Zomato_kolkata.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b84c568e",
   "metadata": {},
   "source": [
    "# Kolkata Zomato Dataset Analysis\n",
    "\n",
    "The dataset contains 7388 rows and 7 columns. The details of the restaurant those provided are –\n",
    "\n",
    "\t'name' [Name of the restaurant]\n",
    "\t'voteCount' [Number of votes received from the user]\n",
    "\t'rating' [rating given to the restaurant by the user] \n",
    "\t'address' [Address of the restaurant]\n",
    "\t'cusine' [Category of cuisine offered] \n",
    "\t'cost' [Average price of the restaurant] \n",
    "\t'timing' [Opening and closing time of the restaurant]\n",
    "\n",
    "We will be performing EDA(exploratory data analysis) on the given Dataset.\n",
    "\n",
    "## Libraries required –\n",
    "\tPandas – Data exploration\n",
    "\tNumpy – Mathematical operation\n",
    "\tMatplotlib –  Data Visualization\n",
    "\tSeaborn –  Data Visualization\n",
    "\n",
    "## WorkFlow – \n",
    "\n",
    "Our workflow will primarily consist of two segments. Data cleaning and data visualization. The data cleaning process will consume more time than the visualization.\n",
    "\n",
    "### Data Cleaning – \n",
    "\n",
    "\tVote count , rating and cost , these three columns are type casted as object. We need to convert them in numerical category for our calculative functionality.\n",
    "\tWe need to find the missing values and then handle them with mean, median or mode operation based on it’s relevance.\n",
    "\tWe need to look for duplicate values in the dataset.\n",
    "\tLet’s create different data frames based on given conditions –\n",
    "\n",
    "•\thigh_end_restos – 1896 , AvgRating – 3.17 , AvgVotecount - 288\n",
    "•\tcheap_restos – 2193 , AvgRating – 2.11 , AvgVotecount - 32\n",
    "•\tmidnight_restos – 670, AvgRating – 2.67 , AvgVotecount - 128\n",
    "•\tgenral_timed_resto – 6718 , AvgRating – 2.49 , AvgVotecount - 107\n",
    "\n",
    "\tLet’s find out the most popular cuisines in the market –\n",
    "\n",
    "•\tNorth Indian\n",
    "•\tChinese\n",
    "•\tFast Food\n",
    "•\tCafé\n",
    "•\tBiriyani\n",
    "•\tBengali\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "321afd27",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b80ff449",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data = pd.read_csv('Zkolkata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0162dff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>voteCount</th>\n",
       "      <th>rating</th>\n",
       "      <th>address</th>\n",
       "      <th>cusine</th>\n",
       "      <th>cost</th>\n",
       "      <th>timing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Peter Cat</td>\n",
       "      <td>12404 votes</td>\n",
       "      <td>4.2</td>\n",
       "      <td>18A, Park Street, Park Street Area, Kolkata</td>\n",
       "      <td>North Indian, Continental</td>\n",
       "      <td>₹1,000</td>\n",
       "      <td>11am – 11:20pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Naturals Ice Cream</td>\n",
       "      <td>2498 votes</td>\n",
       "      <td>4.9</td>\n",
       "      <td>77/1/A, Ground Floor, Near West Bengal Board, ...</td>\n",
       "      <td>North Indian, Continental</td>\n",
       "      <td>₹200</td>\n",
       "      <td>11am – 12midnight (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carpe Diem</td>\n",
       "      <td>4083 votes</td>\n",
       "      <td>4.4</td>\n",
       "      <td>18M, Park Street Area, Kolkata</td>\n",
       "      <td>Ice Cream</td>\n",
       "      <td>₹1,000</td>\n",
       "      <td>12noon – 12midnight (Mon, Tue, Wed, Thu, Fri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barbeque Nation</td>\n",
       "      <td>5455 votes</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1st Floor, 24, Park Center Building, Park Stre...</td>\n",
       "      <td>Ice Cream</td>\n",
       "      <td>₹1,800</td>\n",
       "      <td>12noon – 3:30pm, 6:30pm – 10:45pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Flurys</td>\n",
       "      <td>4709 votes</td>\n",
       "      <td>4.2</td>\n",
       "      <td>15, Apeejay House, Park Street Area, Kolkata</td>\n",
       "      <td>Chinese, North Indian, Continental</td>\n",
       "      <td>₹400</td>\n",
       "      <td>7:30am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KFC</td>\n",
       "      <td>2135 votes</td>\n",
       "      <td>4.1</td>\n",
       "      <td>20K, Park Street, Park Street Area, Kolkata</td>\n",
       "      <td>Chinese, North Indian, Continental</td>\n",
       "      <td>₹450</td>\n",
       "      <td>11am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tung Fong</td>\n",
       "      <td>3644 votes</td>\n",
       "      <td>4.3</td>\n",
       "      <td>25 B, Ground Floor, Karnani Mansion, Free Scho...</td>\n",
       "      <td>North Indian, Chinese, Kebab, BBQ</td>\n",
       "      <td>₹1,000</td>\n",
       "      <td>12noon – 4pm, 6pm – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Arsalan</td>\n",
       "      <td>1500 votes</td>\n",
       "      <td>4.0</td>\n",
       "      <td>119 A, Muzaffar Ahmed Street, Mirza Ghalib Str...</td>\n",
       "      <td>North Indian, Chinese, Kebab, BBQ</td>\n",
       "      <td>₹1,000</td>\n",
       "      <td>11am – 11:45pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kusum Rolls</td>\n",
       "      <td>1973 votes</td>\n",
       "      <td>4.1</td>\n",
       "      <td>21, Karnani Mansion, Park Street Area, Kolkata</td>\n",
       "      <td>Bakery, Desserts, Sandwich</td>\n",
       "      <td>₹300</td>\n",
       "      <td>12noon – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kareem's</td>\n",
       "      <td>1027 votes</td>\n",
       "      <td>4.3</td>\n",
       "      <td>55 B, Mirza Ghalib Street, Park Street Area, K...</td>\n",
       "      <td>Bakery, Desserts, Sandwich</td>\n",
       "      <td>₹1,200</td>\n",
       "      <td>12:30pm – 4:30pm, 6:30pm – 11:30pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>WOW! China</td>\n",
       "      <td>53 votes</td>\n",
       "      <td>3.8</td>\n",
       "      <td>19-57, Park Street Area, Kolkata</td>\n",
       "      <td>Fast Food, Burger</td>\n",
       "      <td>₹650</td>\n",
       "      <td>12noon – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Golden Spoon</td>\n",
       "      <td>1000 votes</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1, Middleton Row, Near Loreto House, Park Stre...</td>\n",
       "      <td>Fast Food, Burger</td>\n",
       "      <td>₹600</td>\n",
       "      <td>12noon – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BarBQ</td>\n",
       "      <td>8321 votes</td>\n",
       "      <td>4.3</td>\n",
       "      <td>43-47-55, Park Street Area, Kolkata</td>\n",
       "      <td>Chinese, Asian</td>\n",
       "      <td>₹1,200</td>\n",
       "      <td>12noon – 11:15pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>The Kebab Factory</td>\n",
       "      <td>Cuisines:</td>\n",
       "      <td>NEW</td>\n",
       "      <td>33, Royd Street, Park Street Area, Kolkata</td>\n",
       "      <td>Chinese, Asian</td>\n",
       "      <td>₹350</td>\n",
       "      <td>11am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Patisserie By Franziska</td>\n",
       "      <td>Cuisines:</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13C, Russell Street, Opposite Bengal Club, Par...</td>\n",
       "      <td>Mughlai, North Indian, Rolls, Biryani</td>\n",
       "      <td>₹450</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>The Sixth Sense</td>\n",
       "      <td>92 votes</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25B, Karnani Mansion, Opposite Westside, Park ...</td>\n",
       "      <td>Mughlai, North Indian, Rolls, Biryani</td>\n",
       "      <td>₹1,500</td>\n",
       "      <td>12noon – 12midnight (Mon-Thu),12noon – 2am...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Food King</td>\n",
       "      <td>23 votes</td>\n",
       "      <td>3.3</td>\n",
       "      <td>90/1, Ground Floor, Muzaffar Ahmed Street, War...</td>\n",
       "      <td>Rolls</td>\n",
       "      <td>₹300</td>\n",
       "      <td>12noon – 11:30pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Burgrill</td>\n",
       "      <td>888 votes</td>\n",
       "      <td>4.2</td>\n",
       "      <td>12B/2, Park Street Park, Opposite Oxford Book ...</td>\n",
       "      <td>Rolls</td>\n",
       "      <td>₹500</td>\n",
       "      <td>11am – 2am (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Zainab Biryani</td>\n",
       "      <td>Cuisines:</td>\n",
       "      <td>NEW</td>\n",
       "      <td>19, Alimuddin Street, Park Street Area, Kolkata</td>\n",
       "      <td>Biryani, North Indian, Mughlai</td>\n",
       "      <td>₹400</td>\n",
       "      <td>11am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>The Crepe Cafe</td>\n",
       "      <td>81 votes</td>\n",
       "      <td>4.4</td>\n",
       "      <td>6/1A, Middleton Street, Park Street Area, Kolkata</td>\n",
       "      <td>Biryani, North Indian, Mughlai</td>\n",
       "      <td>₹800</td>\n",
       "      <td>10:30am – 10:30pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       name    voteCount rating  \\\n",
       "0                 Peter Cat  12404 votes    4.2   \n",
       "1        Naturals Ice Cream   2498 votes    4.9   \n",
       "2                Carpe Diem   4083 votes    4.4   \n",
       "3           Barbeque Nation   5455 votes    4.6   \n",
       "4                    Flurys   4709 votes    4.2   \n",
       "5                       KFC   2135 votes    4.1   \n",
       "6                 Tung Fong   3644 votes    4.3   \n",
       "7                   Arsalan   1500 votes    4.0   \n",
       "8               Kusum Rolls   1973 votes    4.1   \n",
       "9                  Kareem's   1027 votes    4.3   \n",
       "10               WOW! China     53 votes    3.8   \n",
       "11             Golden Spoon   1000 votes    3.6   \n",
       "12                    BarBQ   8321 votes    4.3   \n",
       "13        The Kebab Factory    Cuisines:    NEW   \n",
       "14  Patisserie By Franziska    Cuisines:    NaN   \n",
       "15          The Sixth Sense     92 votes    4.0   \n",
       "16                Food King     23 votes    3.3   \n",
       "17                 Burgrill    888 votes    4.2   \n",
       "18           Zainab Biryani    Cuisines:    NEW   \n",
       "19           The Crepe Cafe     81 votes    4.4   \n",
       "\n",
       "                                              address  \\\n",
       "0         18A, Park Street, Park Street Area, Kolkata   \n",
       "1   77/1/A, Ground Floor, Near West Bengal Board, ...   \n",
       "2                      18M, Park Street Area, Kolkata   \n",
       "3   1st Floor, 24, Park Center Building, Park Stre...   \n",
       "4        15, Apeejay House, Park Street Area, Kolkata   \n",
       "5         20K, Park Street, Park Street Area, Kolkata   \n",
       "6   25 B, Ground Floor, Karnani Mansion, Free Scho...   \n",
       "7   119 A, Muzaffar Ahmed Street, Mirza Ghalib Str...   \n",
       "8      21, Karnani Mansion, Park Street Area, Kolkata   \n",
       "9   55 B, Mirza Ghalib Street, Park Street Area, K...   \n",
       "10                   19-57, Park Street Area, Kolkata   \n",
       "11  1, Middleton Row, Near Loreto House, Park Stre...   \n",
       "12                43-47-55, Park Street Area, Kolkata   \n",
       "13         33, Royd Street, Park Street Area, Kolkata   \n",
       "14  13C, Russell Street, Opposite Bengal Club, Par...   \n",
       "15  25B, Karnani Mansion, Opposite Westside, Park ...   \n",
       "16  90/1, Ground Floor, Muzaffar Ahmed Street, War...   \n",
       "17  12B/2, Park Street Park, Opposite Oxford Book ...   \n",
       "18    19, Alimuddin Street, Park Street Area, Kolkata   \n",
       "19  6/1A, Middleton Street, Park Street Area, Kolkata   \n",
       "\n",
       "                                   cusine    cost  \\\n",
       "0               North Indian, Continental  ₹1,000   \n",
       "1               North Indian, Continental    ₹200   \n",
       "2                               Ice Cream  ₹1,000   \n",
       "3                               Ice Cream  ₹1,800   \n",
       "4      Chinese, North Indian, Continental    ₹400   \n",
       "5      Chinese, North Indian, Continental    ₹450   \n",
       "6       North Indian, Chinese, Kebab, BBQ  ₹1,000   \n",
       "7       North Indian, Chinese, Kebab, BBQ  ₹1,000   \n",
       "8              Bakery, Desserts, Sandwich    ₹300   \n",
       "9              Bakery, Desserts, Sandwich  ₹1,200   \n",
       "10                      Fast Food, Burger    ₹650   \n",
       "11                      Fast Food, Burger    ₹600   \n",
       "12                         Chinese, Asian  ₹1,200   \n",
       "13                         Chinese, Asian    ₹350   \n",
       "14  Mughlai, North Indian, Rolls, Biryani    ₹450   \n",
       "15  Mughlai, North Indian, Rolls, Biryani  ₹1,500   \n",
       "16                                  Rolls    ₹300   \n",
       "17                                  Rolls    ₹500   \n",
       "18         Biryani, North Indian, Mughlai    ₹400   \n",
       "19         Biryani, North Indian, Mughlai    ₹800   \n",
       "\n",
       "                                             timing  \n",
       "0                          11am – 11:20pm (Mon-Sun)  \n",
       "1                       11am – 12midnight (Mon-Sun)  \n",
       "2   12noon – 12midnight (Mon, Tue, Wed, Thu, Fri...  \n",
       "3       12noon – 3:30pm, 6:30pm – 10:45pm (Mon-Sun)  \n",
       "4                           7:30am – 11pm (Mon-Sun)  \n",
       "5                             11am – 11pm (Mon-Sun)  \n",
       "6                12noon – 4pm, 6pm – 11pm (Mon-Sun)  \n",
       "7                          11am – 11:45pm (Mon-Sun)  \n",
       "8                           12noon – 11pm (Mon-Sun)  \n",
       "9      12:30pm – 4:30pm, 6:30pm – 11:30pm (Mon-Sun)  \n",
       "10                          12noon – 11pm (Mon-Sun)  \n",
       "11                          12noon – 11pm (Mon-Sun)  \n",
       "12                       12noon – 11:15pm (Mon-Sun)  \n",
       "13                            11am – 11pm (Mon-Sun)  \n",
       "14                                              NaN  \n",
       "15    12noon – 12midnight (Mon-Thu),12noon – 2am...  \n",
       "16                       12noon – 11:30pm (Mon-Sun)  \n",
       "17                             11am – 2am (Mon-Sun)  \n",
       "18                            11am – 11pm (Mon-Sun)  \n",
       "19                      10:30am – 10:30pm (Mon-Sun)  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "97756032",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7388, 7)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "814dd226",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'voteCount', 'rating', 'address', 'cusine', 'cost', 'timing'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f3140559",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7388 entries, 0 to 7387\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   name       7388 non-null   object\n",
      " 1   voteCount  7388 non-null   object\n",
      " 2   rating     7104 non-null   object\n",
      " 3   address    7387 non-null   object\n",
      " 4   cusine     7388 non-null   object\n",
      " 5   cost       7387 non-null   object\n",
      " 6   timing     7086 non-null   object\n",
      "dtypes: object(7)\n",
      "memory usage: 404.2+ KB\n"
     ]
    }
   ],
   "source": [
    "Data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "37c2fec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name         0.00\n",
       "voteCount    0.00\n",
       "rating       3.84\n",
       "address      0.01\n",
       "cusine       0.00\n",
       "cost         0.01\n",
       "timing       4.09\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(100*(Data.isnull().sum()/len(Data.index)),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "df59ccd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data['cost'] = Data['cost'].str.replace('₹', '')\n",
    "Data['cost'] = Data['cost'].str.replace(',', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a51678ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data['cost'] = pd.to_numeric(Data['cost'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "01cbed80",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data['cost'] = Data['cost'].fillna(Data['cost'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d64e9489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name         0.00\n",
       "voteCount    0.00\n",
       "rating       3.84\n",
       "address      0.01\n",
       "cusine       0.00\n",
       "cost         0.00\n",
       "timing       4.09\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(100*(Data.isnull().sum()/len(Data.index)),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c5390b11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4.2', '4.9', '4.4', '4.6', '4.1', '4.3', '4.0', '3.8', '3.6',\n",
       "       'NEW', nan, '3.3', '3.7', '3.9', '3.5', '3.2', '3.1', '3.0', '2.9',\n",
       "       '-', '3.4', '2.3', '4.5', '2.8', '4.7', '2.7', '2.6', '2.5', '2.1',\n",
       "       '4.8', '2.4', '2.2', '1.9', '2.0'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data['rating'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b1e8acaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data['rating'] = Data['rating'].str.replace('NEW', '0')\n",
    "Data['rating'] = Data['rating'].str.replace('-', '0')\n",
    "Data['rating'] = Data['rating'].str.replace(' ', '0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fccc02ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data['rating'] = pd.to_numeric(Data['rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "33109329",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data['rating'] = Data['rating'].fillna(Data['rating'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "340a2d95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name         0.00\n",
       "voteCount    0.00\n",
       "rating       0.00\n",
       "address      0.01\n",
       "cusine       0.00\n",
       "cost         0.00\n",
       "timing       4.09\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(100*(Data.isnull().sum()/len(Data.index)),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8a7cb20b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7388 entries, 0 to 7387\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   name       7388 non-null   object \n",
      " 1   voteCount  7388 non-null   object \n",
      " 2   rating     7388 non-null   float64\n",
      " 3   address    7387 non-null   object \n",
      " 4   cusine     7388 non-null   object \n",
      " 5   cost       7388 non-null   float64\n",
      " 6   timing     7086 non-null   object \n",
      "dtypes: float64(2), object(5)\n",
      "memory usage: 404.2+ KB\n"
     ]
    }
   ],
   "source": [
    "Data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eed9c782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6926"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data['address'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0fe4fffb",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data['address'] = Data['address'].fillna(Data['address'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7f17e2fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name         0.00\n",
       "voteCount    0.00\n",
       "rating       0.00\n",
       "address      0.00\n",
       "cusine       0.00\n",
       "cost         0.00\n",
       "timing       4.09\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(100*(Data.isnull().sum()/len(Data.index)),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2704d9a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['12404 votes', '2498 votes', '4083 votes', '5455 votes',\n",
       "       '4709 votes', '2135 votes', '3644 votes', '1500 votes',\n",
       "       '1973 votes', '1027 votes', '53 votes', '1000 votes', '8321 votes',\n",
       "       'Cuisines:', '92 votes', '23 votes', '888 votes', '81 votes',\n",
       "       '979 votes', '2552 votes', '1019 votes', '66 votes', '71 votes',\n",
       "       '341 votes', '505 votes', '45 votes', '816 votes', '1770 votes',\n",
       "       '99 votes', '801 votes', '80 votes', '21 votes', '676 votes',\n",
       "       '640 votes', '167 votes', '577 votes', '1465 votes', '366 votes',\n",
       "       '866 votes', '1137 votes', '355 votes', '78 votes', '34 votes',\n",
       "       '85 votes', '918 votes', '6 votes', '159 votes', '504 votes',\n",
       "       '1287 votes', '967 votes', '16 votes', '169 votes', '9 votes',\n",
       "       '51 votes', '321 votes', '124 votes', '411 votes', '67 votes',\n",
       "       '56 votes', '217 votes', '11 votes', '155 votes', '141 votes',\n",
       "       '386 votes', '10 votes', '5 votes', '89 votes', '7 votes',\n",
       "       '388 votes', '144 votes', '593 votes', '32 votes', '4 votes',\n",
       "       '35 votes', '79 votes', '18 votes', '13 votes', '52 votes',\n",
       "       '324 votes', '61 votes', '33 votes', '8 votes', '495 votes',\n",
       "       '43 votes', '30 votes', '50 votes', '17 votes', '332 votes',\n",
       "       '137 votes', '29 votes', '49 votes', '535 votes', '233 votes',\n",
       "       '76 votes', '1728 votes', '2305 votes', '2515 votes', '1837 votes',\n",
       "       '1169 votes', '1910 votes', '2010 votes', '1502 votes',\n",
       "       '2398 votes', '845 votes', '615 votes', '333 votes', '250 votes',\n",
       "       '173 votes', '1394 votes', '580 votes', '2423 votes', '1342 votes',\n",
       "       '964 votes', '1564 votes', '198 votes', '962 votes', '243 votes',\n",
       "       '3763 votes', '94 votes', '609 votes', '125 votes', '259 votes',\n",
       "       '501 votes', '3156 votes', '926 votes', '1096 votes', '1439 votes',\n",
       "       '1071 votes', '806 votes', '969 votes', '858 votes', '178 votes',\n",
       "       '44 votes', '686 votes', '973 votes', '440 votes', '232 votes',\n",
       "       '2727 votes', '683 votes', '1109 votes', '262 votes', '835 votes',\n",
       "       '104 votes', '47 votes', '1037 votes', '983 votes', '326 votes',\n",
       "       '168 votes', '86 votes', '39 votes', '274 votes', '728 votes',\n",
       "       '1186 votes', '24 votes', '323 votes', '284 votes', '483 votes',\n",
       "       '208 votes', '3203 votes', '645 votes', '162 votes', '234 votes',\n",
       "       '381 votes', '994 votes', '238 votes', '20 votes', '748 votes',\n",
       "       '413 votes', '1080 votes', '195 votes', '449 votes', '14 votes',\n",
       "       '399 votes', '1699 votes', '127 votes', '679 votes', '100 votes',\n",
       "       '1193 votes', '31 votes', '128 votes', '55 votes', '182 votes',\n",
       "       '583 votes', '12 votes', '73 votes', '616 votes', '574 votes',\n",
       "       '143 votes', '730 votes', '222 votes', '96 votes', '226 votes',\n",
       "       '184 votes', '467 votes', '19 votes', '75 votes', '133 votes',\n",
       "       '265 votes', '430 votes', '15 votes', '40 votes', '161 votes',\n",
       "       '105 votes', '249 votes', '93 votes', '424 votes', '46 votes',\n",
       "       '64 votes', '82 votes', '1128 votes', '1011 votes', '393 votes',\n",
       "       '887 votes', '148 votes', '171 votes', '257 votes', '352 votes',\n",
       "       '107 votes', '166 votes', '98 votes', '38 votes', '191 votes',\n",
       "       '585 votes', '72 votes', '308 votes', '518 votes', '129 votes',\n",
       "       '531 votes', '559 votes', '242 votes', '54 votes', '111 votes',\n",
       "       '177 votes', '213 votes', '1286 votes', '37 votes', '103 votes',\n",
       "       '28 votes', '83 votes', '90 votes', '263 votes', '241 votes',\n",
       "       '36 votes', '60 votes', '186 votes', '70 votes', '74 votes',\n",
       "       '304 votes', '282 votes', '340 votes', '135 votes', '299 votes',\n",
       "       '59 votes', '523 votes', '69 votes', '48 votes', '193 votes',\n",
       "       '484 votes', '183 votes', '22 votes', '214 votes', '68 votes',\n",
       "       '202 votes', '25 votes', '258 votes', '273 votes', '88 votes',\n",
       "       '216 votes', '235 votes', '26 votes', '2 votes', '455 votes',\n",
       "       '153 votes', '41 votes', '154 votes', '255 votes', '151 votes',\n",
       "       '121 votes', '91 votes', '349 votes', '42 votes', '63 votes',\n",
       "       '77 votes', '58 votes', '172 votes', '116 votes', '301 votes',\n",
       "       '145 votes', '290 votes', '27 votes', '84 votes', '113 votes',\n",
       "       '65 votes', '126 votes', '260 votes', '197 votes', '353 votes',\n",
       "       '724 votes', '520 votes', '164 votes', '371 votes', '112 votes',\n",
       "       '420 votes', '318 votes', '337 votes', '2170 votes', '955 votes',\n",
       "       '2432 votes', '1343 votes', '269 votes', '2512 votes',\n",
       "       '2734 votes', '2851 votes', '649 votes', '1030 votes', '431 votes',\n",
       "       '1236 votes', '815 votes', '1828 votes', '2028 votes', '375 votes',\n",
       "       '275 votes', '432 votes', '860 votes', '405 votes', '293 votes',\n",
       "       '311 votes', '345 votes', '357 votes', '146 votes', '739 votes',\n",
       "       '310 votes', '212 votes', '264 votes', '0 votes', '236 votes',\n",
       "       '542 votes', '239 votes', '203 votes', '62 votes', '196 votes',\n",
       "       '192 votes', '118 votes', '457 votes', '110 votes', '194 votes',\n",
       "       '720 votes', '211 votes', '204 votes', '394 votes', '1241 votes',\n",
       "       '4893 votes', '4662 votes', '1003 votes', '1922 votes',\n",
       "       '296 votes', '579 votes', '4623 votes', '1753 votes', '856 votes',\n",
       "       '1643 votes', '1433 votes', '2416 votes', '1471 votes',\n",
       "       '1428 votes', '2611 votes', '1419 votes', '4434 votes',\n",
       "       '3534 votes', '1967 votes', '1229 votes', '1097 votes',\n",
       "       '907 votes', '218 votes', '2792 votes', '354 votes', '2079 votes',\n",
       "       '778 votes', '1351 votes', '1902 votes', '3379 votes',\n",
       "       '1264 votes', '702 votes', '1955 votes', '2039 votes', '587 votes',\n",
       "       '673 votes', '1675 votes', '3750 votes', '3945 votes',\n",
       "       '2636 votes', '2346 votes', '2248 votes', '312 votes',\n",
       "       '2426 votes', '1108 votes', '465 votes', '2484 votes',\n",
       "       '2355 votes', '1501 votes', '119 votes', '2604 votes', '316 votes',\n",
       "       '3375 votes', '2007 votes', '1429 votes', '912 votes', '919 votes',\n",
       "       '160 votes', '109 votes', '555 votes', '3341 votes', '207 votes',\n",
       "       '512 votes', '622 votes', '206 votes', '658 votes', '201 votes',\n",
       "       '1495 votes', '1358 votes', '2492 votes', '458 votes', '840 votes',\n",
       "       '759 votes', '1398 votes', '540 votes', '1702 votes', '1580 votes',\n",
       "       '950 votes', '1877 votes', '1159 votes', '820 votes', '672 votes',\n",
       "       '509 votes', '712 votes', '1736 votes', '294 votes', '409 votes',\n",
       "       '659 votes', '163 votes', '2114 votes', '834 votes', '1586 votes',\n",
       "       '219 votes', '870 votes', '228 votes', '984 votes', '288 votes',\n",
       "       '2750 votes', '279 votes', '492 votes', '471 votes', '422 votes',\n",
       "       '254 votes', '879 votes', '305 votes', '138 votes', '272 votes',\n",
       "       '705 votes', '456 votes', '57 votes', '436 votes', '229 votes',\n",
       "       '578 votes', '514 votes', '115 votes', '1693 votes', '1612 votes',\n",
       "       '2723 votes', '131 votes', '223 votes', '466 votes', '1204 votes',\n",
       "       '502 votes', '599 votes', '867 votes', '101 votes', '266 votes',\n",
       "       '632 votes', '377 votes', '402 votes', '175 votes', '464 votes',\n",
       "       '707 votes', '247 votes', '397 votes', '929 votes', '149 votes',\n",
       "       '401 votes', '2811 votes', '224 votes', '189 votes', '755 votes',\n",
       "       '176 votes', '470 votes', '446 votes', '576 votes', '416 votes',\n",
       "       '784 votes', '454 votes', '453 votes', '837 votes', '385 votes',\n",
       "       '726 votes', '295 votes', '1391 votes', '185 votes', '1296 votes',\n",
       "       '528 votes', '898 votes', '307 votes', '289 votes', '899 votes',\n",
       "       '939 votes', '627 votes', '654 votes', '521 votes', '407 votes',\n",
       "       '200 votes', '157 votes', '379 votes', '187 votes', '1527 votes',\n",
       "       '190 votes', '230 votes', '339 votes', '619 votes', '370 votes',\n",
       "       '843 votes', '810 votes', '1208 votes', '415 votes', '132 votes',\n",
       "       '1040 votes', '482 votes', '237 votes', '477 votes', '142 votes',\n",
       "       '114 votes', '526 votes', '1410 votes', '643 votes', '691 votes',\n",
       "       '287 votes', '373 votes', '286 votes', '1058 votes', '798 votes',\n",
       "       '123 votes', '1424 votes', '368 votes', '97 votes', '489 votes',\n",
       "       '1354 votes', '1014 votes', '666 votes', '428 votes', '227 votes',\n",
       "       '548 votes', '325 votes', '248 votes', '253 votes', '525 votes',\n",
       "       '139 votes', '1919 votes', '285 votes', '278 votes', '106 votes',\n",
       "       '1129 votes', '336 votes', '569 votes', '136 votes', '317 votes',\n",
       "       '170 votes', '328 votes', '205 votes', '283 votes', '709 votes',\n",
       "       '612 votes', '620 votes', '516 votes', '244 votes', '3242 votes',\n",
       "       '433 votes', '557 votes', '800 votes', '134 votes', '87 votes',\n",
       "       '270 votes', '486 votes', '358 votes', '613 votes', '331 votes',\n",
       "       '102 votes', '681 votes', '359 votes', '805 votes', '181 votes',\n",
       "       '174 votes', '1212 votes', '626 votes', '472 votes', '240 votes',\n",
       "       '717 votes', '117 votes', '343 votes', '150 votes', '180 votes',\n",
       "       '423 votes', '408 votes', '156 votes', '404 votes', '122 votes',\n",
       "       '747 votes', '452 votes', '95 votes', '147 votes', '152 votes',\n",
       "       '674 votes', '372 votes', '158 votes', '511 votes', '417 votes',\n",
       "       '1638 votes', '280 votes', '251 votes', '347 votes', '108 votes',\n",
       "       '225 votes', '414 votes', '360 votes', '551 votes', '120 votes',\n",
       "       '261 votes', '188 votes', '469 votes', '1107 votes', '130 votes',\n",
       "       '335 votes', '140 votes', '546 votes', '268 votes', '781 votes',\n",
       "       '959 votes', '1042 votes', '494 votes', '2781 votes', '2321 votes',\n",
       "       '1050 votes', '2799 votes', '718 votes', '1531 votes', '604 votes',\n",
       "       '1681 votes', '365 votes', '2322 votes', '1088 votes', '610 votes',\n",
       "       '601 votes', '663 votes', '298 votes', '647 votes', '519 votes',\n",
       "       '660 votes', '561 votes', '391 votes', '510 votes', '1529 votes',\n",
       "       '313 votes', '803 votes', '1039 votes', '999 votes', '1299 votes',\n",
       "       '976 votes', '246 votes', '179 votes', '346 votes', '982 votes',\n",
       "       '327 votes', '367 votes', '716 votes', '329 votes', '271 votes',\n",
       "       '437 votes', '2094 votes', '306 votes', '592 votes', '297 votes',\n",
       "       '731 votes', '256 votes', '1104 votes', '496 votes', '644 votes',\n",
       "       '841 votes', '498 votes', '220 votes', '292 votes', '582 votes',\n",
       "       '768 votes', '527 votes', '209 votes', '356 votes', '363 votes',\n",
       "       '378 votes', '374 votes', '635 votes', '383 votes', '621 votes',\n",
       "       '693 votes', '396 votes', '320 votes', '221 votes', '823 votes',\n",
       "       '752 votes', '443 votes', '419 votes', '600 votes', '434 votes',\n",
       "       '442 votes', '608 votes', '334 votes', '554 votes', '392 votes',\n",
       "       '165 votes', '315 votes', '210 votes', '426 votes', '252 votes',\n",
       "       '369 votes', '389 votes', '1774 votes', '302 votes', '300 votes',\n",
       "       '474 votes', '338 votes', '342 votes', '499 votes', '387 votes',\n",
       "       '1401 votes', '900 votes', '400 votes', '892 votes', '571 votes'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data['voteCount'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9840f947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name         0\n",
       "voteCount    0\n",
       "rating       0\n",
       "address      0\n",
       "cusine       0\n",
       "cost         0\n",
       "timing       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate = Data[Data.duplicated()]\n",
    "duplicate.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "781a669f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7388 entries, 0 to 7387\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   name       7388 non-null   object \n",
      " 1   voteCount  7388 non-null   object \n",
      " 2   rating     7388 non-null   float64\n",
      " 3   address    7388 non-null   object \n",
      " 4   cusine     7388 non-null   object \n",
      " 5   cost       7388 non-null   float64\n",
      " 6   timing     7086 non-null   object \n",
      "dtypes: float64(2), object(5)\n",
      "memory usage: 404.2+ KB\n"
     ]
    }
   ],
   "source": [
    "Data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aa4e4aa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                                 Chinese\n",
       "1                                   North Indian, Chinese\n",
       "2                                                  Mishti\n",
       "3                                            North Indian\n",
       "4                                               Fast Food\n",
       "                              ...                        \n",
       "1092                      Cafe, Continental, Tea, Italian\n",
       "1093                              Rolls, Bengali, Chinese\n",
       "1094                         Chinese, North Indian, Kebab\n",
       "1095                   Momos, Sandwich, Burger, Fast Food\n",
       "1096    Healthy Food, Salad, Continental, North Indian...\n",
       "Name: cusine, Length: 1097, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurant_count=pd.DataFrame(Data['cusine'].value_counts().sort_values(ascending=False))\n",
    "restaurant_count.rename(columns = {'cusine':'Count'},inplace=True)\n",
    "restaurant_count.index.rename('cusine',inplace=True)\n",
    "restaurant_count.reset_index(inplace=True)\n",
    "restaurant_count['cusine']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0a46dff5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cusine</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chinese</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>North Indian, Chinese</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mishti</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North Indian</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fast Food</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  cusine  Count\n",
       "0                Chinese    346\n",
       "1  North Indian, Chinese    332\n",
       "2                 Mishti    320\n",
       "3           North Indian    298\n",
       "4              Fast Food    294"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurant_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8b93bea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "split_data = restaurant_count['cusine'].str.split(\",\")\n",
    "data = split_data.to_list()\n",
    "names = ['cusine1','cusine2','cusine3','cusine4','cusine5','cusine6','cusine7','cusine8']\n",
    "restaurant_count = pd.DataFrame(data, columns=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d4e39a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chinese</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fast Food</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cafe</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Biryani</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Cuisines  Count\n",
       "0  North Indian    207\n",
       "1       Chinese    200\n",
       "2     Fast Food     87\n",
       "3          Cafe     74\n",
       "4       Biryani     63"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_cuisines = restaurant_count['cusine1'].value_counts()\n",
    "unique_cuisines\n",
    "for col in names[:1]:\n",
    "    unique_cuisines = unique_cuisines.append(restaurant_count[col].value_counts().sort_values(ascending=False))\n",
    "unique_cuisines = pd.DataFrame(unique_cuisines)\n",
    "unique_cuisines.rename(columns = {'cusine1':'Count'},inplace=True)\n",
    "unique_cuisines.index.rename('Cuisines',inplace=True)\n",
    "unique_cuisines.reset_index(inplace=True)\n",
    "unique_cuisines.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7f4fd2d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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",
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       "      <td>Chinese</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>Fast Food</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cafe</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Biryani</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Cuisines  Count\n",
       "0  North Indian    207\n",
       "1       Chinese    200\n",
       "2     Fast Food     87\n",
       "3          Cafe     74\n",
       "4       Biryani     63"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_cuisines.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7bbcc907",
   "metadata": {},
   "outputs": [],
   "source": [
    "popular_cuisines = unique_cuisines[unique_cuisines['Count']>50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4547912b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>Chinese</td>\n",
       "      <td>200</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Biryani</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bengali</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>North Indian</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Chinese</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Fast Food</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Cafe</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Biryani</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Bengali</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Cuisines  Count\n",
       "0   North Indian    207\n",
       "1        Chinese    200\n",
       "2      Fast Food     87\n",
       "3           Cafe     74\n",
       "4        Biryani     63\n",
       "5        Bengali     55\n",
       "58  North Indian    207\n",
       "59       Chinese    200\n",
       "60     Fast Food     87\n",
       "61          Cafe     74\n",
       "62       Biryani     63\n",
       "63       Bengali     55"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popular_cuisines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f322dc41",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data['voteCount'].replace('Cuisines:','0',inplace=True)\n",
    "Data['voteCount']= Data['voteCount'].apply(lambda x: x.split(' ')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f30d3825",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>voteCount</th>\n",
       "      <th>rating</th>\n",
       "      <th>address</th>\n",
       "      <th>cusine</th>\n",
       "      <th>cost</th>\n",
       "      <th>timing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Peter Cat</td>\n",
       "      <td>12404</td>\n",
       "      <td>4.2</td>\n",
       "      <td>18A, Park Street, Park Street Area, Kolkata</td>\n",
       "      <td>North Indian, Continental</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>11am – 11:20pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Naturals Ice Cream</td>\n",
       "      <td>2498</td>\n",
       "      <td>4.9</td>\n",
       "      <td>77/1/A, Ground Floor, Near West Bengal Board, ...</td>\n",
       "      <td>North Indian, Continental</td>\n",
       "      <td>200.0</td>\n",
       "      <td>11am – 12midnight (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carpe Diem</td>\n",
       "      <td>4083</td>\n",
       "      <td>4.4</td>\n",
       "      <td>18M, Park Street Area, Kolkata</td>\n",
       "      <td>Ice Cream</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>12noon – 12midnight (Mon, Tue, Wed, Thu, Fri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barbeque Nation</td>\n",
       "      <td>5455</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1st Floor, 24, Park Center Building, Park Stre...</td>\n",
       "      <td>Ice Cream</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>12noon – 3:30pm, 6:30pm – 10:45pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Flurys</td>\n",
       "      <td>4709</td>\n",
       "      <td>4.2</td>\n",
       "      <td>15, Apeejay House, Park Street Area, Kolkata</td>\n",
       "      <td>Chinese, North Indian, Continental</td>\n",
       "      <td>400.0</td>\n",
       "      <td>7:30am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name voteCount  rating  \\\n",
       "0           Peter Cat     12404     4.2   \n",
       "1  Naturals Ice Cream      2498     4.9   \n",
       "2          Carpe Diem      4083     4.4   \n",
       "3     Barbeque Nation      5455     4.6   \n",
       "4              Flurys      4709     4.2   \n",
       "\n",
       "                                             address  \\\n",
       "0        18A, Park Street, Park Street Area, Kolkata   \n",
       "1  77/1/A, Ground Floor, Near West Bengal Board, ...   \n",
       "2                     18M, Park Street Area, Kolkata   \n",
       "3  1st Floor, 24, Park Center Building, Park Stre...   \n",
       "4       15, Apeejay House, Park Street Area, Kolkata   \n",
       "\n",
       "                               cusine    cost  \\\n",
       "0           North Indian, Continental  1000.0   \n",
       "1           North Indian, Continental   200.0   \n",
       "2                           Ice Cream  1000.0   \n",
       "3                           Ice Cream  1800.0   \n",
       "4  Chinese, North Indian, Continental   400.0   \n",
       "\n",
       "                                            timing  \n",
       "0                         11am – 11:20pm (Mon-Sun)  \n",
       "1                      11am – 12midnight (Mon-Sun)  \n",
       "2  12noon – 12midnight (Mon, Tue, Wed, Thu, Fri...  \n",
       "3      12noon – 3:30pm, 6:30pm – 10:45pm (Mon-Sun)  \n",
       "4                          7:30am – 11pm (Mon-Sun)  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cf1d07fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7388 entries, 0 to 7387\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   name       7388 non-null   object \n",
      " 1   voteCount  7388 non-null   object \n",
      " 2   rating     7388 non-null   float64\n",
      " 3   address    7388 non-null   object \n",
      " 4   cusine     7388 non-null   object \n",
      " 5   cost       7388 non-null   float64\n",
      " 6   timing     7086 non-null   object \n",
      "dtypes: float64(2), object(5)\n",
      "memory usage: 404.2+ KB\n"
     ]
    }
   ],
   "source": [
    "Data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a9f46c6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name         0.00\n",
       "voteCount    0.00\n",
       "rating       0.00\n",
       "address      0.00\n",
       "cusine       0.00\n",
       "cost         0.00\n",
       "timing       4.09\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(100*(Data.isnull().sum()/len(Data.index)),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "38c51d12",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data['voteCount'] = pd.to_numeric(Data['voteCount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3aec5154",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7388 entries, 0 to 7387\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   name       7388 non-null   object \n",
      " 1   voteCount  7388 non-null   int64  \n",
      " 2   rating     7388 non-null   float64\n",
      " 3   address    7388 non-null   object \n",
      " 4   cusine     7388 non-null   object \n",
      " 5   cost       7388 non-null   float64\n",
      " 6   timing     7086 non-null   object \n",
      "dtypes: float64(2), int64(1), object(4)\n",
      "memory usage: 404.2+ KB\n"
     ]
    }
   ],
   "source": [
    "Data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e8f901bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7388.000000\n",
       "mean      373.263707\n",
       "std       296.051164\n",
       "min       100.000000\n",
       "25%       200.000000\n",
       "50%       300.000000\n",
       "75%       450.000000\n",
       "max      4500.000000\n",
       "Name: cost, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data['cost'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8786a6eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data['timing'] = Data['timing'].fillna(Data['timing'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3f395884",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name         0.0\n",
       "voteCount    0.0\n",
       "rating       0.0\n",
       "address      0.0\n",
       "cusine       0.0\n",
       "cost         0.0\n",
       "timing       0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(100*(Data.isnull().sum()/len(Data.index)),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d6c52ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7388 entries, 0 to 7387\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   name       7388 non-null   object \n",
      " 1   voteCount  7388 non-null   int64  \n",
      " 2   rating     7388 non-null   float64\n",
      " 3   address    7388 non-null   object \n",
      " 4   cusine     7388 non-null   object \n",
      " 5   cost       7388 non-null   float64\n",
      " 6   timing     7388 non-null   object \n",
      "dtypes: float64(2), int64(1), object(4)\n",
      "memory usage: 404.2+ KB\n"
     ]
    }
   ],
   "source": [
    "Data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8f99c9d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "high_end_restos = Data[Data['cost']>=450]\n",
    "cheap_restos = Data[Data['cost']<=200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "711eef8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>voteCount</th>\n",
       "      <th>rating</th>\n",
       "      <th>address</th>\n",
       "      <th>cusine</th>\n",
       "      <th>cost</th>\n",
       "      <th>timing</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Peter Cat</td>\n",
       "      <td>12404</td>\n",
       "      <td>4.2</td>\n",
       "      <td>18A, Park Street, Park Street Area, Kolkata</td>\n",
       "      <td>North Indian, Continental</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>11am – 11:20pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carpe Diem</td>\n",
       "      <td>4083</td>\n",
       "      <td>4.4</td>\n",
       "      <td>18M, Park Street Area, Kolkata</td>\n",
       "      <td>Ice Cream</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>12noon – 12midnight (Mon, Tue, Wed, Thu, Fri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barbeque Nation</td>\n",
       "      <td>5455</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1st Floor, 24, Park Center Building, Park Stre...</td>\n",
       "      <td>Ice Cream</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>12noon – 3:30pm, 6:30pm – 10:45pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KFC</td>\n",
       "      <td>2135</td>\n",
       "      <td>4.1</td>\n",
       "      <td>20K, Park Street, Park Street Area, Kolkata</td>\n",
       "      <td>Chinese, North Indian, Continental</td>\n",
       "      <td>450.0</td>\n",
       "      <td>11am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tung Fong</td>\n",
       "      <td>3644</td>\n",
       "      <td>4.3</td>\n",
       "      <td>25 B, Ground Floor, Karnani Mansion, Free Scho...</td>\n",
       "      <td>North Indian, Chinese, Kebab, BBQ</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>12noon – 4pm, 6pm – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7345</th>\n",
       "      <td>Jyotirbina Hotel And Restaurant</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Delivery Only</td>\n",
       "      <td>North Indian</td>\n",
       "      <td>500.0</td>\n",
       "      <td>10am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7346</th>\n",
       "      <td>Cafe Soy</td>\n",
       "      <td>0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Shop 31, C Block, Downtown Mall, Uniworld City...</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>800.0</td>\n",
       "      <td>11am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7368</th>\n",
       "      <td>Michel Nagar Dhaba</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>National Highway 34, Madhyamgram Municipality ...</td>\n",
       "      <td>Sandwich, Fast Food</td>\n",
       "      <td>450.0</td>\n",
       "      <td>24 Hours (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7370</th>\n",
       "      <td>Perfect 10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21D, Anupama Phase 1, Arjunpur, Kaikhali, Kolkata</td>\n",
       "      <td>South Indian</td>\n",
       "      <td>700.0</td>\n",
       "      <td>11am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7373</th>\n",
       "      <td>Rasana Tripti</td>\n",
       "      <td>0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12, Mahesh Colony, Nistarini Super Market, She...</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>500.0</td>\n",
       "      <td>11am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1896 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 name  voteCount  rating  \\\n",
       "0                           Peter Cat      12404     4.2   \n",
       "2                          Carpe Diem       4083     4.4   \n",
       "3                     Barbeque Nation       5455     4.6   \n",
       "5                                 KFC       2135     4.1   \n",
       "6                           Tung Fong       3644     4.3   \n",
       "...                               ...        ...     ...   \n",
       "7345  Jyotirbina Hotel And Restaurant          0     0.0   \n",
       "7346                         Cafe Soy          0     3.2   \n",
       "7368               Michel Nagar Dhaba          0     0.0   \n",
       "7370                       Perfect 10          0     0.0   \n",
       "7373                    Rasana Tripti          0     3.2   \n",
       "\n",
       "                                                address  \\\n",
       "0           18A, Park Street, Park Street Area, Kolkata   \n",
       "2                        18M, Park Street Area, Kolkata   \n",
       "3     1st Floor, 24, Park Center Building, Park Stre...   \n",
       "5           20K, Park Street, Park Street Area, Kolkata   \n",
       "6     25 B, Ground Floor, Karnani Mansion, Free Scho...   \n",
       "...                                                 ...   \n",
       "7345                                      Delivery Only   \n",
       "7346  Shop 31, C Block, Downtown Mall, Uniworld City...   \n",
       "7368  National Highway 34, Madhyamgram Municipality ...   \n",
       "7370  21D, Anupama Phase 1, Arjunpur, Kaikhali, Kolkata   \n",
       "7373  12, Mahesh Colony, Nistarini Super Market, She...   \n",
       "\n",
       "                                  cusine    cost  \\\n",
       "0              North Indian, Continental  1000.0   \n",
       "2                              Ice Cream  1000.0   \n",
       "3                              Ice Cream  1800.0   \n",
       "5     Chinese, North Indian, Continental   450.0   \n",
       "6      North Indian, Chinese, Kebab, BBQ  1000.0   \n",
       "...                                  ...     ...   \n",
       "7345                        North Indian   500.0   \n",
       "7346                           Fast Food   800.0   \n",
       "7368                 Sandwich, Fast Food   450.0   \n",
       "7370                        South Indian   700.0   \n",
       "7373                           Fast Food   500.0   \n",
       "\n",
       "                                               timing  \n",
       "0                            11am – 11:20pm (Mon-Sun)  \n",
       "2     12noon – 12midnight (Mon, Tue, Wed, Thu, Fri...  \n",
       "3         12noon – 3:30pm, 6:30pm – 10:45pm (Mon-Sun)  \n",
       "5                               11am – 11pm (Mon-Sun)  \n",
       "6                  12noon – 4pm, 6pm – 11pm (Mon-Sun)  \n",
       "...                                               ...  \n",
       "7345                            10am – 11pm (Mon-Sun)  \n",
       "7346                            11am – 11pm (Mon-Sun)  \n",
       "7368                               24 Hours (Mon-Sun)  \n",
       "7370                            11am – 11pm (Mon-Sun)  \n",
       "7373                            11am – 11pm (Mon-Sun)  \n",
       "\n",
       "[1896 rows x 7 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_end_restos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d0f0a08c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>voteCount</th>\n",
       "      <th>rating</th>\n",
       "      <th>address</th>\n",
       "      <th>cusine</th>\n",
       "      <th>cost</th>\n",
       "      <th>timing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Naturals Ice Cream</td>\n",
       "      <td>2498</td>\n",
       "      <td>4.9</td>\n",
       "      <td>77/1/A, Ground Floor, Near West Bengal Board, ...</td>\n",
       "      <td>North Indian, Continental</td>\n",
       "      <td>200.0</td>\n",
       "      <td>11am – 12midnight (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carpe Diem</td>\n",
       "      <td>4083</td>\n",
       "      <td>4.4</td>\n",
       "      <td>18M, Park Street Area, Kolkata</td>\n",
       "      <td>Ice Cream</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>12noon – 12midnight (Mon, Tue, Wed, Thu, Fri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>The Sixth Sense</td>\n",
       "      <td>92</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25B, Karnani Mansion, Opposite Westside, Park ...</td>\n",
       "      <td>Mughlai, North Indian, Rolls, Biryani</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>12noon – 12midnight (Mon-Thu),12noon – 2am...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NFC Food Funda</td>\n",
       "      <td>71</td>\n",
       "      <td>3.9</td>\n",
       "      <td>48, AJC Bose Road, Near Mother Teresa House, P...</td>\n",
       "      <td>Chinese, North Indian, Asian</td>\n",
       "      <td>200.0</td>\n",
       "      <td>12:30pm – 12midnight (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Eaterniaa</td>\n",
       "      <td>505</td>\n",
       "      <td>4.1</td>\n",
       "      <td>111, Ripon Street, Near Ripon Street Police Mu...</td>\n",
       "      <td>Kebab</td>\n",
       "      <td>500.0</td>\n",
       "      <td>11:30am – 12midnight (Mon-Sun)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name  voteCount  rating  \\\n",
       "1   Naturals Ice Cream       2498     4.9   \n",
       "2           Carpe Diem       4083     4.4   \n",
       "15     The Sixth Sense         92     4.0   \n",
       "24      NFC Food Funda         71     3.9   \n",
       "26           Eaterniaa        505     4.1   \n",
       "\n",
       "                                              address  \\\n",
       "1   77/1/A, Ground Floor, Near West Bengal Board, ...   \n",
       "2                      18M, Park Street Area, Kolkata   \n",
       "15  25B, Karnani Mansion, Opposite Westside, Park ...   \n",
       "24  48, AJC Bose Road, Near Mother Teresa House, P...   \n",
       "26  111, Ripon Street, Near Ripon Street Police Mu...   \n",
       "\n",
       "                                   cusine    cost  \\\n",
       "1               North Indian, Continental   200.0   \n",
       "2                               Ice Cream  1000.0   \n",
       "15  Mughlai, North Indian, Rolls, Biryani  1500.0   \n",
       "24           Chinese, North Indian, Asian   200.0   \n",
       "26                                  Kebab   500.0   \n",
       "\n",
       "                                             timing  \n",
       "1                       11am – 12midnight (Mon-Sun)  \n",
       "2   12noon – 12midnight (Mon, Tue, Wed, Thu, Fri...  \n",
       "15    12noon – 12midnight (Mon-Thu),12noon – 2am...  \n",
       "24                   12:30pm – 12midnight (Mon-Sun)  \n",
       "26                   11:30am – 12midnight (Mon-Sun)  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midnight_restos = Data[Data['timing'].str.contains(\"midnight\")]\n",
    "midnight_restos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "de8620b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>voteCount</th>\n",
       "      <th>rating</th>\n",
       "      <th>address</th>\n",
       "      <th>cusine</th>\n",
       "      <th>cost</th>\n",
       "      <th>timing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Naturals Ice Cream</td>\n",
       "      <td>2498</td>\n",
       "      <td>4.9</td>\n",
       "      <td>77/1/A, Ground Floor, Near West Bengal Board, ...</td>\n",
       "      <td>North Indian, Continental</td>\n",
       "      <td>200.0</td>\n",
       "      <td>11am – 12midnight (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carpe Diem</td>\n",
       "      <td>4083</td>\n",
       "      <td>4.4</td>\n",
       "      <td>18M, Park Street Area, Kolkata</td>\n",
       "      <td>Ice Cream</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>12noon – 12midnight (Mon, Tue, Wed, Thu, Fri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>The Sixth Sense</td>\n",
       "      <td>92</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25B, Karnani Mansion, Opposite Westside, Park ...</td>\n",
       "      <td>Mughlai, North Indian, Rolls, Biryani</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>12noon – 12midnight (Mon-Thu),12noon – 2am...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NFC Food Funda</td>\n",
       "      <td>71</td>\n",
       "      <td>3.9</td>\n",
       "      <td>48, AJC Bose Road, Near Mother Teresa House, P...</td>\n",
       "      <td>Chinese, North Indian, Asian</td>\n",
       "      <td>200.0</td>\n",
       "      <td>12:30pm – 12midnight (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Eaterniaa</td>\n",
       "      <td>505</td>\n",
       "      <td>4.1</td>\n",
       "      <td>111, Ripon Street, Near Ripon Street Police Mu...</td>\n",
       "      <td>Kebab</td>\n",
       "      <td>500.0</td>\n",
       "      <td>11:30am – 12midnight (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7329</th>\n",
       "      <td>Yummy Multicuisine Restaurant</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19, Purbayan, Rajarhat, Near Derozio Memorial,...</td>\n",
       "      <td>Momos, Chinese</td>\n",
       "      <td>400.0</td>\n",
       "      <td>11am – 12midnight (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7335</th>\n",
       "      <td>Afghan Biryani</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Delivery Only</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>250.0</td>\n",
       "      <td>11am – 12midnight (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7336</th>\n",
       "      <td>Tiffin Express</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Delivery Only</td>\n",
       "      <td>Chinese, North Indian, Fast Food</td>\n",
       "      <td>200.0</td>\n",
       "      <td>8am – 12midnight (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7342</th>\n",
       "      <td>Wow Now</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Delivery Only</td>\n",
       "      <td>Biryani</td>\n",
       "      <td>200.0</td>\n",
       "      <td>10am – 12midnight (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7360</th>\n",
       "      <td>Super Sunday Suppers</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Christian Para, Kestopur, Kolkata</td>\n",
       "      <td>Chinese</td>\n",
       "      <td>400.0</td>\n",
       "      <td>10am – 12midnight (Mon-Sun)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>670 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               name  voteCount  rating  \\\n",
       "1                Naturals Ice Cream       2498     4.9   \n",
       "2                        Carpe Diem       4083     4.4   \n",
       "15                  The Sixth Sense         92     4.0   \n",
       "24                   NFC Food Funda         71     3.9   \n",
       "26                        Eaterniaa        505     4.1   \n",
       "...                             ...        ...     ...   \n",
       "7329  Yummy Multicuisine Restaurant          0     0.0   \n",
       "7335                 Afghan Biryani          0     0.0   \n",
       "7336                 Tiffin Express          0     0.0   \n",
       "7342                        Wow Now          0     0.0   \n",
       "7360           Super Sunday Suppers          0     0.0   \n",
       "\n",
       "                                                address  \\\n",
       "1     77/1/A, Ground Floor, Near West Bengal Board, ...   \n",
       "2                        18M, Park Street Area, Kolkata   \n",
       "15    25B, Karnani Mansion, Opposite Westside, Park ...   \n",
       "24    48, AJC Bose Road, Near Mother Teresa House, P...   \n",
       "26    111, Ripon Street, Near Ripon Street Police Mu...   \n",
       "...                                                 ...   \n",
       "7329  19, Purbayan, Rajarhat, Near Derozio Memorial,...   \n",
       "7335                                      Delivery Only   \n",
       "7336                                      Delivery Only   \n",
       "7342                                      Delivery Only   \n",
       "7360                  Christian Para, Kestopur, Kolkata   \n",
       "\n",
       "                                     cusine    cost  \\\n",
       "1                 North Indian, Continental   200.0   \n",
       "2                                 Ice Cream  1000.0   \n",
       "15    Mughlai, North Indian, Rolls, Biryani  1500.0   \n",
       "24             Chinese, North Indian, Asian   200.0   \n",
       "26                                    Kebab   500.0   \n",
       "...                                     ...     ...   \n",
       "7329                         Momos, Chinese   400.0   \n",
       "7335                              Fast Food   250.0   \n",
       "7336       Chinese, North Indian, Fast Food   200.0   \n",
       "7342                                Biryani   200.0   \n",
       "7360                                Chinese   400.0   \n",
       "\n",
       "                                               timing  \n",
       "1                         11am – 12midnight (Mon-Sun)  \n",
       "2     12noon – 12midnight (Mon, Tue, Wed, Thu, Fri...  \n",
       "15      12noon – 12midnight (Mon-Thu),12noon – 2am...  \n",
       "24                     12:30pm – 12midnight (Mon-Sun)  \n",
       "26                     11:30am – 12midnight (Mon-Sun)  \n",
       "...                                               ...  \n",
       "7329                      11am – 12midnight (Mon-Sun)  \n",
       "7335                      11am – 12midnight (Mon-Sun)  \n",
       "7336                       8am – 12midnight (Mon-Sun)  \n",
       "7342                      10am – 12midnight (Mon-Sun)  \n",
       "7360                      10am – 12midnight (Mon-Sun)  \n",
       "\n",
       "[670 rows x 7 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midnight_restos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8f683ae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>voteCount</th>\n",
       "      <th>rating</th>\n",
       "      <th>address</th>\n",
       "      <th>cusine</th>\n",
       "      <th>cost</th>\n",
       "      <th>timing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Peter Cat</td>\n",
       "      <td>12404</td>\n",
       "      <td>4.2</td>\n",
       "      <td>18A, Park Street, Park Street Area, Kolkata</td>\n",
       "      <td>North Indian, Continental</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>11am – 11:20pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barbeque Nation</td>\n",
       "      <td>5455</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1st Floor, 24, Park Center Building, Park Stre...</td>\n",
       "      <td>Ice Cream</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>12noon – 3:30pm, 6:30pm – 10:45pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Flurys</td>\n",
       "      <td>4709</td>\n",
       "      <td>4.2</td>\n",
       "      <td>15, Apeejay House, Park Street Area, Kolkata</td>\n",
       "      <td>Chinese, North Indian, Continental</td>\n",
       "      <td>400.0</td>\n",
       "      <td>7:30am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KFC</td>\n",
       "      <td>2135</td>\n",
       "      <td>4.1</td>\n",
       "      <td>20K, Park Street, Park Street Area, Kolkata</td>\n",
       "      <td>Chinese, North Indian, Continental</td>\n",
       "      <td>450.0</td>\n",
       "      <td>11am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tung Fong</td>\n",
       "      <td>3644</td>\n",
       "      <td>4.3</td>\n",
       "      <td>25 B, Ground Floor, Karnani Mansion, Free Scho...</td>\n",
       "      <td>North Indian, Chinese, Kebab, BBQ</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>12noon – 4pm, 6pm – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7383</th>\n",
       "      <td>WOW! ARBA</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Delivery Only</td>\n",
       "      <td>Chinese, Fast Food</td>\n",
       "      <td>150.0</td>\n",
       "      <td>11am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7384</th>\n",
       "      <td>Renu's</td>\n",
       "      <td>50</td>\n",
       "      <td>2.8</td>\n",
       "      <td>89/109, Vivekananda Road, Bangur Park, Hooghly...</td>\n",
       "      <td>Bengali</td>\n",
       "      <td>350.0</td>\n",
       "      <td>11am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7385</th>\n",
       "      <td>The Food Place</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>119, Ramkrishna Road, Radhashree, Landmark- Ai...</td>\n",
       "      <td>Bengali</td>\n",
       "      <td>150.0</td>\n",
       "      <td>8am – 11:45pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7386</th>\n",
       "      <td>New Dalma Restaurant</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Delivery Only</td>\n",
       "      <td>Chinese</td>\n",
       "      <td>250.0</td>\n",
       "      <td>12noon – 10:30pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7387</th>\n",
       "      <td>Baanerjee Cabin</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89, Nripen Sarkar Road, Sahapukur, Sreedurga C...</td>\n",
       "      <td>Chinese</td>\n",
       "      <td>200.0</td>\n",
       "      <td>10am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6718 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      name  voteCount  rating  \\\n",
       "0                Peter Cat      12404     4.2   \n",
       "3          Barbeque Nation       5455     4.6   \n",
       "4                   Flurys       4709     4.2   \n",
       "5                      KFC       2135     4.1   \n",
       "6                Tung Fong       3644     4.3   \n",
       "...                    ...        ...     ...   \n",
       "7383             WOW! ARBA          0     0.0   \n",
       "7384                Renu's         50     2.8   \n",
       "7385        The Food Place          0     0.0   \n",
       "7386  New Dalma Restaurant          0     0.0   \n",
       "7387       Baanerjee Cabin          0     0.0   \n",
       "\n",
       "                                                address  \\\n",
       "0           18A, Park Street, Park Street Area, Kolkata   \n",
       "3     1st Floor, 24, Park Center Building, Park Stre...   \n",
       "4          15, Apeejay House, Park Street Area, Kolkata   \n",
       "5           20K, Park Street, Park Street Area, Kolkata   \n",
       "6     25 B, Ground Floor, Karnani Mansion, Free Scho...   \n",
       "...                                                 ...   \n",
       "7383                                      Delivery Only   \n",
       "7384  89/109, Vivekananda Road, Bangur Park, Hooghly...   \n",
       "7385  119, Ramkrishna Road, Radhashree, Landmark- Ai...   \n",
       "7386                                      Delivery Only   \n",
       "7387  89, Nripen Sarkar Road, Sahapukur, Sreedurga C...   \n",
       "\n",
       "                                  cusine    cost  \\\n",
       "0              North Indian, Continental  1000.0   \n",
       "3                              Ice Cream  1800.0   \n",
       "4     Chinese, North Indian, Continental   400.0   \n",
       "5     Chinese, North Indian, Continental   450.0   \n",
       "6      North Indian, Chinese, Kebab, BBQ  1000.0   \n",
       "...                                  ...     ...   \n",
       "7383                  Chinese, Fast Food   150.0   \n",
       "7384                             Bengali   350.0   \n",
       "7385                             Bengali   150.0   \n",
       "7386                             Chinese   250.0   \n",
       "7387                             Chinese   200.0   \n",
       "\n",
       "                                           timing  \n",
       "0                        11am – 11:20pm (Mon-Sun)  \n",
       "3     12noon – 3:30pm, 6:30pm – 10:45pm (Mon-Sun)  \n",
       "4                         7:30am – 11pm (Mon-Sun)  \n",
       "5                           11am – 11pm (Mon-Sun)  \n",
       "6              12noon – 4pm, 6pm – 11pm (Mon-Sun)  \n",
       "...                                           ...  \n",
       "7383                        11am – 11pm (Mon-Sun)  \n",
       "7384                        11am – 11pm (Mon-Sun)  \n",
       "7385                      8am – 11:45pm (Mon-Sun)  \n",
       "7386                   12noon – 10:30pm (Mon-Sun)  \n",
       "7387                        10am – 11pm (Mon-Sun)  \n",
       "\n",
       "[6718 rows x 7 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genral_timed_resto = Data[Data['timing'].str.contains(\"midnight\")== False]\n",
    "genral_timed_resto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d00d35a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>voteCount</th>\n",
       "      <th>rating</th>\n",
       "      <th>address</th>\n",
       "      <th>cusine</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Naturals Ice Cream</td>\n",
       "      <td>2498</td>\n",
       "      <td>4.9</td>\n",
       "      <td>77/1/A, Ground Floor, Near West Bengal Board, ...</td>\n",
       "      <td>North Indian, Continental</td>\n",
       "      <td>200.0</td>\n",
       "      <td>11am – 12midnight (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NFC Food Funda</td>\n",
       "      <td>71</td>\n",
       "      <td>3.9</td>\n",
       "      <td>48, AJC Bose Road, Near Mother Teresa House, P...</td>\n",
       "      <td>Chinese, North Indian, Asian</td>\n",
       "      <td>200.0</td>\n",
       "      <td>12:30pm – 12midnight (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Hot Kati Roll</td>\n",
       "      <td>816</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1B, Park Estate, Park Street Area, Kolkata</td>\n",
       "      <td>Bakery, Desserts</td>\n",
       "      <td>200.0</td>\n",
       "      <td>11am – 10pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Crazy 4 Cakes</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7/1, Russel Street, Park Street Area, Kolkata</td>\n",
       "      <td>Pizza, Fast Food</td>\n",
       "      <td>200.0</td>\n",
       "      <td>10:30am – 9:30pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>UBQ by Barbeque Nation</td>\n",
       "      <td>67</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1st Floor, Park Center, Building 24, Park Stre...</td>\n",
       "      <td>Biryani, North Indian, Mughlai</td>\n",
       "      <td>200.0</td>\n",
       "      <td>12noon – 3pm, 6:30pm – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7377</th>\n",
       "      <td>Wow! Arba</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Delivery Only</td>\n",
       "      <td>Mishti</td>\n",
       "      <td>200.0</td>\n",
       "      <td>11am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7378</th>\n",
       "      <td>SayShawarma</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ST.7 Bus Stand, Shapoorji Apartments, New Town...</td>\n",
       "      <td>Mithai</td>\n",
       "      <td>160.0</td>\n",
       "      <td>5pm – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7383</th>\n",
       "      <td>WOW! ARBA</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Delivery Only</td>\n",
       "      <td>Chinese, Fast Food</td>\n",
       "      <td>150.0</td>\n",
       "      <td>11am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7385</th>\n",
       "      <td>The Food Place</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>119, Ramkrishna Road, Radhashree, Landmark- Ai...</td>\n",
       "      <td>Bengali</td>\n",
       "      <td>150.0</td>\n",
       "      <td>8am – 11:45pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7387</th>\n",
       "      <td>Baanerjee Cabin</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89, Nripen Sarkar Road, Sahapukur, Sreedurga C...</td>\n",
       "      <td>Chinese</td>\n",
       "      <td>200.0</td>\n",
       "      <td>10am – 11pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2193 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name  voteCount  rating  \\\n",
       "1         Naturals Ice Cream       2498     4.9   \n",
       "24            NFC Food Funda         71     3.9   \n",
       "28             Hot Kati Roll        816     3.8   \n",
       "61             Crazy 4 Cakes          0     0.0   \n",
       "66    UBQ by Barbeque Nation         67     3.5   \n",
       "...                      ...        ...     ...   \n",
       "7377               Wow! Arba          0     0.0   \n",
       "7378             SayShawarma          0     0.0   \n",
       "7383               WOW! ARBA          0     0.0   \n",
       "7385          The Food Place          0     0.0   \n",
       "7387         Baanerjee Cabin          0     0.0   \n",
       "\n",
       "                                                address  \\\n",
       "1     77/1/A, Ground Floor, Near West Bengal Board, ...   \n",
       "24    48, AJC Bose Road, Near Mother Teresa House, P...   \n",
       "28           1B, Park Estate, Park Street Area, Kolkata   \n",
       "61        7/1, Russel Street, Park Street Area, Kolkata   \n",
       "66    1st Floor, Park Center, Building 24, Park Stre...   \n",
       "...                                                 ...   \n",
       "7377                                      Delivery Only   \n",
       "7378  ST.7 Bus Stand, Shapoorji Apartments, New Town...   \n",
       "7383                                      Delivery Only   \n",
       "7385  119, Ramkrishna Road, Radhashree, Landmark- Ai...   \n",
       "7387  89, Nripen Sarkar Road, Sahapukur, Sreedurga C...   \n",
       "\n",
       "                              cusine   cost  \\\n",
       "1          North Indian, Continental  200.0   \n",
       "24      Chinese, North Indian, Asian  200.0   \n",
       "28                  Bakery, Desserts  200.0   \n",
       "61                  Pizza, Fast Food  200.0   \n",
       "66    Biryani, North Indian, Mughlai  200.0   \n",
       "...                              ...    ...   \n",
       "7377                          Mishti  200.0   \n",
       "7378                          Mithai  160.0   \n",
       "7383              Chinese, Fast Food  150.0   \n",
       "7385                         Bengali  150.0   \n",
       "7387                         Chinese  200.0   \n",
       "\n",
       "                                     timing  \n",
       "1               11am – 12midnight (Mon-Sun)  \n",
       "24           12:30pm – 12midnight (Mon-Sun)  \n",
       "28                    11am – 10pm (Mon-Sun)  \n",
       "61               10:30am – 9:30pm (Mon-Sun)  \n",
       "66    12noon – 3pm, 6:30pm – 11pm (Mon-Sun)  \n",
       "...                                     ...  \n",
       "7377                  11am – 11pm (Mon-Sun)  \n",
       "7378                   5pm – 11pm (Mon-Sun)  \n",
       "7383                  11am – 11pm (Mon-Sun)  \n",
       "7385                8am – 11:45pm (Mon-Sun)  \n",
       "7387                  10am – 11pm (Mon-Sun)  \n",
       "\n",
       "[2193 rows x 7 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cheap_restos "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f0bff555",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(Data.corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a0e9ac9",
   "metadata": {},
   "source": [
    "#### The co relation shows no significant connection between rating , cost and votecount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9607acb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\joy\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(popular_cuisines['Cuisines'],popular_cuisines['Count'])\n",
    "plt.rcParams['figure.figsize']=(8,5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "809d96ff",
   "metadata": {},
   "source": [
    "#### As we can see from the plotting , North Indian and chinese cuisines are ruling the bengal market and bengali foods are last in the list for obvius reasons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d1c4e93e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\joy\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(Data['voteCount'],Data['rating'])\n",
    "plt.rcParams['figure.figsize']=(15,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ab4fc245",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\joy\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(Data['cost'],Data['rating'])\n",
    "plt.rcParams['figure.figsize']=(15,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "6a0b60a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\joy\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(Data['cost'],Data['voteCount'])\n",
    "plt.rcParams['figure.figsize']=(15,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f60c3903",
   "metadata": {},
   "source": [
    "#### The three scatterplots above clearly proves three things\n",
    "##### The highest rated restaurant need to be the costliest.\n",
    "##### The costliest restaurant doesn't necessarily promote themselves in zomato with paid votes. rather small restaurants does.\n",
    "##### restaurants which got highest number of votes generally ar mid tier / cheap restaurants with a few exceptions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "e35eea15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7388.000000\n",
       "mean        2.507932\n",
       "std         1.517401\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         3.200000\n",
       "75%         3.500000\n",
       "max         4.900000\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data['rating'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "251733ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     7388.000000\n",
       "mean       109.419058\n",
       "std        372.898968\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%         14.000000\n",
       "75%         64.000000\n",
       "max      12404.000000\n",
       "Name: voteCount, dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data['voteCount'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "cf07f22c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1789029535864977"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_end_restos['rating'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "c676378b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\joy\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(high_end_restos['cost'],high_end_restos['rating'])\n",
    "plt.rcParams['figure.figsize']=(8,5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "03190e70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1180574555403555"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cheap_restos['rating'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "dbe46624",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\joy\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(cheap_restos['rating'],cheap_restos['cost'])\n",
    "plt.rcParams['figure.figsize']=(8,5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "6343596a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "287.6645569620253"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_end_restos['voteCount'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "48b8145b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\joy\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(high_end_restos['voteCount'],high_end_restos['rating'])\n",
    "plt.rcParams['figure.figsize']=(8,5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d92413b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.6046511627907"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cheap_restos['voteCount'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "1817e302",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\joy\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(cheap_restos['voteCount'],cheap_restos['rating'])\n",
    "plt.rcParams['figure.figsize']=(8,5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "12423e0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.6646268656716416"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midnight_restos['rating'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "9fc2c901",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\joy\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(midnight_restos['cost'],midnight_restos['rating'])\n",
    "plt.rcParams['figure.figsize']=(8,5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c694c138",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128.40597014925373"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midnight_restos['voteCount'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "4e467d8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.4923042572194105"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genral_timed_resto['rating'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "6a84fd9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "107.52545400416791"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genral_timed_resto['voteCount'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58918811",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfcd973a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}