Geolocational.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aayushsss1/Exploratory-Analysis-of-Geolocational-Data/blob/main/Geolocational.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8fzIX-laJQpR"
      },
      "source": [
        "#Exploratory Analysis of Geolocational Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oxHvohXhT-se"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wsxikXvSBrrM"
      },
      "outputs": [],
      "source": [
        "import geopy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "RmWmOBTGUk-u",
        "outputId": "325d4c1b-2806-4c64-ce0c-3584b5d51f87"
      },
      "outputs": [
        {
          "data": {
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GPA</th>\n",
              "      <th>Gender</th>\n",
              "      <th>breakfast</th>\n",
              "      <th>calories_chicken</th>\n",
              "      <th>calories_day</th>\n",
              "      <th>calories_scone</th>\n",
              "      <th>coffee</th>\n",
              "      <th>comfort_food</th>\n",
              "      <th>comfort_food_reasons</th>\n",
              "      <th>comfort_food_reasons_coded</th>\n",
              "      <th>cook</th>\n",
              "      <th>comfort_food_reasons_coded.1</th>\n",
              "      <th>cuisine</th>\n",
              "      <th>diet_current</th>\n",
              "      <th>diet_current_coded</th>\n",
              "      <th>drink</th>\n",
              "      <th>eating_changes</th>\n",
              "      <th>eating_changes_coded</th>\n",
              "      <th>eating_changes_coded1</th>\n",
              "      <th>eating_out</th>\n",
              "      <th>employment</th>\n",
              "      <th>ethnic_food</th>\n",
              "      <th>exercise</th>\n",
              "      <th>father_education</th>\n",
              "      <th>father_profession</th>\n",
              "      <th>fav_cuisine</th>\n",
              "      <th>fav_cuisine_coded</th>\n",
              "      <th>fav_food</th>\n",
              "      <th>food_childhood</th>\n",
              "      <th>fries</th>\n",
              "      <th>fruit_day</th>\n",
              "      <th>grade_level</th>\n",
              "      <th>greek_food</th>\n",
              "      <th>healthy_feeling</th>\n",
              "      <th>healthy_meal</th>\n",
              "      <th>ideal_diet</th>\n",
              "      <th>ideal_diet_coded</th>\n",
              "      <th>income</th>\n",
              "      <th>indian_food</th>\n",
              "      <th>italian_food</th>\n",
              "      <th>life_rewarding</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>meals_dinner_friend</th>\n",
              "      <th>mother_education</th>\n",
              "      <th>mother_profession</th>\n",
              "      <th>nutritional_check</th>\n",
              "      <th>on_off_campus</th>\n",
              "      <th>parents_cook</th>\n",
              "      <th>pay_meal_out</th>\n",
              "      <th>persian_food</th>\n",
              "      <th>self_perception_weight</th>\n",
              "      <th>soup</th>\n",
              "      <th>sports</th>\n",
              "      <th>thai_food</th>\n",
              "      <th>tortilla_calories</th>\n",
              "      <th>turkey_calories</th>\n",
              "      <th>type_sports</th>\n",
              "      <th>veggies_day</th>\n",
              "      <th>vitamins</th>\n",
              "      <th>waffle_calories</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>430</td>\n",
              "      <td>NaN</td>\n",
              "      <td>315.0</td>\n",
              "      <td>1</td>\n",
              "      <td>none</td>\n",
              "      <td>we dont have comfort</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>eat good and exercise</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>eat faster</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>profesor</td>\n",
              "      <td>Arabic cuisine</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>rice  and chicken</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>looks not oily</td>\n",
              "      <td>being healthy</td>\n",
              "      <td>8</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>rice, chicken,  soup</td>\n",
              "      <td>1.0</td>\n",
              "      <td>unemployed</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1165.0</td>\n",
              "      <td>345</td>\n",
              "      <td>car racing</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1315</td>\n",
              "      <td>187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.654</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>610</td>\n",
              "      <td>3.0</td>\n",
              "      <td>420.0</td>\n",
              "      <td>2</td>\n",
              "      <td>chocolate, chips, ice cream</td>\n",
              "      <td>Stress, bored, anger</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>I eat about three times a day with some snacks...</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>I eat out more than usual.</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Self employed</td>\n",
              "      <td>Italian</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>chicken and biscuits, beef soup, baked beans</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>Grains, Veggies, (more of grains and veggies),...</td>\n",
              "      <td>Try to eat 5-6 small meals a day. While trying...</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Pasta, steak, chicken</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Nurse RN</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>725.0</td>\n",
              "      <td>690</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>900</td>\n",
              "      <td>155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>720</td>\n",
              "      <td>4.0</td>\n",
              "      <td>420.0</td>\n",
              "      <td>2</td>\n",
              "      <td>frozen yogurt, pizza, fast food</td>\n",
              "      <td>stress, sadness</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>toast and fruit for breakfast, salad for lunch...</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>sometimes choosing to eat fast food instead of...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>owns business</td>\n",
              "      <td>italian</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>mac and cheese, pizza, tacos</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>usually includes natural ingredients; nonproce...</td>\n",
              "      <td>i would say my ideal diet is my current diet</td>\n",
              "      <td>6</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>chicken and rice with veggies, pasta, some kin...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>owns business</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>1165.0</td>\n",
              "      <td>500</td>\n",
              "      <td>none</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>900</td>\n",
              "      <td>I'm not answering this.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>430</td>\n",
              "      <td>3.0</td>\n",
              "      <td>420.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Pizza, Mac and cheese, ice cream</td>\n",
              "      <td>Boredom</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>College diet, cheap and easy foods most nights...</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Accepting cheap and premade/store bought foods</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Mechanic</td>\n",
              "      <td>Turkish</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Beef stroganoff, tacos, pizza</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>Fresh fruits&amp; vegetables, organic meats</td>\n",
              "      <td>Healthy, fresh veggies/fruits &amp; organic foods</td>\n",
              "      <td>2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Grilled chicken \\rStuffed Shells\\rHomemade Chili</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Special Education Teacher</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>725.0</td>\n",
              "      <td>690</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1315</td>\n",
              "      <td>Not sure, 240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>720</td>\n",
              "      <td>2.0</td>\n",
              "      <td>420.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Ice cream, chocolate, chips</td>\n",
              "      <td>Stress, boredom, cravings</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>I try to eat healthy but often struggle becaus...</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>I have eaten generally the same foods but I do...</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>IT</td>\n",
              "      <td>Italian</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Pasta, chicken tender, pizza</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>A lean protein such as grilled chicken, green ...</td>\n",
              "      <td>Ideally I would like to be able to eat healthi...</td>\n",
              "      <td>2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Chicken Parmesan, Pulled Pork, Spaghetti and m...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Substance Abuse Conselor</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>940.0</td>\n",
              "      <td>500</td>\n",
              "      <td>Softball</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>760</td>\n",
              "      <td>190</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     GPA  Gender  ...  waffle_calories                    weight\n",
              "0    2.4       2  ...             1315                       187\n",
              "1  3.654       1  ...              900                       155\n",
              "2    3.3       1  ...              900  I'm not answering this. \n",
              "3    3.2       1  ...             1315             Not sure, 240\n",
              "4    3.5       1  ...              760                       190\n",
              "\n",
              "[5 rows x 61 columns]"
            ]
          },
          "execution_count": 262,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv('food_coded.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dU-GdFU1UtlM",
        "outputId": "6ca763b0-08a4-4d25-d97c-1ad1751cfc22"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 125 entries, 0 to 124\n",
            "Data columns (total 61 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   GPA                           123 non-null    object \n",
            " 1   Gender                        125 non-null    int64  \n",
            " 2   breakfast                     125 non-null    int64  \n",
            " 3   calories_chicken              125 non-null    int64  \n",
            " 4   calories_day                  106 non-null    float64\n",
            " 5   calories_scone                124 non-null    float64\n",
            " 6   coffee                        125 non-null    int64  \n",
            " 7   comfort_food                  124 non-null    object \n",
            " 8   comfort_food_reasons          124 non-null    object \n",
            " 9   comfort_food_reasons_coded    106 non-null    float64\n",
            " 10  cook                          122 non-null    float64\n",
            " 11  comfort_food_reasons_coded.1  125 non-null    int64  \n",
            " 12  cuisine                       108 non-null    float64\n",
            " 13  diet_current                  124 non-null    object \n",
            " 14  diet_current_coded            125 non-null    int64  \n",
            " 15  drink                         123 non-null    float64\n",
            " 16  eating_changes                122 non-null    object \n",
            " 17  eating_changes_coded          125 non-null    int64  \n",
            " 18  eating_changes_coded1         125 non-null    int64  \n",
            " 19  eating_out                    125 non-null    int64  \n",
            " 20  employment                    116 non-null    float64\n",
            " 21  ethnic_food                   125 non-null    int64  \n",
            " 22  exercise                      112 non-null    float64\n",
            " 23  father_education              124 non-null    float64\n",
            " 24  father_profession             122 non-null    object \n",
            " 25  fav_cuisine                   123 non-null    object \n",
            " 26  fav_cuisine_coded             125 non-null    int64  \n",
            " 27  fav_food                      123 non-null    float64\n",
            " 28  food_childhood                124 non-null    object \n",
            " 29  fries                         125 non-null    int64  \n",
            " 30  fruit_day                     125 non-null    int64  \n",
            " 31  grade_level                   125 non-null    int64  \n",
            " 32  greek_food                    125 non-null    int64  \n",
            " 33  healthy_feeling               125 non-null    int64  \n",
            " 34  healthy_meal                  124 non-null    object \n",
            " 35  ideal_diet                    124 non-null    object \n",
            " 36  ideal_diet_coded              125 non-null    int64  \n",
            " 37  income                        124 non-null    float64\n",
            " 38  indian_food                   125 non-null    int64  \n",
            " 39  italian_food                  125 non-null    int64  \n",
            " 40  life_rewarding                124 non-null    float64\n",
            " 41  marital_status                124 non-null    float64\n",
            " 42  meals_dinner_friend           122 non-null    object \n",
            " 43  mother_education              122 non-null    float64\n",
            " 44  mother_profession             123 non-null    object \n",
            " 45  nutritional_check             125 non-null    int64  \n",
            " 46  on_off_campus                 124 non-null    float64\n",
            " 47  parents_cook                  125 non-null    int64  \n",
            " 48  pay_meal_out                  125 non-null    int64  \n",
            " 49  persian_food                  124 non-null    float64\n",
            " 50  self_perception_weight        124 non-null    float64\n",
            " 51  soup                          124 non-null    float64\n",
            " 52  sports                        123 non-null    float64\n",
            " 53  thai_food                     125 non-null    int64  \n",
            " 54  tortilla_calories             124 non-null    float64\n",
            " 55  turkey_calories               125 non-null    int64  \n",
            " 56  type_sports                   104 non-null    object \n",
            " 57  veggies_day                   125 non-null    int64  \n",
            " 58  vitamins                      125 non-null    int64  \n",
            " 59  waffle_calories               125 non-null    int64  \n",
            " 60  weight                        123 non-null    object \n",
            "dtypes: float64(20), int64(27), object(14)\n",
            "memory usage: 59.7+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "rJGjRjSaU6bh",
        "outputId": "c64c8aca-d813-4f57-c2af-8241cc7aa1e3"
      },
      "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>Gender</th>\n",
              "      <th>breakfast</th>\n",
              "      <th>calories_chicken</th>\n",
              "      <th>calories_day</th>\n",
              "      <th>calories_scone</th>\n",
              "      <th>coffee</th>\n",
              "      <th>comfort_food_reasons_coded</th>\n",
              "      <th>cook</th>\n",
              "      <th>comfort_food_reasons_coded.1</th>\n",
              "      <th>cuisine</th>\n",
              "      <th>diet_current_coded</th>\n",
              "      <th>drink</th>\n",
              "      <th>eating_changes_coded</th>\n",
              "      <th>eating_changes_coded1</th>\n",
              "      <th>eating_out</th>\n",
              "      <th>employment</th>\n",
              "      <th>ethnic_food</th>\n",
              "      <th>exercise</th>\n",
              "      <th>father_education</th>\n",
              "      <th>fav_cuisine_coded</th>\n",
              "      <th>fav_food</th>\n",
              "      <th>fries</th>\n",
              "      <th>fruit_day</th>\n",
              "      <th>grade_level</th>\n",
              "      <th>greek_food</th>\n",
              "      <th>healthy_feeling</th>\n",
              "      <th>ideal_diet_coded</th>\n",
              "      <th>income</th>\n",
              "      <th>indian_food</th>\n",
              "      <th>italian_food</th>\n",
              "      <th>life_rewarding</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>mother_education</th>\n",
              "      <th>nutritional_check</th>\n",
              "      <th>on_off_campus</th>\n",
              "      <th>parents_cook</th>\n",
              "      <th>pay_meal_out</th>\n",
              "      <th>persian_food</th>\n",
              "      <th>self_perception_weight</th>\n",
              "      <th>soup</th>\n",
              "      <th>sports</th>\n",
              "      <th>thai_food</th>\n",
              "      <th>tortilla_calories</th>\n",
              "      <th>turkey_calories</th>\n",
              "      <th>veggies_day</th>\n",
              "      <th>vitamins</th>\n",
              "      <th>waffle_calories</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>125.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>106.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>125.00000</td>\n",
              "      <td>106.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>108.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>125.00000</td>\n",
              "      <td>116.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>125.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.392000</td>\n",
              "      <td>1.112000</td>\n",
              "      <td>577.320000</td>\n",
              "      <td>3.028302</td>\n",
              "      <td>505.241935</td>\n",
              "      <td>1.75200</td>\n",
              "      <td>2.698113</td>\n",
              "      <td>2.786885</td>\n",
              "      <td>2.688000</td>\n",
              "      <td>1.388889</td>\n",
              "      <td>1.760000</td>\n",
              "      <td>1.560976</td>\n",
              "      <td>1.536000</td>\n",
              "      <td>4.552000</td>\n",
              "      <td>2.56000</td>\n",
              "      <td>2.448276</td>\n",
              "      <td>3.744000</td>\n",
              "      <td>1.589286</td>\n",
              "      <td>3.483871</td>\n",
              "      <td>2.424000</td>\n",
              "      <td>1.715447</td>\n",
              "      <td>1.088000</td>\n",
              "      <td>4.224000</td>\n",
              "      <td>2.376000</td>\n",
              "      <td>3.488000</td>\n",
              "      <td>5.456000</td>\n",
              "      <td>3.704000</td>\n",
              "      <td>4.532258</td>\n",
              "      <td>3.152000</td>\n",
              "      <td>4.728000</td>\n",
              "      <td>5.104839</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>3.426230</td>\n",
              "      <td>3.152000</td>\n",
              "      <td>1.322581</td>\n",
              "      <td>1.528000</td>\n",
              "      <td>3.408000</td>\n",
              "      <td>2.806452</td>\n",
              "      <td>3.120968</td>\n",
              "      <td>1.217742</td>\n",
              "      <td>1.390244</td>\n",
              "      <td>3.336000</td>\n",
              "      <td>947.580645</td>\n",
              "      <td>555.040000</td>\n",
              "      <td>4.008000</td>\n",
              "      <td>1.512000</td>\n",
              "      <td>1073.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.490161</td>\n",
              "      <td>0.316636</td>\n",
              "      <td>131.214156</td>\n",
              "      <td>0.639308</td>\n",
              "      <td>230.840506</td>\n",
              "      <td>0.43359</td>\n",
              "      <td>1.972042</td>\n",
              "      <td>1.038351</td>\n",
              "      <td>1.910987</td>\n",
              "      <td>0.974759</td>\n",
              "      <td>0.766222</td>\n",
              "      <td>0.498298</td>\n",
              "      <td>0.757159</td>\n",
              "      <td>2.547788</td>\n",
              "      <td>1.13876</td>\n",
              "      <td>0.533158</td>\n",
              "      <td>1.177093</td>\n",
              "      <td>0.665137</td>\n",
              "      <td>1.206243</td>\n",
              "      <td>1.947968</td>\n",
              "      <td>0.910056</td>\n",
              "      <td>0.284435</td>\n",
              "      <td>0.923388</td>\n",
              "      <td>1.133536</td>\n",
              "      <td>1.365567</td>\n",
              "      <td>2.585643</td>\n",
              "      <td>2.086918</td>\n",
              "      <td>1.456332</td>\n",
              "      <td>1.486802</td>\n",
              "      <td>0.587175</td>\n",
              "      <td>3.120399</td>\n",
              "      <td>0.548464</td>\n",
              "      <td>1.170703</td>\n",
              "      <td>1.205204</td>\n",
              "      <td>0.681323</td>\n",
              "      <td>0.746778</td>\n",
              "      <td>1.040285</td>\n",
              "      <td>1.423824</td>\n",
              "      <td>1.115980</td>\n",
              "      <td>0.414385</td>\n",
              "      <td>0.489800</td>\n",
              "      <td>1.436528</td>\n",
              "      <td>202.090179</td>\n",
              "      <td>152.370379</td>\n",
              "      <td>1.081337</td>\n",
              "      <td>0.501867</td>\n",
              "      <td>248.667092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>265.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>315.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>580.000000</td>\n",
              "      <td>345.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>575.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>430.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>725.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>900.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>610.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>940.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>900.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>720.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1165.000000</td>\n",
              "      <td>690.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1315.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>720.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>980.000000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>5.00000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1165.000000</td>\n",
              "      <td>850.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1315.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Gender   breakfast  ...    vitamins  waffle_calories\n",
              "count  125.000000  125.000000  ...  125.000000       125.000000\n",
              "mean     1.392000    1.112000  ...    1.512000      1073.400000\n",
              "std      0.490161    0.316636  ...    0.501867       248.667092\n",
              "min      1.000000    1.000000  ...    1.000000       575.000000\n",
              "25%      1.000000    1.000000  ...    1.000000       900.000000\n",
              "50%      1.000000    1.000000  ...    2.000000       900.000000\n",
              "75%      2.000000    1.000000  ...    2.000000      1315.000000\n",
              "max      2.000000    2.000000  ...    2.000000      1315.000000\n",
              "\n",
              "[8 rows x 47 columns]"
            ]
          },
          "execution_count": 264,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AXXnuFfoJr8q"
      },
      "source": [
        "Filling NaN data with the mode "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "tBXtRKZ1ZGfG",
        "outputId": "86eb91c0-cb86-4c77-d8e5-daca481383e3"
      },
      "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>GPA</th>\n",
              "      <th>Gender</th>\n",
              "      <th>breakfast</th>\n",
              "      <th>calories_chicken</th>\n",
              "      <th>calories_day</th>\n",
              "      <th>calories_scone</th>\n",
              "      <th>coffee</th>\n",
              "      <th>comfort_food</th>\n",
              "      <th>comfort_food_reasons</th>\n",
              "      <th>comfort_food_reasons_coded</th>\n",
              "      <th>cook</th>\n",
              "      <th>comfort_food_reasons_coded.1</th>\n",
              "      <th>cuisine</th>\n",
              "      <th>diet_current</th>\n",
              "      <th>diet_current_coded</th>\n",
              "      <th>drink</th>\n",
              "      <th>eating_changes</th>\n",
              "      <th>eating_changes_coded</th>\n",
              "      <th>eating_changes_coded1</th>\n",
              "      <th>eating_out</th>\n",
              "      <th>employment</th>\n",
              "      <th>ethnic_food</th>\n",
              "      <th>exercise</th>\n",
              "      <th>father_education</th>\n",
              "      <th>father_profession</th>\n",
              "      <th>fav_cuisine</th>\n",
              "      <th>fav_cuisine_coded</th>\n",
              "      <th>fav_food</th>\n",
              "      <th>food_childhood</th>\n",
              "      <th>fries</th>\n",
              "      <th>fruit_day</th>\n",
              "      <th>grade_level</th>\n",
              "      <th>greek_food</th>\n",
              "      <th>healthy_feeling</th>\n",
              "      <th>healthy_meal</th>\n",
              "      <th>ideal_diet</th>\n",
              "      <th>ideal_diet_coded</th>\n",
              "      <th>income</th>\n",
              "      <th>indian_food</th>\n",
              "      <th>italian_food</th>\n",
              "      <th>life_rewarding</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>meals_dinner_friend</th>\n",
              "      <th>mother_education</th>\n",
              "      <th>mother_profession</th>\n",
              "      <th>nutritional_check</th>\n",
              "      <th>on_off_campus</th>\n",
              "      <th>parents_cook</th>\n",
              "      <th>pay_meal_out</th>\n",
              "      <th>persian_food</th>\n",
              "      <th>self_perception_weight</th>\n",
              "      <th>soup</th>\n",
              "      <th>sports</th>\n",
              "      <th>thai_food</th>\n",
              "      <th>tortilla_calories</th>\n",
              "      <th>turkey_calories</th>\n",
              "      <th>type_sports</th>\n",
              "      <th>veggies_day</th>\n",
              "      <th>vitamins</th>\n",
              "      <th>waffle_calories</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>430</td>\n",
              "      <td>3.0</td>\n",
              "      <td>315.0</td>\n",
              "      <td>1</td>\n",
              "      <td>none</td>\n",
              "      <td>we dont have comfort</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>eat good and exercise</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>eat faster</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>profesor</td>\n",
              "      <td>Arabic cuisine</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>rice  and chicken</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>looks not oily</td>\n",
              "      <td>being healthy</td>\n",
              "      <td>8</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>rice, chicken,  soup</td>\n",
              "      <td>1.0</td>\n",
              "      <td>unemployed</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1165.0</td>\n",
              "      <td>345</td>\n",
              "      <td>car racing</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1315</td>\n",
              "      <td>187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.654</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>610</td>\n",
              "      <td>3.0</td>\n",
              "      <td>420.0</td>\n",
              "      <td>2</td>\n",
              "      <td>chocolate, chips, ice cream</td>\n",
              "      <td>Stress, bored, anger</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>I eat about three times a day with some snacks...</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>I eat out more than usual.</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Self employed</td>\n",
              "      <td>Italian</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>chicken and biscuits, beef soup, baked beans</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>Grains, Veggies, (more of grains and veggies),...</td>\n",
              "      <td>Try to eat 5-6 small meals a day. While trying...</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Pasta, steak, chicken</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Nurse RN</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>725.0</td>\n",
              "      <td>690</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>900</td>\n",
              "      <td>155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>720</td>\n",
              "      <td>4.0</td>\n",
              "      <td>420.0</td>\n",
              "      <td>2</td>\n",
              "      <td>frozen yogurt, pizza, fast food</td>\n",
              "      <td>stress, sadness</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>toast and fruit for breakfast, salad for lunch...</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>sometimes choosing to eat fast food instead of...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>owns business</td>\n",
              "      <td>italian</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>mac and cheese, pizza, tacos</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>usually includes natural ingredients; nonproce...</td>\n",
              "      <td>i would say my ideal diet is my current diet</td>\n",
              "      <td>6</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>chicken and rice with veggies, pasta, some kin...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>owns business</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>1165.0</td>\n",
              "      <td>500</td>\n",
              "      <td>none</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>900</td>\n",
              "      <td>I'm not answering this.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>430</td>\n",
              "      <td>3.0</td>\n",
              "      <td>420.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Pizza, Mac and cheese, ice cream</td>\n",
              "      <td>Boredom</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>College diet, cheap and easy foods most nights...</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Accepting cheap and premade/store bought foods</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Mechanic</td>\n",
              "      <td>Turkish</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Beef stroganoff, tacos, pizza</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>Fresh fruits&amp; vegetables, organic meats</td>\n",
              "      <td>Healthy, fresh veggies/fruits &amp; organic foods</td>\n",
              "      <td>2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Grilled chicken \\rStuffed Shells\\rHomemade Chili</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Special Education Teacher</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>725.0</td>\n",
              "      <td>690</td>\n",
              "      <td>none</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1315</td>\n",
              "      <td>Not sure, 240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>720</td>\n",
              "      <td>2.0</td>\n",
              "      <td>420.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Ice cream, chocolate, chips</td>\n",
              "      <td>Stress, boredom, cravings</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>I try to eat healthy but often struggle becaus...</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>I have eaten generally the same foods but I do...</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>IT</td>\n",
              "      <td>Italian</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Pasta, chicken tender, pizza</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>A lean protein such as grilled chicken, green ...</td>\n",
              "      <td>Ideally I would like to be able to eat healthi...</td>\n",
              "      <td>2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Chicken Parmesan, Pulled Pork, Spaghetti and m...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Substance Abuse Conselor</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>940.0</td>\n",
              "      <td>500</td>\n",
              "      <td>Softball</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>760</td>\n",
              "      <td>190</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     GPA  Gender  ...  waffle_calories                    weight\n",
              "0    2.4       2  ...             1315                       187\n",
              "1  3.654       1  ...              900                       155\n",
              "2    3.3       1  ...              900  I'm not answering this. \n",
              "3    3.2       1  ...             1315             Not sure, 240\n",
              "4    3.5       1  ...              760                       190\n",
              "\n",
              "[5 rows x 61 columns]"
            ]
          },
          "execution_count": 265,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "for column in df.columns:\n",
        "    df[column].fillna(df[column].mode()[0], inplace=True)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sJi2PVO8J0BZ"
      },
      "source": [
        "Creating a new dataframe with essential columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FIoA4mu1ZYNY"
      },
      "outputs": [],
      "source": [
        "df1=df[['cook','eating_out','employment','ethnic_food','exercise','fruit_day','income','on_off_campus','pay_meal_out','sports', 'veggies_day']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B0_GYQiyZyDs",
        "outputId": "a81ee9ae-b6cc-4b35-a831-32eb269a5cf9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['GPA', 'Gender', 'breakfast', 'calories_chicken', 'calories_day', 'calories_scone', 'coffee', 'comfort_food', 'comfort_food_reasons', 'comfort_food_reasons_coded', 'cook', 'comfort_food_reasons_coded.1', 'cuisine', 'diet_current', 'diet_current_coded', 'drink', 'eating_changes', 'eating_changes_coded', 'eating_changes_coded1', 'eating_out', 'employment', 'ethnic_food', 'exercise', 'father_education', 'father_profession', 'fav_cuisine', 'fav_cuisine_coded', 'fav_food', 'food_childhood', 'fries', 'fruit_day', 'grade_level', 'greek_food', 'healthy_feeling', 'healthy_meal', 'ideal_diet', 'ideal_diet_coded', 'income', 'indian_food', 'italian_food', 'life_rewarding', 'marital_status', 'meals_dinner_friend', 'mother_education', 'mother_profession', 'nutritional_check', 'on_off_campus', 'parents_cook', 'pay_meal_out', 'persian_food', 'self_perception_weight', 'soup', 'sports', 'thai_food', 'tortilla_calories', 'turkey_calories', 'type_sports', 'veggies_day', 'vitamins', 'waffle_calories', 'weight']\n"
          ]
        }
      ],
      "source": [
        "print(df.columns.tolist())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "76-mxoDUboTP",
        "outputId": "06477682-73fd-4d61-84fc-f409ac211a58"
      },
      "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>cook</th>\n",
              "      <th>eating_out</th>\n",
              "      <th>employment</th>\n",
              "      <th>ethnic_food</th>\n",
              "      <th>exercise</th>\n",
              "      <th>fruit_day</th>\n",
              "      <th>income</th>\n",
              "      <th>on_off_campus</th>\n",
              "      <th>pay_meal_out</th>\n",
              "      <th>sports</th>\n",
              "      <th>veggies_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   cook  eating_out  employment  ...  pay_meal_out  sports  veggies_day\n",
              "0   2.0           3         3.0  ...             2     1.0            5\n",
              "1   3.0           2         2.0  ...             4     1.0            4\n",
              "2   1.0           2         3.0  ...             3     2.0            5\n",
              "3   2.0           2         3.0  ...             2     2.0            3\n",
              "4   1.0           2         2.0  ...             4     1.0            4\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "execution_count": 268,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IxkZfoeybslz",
        "outputId": "9cdbcec1-1458-477e-d9f3-6f16053f8b3f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(125, 11)"
            ]
          },
          "execution_count": 269,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mpIz6onXKFNP"
      },
      "source": [
        "#Removal of Outliers - Using Boxplots\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "AV7dH-LNtWXo",
        "outputId": "f9930c08-aa8a-476f-ea95-f9a2d6ef08ca"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b464c21d0>"
            ]
          },
          "execution_count": 270,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(14, 8))\n",
        "sns.boxplot(data=df1, ax = ax)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qksV6OBVuLWO",
        "outputId": "911486a7-6103-463f-af27-15cdacb553d4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "cook             5.0\n",
              "eating_out       5.0\n",
              "employment       3.0\n",
              "ethnic_food      5.0\n",
              "exercise         3.0\n",
              "fruit_day        5.0\n",
              "income           6.0\n",
              "on_off_campus    3.0\n",
              "pay_meal_out     6.0\n",
              "sports           2.0\n",
              "veggies_day      5.0\n",
              "Name: 0.95, dtype: float64"
            ]
          },
          "execution_count": 271,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "max_threshold = df1.quantile(0.95)\n",
        "max_threshold"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "crvxrWsXup4P"
      },
      "outputs": [],
      "source": [
        "df1 = df1.drop(df1[df1['cook'] > 4].index)\n",
        "df1 = df1.drop(df1[df1['eating_out'] > 4].index)\n",
        "df1 = df1.drop(df1[df1['fruit_day'] < 3].index)\n",
        "df1 = df1.drop(df1[df1['on_off_campus'] > 1].index)\n",
        "df1 = df1.drop(df1[df1['pay_meal_out'] > 5].index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hJ15-FoSwBlJ",
        "outputId": "00f31245-d126-4521-dff4-27d35b50df34"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(75, 11)"
            ]
          },
          "execution_count": 273,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "NP8V7ppIwDty",
        "outputId": "bedd7ca9-f837-45ed-e810-9078fc4b45a4"
      },
      "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>cook</th>\n",
              "      <th>eating_out</th>\n",
              "      <th>employment</th>\n",
              "      <th>ethnic_food</th>\n",
              "      <th>exercise</th>\n",
              "      <th>fruit_day</th>\n",
              "      <th>income</th>\n",
              "      <th>on_off_campus</th>\n",
              "      <th>pay_meal_out</th>\n",
              "      <th>sports</th>\n",
              "      <th>veggies_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   cook  eating_out  employment  ...  pay_meal_out  sports  veggies_day\n",
              "0   2.0           3         3.0  ...             2     1.0            5\n",
              "1   3.0           2         2.0  ...             4     1.0            4\n",
              "3   2.0           2         3.0  ...             2     2.0            3\n",
              "4   1.0           2         2.0  ...             4     1.0            4\n",
              "7   3.0           2         2.0  ...             5     2.0            4\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "execution_count": 274,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "D-x0YDRo0Ra1",
        "outputId": "609fb613-ff3a-43d1-e6bb-dd9bf11c76a6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b4638f650>"
            ]
          },
          "execution_count": 275,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(14, 8))\n",
        "sns.boxplot(data=df1, ax = ax)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WMuJNN8mxq6u"
      },
      "outputs": [],
      "source": [
        "df1 = df1.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gW8SOEuf1B55"
      },
      "outputs": [],
      "source": [
        "df1 = df1.drop(df1[df1['veggies_day'] < 2].index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "4gS3dRwDxrmJ",
        "outputId": "328c7f1b-5aff-481a-a2b7-ad4b4991e124"
      },
      "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>index</th>\n",
              "      <th>cook</th>\n",
              "      <th>eating_out</th>\n",
              "      <th>employment</th>\n",
              "      <th>ethnic_food</th>\n",
              "      <th>exercise</th>\n",
              "      <th>fruit_day</th>\n",
              "      <th>income</th>\n",
              "      <th>on_off_campus</th>\n",
              "      <th>pay_meal_out</th>\n",
              "      <th>sports</th>\n",
              "      <th>veggies_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index  cook  eating_out  ...  pay_meal_out  sports  veggies_day\n",
              "0      0   2.0           3  ...             2     1.0            5\n",
              "1      1   3.0           2  ...             4     1.0            4\n",
              "2      3   2.0           2  ...             2     2.0            3\n",
              "3      4   1.0           2  ...             4     1.0            4\n",
              "4      7   3.0           2  ...             5     2.0            4\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "execution_count": 278,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TsEhyl630n-f",
        "outputId": "5291c180-87a0-40c3-cdd9-44ce58b3684a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0       0\n",
              "1       1\n",
              "2       3\n",
              "3       4\n",
              "4       7\n",
              "     ... \n",
              "70    117\n",
              "71    119\n",
              "72    121\n",
              "73    122\n",
              "74    124\n",
              "Name: index, Length: 74, dtype: int64"
            ]
          },
          "execution_count": 279,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1['index']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2NosxvTn0s9A"
      },
      "outputs": [],
      "source": [
        "df1 = df1.drop([\"index\"], axis = 'columns')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "QQrUbaVq1TqA",
        "outputId": "24fdc54f-784d-4f90-d592-af2a022bcd7b"
      },
      "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>cook</th>\n",
              "      <th>eating_out</th>\n",
              "      <th>employment</th>\n",
              "      <th>ethnic_food</th>\n",
              "      <th>exercise</th>\n",
              "      <th>fruit_day</th>\n",
              "      <th>income</th>\n",
              "      <th>on_off_campus</th>\n",
              "      <th>pay_meal_out</th>\n",
              "      <th>sports</th>\n",
              "      <th>veggies_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   cook  eating_out  employment  ...  pay_meal_out  sports  veggies_day\n",
              "0   2.0           3         3.0  ...             2     1.0            5\n",
              "1   3.0           2         2.0  ...             4     1.0            4\n",
              "2   2.0           2         3.0  ...             2     2.0            3\n",
              "3   1.0           2         2.0  ...             4     1.0            4\n",
              "4   3.0           2         2.0  ...             5     2.0            4\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "execution_count": 281,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "G4GS4sEY1V2V",
        "outputId": "812ba571-1b56-41f0-c9e0-acc39272638d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b464c26d0>"
            ]
          },
          "execution_count": 282,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(14, 8))\n",
        "sns.boxplot(data=df1, ax = ax)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rn0GkfXT1gl3"
      },
      "outputs": [],
      "source": [
        "df1 = df1.drop(df1[df1['veggies_day'] < 3].index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "7DjAQ74O1irt",
        "outputId": "70966292-3ea1-4db1-8dbf-a8c91ade7b55"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b47031b10>"
            ]
          },
          "execution_count": 284,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(14, 8))\n",
        "sns.boxplot(data=df1, ax = ax)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-EgI-giK1mF8",
        "outputId": "00d3d25c-8e65-4424-aba6-2a92e56015a5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(66, 11)"
            ]
          },
          "execution_count": 285,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "yEZocgNH1qjN",
        "outputId": "ae27ce4d-eef2-437e-a9de-b5c066f46dd3"
      },
      "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>cook</th>\n",
              "      <th>eating_out</th>\n",
              "      <th>employment</th>\n",
              "      <th>ethnic_food</th>\n",
              "      <th>exercise</th>\n",
              "      <th>fruit_day</th>\n",
              "      <th>income</th>\n",
              "      <th>on_off_campus</th>\n",
              "      <th>pay_meal_out</th>\n",
              "      <th>sports</th>\n",
              "      <th>veggies_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
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              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   cook  eating_out  employment  ...  pay_meal_out  sports  veggies_day\n",
              "0   2.0           3         3.0  ...             2     1.0            5\n",
              "1   3.0           2         2.0  ...             4     1.0            4\n",
              "2   2.0           2         3.0  ...             2     2.0            3\n",
              "3   1.0           2         2.0  ...             4     1.0            4\n",
              "4   3.0           2         2.0  ...             5     2.0            4\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "execution_count": 286,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GvYb4vVZ13XX"
      },
      "outputs": [],
      "source": [
        "df1 = df1.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0ZyjdVfp17hs"
      },
      "outputs": [],
      "source": [
        "df1 = df1.drop([\"index\"], axis = 'columns')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NfcrHPhaKTml"
      },
      "source": [
        "The new refined dataset"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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              "   cook  eating_out  employment  ...  pay_meal_out  sports  veggies_day\n",
              "0   2.0           3         3.0  ...             2     1.0            5\n",
              "1   3.0           2         2.0  ...             4     1.0            4\n",
              "2   2.0           2         3.0  ...             2     2.0            3\n",
              "3   1.0           2         2.0  ...             4     1.0            4\n",
              "4   3.0           2         2.0  ...             5     2.0            4\n",
              "\n",
              "[5 rows x 11 columns]"
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      "source": [
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      "execution_count": null,
      "metadata": {
        "id": "kpxxvudU7TVt"
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      "source": [
        "X = df1.copy()"
      ]
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      "execution_count": null,
      "metadata": {
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              "   cook  eating_out  employment  ...  pay_meal_out  sports  veggies_day\n",
              "0   2.0           3         3.0  ...             2     1.0            5\n",
              "1   3.0           2         2.0  ...             4     1.0            4\n",
              "2   2.0           2         3.0  ...             2     2.0            3\n",
              "3   1.0           2         2.0  ...             4     1.0            4\n",
              "4   3.0           2         2.0  ...             5     2.0            4\n",
              "\n",
              "[5 rows x 11 columns]"
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        "id": "s2JyUMRiKhld"
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      "source": [
        "Elbow Method to find the number of clusters"
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      "execution_count": null,
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.cluster import KMeans\n",
        "wcss=[]\n",
        "for i in range(1,11):\n",
        "  kmeans=KMeans(n_clusters=i,init = 'k-means++',random_state=42)\n",
        "  kmeans.fit(X)\n",
        "  wcss.append(kmeans.inertia_)\n",
        "plt.plot(range(1,11),wcss)\n",
        "plt.title('The elbow method')\n",
        "plt.xlabel('The number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8rzYKVE8MEA",
        "outputId": "341db714-2a3f-4c81-d0fa-be755f3a8da1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1 2 2 2 1 2 2 0 1 0 2 1 1 0 1 2 1 2 2 0 1 0 2 0 1 2 2 1 2 0 1 2 2 2 0 2 2\n",
            " 1 0 2 1 2 1 0 0 1 0 2 2 1 1 0 2 2 2 2 2 1 2 0 0 0 0 0 0 1]\n"
          ]
        }
      ],
      "source": [
        "kmeans=KMeans(n_clusters=3, init='k-means++',random_state=42)\n",
        "Y_kmeans=kmeans.fit_predict(X) \n",
        "print(Y_kmeans) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rpzgdzc6Kt-a"
      },
      "source": [
        "#Foursquare API - To get geolocational data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w9GG8AOh8uue"
      },
      "outputs": [],
      "source": [
        "import json, requests\n",
        "CLIENT_ID = '' # your Foursquare ID\n",
        "CLIENT_SECRET = '' # your Foursquare Secret\n",
        "VERSION = '20200316'\n",
        "LIMIT = 10000\n",
        "url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
        "    CLIENT_ID, \n",
        "    CLIENT_SECRET, \n",
        "    VERSION, \n",
        "    13.0306, 77.5649,\n",
        "    30000, \n",
        "    LIMIT)\n",
        "data = requests.get(url).json()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Et0mhpWLBkhY",
        "outputId": "8b8a0262-7322-4718-ba08-ba45ef063285"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'meta': {'code': 200, 'requestId': '60b6483fe2d5281cb41a7a0f'},\n",
              " 'response': {'groups': [{'items': [{'reasons': {'count': 0,\n",
              "       'items': [{'reasonName': 'globalInteractionReason',\n",
              "         'summary': 'This spot is popular',\n",
              "         'type': 'general'}]},\n",
              "      'referralId': 'e-0-4f1869c7e4b0ebf9e4ae9134-0',\n",
              "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/mall_',\n",
              "          'suffix': '.png'},\n",
              "         'id': '4bf58dd8d48988d1fd941735',\n",
              "         'name': 'Shopping Mall',\n",
              "         'pluralName': 'Shopping Malls',\n",
              "         'primary': True,\n",
              "         'shortName': 'Mall'}],\n",
              "       'id': '4f1869c7e4b0ebf9e4ae9134',\n",
              "       'location': {'address': '#26/1',\n",
              "        'cc': 'IN',\n",
              "        'city': 'Bangalore',\n",
              "        'country': 'India',\n",
              "        'crossStreet': 'Dr. Rajkumar Rd, Rajajinagar',\n",
              "        'distance': 2398,\n",
              "        'formattedAddress': ['#26/1 (Dr. Rajkumar Rd, Rajajinagar)',\n",
              "         'Bangalore 560055',\n",
              "         'Karnātaka',\n",
              "         'India'],\n",
              "        'labeledLatLngs': [{'label': 'display',\n",
              "          'lat': 13.011313244706537,\n",
              "          'lng': 77.55503923754937}],\n",
              "        'lat': 13.011313244706537,\n",
              "        'lng': 77.55503923754937,\n",
              "        'postalCode': '560055',\n",
              "        'state': 'Karnātaka'},\n",
              "       'name': 'Orion Mall',\n",
              "       'photos': {'count': 0, 'groups': []}}},\n",
              "     {'reasons': {'count': 0,\n",
              "       'items': [{'reasonName': 'globalInteractionReason',\n",
              "         'summary': 'This spot is popular',\n",
              "         'type': 'general'}]},\n",
              "      'referralId': 'e-0-531ed995498ef21f6f904159-1',\n",
              "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/arts_entertainment/movietheater_',\n",
              "          'suffix': '.png'},\n",
              "         'id': '4bf58dd8d48988d180941735',\n",
              "         'name': 'Multiplex',\n",
              "         'pluralName': 'Multiplexes',\n",
              "         'primary': True,\n",
              "         'shortName': 'Cineplex'}],\n",
              "       'id': '531ed995498ef21f6f904159',\n",
              "       'location': {'cc': 'IN',\n",
              "        'country': 'India',\n",
              "        'distance': 2438,\n",
              "        'formattedAddress': ['India'],\n",
              "        'labeledLatLngs': [{'label': 'display',\n",
              "          'lat': 13.010923124966338,\n",
              "          'lng': 77.55501011161395}],\n",
              "        'lat': 13.010923124966338,\n",
              "        'lng': 77.55501011161395},\n",
              "       'name': 'PVR Gold Class, Orion',\n",
              "       'photos': {'count': 0, 'groups': []}}},\n",
              "     {'reasons': {'count': 0,\n",
              "       'items': [{'reasonName': 'globalInteractionReason',\n",
              "         'summary': 'This spot is popular',\n",
              "         'type': 'general'}]},\n",
              "      'referralId': 'e-0-4e1d8edd1838f4ab743f26ae-2',\n",
              "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/bakery_',\n",
              "          'suffix': '.png'},\n",
              "         'id': '4bf58dd8d48988d16a941735',\n",
              "         'name': 'Bakery',\n",
              "         'pluralName': 'Bakeries',\n",
              "         'primary': True,\n",
              "         'shortName': 'Bakery'}],\n",
              "       'id': '4e1d8edd1838f4ab743f26ae',\n",
              "       'location': {'cc': 'IN',\n",
              "        'country': 'India',\n",
              "        'distance': 4486,\n",
              "        'formattedAddress': ['India'],\n",
              "        'labeledLatLngs': [{'label': 'display',\n",
              "          'lat': 12.991577953372982,\n",
              "          'lng': 77.5545605063243}],\n",
              "        'lat': 12.991577953372982,\n",
              "        'lng': 77.5545605063243},\n",
              "       'name': 'O.G. Variar & Sons',\n",
              "       'photos': {'count': 0, 'groups': []}}},\n",
              "     {'reasons': {'count': 0,\n",
              "       'items': [{'reasonName': 'globalInteractionReason',\n",
              "         'summary': 'This spot is popular',\n",
              "         'type': 'general'}]},\n",
              "      'referralId': 'e-0-4db972aba86ee4fa7d37df76-3',\n",
              "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/travel/hotel_',\n",
              "          'suffix': '.png'},\n",
              "         'id': '4bf58dd8d48988d1fa931735',\n",
              "         'name': 'Hotel',\n",
              "         'pluralName': 'Hotels',\n",
              "         'primary': True,\n",
              "         'shortName': 'Hotel'}],\n",
              "       'id': '4db972aba86ee4fa7d37df76',\n",
              "       'location': {'address': '26/1 Dr. Rajkumar Road, Malleswaram-Rajajinagar',\n",
              "        'cc': 'IN',\n",
              "        'city': 'Bangalore',\n",
              "        'country': 'India',\n",
              "        'distance': 2268,\n",
              "        'formattedAddress': ['26/1 Dr. Rajkumar Road, Malleswaram-Rajajinagar',\n",
              "         'Bangalore 560055',\n",
              "         'Karnātaka',\n",
              "         'India'],\n",
              "        'labeledLatLngs': [{'label': 'display',\n",
              "          'lat': 13.0125129,\n",
              "          'lng': 77.5552546}],\n",
              "        'lat': 13.0125129,\n",
              "        'lng': 77.5552546,\n",
              "        'postalCode': '560055',\n",
              "        'state': 'Karnātaka'},\n",
              "       'name': 'Sheraton Bangalore Hotel',\n",
              "       'photos': {'count': 0, 'groups': []}}},\n",
              "     {'reasons': {'count': 0,\n",
              "       'items': [{'reasonName': 'globalInteractionReason',\n",
              "         'summary': 'This spot is popular',\n",
              "         'type': 'general'}]},\n",
              "      'referralId': 'e-0-4d90bab2d4ec8cfacfb5a989-4',\n",
              "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/icecream_',\n",
              "          'suffix': '.png'},\n",
              "         'id': '4bf58dd8d48988d1c9941735',\n",
              "         'name': 'Ice Cream Shop',\n",
              "         'pluralName': 'Ice Cream Shops',\n",
              "         'primary': True,\n",
              "         'shortName': 'Ice Cream'}],\n",
              "       'id': '4d90bab2d4ec8cfacfb5a989',\n",
              "       'location': {'address': 'Nehru Circle',\n",
              "        'cc': 'IN',\n",
              "        'city': 'Bangalore',\n",
              "        'country': 'India',\n",
              "        'crossStreet': 'Near Sheshadripuram College',\n",
              "        'distance': 4891,\n",
              "        'formattedAddress': ['Nehru Circle (Near Sheshadripuram College)',\n",
              "         'Bangalore',\n",
              "         'Karnātaka',\n",
              "         'India'],\n",
              "        'labeledLatLngs': [{'label': 'display',\n",
              "          'lat': 12.98830366372137,\n",
              "          'lng': 77.57712961194349}],\n",
              "        'lat': 12.98830366372137,\n",
              "        'lng': 77.57712961194349,\n",
              "        'state': 'Karnātaka'},\n",
              "       'name': 'Corner House',\n",
              "       'photos': {'count': 0, 'groups': []}}},\n",
              "     {'reasons': {'count': 0,\n",
              "       'items': [{'reasonName': 'globalInteractionReason',\n",
              "         'summary': 'This spot is popular',\n",
              "         'type': 'general'}]},\n",
              "      'referralId': 'e-0-4b7965c1f964a52085f72ee3-5',\n",
              "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/tearoom_',\n",
              "          'suffix': '.png'},\n",
              "         'id': '4bf58dd8d48988d1dc931735',\n",
              "         'name': 'Tea Room',\n",
              "         'pluralName': 'Tea Rooms',\n",
              "         'primary': True,\n",
              "         'shortName': 'Tea Room'}],\n",
              "       'id': '4b7965c1f964a52085f72ee3',\n",
              "       'location': {'address': 'Cunningham Road',\n",
              "        'cc': 'IN',\n",
              "        'city': 'Bangalore',\n",
              "        'country': 'India',\n",
              "        'crossStreet': 'Vasanth Nagar',\n",
              "        'distance': 5824,\n",
              "        'formattedAddress': ['Cunningham Road (Vasanth Nagar)',\n",
              "         'Bangalore 560052',\n",
              "         'Karnātaka',\n",
              "         'India'],\n",
              "        'labeledLatLngs': [{'label': 'display',\n",
              "          'lat': 12.987156756402875,\n",
              "          'lng': 77.59483450996156}],\n",
              "        'lat': 12.987156756402875,\n",
              "        'lng': 77.59483450996156,\n",
              "        'postalCode': '560052',\n",
              "        'state': 'Karnātaka'},\n",
              "       'name': 'Infinitea',\n",
              "       'photos': {'count': 0, 'groups': []}}},\n",
              "     {'reasons': {'count': 0,\n",
              "       'items': [{'reasonName': 'globalInteractionReason',\n",
              "         'summary': 'This spot is popular',\n",
              "         'type': 'general'}]},\n",
              "      'referralId': 'e-0-4d0f41eec3dc3704e9633174-6',\n",
              "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/snacks_',\n",
              "          'suffix': '.png'},\n",
              "         'id': '4bf58dd8d48988d1c7941735',\n",
              "         'name': 'Snack Place',\n",
              "         'pluralName': 'Snack Places',\n",
              "         'primary': True,\n",
              "         'shortName': 'Snacks'}],\n",
              "       'id': '4d0f41eec3dc3704e9633174',\n",
              "       'location': {'cc': 'IN',\n",
              "        'city': 'Bangalore',\n",
              "        'country': 'India',\n",
              "        'distance': 3545,\n",
              "        'formattedAddress': ['Bangalore', 'Karnātaka', 'India'],\n",
              "        'labeledLatLngs': [{'label': 'display',\n",
              "          'lat': 12.999370572624446,\n",
              "          'lng': 77.57132224515472}],\n",
              "        'lat': 12.999370572624446,\n",
              "        'lng': 77.57132224515472,\n",
              "        'state': 'Karnātaka'},\n",
              "       'name': 'Asha Sweet Center',\n",
              "       'photos': {'count': 0, 'groups': []}}},\n",
              "     {'reasons': {'count': 0,\n",
              "       'items': [{'reasonName': 'globalInteractionReason',\n",
              "         'summary': 'This spot is popular',\n",
              "         'type': 'general'}]},\n",
              "      'referralId': 'e-0-4b69572ff964a520509f2be3-7',\n",
              "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/travel/hotel_',\n",
              "          'suffix': '.png'},\n",
              "         'id': '4bf58dd8d48988d1fa931735',\n",
              "         'name': 'Hotel',\n",
              "         'pluralName': 'Hotels',\n",
              "         'primary': True,\n",
              "         'shortName': 'Hotel'}],\n",
              "       'id': '4b69572ff964a520509f2be3',\n",
              "       'location': {'address': 'Racecourse Rd.',\n",
              "        'cc': 'IN',\n",
              "        'city': 'Bangalore',\n",
              "        'country': 'India',\n",
              "        'distance': 5563,\n",
              "        'formattedAddress': ['Racecourse Rd.',\n",
              "         'Bangalore',\n",
              "         'Karnātaka',\n",
              "         'India'],\n",
              "        'labeledLatLngs': [{'label': 'display',\n",
              "          'lat': 12.984572066608893,\n",
              "          'lng': 77.58489294114838}],\n",
              "        'lat': 12.984572066608893,\n",
              "        'lng': 77.58489294114838,\n",
              "        'state': 'Karnātaka'},\n",
              "       'name': 'Taj West End',\n",
              "       'photos': {'count': 0, 'groups': []}}},\n",
              "     {'reasons': {'count': 0,\n",
              "       'items': [{'reasonName': 'globalInteractionReason',\n",
              "         'summary': 'This spot is popular',\n",
              "         'type': 'general'}]},\n",
              "      'referralId': 'e-0-4f93d338e4b01cffa1f2a8a2-8',\n",
              "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/departmentstore_',\n",
              "          'suffix': '.png'},\n",
              "         'id': '4bf58dd8d48988d1f6941735',\n",
              "         'name': 'Department Store',\n",
              "         'pluralName': 'Department Stores',\n",
              "         'primary': True,\n",
              "         'shortName': 'Department Store'}],\n",
              "       'id': '4f93d338e4b01cffa1f2a8a2',\n",
              "       'location': {'address': '26/1 Dr. Rajkumar Road',\n",
              "        'cc': 'IN',\n",
              "        'city': 'Bangalore',\n",
              "        'country': 'India',\n",
              "        'distance': 2404,\n",
              "        'formattedAddress': ['26/1 Dr. Rajkumar Road',\n",
              "         'Bangalore 560055',\n",
              "         'Karnātaka',\n",
              "         'India'],\n",
              "        'labeledLatLngs': [{'label': 'display',\n",
              "          'lat': 13.01126087435231,\n",
              "          'lng': 77.5550342993936}],\n",
              "        'lat': 13.01126087435231,\n",
              "        'lng': 77.5550342993936,\n",
              "        'postalCode': '560055',\n",
              "        'state': 'Karnātaka'},\n",
              "       'name': 'Central',\n",
              "       'photos': {'count': 0, 'groups': []}}},\n",
              "     {'reasons': {'count': 0,\n",
              "       'items': [{'reasonName': 'globalInteractionReason',\n",
              "         'summary': 'This spot is popular',\n",
              "         'type': 'general'}]},\n",
              "      'referralId': 'e-0-4d57a3817c0b37048f4ad808-9',\n",
              "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/icecream_',\n",
              "          'suffix': '.png'},\n",
              "         'id': '4bf58dd8d48988d1c9941735',\n",
              "         'name': 'Ice Cream Shop',\n",
              "         'pluralName': 'Ice Cream Shops',\n",
              "         'primary': True,\n",
              "         'shortName': 'Ice Cream'}],\n",
              "       'id': '4d57a3817c0b37048f4ad808',\n",
              "       'location': {'address': 'Basweshwarnagar',\n",
              "        'cc': 'IN',\n",
              "        'city': 'Bangalore',\n",
              "        'country': 'India',\n",
              "        'distance': 5548,\n",
              "        'formattedAddress': ['Basweshwarnagar',\n",
              "         'Bangalore',\n",
              "         'Karnātaka',\n",
              "         'India'],\n",
              "        'labeledLatLngs': [{'label': 'display',\n",
              "          'lat': 12.985484062294457,\n",
              "          'lng': 77.54315786791148}],\n",
              "        'lat': 12.985484062294457,\n",
              "        'lng': 77.54315786791148,\n",
              "        'state': 'Karnātaka'},\n",
              "       'name': 'Natural Ice Cream',\n",
              "       'photos': {'count': 0, 'groups': []}}},\n",
              "     {'reasons': {'count': 0,\n",
              "       'items': [{'reasonName': 'globalInteractionReason',\n",
              "         'summary': 'This spot is popular',\n",
              "         'type': 'general'}]},\n",
              "      'referralId': 'e-0-50269b31e4b0727083f0dde8-10',\n",
              "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/arts_entertainment/bowling_',\n",
              "          'suffix': '.png'},\n",
              "         'id': '4bf58dd8d48988d1e4931735',\n",
              "         'name': 'Bowling Alley',\n",
              "         'pluralName': 'Bowling Alleys',\n",
              "         'primary': True,\n",
              "         'shortName': 'Bowling Alley'}],\n",
              "       'id': '50269b31e4b0727083f0dde8',\n",
              "       'location': {'address': '4th Flr, Orion Mall',\n",
              "        'cc': 'IN',\n",
              "        'city': 'Bangalore',\n",
              "        'country': 'India',\n",
              "        'crossStreet': 'Rajajinagar',\n",
              "        'distance': 2409,\n",
              "        'formattedAddress': ['4th Flr, Orion Mall (Rajajinagar)',\n",
              "         'Bangalore 560055',\n",
              "         'Karnātaka',\n",
              "         'India'],\n",
              "        'labeledLatLngs': [{'label': 'display',\n",
              "          'lat': 13.011197511086094,\n",
              "          'lng': 77.55505736954053}],\n",
              "        'lat': 13.011197511086094,\n",
              "        'lng': 77.55505736954053,\n",
              "        'postalCode': '560055',\n",
              "        'state': 'Karnātaka'},\n",
              "       'name': 'PVR bluO',\n",
              "       'photos': {'count': 0, 'groups': []}}},\n",
              "     {'reasons': {'count': 0,\n",
              "       'items': [{'reasonName': 'globalInteractionReason',\n",
              "         'summary': 'This spot is popular',\n",
              "         'type': 'general'}]},\n",
              "      'referralId': 'e-0-50927aac498eeb726337e5b9-11',\n",
              "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/arts_entertainment/movietheater_',\n",
              "          'suffix': '.png'},\n",
              "         'id': '4bf58dd8d48988d180941735',\n",
              "         'name': 'Multiplex',\n",
              "         'pluralName': 'Multiplexes',\n",
              "         'primary': True,\n",
              "         'shortName': 'Cineplex'}],\n",
              "       'id': '50927aac498eeb726337e5b9',\n",
              "       'location': {'address': 'ORION Mall, Brigade Gateway',\n",
              "        'cc': 'IN',\n",
              "        'city': 'Bangalore',\n",
              "        'country': 'India',\n",
              "        'crossStreet': '26/1, Dr.Rajkumar Road, Rajaji Nagar',\n",
              "        'distance': 2451,\n",
              "        'formattedAddress': ['ORION Mall, Brigade Gateway (26/1, Dr.Rajkumar Road, Rajaji Nagar)',\n",
              "         'Bangalore 560025',\n",
              "         'Karnātaka',\n",
              "         'India'],\n",
              "        'labeledLatLngs': [{'label': 'display',\n",
              "          'lat': 13.010800728380858,\n",
              "          'lng': 77.55500467509675}],\n",
              "        'lat': 13.010800728380858,\n",
              "        'lng': 77.55500467509675,\n",
              "        'postalCode': '560025',\n",
              "        'state': 'Karnātaka'},\n",
              "       'name': 'PVR Cinemas',\n",
              "       'photos': {'count': 0, 'groups': []}}},\n",
              "     {'reasons': {'count': 0,\n",
              "       'items': [{'reasonName': 'globalInteractionReason',\n",
              "         'summary': 'This spot is popular',\n",
              "         'type': 'general'}]},\n",
              "      'referralId': 'e-0-4f58cf41e4b0fbb1bbb8cae5-12',\n",
              "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/nightlife/default_',\n",
              "          'suffix': '.png'},\n",
              "         'id': '4bf58dd8d48988d121941735',\n",
              "         'name': 'Lounge',\n",
              "         'pluralName': 'Lounges',\n",
              "         'primary': True,\n",
              "         'shortName': 'Lounge'}],\n",
              "       'id': '4f58cf41e4b0fbb1bbb8cae5',\n",
              "       'location': {'cc': 'IN',\n",
              "        'country': 'India',\n",
              "        'distance': 2289,\n",
              "        'formattedAddress': ['India'],\n",
              "        'labeledLatLngs': [{'label': 'display',\n",
              "          'lat': 13.012320458641893,\n",
              "          'lng': 77.55522089158474}],\n",
              "        'lat': 13.012320458641893,\n",
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              "         'primary': True,\n",
              "         'shortName': 'Brewery'}],\n",
              "       'id': '5c849a711953f3002c8a751e',\n",
              "       'location': {'cc': 'IN',\n",
              "        'city': 'Bangalore',\n",
              "        'country': 'India',\n",
              "        'distance': 24522,\n",
              "        'formattedAddress': ['Bangalore 560300', 'Karnātaka', 'India'],\n",
              "        'labeledLatLngs': [{'label': 'display',\n",
              "          'lat': 13.199887,\n",
              "          'lng': 77.709638}],\n",
              "        'lat': 13.199887,\n",
              "        'lng': 77.709638,\n",
              "        'postalCode': '560300',\n",
              "        'state': 'Karnātaka'},\n",
              "       'name': 'Windmills Craftswork',\n",
              "       'photos': {'count': 0, 'groups': []}}},\n",
              "     {'reasons': {'count': 0,\n",
              "       'items': [{'reasonName': 'globalInteractionReason',\n",
              "         'summary': 'This spot is popular',\n",
              "         'type': 'general'}]},\n",
              "      'referralId': 'e-0-52a2d44e11d2c21acc86bc9e-97',\n",
              "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/breakfast_',\n",
              "          'suffix': '.png'},\n",
              "         'id': '4bf58dd8d48988d143941735',\n",
              "         'name': 'Breakfast Spot',\n",
              "         'pluralName': 'Breakfast Spots',\n",
              "         'primary': True,\n",
              "         'shortName': 'Breakfast'}],\n",
              "       'id': '52a2d44e11d2c21acc86bc9e',\n",
              "       'location': {'address': 'Hosakerahalli Cross',\n",
              "        'cc': 'IN',\n",
              "        'city': 'Bangalore',\n",
              "        'country': 'India',\n",
              "        'crossStreet': 'Banashankari',\n",
              "        'distance': 10819,\n",
              "        'formattedAddress': ['Hosakerahalli Cross (Banashankari)',\n",
              "         'Bangalore 560085',\n",
              "         'Karnātaka',\n",
              "         'India'],\n",
              "        'labeledLatLngs': [{'label': 'display',\n",
              "          'lat': 12.934932251251041,\n",
              "          'lng': 77.54731217337678}],\n",
              "        'lat': 12.934932251251041,\n",
              "        'lng': 77.54731217337678,\n",
              "        'postalCode': '560085',\n",
              "        'state': 'Karnātaka'},\n",
              "       'name': 'Slv supreme fastfood',\n",
              "       'photos': {'count': 0, 'groups': []}}},\n",
              "     {'reasons': {'count': 0,\n",
              "       'items': [{'reasonName': 'globalInteractionReason',\n",
              "         'summary': 'This spot is popular',\n",
              "         'type': 'general'}]},\n",
              "      'referralId': 'e-0-503a3143e4b0f874bcc33383-98',\n",
              "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/arts_entertainment/themepark_',\n",
              "          'suffix': '.png'},\n",
              "         'id': '4bf58dd8d48988d182941735',\n",
              "         'name': 'Theme Park',\n",
              "         'pluralName': 'Theme Parks',\n",
              "         'primary': True,\n",
              "         'shortName': 'Theme Park'}],\n",
              "       'id': '503a3143e4b0f874bcc33383',\n",
              "       'location': {'address': 'Near Christ University',\n",
              "        'cc': 'IN',\n",
              "        'city': 'Bangalore',\n",
              "        'country': 'India',\n",
              "        'crossStreet': 'Bangalore',\n",
              "        'distance': 20429,\n",
              "        'formattedAddress': ['Near Christ University (Bangalore)',\n",
              "         'Bangalore',\n",
              "         'Karnātaka',\n",
              "         'India'],\n",
              "        'labeledLatLngs': [{'label': 'display',\n",
              "          'lat': 12.939804009262263,\n",
              "          'lng': 77.40122544047205}],\n",
              "        'lat': 12.939804009262263,\n",
              "        'lng': 77.40122544047205,\n",
              "        'state': 'Karnātaka'},\n",
              "       'name': 'Wonderla Amusement Park',\n",
              "       'photos': {'count': 0, 'groups': []}}},\n",
              "     {'reasons': {'count': 0,\n",
              "       'items': [{'reasonName': 'globalInteractionReason',\n",
              "         'summary': 'This spot is popular',\n",
              "         'type': 'general'}]},\n",
              "      'referralId': 'e-0-4b647dd2f964a520cdb72ae3-99',\n",
              "      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/tearoom_',\n",
              "          'suffix': '.png'},\n",
              "         'id': '4bf58dd8d48988d1dc931735',\n",
              "         'name': 'Tea Room',\n",
              "         'pluralName': 'Tea Rooms',\n",
              "         'primary': True,\n",
              "         'shortName': 'Tea Room'}],\n",
              "       'id': '4b647dd2f964a520cdb72ae3',\n",
              "       'location': {'address': '293, Banashankari 3rd Stage',\n",
              "        'cc': 'IN',\n",
              "        'city': 'Bangalore',\n",
              "        'country': 'India',\n",
              "        'crossStreet': '100ft Ring Rd',\n",
              "        'distance': 12006,\n",
              "        'formattedAddress': ['293, Banashankari 3rd Stage (100ft Ring Rd)',\n",
              "         'Bangalore 560085',\n",
              "         'Karnātaka',\n",
              "         'India'],\n",
              "        'labeledLatLngs': [{'label': 'display',\n",
              "          'lat': 12.92308552482382,\n",
              "          'lng': 77.55603208723464}],\n",
              "        'lat': 12.92308552482382,\n",
              "        'lng': 77.55603208723464,\n",
              "        'postalCode': '560085',\n",
              "        'state': 'Karnātaka'},\n",
              "       'name': 'My Tea House',\n",
              "       'photos': {'count': 0, 'groups': []}}}],\n",
              "    'name': 'recommended',\n",
              "    'type': 'Recommended Places'}],\n",
              "  'headerFullLocation': 'Bangalore',\n",
              "  'headerLocation': 'Bangalore',\n",
              "  'headerLocationGranularity': 'city',\n",
              "  'suggestedBounds': {'ne': {'lat': 13.30060027000027,\n",
              "    'lng': 77.8415191509194},\n",
              "   'sw': {'lat': 12.76059972999973, 'lng': 77.28828084908059}},\n",
              "  'suggestedFilters': {'filters': [{'key': 'openNow', 'name': 'Open now'}],\n",
              "   'header': 'Tap to show:'},\n",
              "  'totalResults': 162}}"
            ]
          },
          "execution_count": 295,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8tv6WS6LK6Xm"
      },
      "source": [
        "Converting the Json Response to a Dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VRW2l3XZQqow"
      },
      "outputs": [],
      "source": [
        "dataframe = pd.json_normalize(data['response']['groups'][0]['items'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 719
        },
        "id": "KiqOJci3af-u",
        "outputId": "67381cec-5fae-425f-be9b-b8d8b4715531"
      },
      "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>referralId</th>\n",
              "      <th>reasons.count</th>\n",
              "      <th>reasons.items</th>\n",
              "      <th>venue.id</th>\n",
              "      <th>venue.name</th>\n",
              "      <th>venue.location.address</th>\n",
              "      <th>venue.location.crossStreet</th>\n",
              "      <th>venue.location.lat</th>\n",
              "      <th>venue.location.lng</th>\n",
              "      <th>venue.location.labeledLatLngs</th>\n",
              "      <th>venue.location.distance</th>\n",
              "      <th>venue.location.postalCode</th>\n",
              "      <th>venue.location.cc</th>\n",
              "      <th>venue.location.city</th>\n",
              "      <th>venue.location.state</th>\n",
              "      <th>venue.location.country</th>\n",
              "      <th>venue.location.formattedAddress</th>\n",
              "      <th>venue.categories</th>\n",
              "      <th>venue.photos.count</th>\n",
              "      <th>venue.photos.groups</th>\n",
              "      <th>venue.venuePage.id</th>\n",
              "      <th>venue.location.neighborhood</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>e-0-4f1869c7e4b0ebf9e4ae9134-0</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4f1869c7e4b0ebf9e4ae9134</td>\n",
              "      <td>Orion Mall</td>\n",
              "      <td>#26/1</td>\n",
              "      <td>Dr. Rajkumar Rd, Rajajinagar</td>\n",
              "      <td>13.011313</td>\n",
              "      <td>77.555039</td>\n",
              "      <td>[{'label': 'display', 'lat': 13.01131324470653...</td>\n",
              "      <td>2398</td>\n",
              "      <td>560055</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[#26/1 (Dr. Rajkumar Rd, Rajajinagar), Bangalo...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1fd941735', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>e-0-531ed995498ef21f6f904159-1</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>531ed995498ef21f6f904159</td>\n",
              "      <td>PVR Gold Class, Orion</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.010923</td>\n",
              "      <td>77.555010</td>\n",
              "      <td>[{'label': 'display', 'lat': 13.01092312496633...</td>\n",
              "      <td>2438</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>[India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d180941735', 'name': 'M...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>e-0-4e1d8edd1838f4ab743f26ae-2</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4e1d8edd1838f4ab743f26ae</td>\n",
              "      <td>O.G. Variar &amp; Sons</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.991578</td>\n",
              "      <td>77.554561</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.99157795337298...</td>\n",
              "      <td>4486</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>[India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16a941735', 'name': 'B...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>e-0-4db972aba86ee4fa7d37df76-3</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4db972aba86ee4fa7d37df76</td>\n",
              "      <td>Sheraton Bangalore Hotel</td>\n",
              "      <td>26/1 Dr. Rajkumar Road, Malleswaram-Rajajinagar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.012513</td>\n",
              "      <td>77.555255</td>\n",
              "      <td>[{'label': 'display', 'lat': 13.0125129, 'lng'...</td>\n",
              "      <td>2268</td>\n",
              "      <td>560055</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[26/1 Dr. Rajkumar Road, Malleswaram-Rajajinag...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1fa931735', 'name': 'H...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>e-0-4d90bab2d4ec8cfacfb5a989-4</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4d90bab2d4ec8cfacfb5a989</td>\n",
              "      <td>Corner House</td>\n",
              "      <td>Nehru Circle</td>\n",
              "      <td>Near Sheshadripuram College</td>\n",
              "      <td>12.988304</td>\n",
              "      <td>77.577130</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.98830366372137...</td>\n",
              "      <td>4891</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Nehru Circle (Near Sheshadripuram College), B...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1c9941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>e-0-4b7965c1f964a52085f72ee3-5</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4b7965c1f964a52085f72ee3</td>\n",
              "      <td>Infinitea</td>\n",
              "      <td>Cunningham Road</td>\n",
              "      <td>Vasanth Nagar</td>\n",
              "      <td>12.987157</td>\n",
              "      <td>77.594835</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.98715675640287...</td>\n",
              "      <td>5824</td>\n",
              "      <td>560052</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Cunningham Road (Vasanth Nagar), Bangalore 56...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1dc931735', 'name': 'T...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>e-0-4d0f41eec3dc3704e9633174-6</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4d0f41eec3dc3704e9633174</td>\n",
              "      <td>Asha Sweet Center</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.999371</td>\n",
              "      <td>77.571322</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.99937057262444...</td>\n",
              "      <td>3545</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1c7941735', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>e-0-4b69572ff964a520509f2be3-7</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4b69572ff964a520509f2be3</td>\n",
              "      <td>Taj West End</td>\n",
              "      <td>Racecourse Rd.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.984572</td>\n",
              "      <td>77.584893</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.98457206660889...</td>\n",
              "      <td>5563</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Racecourse Rd., Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1fa931735', 'name': 'H...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>e-0-4f93d338e4b01cffa1f2a8a2-8</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4f93d338e4b01cffa1f2a8a2</td>\n",
              "      <td>Central</td>\n",
              "      <td>26/1 Dr. Rajkumar Road</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.011261</td>\n",
              "      <td>77.555034</td>\n",
              "      <td>[{'label': 'display', 'lat': 13.01126087435231...</td>\n",
              "      <td>2404</td>\n",
              "      <td>560055</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[26/1 Dr. Rajkumar Road, Bangalore 560055, Kar...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1f6941735', 'name': 'D...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>e-0-4d57a3817c0b37048f4ad808-9</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4d57a3817c0b37048f4ad808</td>\n",
              "      <td>Natural Ice Cream</td>\n",
              "      <td>Basweshwarnagar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.985484</td>\n",
              "      <td>77.543158</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.98548406229445...</td>\n",
              "      <td>5548</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Basweshwarnagar, Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1c9941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       referralId  ...  venue.location.neighborhood\n",
              "0  e-0-4f1869c7e4b0ebf9e4ae9134-0  ...                          NaN\n",
              "1  e-0-531ed995498ef21f6f904159-1  ...                          NaN\n",
              "2  e-0-4e1d8edd1838f4ab743f26ae-2  ...                          NaN\n",
              "3  e-0-4db972aba86ee4fa7d37df76-3  ...                          NaN\n",
              "4  e-0-4d90bab2d4ec8cfacfb5a989-4  ...                          NaN\n",
              "5  e-0-4b7965c1f964a52085f72ee3-5  ...                          NaN\n",
              "6  e-0-4d0f41eec3dc3704e9633174-6  ...                          NaN\n",
              "7  e-0-4b69572ff964a520509f2be3-7  ...                          NaN\n",
              "8  e-0-4f93d338e4b01cffa1f2a8a2-8  ...                          NaN\n",
              "9  e-0-4d57a3817c0b37048f4ad808-9  ...                          NaN\n",
              "\n",
              "[10 rows x 22 columns]"
            ]
          },
          "execution_count": 297,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataframe.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xv-Xhd74LA4i"
      },
      "source": [
        "Finding the number of restaurants and other amenities like gyms, parks in the given location (using lat and lng)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "g1-Z9u3Zrx3N",
        "outputId": "872d0a07-7d66-4b3d-cfdc-8dc3aa78c683"
      },
      "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>referralId</th>\n",
              "      <th>reasons.count</th>\n",
              "      <th>reasons.items</th>\n",
              "      <th>venue.id</th>\n",
              "      <th>venue.name</th>\n",
              "      <th>venue.location.address</th>\n",
              "      <th>venue.location.crossStreet</th>\n",
              "      <th>venue.location.lat</th>\n",
              "      <th>venue.location.lng</th>\n",
              "      <th>venue.location.labeledLatLngs</th>\n",
              "      <th>venue.location.distance</th>\n",
              "      <th>venue.location.postalCode</th>\n",
              "      <th>venue.location.cc</th>\n",
              "      <th>venue.location.city</th>\n",
              "      <th>venue.location.state</th>\n",
              "      <th>venue.location.country</th>\n",
              "      <th>venue.location.formattedAddress</th>\n",
              "      <th>venue.categories</th>\n",
              "      <th>venue.photos.count</th>\n",
              "      <th>venue.photos.groups</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>e-0-4b647dd2f964a520cdb72ae3-0</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4b647dd2f964a520cdb72ae3</td>\n",
              "      <td>My Tea House</td>\n",
              "      <td>293, Banashankari 3rd Stage</td>\n",
              "      <td>100ft Ring Rd</td>\n",
              "      <td>12.923086</td>\n",
              "      <td>77.556032</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92308552482382...</td>\n",
              "      <td>0</td>\n",
              "      <td>560085</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[293, Banashankari 3rd Stage (100ft Ring Rd), ...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1dc931735', 'name': 'T...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>e-0-548ff23c498e3cbb402f3df6-1</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>548ff23c498e3cbb402f3df6</td>\n",
              "      <td>Vinny's</td>\n",
              "      <td>Opp. BDA Complex</td>\n",
              "      <td>Bsk 2 Nd Stage</td>\n",
              "      <td>12.924439</td>\n",
              "      <td>77.564848</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92443909349938...</td>\n",
              "      <td>968</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Opp. BDA Complex (Bsk 2 Nd Stage), Bangalore,...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1ca941735', 'name': 'P...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>e-0-4f72ac4fe4b0a60ba514108e-2</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4f72ac4fe4b0a60ba514108e</td>\n",
              "      <td>Kavali</td>\n",
              "      <td>Opp. Chennamanakere Bus Stand</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.928059</td>\n",
              "      <td>77.556010</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92805908794264...</td>\n",
              "      <td>553</td>\n",
              "      <td>560085</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Opp. Chennamanakere Bus Stand, Bangalore 5600...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d143941735', 'name': 'B...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>e-0-4fe3013be4b0abb74945f5e7-3</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4fe3013be4b0abb74945f5e7</td>\n",
              "      <td>Ibaco</td>\n",
              "      <td>#31,21st Main Rd, Banashankari</td>\n",
              "      <td>Opp. BDA Complex</td>\n",
              "      <td>12.925808</td>\n",
              "      <td>77.564340</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92580773987106...</td>\n",
              "      <td>950</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[#31,21st Main Rd, Banashankari (Opp. BDA Comp...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1c9941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>e-0-4d98411c744f3704907fea57-4</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4d98411c744f3704907fea57</td>\n",
              "      <td>Casablanca</td>\n",
              "      <td>Sri Sai Plaza, No. 97, 22nd Main Rd, BSK 2nd S...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.926261</td>\n",
              "      <td>77.564343</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92626050831024...</td>\n",
              "      <td>968</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Sri Sai Plaza, No. 97, 22nd Main Rd, BSK 2nd ...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16c941735', 'name': 'B...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>e-0-4f00421f775bec6b446e0218-5</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4f00421f775bec6b446e0218</td>\n",
              "      <td>Poonam Sweet Centre</td>\n",
              "      <td>BSK BDA Complex</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.924890</td>\n",
              "      <td>77.564926</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92488962738543...</td>\n",
              "      <td>985</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>[BSK BDA Complex, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16e941735', 'name': 'F...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>e-0-4d1ca0a6c50e6ea8b41bd4b8-6</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4d1ca0a6c50e6ea8b41bd4b8</td>\n",
              "      <td>Anna Kuteera</td>\n",
              "      <td>Kathriguppe Main Road</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.926094</td>\n",
              "      <td>77.550367</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92609373090117...</td>\n",
              "      <td>699</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Kathriguppe Main Road, Bangalore, Karnātaka, ...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d10f941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>e-0-4fc1f35ce4b01d437e407d17-7</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4fc1f35ce4b01d437e407d17</td>\n",
              "      <td>Hara</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.924391</td>\n",
              "      <td>77.565137</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92439074016553...</td>\n",
              "      <td>998</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>[India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d10f941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>e-0-55f9259f498e9521e9f1a78e-8</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>55f9259f498e9521e9f1a78e</td>\n",
              "      <td>Smacznego</td>\n",
              "      <td>#2470, 21th Main</td>\n",
              "      <td>25th Cross, Bsk 2nd Stage</td>\n",
              "      <td>12.924022</td>\n",
              "      <td>77.564215</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92402228255212...</td>\n",
              "      <td>893</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[#2470, 21th Main (25th Cross, Bsk 2nd Stage),...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16e941735', 'name': 'F...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>e-0-5221f3dc11d2bc869d73a4c0-9</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>5221f3dc11d2bc869d73a4c0</td>\n",
              "      <td>Roving Feast</td>\n",
              "      <td>Next to Polar Bear</td>\n",
              "      <td>Opp to Banashankari BDA Complex</td>\n",
              "      <td>12.924468</td>\n",
              "      <td>77.565019</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92446794292586...</td>\n",
              "      <td>987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Next to Polar Bear (Opp to Banashankari BDA C...</td>\n",
              "      <td>[{'id': '54135bf5e4b08f3d2429dfdf', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>e-0-4c278977aa77ef3bcef798b0-10</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c278977aa77ef3bcef798b0</td>\n",
              "      <td>1947</td>\n",
              "      <td>Banashankari 3rd Stage</td>\n",
              "      <td>Ring Road</td>\n",
              "      <td>12.922878</td>\n",
              "      <td>77.556389</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92287772608116...</td>\n",
              "      <td>45</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banashankari 3rd Stage (Ring Road), Bangalore...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d10f941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>e-0-52f7973d498e08f2ba6b6384-11</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>52f7973d498e08f2ba6b6384</td>\n",
              "      <td>Maiya's Express</td>\n",
              "      <td>Padmanabhanagar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.918197</td>\n",
              "      <td>77.556125</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.91819673239357...</td>\n",
              "      <td>544</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>[Padmanabhanagar, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1c7941735', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>e-0-4c9e0c908afca0934f90fc15-12</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c9e0c908afca0934f90fc15</td>\n",
              "      <td>Cake of the Day</td>\n",
              "      <td>Outer Ring Road, Kathreguppe IV Phase,</td>\n",
              "      <td>Banashankari 3rd stage,</td>\n",
              "      <td>12.922886</td>\n",
              "      <td>77.556190</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92288594375253...</td>\n",
              "      <td>28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Outer Ring Road, Kathreguppe IV Phase, (Banas...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16a941735', 'name': 'B...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>e-0-4da9c8ddfa8cc764972312bb-13</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4da9c8ddfa8cc764972312bb</td>\n",
              "      <td>Anand Sweets And Savouries</td>\n",
              "      <td>#70,1st Main Rd,Banashankari</td>\n",
              "      <td>Near BDA Complex</td>\n",
              "      <td>12.926310</td>\n",
              "      <td>77.564598</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92631039507795...</td>\n",
              "      <td>996</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[#70,1st Main Rd,Banashankari (Near BDA Comple...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1c7941735', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>e-0-4c5f9cec47429c7410b8197a-14</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c5f9cec47429c7410b8197a</td>\n",
              "      <td>cafe coffee day</td>\n",
              "      <td>Banshankri 3rd stage</td>\n",
              "      <td>1st cross,kathrigupe</td>\n",
              "      <td>12.930137</td>\n",
              "      <td>77.559764</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.93013742112125...</td>\n",
              "      <td>883</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banshankri 3rd stage (1st cross,kathrigupe), ...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16d941735', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>e-0-4ba787a5f964a520f89a39e3-15</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4ba787a5f964a520f89a39e3</td>\n",
              "      <td>J Cubez</td>\n",
              "      <td># 1431, Banashankri 2nd Stage,</td>\n",
              "      <td>22nd Main</td>\n",
              "      <td>12.925807</td>\n",
              "      <td>77.564023</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92580652390511...</td>\n",
              "      <td>918</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[# 1431, Banashankri 2nd Stage, (22nd Main), B...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d11b941735', 'name': 'P...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>e-0-4c27565caa77ef3b78ec98b0-16</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c27565caa77ef3b78ec98b0</td>\n",
              "      <td>Megamart</td>\n",
              "      <td>near Devegowda Petrol Bunk</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>12.923020</td>\n",
              "      <td>77.560389</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92302019087350...</td>\n",
              "      <td>472</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[near Devegowda Petrol Bunk (Banashankari), Ba...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1fd941735', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>e-0-4c7a2e9d81bca0938accff14-17</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c7a2e9d81bca0938accff14</td>\n",
              "      <td>Pizza Hut</td>\n",
              "      <td>92/4, Kathriguppe, 80 feet Rd</td>\n",
              "      <td>Banashankari 3rd Stage</td>\n",
              "      <td>12.924959</td>\n",
              "      <td>77.550755</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92495906409410...</td>\n",
              "      <td>609</td>\n",
              "      <td>560085</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[92/4, Kathriguppe, 80 feet Rd (Banashankari 3...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1ca941735', 'name': 'P...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>e-0-4fd6068de4b02c7fc50d3647-18</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4fd6068de4b02c7fc50d3647</td>\n",
              "      <td>The Platter</td>\n",
              "      <td>No. 10,30th Main, College Teachers Layout, Ban...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.927046</td>\n",
              "      <td>77.561539</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92704630115649...</td>\n",
              "      <td>742</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[No. 10,30th Main, College Teachers Layout, Ba...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d142941735', 'name': 'A...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>e-0-4dd95fc07d8b3226641f141d-19</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4dd95fc07d8b3226641f141d</td>\n",
              "      <td>Kamakya theatre</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.923320</td>\n",
              "      <td>77.553383</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92331972119427...</td>\n",
              "      <td>288</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>[India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d17f941735', 'name': 'M...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>e-0-56361097498e22d081e12fd4-20</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>56361097498e22d081e12fd4</td>\n",
              "      <td>Caf-Eleven</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>Near Devegowda petrol bunk</td>\n",
              "      <td>12.923231</td>\n",
              "      <td>77.554920</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92323072958486...</td>\n",
              "      <td>121</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banashankari (Near Devegowda petrol bunk), Ba...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d119941735', 'name': 'H...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>e-0-4ed208ded3e3521ce7a510f4-21</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4ed208ded3e3521ce7a510f4</td>\n",
              "      <td>Tanishq</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.922339</td>\n",
              "      <td>77.555552</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92233859797268...</td>\n",
              "      <td>98</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banashankari, Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d111951735', 'name': 'J...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>e-0-4e819ec1f79047b5689530dc-22</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4e819ec1f79047b5689530dc</td>\n",
              "      <td>Croma</td>\n",
              "      <td>100 Ft Ring Road</td>\n",
              "      <td>Bsk 3rd Stage</td>\n",
              "      <td>12.924129</td>\n",
              "      <td>77.552998</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92412920338409...</td>\n",
              "      <td>349</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[100 Ft Ring Road (Bsk 3rd Stage), Bangalore, ...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d122951735', 'name': 'E...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>e-0-4c2adec1b5fbb713547449df-23</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c2adec1b5fbb713547449df</td>\n",
              "      <td>Shoprite Super Market</td>\n",
              "      <td># 278, M.K.Puttalingaiah Road,</td>\n",
              "      <td>Near D.G. Hospital, Padmanabhanagar,</td>\n",
              "      <td>12.920235</td>\n",
              "      <td>77.560010</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92023519571532...</td>\n",
              "      <td>535</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[# 278, M.K.Puttalingaiah Road, (Near D.G. Hos...</td>\n",
              "      <td>[{'id': '4d954b0ea243a5684a65b473', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>e-0-4e36e3a562e17d290842a6fe-24</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4e36e3a562e17d290842a6fe</td>\n",
              "      <td>McDonald's</td>\n",
              "      <td>BSK 2nd Stage, 21st Main,</td>\n",
              "      <td>Opp. BDA Complex,</td>\n",
              "      <td>12.924456</td>\n",
              "      <td>77.564968</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92445620485228...</td>\n",
              "      <td>981</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[BSK 2nd Stage, 21st Main, (Opp. BDA Complex,)...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16e941735', 'name': 'F...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>e-0-5644161e38fadb768be959b9-25</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>5644161e38fadb768be959b9</td>\n",
              "      <td>South Kitchen</td>\n",
              "      <td>Bhuvaneshwari Nagar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.921313</td>\n",
              "      <td>77.553296</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92131270970515...</td>\n",
              "      <td>356</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Bhuvaneshwari Nagar, Bangalore, Karnātaka, In...</td>\n",
              "      <td>[{'id': '54135bf5e4b08f3d2429dfde', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>e-0-4c700db9f52cb713d664acaf-26</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c700db9f52cb713d664acaf</td>\n",
              "      <td>Gustoes</td>\n",
              "      <td>No 10, College Teachers HBCS</td>\n",
              "      <td>Banashankari 3rd Stage</td>\n",
              "      <td>12.927095</td>\n",
              "      <td>77.561462</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92709464400327...</td>\n",
              "      <td>739</td>\n",
              "      <td>india</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[No 10, College Teachers HBCS (Banashankari 3r...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1e1931735', 'name': 'A...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>e-0-4c5f9fa947429c746bd6197a-27</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c5f9fa947429c746bd6197a</td>\n",
              "      <td>Cafe Coffee Day</td>\n",
              "      <td>1431, Banshankri 2nd Stage</td>\n",
              "      <td>22nd Cross</td>\n",
              "      <td>12.925870</td>\n",
              "      <td>77.564142</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92587049086617...</td>\n",
              "      <td>932</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[1431, Banshankri 2nd Stage (22nd Cross), Bang...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16d941735', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>e-0-4c275892aa77ef3be8ec98b0-28</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c275892aa77ef3be8ec98b0</td>\n",
              "      <td>Banashankari BDA Shopping Complex</td>\n",
              "      <td>24th Cross Rd</td>\n",
              "      <td>2nd Stage, Banashankari</td>\n",
              "      <td>12.925118</td>\n",
              "      <td>77.564614</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92511811062417...</td>\n",
              "      <td>958</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[24th Cross Rd (2nd Stage, Banashankari), Bang...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1fd941735', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>e-0-54929792498e88761038e3f0-29</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>54929792498e88761038e3f0</td>\n",
              "      <td>Chocolateria San Churro</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>12.927783</td>\n",
              "      <td>77.562078</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92778341114295...</td>\n",
              "      <td>838</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banashankari, Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1d0941735', 'name': 'D...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>e-0-4b7a9e40f964a520cd332fe3-30</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4b7a9e40f964a520cd332fe3</td>\n",
              "      <td>Fitness Cube</td>\n",
              "      <td>padmanabha nagar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.919037</td>\n",
              "      <td>77.557713</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.91903684195092...</td>\n",
              "      <td>486</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[padmanabha nagar, Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d175941735', 'name': 'G...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>e-0-4bebad6eb3352d7f85c456d2-31</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4bebad6eb3352d7f85c456d2</td>\n",
              "      <td>Cafe coffee day</td>\n",
              "      <td>Katriguppe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.926020</td>\n",
              "      <td>77.550306</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92601985435143...</td>\n",
              "      <td>701</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Katriguppe, Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16d941735', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>e-0-4c948ff0f7cfa1cdebdfb415-32</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c948ff0f7cfa1cdebdfb415</td>\n",
              "      <td>Pizza Hut</td>\n",
              "      <td>Banshankri, 2nd Stage, 24th Main</td>\n",
              "      <td>Opp. Banshankri shopping complex,</td>\n",
              "      <td>12.924515</td>\n",
              "      <td>77.564497</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92451500745971...</td>\n",
              "      <td>932</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banshankri, 2nd Stage, 24th Main (Opp. Bansha...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1ca941735', 'name': 'P...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>e-0-4edf823ab6346b119e22784a-33</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4edf823ab6346b119e22784a</td>\n",
              "      <td>Reliance Trends</td>\n",
              "      <td>Next to Big Bazaar, Katriguppe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.926891</td>\n",
              "      <td>77.551068</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92689089784805...</td>\n",
              "      <td>685</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Next to Big Bazaar, Katriguppe, Bangalore, Ka...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d103951735', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>e-0-4f546ac2e4b09f74b9c7efd3-34</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4f546ac2e4b09f74b9c7efd3</td>\n",
              "      <td>Adyar Ananda Bhavan</td>\n",
              "      <td>80 Feet Rd, Banashankari</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.929356</td>\n",
              "      <td>77.552162</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92935588637881...</td>\n",
              "      <td>814</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[80 Feet Rd, Banashankari, Bangalore, Karnātak...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16e941735', 'name': 'F...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>e-0-50150cb1e4b0073bd0ac7daf-35</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>50150cb1e4b0073bd0ac7daf</td>\n",
              "      <td>Puma store banshankri</td>\n",
              "      <td>92/3 80 Ft Road Opposite Big Bazar</td>\n",
              "      <td>3rd Stage</td>\n",
              "      <td>12.926899</td>\n",
              "      <td>77.550973</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92689869563763...</td>\n",
              "      <td>693</td>\n",
              "      <td>560085</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[92/3 80 Ft Road Opposite Big Bazar (3rd Stage...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d107951735', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>e-0-4ef33feef9ab11b14fb69dbe-36</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4ef33feef9ab11b14fb69dbe</td>\n",
              "      <td>kathriguppe junction</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.925483</td>\n",
              "      <td>77.549834</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92548347632488...</td>\n",
              "      <td>723</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d103951735', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>e-0-4e8d588eb803ca3dfcc789bb-37</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4e8d588eb803ca3dfcc789bb</td>\n",
              "      <td>Nandhini Deluxe</td>\n",
              "      <td>Kathriguppe Signal</td>\n",
              "      <td>100ft Ring Road, Banashankari 3rd Stage</td>\n",
              "      <td>12.925742</td>\n",
              "      <td>77.549423</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92574166610375...</td>\n",
              "      <td>775</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>KAR</td>\n",
              "      <td>India</td>\n",
              "      <td>[Kathriguppe Signal (100ft Ring Road, Banashan...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d10f941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>e-0-50e96377e4b007fcc114b421-38</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>50e96377e4b007fcc114b421</td>\n",
              "      <td>Goli Vadapav</td>\n",
              "      <td>BDA Complex</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.924699</td>\n",
              "      <td>77.564212</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92469911669346...</td>\n",
              "      <td>905</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>[BDA Complex, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16c941735', 'name': 'B...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>e-0-53ff45e7498e053f1868a162-39</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>53ff45e7498e053f1868a162</td>\n",
              "      <td>Jalsa</td>\n",
              "      <td>22nd Main, BSK 2nd Stage</td>\n",
              "      <td>Next To Banashankari BDA Complex</td>\n",
              "      <td>12.926183</td>\n",
              "      <td>77.564653</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92618269647203...</td>\n",
              "      <td>996</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[22nd Main, BSK 2nd Stage (Next To Banashankar...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d10f941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>e-0-4e356fce45dd1c42bf1e6674-40</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4e356fce45dd1c42bf1e6674</td>\n",
              "      <td>Nandini Dosa Camp</td>\n",
              "      <td>Bsk 2nd stage</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.927030</td>\n",
              "      <td>77.564324</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92702986937003...</td>\n",
              "      <td>1001</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Bsk 2nd stage, Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4d4b7105d754a06374d81259', 'name': 'F...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         referralId  ...  venue.photos.groups\n",
              "0    e-0-4b647dd2f964a520cdb72ae3-0  ...                   []\n",
              "1    e-0-548ff23c498e3cbb402f3df6-1  ...                   []\n",
              "2    e-0-4f72ac4fe4b0a60ba514108e-2  ...                   []\n",
              "3    e-0-4fe3013be4b0abb74945f5e7-3  ...                   []\n",
              "4    e-0-4d98411c744f3704907fea57-4  ...                   []\n",
              "5    e-0-4f00421f775bec6b446e0218-5  ...                   []\n",
              "6    e-0-4d1ca0a6c50e6ea8b41bd4b8-6  ...                   []\n",
              "7    e-0-4fc1f35ce4b01d437e407d17-7  ...                   []\n",
              "8    e-0-55f9259f498e9521e9f1a78e-8  ...                   []\n",
              "9    e-0-5221f3dc11d2bc869d73a4c0-9  ...                   []\n",
              "10  e-0-4c278977aa77ef3bcef798b0-10  ...                   []\n",
              "11  e-0-52f7973d498e08f2ba6b6384-11  ...                   []\n",
              "12  e-0-4c9e0c908afca0934f90fc15-12  ...                   []\n",
              "13  e-0-4da9c8ddfa8cc764972312bb-13  ...                   []\n",
              "14  e-0-4c5f9cec47429c7410b8197a-14  ...                   []\n",
              "15  e-0-4ba787a5f964a520f89a39e3-15  ...                   []\n",
              "16  e-0-4c27565caa77ef3b78ec98b0-16  ...                   []\n",
              "17  e-0-4c7a2e9d81bca0938accff14-17  ...                   []\n",
              "18  e-0-4fd6068de4b02c7fc50d3647-18  ...                   []\n",
              "19  e-0-4dd95fc07d8b3226641f141d-19  ...                   []\n",
              "20  e-0-56361097498e22d081e12fd4-20  ...                   []\n",
              "21  e-0-4ed208ded3e3521ce7a510f4-21  ...                   []\n",
              "22  e-0-4e819ec1f79047b5689530dc-22  ...                   []\n",
              "23  e-0-4c2adec1b5fbb713547449df-23  ...                   []\n",
              "24  e-0-4e36e3a562e17d290842a6fe-24  ...                   []\n",
              "25  e-0-5644161e38fadb768be959b9-25  ...                   []\n",
              "26  e-0-4c700db9f52cb713d664acaf-26  ...                   []\n",
              "27  e-0-4c5f9fa947429c746bd6197a-27  ...                   []\n",
              "28  e-0-4c275892aa77ef3be8ec98b0-28  ...                   []\n",
              "29  e-0-54929792498e88761038e3f0-29  ...                   []\n",
              "30  e-0-4b7a9e40f964a520cd332fe3-30  ...                   []\n",
              "31  e-0-4bebad6eb3352d7f85c456d2-31  ...                   []\n",
              "32  e-0-4c948ff0f7cfa1cdebdfb415-32  ...                   []\n",
              "33  e-0-4edf823ab6346b119e22784a-33  ...                   []\n",
              "34  e-0-4f546ac2e4b09f74b9c7efd3-34  ...                   []\n",
              "35  e-0-50150cb1e4b0073bd0ac7daf-35  ...                   []\n",
              "36  e-0-4ef33feef9ab11b14fb69dbe-36  ...                   []\n",
              "37  e-0-4e8d588eb803ca3dfcc789bb-37  ...                   []\n",
              "38  e-0-50e96377e4b007fcc114b421-38  ...                   []\n",
              "39  e-0-53ff45e7498e053f1868a162-39  ...                   []\n",
              "40  e-0-4e356fce45dd1c42bf1e6674-40  ...                   []\n",
              "\n",
              "[41 rows x 20 columns]"
            ]
          },
          "execution_count": 298,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "resta=[]\n",
        "oth=[]\n",
        "for lat,lng in zip(dataframe['venue.location.lat'],dataframe['venue.location.lng']):\n",
        "    url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
        "      CLIENT_ID, \n",
        "      CLIENT_SECRET, \n",
        "      VERSION, \n",
        "      lat,lng,\n",
        "      1000, \n",
        "      100)\n",
        "    res = requests.get(url).json()\n",
        "    venue = res['response']['groups'][0]['items']\n",
        "    dataframe = pd.json_normalize(venue)\n",
        "    df=dataframe['venue.categories']\n",
        "\n",
        "    g=[]\n",
        "    for i in range(0,df.size):\n",
        "      g.append(df[i][0]['icon']['prefix'].find('food'))\n",
        "    co=0\n",
        "    for i in g:\n",
        "      if i>1:\n",
        "        co+=1\n",
        "    resta.append(co)\n",
        "    oth.append(len(g)-co)\n",
        "dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w8wBZFdztdz6"
      },
      "outputs": [],
      "source": [
        "dataframe['restaurant'] = pd.Series(resta)\n",
        "dataframe['others']=pd.Series(oth)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dj6TbMsmr9od",
        "outputId": "5fb40c3a-fbff-442b-c2ea-23b8e090beca"
      },
      "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>referralId</th>\n",
              "      <th>reasons.count</th>\n",
              "      <th>reasons.items</th>\n",
              "      <th>venue.id</th>\n",
              "      <th>venue.name</th>\n",
              "      <th>venue.location.address</th>\n",
              "      <th>venue.location.crossStreet</th>\n",
              "      <th>venue.location.lat</th>\n",
              "      <th>venue.location.lng</th>\n",
              "      <th>venue.location.labeledLatLngs</th>\n",
              "      <th>venue.location.distance</th>\n",
              "      <th>venue.location.postalCode</th>\n",
              "      <th>venue.location.cc</th>\n",
              "      <th>venue.location.city</th>\n",
              "      <th>venue.location.state</th>\n",
              "      <th>venue.location.country</th>\n",
              "      <th>venue.location.formattedAddress</th>\n",
              "      <th>venue.categories</th>\n",
              "      <th>venue.photos.count</th>\n",
              "      <th>venue.photos.groups</th>\n",
              "      <th>restaurant</th>\n",
              "      <th>others</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>e-0-4b647dd2f964a520cdb72ae3-0</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4b647dd2f964a520cdb72ae3</td>\n",
              "      <td>My Tea House</td>\n",
              "      <td>293, Banashankari 3rd Stage</td>\n",
              "      <td>100ft Ring Rd</td>\n",
              "      <td>12.923086</td>\n",
              "      <td>77.556032</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92308552482382...</td>\n",
              "      <td>0</td>\n",
              "      <td>560085</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[293, Banashankari 3rd Stage (100ft Ring Rd), ...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1dc931735', 'name': 'T...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>e-0-548ff23c498e3cbb402f3df6-1</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>548ff23c498e3cbb402f3df6</td>\n",
              "      <td>Vinny's</td>\n",
              "      <td>Opp. BDA Complex</td>\n",
              "      <td>Bsk 2 Nd Stage</td>\n",
              "      <td>12.924439</td>\n",
              "      <td>77.564848</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92443909349938...</td>\n",
              "      <td>968</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Opp. BDA Complex (Bsk 2 Nd Stage), Bangalore,...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1ca941735', 'name': 'P...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>35</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>e-0-4f72ac4fe4b0a60ba514108e-2</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4f72ac4fe4b0a60ba514108e</td>\n",
              "      <td>Kavali</td>\n",
              "      <td>Opp. Chennamanakere Bus Stand</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.928059</td>\n",
              "      <td>77.556010</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92805908794264...</td>\n",
              "      <td>553</td>\n",
              "      <td>560085</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Opp. Chennamanakere Bus Stand, Bangalore 5600...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d143941735', 'name': 'B...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>22</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>e-0-4fe3013be4b0abb74945f5e7-3</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4fe3013be4b0abb74945f5e7</td>\n",
              "      <td>Ibaco</td>\n",
              "      <td>#31,21st Main Rd, Banashankari</td>\n",
              "      <td>Opp. BDA Complex</td>\n",
              "      <td>12.925808</td>\n",
              "      <td>77.564340</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92580773987106...</td>\n",
              "      <td>950</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[#31,21st Main Rd, Banashankari (Opp. BDA Comp...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1c9941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>31</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>e-0-4d98411c744f3704907fea57-4</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4d98411c744f3704907fea57</td>\n",
              "      <td>Casablanca</td>\n",
              "      <td>Sri Sai Plaza, No. 97, 22nd Main Rd, BSK 2nd S...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.926261</td>\n",
              "      <td>77.564343</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92626050831024...</td>\n",
              "      <td>968</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Sri Sai Plaza, No. 97, 22nd Main Rd, BSK 2nd ...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16c941735', 'name': 'B...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>49</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>e-0-4f00421f775bec6b446e0218-5</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4f00421f775bec6b446e0218</td>\n",
              "      <td>Poonam Sweet Centre</td>\n",
              "      <td>BSK BDA Complex</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.924890</td>\n",
              "      <td>77.564926</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92488962738543...</td>\n",
              "      <td>985</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>[BSK BDA Complex, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16e941735', 'name': 'F...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>50</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>e-0-4d1ca0a6c50e6ea8b41bd4b8-6</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4d1ca0a6c50e6ea8b41bd4b8</td>\n",
              "      <td>Anna Kuteera</td>\n",
              "      <td>Kathriguppe Main Road</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.926094</td>\n",
              "      <td>77.550367</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92609373090117...</td>\n",
              "      <td>699</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Kathriguppe Main Road, Bangalore, Karnātaka, ...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d10f941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>52</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>e-0-4fc1f35ce4b01d437e407d17-7</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4fc1f35ce4b01d437e407d17</td>\n",
              "      <td>Hara</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.924391</td>\n",
              "      <td>77.565137</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92439074016553...</td>\n",
              "      <td>998</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>[India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d10f941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>27</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>e-0-55f9259f498e9521e9f1a78e-8</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>55f9259f498e9521e9f1a78e</td>\n",
              "      <td>Smacznego</td>\n",
              "      <td>#2470, 21th Main</td>\n",
              "      <td>25th Cross, Bsk 2nd Stage</td>\n",
              "      <td>12.924022</td>\n",
              "      <td>77.564215</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92402228255212...</td>\n",
              "      <td>893</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[#2470, 21th Main (25th Cross, Bsk 2nd Stage),...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16e941735', 'name': 'F...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>34</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>e-0-5221f3dc11d2bc869d73a4c0-9</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>5221f3dc11d2bc869d73a4c0</td>\n",
              "      <td>Roving Feast</td>\n",
              "      <td>Next to Polar Bear</td>\n",
              "      <td>Opp to Banashankari BDA Complex</td>\n",
              "      <td>12.924468</td>\n",
              "      <td>77.565019</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92446794292586...</td>\n",
              "      <td>987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Next to Polar Bear (Opp to Banashankari BDA C...</td>\n",
              "      <td>[{'id': '54135bf5e4b08f3d2429dfdf', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>26</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>e-0-4c278977aa77ef3bcef798b0-10</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c278977aa77ef3bcef798b0</td>\n",
              "      <td>1947</td>\n",
              "      <td>Banashankari 3rd Stage</td>\n",
              "      <td>Ring Road</td>\n",
              "      <td>12.922878</td>\n",
              "      <td>77.556389</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92287772608116...</td>\n",
              "      <td>45</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banashankari 3rd Stage (Ring Road), Bangalore...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d10f941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>35</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>e-0-52f7973d498e08f2ba6b6384-11</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>52f7973d498e08f2ba6b6384</td>\n",
              "      <td>Maiya's Express</td>\n",
              "      <td>Padmanabhanagar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.918197</td>\n",
              "      <td>77.556125</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.91819673239357...</td>\n",
              "      <td>544</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>[Padmanabhanagar, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1c7941735', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>35</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>e-0-4c9e0c908afca0934f90fc15-12</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c9e0c908afca0934f90fc15</td>\n",
              "      <td>Cake of the Day</td>\n",
              "      <td>Outer Ring Road, Kathreguppe IV Phase,</td>\n",
              "      <td>Banashankari 3rd stage,</td>\n",
              "      <td>12.922886</td>\n",
              "      <td>77.556190</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92288594375253...</td>\n",
              "      <td>28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Outer Ring Road, Kathreguppe IV Phase, (Banas...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16a941735', 'name': 'B...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>32</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>e-0-4da9c8ddfa8cc764972312bb-13</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4da9c8ddfa8cc764972312bb</td>\n",
              "      <td>Anand Sweets And Savouries</td>\n",
              "      <td>#70,1st Main Rd,Banashankari</td>\n",
              "      <td>Near BDA Complex</td>\n",
              "      <td>12.926310</td>\n",
              "      <td>77.564598</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92631039507795...</td>\n",
              "      <td>996</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[#70,1st Main Rd,Banashankari (Near BDA Comple...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1c7941735', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>37</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>e-0-4c5f9cec47429c7410b8197a-14</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c5f9cec47429c7410b8197a</td>\n",
              "      <td>cafe coffee day</td>\n",
              "      <td>Banshankri 3rd stage</td>\n",
              "      <td>1st cross,kathrigupe</td>\n",
              "      <td>12.930137</td>\n",
              "      <td>77.559764</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.93013742112125...</td>\n",
              "      <td>883</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banshankri 3rd stage (1st cross,kathrigupe), ...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16d941735', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>44</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>e-0-4ba787a5f964a520f89a39e3-15</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4ba787a5f964a520f89a39e3</td>\n",
              "      <td>J Cubez</td>\n",
              "      <td># 1431, Banashankri 2nd Stage,</td>\n",
              "      <td>22nd Main</td>\n",
              "      <td>12.925807</td>\n",
              "      <td>77.564023</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92580652390511...</td>\n",
              "      <td>918</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[# 1431, Banashankri 2nd Stage, (22nd Main), B...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d11b941735', 'name': 'P...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>34</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>e-0-4c27565caa77ef3b78ec98b0-16</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c27565caa77ef3b78ec98b0</td>\n",
              "      <td>Megamart</td>\n",
              "      <td>near Devegowda Petrol Bunk</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>12.923020</td>\n",
              "      <td>77.560389</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92302019087350...</td>\n",
              "      <td>472</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[near Devegowda Petrol Bunk (Banashankari), Ba...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1fd941735', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>e-0-4c7a2e9d81bca0938accff14-17</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c7a2e9d81bca0938accff14</td>\n",
              "      <td>Pizza Hut</td>\n",
              "      <td>92/4, Kathriguppe, 80 feet Rd</td>\n",
              "      <td>Banashankari 3rd Stage</td>\n",
              "      <td>12.924959</td>\n",
              "      <td>77.550755</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92495906409410...</td>\n",
              "      <td>609</td>\n",
              "      <td>560085</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[92/4, Kathriguppe, 80 feet Rd (Banashankari 3...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1ca941735', 'name': 'P...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>e-0-4fd6068de4b02c7fc50d3647-18</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4fd6068de4b02c7fc50d3647</td>\n",
              "      <td>The Platter</td>\n",
              "      <td>No. 10,30th Main, College Teachers Layout, Ban...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.927046</td>\n",
              "      <td>77.561539</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92704630115649...</td>\n",
              "      <td>742</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[No. 10,30th Main, College Teachers Layout, Ba...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d142941735', 'name': 'A...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>44</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>e-0-4dd95fc07d8b3226641f141d-19</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4dd95fc07d8b3226641f141d</td>\n",
              "      <td>Kamakya theatre</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.923320</td>\n",
              "      <td>77.553383</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92331972119427...</td>\n",
              "      <td>288</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>[India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d17f941735', 'name': 'M...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>57</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>e-0-56361097498e22d081e12fd4-20</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>56361097498e22d081e12fd4</td>\n",
              "      <td>Caf-Eleven</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>Near Devegowda petrol bunk</td>\n",
              "      <td>12.923231</td>\n",
              "      <td>77.554920</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92323072958486...</td>\n",
              "      <td>121</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banashankari (Near Devegowda petrol bunk), Ba...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d119941735', 'name': 'H...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>59</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>e-0-4ed208ded3e3521ce7a510f4-21</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4ed208ded3e3521ce7a510f4</td>\n",
              "      <td>Tanishq</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.922339</td>\n",
              "      <td>77.555552</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92233859797268...</td>\n",
              "      <td>98</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banashankari, Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d111951735', 'name': 'J...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>58</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>e-0-4e819ec1f79047b5689530dc-22</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4e819ec1f79047b5689530dc</td>\n",
              "      <td>Croma</td>\n",
              "      <td>100 Ft Ring Road</td>\n",
              "      <td>Bsk 3rd Stage</td>\n",
              "      <td>12.924129</td>\n",
              "      <td>77.552998</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92412920338409...</td>\n",
              "      <td>349</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[100 Ft Ring Road (Bsk 3rd Stage), Bangalore, ...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d122951735', 'name': 'E...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>41</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>e-0-4c2adec1b5fbb713547449df-23</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c2adec1b5fbb713547449df</td>\n",
              "      <td>Shoprite Super Market</td>\n",
              "      <td># 278, M.K.Puttalingaiah Road,</td>\n",
              "      <td>Near D.G. Hospital, Padmanabhanagar,</td>\n",
              "      <td>12.920235</td>\n",
              "      <td>77.560010</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92023519571532...</td>\n",
              "      <td>535</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[# 278, M.K.Puttalingaiah Road, (Near D.G. Hos...</td>\n",
              "      <td>[{'id': '4d954b0ea243a5684a65b473', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>18</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>e-0-4e36e3a562e17d290842a6fe-24</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4e36e3a562e17d290842a6fe</td>\n",
              "      <td>McDonald's</td>\n",
              "      <td>BSK 2nd Stage, 21st Main,</td>\n",
              "      <td>Opp. BDA Complex,</td>\n",
              "      <td>12.924456</td>\n",
              "      <td>77.564968</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92445620485228...</td>\n",
              "      <td>981</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[BSK 2nd Stage, 21st Main, (Opp. BDA Complex,)...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16e941735', 'name': 'F...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>60</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>e-0-5644161e38fadb768be959b9-25</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>5644161e38fadb768be959b9</td>\n",
              "      <td>South Kitchen</td>\n",
              "      <td>Bhuvaneshwari Nagar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.921313</td>\n",
              "      <td>77.553296</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92131270970515...</td>\n",
              "      <td>356</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Bhuvaneshwari Nagar, Bangalore, Karnātaka, In...</td>\n",
              "      <td>[{'id': '54135bf5e4b08f3d2429dfde', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>56</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>e-0-4c700db9f52cb713d664acaf-26</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c700db9f52cb713d664acaf</td>\n",
              "      <td>Gustoes</td>\n",
              "      <td>No 10, College Teachers HBCS</td>\n",
              "      <td>Banashankari 3rd Stage</td>\n",
              "      <td>12.927095</td>\n",
              "      <td>77.561462</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92709464400327...</td>\n",
              "      <td>739</td>\n",
              "      <td>india</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[No 10, College Teachers HBCS (Banashankari 3r...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1e1931735', 'name': 'A...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>59</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>e-0-4c5f9fa947429c746bd6197a-27</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c5f9fa947429c746bd6197a</td>\n",
              "      <td>Cafe Coffee Day</td>\n",
              "      <td>1431, Banshankri 2nd Stage</td>\n",
              "      <td>22nd Cross</td>\n",
              "      <td>12.925870</td>\n",
              "      <td>77.564142</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92587049086617...</td>\n",
              "      <td>932</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[1431, Banshankri 2nd Stage (22nd Cross), Bang...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16d941735', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>56</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>e-0-4c275892aa77ef3be8ec98b0-28</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c275892aa77ef3be8ec98b0</td>\n",
              "      <td>Banashankari BDA Shopping Complex</td>\n",
              "      <td>24th Cross Rd</td>\n",
              "      <td>2nd Stage, Banashankari</td>\n",
              "      <td>12.925118</td>\n",
              "      <td>77.564614</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92511811062417...</td>\n",
              "      <td>958</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[24th Cross Rd (2nd Stage, Banashankari), Bang...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1fd941735', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>47</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>e-0-54929792498e88761038e3f0-29</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>54929792498e88761038e3f0</td>\n",
              "      <td>Chocolateria San Churro</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>12.927783</td>\n",
              "      <td>77.562078</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92778341114295...</td>\n",
              "      <td>838</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banashankari, Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1d0941735', 'name': 'D...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>59</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>e-0-4b7a9e40f964a520cd332fe3-30</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4b7a9e40f964a520cd332fe3</td>\n",
              "      <td>Fitness Cube</td>\n",
              "      <td>padmanabha nagar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.919037</td>\n",
              "      <td>77.557713</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.91903684195092...</td>\n",
              "      <td>486</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[padmanabha nagar, Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d175941735', 'name': 'G...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>59</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>e-0-4bebad6eb3352d7f85c456d2-31</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4bebad6eb3352d7f85c456d2</td>\n",
              "      <td>Cafe coffee day</td>\n",
              "      <td>Katriguppe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.926020</td>\n",
              "      <td>77.550306</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92601985435143...</td>\n",
              "      <td>701</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Katriguppe, Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16d941735', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>57</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>e-0-4c948ff0f7cfa1cdebdfb415-32</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c948ff0f7cfa1cdebdfb415</td>\n",
              "      <td>Pizza Hut</td>\n",
              "      <td>Banshankri, 2nd Stage, 24th Main</td>\n",
              "      <td>Opp. Banshankri shopping complex,</td>\n",
              "      <td>12.924515</td>\n",
              "      <td>77.564497</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92451500745971...</td>\n",
              "      <td>932</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banshankri, 2nd Stage, 24th Main (Opp. Bansha...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1ca941735', 'name': 'P...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>58</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>e-0-4edf823ab6346b119e22784a-33</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4edf823ab6346b119e22784a</td>\n",
              "      <td>Reliance Trends</td>\n",
              "      <td>Next to Big Bazaar, Katriguppe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.926891</td>\n",
              "      <td>77.551068</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92689089784805...</td>\n",
              "      <td>685</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Next to Big Bazaar, Katriguppe, Bangalore, Ka...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d103951735', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>51</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>e-0-4f546ac2e4b09f74b9c7efd3-34</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4f546ac2e4b09f74b9c7efd3</td>\n",
              "      <td>Adyar Ananda Bhavan</td>\n",
              "      <td>80 Feet Rd, Banashankari</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.929356</td>\n",
              "      <td>77.552162</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92935588637881...</td>\n",
              "      <td>814</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[80 Feet Rd, Banashankari, Bangalore, Karnātak...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16e941735', 'name': 'F...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>55</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>e-0-50150cb1e4b0073bd0ac7daf-35</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>50150cb1e4b0073bd0ac7daf</td>\n",
              "      <td>Puma store banshankri</td>\n",
              "      <td>92/3 80 Ft Road Opposite Big Bazar</td>\n",
              "      <td>3rd Stage</td>\n",
              "      <td>12.926899</td>\n",
              "      <td>77.550973</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92689869563763...</td>\n",
              "      <td>693</td>\n",
              "      <td>560085</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[92/3 80 Ft Road Opposite Big Bazar (3rd Stage...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d107951735', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>34</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>e-0-4ef33feef9ab11b14fb69dbe-36</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4ef33feef9ab11b14fb69dbe</td>\n",
              "      <td>kathriguppe junction</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.925483</td>\n",
              "      <td>77.549834</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92548347632488...</td>\n",
              "      <td>723</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d103951735', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>67</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>e-0-4e8d588eb803ca3dfcc789bb-37</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4e8d588eb803ca3dfcc789bb</td>\n",
              "      <td>Nandhini Deluxe</td>\n",
              "      <td>Kathriguppe Signal</td>\n",
              "      <td>100ft Ring Road, Banashankari 3rd Stage</td>\n",
              "      <td>12.925742</td>\n",
              "      <td>77.549423</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92574166610375...</td>\n",
              "      <td>775</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>KAR</td>\n",
              "      <td>India</td>\n",
              "      <td>[Kathriguppe Signal (100ft Ring Road, Banashan...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d10f941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>34</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>e-0-50e96377e4b007fcc114b421-38</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>50e96377e4b007fcc114b421</td>\n",
              "      <td>Goli Vadapav</td>\n",
              "      <td>BDA Complex</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.924699</td>\n",
              "      <td>77.564212</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92469911669346...</td>\n",
              "      <td>905</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>[BDA Complex, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16c941735', 'name': 'B...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>28</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>e-0-53ff45e7498e053f1868a162-39</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>53ff45e7498e053f1868a162</td>\n",
              "      <td>Jalsa</td>\n",
              "      <td>22nd Main, BSK 2nd Stage</td>\n",
              "      <td>Next To Banashankari BDA Complex</td>\n",
              "      <td>12.926183</td>\n",
              "      <td>77.564653</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92618269647203...</td>\n",
              "      <td>996</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[22nd Main, BSK 2nd Stage (Next To Banashankar...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d10f941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>49</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>e-0-4e356fce45dd1c42bf1e6674-40</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4e356fce45dd1c42bf1e6674</td>\n",
              "      <td>Nandini Dosa Camp</td>\n",
              "      <td>Bsk 2nd stage</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.927030</td>\n",
              "      <td>77.564324</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92702986937003...</td>\n",
              "      <td>1001</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Bsk 2nd stage, Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4d4b7105d754a06374d81259', 'name': 'F...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>37</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         referralId  reasons.count  ... restaurant others\n",
              "0    e-0-4b647dd2f964a520cdb72ae3-0              0  ...         33     30\n",
              "1    e-0-548ff23c498e3cbb402f3df6-1              0  ...         35     31\n",
              "2    e-0-4f72ac4fe4b0a60ba514108e-2              0  ...         22      6\n",
              "3    e-0-4fe3013be4b0abb74945f5e7-3              0  ...         31     30\n",
              "4    e-0-4d98411c744f3704907fea57-4              0  ...         49     28\n",
              "5    e-0-4f00421f775bec6b446e0218-5              0  ...         50     16\n",
              "6    e-0-4d1ca0a6c50e6ea8b41bd4b8-6              0  ...         52     21\n",
              "7    e-0-4fc1f35ce4b01d437e407d17-7              0  ...         27     21\n",
              "8    e-0-55f9259f498e9521e9f1a78e-8              0  ...         34     30\n",
              "9    e-0-5221f3dc11d2bc869d73a4c0-9              0  ...         26      4\n",
              "10  e-0-4c278977aa77ef3bcef798b0-10              0  ...         35     30\n",
              "11  e-0-52f7973d498e08f2ba6b6384-11              0  ...         35     31\n",
              "12  e-0-4c9e0c908afca0934f90fc15-12              0  ...         32     30\n",
              "13  e-0-4da9c8ddfa8cc764972312bb-13              0  ...         37     32\n",
              "14  e-0-4c5f9cec47429c7410b8197a-14              0  ...         44     19\n",
              "15  e-0-4ba787a5f964a520f89a39e3-15              0  ...         34     29\n",
              "16  e-0-4c27565caa77ef3b78ec98b0-16              0  ...          4      6\n",
              "17  e-0-4c7a2e9d81bca0938accff14-17              0  ...         12      3\n",
              "18  e-0-4fd6068de4b02c7fc50d3647-18              0  ...         44     30\n",
              "19  e-0-4dd95fc07d8b3226641f141d-19              0  ...         57     43\n",
              "20  e-0-56361097498e22d081e12fd4-20              0  ...         59     41\n",
              "21  e-0-4ed208ded3e3521ce7a510f4-21              0  ...         58     42\n",
              "22  e-0-4e819ec1f79047b5689530dc-22              0  ...         41      7\n",
              "23  e-0-4c2adec1b5fbb713547449df-23              0  ...         18      9\n",
              "24  e-0-4e36e3a562e17d290842a6fe-24              0  ...         60     40\n",
              "25  e-0-5644161e38fadb768be959b9-25              0  ...         56     44\n",
              "26  e-0-4c700db9f52cb713d664acaf-26              0  ...         59     41\n",
              "27  e-0-4c5f9fa947429c746bd6197a-27              0  ...         56     44\n",
              "28  e-0-4c275892aa77ef3be8ec98b0-28              0  ...         47     16\n",
              "29  e-0-54929792498e88761038e3f0-29              0  ...         59     41\n",
              "30  e-0-4b7a9e40f964a520cd332fe3-30              0  ...         59     41\n",
              "31  e-0-4bebad6eb3352d7f85c456d2-31              0  ...         57     43\n",
              "32  e-0-4c948ff0f7cfa1cdebdfb415-32              0  ...         58     42\n",
              "33  e-0-4edf823ab6346b119e22784a-33              0  ...         51     24\n",
              "34  e-0-4f546ac2e4b09f74b9c7efd3-34              0  ...         55     45\n",
              "35  e-0-50150cb1e4b0073bd0ac7daf-35              0  ...         34     20\n",
              "36  e-0-4ef33feef9ab11b14fb69dbe-36              0  ...         67     33\n",
              "37  e-0-4e8d588eb803ca3dfcc789bb-37              0  ...         34     30\n",
              "38  e-0-50e96377e4b007fcc114b421-38              0  ...         28      5\n",
              "39  e-0-53ff45e7498e053f1868a162-39              0  ...         49     14\n",
              "40  e-0-4e356fce45dd1c42bf1e6674-40              0  ...         37      7\n",
              "\n",
              "[41 rows x 22 columns]"
            ]
          },
          "execution_count": 300,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lTfyFG72yH1w",
        "outputId": "067cabfe-c352-4466-f88b-2df6cd19dc9d"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>referralId</th>\n",
              "      <th>reasons.count</th>\n",
              "      <th>reasons.items</th>\n",
              "      <th>venue.id</th>\n",
              "      <th>venue.name</th>\n",
              "      <th>venue.location.address</th>\n",
              "      <th>venue.location.crossStreet</th>\n",
              "      <th>venue.location.lat</th>\n",
              "      <th>venue.location.lng</th>\n",
              "      <th>venue.location.labeledLatLngs</th>\n",
              "      <th>venue.location.distance</th>\n",
              "      <th>venue.location.postalCode</th>\n",
              "      <th>venue.location.cc</th>\n",
              "      <th>venue.location.city</th>\n",
              "      <th>venue.location.state</th>\n",
              "      <th>venue.location.country</th>\n",
              "      <th>venue.location.formattedAddress</th>\n",
              "      <th>venue.categories</th>\n",
              "      <th>venue.photos.count</th>\n",
              "      <th>venue.photos.groups</th>\n",
              "      <th>restaurant</th>\n",
              "      <th>others</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>e-0-4b647dd2f964a520cdb72ae3-0</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4b647dd2f964a520cdb72ae3</td>\n",
              "      <td>My Tea House</td>\n",
              "      <td>293, Banashankari 3rd Stage</td>\n",
              "      <td>100ft Ring Rd</td>\n",
              "      <td>12.923086</td>\n",
              "      <td>77.556032</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92308552482382...</td>\n",
              "      <td>0</td>\n",
              "      <td>560085</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[293, Banashankari 3rd Stage (100ft Ring Rd), ...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1dc931735', 'name': 'T...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>e-0-548ff23c498e3cbb402f3df6-1</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>548ff23c498e3cbb402f3df6</td>\n",
              "      <td>Vinny's</td>\n",
              "      <td>Opp. BDA Complex</td>\n",
              "      <td>Bsk 2 Nd Stage</td>\n",
              "      <td>12.924439</td>\n",
              "      <td>77.564848</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92443909349938...</td>\n",
              "      <td>968</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Opp. BDA Complex (Bsk 2 Nd Stage), Bangalore,...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1ca941735', 'name': 'P...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>35</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>e-0-4f72ac4fe4b0a60ba514108e-2</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4f72ac4fe4b0a60ba514108e</td>\n",
              "      <td>Kavali</td>\n",
              "      <td>Opp. Chennamanakere Bus Stand</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.928059</td>\n",
              "      <td>77.556010</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92805908794264...</td>\n",
              "      <td>553</td>\n",
              "      <td>560085</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Opp. Chennamanakere Bus Stand, Bangalore 5600...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d143941735', 'name': 'B...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>22</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>e-0-4fe3013be4b0abb74945f5e7-3</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4fe3013be4b0abb74945f5e7</td>\n",
              "      <td>Ibaco</td>\n",
              "      <td>#31,21st Main Rd, Banashankari</td>\n",
              "      <td>Opp. BDA Complex</td>\n",
              "      <td>12.925808</td>\n",
              "      <td>77.564340</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92580773987106...</td>\n",
              "      <td>950</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[#31,21st Main Rd, Banashankari (Opp. BDA Comp...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1c9941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>31</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>e-0-4d98411c744f3704907fea57-4</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4d98411c744f3704907fea57</td>\n",
              "      <td>Casablanca</td>\n",
              "      <td>Sri Sai Plaza, No. 97, 22nd Main Rd, BSK 2nd S...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.926261</td>\n",
              "      <td>77.564343</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92626050831024...</td>\n",
              "      <td>968</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Sri Sai Plaza, No. 97, 22nd Main Rd, BSK 2nd ...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16c941735', 'name': 'B...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>49</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>e-0-4f00421f775bec6b446e0218-5</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4f00421f775bec6b446e0218</td>\n",
              "      <td>Poonam Sweet Centre</td>\n",
              "      <td>BSK BDA Complex</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.924890</td>\n",
              "      <td>77.564926</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92488962738543...</td>\n",
              "      <td>985</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>[BSK BDA Complex, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16e941735', 'name': 'F...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>50</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>e-0-4d1ca0a6c50e6ea8b41bd4b8-6</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4d1ca0a6c50e6ea8b41bd4b8</td>\n",
              "      <td>Anna Kuteera</td>\n",
              "      <td>Kathriguppe Main Road</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.926094</td>\n",
              "      <td>77.550367</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92609373090117...</td>\n",
              "      <td>699</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Kathriguppe Main Road, Bangalore, Karnātaka, ...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d10f941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>52</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>e-0-4fc1f35ce4b01d437e407d17-7</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4fc1f35ce4b01d437e407d17</td>\n",
              "      <td>Hara</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.924391</td>\n",
              "      <td>77.565137</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92439074016553...</td>\n",
              "      <td>998</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>[India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d10f941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>27</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>e-0-55f9259f498e9521e9f1a78e-8</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>55f9259f498e9521e9f1a78e</td>\n",
              "      <td>Smacznego</td>\n",
              "      <td>#2470, 21th Main</td>\n",
              "      <td>25th Cross, Bsk 2nd Stage</td>\n",
              "      <td>12.924022</td>\n",
              "      <td>77.564215</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92402228255212...</td>\n",
              "      <td>893</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[#2470, 21th Main (25th Cross, Bsk 2nd Stage),...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16e941735', 'name': 'F...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>34</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>e-0-5221f3dc11d2bc869d73a4c0-9</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>5221f3dc11d2bc869d73a4c0</td>\n",
              "      <td>Roving Feast</td>\n",
              "      <td>Next to Polar Bear</td>\n",
              "      <td>Opp to Banashankari BDA Complex</td>\n",
              "      <td>12.924468</td>\n",
              "      <td>77.565019</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92446794292586...</td>\n",
              "      <td>987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Next to Polar Bear (Opp to Banashankari BDA C...</td>\n",
              "      <td>[{'id': '54135bf5e4b08f3d2429dfdf', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>26</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>e-0-4c278977aa77ef3bcef798b0-10</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c278977aa77ef3bcef798b0</td>\n",
              "      <td>1947</td>\n",
              "      <td>Banashankari 3rd Stage</td>\n",
              "      <td>Ring Road</td>\n",
              "      <td>12.922878</td>\n",
              "      <td>77.556389</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92287772608116...</td>\n",
              "      <td>45</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banashankari 3rd Stage (Ring Road), Bangalore...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d10f941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>35</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>e-0-52f7973d498e08f2ba6b6384-11</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>52f7973d498e08f2ba6b6384</td>\n",
              "      <td>Maiya's Express</td>\n",
              "      <td>Padmanabhanagar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.918197</td>\n",
              "      <td>77.556125</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.91819673239357...</td>\n",
              "      <td>544</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>[Padmanabhanagar, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1c7941735', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>35</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>e-0-4c9e0c908afca0934f90fc15-12</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c9e0c908afca0934f90fc15</td>\n",
              "      <td>Cake of the Day</td>\n",
              "      <td>Outer Ring Road, Kathreguppe IV Phase,</td>\n",
              "      <td>Banashankari 3rd stage,</td>\n",
              "      <td>12.922886</td>\n",
              "      <td>77.556190</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92288594375253...</td>\n",
              "      <td>28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Outer Ring Road, Kathreguppe IV Phase, (Banas...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16a941735', 'name': 'B...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>32</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>e-0-4da9c8ddfa8cc764972312bb-13</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4da9c8ddfa8cc764972312bb</td>\n",
              "      <td>Anand Sweets And Savouries</td>\n",
              "      <td>#70,1st Main Rd,Banashankari</td>\n",
              "      <td>Near BDA Complex</td>\n",
              "      <td>12.926310</td>\n",
              "      <td>77.564598</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92631039507795...</td>\n",
              "      <td>996</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[#70,1st Main Rd,Banashankari (Near BDA Comple...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1c7941735', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>37</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>e-0-4c5f9cec47429c7410b8197a-14</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c5f9cec47429c7410b8197a</td>\n",
              "      <td>cafe coffee day</td>\n",
              "      <td>Banshankri 3rd stage</td>\n",
              "      <td>1st cross,kathrigupe</td>\n",
              "      <td>12.930137</td>\n",
              "      <td>77.559764</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.93013742112125...</td>\n",
              "      <td>883</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banshankri 3rd stage (1st cross,kathrigupe), ...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16d941735', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>44</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>e-0-4ba787a5f964a520f89a39e3-15</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4ba787a5f964a520f89a39e3</td>\n",
              "      <td>J Cubez</td>\n",
              "      <td># 1431, Banashankri 2nd Stage,</td>\n",
              "      <td>22nd Main</td>\n",
              "      <td>12.925807</td>\n",
              "      <td>77.564023</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92580652390511...</td>\n",
              "      <td>918</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[# 1431, Banashankri 2nd Stage, (22nd Main), B...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d11b941735', 'name': 'P...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>34</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>e-0-4c27565caa77ef3b78ec98b0-16</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c27565caa77ef3b78ec98b0</td>\n",
              "      <td>Megamart</td>\n",
              "      <td>near Devegowda Petrol Bunk</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>12.923020</td>\n",
              "      <td>77.560389</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92302019087350...</td>\n",
              "      <td>472</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[near Devegowda Petrol Bunk (Banashankari), Ba...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1fd941735', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>e-0-4c7a2e9d81bca0938accff14-17</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c7a2e9d81bca0938accff14</td>\n",
              "      <td>Pizza Hut</td>\n",
              "      <td>92/4, Kathriguppe, 80 feet Rd</td>\n",
              "      <td>Banashankari 3rd Stage</td>\n",
              "      <td>12.924959</td>\n",
              "      <td>77.550755</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92495906409410...</td>\n",
              "      <td>609</td>\n",
              "      <td>560085</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[92/4, Kathriguppe, 80 feet Rd (Banashankari 3...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1ca941735', 'name': 'P...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>e-0-4fd6068de4b02c7fc50d3647-18</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4fd6068de4b02c7fc50d3647</td>\n",
              "      <td>The Platter</td>\n",
              "      <td>No. 10,30th Main, College Teachers Layout, Ban...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.927046</td>\n",
              "      <td>77.561539</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92704630115649...</td>\n",
              "      <td>742</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[No. 10,30th Main, College Teachers Layout, Ba...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d142941735', 'name': 'A...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>44</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>e-0-4dd95fc07d8b3226641f141d-19</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4dd95fc07d8b3226641f141d</td>\n",
              "      <td>Kamakya theatre</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.923320</td>\n",
              "      <td>77.553383</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92331972119427...</td>\n",
              "      <td>288</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>[India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d17f941735', 'name': 'M...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>57</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>e-0-56361097498e22d081e12fd4-20</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>56361097498e22d081e12fd4</td>\n",
              "      <td>Caf-Eleven</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>Near Devegowda petrol bunk</td>\n",
              "      <td>12.923231</td>\n",
              "      <td>77.554920</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92323072958486...</td>\n",
              "      <td>121</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banashankari (Near Devegowda petrol bunk), Ba...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d119941735', 'name': 'H...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>59</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>e-0-4ed208ded3e3521ce7a510f4-21</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4ed208ded3e3521ce7a510f4</td>\n",
              "      <td>Tanishq</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.922339</td>\n",
              "      <td>77.555552</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92233859797268...</td>\n",
              "      <td>98</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banashankari, Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d111951735', 'name': 'J...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>58</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>e-0-4e819ec1f79047b5689530dc-22</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4e819ec1f79047b5689530dc</td>\n",
              "      <td>Croma</td>\n",
              "      <td>100 Ft Ring Road</td>\n",
              "      <td>Bsk 3rd Stage</td>\n",
              "      <td>12.924129</td>\n",
              "      <td>77.552998</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92412920338409...</td>\n",
              "      <td>349</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[100 Ft Ring Road (Bsk 3rd Stage), Bangalore, ...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d122951735', 'name': 'E...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>41</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>e-0-4c2adec1b5fbb713547449df-23</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c2adec1b5fbb713547449df</td>\n",
              "      <td>Shoprite Super Market</td>\n",
              "      <td># 278, M.K.Puttalingaiah Road,</td>\n",
              "      <td>Near D.G. Hospital, Padmanabhanagar,</td>\n",
              "      <td>12.920235</td>\n",
              "      <td>77.560010</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92023519571532...</td>\n",
              "      <td>535</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[# 278, M.K.Puttalingaiah Road, (Near D.G. Hos...</td>\n",
              "      <td>[{'id': '4d954b0ea243a5684a65b473', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>18</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>e-0-4e36e3a562e17d290842a6fe-24</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4e36e3a562e17d290842a6fe</td>\n",
              "      <td>McDonald's</td>\n",
              "      <td>BSK 2nd Stage, 21st Main,</td>\n",
              "      <td>Opp. BDA Complex,</td>\n",
              "      <td>12.924456</td>\n",
              "      <td>77.564968</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92445620485228...</td>\n",
              "      <td>981</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[BSK 2nd Stage, 21st Main, (Opp. BDA Complex,)...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16e941735', 'name': 'F...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>60</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>e-0-5644161e38fadb768be959b9-25</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>5644161e38fadb768be959b9</td>\n",
              "      <td>South Kitchen</td>\n",
              "      <td>Bhuvaneshwari Nagar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.921313</td>\n",
              "      <td>77.553296</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92131270970515...</td>\n",
              "      <td>356</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Bhuvaneshwari Nagar, Bangalore, Karnātaka, In...</td>\n",
              "      <td>[{'id': '54135bf5e4b08f3d2429dfde', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>56</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>e-0-4c700db9f52cb713d664acaf-26</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c700db9f52cb713d664acaf</td>\n",
              "      <td>Gustoes</td>\n",
              "      <td>No 10, College Teachers HBCS</td>\n",
              "      <td>Banashankari 3rd Stage</td>\n",
              "      <td>12.927095</td>\n",
              "      <td>77.561462</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92709464400327...</td>\n",
              "      <td>739</td>\n",
              "      <td>india</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[No 10, College Teachers HBCS (Banashankari 3r...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1e1931735', 'name': 'A...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>59</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>e-0-4c5f9fa947429c746bd6197a-27</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c5f9fa947429c746bd6197a</td>\n",
              "      <td>Cafe Coffee Day</td>\n",
              "      <td>1431, Banshankri 2nd Stage</td>\n",
              "      <td>22nd Cross</td>\n",
              "      <td>12.925870</td>\n",
              "      <td>77.564142</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92587049086617...</td>\n",
              "      <td>932</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[1431, Banshankri 2nd Stage (22nd Cross), Bang...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16d941735', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>56</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>e-0-4c275892aa77ef3be8ec98b0-28</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c275892aa77ef3be8ec98b0</td>\n",
              "      <td>Banashankari BDA Shopping Complex</td>\n",
              "      <td>24th Cross Rd</td>\n",
              "      <td>2nd Stage, Banashankari</td>\n",
              "      <td>12.925118</td>\n",
              "      <td>77.564614</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92511811062417...</td>\n",
              "      <td>958</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[24th Cross Rd (2nd Stage, Banashankari), Bang...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1fd941735', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>47</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>e-0-54929792498e88761038e3f0-29</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>54929792498e88761038e3f0</td>\n",
              "      <td>Chocolateria San Churro</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Banashankari</td>\n",
              "      <td>12.927783</td>\n",
              "      <td>77.562078</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92778341114295...</td>\n",
              "      <td>838</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banashankari, Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1d0941735', 'name': 'D...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>59</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>e-0-4b7a9e40f964a520cd332fe3-30</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4b7a9e40f964a520cd332fe3</td>\n",
              "      <td>Fitness Cube</td>\n",
              "      <td>padmanabha nagar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.919037</td>\n",
              "      <td>77.557713</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.91903684195092...</td>\n",
              "      <td>486</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[padmanabha nagar, Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d175941735', 'name': 'G...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>59</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>e-0-4bebad6eb3352d7f85c456d2-31</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4bebad6eb3352d7f85c456d2</td>\n",
              "      <td>Cafe coffee day</td>\n",
              "      <td>Katriguppe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.926020</td>\n",
              "      <td>77.550306</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92601985435143...</td>\n",
              "      <td>701</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Katriguppe, Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16d941735', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>57</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>e-0-4c948ff0f7cfa1cdebdfb415-32</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4c948ff0f7cfa1cdebdfb415</td>\n",
              "      <td>Pizza Hut</td>\n",
              "      <td>Banshankri, 2nd Stage, 24th Main</td>\n",
              "      <td>Opp. Banshankri shopping complex,</td>\n",
              "      <td>12.924515</td>\n",
              "      <td>77.564497</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92451500745971...</td>\n",
              "      <td>932</td>\n",
              "      <td>560070</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Banshankri, 2nd Stage, 24th Main (Opp. Bansha...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d1ca941735', 'name': 'P...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>58</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>e-0-4edf823ab6346b119e22784a-33</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4edf823ab6346b119e22784a</td>\n",
              "      <td>Reliance Trends</td>\n",
              "      <td>Next to Big Bazaar, Katriguppe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.926891</td>\n",
              "      <td>77.551068</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92689089784805...</td>\n",
              "      <td>685</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Next to Big Bazaar, Katriguppe, Bangalore, Ka...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d103951735', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>51</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>e-0-4f546ac2e4b09f74b9c7efd3-34</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4f546ac2e4b09f74b9c7efd3</td>\n",
              "      <td>Adyar Ananda Bhavan</td>\n",
              "      <td>80 Feet Rd, Banashankari</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.929356</td>\n",
              "      <td>77.552162</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92935588637881...</td>\n",
              "      <td>814</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[80 Feet Rd, Banashankari, Bangalore, Karnātak...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16e941735', 'name': 'F...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>55</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>e-0-50150cb1e4b0073bd0ac7daf-35</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>50150cb1e4b0073bd0ac7daf</td>\n",
              "      <td>Puma store banshankri</td>\n",
              "      <td>92/3 80 Ft Road Opposite Big Bazar</td>\n",
              "      <td>3rd Stage</td>\n",
              "      <td>12.926899</td>\n",
              "      <td>77.550973</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92689869563763...</td>\n",
              "      <td>693</td>\n",
              "      <td>560085</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[92/3 80 Ft Road Opposite Big Bazar (3rd Stage...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d107951735', 'name': 'S...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>34</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>e-0-4ef33feef9ab11b14fb69dbe-36</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4ef33feef9ab11b14fb69dbe</td>\n",
              "      <td>kathriguppe junction</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.925483</td>\n",
              "      <td>77.549834</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92548347632488...</td>\n",
              "      <td>723</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d103951735', 'name': 'C...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>67</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>e-0-4e8d588eb803ca3dfcc789bb-37</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4e8d588eb803ca3dfcc789bb</td>\n",
              "      <td>Nandhini Deluxe</td>\n",
              "      <td>Kathriguppe Signal</td>\n",
              "      <td>100ft Ring Road, Banashankari 3rd Stage</td>\n",
              "      <td>12.925742</td>\n",
              "      <td>77.549423</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92574166610375...</td>\n",
              "      <td>775</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>KAR</td>\n",
              "      <td>India</td>\n",
              "      <td>[Kathriguppe Signal (100ft Ring Road, Banashan...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d10f941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>34</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>e-0-50e96377e4b007fcc114b421-38</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>50e96377e4b007fcc114b421</td>\n",
              "      <td>Goli Vadapav</td>\n",
              "      <td>BDA Complex</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.924699</td>\n",
              "      <td>77.564212</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92469911669346...</td>\n",
              "      <td>905</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>[BDA Complex, India]</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d16c941735', 'name': 'B...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>28</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>e-0-53ff45e7498e053f1868a162-39</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>53ff45e7498e053f1868a162</td>\n",
              "      <td>Jalsa</td>\n",
              "      <td>22nd Main, BSK 2nd Stage</td>\n",
              "      <td>Next To Banashankari BDA Complex</td>\n",
              "      <td>12.926183</td>\n",
              "      <td>77.564653</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92618269647203...</td>\n",
              "      <td>996</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[22nd Main, BSK 2nd Stage (Next To Banashankar...</td>\n",
              "      <td>[{'id': '4bf58dd8d48988d10f941735', 'name': 'I...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>49</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>e-0-4e356fce45dd1c42bf1e6674-40</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
              "      <td>4e356fce45dd1c42bf1e6674</td>\n",
              "      <td>Nandini Dosa Camp</td>\n",
              "      <td>Bsk 2nd stage</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.927030</td>\n",
              "      <td>77.564324</td>\n",
              "      <td>[{'label': 'display', 'lat': 12.92702986937003...</td>\n",
              "      <td>1001</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IN</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnātaka</td>\n",
              "      <td>India</td>\n",
              "      <td>[Bsk 2nd stage, Bangalore, Karnātaka, India]</td>\n",
              "      <td>[{'id': '4d4b7105d754a06374d81259', 'name': 'F...</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>37</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         referralId  reasons.count  ... restaurant others\n",
              "0    e-0-4b647dd2f964a520cdb72ae3-0              0  ...         33     30\n",
              "1    e-0-548ff23c498e3cbb402f3df6-1              0  ...         35     31\n",
              "2    e-0-4f72ac4fe4b0a60ba514108e-2              0  ...         22      6\n",
              "3    e-0-4fe3013be4b0abb74945f5e7-3              0  ...         31     30\n",
              "4    e-0-4d98411c744f3704907fea57-4              0  ...         49     28\n",
              "5    e-0-4f00421f775bec6b446e0218-5              0  ...         50     16\n",
              "6    e-0-4d1ca0a6c50e6ea8b41bd4b8-6              0  ...         52     21\n",
              "7    e-0-4fc1f35ce4b01d437e407d17-7              0  ...         27     21\n",
              "8    e-0-55f9259f498e9521e9f1a78e-8              0  ...         34     30\n",
              "9    e-0-5221f3dc11d2bc869d73a4c0-9              0  ...         26      4\n",
              "10  e-0-4c278977aa77ef3bcef798b0-10              0  ...         35     30\n",
              "11  e-0-52f7973d498e08f2ba6b6384-11              0  ...         35     31\n",
              "12  e-0-4c9e0c908afca0934f90fc15-12              0  ...         32     30\n",
              "13  e-0-4da9c8ddfa8cc764972312bb-13              0  ...         37     32\n",
              "14  e-0-4c5f9cec47429c7410b8197a-14              0  ...         44     19\n",
              "15  e-0-4ba787a5f964a520f89a39e3-15              0  ...         34     29\n",
              "16  e-0-4c27565caa77ef3b78ec98b0-16              0  ...          4      6\n",
              "17  e-0-4c7a2e9d81bca0938accff14-17              0  ...         12      3\n",
              "18  e-0-4fd6068de4b02c7fc50d3647-18              0  ...         44     30\n",
              "19  e-0-4dd95fc07d8b3226641f141d-19              0  ...         57     43\n",
              "20  e-0-56361097498e22d081e12fd4-20              0  ...         59     41\n",
              "21  e-0-4ed208ded3e3521ce7a510f4-21              0  ...         58     42\n",
              "22  e-0-4e819ec1f79047b5689530dc-22              0  ...         41      7\n",
              "23  e-0-4c2adec1b5fbb713547449df-23              0  ...         18      9\n",
              "24  e-0-4e36e3a562e17d290842a6fe-24              0  ...         60     40\n",
              "25  e-0-5644161e38fadb768be959b9-25              0  ...         56     44\n",
              "26  e-0-4c700db9f52cb713d664acaf-26              0  ...         59     41\n",
              "27  e-0-4c5f9fa947429c746bd6197a-27              0  ...         56     44\n",
              "28  e-0-4c275892aa77ef3be8ec98b0-28              0  ...         47     16\n",
              "29  e-0-54929792498e88761038e3f0-29              0  ...         59     41\n",
              "30  e-0-4b7a9e40f964a520cd332fe3-30              0  ...         59     41\n",
              "31  e-0-4bebad6eb3352d7f85c456d2-31              0  ...         57     43\n",
              "32  e-0-4c948ff0f7cfa1cdebdfb415-32              0  ...         58     42\n",
              "33  e-0-4edf823ab6346b119e22784a-33              0  ...         51     24\n",
              "34  e-0-4f546ac2e4b09f74b9c7efd3-34              0  ...         55     45\n",
              "35  e-0-50150cb1e4b0073bd0ac7daf-35              0  ...         34     20\n",
              "36  e-0-4ef33feef9ab11b14fb69dbe-36              0  ...         67     33\n",
              "37  e-0-4e8d588eb803ca3dfcc789bb-37              0  ...         34     30\n",
              "38  e-0-50e96377e4b007fcc114b421-38              0  ...         28      5\n",
              "39  e-0-53ff45e7498e053f1868a162-39              0  ...         49     14\n",
              "40  e-0-4e356fce45dd1c42bf1e6674-40              0  ...         37      7\n",
              "\n",
              "[41 rows x 22 columns]"
            ]
          },
          "execution_count": 301,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nyiaw9adyMJ3"
      },
      "outputs": [],
      "source": [
        "dataframe=dataframe[['venue.location.lat','venue.location.lng','restaurant','others','venue.location.address']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "DKg35jHl1az7",
        "outputId": "04209247-8382-468e-cf6b-20f1a6edfa63"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>venue.location.lat</th>\n",
              "      <th>venue.location.lng</th>\n",
              "      <th>restaurant</th>\n",
              "      <th>others</th>\n",
              "      <th>venue.location.address</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>12.923086</td>\n",
              "      <td>77.556032</td>\n",
              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "      <td>293, Banashankari 3rd Stage</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12.924439</td>\n",
              "      <td>77.564848</td>\n",
              "      <td>35</td>\n",
              "      <td>31</td>\n",
              "      <td>Opp. BDA Complex</td>\n",
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              "      <td>12.928059</td>\n",
              "      <td>77.556010</td>\n",
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              "      <td>6</td>\n",
              "      <td>Opp. Chennamanakere Bus Stand</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12.925808</td>\n",
              "      <td>77.564340</td>\n",
              "      <td>31</td>\n",
              "      <td>30</td>\n",
              "      <td>#31,21st Main Rd, Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12.926261</td>\n",
              "      <td>77.564343</td>\n",
              "      <td>49</td>\n",
              "      <td>28</td>\n",
              "      <td>Sri Sai Plaza, No. 97, 22nd Main Rd, BSK 2nd S...</td>\n",
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              "      <td>12.924890</td>\n",
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              "      <td>77.550367</td>\n",
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              "      <td>21</td>\n",
              "      <td>Kathriguppe Main Road</td>\n",
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              "      <td>27</td>\n",
              "      <td>21</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>34</td>\n",
              "      <td>30</td>\n",
              "      <td>#2470, 21th Main</td>\n",
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              "      <td>77.565019</td>\n",
              "      <td>26</td>\n",
              "      <td>4</td>\n",
              "      <td>Next to Polar Bear</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   venue.location.lat  ...                             venue.location.address\n",
              "0           12.923086  ...                        293, Banashankari 3rd Stage\n",
              "1           12.924439  ...                                   Opp. BDA Complex\n",
              "2           12.928059  ...                      Opp. Chennamanakere Bus Stand\n",
              "3           12.925808  ...                     #31,21st Main Rd, Banashankari\n",
              "4           12.926261  ...  Sri Sai Plaza, No. 97, 22nd Main Rd, BSK 2nd S...\n",
              "5           12.924890  ...                                    BSK BDA Complex\n",
              "6           12.926094  ...                              Kathriguppe Main Road\n",
              "7           12.924391  ...                                                NaN\n",
              "8           12.924022  ...                                   #2470, 21th Main\n",
              "9           12.924468  ...                                 Next to Polar Bear\n",
              "\n",
              "[10 rows x 5 columns]"
            ]
          },
          "execution_count": 303,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataframe.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "io5ZO50Zu5YE"
      },
      "outputs": [],
      "source": [
        "dataframe = dataframe.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ow8EQ7cAx-Fc",
        "outputId": "eeb3c59d-5c9e-49ec-98b2-003426c294a9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "venue.location.lat        0\n",
              "venue.location.lng        0\n",
              "restaurant                0\n",
              "others                    0\n",
              "venue.location.address    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 305,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataframe.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "05L6SvIG1f1h"
      },
      "outputs": [],
      "source": [
        "dataframe = dataframe.rename(columns={'venue.location.lat': 'lat', 'venue.location.lng': 'lng'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "s5uQM89r1tBQ",
        "outputId": "02c3aede-2d8b-43b0-c628-064c0e896da4"
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      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>lat</th>\n",
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              "      <td>#31,21st Main Rd, Banashankari</td>\n",
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            "text/plain": [
              "         lat  ...                             venue.location.address\n",
              "0  12.923086  ...                        293, Banashankari 3rd Stage\n",
              "1  12.924439  ...                                   Opp. BDA Complex\n",
              "2  12.928059  ...                      Opp. Chennamanakere Bus Stand\n",
              "3  12.925808  ...                     #31,21st Main Rd, Banashankari\n",
              "4  12.926261  ...  Sri Sai Plaza, No. 97, 22nd Main Rd, BSK 2nd S...\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "execution_count": 307,
          "metadata": {
            "tags": []
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          "output_type": "execute_result"
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      "source": [
        "dataframe.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vbgeGfZ32UYf"
      },
      "outputs": [],
      "source": [
        "X1 = dataframe.copy()\n",
        "X1 = X1.drop(['venue.location.address'],axis =1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "YgpwY7Cd2xha",
        "outputId": "82380fbd-1973-496a-e74b-cc15f5b48220"
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      "outputs": [
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              "      <td>77.564598</td>\n",
              "      <td>37</td>\n",
              "      <td>32</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>12.930137</td>\n",
              "      <td>77.559764</td>\n",
              "      <td>44</td>\n",
              "      <td>19</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
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              "      <td>6</td>\n",
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              "      <td>3</td>\n",
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              "      <td>77.555552</td>\n",
              "      <td>58</td>\n",
              "      <td>42</td>\n",
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              "      <td>77.552998</td>\n",
              "      <td>41</td>\n",
              "      <td>7</td>\n",
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              "    <tr>\n",
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              "      <td>77.560010</td>\n",
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              "      <td>9</td>\n",
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              "      <td>77.561462</td>\n",
              "      <td>59</td>\n",
              "      <td>41</td>\n",
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            ],
            "text/plain": [
              "          lat        lng  restaurant  others\n",
              "0   12.923086  77.556032          33      30\n",
              "1   12.924439  77.564848          35      31\n",
              "2   12.928059  77.556010          22       6\n",
              "3   12.925808  77.564340          31      30\n",
              "4   12.926261  77.564343          49      28\n",
              "5   12.924890  77.564926          50      16\n",
              "6   12.926094  77.550367          52      21\n",
              "8   12.924022  77.564215          34      30\n",
              "9   12.924468  77.565019          26       4\n",
              "10  12.922878  77.556389          35      30\n",
              "11  12.918197  77.556125          35      31\n",
              "12  12.922886  77.556190          32      30\n",
              "13  12.926310  77.564598          37      32\n",
              "14  12.930137  77.559764          44      19\n",
              "15  12.925807  77.564023          34      29\n",
              "16  12.923020  77.560389           4       6\n",
              "17  12.924959  77.550755          12       3\n",
              "18  12.927046  77.561539          44      30\n",
              "20  12.923231  77.554920          59      41\n",
              "21  12.922339  77.555552          58      42\n",
              "22  12.924129  77.552998          41       7\n",
              "23  12.920235  77.560010          18       9\n",
              "24  12.924456  77.564968          60      40\n",
              "25  12.921313  77.553296          56      44\n",
              "26  12.927095  77.561462          59      41"
            ]
          },
          "execution_count": 309,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X1.head(25)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1XcRdU0ILTDu"
      },
      "source": [
        "#KMeans Clustering - Finding the Clusters and Centroids, using the new FourSquare Dataframe (X1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Q5wTvhAj2N5d",
        "outputId": "35e03490-8005-4c84-89ca-c307f360278a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.cluster import KMeans\n",
        "wcss=[]\n",
        "for i in range(1,11):\n",
        "  kmeans=KMeans(n_clusters=i,init = 'k-means++',random_state=42)\n",
        "  kmeans.fit(X1)\n",
        "  wcss.append(kmeans.inertia_)\n",
        "plt.plot(range(1,11),wcss)\n",
        "plt.title('The elbow method')\n",
        "plt.xlabel('The number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cDb-PaY518XX"
      },
      "outputs": [],
      "source": [
        "kmeans=KMeans(n_clusters=5, init='k-means++',random_state=42)\n",
        "X1['cluster']=kmeans.fit_predict(X1) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 824
        },
        "id": "Dn_occlS2-w1",
        "outputId": "45d01f16-2239-4fec-fe86-01f1fdf40cb8"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>restaurant</th>\n",
              "      <th>others</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12.924439</td>\n",
              "      <td>77.564848</td>\n",
              "      <td>35</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12.928059</td>\n",
              "      <td>77.556010</td>\n",
              "      <td>22</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12.925808</td>\n",
              "      <td>77.564340</td>\n",
              "      <td>31</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12.926261</td>\n",
              "      <td>77.564343</td>\n",
              "      <td>49</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>12.924890</td>\n",
              "      <td>77.564926</td>\n",
              "      <td>50</td>\n",
              "      <td>16</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>12.926094</td>\n",
              "      <td>77.550367</td>\n",
              "      <td>52</td>\n",
              "      <td>21</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>12.924022</td>\n",
              "      <td>77.564215</td>\n",
              "      <td>34</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>12.924468</td>\n",
              "      <td>77.565019</td>\n",
              "      <td>26</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>12.922878</td>\n",
              "      <td>77.556389</td>\n",
              "      <td>35</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12.918197</td>\n",
              "      <td>77.556125</td>\n",
              "      <td>35</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>12.922886</td>\n",
              "      <td>77.556190</td>\n",
              "      <td>32</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>12.926310</td>\n",
              "      <td>77.564598</td>\n",
              "      <td>37</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>12.930137</td>\n",
              "      <td>77.559764</td>\n",
              "      <td>44</td>\n",
              "      <td>19</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>12.925807</td>\n",
              "      <td>77.564023</td>\n",
              "      <td>34</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>12.923020</td>\n",
              "      <td>77.560389</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>12.924959</td>\n",
              "      <td>77.550755</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>12.927046</td>\n",
              "      <td>77.561539</td>\n",
              "      <td>44</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>12.923231</td>\n",
              "      <td>77.554920</td>\n",
              "      <td>59</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>12.922339</td>\n",
              "      <td>77.555552</td>\n",
              "      <td>58</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>12.924129</td>\n",
              "      <td>77.552998</td>\n",
              "      <td>41</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>12.920235</td>\n",
              "      <td>77.560010</td>\n",
              "      <td>18</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>12.924456</td>\n",
              "      <td>77.564968</td>\n",
              "      <td>60</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>12.921313</td>\n",
              "      <td>77.553296</td>\n",
              "      <td>56</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>12.927095</td>\n",
              "      <td>77.561462</td>\n",
              "      <td>59</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          lat        lng  restaurant  others  cluster\n",
              "0   12.923086  77.556032          33      30        0\n",
              "1   12.924439  77.564848          35      31        0\n",
              "2   12.928059  77.556010          22       6        2\n",
              "3   12.925808  77.564340          31      30        0\n",
              "4   12.926261  77.564343          49      28        4\n",
              "5   12.924890  77.564926          50      16        4\n",
              "6   12.926094  77.550367          52      21        4\n",
              "8   12.924022  77.564215          34      30        0\n",
              "9   12.924468  77.565019          26       4        2\n",
              "10  12.922878  77.556389          35      30        0\n",
              "11  12.918197  77.556125          35      31        0\n",
              "12  12.922886  77.556190          32      30        0\n",
              "13  12.926310  77.564598          37      32        0\n",
              "14  12.930137  77.559764          44      19        4\n",
              "15  12.925807  77.564023          34      29        0\n",
              "16  12.923020  77.560389           4       6        3\n",
              "17  12.924959  77.550755          12       3        3\n",
              "18  12.927046  77.561539          44      30        0\n",
              "20  12.923231  77.554920          59      41        1\n",
              "21  12.922339  77.555552          58      42        1\n",
              "22  12.924129  77.552998          41       7        2\n",
              "23  12.920235  77.560010          18       9        3\n",
              "24  12.924456  77.564968          60      40        1\n",
              "25  12.921313  77.553296          56      44        1\n",
              "26  12.927095  77.561462          59      41        1"
            ]
          },
          "execution_count": 312,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X1.head(25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dEq5t1sd7_xH",
        "outputId": "e2836abc-952b-46a6-f9f9-3935aa10d470"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  iloc._setitem_with_indexer(indexer, value)\n"
          ]
        }
      ],
      "source": [
        "closest = kmeans.cluster_centers_\n",
        "X1[\"centroids\"] = 0\n",
        "for i in range(len(closest)):\n",
        "    X1[\"centroids\"].iloc[i] = 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "e_FRVIWY9cvk",
        "outputId": "826533f5-2c17-455a-b2bd-eeed5cac5433"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>restaurant</th>\n",
              "      <th>others</th>\n",
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              "      <th>1</th>\n",
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              "      <td>31</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>6</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "      <td>28</td>\n",
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              "      <td>1</td>\n",
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            ],
            "text/plain": [
              "         lat        lng  restaurant  others  cluster  centroids\n",
              "0  12.923086  77.556032          33      30        0          1\n",
              "1  12.924439  77.564848          35      31        0          1\n",
              "2  12.928059  77.556010          22       6        2          1\n",
              "3  12.925808  77.564340          31      30        0          1\n",
              "4  12.926261  77.564343          49      28        4          1"
            ]
          },
          "execution_count": 314,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X1[X1['centroids']==1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
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        "id": "kdkJfVCOAT2J",
        "outputId": "a3e45730-9b3f-48b2-91e4-0dfb3bcb80e1"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>restaurant</th>\n",
              "      <th>others</th>\n",
              "      <th>cluster</th>\n",
              "      <th>centroids</th>\n",
              "      <th>venue.location.formattedAddress</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>293, Banashankari 3rd Stage</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12.924439</td>\n",
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              "      <td>31</td>\n",
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              "      <td>1</td>\n",
              "      <td>Opp. BDA Complex</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>77.556010</td>\n",
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              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Opp. Chennamanakere Bus Stand</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>77.564340</td>\n",
              "      <td>31</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>#31,21st Main Rd, Banashankari</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12.926261</td>\n",
              "      <td>77.564343</td>\n",
              "      <td>49</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Sri Sai Plaza, No. 97, 22nd Main Rd, BSK 2nd S...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         lat  ...                    venue.location.formattedAddress\n",
              "0  12.923086  ...                        293, Banashankari 3rd Stage\n",
              "1  12.924439  ...                                   Opp. BDA Complex\n",
              "2  12.928059  ...                      Opp. Chennamanakere Bus Stand\n",
              "3  12.925808  ...                     #31,21st Main Rd, Banashankari\n",
              "4  12.926261  ...  Sri Sai Plaza, No. 97, 22nd Main Rd, BSK 2nd S...\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "execution_count": 315,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X1['venue.location.formattedAddress'] = dataframe['venue.location.address']\n",
        "X1.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oib6z4H2L75W"
      },
      "source": [
        "Folium to plot the clusters on a map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xpq4qyhDDAMX"
      },
      "outputs": [],
      "source": [
        "import folium\n",
        "m = folium.Map(location=[12, -122.6750])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k1xoKHl_BgAJ",
        "outputId": "63763b18-995c-4b15-ceba-427acf788419"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Bengaluru, Bangalore North, Bangalore Urban, Karnataka, India\n",
            "[lat, lng]: [12.9791198, 77.5912997]\n"
          ]
        }
      ],
      "source": [
        "city = \"Bangalore\"\n",
        "## get location\n",
        "locator = geopy.geocoders.Nominatim(user_agent=\"MyCoder\")\n",
        "location = locator.geocode(city)\n",
        "print(location)\n",
        "## keep latitude and longitude only\n",
        "location = [location.latitude, location.longitude]\n",
        "print(\"[lat, lng]:\", location)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dn2e9SsAMAZa"
      },
      "source": [
        "#Plotting the Clusters on a geographical location"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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onload=\"this.contentDocument.open();this.contentDocument.write(    decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
            ],
            "text/plain": [
              "<folium.folium.Map at 0x7f2b4623e790>"
            ]
          },
          "execution_count": 321,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import sklearn.preprocessing\n",
        "import folium\n",
        "x, y = \"lat\", \"lng\"\n",
        "color = \"cluster\"\n",
        "size = \"restaurant\"\n",
        "popup = \"venue.location.formattedAddress\"\n",
        "marker = \"centroids\"\n",
        "data = X1.copy()\n",
        "## create color column\n",
        "lst_elements = sorted(list(X1[color].unique()))\n",
        "lst_colors = ['#%06X' % np.random.randint(0, 0xFFFFFF) for i in \n",
        "              range(len(lst_elements))]\n",
        "data[\"color\"] = data[color].apply(lambda x: \n",
        "                lst_colors[lst_elements.index(x)])\n",
        "## create size column (scaled)\n",
        "scaler = sklearn.preprocessing.MinMaxScaler(feature_range=(3,15))\n",
        "data[\"size\"] = scaler.fit_transform(\n",
        "               data[size].values.reshape(-1,1)).reshape(-1)\n",
        "## initialize the map with the starting location\n",
        "map_ = folium.Map(location=location, tiles=\"cartodbpositron\",\n",
        "                  zoom_start=11)\n",
        "## add points\n",
        "data.apply(lambda row: folium.CircleMarker(\n",
        "           location=[row[x],row[y]], \n",
        "           color=row[\"color\"], fill=True,popup=row[popup],\n",
        "           radius=row[\"size\"]).add_to(map_), axis=1)\n",
        "## add html legend\n",
        "legend_html = \"\"\"<div style=\"position:fixed; bottom:10px; left:10px; border:2px solid black; z-index:9999; font-size:14px;\">&nbsp;<b>\"\"\"+color+\"\"\":</b><br>\"\"\"\n",
        "for i in lst_elements:\n",
        "     legend_html = legend_html+\"\"\"&nbsp;<i class=\"fa fa-circle \n",
        "     fa-1x\" style=\"color:\"\"\"+lst_colors[lst_elements.index(i)]+\"\"\"\">\n",
        "     </i>&nbsp;\"\"\"+str(i)+\"\"\"<br>\"\"\"\n",
        "legend_html = legend_html+\"\"\"</div>\"\"\"\n",
        "map_.get_root().html.add_child(folium.Element(legend_html))\n",
        "## add centroids marker\n",
        "lst_elements = sorted(list(X1[marker].unique()))\n",
        "data[data[marker]==1].apply(lambda row: \n",
        "           folium.Marker(location=[row[x],row[y]], \n",
        "           draggable=False,  popup=row[popup] ,       \n",
        "           icon=folium.Icon(color=\"black\")).add_to(map_), axis=1)\n",
        "## plot the map\n",
        "map_"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "include_colab_link": true,
      "name": "Geolocational.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}