVV09A0HaqmoqkKaKCooNOK/uhV0KmgaKBTlWOfa+hUzX+7cuLnPrXoo6ah+LSijyqgqUUfARQF7dhHUI7fWPx2pYTbARRdCffn3+McO6aGUvLt/uPy73Mox+7vP+6/H4tuV/zfOv65/3al+M+hnXyME39eTvr5jv9QJ/7c2gn1KqBpJ9R5Qh3F6i32c59Qp/bv58efKqFdfVpf1Y3SevXVV3n66acBCA0N5aqrruKWW25BVVUef/xx3G43ycnJdOvWjeDgYHbs2EF+fj5utxu3201qaiq1a9dmypQp3HPPPTidTnJycoiIiCAuLo7MzEzUY28Mjhw5wrPPPsuSJUvIyMjA5/Nht9t57733uPfee7nkkktYt24dhYWFeDwe7rjjDv744w9SUlKYNWsWw4YN4/rrr+fQoUMoisKePXvIyMhAr9djMpkIDw9nwYIFtGrV6rQ/7/fff8/dd99NZmZmqV+zyqhajXTlmq4oT3avwpN7mvF247X0KfyCVEsbsmiFwxlChDcSxWzFawTN4MAQ7EIL0eMwmbEZDRToFQo1cKPgxogHHYrPixEfBp+GQfVh8KnoVS8mn5dg1YnF50CneQAvmuZG1byoigdF5wOl6I+meNEUH6riRcXn/8+DD4+i4NYpuBUFNwouRcMDuNDwoKH6QFNVNA00VSv649NAVdFU0Hw+UIsCW6eBUdVhQMGo6jGgYFAVDJqCUdOhP/a5QdVh0PB/rVcV9MfeMOhVpejj8a99oNO0Yx9B5/v3DYRO1dD5NBRVRXesBkXV0PlUFFUrekPhU1FUFY59/u9H3wl/VPD++7Xm84HXG+h/SgBEh9wJlD5077rrLp5++mkMhqJf29988w3Tpk2jVatWjB8/nv/7v/8DYM2aNVx22WX+U8oGgwG3201aWhq1a9cmMzMTp7No3/HIyEgAzGYzmqbx22+/8fLLL7Ny5Uo0TaNjx4688847rFy5kldeeYXHH3+c++67z3992GKx8OGHHwLQokULUlJSitW8YsUKxo0bx4ABAxg6dCgFBQXYbDY2b95Mo0aNit134cKFvPrqq/43C16vF6fTid1uJzg4uNSvW2VTrUK3rI0KQpxM0xQe3N2JLhHN+NT0BR2OTKDAEsbyxIuwOaJRcqMI8zZG84TgNIJDy8OouamrNxGkmAk2h6FZdHj1OtyKEZcCPpMdzVwApgI0YwG+IBcekwWnMQinIQSHzoJdb8auGHGqZlxeCx6fGY/PhMtjwO3V4/Hq8HgUFK8GXg28PoJUJ0Gqi2DVRZDmxKI6CVEdRKkurDoXVsVNiM5FsOImBDdBOLHgxKy5MGtOjKoXo8+BUXWgqh58mg+P4sOr+XDrFDwGEy69EbfBiFtnxK3X49YbcSl6PHo9bp0Ol6LDdeyjW1FwK8feeCjasfAHFypuTcWjqbg0H27Nh1vz4lZ9uDQPHtWLW/Xg8rnxqOXRdGNArymYND0mDJg0PUZ0GDV90eeaDqOmw4Qew7HPjegwqfpjbywUDOiK3oQce2Nh1BT02r9vNIo+Hnvzceyjzv/mo+iPs1kkceXw02iaxvTp02nVqhUPPvggf/75JwDXXnstU6dO9X9ep04dmjdvzkMPPcS8efN49NFHWbp0qf9aq91uZ+fOnYSFhdG6dWtcLheFhYU88sgjjBgxgieffJJVq1Zx1113kZGRgdfr5dZbb+WTTz7BbDbjdDpZunQp9erVY8mSJadcf01PT8dsNvPMM88AYDQaiYmJIT8/nxkzZvDkk0+yZMkS+vbty8aNG7nyyiu55557eOWVV4iKimLJkiXcdddduN1uCd3KymyoVn1hohJZlRtGu7wHeb/xagYe+ZgByUWXMdx6MysaXMRyazSmdI2kVDOR7rr4dPXID44j3aXiteWhU7LxODNw2bII0VuJjqhLZEgdrMZIjJoZjwIOvDiMHuwGD4WKkwLVgVPLBUMBeoOd4GAPliAvZrMbk8WJonfi0Jtx6iwUKEHYFQt2XRiFSgSFhJJFLWxaEDbNjMurw+PR4/GC6lUw+MDkA6MKeq+K4isKb83jw+dR8XpU3G4fLrcPh9uHx+3GpLmIMHiINHqIMHgIN3gI03sI1XkI1buxKi4idW6CFRfBuAjC5Q91k+rCpDowqk70PgcGrxOd147OawePHUUteTSqoeDWm3AbLbgNJtwGM+5j4e8xmHDpDMfeBBhw6/S49QZcOh0enR63osOlKLh1OtyAW/l39O/m+JsAFZdW9CbApfnIPxb+bs2JW/Xi8nnwqB7cx96ElMW9id3oUIbH/+c//wEgKCiIxx9/nLFjx9KyZUuWLl3K/v37+e233wDwer0UFhYSEhJC48aNCQsLA2Dp0qVA0anj4yPVuLg4Zs6cSU5ODgBff/01l1xyCQ6Hgz///JMXX3yRnj17otfrgaLOZ4CoqCiysrJ47rnneO211/juu+/8dd5www3ExMQQFRWFy+Vi+vTpdO7cmdzcXH8wP/XUU3z77bfMmzePtLQ0/vrrLzweD3/88QetWrXilVdeYf/+/QDk5eXx2GOPMWfOHFwuF506deLNN9+kXbt2ZXg1A6daXdM9anPR+ZWFgS5DVHM9I/P4MPQzQjPWFLvdp+hZX689i6ISWGfPoUOGlS6pQcSk+nAHN8IW34o8Qxy5BSoGiw2zJRdNzcJVmE7+0VSchTaio+oSF92AqNDahJmjCNKFYnQboVDDqfPgCPFht3ixG90UKi4KNScFHjs2RyEFdhtms4+wMAWrVSM42IslyIPJ5MJocKDX20Gxoao27BgoVEJx6GNx6GKw66Kw6yIpJJQCrBQcC+t81USeT0e+TyHPo2LQwKoqBGtg0RTMPjCqGkZVQ+/Vjo28VVSPis+r4nUXhbfT7cPp9lLo8mJ3+zj5t06Q3keU0UuU0UuksSjMww0ewgxuwo6FeojiIkQ5FuqKC4vmwoILk+bErDoxqg6MPgd6nxO914Hea0fx2sHjQNHKfunJq+j/DX+jBbfeWPS5/tgZAP2/o3+3Tn/sT9Go36PT0a7xlbRvd0upnjsrK4tmzZqRnZ1N165dady4MQsWLODo0aMEBwfTrFkztm7disfjoVevXmzbto3s7Gz/NdoTHe9sLiwsZOjQofz0008EBQVRWFhIt27dWLNmDd6TTskbDAbCwsIYOHAg33zzDQCNGzcmISGBVatW4fF4UBQFVVV57bXXMJvNPPTQQ8TExKCqqn8UHRkZidvt5vXXX+fee++lYcOG7N69m40bN9K+fXtMJhPdunWjX79+vPfee2RkZNC7d2/CwsJ4/vnnCQ8P56OPPmLKlCns2rWLqKioUr2egVStQtfu9tLy+d8DXYaoAfSKygeNV9E//RMUr6PE++xIaMmiWg1Z5MvDnpfLoJx6dDhkIGb7ETyGGOxNu5IX0Zgcbxg5WT5MQS6Cw2wYjLn43Edx5KeRfzQVr7toofzg4HDi45oQHV6H8OBYQvThmH0WdHYFrcALKriDVRwhKvagomC269wUaE4KvHYKnIXkF9r8XaZ6vUZ4uI7QUAgJUQkK8mK2uDAZXegNdnS6AtBs+NRcvN4cNM2NWxeN0xCPUx+PQx+DXRdNoRKBXQkrCmuCsalFYZ2v6snzKeR5NZwnXPpRNI1gTSEUBYuqEKRpWFQw+MDg09D7NBSPBr5j4e1R8Xh8ReHtKhp524+F97kKNXiJNnqJMnkJ17sJN3oJ1x8PdTchOjdWnbso1HEdO/Xu8p96N6sODKoTo69opK732tF5HSge+2n//k8x8H/Q7Z5zrvlELpeLWrVqFZtLC0UBqmkaOp2OgoICVFXlkUce4csvv6R9+/YsWrQInU53Svh++eWXjBw5ksTEROx2O1lZWSU+b/v27dmwYQONGzdm7969HI+LiIgIQkJCOHToEC6Xi9DQUHw+H2azmTZt2jBhwgTuvPNOsrKyWLduHTfccAMrV67EYDDQrFkzXnvtNTIzMxk5ciQAOTk53HDDDcyfPx+9Xo/BYEBVVTweDxaLhdzcXMxms7+uJk2a8MQTT3DnnXeW6vUMpGp1ejnIqC9qQqw2byNEZeXTdNy5uxu9olrwfuSnWI+uO+U+LdK20SJtG/cBKdENWNRQ4+NGNjZ1OEJjt5uBOatps38V9bcewpdnx9WmFwXmtuQpDcmyN8du92IIUYmo48ISkodOl43DmcHOw6uwZWagnTB60+tN1IprQExQPSKJJ8wbSZw3HIPTADYf2glz2N0WFadVw2HxYDd5KVRdFNqcZOc4KHAVYisswOlynvLzBAUrhIdxbBTtIyLIg8mcitGwG52+EEUpQNPy8fly8Hrz+LdFGLyKFaehNg5DLZz6OOy6aOxKFAVKGIXHw1qzYFNN5PgM5PsUcr0a9jP0aegpCu+Q4yNvVcGsahiPhbfOp6F4VfBqqB4Vr8eH06OS7/aR7PbhKPRid/nKvAe3omjFRukRBg9hhqJwD9N7sOpdWBUPrYM6cvpe3TMzm83ExMTgcDiIiooiLCyMvXv3YrPZUBSFrl27+hfHqF27NllZWezduxeAqVOnsn37dl555RX/8Y6f5j106BAtWrTwX1vNzMwkKSmJw4cPk5ubyxVXXAGAz+ejYcOGpKWl4XA4yM3N5Y477gDg/vvvR6fTccstt/D0008za9YsBg4cyGOPPca0adO47777yM3NpXbt2ixbtoxt27ZhtVoZNGgQ9913n7+W7t27s2vXLu677z769etHWFgYY8aM4fvvvycyMhKd7t/Lhw6Hgz179pTy1QysahW6iqIQZNSf1ztgIcriz+wI2uU8wsdNVnBp2qco3lPDCiAxaz+jsvYzCsgMrcXiek1ZFOviw/g9eLp4aOeqxeVZGbTc9xtxWw7QJDsHb50mOFr0wGZuTo6vDllHa+N1F4VncKwPa0QhZkte0Slqezo5R1M5nLarxOePiEigVkxDosJqE2aJJlgXRpjbipIDWuGp11O9Jg2nVcUR7MVu9GI3uCnASaHPgS2nkMOpBdgddsAChJ3yeEXRCAvTERZ2bBQd7MVi8RBmcmEwZKPTpQA2VDUPrzcHVS15tOhTgnAaEo6NrGOLRtdKJIVK+LGwDsGmBR0bWRs46tOR5wWb7/icHf1Z/w4NmkaIphCCQpAPLBqYVTD6ijqt9T4NPCqaV0XzanjdPjweHy63itPtxeHyUeDSk1VoBIJO+zxvd6lb6tA9LiwsDK/Xy5EjR0hKSiIiIoJNmzaxevVq/31ef/11APbt2wcULR85c+bMYseJjY0FipqyBgwYQIMGDQgODsZsNrN9+3b/ClahoaFA0WIZWVlZjBkzhgkTJgDw2Wef0alTJ7744gsSEhJQVZUmTZqQk5PDb7/9xkcffeSf7nP06FEKCwvp3r07GzZsICEhoVg9Xbp0Ye/evRgMBoKDg2nfvj0AHTp0YNasWWiahsfjITo6mhtvvJG7776biIiIMr6agVGtTi8DdHp5gexbKQLi0ugc3g3+hJCjG875MQWWMP5scBGLgi38ZduH3WsHoKczkUuPxtJ0j4OgLfvQbAWoBhPupK7YG15EvrU+Wc4Q8nI8Jw4oMQe7CQkrwGDMQfUexZGfTl7GIf8p6pJYLFZqxTUmJqIuEcFxhBgiMPss6J16NJsXfCX/ivAZtKIRc7AXh9lLocFFIa6iYHbbKXAUUGAv5Fx+xZjNEBamHDvV7S1qGDO5MBqd6A12/yha9eXi9eaiceY31qpiwqWPx2mIx6GPw66LwaFEUqgLp5Awf1jbNBP5PiN5Ph15Psj3qpT26q9JgxAgWFUI1hTMPg2zSlE3s1flwYvq0yex9NcgmzRpQmFhIZMnT+aaa64pdtpYr9fjO7Y40GuvvcbixYv55ZdfAGjbti0vv/yyf+UqgMWLF3PVVVdhs9kAGDlyJPPmzSM/P7/YMo3HTy8PGTKEn376ifj4eNLT09HpdCQlJbF3714cDgeKoviv6YaEhOByuTAajTgcDpKSkti5cyeqqhIREUH37t355Zdf2LZtm3+e7tSpU1mzZg1z5swhIyOD6OhoCgsLsdlseL1eunXrxvTp00lJSSElJcU/PaoqqlYjXYBQi1FCVwTEH1mRtM95nE8b/0Wvw5+h+M7+79DqzGfQjsUM4t9O6EXhkSyxp7DckgKJYOito6+9Kb0yImmUnEX0gg+JcjhpAPgi43C26kVBQityDXFk5gWRnW4CooDGABhCNMLrOAkKyUeny8brzKAg5zC2zCNomorTWcCBgxs5cHDjKfXpdHpiYxoQG12PSGsCoaYoggjB4DZisPkIyVUJyTUBJuDUaR2qTsNlPXad2Vw0Yi5UXBSqjmMNYAXYCgtwuVSOHtU4ehSKRqd6wEzJo2gIDS0K6OMNY2aLB5PJidHgQKcrBMWGRc3D6tmKz7nqHP8Gi7qli8I6oeg0uP5Yk5kSQQFhFPrD2ky+aiTfpyfPB3letahDGsjRa4B20m9XhTGhpnOuoyQ+n4/s7Gzeeecd/2IWx+fsnvjGpkmTJhiNRv7++29yc3P913f/7//+j2+//dZ/v7CwMCwWC1lZWbRt25Y5c+b4j2O1WikoKCAyMpKEhAT/qdzrr7+euXPnkpKSwqWXXkpaWhoul4v4+HhycnJwOBx89dVXbNy4kbFjxwLw008/YTQa6dmzJzk5Ofz666889thjLFmyBJPJhNvtZsiQIezZswe3242iKFgsFubNm8frr7/O9OnT2bZtGzt37qRZs2bodDqeeeYZrr76ajp16lSm1zQQqt1I9+r3l7P+YG6gyxA13OUx2bxl+YjgzM2levyJndB/uNI57Mjwf8+iGRhQ2JCeh0NJ3JWLfvveYgtAuBu3w9G0K/mRTcj2hpOT7UMtYbSqN/qwRtgxWfJAzcRtP0J+ZiqO/NxzrjM0LJb42EZEh9UhzBJDsD4Us9eCUnjstPU5/HbRFA1XsIbD6sNh8Z3ame0sxFZoO6Wj9lwZjcdH0doJDWNuTEYner0dRVcAWv6xhrFcNK10c4NduhhcxgSculrY9TE4jjWZFR5rMhvdvCv1QiJLdWzA38x0ouNdwunp6f45urNmzSI4OJgrrrjCPxLWNI3+/fuzcGHR7A6LxYLL5ULTNPr27cvmzZtLXPmpb9++/P3338TFxZGSkoKiKMTFxeF0OmnSpAnbtm3D4XCg1+tp1aoVmzZtIjg4mLCwMGJiYti2bZt/BH7ttdeyaNEicnNz0el0dOvWjb///tv/XIqi+JvCYmJiOHLkCGvWrKFLly7o9XrMZjMul4uEhAR69erFq6++SmJiYqlfz0CpdiPdqOCyvZsUojzMz4yig24MnzVeRs/DX5zTqPdEes1HpwNr6XQAxgDbE1ryx7FO6OSCg8yxJjOnGdAMwgdZGGRrSJdUC7V3ZGFK3oxpz0bCgURAtYTgan0JhYntyAuqQ2aBmcJ8Lz6PnryjoUAoUPfYE0N4HQ/BYQUYjTmo3kwctjTyjhzC63adUqct/yi2/KMkn/IdMBmDqBXXiJjIRCJDahFijMCiBv972tp7bIFGTcFSqGAp1BGJkaLrxKdyB6k4rCoOixe7yUOhzk2h5qDAW9QAll9ow13CaXSPB7KyNIoadHUUjcpNgLXE5wkJUQgLo8RpVzq9HUWxoWn5eL05+Hz5/seZ1UzMrkzCKPmNVi3DihJvPxfKiUtonsDlcjFx4kSGDh2KTqdDr9czbNgwVFWlTp06/hWili1bxvr16wHo3bs3H374ITfffDNr167l0KFDzJ8/n169elFQUEDDhg2JjY1l1aqiMwQ6nc4/j3fatGnMnz+fffv20a9fP0JCQti/fz+HDh1ix44dAMydO5dVq1YxZ84cAEaPHs0VV1yBpmnUqVOH3Nxcrr32Wi6//HJ/6E6bNo3PPvuMdevWERQUxDvvvMP27dv56KOPCA0NpVmzZrRv356ZM2fSpUsXvv7661K/loFW7Ua6j87YyA/rDgW6DCH8rojN5A3TRwRlbS2X46VEN2BRnSQWKU422fahnjQHNU4N4cqchnQ8ZCBu+xHYl3LKMXwJDbEnXYwttjk5SjRZ2fibtEqmERLuJMhqQ6fLwus6SmHOYfIz09FKs5C/ohATneifkxxqiiJIsWL0GFEKNDRH6ZohPeZjI+ZgH3ajB7u+6Drz8WC22QtwOM9xis850OshPFw5adrVsVG0wYFOZwPNhqrl0bPHEnQ6Y6meJz09ne7du3Po0CGGDBnC3LlzGTVqFLNmzeKTTz4hIyOD++67D71e73/jMXnyZO6//34AmjVrRr169Vi4cCEPPPAAr7/+OpdffjmrV68mKCgIm83mP7VrMBh4//33ueOOO+jbty8rVqzg4osvZsGCBUyYMIHDhw+zYcMGFi5cSKNGjbjnnnuYMGECTqfTf6q5e/fu5ObmsnTpUu69915+/fVX9u3bh06nw+v1kpmZSXR0NDExMWRlZZGTk4PX6yU2Npa+ffuyevVqgoODufPOOzl48CB5eXnMnj2b33//nYEDB5KVlVUl5+hCNQzdl+dt49O/9gW6DCGKCdL7+KLRUrqmTjnt6kulUdQJ3ZZFJoVV+XtKXDaxkTeSgTmJtDugELU1Fe1w+in30fQG3C26UtioE/mh9cl2hpCbc/bTwzqDSmiEvWihD7Jw249gO3oI+3mcoi6J1RpJXGwjYsLrEh4US7A+7N85ybZzO219Oj6jVjRiDvZhN3uw690UHu/Mdtux2QsotBeWqf6TWSwWnnzyyTId4+qrr2bOnDm89dZbPP/88+Tm5nL//fezbNkycnJyyMzMxGQykZuby1tvvcX48eM5WnSRnJEjR/LHH3/gcDjIzMzEYrGQkJBARkYGTZo04fDhw/77Hr+e2q9fP9566y2efPJJvF4vXq+XkJAQJk+eTNeuXbnmmmvYtm0bsbGxZGZm+hupBg0aRFRUFDabjZ9++sl/ennUqFFs376dVatWoWkaycnJ3HTTTaxZswaTyYROp8PhcDB69GgSExN55JFHeOONN0hISKBDhw7odDomTpzIzz//TGpqarEpRFVJtTu9HBkip5dF5ePw6flP8qUMiWvDa4YPsGTvKJfjxtiOcN3WBVwH2CzhLGvQ8ZRO6L2GHN6PzYFYoBO09tRhQGYCLff7CN9yEDUzC8Xnxbx1Oeaty4kCGgBqeAzO1pdgS2hDrqkWWXkGnCdNL1K9OvIyrRSdqv33FHVYbQ/W8KKFPo6fos7PSMVTwvzfkhQU5FBQsJa9rD3lewaDiVpxDYmJrE+ktRZWYyQWNRiDy4Bm88FZ9tXWexSsOXqsOXqKTjOHnHIfn17DFaIWrQB2UgOYzV3UAFZQWHBOndlQ1JhUVsnJyWiaxsMPP4yqqhgMBv810Fq1avmviZ5OWloa8fHxzJs3jz///JNPPvmEwsJCOnbsyIYNGwgJCcFut7N06VL/dnvPP/88ZrOZUaNG8eGHH9KwYUMeeOABnE4nHo8Ho9HII488wjXXXMOrr77KF198we+//86CBQt4++23z/jzDBkyhPr16/Pkk08ya9Ysf7PW/Pnzefnll3nxxRf57LPPiImJITk5Gb1eT+fOnfnll1/KNXCPz1E+vuPShVbtQjdKQldUYj9lxPKH/lm+bLSIiw5NRSnjer4nCnXm+TuhXQYLK+p35I9jndDZrlz//bYYj7Al4QgkAN2hq6s+l2XE0Xyfi+DN+9Hyi65T6vIyCV4+i2BmUevYYz2N2mJv2o38qMbk+CLIziq5ScvtMJLtiKKoi7poNxldsEZ0wvFT1Dl4XRkU5qSe9ylqr9dN6uGdpB7eWeL3o6LqEBfdkKiwBMLMMQQpVkxeE0oBaPZzO8ug9ykE5+sJztcTfabO7BANR4gPh/nfpTkLVAeFHgc2Z1Fnts/n8893La0RI0awdetWBg0aRPfu3ZkwYQL//PMPixYtYvTo0UDRVniDBw+msLBolB4cHMzTTz/N+PHjAYiOjmbPnj2YTCYGDx7MhAkTCAkJYdWqVYwZMwaTqeh35/HAHTduHMuWLWPnzp3+eb3x8fFs2bLFX5fH4+Gpp57iqaeeAoo2NUhMTKRPnz7+NaFPlJCQgKZpZGVlsXPnTj755BMuueQSxo8fz19//cUll1zCK6+8wrBhw9i4cSPR0dH+a9HVRbUL3XpR1Wc3ClE9Ffp0XJvcn+G12vGq7gPMOSWHR1mYvU567/mb3py5ExpgpTmVlYmpkAj6Xgq97U3ofSSKxnsKMW/Zi+b49xqoce8mwvduKtak5W7Vk4J67ckLqktWoZmCvJKDTUGhMC+IwrwgIA5oDkBQtFq00EdQPoqWhct+BFtWKvbc7FL97NnZqWRnp5b4veDgMGrFNCI6oi7hIXFY9eGYfBb0jmPNXeexU5lOVQiyKQTZdEDJC2NoioY7SMMUFV2qn+W4Q4eK+lSWL1/OoEGD0Ov1tG7dmt27dwP412Q+HrShoaEUFhb6NygAaNmyJY888gjXXnstDRs25NChQ7jdbg4cOMAnn3zCxx9/zPXXX8+iRYto164da9euZfHixXi9Xv73v/8B8NdffwHw8MMP8+677/L4448zYcIEGjRowP79+/H5fIwaNeq0jV/HRUZGEh0dzccff0xCQgIHDx4s8+n3qqLahW7DmFNPFQlRGf14JI7fDc/yVcOFdDj0dbmOek90tk7oE/nQ+CN4P3803A8NwXyZnv6FLbg4LYz6yfkYtu9FO6FDWOcsxLJ2Ppa184mhaGawN6EBjqSLKYhtUdSklQMe1+lHsqpXR37m8S7qOkU3KhBa24M1rACjKRfVl4nTVrTQh6cMjVB2ez77Dm5g38ENp75OegOxsQ2IPXba2j8n2WUsWkrzjI1mJVM0BbNdIdRcttPLycnJxMXFERUVxc8//3zK9x0OB4cOHeLrr7/mgQceIDU1FU3T/KdLvV4vVquVf/75h48//hhFUYiJicFgMFBQUIDFYmHPnj3cfffdXHHFFXg8Hho3bsyQIUNYuXIlQ4cO5f333+e7776jZcuWJCQkYDAY+Pjjj9E0jX379nHzzTfz1VdfMWrUKADWrVvHjh07sFgsNGjQgIiICOLj44GijujLLruMGTNm8PXXX2MymbjhhhuKrax1stWrV/P000+zfv16PB4P7du3580336Rjx47/vt6KwieffMLPP//M77//Tp06dZg0aVKxhUF++eUXRo8eTUpKCt26dfOv/1xRql3oJoRbCDLqy7yeqhAVodCrZ3jyAK6Lb8crvI8pd/cFf86ktG0knbgm9LFO6I35e9FO6lByKT7mWXczrynQFEIHmrmioBndUoOpvTMb3a59RRvGn8CQtp/QtP2EUnQGW9Ppcbfoir1RJ/LDGpDtCiE323vWNdI9DiM5jkggEmgIgC5IIzrehSXEhl6fjdedgT0njfzMNFRf2f6f9/m8pKfvJj295L+D8PBa/qU0w4NiCdaFYvKYUexnn5NsiCh5GtS5uuyyy8jNzWXkyJFcf/31p+wvGxcXR5s2bQgJCSEnJ4cOHTpw5MgRZsyYwf3338+KFSto3bo1U6ZMoV27dhw5coTY2FjCwsLQ6/V069aN8ePH88wzz2A0Gvnll19YvHgxP/zwA/v378dgMDB58uRizzlx4kR69erFVVddRXx8PGFhYVx++eUkJiYybdo01qxZwzfffEOHDh1Yv349d9xxB/fc8++GD23atOGhhx6idu3abN68mTvuuIMJEyYwbNiwEl8Dm83GyJEjeffdd9E0jUmTJjFo0CCSk5OLnb5/6aWXmDhxIq+99hrvvvsuN910EwcOHCAqKoqUlBSGDx/Offfdx5133smaNWt49NFHy/R3c76qXfcywBVvL2N7Wv7Z7yhEJRJq8PJ1wwW0PTStXLaiO1+ZobX4o15b/jhDJ/TJ4nxWBuXV56JDJmrtyIC9B89pxxE1LBpn60soqN2GXGM8mfmnNmmdD51exRphxxych6Jl43YcwZZ5iMJSnqI+XyZTMPHH5iRHhNTCaojArAb5T1vH3NoKS5PSL4wxatQocnNzmT17No0bN/bv7jN79myuvvpqFEXh1VdfZe3atRw+fJht27YRFhbG/v37/ddKr7zySubOnUtsbCwffvgh1157LSaTicsvv5y1a9cyc+ZMLr/8clwuFzabjdzcXFq1akVubi6LFi2ifv367N69m+nTp/Ppp5+SnJxM165dueOOO/jggw/wer1MnTqVG264gSZNmjBu3LhiyzW+/PLL/PLLL/65uTt27GDUqFFs2LCBFi1acPPNNzN9+nTWrCnaMvPFF19k9uzZjB49mtGjR5Obm1vsNTm+rOQ333zDlVdeCRSNdJ999lnGjRsHQGFhIVarlV9//ZWBAwfy9NNPM2fOHLZu/Xf63pNPPsmECROkkaosGsWGSOiKKsfmNTA0+QpuTGjPS+pkjHl7z/6gchRjO8L1WxdwPafvhD5Zhr6AKVFbmRIFtIX63miuyEmkXYqOmG1paCmHS3ycLj+L4L9nE8xs4oBmgKdBaxzNu5Ef1YRsX+Rpm7RKovp05GdZIctK0SnqNqBAWIKXkPACjKYcVF8WTlvRdoluR8k/T2m53XYOHtrCwUNbTvmeouj4b9inp1ny4+yOHj3KP//8Q0pKCmazGYPBgNvtZtq0aYSEFF1Oe//99xk/fjypqUXXsvV6Pbm5uVitVn9Hs91uR1EUevXqxZIlS+jXrx8ej4eLL76Yv//+m0ceeYRGjRqxfft2PvvsMx544AHeffddbrnlFoYPH47b7aZ+/foMHDgQm83GsGHD6N27N6+99hoZGRn8/PPPDBs2jMLCQvbs2cN///tfRowYgdfr9W/LFx4e7v+5br31Vnbs2EFoaCi7d+/m2WefJSzs1GU/b7jhBgYNGsSRI0d49tlnWbJkCRkZGfh8Pux2OwcPFr9E0rZtW//nISEhhIWFkZFR1Mewfft2unbtWuz+3bt3L+XfTOlUz9CV67qiCvsmLYGfjS8yrcHvtDr0bUBGvad0Qje4iEVhESw9qRP6ZAcMuXwYm1s0PakjtPQkcHlWbVof0AjfchAt49SlBo8z7t+Ccf8WwiiafKSZg3C26klhvQ7kBdclq9By2iat03E7DbidEUAE/lPUFoiKdREcmo9On4PPnUFh7mHyj5b9FHVJdHodoTGlb6S65ppryM7OpmPHjkybNo0jR44wYsQI3n33XX/z0d13383dd99NUlIS/fv358EHHzzlOPXq1QOgT58+fPzxxyxbtoyuXbvy5JNPsmLFCrZt24bL5WLMmDE88MADANStWzQNbP/+/f5RoKqqDBkyBJ1Ox7Rp01AUhdTUVG666SbMZrN/RPree+/x0EMPcdNNN7Ft2zY+//xz9PqiXZ/++ecfVqxYQd++fZk4cSLh4eFMnz6dSZMmFatZ0zSCgoIICgryL4rx9ttvU79+fcxmM927dz9lFTKjsfgCJMfnD1cW1TN0YyV0RdWW5zFwZfJgRtTuwHO+9zDm7Q9YLWavk967l59TJ/TJthmPsi3+KMQDXaGTqx79jtaixT43IVsPoOXknvaxistB0LqFBK1b6G/S8sXVw9HyYmy1kshVYsg8S5PW6dhtZuy2WIreHTQr+jkjVUIjHJiD8lDIKjpFnXWYwpzTv1E4F+G1EtDpzr7FYElyc3NZtmwZAwYMwGKxUL9+ferXr8/MmTNp3749//zzDwAHDx7kgQceIDk5meTkZNLT03n33XepVatosteJp6d79+7N6NGjeeKJJ/zTiw4fPkxyctFinq+++iqvvvqqf2tAgLVr1zJmzBi2bdvmX/hizZo1eL1eZs2axZIlS3j//fcBqFWrFrVr12bevHm0bt2aCRMmULt2bcxms3+t5B49egBFux117tyZF154ge3bt5OXl8d3333H+++/z/Lly6lduzZTpkxh9OjR+Hw+3n//fQYNGsRPP/3Es88+S2ZmJs888wxLly5l1qxZQNG825dffpmdO3f65x7nH5sGl5SUxNy5c4u9xsf3Ia4oVXNJj7NoGFP2iehCVAZTD9emS/ZYtif+B40zT8OoCMc7ocesn8fv29Yww2nlrvA2NLGe28Lza8yH+V/d9Yy6ZCvX31XA5NGN2HpjZ1xd26CEnP3Nsj7jINYl35Dw3XMkTb+Hixfcz8VHptLBup3GtZ1ERhs4y2yV09J8OvKzQjh6qDYZh9qQm9UPHyMIS3iQhBa3Ua/tNSS26kVM/WaYgs59amJU7bqlK4iiRTWsVisHDx4sNlpr06YNN910E++88w4AQ4cOJTs7m6+//hqDwcCSJUsYPHgwycnJzJkzp1iwtG3blsjISHbu3OkfvR4fYSqKwp49e0hLSyu2mcAzzzzDpEmTePnll0lNTSUuLo7Q0FDatm3LyJEjefbZZwkPD/dvSP/SSy8xZ84c6taty5EjR+jRowcPPvggb7zxBoB/KtPgwYP5559/CAkJYf78+UDRNdaHHnqI++67r1iDVNOmTfnqq6/48MMPGTZsGLm5uVgsFu69917/3GIo6tQeN24cGzduZPbs2aiqyqeffgoUnRFITk7m8ccfZ+fOnXzzzTfFplVVhGrZSJXv9ND2xfmBLkOIcnVbnRSe9kzGkH/w7HcOgIMxDfmjdovTdkKfjUnTc5m9PpekR9Ag2YZx214016mbLJyNGhqFs00vChJak2tOICvfgKOg/JbePC4o1ElwqA29Pgef+yj2vDTyMg6j+oo/V9err+fi/4wo9fP88MMP3Hjjjf59ZXv37s1//vMfwsLCaN68OW63G71ez759+0hMTGT16tWMHj2av//+m6CgIJo2bYper6devXrMnj0bgGHDhvHTTz/Ro0cPli1bhqqqmM1mYmJiSEtL8z/38dWaFi5cyGWXXUbfvn1ZsmTJaWt94YUXePHFF0lOTiYpKYmkpCR27dqFyWTC6/Uybdo0hg8fDhR1XBcUFKDX6xk8eDDNmjVj3LhxvPXWWzz00EOnNFItXryYO++8k7Vr12K1Wvn888957LHH/N+HojcNs2bNKtYBHRoaSkFBATabDavVyrx583j44YdJSUmhS5cu3Hrrrdx2220V1khVLUMXZDN7UT3Fmjx8XX8ezVK+RynLAsQX2Imd0Cvzd+MtxXrTVs3EAFtDuh8Ooe6uHHQ79xXbwvB8eOq3xNG8O/lRTcnRIsjOUvF5y//1U3Qq1ggHluB8FKVoLepOQ66kebeyNes4nU6WLVvGihUr+PXXX1m1ahWffvopo0aN4p133uHNN98sdjoYihagePvttxkxYkSx08vHjR49mg0bNvhDtE+fPrRv35633nrLf5/joZuRkeFflWr9+vV07NiRAwcO+K8Tn+ypp55iy5Yt/PTTTwC43W4SEhKYMWMGl112GQDt27dn2LBhvPjii0DRdeOGDRvy119/0bNnT/+xjp9ePn6tODg4mMmTJ3PrrbeW+Nxr167lxRdfZOPGjeTk5KCqKna7na1bt9KyZctzer0vpGp5TRegUYyVzIKKmS4gREU56jYyIPlq7qp7EY+73sVgK3nlpUArTSf0yQoUNz+E7eSHMKAFRKvBDMqrT6dUCwk7MmH3/nOangRgPLAN44Ft/zZpmSy4jjdphSSSZQ/Cllu6fXRPpKk6bNkh2LJDKJql3JqYem3KdMwTw7B///4899xz3H777f5R3vHN4qFo5apGjRrRrFmzYsfQ6XRomlbiSlGKohTb3L4kJzYnHT/G6ZqTfD4fX375Jenp6RgMhmK3f/755/7QPZ2Qs1xmCAo6deWv4woLCxkwYAADBgxg2rRpxMbGcvDgQQYMGFDito+BUG1Dt2FMCKv2S+iK6umjQ/WYY36Fr+v9RJOUHwJdzhmdrhN6SeFBctx553ycLJ2dryK381Uk0Brq+CIZnFOP9ikGYrenox049y09FbcTy/pFWNYvIpqi1aF9sXVxtLqEgrgkcnSxZOUquJ1l62bWG3RE1ir/pWlbtmzJd999h16vJykpiZSUFFJSUvjyyy/9Sznm5ub6R3axsbH+NZO/+OILBg4cyFVXXYXBYODHH38kIiKCzz//3L8jUFn88ssv2Gw21q9f7+9WBtiyZQu33norubm5ZTqN27ZtWxYtWlTiSHfHjh1kZWXxv//9z39N+vi838qi2oZukzhpphLVW7rLRL/ka7gv8SIecbyHvqDkObGVycmd0OvqteePc+yEPlmqPp+PY7ZADNABmnprcUVWHVofgMith9DSz+94+qOHsC75FitFzdaaTo+72UXYG3cmP6wROR4rOdlezmcGV3SdEHT60verZmVlsXHjRiwWC5s2bSI0NJQ1a9YwceJEOnTowKZNm+jXr5+/sWrv3r088cQTLFq0iISEBDp16gTApZdeymuvvQYUzdf94IMP2LlzJx06dPAvzdigQQNWrlzJ/v37sVqtpd6v9rPPPmPw4MG0a9eu2O0tW7bk4YcfZtq0adx3332lfk1eeOEFLrvsMho3bsx//vMfvF4vv/zyC2PGjKFevXqYTCbeffdd7r77brZs2eJfKKOyqJbdywAd6kUEugQhKsTklAZcXDCefXWHBbqU86LXfHQuQyf0yZINWbxTaxN3dtnEdbdmM/7Ruqwe0YnCXu1Ros5/NShF9WHesYrInydT/9tHaf/9XfRe9QzdnL/TJvowibU1gq1nHrfE1ivb7kJWq5XQ0FDWr19Pr169aN26Nc899xx33HEHN998c1GdisKcOXPw+XwcPnyYZ599lrZt25Kfn++fEjRgwACee+45AB5//HFsNhsjRhRv7nrsscfQ6/W0bNnSf1r2fB05coSff/6Za6655pTv6XQ6rr76aj777LPzPu6J+vTpw8yZM5k7dy7t27fn0ksvZdWqVUDRiH7KlCnMnDmTli1b8r///Y/XX3+9TM9X3qptI5Xbq9L2pd9xnmVvTSGqk9H19vJA4XvoC0/dqL4qKWsn9MkUDXo463Lp0Via7nUStGUfmq2gXGr11GuBo3kPbNFNydYiyc7W8B37vdP35ha0vLh2mY5fUoMTnNpgdNNNNxEXF8ebb74JFDUqjR492r8BARQFtMViKXbaF2Dbtm2nbYoS5avanl42GXS0T4xgxV65ritqjrcONuJ7y6t8U/dH6h36KdDllFq9zH2MytzHKMqnE1pTYHnQIZbXOwT1wNBbx6X2plxyJJJGuwswbd2L5nSWqlbjwR0YD+4gjKIFKFWTGXfLHhTW70jteu1LdczzlZuby48//ujfeg/g5ptv5rPPPisWugBvvvkm/fr1K3Zb7dr/vjE4OcyPT93ZsGHDhSq/Rqm2oQvQpWG0hK6ocQ45zfTa/X88Vr8z99reRWc/GuiSyqQ8OqFP5lVU5ofsY36jfdAILP0NDCxIokdaKIm78tBv31Pq6Uk6twvLhsUEJ68i/NXbSnUMKFpF6ssvvwRg6dKlfPXVV3Tu3JmJEyfStm1bcnNz/WsZf/PNNzidzmLrCmuahqqq7Nq1q1g3c3x8PE2aNDnt8x5f61hcGNX2mi5A14alawQQojp4/UATLnVM4FDdwYEupdwc74SetO5X/ty3n/f0iVwd2YZIU/jZH3wGTsXL7NBknmi2jv+7cg93PGph9j1tSB3aGa15I9Cd/6/KoHZtUUrxuBMNHDiQe+65h5YtW7Jo0SIMBoN/R51169b5w/Szzz7j0UcfZfXq1WzYsIENGzawceNGLrnkEj7//PPzqzsoiLi4uDLVLU6vWo90O9aLxKhX8JzjTiVCVDf7HRYu3n0TT9bvzJ2299DZy7aOcGVSnp3QJ8vTOfkmYjvfRAAtId4XxqDc+nQ8ZKTW9gy0fWdvMgq66KIy1QBgNpt57LHH+OKLL/j888+59tpr+fnnnxk7dizffvstXq+XCRMmsG7dOjZv3kzr1q0ZMmQI999/P3/++ad/h6LWrVv7G692795d4nzd3r17s2TJklNOL4vyVa1HukEmPa3rlO0dsBDVwf8ONKOfcyKH6wwMdCkXRHl3Qp8sXV/A59Fbub/dBq77z2GefDyWpf/tQE7/i1Bqx5f4mOCLOpXLczdq1Ig///yTrVu3cs8996DT6fjll1/8m8q/8sor1K1bl507dzJgwACcTicXXXQRP//8M8uWLcPn8zFy5Eh/h+/jjz9+aq3BwfTq1atc6hVnVq1HugBdG0az/mBuoMsQIuD22i302DOC5xp04da8yegcWYEu6YJJSttGUto27qeoE3pR7eb8objKpRMaYK8hh8lxORAHdII27tpcnlWblvt9hG85gJZfQFCH9mV+nnnz5mG1Fq05UFhYSEJCAvPmzaNjx47s378fgHHjxvHQQw8Ve9xjjz3m/1xVVa688kpmzJjByZNVnE4nffr0ITY21r8co7iwakDoRvHh0j2BLkOISmPc/hZMD5nIV7WnE394QaDLueDqZe7j1sx93Er5dEKXZLMpg80JGUUrP3aHGyP68dSxjdvLom/fvnzwwQcA5OTk8P7773PFFVf4R62AfwGM43w+H+PHj2fGjBmkpqbidrtxuVwEB5+6MtZtt92GzWZjwYIF6Mp4/Vmcm2ofup0aRKJTQJXLukL4JRcG0W3vrbzUsCu35ExG58wJdEkV4uRO6D8bdOCP4KAydUKXJLphUrkcJyQkpFin8aeffkp4eDiffPIJt99+u/8+J3rttdd4++23eeutt2jTpg0hISGMHj36lLWHX375ZX7//XdWrVpVbAs9cWFV+7c2oRYjSQlhgS5DiErphX0tGeR9jYzaZ16EvjoKdeYxeMeScu+EBuhRu0c5VHgqRVHQ6XQ4HI7T3mf58uUMHTqUm2++maFDhzJ37lx27dpV7D4//PADY8eOZcaMGTRu3LjUtZy4a1Fp9OnTx78t34W0ZMkSFEWpFM1h1T50AbrI1CEhTmtHQTBd9v6XbxKeQjPXzMbD453QY9f9zOJd2/hcjePmiDbUDjr/qTOR5khaRpd+C7kTg8jlcpGenk56ejrjx48nKCiIgoIChgwZctrHN23alAULFvD3338zbdo0Nm/ezJEjR4CioDy+3d+YMWNo1aqV//jZ2eW7psGoUaOK7WsL8P3332OxWJg0aRIAP/74Y6VbG/lCqxGh27NxTKBLEKLSe3pfG65UXycroXegSwmook7oNYxZ/3OpOqF71OmBTimfX62//fYbCQkJJCQkMG7cOHw+HzNnzqRPnz6nfcyzzz5Lx44dGTBgAMOHD6du3brFwm/Pnj3Y7XZefvll/7ETEhL8m8tfKJ9++ik33XQTH3zwAY8++igAUVFRNe7Udo0I3UuaxRBqrvaXr4Uos622EC7adxczao9BM8tlGSjqhL5/w8/M2rycn216HgltRbuwxiicOtcV4JI6l5TL806ZMgVN0/x/PvjgA6xWq38zgRdffJGhQ4eycOFCEhISiI6O5r777iM0NJTZs2djs9kICgoiOjqaL7/80r+M47vvvgtA/fr1/ceePXs2+fn5WCwWxo4dy8MPP4z32IpcL774IjNnzqRXr15YLBZatmzJggXn14A3ceJEHnjgAaZPn15sS76TTy83aNCA8ePHc9tttxEaGkq9evX4+OOPix3r77//pn379lgsFjp16sTs2bNRFKXYMpW//PILzZo1IygoiL59+/o7vU/0ww8/0KpVK8xmMw0aNPCPvk+s5eWXX2bEiBFYrVbq16/P3LlzOXr0KEOHDsVqtdK2bdvz3jqwRoSu2aCnf8tagS5DiCrjib3tuFp7nez4iwNdSqVSL3Mft276la83LuaPTCfPBTenZ0QLDLqiN/UGxcAldcsndM/F4sWL2bNnD4sXL+bLL79kypQpTJkypcT7rl69GijaTzctLc3/9bJlyxgxYgQPPfQQ27Zt46OPPmLKlCm88sorQNGUo+HDh2MymVi5ciUffvghY8aMOecax4wZw7hx45g3bx5XX331We8/adIkOnXqxPr167n33nu555572LlzJwD5+fkMGTKENm3asG7dOsaNG3dKLSkpKQwfPpwhQ4awYcMGbr/9dp588sli91m7di3XX389//nPf9i8eTMvvvgizz333Cmv3ZtvvknPnj1Zv349gwcP5pZbbmHEiBHcfPPNrFu3jsaNGzNixIhTpmKdSY0IXYAr2yUEugQhqpQN+VY67r+XH+s8jmaS/alPdrwT+sP18/kzNYv/mRtzZ70rCDNV3BmCyMhI3nvvPVq0aMGVV17J4MGDWbRoUYn3jY2NBSAiIoL4+Hj/1y+99BJPPvkkI0eOpFGjRvTv359x48bx0UcfAbBw4UJ27NjB1KlTadeuHb169WL8+PHnVN+vv/7KxIkTmTNnDpdddm7NeoMGDeLee++lSZMmjBkzhpiYGBYvXgwUrTGtKAqffPIJLVu25IorrjhlsY8PPviAxo0bM2nSJJo3b85NN910yqYPb7zxBpdddhnPPfcczZo1Y9SoUdx///3+PYdPrOWuu+6iadOmPP/88+Tn59O5c2euu+46mjVrxpgxY9i+fbv/mvm5qDGhe0nTWMKDjIEuQ4gq55E9HbhGeYPc+AvTkVsdFHVCL+aekEYV+rytWrUqtk1fQkICGRnntwTmxo0bGTt2LFar1f/njjvuIC0tDbvdzvbt20lMTCy2E1H37t3P6dht27alQYMGvPDCCxQUnNtWim3btvV/rigK8fHx/p9p586dtG3bFovF4r9Ply5dij1++/btxTZ+KKne7du307Nnz2K39ezZk+TkZHw+X4m11KpVdLa0TZs2p9x2Pq95jQldo17H5XKKWYhSWZdnpcOB+5hT51E0U8jZH1ATKTpIuqrMhwkLCyMvL++U20/cVeg4o7H4QEJRFFT1/PYQLygo4KWXXvJvlLBhwwY2b95McnJysXArjTp16rBkyRJSU1MZOHAgNpvtrI8pj5+pvJxYy/H1qku67XzqqzGhC3Blu7JtJi1ETaZpCg/tuYgbdJPIq9Ut0OVUPvW6g7Xsu/M0b96cdevWnXL7ibsKlZbRaCw2kgPo2LEjO3fupEmTJqf80el0JCUlkZKSQlpamv8xK1asOOfnrF+/PkuXLiU9Pf2cg/d0mjdvzubNm3G5XP7bjl+bPi4pKanYil0l1ZuUlMTy5cuL3bZ8+XKaNWtW7MzBhVCjQrdn42iiQkyBLkOIKm1VbhjtDz7AL3VHoxlPXVqwxmo5rFwOc88997Br1y4efPBBNm3axM6dO3njjTf49ttv/VNtSqtBgwYsWrSI9PR0cnKKViF7/vnnmTp1Ki+99BJbt25l+/btTJ8+nWeffRaAfv360axZM0aOHMnGjRtZtmwZzzzzzHk9b2JiIkuWLCEjI4MBAwaQn59fqvpvvPFGVFXlzjvvZPv27fz++++8/vrrwL+jzrvvvpvk5GQef/xxdu7cyTfffHNKg9Sjjz7KokWLGDduHLt27eLLL7/kvffeK7Zm9YVSo0LXoNcxoFXJO4IIIc6dpincu7sLNxomkR/XOdDlBJ6ig6TTL1hxPo7vKrRjxw769etH165dmTFjBjNnzmTgwLLtEjVp0iQWLFhAYmIiHTp0AGDAgAHMmzeP+fPn07lzZ7p168abb75J/fr1AdDpdMyaNQuHw0GXLl24/fbb/Z3N56Nu3bosWbKEzMzMUgdvWFgYP/30Exs2bKB9+/Y888wzPP/88wD+U+H16tXjhx9+YPbs2bRr144PP/zwlMavjh07MmPGDKZPn07r1q15/vnnGTt27CkNVxeCop1Pr3M1sHx3Jjd9ujLQZQhRbegVlcmNVzMg/WMU7+mXJ6zWmvSDm38IdBU10rRp07j11lvJy8sjKCgo0OWcVY1bMaJbo2hirCYyC9xnv7MQ4qx8mo67d3elV1Rz3o/6DGvG2kCXVPEuGhXoCmqMqVOn0qhRI+rUqcPGjRsZM2YM119/fZUIXKhhp5cB9DqFK1rLnF0hytuf2RG0S3mYhXXvRzOUreu1SrHWgmZXBLqKGiM9PZ2bb76ZpKQkHn74Ya677rpTVq2qzGrc6WWAlXuzuOHjc+++E0Kcnz5ROUwO+YSQoxsCXcqFd/Ej0O+FQFchqogaN9KFol2HGsXKXEMhLpQl2ZG0T32cJYn3ounLvpl75aVAxxGBLkJUITUydBVFYUS3+oEuQ4hqzaMqjEq+mDuDJmGPaXv2B1RFjXpDVMNAVyGqkBoZugDXdkrEKjsPCXHBLciMosPhJ/gr8S40fTWbJ99xZKArEFVMjQ1dq9nA8I51Al2GEDWCS9Vxc3Jv7g1+HUd060CXUz6CY6DFlYGuQlQxNTZ0AUZ0b4BS8paYQogL4NejMXRMH8M/iXeg6ar4BiTt/w8M1WzkLi64Gh26TeKsXNwkJtBlCFGjOHx6/i+5Lw9aX8cZlRTockpJgY6jAl2EqIJqdOgC3HaxNEEIEQg/ZcRy0ZFnWJ14G5quivVXtBgMMU0CXYWogmp86PZtHkeL+NBAlyFEjVTo03Fdcj8eDX0dV2TzQJdz7i5+ONAViCqqxocuwB2XVOzG00KI4n48EkfHo8+yNnEUmnJht1Yrs/oXQ91Oga5CVFESusBV7WuTEF6Dlq0TohIq9Oq5JvlyHg9/HXdEJT51K6NcUQYSuoBRr+O2nnJtV4jK4Pv0WlyU+TwbE29BUyrZr6j4ttC0X6CroEGDBrz11lsX5NiKojB79uwLcmwhoev3f13rEWapYs0cQlRTNq+BoclX8EzEa7gjKtHln95jSv3QPn36MHr06FNunzJlChEREed1rNWrV3PnnXf6v67IoDx69Cj33HMP9erVw2w2Ex8fz4ABA1i+fHmZ67mQbyYqCwndY6xmA3f1bhzoMoQQJ/gmLYHOWS+yOfGmwI96a7Up6lquBGJjYwkODg7Ic19zzTWsX7+eL7/8kl27djF37lz69OlDVlZWQOqpaiR0T/DfixtSW67tClGp5HkMDEkezAuRE/GENwhcIb2foCJW0xk1ahTDhg3j9ddfJyEhgejoaO677z48Ho//PieOCBs0aADA1VdfjaIo/q8B5syZQ8eOHbFYLDRq1IiXXnoJr9fr/35ycjK9evXCYrHQsmVLFixYcMbacnNzWbZsGRMmTKBv377Ur1+fLl268NRTT3HVVVedsZ49e/YwdOhQatWqhdVqpXPnzixcuNB/7D59+nDgwAEefvhhFEVBOeG1/uuvv7jkkksICgoiMTGRBx98kMLCQv/333//fZo2bYrFYqFWrVpce+215/x6VzQJ3RNYjHoevbwKTVsQogaZerg2XbLHsj3xP2hU8FJyca0gaUiFPd3ixYvZs2cPixcv5ssvv2TKlClMmTKlxPuuXr0agC+++IK0tDT/18uWLWPEiBE89NBDbNu2jY8++ogpU6bwyiuvAKCqKsOHD8dkMrFy5Uo+/PBDxow58+lzq9WK1Wpl9uzZuFyu86qnoKCAQYMGsWjRItavX8/AgQMZMmQIBw8eBODHH3+kbt26jB07lrS0NNLS0oCisB44cCDXXHMNmzZt4rvvvuOvv/7i/vvvB2DNmjU8+OCDjB07lp07d/Lbb7/Rq1evc32pK5yE7kmu7lCHVrXDAl2GEKIEOR4DVyRfxbjo/+ENq1dxT3zZ8xUyyj0uMjKS9957jxYtWnDllVcyePBgFi1aVOJ9Y2NjAYiIiCA+Pt7/9UsvvcSTTz7JyJEjadSoEf3792fcuHF89NFHACxcuJAdO3YwdepU2rVrR69evRg/fvwZ6zIYDEyZMoUvv/ySiIgIevbsydNPP82mTZvOWk+7du246667aN26NU2bNmXcuHE0btyYuXPnAhAVFYVeryc0NJT4+Hji4+MBePXVV7npppsYPXo0TZs2pUePHrzzzjtMnToVp9PJwYMHCQkJ4corr6R+/fp06NCBBx98sLQv/QUnoXsSnU7h6UFVdWk6IWqGz1MT6Z47jp2J1134UW/jS6H5wAv7HCdp1aoVev2/85UTEhLIyMg4r2Ns3LiRsWPH+kenVquVO+64g7S0NOx2O9u3bycxMZHatWv7H9O9e/ezHveaa67h8OHDzJ07l4EDB7JkyRI6dux42pH4cQUFBTz22GMkJSURERGB1Wpl+/bt/pHumX6OKVOmFPs5BgwYgKqq7Nu3j/79+1O/fn0aNWrELbfcwrRp07Db7ef0GgWChG4JejaJoU/z2ECXIYQ4g6NuIwOSr+Z/Ma/iDb1AO4YpehjwarkcKiwsjLy8vFNuz83NJTw8vNhtRmPxzSAURUFV1fN6voKCAl566SU2bNjg/7N582aSk5OxWMrWu2KxWOjfvz/PPfccf//9N6NGjeKFF14442Mee+wxZs2axfjx41m2bBkbNmygTZs2uN3us/4cd911V7GfY+PGjSQnJ9O4cWNCQ0NZt24d3377LQkJCTz//PO0a9eO3NzcMv2MF4rMkTmNpwclsSw5E5+qBboUIcQZfHSoHrPM4/mm3lyapPxQvgfvdBvEtSiXQzVv3pz58+efcvu6deto1qxZmY5tNBrx+XzFbuvYsSM7d+6kSZOSFxpJSkoiJSWFtLQ0EhISAFixYkWpnr9ly5bFpgiVVM/y5csZNWoUV199NVAUpvv37y92H5PJVOLPsW3bttP+HFB02rtfv37069ePF154gYiICP744w+GDx9eqp/nQpKR7mk0qxXKdRfVDXQZQohzkOEy0i/5GibEjsdnrX32B5wLSwT0fbp8jgXcc8897Nq1iwcffJBNmzaxc+dO3njjDb799lseffTRMh27QYMGLFq0iPT0dHJycgB4/vnnmTp1Ki+99BJbt25l+/btTJ8+nWeffRaAfv360axZM0aOHMnGjRtZtmwZzzzzzBmfJysri0svvZSvv/6aTZs2sW/fPmbOnMnEiRMZOnToGetp2rQpP/74o3+keuONN54yem/QoAF//vknqampZGZmAjBmzBj+/vtv7r//fjZs2EBycjJz5szxN1LNmzePd955hw0bNnDgwAGmTp2Kqqo0b145m2IldM/gkf7NCDZV8nVghRB+H6Q04OKC8eyrO6zsB+vzJARHlf04xzRq1Ig///yTHTt20K9fP7p27cqMGTOYOXMmAweW7ZrxpEmTWLBgAYmJiXTo0AGAAQMGMG/ePObPn0/nzp3p1q0bb775JvXr1wdAp9Mxa9YsHA4HXbp04fbbb/d3Np+O1Wqla9euvPnmm/Tq1YvWrVvz3HPPcccdd/Dee++dsZ433niDyMhIevTowZAhQxgwYAAdO3YsdvyxY8eyf/9+Gjdu7G/Aatu2LUuXLmXXrl1ccskldOjQgeeff95/LToiIoIff/yRSy+9lKSkJD788EO+/fZbWrVqVabX9EJRNE2T86dn8OaCXby9KDnQZQghztND9fbyYOF76AvTz//BMc3gnn9AL1fgRPmSke5Z3NW7EbGh5kCXIYQ4T28fbESvwlc5UPeq83/w5a9I4IoLQkL3LIJNBsYMLJ9GCiFExUp1mum9+z+8GzcOX0jcuT2oST9odvmFLUzUWHJ6+RzdNmU1f+w4v3lyQojKo16Qk2l1fiDx0M+nv5PBAncvh5hKvLWgqNJkpHuOXh3ehvAg49nvKISolA46LFyy+yY+qPUSanBMyXfq86QErrigZKR7HmatP8TD320MdBlCiDJqFOzk64QZ1E797d8ba3eA2xeBTmYsiAtHRrrn4eoOdbm8Za1AlyGEKKO9dgs99ozgs/jnUYOiQWeEoZMlcMUFJyPd85RZ4OLyN/8ku/DMS5cJIaqGpiEOvrzMQ+0e/xfoUkQNICPd8xRjNTNuaOtAlyGEKCdBkfHEdb0h0GWIGkJCtxQGt03gyrYJgS5DCFFGFqOON29oj0EvvwpFxZB/aaU0bmhrYqyyaIYQVdmTA1vQONYa6DJEDSKhW0qRISZeHd4m0GUIIUrpkqYxjOzRINBliBpGQrcM+resxfAOF2gfTyHEBRMeZOS1a9uhKEqgSxE1jIRuGb1wVSvqRwcHugwhxDnSKfDG9e2IDy/bRu5ClIaEbhmFBxn5dEQnrGZZHF2IquCR/s24LEnm24vAkNAtB01rhfLG9e2QM1VCVG6D2sRz/6VNA12GqMEkdMvJ5a3iGX1Zs0CXIYQ4jRbxobx+XbtAlyFqOAndcvTgZU24onV8oMsQQpwkMtjIJyM6EWySy0AisCR0y5GiKEy6vh0t4kMDXYoQ4hi9TuG9GzuSGCUNjyLwJHTLWbDJwCcjOhEZLNsAClEZPD0oiZ5NTrOVnxAVTEL3AkiMCmbyTR0x6KSzSohAuvaiuvz34oaBLkMIPwndC6RH4xieHZwU6DKEqLHaJUbwytWyOYmoXCR0L6BRPRtyQ6fEQJchRI0TG2rmo5svwmyQ/XFF5SKhe4GNG9aaLg2jAl2GEDWG1Wzg0xGdZMUpUSlJ6F5gJoOOz0Z2ok2d8ECXIkS1F2zS88WtnWmXGBHoUoQokYRuBQi1GJl6WxeaxskWYkJcKBajjk9HdqJzAzmzJCovCd0KEhli4uvbu1JP5goKUe5MBh0f3dKJHo1lapCo3CR0K1CtMAvTbu9KfJhcaxKivBj1CpNv7EjvZrGBLkWIs5LQrWCJUcF8fXtXYkPNgS5FiCpPr1N4+z8d6N9Sdg0SVYOEbgA0ibPy7R0SvEKUhU6BSde1Y1CbhECXIsQ5k9ANkCZxoXx7RzcJXiFKQVHgf8PbMqxDnUCXIsR5kdANoKIRrwSvEOdr7NDWXN9ZFp4RVY+EboAdD944CV4hzkpR4PkrW3JLt/qBLkWIUlE0TdMCXYSAlGw7t05Zze6MgkCXIkSlZDLoeP26dlzVrnagSxGi1CR0K5E8h4d7vl7L33uyAl2KEJVKZLCRj0fIwhei6pPQrWQ8PpWnf9zMzLWHAl2KEJVCg+hgvri1Cw1jQgJdihBlJqFbSb33RzKTFuxC/nZETXZR/Ug+GdGJqBBToEsRolxI6FZiczce5rGZG3F71UCXIkSFG9w2gUnXtcNilO35RPUhoVvJrdmfzZ1frSW70B3oUoSoMHf3bsyYgc1RFCXQpQhRriR0q4D9mYXcNmU1ezMLA12KEBeUQacwblhr/q9LvUCXIsQFIaFbReTa3dz51VpW7csOdClCXBBWs4HJN8nGBaJ6k9CtQtxeledmb+G7NSmBLkWIctUoNoQPbrqI5vGhgS5FiAtKQrcK+nlTGk/9uIl8pzfQpQhRZsM71OHlq1sTbDIEuhQhLjgJ3SrqcK6D0d9tkNPNosoKNukZO7Q1115UN9ClCFFhJHSrMFXVeH/Jbt5amIxXlb9GUXW0iA/lvRs70iTOGuhShKhQErrVwPqDOYz+bgMHsuyBLkWIM1IUGNGtPk8NSpL5t6JGktCtJgpcXp6fs4Uf16UGuhQhSlQrzMzEa9tJd7Ko0SR0q5m5Gw/zzKzN2KTJSlQig9sk8MrVrYkIluUcRc0moVsNHcqx8/B3G1i9PyfQpYgaLtRi4KWrWjG8ozRLCQESutWWT9WY+s9+3lywS6YWiYAY1r42Tw9KIi7MEuhShKg0JHSruawCFxN/28nMtSlIg7OoCEkJYYwd2kr2vhWiBBK6NcSmQ7k8P2crG1JyA12KqKbCg4w8dnkzbuxaH71ONioQoiQSujWIpml8v/YQE37bSWaBK9DliGpCp8ANnevx+IDmsu+tEGchoVsD2Zwe3lqYzJd/75dFNUSZdKgXwdirWtOmbnigSxGiSpDQrcGSj9h46adt/LU7M9CliComxmpmzMDmXHtRXdnzVojzIKEr+G1LGq/8sp2UbEegSxGVnNmg4+Zu9XmoX1PCLMZAlyNElSOhKwDw+FR+WHuIyUt2S/iKU1jNBm7qVo/bL25EbKg50OUIUWVJ6IpivD6VH9en8v7i3eyXtZxrvIhgI7f2aMioHg0ID5aRrRBlJaErSuRTNWavT2Xykt3sPVoY6HJEBYsLNXPHJY24sWs9Qsyyz60Q5UVCV5yRpmks3J7Bx3/ukWUla4DEqCDu6tWY6zrVxWyQXYCEKG8SuuKcrTuYw8dL9zJ/W7qsblXNNI2zck+fxlzVrjYGvS7Q5QhRbUnoivO2L7OQL//ez9yNh8kudAe6HFFKBp1Cn+ax3NC5Hv2S4mTqjxAVQEJXlJrHp7J4RwY/rDvE4h1HcfvUQJckzkFSQhjXXlSXoe1rE2OVTmQhKpKErigXOYVuftp0mB/WHmLjobxAlyNOEh1iYmj7Olx7UV1a1g4LdDlC1FgSuqLc7c6w8cO6VGatSyU93xnocmosk15H3xaxXHtRIn2bx8q1WiEqAQldccGoqsbyPZn8sPYQv289gsPjC3RJNUKbOuFc07EOQ9vXIVI2IBCiUpHQFRXC4faxYm8Wy5IzWZZ8lOSMgkCXVG0EGfX0bBJNn+Zx9G0RR52IoECXJIQ4DQldERDpeU7+TD7KX8mZLN+dSZZ0QZ+XRjEh9GoWS98WcXRrFCVzaoWoIiR0RcBpmsbWw/n+UfCaAzm4vdIJfaLa4RZ6NImhR+NoejSOIT7cEuiShBClIKErKh2H28fKfVms3JfNjrR8dqTbSMurOQ1ZoWYDzeNDSUoIo1XtMLo1iqZBTEigyxJClAMJXVEl5Nk97EgvCuDjH3el2yh0V93mLJ0CDaJDaJEQSov4MJISwmgRH0piVHCgSxNCXCASuqLK0jSNg9n2oiBOKwrjvUcLOWJzkmv3BLo8vzCLgdhQM3GhFprVshaFa0IYzWuFEmSSa7FC1CQSuqJacnp8ZOS7SM93cuTYn+xCN7kOD7l2NzmFHnLsbvIcHgqcXo7/T1BsIcSTVkU88UuDXkdUiIlYq5mYUDMxVhMxVvOxr4s+j7GaibaapMlJCOEnoSuEEEJUEFmiRgghhKggErpCCCFEBZHQFUIIISqIhK4QQghRQSR0hRBCiAoioSuEEEJUEAldIYQQooJI6AohhBAVREJXCCGEqCASukIIIUQFkdAVQgghKoiErhBCCFFBJHSFEEKICiKhK4QQQlQQCV0hhBCigkjoCiGEEBVEQlcIIYSoIBK6QgghRAWR0BVCCCEqiISuEEIIUUEkdIUQQogKIqErhBBCVBAJXSGEEKKCSOgKIYQQFURCVwghhKggErpCCCFEBZHQFUIIISqIhK4QQghRQSR0hRBCiAoioSuEEEJUEAldIYQQooJI6AohhBAVREJXCCGEqCASukIIIUQFkdAVQgghKoiErhBCCFFBJHSFEEKICiKhK4QQQlQQCV0hhBCigkjoCiGEEBVEQlcIIYSoIBK6QgghRAWR0BVCCCEqiISuEEIIUUEkdIUQQogKIqErhBBCVJD/B2EeZzbUO5bmAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Pie charts\n",
    "plt.pie(country_value, labels=country_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e01694e1-18ba-448c-9b6e-8fb01c942944",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1e7bf603f20>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7bf5ffda0>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7bf6302f0>],\n",
       " [Text(-1.0829742700952103, 0.19278674827836725, 'India'),\n",
       "  Text(1.077281715838356, -0.22240527134123297, 'United States'),\n",
       "  Text(1.0995865153823035, -0.03015783794312073, 'United Kingdom')])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Pie charts - Top 3 countries that uses zomato\n",
    "plt.pie(country_value[:3], labels=country_names[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0c047b33-f361-4303-b4aa-a56b5fc07afb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1e7d019cda0>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7d019ca40>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7d019d5b0>],\n",
       " [Text(-1.0829742700952103, 0.19278674827836725, 'India'),\n",
       "  Text(1.077281715838356, -0.22240527134123297, 'United States'),\n",
       "  Text(1.0995865153823035, -0.03015783794312073, 'United Kingdom')],\n",
       " [Text(-0.590713238233751, 0.10515640815183668, '94.39%'),\n",
       "  Text(0.5876082086391032, -0.12131196618612707, '4.73%'),\n",
       "  Text(0.5997744629358018, -0.01644972978715676, '0.87%')])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(country_value[:3], labels=country_names[:3],autopct='%1.2f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba1efe01-2456-4d4e-b10d-752bbc62814f",
   "metadata": {},
   "source": [
    "## Observation:\n",
    "The record maximum transaction of zomato is from India where United States, United Kingdom further follows it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c2ea6c2e-e9ad-4ee8-a8a8-b5fd7a51cabf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',\n",
       "       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',\n",
       "       'Average Cost for two', 'Currency', 'Has Table booking',\n",
       "       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
       "       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',\n",
       "       'Votes', 'Country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a481b7f1-83fd-457c-9404-64ae84e0cae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001E7CE2E66F0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.groupby(['Aggregate rating', 'Rating color', 'Rating text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ac5830bb-0ede-4f2f-a6fe-e530abd4f60e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Aggregate rating  Rating color  Rating text\n",
       "0.0               White         Not rated      2148\n",
       "1.8               Red           Poor              1\n",
       "1.9               Red           Poor              2\n",
       "2.0               Red           Poor              7\n",
       "2.1               Red           Poor             15\n",
       "2.2               Red           Poor             27\n",
       "2.3               Red           Poor             47\n",
       "2.4               Red           Poor             87\n",
       "2.5               Orange        Average         110\n",
       "2.6               Orange        Average         191\n",
       "2.7               Orange        Average         250\n",
       "2.8               Orange        Average         315\n",
       "2.9               Orange        Average         381\n",
       "3.0               Orange        Average         468\n",
       "3.1               Orange        Average         519\n",
       "3.2               Orange        Average         522\n",
       "3.3               Orange        Average         483\n",
       "3.4               Orange        Average         498\n",
       "3.5               Yellow        Good            480\n",
       "3.6               Yellow        Good            458\n",
       "3.7               Yellow        Good            427\n",
       "3.8               Yellow        Good            400\n",
       "3.9               Yellow        Good            335\n",
       "4.0               Green         Very Good       266\n",
       "4.1               Green         Very Good       274\n",
       "4.2               Green         Very Good       221\n",
       "4.3               Green         Very Good       174\n",
       "4.4               Green         Very Good       144\n",
       "4.5               Dark Green    Excellent        95\n",
       "4.6               Dark Green    Excellent        78\n",
       "4.7               Dark Green    Excellent        42\n",
       "4.8               Dark Green    Excellent        25\n",
       "4.9               Dark Green    Excellent        61\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.groupby(['Aggregate rating', 'Rating color', 'Rating text']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c2bf88ed-f022-4f61-8d1b-ba503d1aead1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Not rated</td>\n",
       "      <td>2148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.8</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.9</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.1</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Aggregate rating Rating color Rating text     0\n",
       "0               0.0        White   Not rated  2148\n",
       "1               1.8          Red        Poor     1\n",
       "2               1.9          Red        Poor     2\n",
       "3               2.0          Red        Poor     7\n",
       "4               2.1          Red        Poor    15"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings=final_df.groupby(['Aggregate rating', 'Rating color', 'Rating text']).size().reset_index()\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ea36f496-2bcc-4314-84ac-afd9ea45a64f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Rating Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Not rated</td>\n",
       "      <td>2148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.8</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.9</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.1</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Aggregate rating Rating color Rating text  Rating Count\n",
       "0               0.0        White   Not rated          2148\n",
       "1               1.8          Red        Poor             1\n",
       "2               1.9          Red        Poor             2\n",
       "3               2.0          Red        Poor             7\n",
       "4               2.1          Red        Poor            15"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings=final_df.groupby(['Aggregate rating', 'Rating color', 'Rating text']).size().reset_index().rename(columns={0:'Rating Count'})\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e27f043-fa8a-4fa1-97b2-4d6f05f9f9d4",
   "metadata": {},
   "source": [
    "## Observation:\n",
    "1. When rating is between 4.5 and 4.9 --> Excellent\n",
    "2. When rating is between 4.0 and 4.4 --> Very good\n",
    "3. When rating is between 3.5 and 3.9 --> Good\n",
    "4. When rating is between 2.5 and 3.4 --> Average\n",
    "5. When rating is between 0.0 and 2.4 --> Poor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1e6fc506-bd29-4818-b3b7-f1b21498c808",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Rating Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Not rated</td>\n",
       "      <td>2148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.8</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.9</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.1</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Aggregate rating Rating color Rating text  Rating Count\n",
       "0               0.0        White   Not rated          2148\n",
       "1               1.8          Red        Poor             1\n",
       "2               1.9          Red        Poor             2\n",
       "3               2.0          Red        Poor             7\n",
       "4               2.1          Red        Poor            15"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "90ea2497-f8e2-42a8-a96d-be3d6a1f5b2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Aggregate rating', ylabel='Rating Count'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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bt265tnft2lV79+71SlEAAAAAUNxZHrbKly+vHTt25Nq+Y8cOhYSEeKMmAAAAACj2LJ9G2L17d/Xo0UO//PKLGjVqJEnasGGD/vnPf6pfv35eLxAAAAAAiiPLw9Ybb7yhwMBAjRs3ToMGDZIkRUREaPjw4frb3/7m9QIBAAAAoDiyPGw5HA717dtXffv21dmzZyVJgYGBXi8MAAAAAIoztz+z9ccff+jrr792DljSn0NWYGCgUlNT9fXXXys9Pd2WIgEAAACguHF72Proo480adKkPI9iBQUF6b333tMnn3zi1eIAAAAAoLhye9iaO3eu+vTpc8X9ffr00axZs7xREwAAAAAUe24PWwcPHlTdunWvuL9OnTo6ePCgV4oCAAAAgOLO7WErMzNTJ06cuOL+EydOKDMz0ytFAQAAAEBx5/awVatWLX3//fdX3L98+XLVqlXLK0UBAAAAQHHn9rDVtWtXvfXWW1q8eHGufd98841Gjhyprl27erU4AAAAACiu3P6erR49emjt2rV68sknVaNGDUVHR0uS9u/frwMHDqht27bq0aOHbYUCAAAAQHHi9pEtSfrXv/6l+fPnq3r16jpw4IASExMVHR2tefPmad68eXbVCAAAAADFjttHtnK0bdtWbdu2taMWAAAAALhuWDqyBQAAAABwD8MWAAAAANiAYQsAAAAAbMCwBQAAAAA2YNgCAAAAABtYvhrh008/LYfDkWu7w+FQQECAbr/9dr3wwgvO7+ECAAAAgBuR5SNbwcHBWrlypX788Uc5HA45HA5t375dK1euVGZmpj7//HPVrVtXGzZssKNeAAAAACgWLB/ZCgsL0wsvvKAPPvhAJUr8OatlZ2erd+/eCgwM1Pz58/XKK69owIABWr9+vdcLBgAAAIDiwPKRrenTp6tPnz7OQUuSSpQooV69eumjjz6Sw+FQz549tXv3bq8WCgAAAADFieVhKzMzU/v378+1ff/+/crKypIkBQQE5Pm5LgAAAAC4UVg+jbBjx47q1q2b/vGPf+iee+6RJG3btk2jRo1Sp06dJElr1qxRrVq1vFspAAAAABQjloetCRMmKDQ0VGPGjFFKSookKTQ0VH379tWAAQMkSc2bN9djjz3m3UoBAAAAoBixPGz5+Pho8ODBGjx4sFJTUyVJQUFBLmsqVqzoneoAAAAAoJiyPGxd6vIhCwAAAADwJ8sXyEhJSVHHjh0VEREhX19f+fj4uNwAAAAAAB4c2erSpYsOHz6sN954Q+Hh4Vx1EAAAAADyYPnI1vr16zV37ly9+uqrat26tZ566imXmxVr165Vq1atFBERIYfDoUWLFrnsN8Zo6NChCg8PV6lSpdSsWTMdPHjQZc2pU6fUoUMHBQUFqUyZMurWrZvS0tJc1uzatUsPPvigAgICFBkZqTFjxlh92QAAAABgieVhKzIyUsYYrzz5uXPnVLduXU2ePDnP/WPGjNF7772nadOmacuWLSpdurRiY2N14cIF55oOHTpoz549io+P1+LFi7V27Vr16NHDuT81NVXNmzdXVFSUEhISNHbsWA0fPlwfffSRV14DAAAAAOTFYSxOTsuXL9e4ceP04YcfqlKlSt4rxOHQwoUL1bp1a0l/HtWKiIjQ3//+d/Xv31+SdObMGYWGhmrmzJlq37699u3bp5o1a2rbtm1q0KCBJGnZsmVq0aKFfvvtN0VERGjq1KkaPHiwkpOT5efnJ0kaOHCgFi1alOeXM+clNTVVwcHBOnPmjIKCglT/tdlee90JYzt5LQsAAACAvS6fDfJj+chWu3bttHr1alWtWlWBgYEqV66cy81bkpKSlJycrGbNmjm3BQcHq2HDhtq0aZMkadOmTSpTpoxz0JKkZs2aqUSJEtqyZYtzTePGjZ2DliTFxsYqMTFRv//+e57PnZ6ertTUVJcbAAAAAFhh+QIZEydOtKGM3JKTkyX9+YXJlwoNDXXuS05OVkhIiMt+X19flStXzmVN5cqVc2Xk7Ctbtmyu5x49erRGjBjhnRcCAAAA4IZkedjq3LmzHXVcUwYNGqR+/fo576empioyMrIIKwIAAABQ3Lg1bKWmpjrPRyzolDpvfdFxWFiYpD+/1ys8PNy5PSUlRfXq1XOuOX78uMvjMjMzderUKefjw8LClJKS4rIm537Omsv5+/vL39/fK68DAAAAwI3Jrc9slS1b1jnUlClTRmXLls11y9nuLZUrV1ZYWJhWrFjh3JaamqotW7YoJiZGkhQTE6PTp08rISHBuWblypXKzs5Ww4YNnWvWrl2rjIwM55r4+HhFR0d7tV4AAAAAuJRbR7ZWrlzpvPjFqlWrvPbkaWlp+vnnn533k5KStGPHDpUrV04VK1ZUnz599Pbbb6tatWqqXLmy3njjDUVERDivWHjHHXfoscceU/fu3TVt2jRlZGSoZ8+eat++vSIiIiRJL7zwgkaMGKFu3bppwIAB2r17tyZNmqQJEyZ47XUAAAAAwOXcGraaNGni/HXlypUVGRkph8PhssYYoyNHjlh68h9++EEPP/yw837O56Q6d+6smTNn6vXXX9e5c+fUo0cPnT59Wg888ICWLVumgIAA52Pmzp2rnj17qmnTpipRooTatGmj9957z7k/ODhYy5cvV1xcnOrXr69bb71VQ4cOdfkuLgAAAADwNsvfs+Xj46Njx47lugrgyZMnFRISoqysLK8WeC3ge7YAAAAASDZ/z5YxJtdRLenPUwIvPeIEAAAAADcyty/9nnOKn8Ph0BtvvKGbbrrJuS8rK0tbtmxxXiUQAAAAAG50bg9b27dvl/Tnka2ffvpJfn5+zn1+fn6qW7eu+vfv7/0KAQAAAKAYcnvYyrkK4UsvvaRJkyZ57fu0AAAAAOB65PawlWPGjBl21AEAAAAA1xXLw5b05yXbv/jiCx0+fFgXL1502bdgwQKvFAYAAAAAxZnlqxHOnz9fjRo10r59+7Rw4UJlZGRoz549WrlypYKDg+2oEQAAAACKHcvD1qhRozRhwgR988038vPz06RJk7R//361bdtWFStWtKNGAAAAACh2LA9bhw4dUsuWLSX9eRXCc+fOyeFwqG/fvvroo4+8XiAAAAAAFEeWh62yZcvq7NmzkqQKFSpo9+7dkqTTp0/r/Pnz3q0OAAAAAIopyxfIaNy4seLj41W7dm0999xz6t27t1auXKn4+Hg1bdrUjhoBAAAAoNixPGx98MEHunDhgiRp8ODBKlmypDZu3Kg2bdpoyJAhXi8QAAAAAIojy8NWuXLlnL8uUaKEBg4c6Lz/xx9/eKcqAAAAACjmLH9mKy/p6ekaP368Kleu7I04AAAAACj23B620tPTNWjQIDVo0ECNGjXSokWLJEkzZsxQ5cqVNWHCBPXt29euOgEAAACgWHH7NMKhQ4fqww8/VLNmzbRx40Y999xzeumll7R582aNHz9ezz33nHx8fOysFQAAAACKDbeHrS+//FKzZ8/Wk08+qd27d6tOnTrKzMzUzp075XA47KwRAAAAAIodt08j/O2331S/fn1J0p133il/f3/17duXQQsAAAAA8uD2sJWVlSU/Pz/nfV9fX9188822FAUAAAAAxZ3bpxEaY9SlSxf5+/tLki5cuKBXXnlFpUuXdlm3YMEC71YIAAAAAMWQ28NW586dXe6/+OKLXi8GAAAAAK4Xbg9bM2bMsLMOAAAAALiueOVLjQEAAAAArhi2AAAAAMAGDFsAAAAAYAOGLQAAAACwAcMWAAAAANiAYQsAAAAAbMCwBQAAAAA2YNgCAAAAABswbAEAAACADRi2AAAAAMAGDFsAAAAAYAOGLQAAAACwAcMWAAAAANiAYQsAAAAAbMCwBQAAAAA2YNgCAAAAABswbAEAAACADRi2AAAAAMAGDFsAAAAAYAOGLQAAAACwAcMWAAAAANiAYQsAAAAAbMCwBQAAAAA2YNgCAAAAABswbAEAAACADRi2AAAAAMAGDFsAAAAAYAOGLQAAAACwAcMWAAAAANiAYQsAAAAAbMCwBQAAAAA2YNgCAAAAABswbAEAAACADRi2AAAAAMAGDFsAAAAAYINretgaPny4HA6Hy61GjRrO/RcuXFBcXJxuueUW3XzzzWrTpo1SUlJcMg4fPqyWLVvqpptuUkhIiF577TVlZmZe7ZcCAAAA4AbjW9QFFKRWrVr6/vvvnfd9ff//kvv27aslS5boyy+/VHBwsHr27KlnnnlGGzZskCRlZWWpZcuWCgsL08aNG3Xs2DF16tRJJUuW1KhRo676awEAFE7912Z7LSthbCevZQEAkJdrftjy9fVVWFhYru1nzpzR9OnT9dlnn+mRRx6RJM2YMUN33HGHNm/erPvuu0/Lly/X3r179f333ys0NFT16tXTW2+9pQEDBmj48OHy8/O72i8HAK573hqIGIYAAMXdNX0aoSQdPHhQERERqlKlijp06KDDhw9LkhISEpSRkaFmzZo519aoUUMVK1bUpk2bJEmbNm1S7dq1FRoa6lwTGxur1NRU7dmz54rPmZ6ertTUVJcbAAAAAFhxTQ9bDRs21MyZM7Vs2TJNnTpVSUlJevDBB3X27FklJyfLz89PZcqUcXlMaGiokpOTJUnJyckug1bO/px9VzJ69GgFBwc7b5GRkd59YQAAAACue9f0aYSPP/6489d16tRRw4YNFRUVpS+++EKlSpWy7XkHDRqkfv36Oe+npqYycAEACoXPmwHAjeeaPrJ1uTJlyqh69er6+eefFRYWposXL+r06dMua1JSUpyf8QoLC8t1dcKc+3l9DiyHv7+/goKCXG4AAAAAYMU1fWTrcmlpaTp06JA6duyo+vXrq2TJklqxYoXatGkjSUpMTNThw4cVExMjSYqJidHIkSN1/PhxhYSESJLi4+MVFBSkmjVrFtnrAABcm4rrxT04agYA16Zretjq37+/WrVqpaioKB09elTDhg2Tj4+Pnn/+eQUHB6tbt27q16+fypUrp6CgIPXq1UsxMTG67777JEnNmzdXzZo11bFjR40ZM0bJyckaMmSI4uLi5O/vX8SvDgAAAMD17Joetn777Tc9//zzOnnypMqXL68HHnhAmzdvVvny5SVJEyZMUIkSJdSmTRulp6crNjZWU6ZMcT7ex8dHixcv1quvvqqYmBiVLl1anTt31ptvvllULwkAihxHQQAAuDqu6WFr/vz5+e4PCAjQ5MmTNXny5CuuiYqK0tKlS71dGgAANwwGdADwTLG6QAYAAAAAFBcMWwAAAABgA4YtAAAAALDBNf2ZLQAAcH3j82AArmcc2QIAAAAAGzBsAQAAAIANGLYAAAAAwAZ8ZgsAAFy3+EwYgKLEkS0AAAAAsAHDFgAAAADYgNMIAeAa5K1TnzjtCQCAosORLQAAAACwAcMWAAAAANiAYQsAAAAAbMCwBQAAAAA2YNgCAAAAABtwNUIAAAAP8IXJAArCkS0AAAAAsAHDFgAAAADYgNMIAcADnD4EAAAKwpEtAAAAALABwxYAAAAA2IBhCwAAAABswLAFAAAAADbgAhkAAAA3GC7yA1wdDFsAAADXIAYioPjjNEIAAAAAsAHDFgAAAADYgNMIAVy3vHUKDqffAAAAT3BkCwAAAABswLAFAAAAADZg2AIAAAAAG/CZLQAAAHgVl60H/sSRLQAAAACwAcMWAAAAANiAYQsAAAAAbMBntgAUGc7pBwAA1zOObAEAAACADTiyBQAAgGKDsyJQnHBkCwAAAABswLAFAAAAADZg2AIAAAAAG/CZLQAAAOD/4TNh8CaObAEAAACADRi2AAAAAMAGDFsAAAAAYAOGLQAAAACwARfIAJAvb31QmA8JAwCAGw1HtgAAAADABhzZAgAAAK4CLit/42HYAoo5/uAGAAC4NjFsAQAAANcB/gP22sNntgAAAADABhzZAgAAAHDdKsojfhzZAgAAAAAbcGQLuAr4rioAAIAbD8MWAAAAgHxx8Q3PcBohAAAAANjghjqyNXnyZI0dO1bJycmqW7eu3n//fd17771FXRauAfxvDQAAQNG5Xv8tdsMc2fr888/Vr18/DRs2TD/++KPq1q2r2NhYHT9+vKhLAwAAAHAdumGObI0fP17du3fXSy+9JEmaNm2alixZok8//VQDBw4s4urgDi4yAQAAgOLkhhi2Ll68qISEBA0aNMi5rUSJEmrWrJk2bdqUa316errS09Od98+cOSNJSk1NlSRlpf/htdpyMq+WxkPmeS1r7dvPX7VsyXvve17vud2/p9RO7UWVT+1Fk3+1ay8u70te+dSed7bd+dSed7bd+dSed7bd+d7Ozsk3xhS43mHcWVXMHT16VBUqVNDGjRsVExPj3P76669rzZo12rJli8v64cOHa8SIEVe7TAAAAADFxJEjR3Tbbbflu+aGOLJl1aBBg9SvXz/n/ezsbJ06dUq33HKLHA5HgY9PTU1VZGSkjhw5oqCgIK/XZ2c+tRdNPrUXTX5xzbY7n9qLJp/aiyaf2osmn9qLJp/avZNvjNHZs2cVERFRYO4NMWzdeuut8vHxUUpKisv2lJQUhYWF5Vrv7+8vf39/l21lypSx/LxBQUG2/DBcjXxqL5p8ai+a/OKabXc+tRdNPrUXTT61F00+tRdNPrUXPj84ONitvBviaoR+fn6qX7++VqxY4dyWnZ2tFStWuJxWCAAAAADeckMc2ZKkfv36qXPnzmrQoIHuvfdeTZw4UefOnXNenRAAAAAAvOmGGbbatWunEydOaOjQoUpOTla9evW0bNkyhYaGev25/P39NWzYsFynIhaHfGovmnxqL5r84pptdz61F00+tRdNPrUXTT61F00+tV/9/BviaoQAAAAAcLXdEJ/ZAgAAAICrjWELAAAAAGzAsAUAAAAANmDYAgAAAAAbMGx5aPLkyapUqZICAgLUsGFDbd26Nd/1X375pWrUqKGAgADVrl1bS5cuzXPd2rVr1apVK0VERMjhcGjRokUF1jJ37lzVrVtXN910k8LDw9W1a1edPHnSa/mTJ0/WHXfcoVKlSik6OlqzZ8/Oc93o0aN1zz33KDAwUCEhIWrdurUSExMLzHfnvfEke8+ePWrTpo0qVaokh8OhiRMnXnGtJ/kff/yxHnzwQZUtW1Zly5ZVs2bN8vw58CR7wYIFatCggcqUKaPSpUurXr16mjNnjtdqv9T8+fPlcDjUunVrr2TPnDlTDofD5RYQEODV2k+fPq24uDiFh4fL399f1atXz/Vz40n2Qw89lKt2h8Ohli1beqXuiRMnKjo6WqVKlVJkZKT69u2rCxcueOV9ycjI0JtvvqmqVasqICBAdevW1bJly/JcO3XqVNWpU8f5xY0xMTH69ttv8813988wq9lW+tSTfHf71JNsK33qSf6l8utTT7Kt9KkndbvTo57mu9unntbubp96km+lTy/3zjvvyOFwqE+fPvmuc7dXrWZb7VWr+VZ61ZN8q/1qJftSBfWq1Wwrvepp7Vb61Uq2lV71tHYr/WoluzC9mouBZfPnzzd+fn7m008/NXv27DHdu3c3ZcqUMSkpKXmu37Bhg/Hx8TFjxowxe/fuNUOGDDElS5Y0P/30U661S5cuNYMHDzYLFiwwkszChQvzrWX9+vWmRIkSZtKkSeaXX34x69atM7Vq1TJPP/10nuut5k+ZMsUEBgaa+fPnm0OHDpl58+aZm2++2Xz99de51sbGxpoZM2aY3bt3mx07dpgWLVqYihUrmrS0tCvmu/veeJK9detW079/fzNv3jwTFhZmJkyYcMW1nuS/8MILZvLkyWb79u1m3759pkuXLiY4ONj89ttvhc5etWqVWbBggdm7d6/5+eefzcSJE42Pj49ZtmyZV2rPkZSUZCpUqGAefPBB89RTT3kle8aMGSYoKMgcO3bMeUtOTs5zrSf56enppkGDBqZFixZm/fr1Jikpyaxevdrs2LGj0NknT550qXv37t3Gx8fHzJgxo9DZc+fONf7+/mbu3LkmKSnJfPfddyY8PNz07dvXK+/L66+/biIiIsySJUvMoUOHzJQpU0xAQID58ccfc639+uuvzZIlS8yBAwdMYmKi+cc//mFKlixpdu/enWe2lT/DrGZb6VNP8t3tU0+yrfSpJ/k5CupTT7Kt9KnVbHd71NN8d/vUk2wrfepJvpU+vdTWrVtNpUqVTJ06dUzv3r2vuM5Kr1rNttqrVvOt9Kon+Vb71Up2Dnd61Wq2lV71JN9qv1rJttKrnuRb7Vcr2Z72al4Ytjxw7733mri4OOf9rKwsExERYUaPHp3n+rZt25qWLVu6bGvYsKH5y1/+ku/zuDMMjR071lSpUsVl23vvvWcqVKiQ7+PczY+JiTH9+/d32davXz9z//33F5h//PhxI8msWbPmims8fW/cyb5UVFSU238xeJJvjDGZmZkmMDDQzJo1y+vZxhhz1113mSFDhhS4zt38zMxM06hRI/PJJ5+Yzp07F/gXg7vZM2bMMMHBwQVmeZo/depUU6VKFXPx4kWvZ19uwoQJJjAwsMDB1Z3suLg488gjj7hs82YvhYeHmw8++MBl2zPPPGM6dOhQYL4xxpQtW9Z88sknee7ztE/dyb6U1T61mm+M+33qSbYx7vepu/me9Kk72YXp04KyPe1Rd/Mv526fupNdmD51J9+TPj179qypVq2aiY+PN02aNMn3H4hWe9VK9qXc7VVP841xr1cLk29M/v1qNdtKr1rJ9qRXreRb7dfCvOfu9KqVfKv9aiW7sH+nXorTCC26ePGiEhIS1KxZM+e2EiVKqFmzZtq0aVOej9m0aZPLekmKjY294norYmJidOTIES1dulTGGKWkpOjf//63WrRoUehsSUpPT891uLpUqVLaunWrMjIy8n3smTNnJEnlypW74hpP3xt3sgvDk/zz588rIyOjwMdYzTbGaMWKFUpMTFTjxo0LXO9u/ptvvqmQkBB169bNrTqsZKelpSkqKkqRkZF66qmntGfPHq/lf/3114qJiVFcXJxCQ0N15513atSoUcrKyvJK7ZeaPn262rdvr9KlSxc6u1GjRkpISHCeFvPLL79o6dKlbvWqO/lX6tX169fnm52VlaX58+fr3LlziomJyXONp33qTnZheJLvbp9azbbap+7me9Kn7mZ70qfuZHvao1Zqv5S7fepOdmH61J18T/o0Li5OLVu2zNWDebHaq1ayPVGYfHd61dN8d/rVaraVXrWabbVXreRb7dfC/J6606tW8q32q5VsT/9OzZPl8ewG97///c9IMhs3bnTZ/tprr5l77703z8eULFnSfPbZZy7bJk+ebEJCQvJ9Lrlx5MkYY7744gtz8803G19fXyPJtGrVyq3/oXAnf9CgQSYsLMz88MMPJjs722zbts2EhoYaSebo0aNXfFxWVpZp2bJlgf8b6Ml74272paz8j7kn+cYY8+qrr5oqVaqYP/74wyvZp0+fNqVLlza+vr7G39/fTJ8+3Wu1r1u3zlSoUMGcOHHCGGPc+h9zd7M3btxoZs2aZbZv325Wr15tnnjiCRMUFGSOHDnilfzo6Gjj7+9vunbtan744Qczf/58U65cOTN8+PBCZ19qy5YtRpLZsmWLV+o2xphJkyaZkiVLOnv1lVdeKfAx7uY///zzpmbNmubAgQMmKyvLLF++3JQqVcr4+fnluX7Xrl2mdOnSxsfHxwQHB5slS5ZcMdtqn1rJvpS7feppvjEF96nVbKt9aiXfap9aybbap1ayPelRT39P3elTq9lW+9RKvtU+nTdvnrnzzjudP68F/W+8lV61mn0pd3q1MPnGFNyrnuS7269Ws630qtVsq71qNd9Kvxbm99SdXvUk391+tZpttVfzw7Bl0bU2bO3Zs8eEh4ebMWPGmJ07d5ply5aZ2rVrm65duxb4WtzJP3/+vHnppZeMr6+v8fHxMREREeb11183kvI9Z/iVV14xUVFRBf4D25P3xt3sS1kZtjzJHz16tClbtqzZuXOn17KzsrLMwYMHzfbt2827775rgoODzapVqwqdn5qaaipVqmSWLl3q3ObOsOXJ+2KMMRcvXjRVq1Yt8NQqd/OrVatmIiMjTWZmpnPbuHHjTFhYmFdr79Gjh6ldu3aB69zNXrVqlQkNDTUff/yx2bVrl1mwYIGJjIw0b775plfyjx8/bp566ilTokQJ4+PjY6pXr27++te/moCAgDzXp6enm4MHD5offvjBDBw40Nx6661mz549ea612qdWsi/lbp96mu9On1rNttqn7uZ70qeevi/GFNynVrI96VFPa3enT61ke9KnVvKt9Onhw4dNSEiIy8+rt4YtT7IvVVCvFja/oF71NN+dfrWabaVXC/u+GJN/r3qS726/Frb2gnrVk3x3+9WTbKt/p+aHYcui9PR04+Pjk2tI6dSpk3nyySfzfExkZGSuP5SGDh1q6tSpk+9zuTMMvfjii+bZZ5912bZu3boCjzy5m5/j4sWL5siRIyYzM9N50YysrKw818bFxZnbbrvN/PLLLwXmWn1vrGRfyt1/xHmSP3bsWBMcHGy2bdvm9exLdevWzTRv3rzQ+du3bzeSjI+Pj/PmcDiMw+EwPj4+5ueff/Z67c8++6xp3759oWs3xpjGjRubpk2bumxbunSpkWTS09MLlZ0jLS3NBAUFmYkTJ+a7zkr2Aw88kOvzj3PmzDGlSpXySi/l+OOPP8xvv/1msrOzzeuvv25q1qzp1uOaNm1qevTokec+T/8Mcyf7Up5+ZsudfHf71JPsSxXUp+7me9Knha29oD51N9tqj1rNz+Fun1rJ9qRPreTncKdPFy5cmOtnQJLzZ+DSfxzncLdXPcm+VEG9Wph8d3q1sPXnyKtfrWZb6VVv1X2lXvUk391+LUzt7vSqJ/nu9mthavf079RL+Vo/8fDG5ufnp/r162vFihXOy3pmZ2drxYoV6tmzZ56PiYmJ0YoVK1wuMRkfH++VzzCcP39evr6uv40+Pj6S/jwn2VtKliyp2267TdKflzV94oknVKKE60f+jDHq1auXFi5cqNWrV6ty5coF5rr73niSbYWn+WPGjNHIkSP13XffqUGDBl7Nvlx2drbS09MLnV+jRg399NNPLtuGDBmis2fPatKkSYqMjPRq7VlZWfrpp5/yPIfak/z7779fn332mbKzs50/gwcOHFB4eLj8/Py8UvuXX36p9PR0vfjii3nu9yT7/PnzuXrmSr1amNoDAgJUoUIFZWRk6KuvvlLbtm3detyVfr6kwv8Zll+2NxSU706feppt13orfeqNWvLrU6vZ7vZoYWsvqE89ybbSp57k53CnT5s2bZrrZ+Cll15SjRo1NGDAAGddl3K3Vz3JtsLTfHd71Vv15/V7ZTXbSq96o+78etWTfHf7tTC1u9OrnuS726+Fqd3Tv1NdWB7PYObPn2/8/f3NzJkzzd69e02PHj1MmTJlnKfVdezY0QwcONC5fsOGDcbX19e8++67Zt++fWbYsGFXvBTr2bNnzfbt253/UzJ+/Hizfft289///tcYY8zAgQNNx44dnetnzJhhfH19zZQpU8yhQ4fM+vXrTYMGDa54SqPV/MTERDNnzhxz4MABs2XLFtOuXTtTrlw5k5SUlCv71VdfNcHBwWb16tUul/o8f/68c42n740n2enp6c7XGh4ebvr372+2b99uDh486JXa33nnHePn52f+/e9/uzzm7Nmzhc4eNWqUWb58uTl06JDZu3eveffdd42vr6/5+OOPvVL75a50yoMn2SNGjDDfffedOXTokElISDDt27c3AQEBeZ5W40n+4cOHTWBgoOnZs6dJTEw0ixcvNiEhIebtt9/22vvywAMPmHbt2l3x/fIke9iwYSYwMNDMmzfP/PLLL2b58uWmatWqpm3btl7J37x5s/nqq6/MoUOHzNq1a80jjzxiKleubH7//fdc+QMHDjRr1qwxSUlJZteuXWbgwIHG4XCY5cuX55lt5c8wq9lW+tSTfHf71JNsK33qSf7l8juN0Gq2lT61mu1ujxb2fSmoTz3JttKnnuRb6dO8XH7qU2F61Wq21V61mm+lVz3Jt9qvVrIvZ+XKoQVlW+lVT/Kt9quV7Bzu9Kon+Vb71Up2YXv1UgxbHnr//fdNxYoVjZ+fn7n33nvN5s2bnfuaNGliOnfu7LL+iy++MNWrVzd+fn6mVq1aV/wA7apVq4ykXLecvM6dO5smTZq4POa9994zNWvWNKVKlTLh4eGmQ4cOV/xeCqv5e/fuNfXq1TOlSpUyQUFB5qmnnjL79+/PMzuvXEku36fg6XvjSXZSUlKej7n8/fM0PyoqKs/HDBs2rNDZgwcPNrfffrsJCAgwZcuWNTExMWb+/Pl5vOuev++XutJfDJ5k9+nTx9kboaGhpkWLFlf8XgpPa9+4caNp2LCh8ff3N1WqVDEjR47MdRqAp9n79+83kpz/WPJW3RkZGWb48OGmatWqJiAgwERGRpq//vWvef7B7Un+6tWrzR133GH8/f3NLbfcYjp27Gj+97//5Vl/165dTVRUlPHz8zPly5c3TZs2dXm9hfkzzGq2lT71JN/dPvUk20qfepJ/ufz+AWc120qfelK3Oz1amHx3+tSTbCt96km+lT7Ny+X/QCxMr1rNttqrVvOt9Kon+Vb71Ur25QozbBWmVz2t3Uq/Ws12t1c9ybfar1ayC9url3IY48VzzQAAAAAAkiS+ZwsAAAAAbMCwBQAAAAA2YNgCAAAAABswbAEAAACADRi2AAAAAMAGDFsAAAAAYAOGLQAAAACwAcMWAADXiYceekh9+vQp6jIAAP8PwxYAwKs2bdokHx8ftWzZsqhLsY3D4dCiRYuK7PlXr14th8Oh06dPu2xfsGCB3nrrraIpCgCQC8MWAMCrpk+frl69emnt2rU6evToVXnOixcvXpXnsVthX0e5cuUUGBjopWoAAIXFsAUA8Jq0tDR9/vnnevXVV9WyZUvNnDkz15qvv/5a1apVU0BAgB5++GHNmjUr11Gajz/+WJGRkbrpppv09NNPa/z48SpTpoxz//Dhw1WvXj198sknqly5sgICAiRJp0+f1ssvv6zy5csrKChIjzzyiHbu3Ony/G+//bZCQkIUGBiol19+WQMHDlS9evWc+7dt26ZHH31Ut956q4KDg9WkSRP9+OOPzv2VKlWSJD399NNyOBzO+5L0n//8R3fffbcCAgJUpUoVjRgxQpmZmVd8v7p06aLWrVtr5MiRioiIUHR0tCRpzpw5atCggQIDAxUWFqYXXnhBx48flyT9+uuvevjhhyVJZcuWlcPhUJcuXSTlPo2wUqVKGjVqlLp27arAwEBVrFhRH330kUsNGzduVL169RQQEKAGDRpo0aJFcjgc2rFjxxXrBgC4h2ELAOA1X3zxhWrUqKHo6Gi9+OKL+vTTT2WMce5PSkrSs88+q9atW2vnzp36y1/+osGDB7tkbNiwQa+88op69+6tHTt26NFHH9XIkSNzPdfPP/+sr776SgsWLHAOBs8995yOHz+ub7/9VgkJCbr77rvVtGlTnTp1SpI0d+5cjRw5Uv/85z+VkJCgihUraurUqS65Z8+eVefOnbV+/Xpt3rxZ1apVU4sWLXT27FlJfw5jkjRjxgwdO3bMeX/dunXq1KmTevfurb179+rDDz/UzJkz86z9UitWrFBiYqLi4+O1ePFiSVJGRobeeust7dy5U4sWLdKvv/7qHKgiIyP11VdfSZISExN17NgxTZo06Yr548aNU4MGDbR9+3b99a9/1auvvqrExERJUmpqqlq1aqXatWvrxx9/1FtvvaUBAwbkWy8AwAIDAICXNGrUyEycONEYY0xGRoa59dZbzapVq5z7BwwYYO68806XxwwePNhIMr///rsxxph27dqZli1buqzp0KGDCQ4Odt4fNmyYKVmypDl+/Lhz27p160xQUJC5cOGCy2OrVq1qPvzwQ2OMMQ0bNjRxcXEu+++//35Tt27dK76mrKwsExgYaL755hvnNklm4cKFLuuaNm1qRo0a5bJtzpw5Jjw8/IrZnTt3NqGhoSY9Pf2Ka4wxZtu2bUaSOXv2rDHGmFWrVrm8ZzmaNGlievfu7bwfFRVlXnzxRef97OxsExISYqZOnWqMMWbq1KnmlltuMX/88Ydzzccff2wkme3bt+dbEwCgYBzZAgB4RWJiorZu3arnn39ekuTr66t27dpp+vTpLmvuuecel8fde++9uXIu33b5fUmKiopS+fLlnfd37typtLQ03XLLLbr55pudt6SkJB06dMjt7JSUFHXv3l3VqlVTcHCwgoKClJaWpsOHD+f7+nfu3Kk333zT5bm7d++uY8eO6fz581d8XO3ateXn5+eyLSEhQa1atVLFihUVGBioJk2aSFKBNeSlTp06zl87HA6FhYU5T0lMTExUnTp1nKdhSnm/1wAAz/gWdQEAgOvD9OnTlZmZqYiICOc2Y4z8/f31wQcfKDg42KvPV7p0aZf7aWlpCg8P1+rVq3OtvfTzXgXp3LmzTp48qUmTJikqKkr+/v6KiYkp8OIVaWlpGjFihJ555plc+y4dZi53+es4d+6cYmNjFRsbq7lz56p8+fI6fPiwYmNjPbqARsmSJV3uOxwOZWdnW84BAFjHsAUAKLTMzEzNnj1b48aNU/PmzV32tW7dWvPmzdMrr7yi6OhoLV261GV/zmeeckRHR+fadvn9vNx9991KTk6Wr6+vy0Ur8sru1KnTFbM3bNigKVOmqEWLFpKkI0eO6P/+7/9c1pQsWVJZWVm5nj8xMVG33357gbXmZ//+/Tp58qTeeecdRUZGSpJ++OEHlzU5R8Iur8Gq6Oho/etf/1J6err8/f0lufdeAwDcw2mEAIBCW7x4sX7//Xd169ZNd955p8utTZs2zlMJ//KXv2j//v0aMGCADhw4oC+++MJ5xUKHwyFJ6tWrl5YuXarx48fr4MGD+vDDD/Xtt986919Js2bNFBMTo9atW2v58uX69ddftXHjRg0ePNg5rPTq1UvTp0/XrFmzdPDgQb399tvatWuXS3a1atU0Z84c7du3T1u2bFGHDh1UqlQpl+eqVKmSVqxYoeTkZP3++++SpKFDh2r27NkaMWKE9uzZo3379mn+/PkaMmSIpfeyYsWK8vPz0/vvv69ffvlFX3/9da7vzoqKipLD4dDixYt14sQJpaWlWXqOHC+88IKys7PVo0cP7du3T999953effddSSrw/QYAFIxhCwBQaNOnT1ezZs3yPFWwTZs2+uGHH7Rr1y5VrlxZ//73v7VgwQLVqVNHU6dOdV6NMOfIyv33369p06Zp/Pjxqlu3rpYtW6a+ffvmeyqe9OdwsHTpUjVu3FgvvfSSqlevrvbt2+u///2vQkNDJUkdOnTQoEGD1L9/f919991KSkpSly5dXLKnT5+u33//XXfffbc6duyov/3tbwoJCXF5rnHjxik+Pl6RkZG66667JEmxsbFavHixli9frnvuuUf33XefJkyYoKioKEvvZfny5TVz5kx9+eWXqlmzpt555x3nAJSjQoUKGjFihAYOHKjQ0FD17NnT0nPkCAoK0jfffKMdO3aoXr16Gjx4sIYOHSop/1MfAQDucRhzyTV5AQC4ykaOHKlp06bpyJEjV1zTvXt37d+/X+vWrfP68z/66KMKCwvTnDlzvJ5dHM2dO1cvvfSSzpw5k+uIHgDAGj6zBQC4qqZMmaJ77rlHt9xyizZs2KCxY8fmOjLz7rvv6tFHH1Xp0qX17bffatasWZoyZUqhn/v8+fOaNm2aYmNj5ePjo3nz5un7779XfHx8obOLq9mzZ6tKlSqqUKGCdu7cqQEDBqht27YMWgDgBQxbAICrKuezUqdOnVLFihX197//XYMGDXJZs3XrVo0ZM0Znz55VlSpV9N577+nll18u9HPnnGo4cuRIXbhwQdHR0frqq6/UrFmzQmcXV8nJyRo6dKiSk5MVHh6u5557rsAvYgYAuIfTCAEAAADABlwgAwAAAABswLAFAAAAADZg2AIAAAAAGzBsAQAAAIANGLYAAAAAwAYMWwAAAABgA4YtAAAAALABwxYAAAAA2IBhCwAAAABs8P8BoDuXMd5Cbm8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (10,4)\n",
    "sns.barplot(x='Aggregate rating', y='Rating Count', data=ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d515cb8f-7b87-4e89-af56-7cf4fe2024af",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Aggregate rating', ylabel='Rating Count'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Aggregate rating', y='Rating Count', hue='Rating color', data=ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "043d50b4-4ef0-484d-bfcb-298f8050cb92",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Aggregate rating', ylabel='Rating Count'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Aggregate rating', y='Rating Count',hue='Rating color', data=ratings, palette=['white','red','orange','yellow','green','green'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e01cd7-551e-4adf-8476-b217b6bb9af5",
   "metadata": {},
   "source": [
    "## Observation:\n",
    "1. Not Rated count is very high\n",
    "2. Max rating is between 2.5 and 3.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "99e61281-762c-43f2-bae8-d328b48ba22e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Rating color', ylabel='count'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Count plot\n",
    "sns.countplot(x='Rating color', data=ratings, palette=['white','red','orange','yellow','green','green'], hue='Rating color')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1fa4ba64-ed1f-449f-8af0-c062fafbdd6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Country</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>India</td>\n",
       "      <td>2139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Aggregate rating         Country     0\n",
       "0               0.0          Brazil     5\n",
       "1               0.0           India  2139\n",
       "2               0.0  United Kingdom     1\n",
       "3               0.0   United States     3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### QUES: Find countries name that has given 0 ratings\n",
    "final_df.groupby(['Aggregate rating', 'Country']).size().reset_index().head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0222a0b8-066e-42ed-b19d-a4713d0307ad",
   "metadata": {},
   "source": [
    "Observation: Maximum number of 0 ratings are from Indian customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c62262bf-1edd-4e88-9b89-97b7d06a202c",
   "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>Currency</th>\n",
       "      <th>Country</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Phillipines</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brazilian Real(R$)</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dollar($)</td>\n",
       "      <td>Australia</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dollar($)</td>\n",
       "      <td>Canada</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dollar($)</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dollar($)</td>\n",
       "      <td>United States</td>\n",
       "      <td>434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Emirati Diram(AED)</td>\n",
       "      <td>UAE</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>India</td>\n",
       "      <td>8652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Indonesian Rupiah(IDR)</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NewZealand($)</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pounds(£)</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Qatari Rial(QR)</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Rand(R)</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sri Lankan Rupee(LKR)</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Currency         Country     0\n",
       "0         Botswana Pula(P)     Phillipines    22\n",
       "1       Brazilian Real(R$)          Brazil    60\n",
       "2                Dollar($)       Australia    24\n",
       "3                Dollar($)          Canada     4\n",
       "4                Dollar($)       Singapore    20\n",
       "5                Dollar($)   United States   434\n",
       "6       Emirati Diram(AED)             UAE    60\n",
       "7       Indian Rupees(Rs.)           India  8652\n",
       "8   Indonesian Rupiah(IDR)       Indonesia    21\n",
       "9            NewZealand($)     New Zealand    40\n",
       "10              Pounds(£)  United Kingdom    80\n",
       "11         Qatari Rial(QR)           Qatar    20\n",
       "12                 Rand(R)    South Africa    60\n",
       "13   Sri Lankan Rupee(LKR)       Sri Lanka    20\n",
       "14        Turkish Lira(TL)          Turkey    34"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### QUES: Find out which currency is used by which country.\n",
    "final_df.groupby(['Currency','Country']).size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cb213032-4c3b-4033-99ba-650deb967b4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "India    2423\n",
       "UAE        28\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## QUES: Which country has online deliveries options?\n",
    "final_df[final_df['Has Online delivery'] == 'Yes'].Country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "65c9958e-91de-40b6-ac1f-e96fee053e72",
   "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>Has Online delivery</th>\n",
       "      <th>Country</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>Australia</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Canada</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>India</td>\n",
       "      <td>6229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>No</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>No</td>\n",
       "      <td>Phillipines</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>No</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>No</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>No</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>No</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>No</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>No</td>\n",
       "      <td>UAE</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>No</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Yes</td>\n",
       "      <td>India</td>\n",
       "      <td>2423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Yes</td>\n",
       "      <td>UAE</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Has Online delivery         Country     0\n",
       "0                   No       Australia    24\n",
       "1                   No          Brazil    60\n",
       "2                   No          Canada     4\n",
       "3                   No           India  6229\n",
       "4                   No       Indonesia    21\n",
       "5                   No     New Zealand    40\n",
       "6                   No     Phillipines    22\n",
       "7                   No           Qatar    20\n",
       "8                   No       Singapore    20\n",
       "9                   No    South Africa    60\n",
       "10                  No       Sri Lanka    20\n",
       "11                  No          Turkey    34\n",
       "12                  No             UAE    32\n",
       "13                  No  United Kingdom    80\n",
       "14                  No   United States   434\n",
       "15                 Yes           India  2423\n",
       "16                 Yes             UAE    28"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.groupby(['Has Online delivery','Country']).size().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb216728-4a4e-48d9-a1e6-23a0648f555e",
   "metadata": {},
   "source": [
    "Observations: Countries that has online delivery are India and UAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cd8919b0-c742-4dac-81b2-ecb66636cab1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['New Delhi', 'Gurgaon', 'Noida', 'Faridabad', 'Ghaziabad',\n",
       "       'Bhubaneshwar', 'Amritsar', 'Ahmedabad', 'Lucknow', 'Guwahati',\n",
       "       ...\n",
       "       'Ojo Caliente', 'Montville', 'Monroe', 'Miller', 'Middleton Beach',\n",
       "       'Panchkula', 'Mc Millan', 'Mayfield', 'Macedon', 'Vineland Station'],\n",
       "      dtype='object', name='City', length=141)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### QUES: Create a Pie Chart for cities distribution\n",
    "final_df.City.value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e1afc44e-7f94-4840-a6d9-de6d0d6e1ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "city_values=final_df.City.value_counts().values\n",
    "city_labels=final_df.City.value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "657019e1-76c4-4b92-b322-8019cb8b4789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x23d6b2eb170>,\n",
       "  <matplotlib.patches.Wedge at 0x23d6b2d3410>,\n",
       "  <matplotlib.patches.Wedge at 0x23d6b2eba10>,\n",
       "  <matplotlib.patches.Wedge at 0x23d6b2ebfb0>,\n",
       "  <matplotlib.patches.Wedge at 0x23d6b328650>],\n",
       " [Text(-0.6145352824185932, 0.9123301960708633, 'New Delhi'),\n",
       "  Text(0.0623675251198054, -1.0982305276263407, 'Gurgaon'),\n",
       "  Text(0.8789045225625368, -0.6614581167535246, 'Noida'),\n",
       "  Text(1.0922218418223437, -0.13058119407559224, 'Faridabad'),\n",
       "  Text(1.099946280005612, -0.010871113182029924, 'Ghaziabad')],\n",
       " [Text(-0.3352010631374145, 0.497634652402289, '68.87%'),\n",
       "  Text(0.0340186500653484, -0.5990348332507311, '14.07%'),\n",
       "  Text(0.47940246685229276, -0.36079533641101336, '13.59%'),\n",
       "  Text(0.5957573682667329, -0.07122610585941394, '3.16%'),\n",
       "  Text(0.5999706981848791, -0.005929698099289049, '0.31%')])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(city_values[:5],labels=city_labels[:5], autopct='%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "572a6051-e2d6-4fbe-9c4a-c28ffabf10b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>North Indian</td>\n",
       "      <td>936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>North Indian, Chinese</td>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chinese</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fast Food</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>North Indian, Mughlai</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cafe</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bakery</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bakery, Desserts</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Street Food</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Cuisines  count\n",
       "0                    North Indian    936\n",
       "1           North Indian, Chinese    511\n",
       "2                         Chinese    354\n",
       "3                       Fast Food    354\n",
       "4           North Indian, Mughlai    334\n",
       "5                            Cafe    299\n",
       "6                          Bakery    218\n",
       "7  North Indian, Mughlai, Chinese    197\n",
       "8                Bakery, Desserts    170\n",
       "9                     Street Food    149"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### QUES: Find top 10 Cuisines\n",
    "final_df['Cuisines'].value_counts().reset_index().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23b07d87-cd3d-49c5-a838-bacd3267678b",
   "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": "3.12.1"
  }
 },
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}