{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "champion-multimedia",
   "metadata": {},
   "source": [
    "# Obsessed with Boba? Analyzing Bubble Tea Shops in NYC Using the Yelp Fusion API\n",
    "Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6fd94ed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # imports for Google Colab Sessions\n",
    "# !apt install gdal-bin python-gdal python3-gdal \n",
    "# # Install rtree - Geopandas requirment\n",
    "# !apt install python3-rtree \n",
    "# # Install Geopandas\n",
    "# !pip install git+git://github.com/geopandas/geopandas.git\n",
    "# # Install descartes - Geopandas requirment\n",
    "# !pip install descartes \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "00399d87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>alias</th>\n",
       "      <th>name</th>\n",
       "      <th>image_url</th>\n",
       "      <th>is_closed</th>\n",
       "      <th>url</th>\n",
       "      <th>review_count</th>\n",
       "      <th>categories</th>\n",
       "      <th>rating</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>transactions</th>\n",
       "      <th>price</th>\n",
       "      <th>location</th>\n",
       "      <th>phone</th>\n",
       "      <th>display_phone</th>\n",
       "      <th>distance</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q3fmTHT7zilDWtfzLK9lMA</td>\n",
       "      <td>truedan-new-york-3</td>\n",
       "      <td>Truedan</td>\n",
       "      <td>https://s3-media2.fl.yelpcdn.com/bphoto/BmUZXO...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/truedan-new-york-3?ad...</td>\n",
       "      <td>99</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.5</td>\n",
       "      <td>{'latitude': 40.7191742, 'longitude': -73.9962...</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>$$</td>\n",
       "      <td>{'address1': '208 Grand St', 'address2': None,...</td>\n",
       "      <td>1.646559e+10</td>\n",
       "      <td>(646) 559-2886</td>\n",
       "      <td>1535.241614</td>\n",
       "      <td>40.719174</td>\n",
       "      <td>-73.996220</td>\n",
       "      <td>POINT (-73.9962197 40.7191742)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7-bx74TooPuZKZDNW4WFcQ</td>\n",
       "      <td>chun-yang-tea-new-york</td>\n",
       "      <td>Chun Yang Tea</td>\n",
       "      <td>https://s3-media1.fl.yelpcdn.com/bphoto/JrGwrZ...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/chun-yang-tea-new-yor...</td>\n",
       "      <td>91</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.5</td>\n",
       "      <td>{'latitude': 40.71617, 'longitude': -73.9971}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>$$</td>\n",
       "      <td>{'address1': '26B Elizabeth St', 'address2': '...</td>\n",
       "      <td>1.212420e+10</td>\n",
       "      <td>(212) 420-0123</td>\n",
       "      <td>1212.877370</td>\n",
       "      <td>40.716170</td>\n",
       "      <td>-73.997100</td>\n",
       "      <td>POINT (-73.9971 40.71617)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3aypSFXLfkAL4dhHVFobKg</td>\n",
       "      <td>lazy-sundaes-new-york-6</td>\n",
       "      <td>Lazy Sundaes</td>\n",
       "      <td>https://s3-media3.fl.yelpcdn.com/bphoto/kEfCYC...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/lazy-sundaes-new-york...</td>\n",
       "      <td>32</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}...</td>\n",
       "      <td>4.5</td>\n",
       "      <td>{'latitude': 40.720597, 'longitude': -73.984539}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>$$</td>\n",
       "      <td>{'address1': '23 Clinton St', 'address2': None...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1877.016204</td>\n",
       "      <td>40.720597</td>\n",
       "      <td>-73.984539</td>\n",
       "      <td>POINT (-73.984539 40.720597)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bVJQEeRNi34-3XN_F1AZEg</td>\n",
       "      <td>xing-fu-tang-new-york</td>\n",
       "      <td>Xing Fu Tang</td>\n",
       "      <td>https://s3-media2.fl.yelpcdn.com/bphoto/cyFHFE...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/xing-fu-tang-new-york...</td>\n",
       "      <td>91</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.5</td>\n",
       "      <td>{'latitude': 40.7288, 'longitude': -73.98775}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'address1': '133 2nd Ave', 'address2': '', 'a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2656.450427</td>\n",
       "      <td>40.728800</td>\n",
       "      <td>-73.987750</td>\n",
       "      <td>POINT (-73.98775000000001 40.7288)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>zupVwJAFYkHDwrsQd2ktXA</td>\n",
       "      <td>fiftylan-union-square-new-york-3</td>\n",
       "      <td>FIFTYLAN Union Square</td>\n",
       "      <td>https://s3-media3.fl.yelpcdn.com/bphoto/UIwR20...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/fiftylan-union-square...</td>\n",
       "      <td>60</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>{'latitude': 40.735516, 'longitude': -73.989191}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'address1': '32 Union Square E', 'address2': ...</td>\n",
       "      <td>1.646767e+10</td>\n",
       "      <td>(646) 767-0085</td>\n",
       "      <td>3367.900728</td>\n",
       "      <td>40.735516</td>\n",
       "      <td>-73.989191</td>\n",
       "      <td>POINT (-73.98919100000001 40.735516)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       id                             alias  \\\n",
       "0  Q3fmTHT7zilDWtfzLK9lMA                truedan-new-york-3   \n",
       "1  7-bx74TooPuZKZDNW4WFcQ            chun-yang-tea-new-york   \n",
       "2  3aypSFXLfkAL4dhHVFobKg           lazy-sundaes-new-york-6   \n",
       "3  bVJQEeRNi34-3XN_F1AZEg             xing-fu-tang-new-york   \n",
       "4  zupVwJAFYkHDwrsQd2ktXA  fiftylan-union-square-new-york-3   \n",
       "\n",
       "                    name                                          image_url  \\\n",
       "0                Truedan  https://s3-media2.fl.yelpcdn.com/bphoto/BmUZXO...   \n",
       "1          Chun Yang Tea  https://s3-media1.fl.yelpcdn.com/bphoto/JrGwrZ...   \n",
       "2           Lazy Sundaes  https://s3-media3.fl.yelpcdn.com/bphoto/kEfCYC...   \n",
       "3           Xing Fu Tang  https://s3-media2.fl.yelpcdn.com/bphoto/cyFHFE...   \n",
       "4  FIFTYLAN Union Square  https://s3-media3.fl.yelpcdn.com/bphoto/UIwR20...   \n",
       "\n",
       "   is_closed                                                url  review_count  \\\n",
       "0      False  https://www.yelp.com/biz/truedan-new-york-3?ad...            99   \n",
       "1      False  https://www.yelp.com/biz/chun-yang-tea-new-yor...            91   \n",
       "2      False  https://www.yelp.com/biz/lazy-sundaes-new-york...            32   \n",
       "3      False  https://www.yelp.com/biz/xing-fu-tang-new-york...            91   \n",
       "4      False  https://www.yelp.com/biz/fiftylan-union-square...            60   \n",
       "\n",
       "                                          categories  rating  \\\n",
       "0    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.5   \n",
       "1    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.5   \n",
       "2  [{'alias': 'bubbletea', 'title': 'Bubble Tea'}...     4.5   \n",
       "3    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.5   \n",
       "4    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.0   \n",
       "\n",
       "                                         coordinates            transactions  \\\n",
       "0  {'latitude': 40.7191742, 'longitude': -73.9962...  ['pickup', 'delivery']   \n",
       "1      {'latitude': 40.71617, 'longitude': -73.9971}  ['pickup', 'delivery']   \n",
       "2   {'latitude': 40.720597, 'longitude': -73.984539}  ['pickup', 'delivery']   \n",
       "3      {'latitude': 40.7288, 'longitude': -73.98775}  ['pickup', 'delivery']   \n",
       "4   {'latitude': 40.735516, 'longitude': -73.989191}  ['pickup', 'delivery']   \n",
       "\n",
       "  price                                           location         phone  \\\n",
       "0    $$  {'address1': '208 Grand St', 'address2': None,...  1.646559e+10   \n",
       "1    $$  {'address1': '26B Elizabeth St', 'address2': '...  1.212420e+10   \n",
       "2    $$  {'address1': '23 Clinton St', 'address2': None...           NaN   \n",
       "3   NaN  {'address1': '133 2nd Ave', 'address2': '', 'a...           NaN   \n",
       "4   NaN  {'address1': '32 Union Square E', 'address2': ...  1.646767e+10   \n",
       "\n",
       "    display_phone     distance   latitude  longitude  \\\n",
       "0  (646) 559-2886  1535.241614  40.719174 -73.996220   \n",
       "1  (212) 420-0123  1212.877370  40.716170 -73.997100   \n",
       "2             NaN  1877.016204  40.720597 -73.984539   \n",
       "3             NaN  2656.450427  40.728800 -73.987750   \n",
       "4  (646) 767-0085  3367.900728  40.735516 -73.989191   \n",
       "\n",
       "                               geometry  \n",
       "0        POINT (-73.9962197 40.7191742)  \n",
       "1             POINT (-73.9971 40.71617)  \n",
       "2          POINT (-73.984539 40.720597)  \n",
       "3    POINT (-73.98775000000001 40.7288)  \n",
       "4  POINT (-73.98919100000001 40.735516)  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# google colab path to data\n",
    "url = 'https://raw.githubusercontent.com/mebauer/boba-nyc/master/teabook/boba-nyc.csv'\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "# # local path to data\n",
    "# df = pd.read_csv('boba-nyc.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8ab37863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>alias</th>\n",
       "      <th>name</th>\n",
       "      <th>image_url</th>\n",
       "      <th>is_closed</th>\n",
       "      <th>url</th>\n",
       "      <th>review_count</th>\n",
       "      <th>categories</th>\n",
       "      <th>rating</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>transactions</th>\n",
       "      <th>price</th>\n",
       "      <th>location</th>\n",
       "      <th>phone</th>\n",
       "      <th>display_phone</th>\n",
       "      <th>distance</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>QBebLBqhiuDlWasPgp6s4Q</td>\n",
       "      <td>tbaar-flushing-5</td>\n",
       "      <td>TBaar</td>\n",
       "      <td>https://s3-media1.fl.yelpcdn.com/bphoto/bgao-b...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/tbaar-flushing-5?adju...</td>\n",
       "      <td>2</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>3.5</td>\n",
       "      <td>{'latitude': 40.759357, 'longitude': -73.830396}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'address1': '135-52 Roosevelt Ave', 'address2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15054.917573</td>\n",
       "      <td>40.759357</td>\n",
       "      <td>-73.830396</td>\n",
       "      <td>POINT (-73.83039599999999 40.759357)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>7apqk24GsFC9dptoD5dDZg</td>\n",
       "      <td>uncleman-cafe-new-york</td>\n",
       "      <td>Uncleman Cafe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/uncleman-cafe-new-yor...</td>\n",
       "      <td>1</td>\n",
       "      <td>[{'alias': 'desserts', 'title': 'Desserts'}, {...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{'latitude': 40.71781, 'longitude': -73.99795}</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'address1': '122 Mulberry St', 'address2': No...</td>\n",
       "      <td>1.212967e+10</td>\n",
       "      <td>(212) 966-6206</td>\n",
       "      <td>1414.238245</td>\n",
       "      <td>40.717810</td>\n",
       "      <td>-73.997950</td>\n",
       "      <td>POINT (-73.99795 40.71781)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>Vz6hbICqQfyMWq9wzAKakw</td>\n",
       "      <td>tbaar-flushing-4</td>\n",
       "      <td>TBaar</td>\n",
       "      <td>https://s3-media1.fl.yelpcdn.com/bphoto/phUYWa...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/tbaar-flushing-4?adju...</td>\n",
       "      <td>6</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>{'latitude': 40.7613116, 'longitude': -73.8308...</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'address1': '37-11 Main St', 'address2': '', ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15147.410738</td>\n",
       "      <td>40.761312</td>\n",
       "      <td>-73.830812</td>\n",
       "      <td>POINT (-73.8308122 40.7613116)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>YBgAPbviWYtVP2gAUhc7_A</td>\n",
       "      <td>t-baar-flushing</td>\n",
       "      <td>TBaar</td>\n",
       "      <td>https://s3-media4.fl.yelpcdn.com/bphoto/8pk-_Q...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/t-baar-flushing?adjus...</td>\n",
       "      <td>8</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>3.5</td>\n",
       "      <td>{'latitude': 40.7587509, 'longitude': -73.830162}</td>\n",
       "      <td>[]</td>\n",
       "      <td>$</td>\n",
       "      <td>{'address1': '135-36 40th Rd', 'address2': '',...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15045.970192</td>\n",
       "      <td>40.758751</td>\n",
       "      <td>-73.830162</td>\n",
       "      <td>POINT (-73.830162 40.7587509)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>f5UVQXvDpOrrIZdCjsw47w</td>\n",
       "      <td>maxins-cafe-flushing</td>\n",
       "      <td>Maxin's Cafe</td>\n",
       "      <td>https://s3-media1.fl.yelpcdn.com/bphoto/S3vWvD...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/maxins-cafe-flushing?...</td>\n",
       "      <td>15</td>\n",
       "      <td>[{'alias': 'bakeries', 'title': 'Bakeries'}, {...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>{'latitude': 40.7585, 'longitude': -73.83064}</td>\n",
       "      <td>[]</td>\n",
       "      <td>$</td>\n",
       "      <td>{'address1': '135-24 40th Rd', 'address2': '',...</td>\n",
       "      <td>1.718887e+10</td>\n",
       "      <td>(718) 886-9200</td>\n",
       "      <td>14997.842205</td>\n",
       "      <td>40.758500</td>\n",
       "      <td>-73.830640</td>\n",
       "      <td>POINT (-73.83064 40.7585)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id                   alias           name  \\\n",
       "409  QBebLBqhiuDlWasPgp6s4Q        tbaar-flushing-5          TBaar   \n",
       "410  7apqk24GsFC9dptoD5dDZg  uncleman-cafe-new-york  Uncleman Cafe   \n",
       "411  Vz6hbICqQfyMWq9wzAKakw        tbaar-flushing-4          TBaar   \n",
       "412  YBgAPbviWYtVP2gAUhc7_A         t-baar-flushing          TBaar   \n",
       "413  f5UVQXvDpOrrIZdCjsw47w    maxins-cafe-flushing   Maxin's Cafe   \n",
       "\n",
       "                                             image_url  is_closed  \\\n",
       "409  https://s3-media1.fl.yelpcdn.com/bphoto/bgao-b...      False   \n",
       "410                                                NaN      False   \n",
       "411  https://s3-media1.fl.yelpcdn.com/bphoto/phUYWa...      False   \n",
       "412  https://s3-media4.fl.yelpcdn.com/bphoto/8pk-_Q...      False   \n",
       "413  https://s3-media1.fl.yelpcdn.com/bphoto/S3vWvD...      False   \n",
       "\n",
       "                                                   url  review_count  \\\n",
       "409  https://www.yelp.com/biz/tbaar-flushing-5?adju...             2   \n",
       "410  https://www.yelp.com/biz/uncleman-cafe-new-yor...             1   \n",
       "411  https://www.yelp.com/biz/tbaar-flushing-4?adju...             6   \n",
       "412  https://www.yelp.com/biz/t-baar-flushing?adjus...             8   \n",
       "413  https://www.yelp.com/biz/maxins-cafe-flushing?...            15   \n",
       "\n",
       "                                            categories  rating  \\\n",
       "409    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     3.5   \n",
       "410  [{'alias': 'desserts', 'title': 'Desserts'}, {...     1.0   \n",
       "411  [{'alias': 'bubbletea', 'title': 'Bubble Tea'}...     4.0   \n",
       "412    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     3.5   \n",
       "413  [{'alias': 'bakeries', 'title': 'Bakeries'}, {...     3.5   \n",
       "\n",
       "                                           coordinates  \\\n",
       "409   {'latitude': 40.759357, 'longitude': -73.830396}   \n",
       "410     {'latitude': 40.71781, 'longitude': -73.99795}   \n",
       "411  {'latitude': 40.7613116, 'longitude': -73.8308...   \n",
       "412  {'latitude': 40.7587509, 'longitude': -73.830162}   \n",
       "413      {'latitude': 40.7585, 'longitude': -73.83064}   \n",
       "\n",
       "               transactions price  \\\n",
       "409  ['pickup', 'delivery']   NaN   \n",
       "410                      []   NaN   \n",
       "411                      []   NaN   \n",
       "412                      []     $   \n",
       "413                      []     $   \n",
       "\n",
       "                                              location         phone  \\\n",
       "409  {'address1': '135-52 Roosevelt Ave', 'address2...           NaN   \n",
       "410  {'address1': '122 Mulberry St', 'address2': No...  1.212967e+10   \n",
       "411  {'address1': '37-11 Main St', 'address2': '', ...           NaN   \n",
       "412  {'address1': '135-36 40th Rd', 'address2': '',...           NaN   \n",
       "413  {'address1': '135-24 40th Rd', 'address2': '',...  1.718887e+10   \n",
       "\n",
       "      display_phone      distance   latitude  longitude  \\\n",
       "409             NaN  15054.917573  40.759357 -73.830396   \n",
       "410  (212) 966-6206   1414.238245  40.717810 -73.997950   \n",
       "411             NaN  15147.410738  40.761312 -73.830812   \n",
       "412             NaN  15045.970192  40.758751 -73.830162   \n",
       "413  (718) 886-9200  14997.842205  40.758500 -73.830640   \n",
       "\n",
       "                                 geometry  \n",
       "409  POINT (-73.83039599999999 40.759357)  \n",
       "410            POINT (-73.99795 40.71781)  \n",
       "411        POINT (-73.8308122 40.7613116)  \n",
       "412         POINT (-73.830162 40.7587509)  \n",
       "413             POINT (-73.83064 40.7585)  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview last five rows\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "compressed-arrow",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of rows: 414\n",
      "number of columns: 19\n"
     ]
    }
   ],
   "source": [
    "rows, columns = df.shape\n",
    "print('number of rows: {}\\nnumber of columns: {}'.format(rows, columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "composed-bradford",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 414 entries, 0 to 413\n",
      "Data columns (total 19 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   id             414 non-null    object \n",
      " 1   alias          414 non-null    object \n",
      " 2   name           414 non-null    object \n",
      " 3   image_url      409 non-null    object \n",
      " 4   is_closed      414 non-null    bool   \n",
      " 5   url            414 non-null    object \n",
      " 6   review_count   414 non-null    int64  \n",
      " 7   categories     414 non-null    object \n",
      " 8   rating         414 non-null    float64\n",
      " 9   coordinates    414 non-null    object \n",
      " 10  transactions   414 non-null    object \n",
      " 11  price          258 non-null    object \n",
      " 12  location       414 non-null    object \n",
      " 13  phone          362 non-null    float64\n",
      " 14  display_phone  362 non-null    object \n",
      " 15  distance       414 non-null    float64\n",
      " 16  latitude       414 non-null    float64\n",
      " 17  longitude      414 non-null    float64\n",
      " 18  geometry       414 non-null    object \n",
      "dtypes: bool(1), float64(5), int64(1), object(12)\n",
      "memory usage: 58.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# review concise summary of data\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "talented-chorus",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>null_count</th>\n",
       "      <th>null_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>156</td>\n",
       "      <td>37.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>display_phone</th>\n",
       "      <td>52</td>\n",
       "      <td>12.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phone</th>\n",
       "      <td>52</td>\n",
       "      <td>12.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>image_url</th>\n",
       "      <td>5</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transactions</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coordinates</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alias</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>categories</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_count</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>url</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_closed</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geometry</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               null_count  null_perc\n",
       "price                 156      37.68\n",
       "display_phone          52      12.56\n",
       "phone                  52      12.56\n",
       "image_url               5       1.21\n",
       "id                      0       0.00\n",
       "transactions            0       0.00\n",
       "longitude               0       0.00\n",
       "latitude                0       0.00\n",
       "distance                0       0.00\n",
       "location                0       0.00\n",
       "coordinates             0       0.00\n",
       "alias                   0       0.00\n",
       "rating                  0       0.00\n",
       "categories              0       0.00\n",
       "review_count            0       0.00\n",
       "url                     0       0.00\n",
       "is_closed               0       0.00\n",
       "name                    0       0.00\n",
       "geometry                0       0.00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# identifiying number of nulls and percentage of total per column\n",
    "ser1 = df.isnull().sum().sort_values(ascending=False)\n",
    "ser2 = round((df.isnull().sum().sort_values(ascending=False) / len(df)) * 100, 2)\n",
    "\n",
    "pd.concat([ser1.rename('null_count'), ser2.rename('null_perc')], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "touched-approval",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_count</th>\n",
       "      <th>rating</th>\n",
       "      <th>phone</th>\n",
       "      <th>distance</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>414.000000</td>\n",
       "      <td>414.000000</td>\n",
       "      <td>3.620000e+02</td>\n",
       "      <td>414.000000</td>\n",
       "      <td>414.000000</td>\n",
       "      <td>414.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>89.722222</td>\n",
       "      <td>3.985507</td>\n",
       "      <td>1.613962e+10</td>\n",
       "      <td>8980.359386</td>\n",
       "      <td>40.716170</td>\n",
       "      <td>-73.941997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>116.765312</td>\n",
       "      <td>0.608481</td>\n",
       "      <td>2.375761e+09</td>\n",
       "      <td>5684.076763</td>\n",
       "      <td>0.058255</td>\n",
       "      <td>0.084129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.201432e+10</td>\n",
       "      <td>953.875131</td>\n",
       "      <td>40.537609</td>\n",
       "      <td>-74.166080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.250000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.347732e+10</td>\n",
       "      <td>3954.891025</td>\n",
       "      <td>40.689505</td>\n",
       "      <td>-73.994920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.718286e+10</td>\n",
       "      <td>8254.243639</td>\n",
       "      <td>40.731610</td>\n",
       "      <td>-73.975096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>109.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.718886e+10</td>\n",
       "      <td>13019.588280</td>\n",
       "      <td>40.757300</td>\n",
       "      <td>-73.880179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>740.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.934235e+10</td>\n",
       "      <td>25132.157979</td>\n",
       "      <td>40.886959</td>\n",
       "      <td>-73.702410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       review_count      rating         phone      distance    latitude  \\\n",
       "count    414.000000  414.000000  3.620000e+02    414.000000  414.000000   \n",
       "mean      89.722222    3.985507  1.613962e+10   8980.359386   40.716170   \n",
       "std      116.765312    0.608481  2.375761e+09   5684.076763    0.058255   \n",
       "min        1.000000    1.000000  1.201432e+10    953.875131   40.537609   \n",
       "25%       16.250000    3.500000  1.347732e+10   3954.891025   40.689505   \n",
       "50%       49.000000    4.000000  1.718286e+10   8254.243639   40.731610   \n",
       "75%      109.000000    4.500000  1.718886e+10  13019.588280   40.757300   \n",
       "max      740.000000    5.000000  1.934235e+10  25132.157979   40.886959   \n",
       "\n",
       "        longitude  \n",
       "count  414.000000  \n",
       "mean   -73.941997  \n",
       "std      0.084129  \n",
       "min    -74.166080  \n",
       "25%    -73.994920  \n",
       "50%    -73.975096  \n",
       "75%    -73.880179  \n",
       "max    -73.702410  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive statistics of numeric columns\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "hindu-seafood",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>Q3fmTHT7zilDWtfzLK9lMA</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alias</th>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>truedan-new-york-3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>414</td>\n",
       "      <td>259</td>\n",
       "      <td>Kung Fu Tea</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>image_url</th>\n",
       "      <td>409</td>\n",
       "      <td>409</td>\n",
       "      <td>https://s3-media2.fl.yelpcdn.com/bphoto/BmUZXO...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>url</th>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>https://www.yelp.com/biz/truedan-new-york-3?ad...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>categories</th>\n",
       "      <td>414</td>\n",
       "      <td>177</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coordinates</th>\n",
       "      <td>414</td>\n",
       "      <td>409</td>\n",
       "      <td>{'latitude': 40.71697, 'longitude': -73.99471}</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transactions</th>\n",
       "      <td>414</td>\n",
       "      <td>5</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>258</td>\n",
       "      <td>3</td>\n",
       "      <td>$</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <td>414</td>\n",
       "      <td>411</td>\n",
       "      <td>{'address1': '90-15 Queens Blvd', 'address2': ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>display_phone</th>\n",
       "      <td>362</td>\n",
       "      <td>357</td>\n",
       "      <td>(718) 395-8297</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geometry</th>\n",
       "      <td>414</td>\n",
       "      <td>409</td>\n",
       "      <td>POINT (-73.99471 40.71697)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count unique                                                top  \\\n",
       "id              414    414                             Q3fmTHT7zilDWtfzLK9lMA   \n",
       "alias           414    414                                 truedan-new-york-3   \n",
       "name            414    259                                        Kung Fu Tea   \n",
       "image_url       409    409  https://s3-media2.fl.yelpcdn.com/bphoto/BmUZXO...   \n",
       "url             414    414  https://www.yelp.com/biz/truedan-new-york-3?ad...   \n",
       "categories      414    177    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]   \n",
       "coordinates     414    409     {'latitude': 40.71697, 'longitude': -73.99471}   \n",
       "transactions    414      5                             ['pickup', 'delivery']   \n",
       "price           258      3                                                  $   \n",
       "location        414    411  {'address1': '90-15 Queens Blvd', 'address2': ...   \n",
       "display_phone   362    357                                     (718) 395-8297   \n",
       "geometry        414    409                         POINT (-73.99471 40.71697)   \n",
       "\n",
       "              freq  \n",
       "id               1  \n",
       "alias            1  \n",
       "name            26  \n",
       "image_url        1  \n",
       "url              1  \n",
       "categories     115  \n",
       "coordinates      2  \n",
       "transactions   229  \n",
       "price          166  \n",
       "location         2  \n",
       "display_phone    2  \n",
       "geometry         2  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive statistics of string/object columns\n",
    "df.describe(include=['O']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "motivated-yeast",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id is unique: True\n"
     ]
    }
   ],
   "source": [
    "# confirm that unique id is actually unique\n",
    "print('id is unique: {}'.format(df['id'].is_unique))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "coral-bradford",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>alias</th>\n",
       "      <th>name</th>\n",
       "      <th>image_url</th>\n",
       "      <th>is_closed</th>\n",
       "      <th>url</th>\n",
       "      <th>review_count</th>\n",
       "      <th>categories</th>\n",
       "      <th>rating</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>transactions</th>\n",
       "      <th>price</th>\n",
       "      <th>location</th>\n",
       "      <th>phone</th>\n",
       "      <th>display_phone</th>\n",
       "      <th>distance</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q3fmTHT7zilDWtfzLK9lMA</td>\n",
       "      <td>truedan-new-york-3</td>\n",
       "      <td>Truedan</td>\n",
       "      <td>https://s3-media2.fl.yelpcdn.com/bphoto/BmUZXO...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/truedan-new-york-3?ad...</td>\n",
       "      <td>99</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.5</td>\n",
       "      <td>{'latitude': 40.7191742, 'longitude': -73.9962...</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>$$</td>\n",
       "      <td>{'address1': '208 Grand St', 'address2': None,...</td>\n",
       "      <td>1.646559e+10</td>\n",
       "      <td>(646) 559-2886</td>\n",
       "      <td>1535.241614</td>\n",
       "      <td>40.719174</td>\n",
       "      <td>-73.996220</td>\n",
       "      <td>POINT (-73.9962197 40.7191742)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7-bx74TooPuZKZDNW4WFcQ</td>\n",
       "      <td>chun-yang-tea-new-york</td>\n",
       "      <td>Chun Yang Tea</td>\n",
       "      <td>https://s3-media1.fl.yelpcdn.com/bphoto/JrGwrZ...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/chun-yang-tea-new-yor...</td>\n",
       "      <td>91</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.5</td>\n",
       "      <td>{'latitude': 40.71617, 'longitude': -73.9971}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>$$</td>\n",
       "      <td>{'address1': '26B Elizabeth St', 'address2': '...</td>\n",
       "      <td>1.212420e+10</td>\n",
       "      <td>(212) 420-0123</td>\n",
       "      <td>1212.877370</td>\n",
       "      <td>40.716170</td>\n",
       "      <td>-73.997100</td>\n",
       "      <td>POINT (-73.9971 40.71617)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3aypSFXLfkAL4dhHVFobKg</td>\n",
       "      <td>lazy-sundaes-new-york-6</td>\n",
       "      <td>Lazy Sundaes</td>\n",
       "      <td>https://s3-media3.fl.yelpcdn.com/bphoto/kEfCYC...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/lazy-sundaes-new-york...</td>\n",
       "      <td>32</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}...</td>\n",
       "      <td>4.5</td>\n",
       "      <td>{'latitude': 40.720597, 'longitude': -73.984539}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>$$</td>\n",
       "      <td>{'address1': '23 Clinton St', 'address2': None...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1877.016204</td>\n",
       "      <td>40.720597</td>\n",
       "      <td>-73.984539</td>\n",
       "      <td>POINT (-73.984539 40.720597)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bVJQEeRNi34-3XN_F1AZEg</td>\n",
       "      <td>xing-fu-tang-new-york</td>\n",
       "      <td>Xing Fu Tang</td>\n",
       "      <td>https://s3-media2.fl.yelpcdn.com/bphoto/cyFHFE...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/xing-fu-tang-new-york...</td>\n",
       "      <td>91</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.5</td>\n",
       "      <td>{'latitude': 40.7288, 'longitude': -73.98775}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'address1': '133 2nd Ave', 'address2': '', 'a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2656.450427</td>\n",
       "      <td>40.728800</td>\n",
       "      <td>-73.987750</td>\n",
       "      <td>POINT (-73.98775000000001 40.7288)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>zupVwJAFYkHDwrsQd2ktXA</td>\n",
       "      <td>fiftylan-union-square-new-york-3</td>\n",
       "      <td>FIFTYLAN Union Square</td>\n",
       "      <td>https://s3-media3.fl.yelpcdn.com/bphoto/UIwR20...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/fiftylan-union-square...</td>\n",
       "      <td>60</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>{'latitude': 40.735516, 'longitude': -73.989191}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'address1': '32 Union Square E', 'address2': ...</td>\n",
       "      <td>1.646767e+10</td>\n",
       "      <td>(646) 767-0085</td>\n",
       "      <td>3367.900728</td>\n",
       "      <td>40.735516</td>\n",
       "      <td>-73.989191</td>\n",
       "      <td>POINT (-73.98919100000001 40.735516)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       id                             alias  \\\n",
       "0  Q3fmTHT7zilDWtfzLK9lMA                truedan-new-york-3   \n",
       "1  7-bx74TooPuZKZDNW4WFcQ            chun-yang-tea-new-york   \n",
       "2  3aypSFXLfkAL4dhHVFobKg           lazy-sundaes-new-york-6   \n",
       "3  bVJQEeRNi34-3XN_F1AZEg             xing-fu-tang-new-york   \n",
       "4  zupVwJAFYkHDwrsQd2ktXA  fiftylan-union-square-new-york-3   \n",
       "\n",
       "                    name                                          image_url  \\\n",
       "0                Truedan  https://s3-media2.fl.yelpcdn.com/bphoto/BmUZXO...   \n",
       "1          Chun Yang Tea  https://s3-media1.fl.yelpcdn.com/bphoto/JrGwrZ...   \n",
       "2           Lazy Sundaes  https://s3-media3.fl.yelpcdn.com/bphoto/kEfCYC...   \n",
       "3           Xing Fu Tang  https://s3-media2.fl.yelpcdn.com/bphoto/cyFHFE...   \n",
       "4  FIFTYLAN Union Square  https://s3-media3.fl.yelpcdn.com/bphoto/UIwR20...   \n",
       "\n",
       "   is_closed                                                url  review_count  \\\n",
       "0      False  https://www.yelp.com/biz/truedan-new-york-3?ad...            99   \n",
       "1      False  https://www.yelp.com/biz/chun-yang-tea-new-yor...            91   \n",
       "2      False  https://www.yelp.com/biz/lazy-sundaes-new-york...            32   \n",
       "3      False  https://www.yelp.com/biz/xing-fu-tang-new-york...            91   \n",
       "4      False  https://www.yelp.com/biz/fiftylan-union-square...            60   \n",
       "\n",
       "                                          categories  rating  \\\n",
       "0    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.5   \n",
       "1    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.5   \n",
       "2  [{'alias': 'bubbletea', 'title': 'Bubble Tea'}...     4.5   \n",
       "3    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.5   \n",
       "4    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.0   \n",
       "\n",
       "                                         coordinates            transactions  \\\n",
       "0  {'latitude': 40.7191742, 'longitude': -73.9962...  ['pickup', 'delivery']   \n",
       "1      {'latitude': 40.71617, 'longitude': -73.9971}  ['pickup', 'delivery']   \n",
       "2   {'latitude': 40.720597, 'longitude': -73.984539}  ['pickup', 'delivery']   \n",
       "3      {'latitude': 40.7288, 'longitude': -73.98775}  ['pickup', 'delivery']   \n",
       "4   {'latitude': 40.735516, 'longitude': -73.989191}  ['pickup', 'delivery']   \n",
       "\n",
       "  price                                           location         phone  \\\n",
       "0    $$  {'address1': '208 Grand St', 'address2': None,...  1.646559e+10   \n",
       "1    $$  {'address1': '26B Elizabeth St', 'address2': '...  1.212420e+10   \n",
       "2    $$  {'address1': '23 Clinton St', 'address2': None...           NaN   \n",
       "3   NaN  {'address1': '133 2nd Ave', 'address2': '', 'a...           NaN   \n",
       "4   NaN  {'address1': '32 Union Square E', 'address2': ...  1.646767e+10   \n",
       "\n",
       "    display_phone     distance   latitude  longitude  \\\n",
       "0  (646) 559-2886  1535.241614  40.719174 -73.996220   \n",
       "1  (212) 420-0123  1212.877370  40.716170 -73.997100   \n",
       "2             NaN  1877.016204  40.720597 -73.984539   \n",
       "3             NaN  2656.450427  40.728800 -73.987750   \n",
       "4  (646) 767-0085  3367.900728  40.735516 -73.989191   \n",
       "\n",
       "                               geometry  \n",
       "0        POINT (-73.9962197 40.7191742)  \n",
       "1             POINT (-73.9971 40.71617)  \n",
       "2          POINT (-73.984539 40.720597)  \n",
       "3    POINT (-73.98775000000001 40.7288)  \n",
       "4  POINT (-73.98919100000001 40.735516)  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "promotional-acceptance",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of unique bubble tea shops: 259\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kung Fu Tea</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vivi Bubble Tea</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gong Cha</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CoCo Fresh Tea &amp; Juice</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Möge Tee</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>Chokolat Patisserie &amp; Culture Tea Bar</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>Siips Bubble</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>Yummy Boba Desserts</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>Bread &amp; Joe</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>Maxin's Cafe</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      name  counts\n",
       "0                              Kung Fu Tea      26\n",
       "1                          Vivi Bubble Tea      25\n",
       "2                                 Gong Cha      24\n",
       "3                   CoCo Fresh Tea & Juice      10\n",
       "4                                 Möge Tee       9\n",
       "..                                     ...     ...\n",
       "254  Chokolat Patisserie & Culture Tea Bar       1\n",
       "255                           Siips Bubble       1\n",
       "256                    Yummy Boba Desserts       1\n",
       "257                            Bread & Joe       1\n",
       "258                           Maxin's Cafe       1\n",
       "\n",
       "[259 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# identify number of unique bubble tea shop entries\n",
    "names_counts = df['name'].value_counts().reset_index()\n",
    "names_counts = names_counts.rename(columns={'index':'name', 'name':'counts'})\n",
    "\n",
    "print('number of unique bubble tea shops: {}'.format(len(names_counts)))\n",
    "# save file\n",
    "name_counts_file_path = '../teaapp/name_counts.csv'\n",
    "names_counts.to_csv(name_counts_file_path)\n",
    "# view dataframe\n",
    "names_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "absolute-crazy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kung Fu Tea</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vivi Bubble Tea</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gong Cha</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CoCo Fresh Tea &amp; Juice</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Möge Tee</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>Chokolat Patisserie &amp; Culture Tea Bar</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>Siips Bubble</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>Yummy Boba Desserts</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>Bread &amp; Joe</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>Maxin's Cafe</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     index  name\n",
       "0                              Kung Fu Tea    26\n",
       "1                          Vivi Bubble Tea    25\n",
       "2                                 Gong Cha    24\n",
       "3                   CoCo Fresh Tea & Juice    10\n",
       "4                                 Möge Tee     9\n",
       "..                                     ...   ...\n",
       "254  Chokolat Patisserie & Culture Tea Bar     1\n",
       "255                           Siips Bubble     1\n",
       "256                    Yummy Boba Desserts     1\n",
       "257                            Bread & Joe     1\n",
       "258                           Maxin's Cafe     1\n",
       "\n",
       "[259 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name'].value_counts().reset_index(drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cross-startup",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names_counts = df['name'].value_counts().reset_index(drop=False)\n",
    "names_counts = names_counts.rename(columns={'index':'names', 'name':'counts'})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "sns.barplot(x='counts',\n",
    "            y=\"names\", \n",
    "            data=names_counts.head(10), \n",
    "            ax=ax)\n",
    "\n",
    "plt.title('Number of bubble tea shops by business in nyc', fontsize=15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "italian-welding",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>review_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ten Ren's Tea Time</td>\n",
       "      <td>656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hanco's</td>\n",
       "      <td>519.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ajisen Ramen</td>\n",
       "      <td>438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Teado Tea Shop</td>\n",
       "      <td>428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>520 Dessert</td>\n",
       "      <td>358.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name  review_count\n",
       "0  Ten Ren's Tea Time         656.0\n",
       "1             Hanco's         519.0\n",
       "2        Ajisen Ramen         438.0\n",
       "3      Teado Tea Shop         428.0\n",
       "4         520 Dessert         358.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_count_df = df.groupby(by='name')['review_count'].mean().sort_values(ascending=False)\n",
    "review_count_df = round(review_count_df, 2)\n",
    "review_count_df = review_count_df.reset_index()\n",
    "\n",
    "review_count_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "median-yemen",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\datal\\AppData\\Local\\Temp\\ipykernel_45984\\1422840523.py:9: UserWarning: Glyph 27792 (\\N{CJK UNIFIED IDEOGRAPH-6C90}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\datal\\AppData\\Local\\Temp\\ipykernel_45984\\1422840523.py:9: UserWarning: Glyph 30333 (\\N{CJK UNIFIED IDEOGRAPH-767D}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\datal\\anaconda3\\envs\\boba-nyc\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 27792 (\\N{CJK UNIFIED IDEOGRAPH-6C90}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\datal\\anaconda3\\envs\\boba-nyc\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 30333 (\\N{CJK UNIFIED IDEOGRAPH-767D}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "sns.barplot(x=\"review_count\", \n",
    "            y=\"name\", \n",
    "            data=review_count_df.head(20), \n",
    "            ax=ax)\n",
    "\n",
    "plt.title('Average number of reviews per business in nyc', fontsize=15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "accepting-organic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>alias</th>\n",
       "      <th>name</th>\n",
       "      <th>image_url</th>\n",
       "      <th>is_closed</th>\n",
       "      <th>url</th>\n",
       "      <th>review_count</th>\n",
       "      <th>categories</th>\n",
       "      <th>rating</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>transactions</th>\n",
       "      <th>price</th>\n",
       "      <th>location</th>\n",
       "      <th>phone</th>\n",
       "      <th>display_phone</th>\n",
       "      <th>distance</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>bn0zZ9nT_j1INOwfpxWmEw</td>\n",
       "      <td>tiger-sugar-flushing</td>\n",
       "      <td>tigersugar</td>\n",
       "      <td>https://s3-media2.fl.yelpcdn.com/bphoto/hai87B...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/tiger-sugar-flushing?...</td>\n",
       "      <td>740</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>{'latitude': 40.75922023369396, 'longitude': -...</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>$$</td>\n",
       "      <td>{'address1': '40-10 Main St', 'address2': 'Ste...</td>\n",
       "      <td>1.347732e+10</td>\n",
       "      <td>(347) 732-4001</td>\n",
       "      <td>15055.920826</td>\n",
       "      <td>40.759220</td>\n",
       "      <td>-73.830305</td>\n",
       "      <td>POINT (-73.83030496537685 40.75922023369396)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>NqnYnZtajJ4Y-Lmxa26VdA</td>\n",
       "      <td>hancos-brooklyn</td>\n",
       "      <td>Hanco's</td>\n",
       "      <td>https://s3-media3.fl.yelpcdn.com/bphoto/_vQWWX...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/hancos-brooklyn?adjus...</td>\n",
       "      <td>704</td>\n",
       "      <td>[{'alias': 'vietnamese', 'title': 'Vietnamese'...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>{'latitude': 40.6870318621828, 'longitude': -7...</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>$</td>\n",
       "      <td>{'address1': '134 Smith St', 'address2': '', '...</td>\n",
       "      <td>1.718859e+10</td>\n",
       "      <td>(718) 858-6818</td>\n",
       "      <td>2073.182530</td>\n",
       "      <td>40.687032</td>\n",
       "      <td>-73.990429</td>\n",
       "      <td>POINT (-73.9904292380982 40.6870318621828)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>WxNlqGSsj_2TCodkx9Sa4A</td>\n",
       "      <td>vivi-bubble-tea-new-york-6</td>\n",
       "      <td>Vivi Bubble Tea</td>\n",
       "      <td>https://s3-media2.fl.yelpcdn.com/bphoto/Fw3SYB...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/vivi-bubble-tea-new-y...</td>\n",
       "      <td>658</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>{'latitude': 40.715099, 'longitude': -73.997535}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>$</td>\n",
       "      <td>{'address1': '49 Bayard St', 'address2': '', '...</td>\n",
       "      <td>1.212567e+10</td>\n",
       "      <td>(212) 566-6833</td>\n",
       "      <td>1114.073275</td>\n",
       "      <td>40.715099</td>\n",
       "      <td>-73.997535</td>\n",
       "      <td>POINT (-73.997535 40.715099)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>SqVHETmH6bf3rPKmdftvvw</td>\n",
       "      <td>ten-rens-tea-time-new-york-2</td>\n",
       "      <td>Ten Ren's Tea Time</td>\n",
       "      <td>https://s3-media1.fl.yelpcdn.com/bphoto/UChdn5...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/ten-rens-tea-time-new...</td>\n",
       "      <td>656</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>{'latitude': 40.7163819, 'longitude': -73.9982...</td>\n",
       "      <td>['delivery']</td>\n",
       "      <td>$$</td>\n",
       "      <td>{'address1': '73 Mott St', 'address2': '', 'ad...</td>\n",
       "      <td>1.212733e+10</td>\n",
       "      <td>(212) 732-7178</td>\n",
       "      <td>1261.450313</td>\n",
       "      <td>40.716382</td>\n",
       "      <td>-73.998268</td>\n",
       "      <td>POINT (-73.99826830000001 40.7163819)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>ZLo3HRSSvN--Mw4eCBLxCg</td>\n",
       "      <td>mango-mango-dessert-flushing-3</td>\n",
       "      <td>Mango Mango Dessert</td>\n",
       "      <td>https://s3-media3.fl.yelpcdn.com/bphoto/X7bnQ9...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/mango-mango-dessert-f...</td>\n",
       "      <td>622</td>\n",
       "      <td>[{'alias': 'desserts', 'title': 'Desserts'}, {...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>{'latitude': 40.7603809317017, 'longitude': -7...</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>$</td>\n",
       "      <td>{'address1': '136-28 39th Ave', 'address2': ''...</td>\n",
       "      <td>1.917563e+10</td>\n",
       "      <td>(917) 563-1847</td>\n",
       "      <td>15199.500511</td>\n",
       "      <td>40.760381</td>\n",
       "      <td>-73.829452</td>\n",
       "      <td>POINT (-73.8294516357502 40.7603809317017)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id                           alias  \\\n",
       "170  bn0zZ9nT_j1INOwfpxWmEw            tiger-sugar-flushing   \n",
       "90   NqnYnZtajJ4Y-Lmxa26VdA                 hancos-brooklyn   \n",
       "51   WxNlqGSsj_2TCodkx9Sa4A      vivi-bubble-tea-new-york-6   \n",
       "45   SqVHETmH6bf3rPKmdftvvw    ten-rens-tea-time-new-york-2   \n",
       "266  ZLo3HRSSvN--Mw4eCBLxCg  mango-mango-dessert-flushing-3   \n",
       "\n",
       "                    name                                          image_url  \\\n",
       "170           tigersugar  https://s3-media2.fl.yelpcdn.com/bphoto/hai87B...   \n",
       "90               Hanco's  https://s3-media3.fl.yelpcdn.com/bphoto/_vQWWX...   \n",
       "51       Vivi Bubble Tea  https://s3-media2.fl.yelpcdn.com/bphoto/Fw3SYB...   \n",
       "45    Ten Ren's Tea Time  https://s3-media1.fl.yelpcdn.com/bphoto/UChdn5...   \n",
       "266  Mango Mango Dessert  https://s3-media3.fl.yelpcdn.com/bphoto/X7bnQ9...   \n",
       "\n",
       "     is_closed                                                url  \\\n",
       "170      False  https://www.yelp.com/biz/tiger-sugar-flushing?...   \n",
       "90       False  https://www.yelp.com/biz/hancos-brooklyn?adjus...   \n",
       "51       False  https://www.yelp.com/biz/vivi-bubble-tea-new-y...   \n",
       "45       False  https://www.yelp.com/biz/ten-rens-tea-time-new...   \n",
       "266      False  https://www.yelp.com/biz/mango-mango-dessert-f...   \n",
       "\n",
       "     review_count                                         categories  rating  \\\n",
       "170           740  [{'alias': 'bubbletea', 'title': 'Bubble Tea'}...     4.0   \n",
       "90            704  [{'alias': 'vietnamese', 'title': 'Vietnamese'...     4.0   \n",
       "51            658    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.0   \n",
       "45            656    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.0   \n",
       "266           622  [{'alias': 'desserts', 'title': 'Desserts'}, {...     4.0   \n",
       "\n",
       "                                           coordinates  \\\n",
       "170  {'latitude': 40.75922023369396, 'longitude': -...   \n",
       "90   {'latitude': 40.6870318621828, 'longitude': -7...   \n",
       "51    {'latitude': 40.715099, 'longitude': -73.997535}   \n",
       "45   {'latitude': 40.7163819, 'longitude': -73.9982...   \n",
       "266  {'latitude': 40.7603809317017, 'longitude': -7...   \n",
       "\n",
       "               transactions price  \\\n",
       "170  ['pickup', 'delivery']    $$   \n",
       "90   ['pickup', 'delivery']     $   \n",
       "51   ['pickup', 'delivery']     $   \n",
       "45             ['delivery']    $$   \n",
       "266  ['pickup', 'delivery']     $   \n",
       "\n",
       "                                              location         phone  \\\n",
       "170  {'address1': '40-10 Main St', 'address2': 'Ste...  1.347732e+10   \n",
       "90   {'address1': '134 Smith St', 'address2': '', '...  1.718859e+10   \n",
       "51   {'address1': '49 Bayard St', 'address2': '', '...  1.212567e+10   \n",
       "45   {'address1': '73 Mott St', 'address2': '', 'ad...  1.212733e+10   \n",
       "266  {'address1': '136-28 39th Ave', 'address2': ''...  1.917563e+10   \n",
       "\n",
       "      display_phone      distance   latitude  longitude  \\\n",
       "170  (347) 732-4001  15055.920826  40.759220 -73.830305   \n",
       "90   (718) 858-6818   2073.182530  40.687032 -73.990429   \n",
       "51   (212) 566-6833   1114.073275  40.715099 -73.997535   \n",
       "45   (212) 732-7178   1261.450313  40.716382 -73.998268   \n",
       "266  (917) 563-1847  15199.500511  40.760381 -73.829452   \n",
       "\n",
       "                                         geometry  \n",
       "170  POINT (-73.83030496537685 40.75922023369396)  \n",
       "90     POINT (-73.9904292380982 40.6870318621828)  \n",
       "51                   POINT (-73.997535 40.715099)  \n",
       "45          POINT (-73.99826830000001 40.7163819)  \n",
       "266    POINT (-73.8294516357502 40.7603809317017)  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_reviewed = df.sort_values(by='review_count', ascending=False).head(20)\n",
    "\n",
    "most_reviewed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "advanced-wayne",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "sns.barplot(x=\"review_count\", \n",
    "            y=\"alias\", \n",
    "            data=most_reviewed, \n",
    "            ax=ax)\n",
    "\n",
    "plt.title('Most reviews per business location in nyc', fontsize=15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "green-miniature",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    414.000000\n",
       "mean       3.985507\n",
       "std        0.608481\n",
       "min        1.000000\n",
       "25%        3.500000\n",
       "50%        4.000000\n",
       "75%        4.500000\n",
       "max        5.000000\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rating'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "encouraging-franklin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "sns.countplot(data=df, \n",
    "             x=\"rating\")\n",
    "\n",
    "plt.title('Count of Yelp ratings per business location in nyc', fontsize=15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "increased-species",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>$</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>$$</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>$$$$</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  price  counts\n",
       "0     $     166\n",
       "1    $$      91\n",
       "2  $$$$       1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_df = df['price'].dropna().value_counts()\n",
    "price_df = price_df.reset_index()\n",
    "price_df.columns = ['price', 'counts']\n",
    "\n",
    "price_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "forced-heating",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price  counts\n",
       "0      1     166\n",
       "1      2      91\n",
       "2      4       1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_df['price'] = price_df['price'].str.count('\\\\$')\n",
    "\n",
    "price_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "suitable-acrobat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "sns.barplot(y=\"counts\", \n",
    "            x=\"price\", \n",
    "            data=price_df, \n",
    "            ax=ax)\n",
    "\n",
    "plt.title('Yelp price level (1 = $) per business location in NYC', fontsize=15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "unknown-lesbian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>boro_code</th>\n",
       "      <th>boro_name</th>\n",
       "      <th>county_fip</th>\n",
       "      <th>ntacode</th>\n",
       "      <th>ntaname</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Queens</td>\n",
       "      <td>081</td>\n",
       "      <td>QN51</td>\n",
       "      <td>Murray Hill</td>\n",
       "      <td>5.248828e+07</td>\n",
       "      <td>33266.904856</td>\n",
       "      <td>POLYGON ((-73.80379 40.77561, -73.80099 40.775...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Queens</td>\n",
       "      <td>081</td>\n",
       "      <td>QN27</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>1.972685e+07</td>\n",
       "      <td>19816.711894</td>\n",
       "      <td>POLYGON ((-73.86110 40.76366, -73.85993 40.762...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Queens</td>\n",
       "      <td>081</td>\n",
       "      <td>QN41</td>\n",
       "      <td>Fresh Meadows-Utopia</td>\n",
       "      <td>2.777485e+07</td>\n",
       "      <td>22106.431272</td>\n",
       "      <td>POLYGON ((-73.77758 40.73019, -73.77849 40.729...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN17</td>\n",
       "      <td>Midtown-Midtown South</td>\n",
       "      <td>3.019153e+07</td>\n",
       "      <td>27032.700375</td>\n",
       "      <td>POLYGON ((-73.97301 40.76428, -73.97141 40.763...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>005</td>\n",
       "      <td>BX09</td>\n",
       "      <td>Soundview-Castle Hill-Clason Point-Harding Park</td>\n",
       "      <td>5.198380e+07</td>\n",
       "      <td>67340.977626</td>\n",
       "      <td>MULTIPOLYGON (((-73.88064 40.81852, -73.88098 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   boro_code  boro_name county_fip ntacode  \\\n",
       "0        4.0     Queens        081    QN51   \n",
       "1        4.0     Queens        081    QN27   \n",
       "2        4.0     Queens        081    QN41   \n",
       "3        1.0  Manhattan        061    MN17   \n",
       "4        2.0      Bronx        005    BX09   \n",
       "\n",
       "                                           ntaname    shape_area  \\\n",
       "0                                      Murray Hill  5.248828e+07   \n",
       "1                                    East Elmhurst  1.972685e+07   \n",
       "2                             Fresh Meadows-Utopia  2.777485e+07   \n",
       "3                            Midtown-Midtown South  3.019153e+07   \n",
       "4  Soundview-Castle Hill-Clason Point-Harding Park  5.198380e+07   \n",
       "\n",
       "     shape_leng                                           geometry  \n",
       "0  33266.904856  POLYGON ((-73.80379 40.77561, -73.80099 40.775...  \n",
       "1  19816.711894  POLYGON ((-73.86110 40.76366, -73.85993 40.762...  \n",
       "2  22106.431272  POLYGON ((-73.77758 40.73019, -73.77849 40.729...  \n",
       "3  27032.700375  POLYGON ((-73.97301 40.76428, -73.97141 40.763...  \n",
       "4  67340.977626  MULTIPOLYGON (((-73.88064 40.81852, -73.88098 ...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://data.cityofnewyork.us/api/geospatial/cpf4-rkhq?method=export&format=Shapefile'\n",
    "neighborhoods = gpd.read_file(url)\n",
    "\n",
    "neighborhoods.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "registered-easter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: GEOGCS[\"WGS84(DD)\",DATUM[\"WGS84\",SPHEROID[\"WGS84\", ...>\n",
       "Name: WGS84(DD)\n",
       "Axis Info [ellipsoidal]:\n",
       "- lon[east]: Longitude (degree)\n",
       "- lat[north]: Latitude (degree)\n",
       "Area of Use:\n",
       "- undefined\n",
       "Datum: WGS84\n",
       "- Ellipsoid: WGS84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8adce682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: EPSG:4326>\n",
       "Name: WGS 84\n",
       "Axis Info [ellipsoidal]:\n",
       "- Lat[north]: Geodetic latitude (degree)\n",
       "- Lon[east]: Geodetic longitude (degree)\n",
       "Area of Use:\n",
       "- name: World.\n",
       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
       "Datum: World Geodetic System 1984 ensemble\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods = neighborhoods.to_crs('EPSG:4326')\n",
    "\n",
    "neighborhoods.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "lasting-characterization",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>alias</th>\n",
       "      <th>name</th>\n",
       "      <th>image_url</th>\n",
       "      <th>is_closed</th>\n",
       "      <th>url</th>\n",
       "      <th>review_count</th>\n",
       "      <th>categories</th>\n",
       "      <th>rating</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>transactions</th>\n",
       "      <th>price</th>\n",
       "      <th>location</th>\n",
       "      <th>phone</th>\n",
       "      <th>display_phone</th>\n",
       "      <th>distance</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q3fmTHT7zilDWtfzLK9lMA</td>\n",
       "      <td>truedan-new-york-3</td>\n",
       "      <td>Truedan</td>\n",
       "      <td>https://s3-media2.fl.yelpcdn.com/bphoto/BmUZXO...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/truedan-new-york-3?ad...</td>\n",
       "      <td>99</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.5</td>\n",
       "      <td>{'latitude': 40.7191742, 'longitude': -73.9962...</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>$$</td>\n",
       "      <td>{'address1': '208 Grand St', 'address2': None,...</td>\n",
       "      <td>1.646559e+10</td>\n",
       "      <td>(646) 559-2886</td>\n",
       "      <td>1535.241614</td>\n",
       "      <td>40.719174</td>\n",
       "      <td>-73.996220</td>\n",
       "      <td>POINT (-73.9962197 40.7191742)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7-bx74TooPuZKZDNW4WFcQ</td>\n",
       "      <td>chun-yang-tea-new-york</td>\n",
       "      <td>Chun Yang Tea</td>\n",
       "      <td>https://s3-media1.fl.yelpcdn.com/bphoto/JrGwrZ...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/chun-yang-tea-new-yor...</td>\n",
       "      <td>91</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.5</td>\n",
       "      <td>{'latitude': 40.71617, 'longitude': -73.9971}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>$$</td>\n",
       "      <td>{'address1': '26B Elizabeth St', 'address2': '...</td>\n",
       "      <td>1.212420e+10</td>\n",
       "      <td>(212) 420-0123</td>\n",
       "      <td>1212.877370</td>\n",
       "      <td>40.716170</td>\n",
       "      <td>-73.997100</td>\n",
       "      <td>POINT (-73.9971 40.71617)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3aypSFXLfkAL4dhHVFobKg</td>\n",
       "      <td>lazy-sundaes-new-york-6</td>\n",
       "      <td>Lazy Sundaes</td>\n",
       "      <td>https://s3-media3.fl.yelpcdn.com/bphoto/kEfCYC...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/lazy-sundaes-new-york...</td>\n",
       "      <td>32</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}...</td>\n",
       "      <td>4.5</td>\n",
       "      <td>{'latitude': 40.720597, 'longitude': -73.984539}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>$$</td>\n",
       "      <td>{'address1': '23 Clinton St', 'address2': None...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1877.016204</td>\n",
       "      <td>40.720597</td>\n",
       "      <td>-73.984539</td>\n",
       "      <td>POINT (-73.984539 40.720597)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bVJQEeRNi34-3XN_F1AZEg</td>\n",
       "      <td>xing-fu-tang-new-york</td>\n",
       "      <td>Xing Fu Tang</td>\n",
       "      <td>https://s3-media2.fl.yelpcdn.com/bphoto/cyFHFE...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/xing-fu-tang-new-york...</td>\n",
       "      <td>91</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.5</td>\n",
       "      <td>{'latitude': 40.7288, 'longitude': -73.98775}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'address1': '133 2nd Ave', 'address2': '', 'a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2656.450427</td>\n",
       "      <td>40.728800</td>\n",
       "      <td>-73.987750</td>\n",
       "      <td>POINT (-73.98775000000001 40.7288)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>zupVwJAFYkHDwrsQd2ktXA</td>\n",
       "      <td>fiftylan-union-square-new-york-3</td>\n",
       "      <td>FIFTYLAN Union Square</td>\n",
       "      <td>https://s3-media3.fl.yelpcdn.com/bphoto/UIwR20...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/fiftylan-union-square...</td>\n",
       "      <td>60</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>{'latitude': 40.735516, 'longitude': -73.989191}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'address1': '32 Union Square E', 'address2': ...</td>\n",
       "      <td>1.646767e+10</td>\n",
       "      <td>(646) 767-0085</td>\n",
       "      <td>3367.900728</td>\n",
       "      <td>40.735516</td>\n",
       "      <td>-73.989191</td>\n",
       "      <td>POINT (-73.98919100000001 40.735516)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       id                             alias  \\\n",
       "0  Q3fmTHT7zilDWtfzLK9lMA                truedan-new-york-3   \n",
       "1  7-bx74TooPuZKZDNW4WFcQ            chun-yang-tea-new-york   \n",
       "2  3aypSFXLfkAL4dhHVFobKg           lazy-sundaes-new-york-6   \n",
       "3  bVJQEeRNi34-3XN_F1AZEg             xing-fu-tang-new-york   \n",
       "4  zupVwJAFYkHDwrsQd2ktXA  fiftylan-union-square-new-york-3   \n",
       "\n",
       "                    name                                          image_url  \\\n",
       "0                Truedan  https://s3-media2.fl.yelpcdn.com/bphoto/BmUZXO...   \n",
       "1          Chun Yang Tea  https://s3-media1.fl.yelpcdn.com/bphoto/JrGwrZ...   \n",
       "2           Lazy Sundaes  https://s3-media3.fl.yelpcdn.com/bphoto/kEfCYC...   \n",
       "3           Xing Fu Tang  https://s3-media2.fl.yelpcdn.com/bphoto/cyFHFE...   \n",
       "4  FIFTYLAN Union Square  https://s3-media3.fl.yelpcdn.com/bphoto/UIwR20...   \n",
       "\n",
       "   is_closed                                                url  review_count  \\\n",
       "0      False  https://www.yelp.com/biz/truedan-new-york-3?ad...            99   \n",
       "1      False  https://www.yelp.com/biz/chun-yang-tea-new-yor...            91   \n",
       "2      False  https://www.yelp.com/biz/lazy-sundaes-new-york...            32   \n",
       "3      False  https://www.yelp.com/biz/xing-fu-tang-new-york...            91   \n",
       "4      False  https://www.yelp.com/biz/fiftylan-union-square...            60   \n",
       "\n",
       "                                          categories  rating  \\\n",
       "0    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.5   \n",
       "1    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.5   \n",
       "2  [{'alias': 'bubbletea', 'title': 'Bubble Tea'}...     4.5   \n",
       "3    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.5   \n",
       "4    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.0   \n",
       "\n",
       "                                         coordinates            transactions  \\\n",
       "0  {'latitude': 40.7191742, 'longitude': -73.9962...  ['pickup', 'delivery']   \n",
       "1      {'latitude': 40.71617, 'longitude': -73.9971}  ['pickup', 'delivery']   \n",
       "2   {'latitude': 40.720597, 'longitude': -73.984539}  ['pickup', 'delivery']   \n",
       "3      {'latitude': 40.7288, 'longitude': -73.98775}  ['pickup', 'delivery']   \n",
       "4   {'latitude': 40.735516, 'longitude': -73.989191}  ['pickup', 'delivery']   \n",
       "\n",
       "  price                                           location         phone  \\\n",
       "0    $$  {'address1': '208 Grand St', 'address2': None,...  1.646559e+10   \n",
       "1    $$  {'address1': '26B Elizabeth St', 'address2': '...  1.212420e+10   \n",
       "2    $$  {'address1': '23 Clinton St', 'address2': None...           NaN   \n",
       "3   NaN  {'address1': '133 2nd Ave', 'address2': '', 'a...           NaN   \n",
       "4   NaN  {'address1': '32 Union Square E', 'address2': ...  1.646767e+10   \n",
       "\n",
       "    display_phone     distance   latitude  longitude  \\\n",
       "0  (646) 559-2886  1535.241614  40.719174 -73.996220   \n",
       "1  (212) 420-0123  1212.877370  40.716170 -73.997100   \n",
       "2             NaN  1877.016204  40.720597 -73.984539   \n",
       "3             NaN  2656.450427  40.728800 -73.987750   \n",
       "4  (646) 767-0085  3367.900728  40.735516 -73.989191   \n",
       "\n",
       "                               geometry  \n",
       "0        POINT (-73.9962197 40.7191742)  \n",
       "1             POINT (-73.9971 40.71617)  \n",
       "2          POINT (-73.984539 40.720597)  \n",
       "3    POINT (-73.98775000000001 40.7288)  \n",
       "4  POINT (-73.98919100000001 40.735516)  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "painful-breathing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>alias</th>\n",
       "      <th>name</th>\n",
       "      <th>image_url</th>\n",
       "      <th>is_closed</th>\n",
       "      <th>url</th>\n",
       "      <th>review_count</th>\n",
       "      <th>categories</th>\n",
       "      <th>rating</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>transactions</th>\n",
       "      <th>price</th>\n",
       "      <th>location</th>\n",
       "      <th>phone</th>\n",
       "      <th>display_phone</th>\n",
       "      <th>distance</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q3fmTHT7zilDWtfzLK9lMA</td>\n",
       "      <td>truedan-new-york-3</td>\n",
       "      <td>Truedan</td>\n",
       "      <td>https://s3-media2.fl.yelpcdn.com/bphoto/BmUZXO...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/truedan-new-york-3?ad...</td>\n",
       "      <td>99</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.5</td>\n",
       "      <td>{'latitude': 40.7191742, 'longitude': -73.9962...</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>$$</td>\n",
       "      <td>{'address1': '208 Grand St', 'address2': None,...</td>\n",
       "      <td>1.646559e+10</td>\n",
       "      <td>(646) 559-2886</td>\n",
       "      <td>1535.241614</td>\n",
       "      <td>40.719174</td>\n",
       "      <td>-73.996220</td>\n",
       "      <td>POINT (-73.99622 40.71917)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7-bx74TooPuZKZDNW4WFcQ</td>\n",
       "      <td>chun-yang-tea-new-york</td>\n",
       "      <td>Chun Yang Tea</td>\n",
       "      <td>https://s3-media1.fl.yelpcdn.com/bphoto/JrGwrZ...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/chun-yang-tea-new-yor...</td>\n",
       "      <td>91</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.5</td>\n",
       "      <td>{'latitude': 40.71617, 'longitude': -73.9971}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>$$</td>\n",
       "      <td>{'address1': '26B Elizabeth St', 'address2': '...</td>\n",
       "      <td>1.212420e+10</td>\n",
       "      <td>(212) 420-0123</td>\n",
       "      <td>1212.877370</td>\n",
       "      <td>40.716170</td>\n",
       "      <td>-73.997100</td>\n",
       "      <td>POINT (-73.99710 40.71617)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3aypSFXLfkAL4dhHVFobKg</td>\n",
       "      <td>lazy-sundaes-new-york-6</td>\n",
       "      <td>Lazy Sundaes</td>\n",
       "      <td>https://s3-media3.fl.yelpcdn.com/bphoto/kEfCYC...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/lazy-sundaes-new-york...</td>\n",
       "      <td>32</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}...</td>\n",
       "      <td>4.5</td>\n",
       "      <td>{'latitude': 40.720597, 'longitude': -73.984539}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>$$</td>\n",
       "      <td>{'address1': '23 Clinton St', 'address2': None...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1877.016204</td>\n",
       "      <td>40.720597</td>\n",
       "      <td>-73.984539</td>\n",
       "      <td>POINT (-73.98454 40.72060)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bVJQEeRNi34-3XN_F1AZEg</td>\n",
       "      <td>xing-fu-tang-new-york</td>\n",
       "      <td>Xing Fu Tang</td>\n",
       "      <td>https://s3-media2.fl.yelpcdn.com/bphoto/cyFHFE...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/xing-fu-tang-new-york...</td>\n",
       "      <td>91</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.5</td>\n",
       "      <td>{'latitude': 40.7288, 'longitude': -73.98775}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'address1': '133 2nd Ave', 'address2': '', 'a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2656.450427</td>\n",
       "      <td>40.728800</td>\n",
       "      <td>-73.987750</td>\n",
       "      <td>POINT (-73.98775 40.72880)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>zupVwJAFYkHDwrsQd2ktXA</td>\n",
       "      <td>fiftylan-union-square-new-york-3</td>\n",
       "      <td>FIFTYLAN Union Square</td>\n",
       "      <td>https://s3-media3.fl.yelpcdn.com/bphoto/UIwR20...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/fiftylan-union-square...</td>\n",
       "      <td>60</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>{'latitude': 40.735516, 'longitude': -73.989191}</td>\n",
       "      <td>['pickup', 'delivery']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'address1': '32 Union Square E', 'address2': ...</td>\n",
       "      <td>1.646767e+10</td>\n",
       "      <td>(646) 767-0085</td>\n",
       "      <td>3367.900728</td>\n",
       "      <td>40.735516</td>\n",
       "      <td>-73.989191</td>\n",
       "      <td>POINT (-73.98919 40.73552)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       id                             alias  \\\n",
       "0  Q3fmTHT7zilDWtfzLK9lMA                truedan-new-york-3   \n",
       "1  7-bx74TooPuZKZDNW4WFcQ            chun-yang-tea-new-york   \n",
       "2  3aypSFXLfkAL4dhHVFobKg           lazy-sundaes-new-york-6   \n",
       "3  bVJQEeRNi34-3XN_F1AZEg             xing-fu-tang-new-york   \n",
       "4  zupVwJAFYkHDwrsQd2ktXA  fiftylan-union-square-new-york-3   \n",
       "\n",
       "                    name                                          image_url  \\\n",
       "0                Truedan  https://s3-media2.fl.yelpcdn.com/bphoto/BmUZXO...   \n",
       "1          Chun Yang Tea  https://s3-media1.fl.yelpcdn.com/bphoto/JrGwrZ...   \n",
       "2           Lazy Sundaes  https://s3-media3.fl.yelpcdn.com/bphoto/kEfCYC...   \n",
       "3           Xing Fu Tang  https://s3-media2.fl.yelpcdn.com/bphoto/cyFHFE...   \n",
       "4  FIFTYLAN Union Square  https://s3-media3.fl.yelpcdn.com/bphoto/UIwR20...   \n",
       "\n",
       "   is_closed                                                url  review_count  \\\n",
       "0      False  https://www.yelp.com/biz/truedan-new-york-3?ad...            99   \n",
       "1      False  https://www.yelp.com/biz/chun-yang-tea-new-yor...            91   \n",
       "2      False  https://www.yelp.com/biz/lazy-sundaes-new-york...            32   \n",
       "3      False  https://www.yelp.com/biz/xing-fu-tang-new-york...            91   \n",
       "4      False  https://www.yelp.com/biz/fiftylan-union-square...            60   \n",
       "\n",
       "                                          categories  rating  \\\n",
       "0    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.5   \n",
       "1    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.5   \n",
       "2  [{'alias': 'bubbletea', 'title': 'Bubble Tea'}...     4.5   \n",
       "3    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.5   \n",
       "4    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.0   \n",
       "\n",
       "                                         coordinates            transactions  \\\n",
       "0  {'latitude': 40.7191742, 'longitude': -73.9962...  ['pickup', 'delivery']   \n",
       "1      {'latitude': 40.71617, 'longitude': -73.9971}  ['pickup', 'delivery']   \n",
       "2   {'latitude': 40.720597, 'longitude': -73.984539}  ['pickup', 'delivery']   \n",
       "3      {'latitude': 40.7288, 'longitude': -73.98775}  ['pickup', 'delivery']   \n",
       "4   {'latitude': 40.735516, 'longitude': -73.989191}  ['pickup', 'delivery']   \n",
       "\n",
       "  price                                           location         phone  \\\n",
       "0    $$  {'address1': '208 Grand St', 'address2': None,...  1.646559e+10   \n",
       "1    $$  {'address1': '26B Elizabeth St', 'address2': '...  1.212420e+10   \n",
       "2    $$  {'address1': '23 Clinton St', 'address2': None...           NaN   \n",
       "3   NaN  {'address1': '133 2nd Ave', 'address2': '', 'a...           NaN   \n",
       "4   NaN  {'address1': '32 Union Square E', 'address2': ...  1.646767e+10   \n",
       "\n",
       "    display_phone     distance   latitude  longitude  \\\n",
       "0  (646) 559-2886  1535.241614  40.719174 -73.996220   \n",
       "1  (212) 420-0123  1212.877370  40.716170 -73.997100   \n",
       "2             NaN  1877.016204  40.720597 -73.984539   \n",
       "3             NaN  2656.450427  40.728800 -73.987750   \n",
       "4  (646) 767-0085  3367.900728  40.735516 -73.989191   \n",
       "\n",
       "                     geometry  \n",
       "0  POINT (-73.99622 40.71917)  \n",
       "1  POINT (-73.99710 40.71617)  \n",
       "2  POINT (-73.98454 40.72060)  \n",
       "3  POINT (-73.98775 40.72880)  \n",
       "4  POINT (-73.98919 40.73552)  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf = gpd.GeoDataFrame(df, crs=4326,\n",
    "      geometry=gpd.points_from_xy(df.longitude, df.latitude))\n",
    "\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "headed-premiere",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\datal\\anaconda3\\envs\\boba-nyc\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3377: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "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>id</th>\n",
       "      <th>alias</th>\n",
       "      <th>name</th>\n",
       "      <th>image_url</th>\n",
       "      <th>is_closed</th>\n",
       "      <th>url</th>\n",
       "      <th>review_count</th>\n",
       "      <th>categories</th>\n",
       "      <th>rating</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>...</th>\n",
       "      <th>longitude</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>boro_code</th>\n",
       "      <th>boro_name</th>\n",
       "      <th>county_fip</th>\n",
       "      <th>ntacode</th>\n",
       "      <th>ntaname</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>shape_leng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q3fmTHT7zilDWtfzLK9lMA</td>\n",
       "      <td>truedan-new-york-3</td>\n",
       "      <td>Truedan</td>\n",
       "      <td>https://s3-media2.fl.yelpcdn.com/bphoto/BmUZXO...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/truedan-new-york-3?ad...</td>\n",
       "      <td>99</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.5</td>\n",
       "      <td>{'latitude': 40.7191742, 'longitude': -73.9962...</td>\n",
       "      <td>...</td>\n",
       "      <td>-73.996220</td>\n",
       "      <td>POINT (-73.99622 40.71917)</td>\n",
       "      <td>94</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN24</td>\n",
       "      <td>SoHo-TriBeCa-Civic Center-Little Italy</td>\n",
       "      <td>2.503268e+07</td>\n",
       "      <td>26855.031527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DbhBca8LzskjS0B-L4QfZg</td>\n",
       "      <td>tiger-sugar-new-york</td>\n",
       "      <td>tigersugar</td>\n",
       "      <td>https://s3-media3.fl.yelpcdn.com/bphoto/5wNVyj...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/tiger-sugar-new-york?...</td>\n",
       "      <td>515</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>{'latitude': 40.717891007822274, 'longitude': ...</td>\n",
       "      <td>...</td>\n",
       "      <td>-73.999780</td>\n",
       "      <td>POINT (-73.99978 40.71789)</td>\n",
       "      <td>94</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN24</td>\n",
       "      <td>SoHo-TriBeCa-Civic Center-Little Italy</td>\n",
       "      <td>2.503268e+07</td>\n",
       "      <td>26855.031527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>bxCjNLVDQggNTbpviV0RMw</td>\n",
       "      <td>lazy-sundaes-new-york-4</td>\n",
       "      <td>Lazy Sundaes</td>\n",
       "      <td>https://s3-media3.fl.yelpcdn.com/bphoto/iDul5E...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/lazy-sundaes-new-york...</td>\n",
       "      <td>65</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>{'latitude': 40.7189689, 'longitude': -74.0006...</td>\n",
       "      <td>...</td>\n",
       "      <td>-74.000677</td>\n",
       "      <td>POINT (-74.00068 40.71897)</td>\n",
       "      <td>94</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN24</td>\n",
       "      <td>SoHo-TriBeCa-Civic Center-Little Italy</td>\n",
       "      <td>2.503268e+07</td>\n",
       "      <td>26855.031527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>jsJdz6pvyYZjtut9vkUwBw</td>\n",
       "      <td>yaya-tea-grand-new-york-2</td>\n",
       "      <td>Yaya Tea Grand</td>\n",
       "      <td>https://s3-media2.fl.yelpcdn.com/bphoto/iutBCc...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/yaya-tea-grand-new-yo...</td>\n",
       "      <td>295</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>{'latitude': 40.7192268371582, 'longitude': -7...</td>\n",
       "      <td>...</td>\n",
       "      <td>-73.996277</td>\n",
       "      <td>POINT (-73.99628 40.71923)</td>\n",
       "      <td>94</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN24</td>\n",
       "      <td>SoHo-TriBeCa-Civic Center-Little Italy</td>\n",
       "      <td>2.503268e+07</td>\n",
       "      <td>26855.031527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>uge-Lp_kY1J6Z4CzzceULw</td>\n",
       "      <td>honestea-new-york</td>\n",
       "      <td>Honestea</td>\n",
       "      <td>https://s3-media1.fl.yelpcdn.com/bphoto/77X7Xd...</td>\n",
       "      <td>False</td>\n",
       "      <td>https://www.yelp.com/biz/honestea-new-york?adj...</td>\n",
       "      <td>104</td>\n",
       "      <td>[{'alias': 'bubbletea', 'title': 'Bubble Tea'}...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>{'latitude': 40.71789, 'longitude': -73.99692}</td>\n",
       "      <td>...</td>\n",
       "      <td>-73.996920</td>\n",
       "      <td>POINT (-73.99692 40.71789)</td>\n",
       "      <td>94</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN24</td>\n",
       "      <td>SoHo-TriBeCa-Civic Center-Little Italy</td>\n",
       "      <td>2.503268e+07</td>\n",
       "      <td>26855.031527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        id                      alias            name  \\\n",
       "0   Q3fmTHT7zilDWtfzLK9lMA         truedan-new-york-3         Truedan   \n",
       "16  DbhBca8LzskjS0B-L4QfZg       tiger-sugar-new-york      tigersugar   \n",
       "29  bxCjNLVDQggNTbpviV0RMw    lazy-sundaes-new-york-4    Lazy Sundaes   \n",
       "60  jsJdz6pvyYZjtut9vkUwBw  yaya-tea-grand-new-york-2  Yaya Tea Grand   \n",
       "72  uge-Lp_kY1J6Z4CzzceULw          honestea-new-york        Honestea   \n",
       "\n",
       "                                            image_url  is_closed  \\\n",
       "0   https://s3-media2.fl.yelpcdn.com/bphoto/BmUZXO...      False   \n",
       "16  https://s3-media3.fl.yelpcdn.com/bphoto/5wNVyj...      False   \n",
       "29  https://s3-media3.fl.yelpcdn.com/bphoto/iDul5E...      False   \n",
       "60  https://s3-media2.fl.yelpcdn.com/bphoto/iutBCc...      False   \n",
       "72  https://s3-media1.fl.yelpcdn.com/bphoto/77X7Xd...      False   \n",
       "\n",
       "                                                  url  review_count  \\\n",
       "0   https://www.yelp.com/biz/truedan-new-york-3?ad...            99   \n",
       "16  https://www.yelp.com/biz/tiger-sugar-new-york?...           515   \n",
       "29  https://www.yelp.com/biz/lazy-sundaes-new-york...            65   \n",
       "60  https://www.yelp.com/biz/yaya-tea-grand-new-yo...           295   \n",
       "72  https://www.yelp.com/biz/honestea-new-york?adj...           104   \n",
       "\n",
       "                                           categories  rating  \\\n",
       "0     [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.5   \n",
       "16    [{'alias': 'bubbletea', 'title': 'Bubble Tea'}]     4.0   \n",
       "29  [{'alias': 'bubbletea', 'title': 'Bubble Tea'}...     4.0   \n",
       "60  [{'alias': 'bubbletea', 'title': 'Bubble Tea'}...     4.0   \n",
       "72  [{'alias': 'bubbletea', 'title': 'Bubble Tea'}...     3.5   \n",
       "\n",
       "                                          coordinates  ...  longitude  \\\n",
       "0   {'latitude': 40.7191742, 'longitude': -73.9962...  ... -73.996220   \n",
       "16  {'latitude': 40.717891007822274, 'longitude': ...  ... -73.999780   \n",
       "29  {'latitude': 40.7189689, 'longitude': -74.0006...  ... -74.000677   \n",
       "60  {'latitude': 40.7192268371582, 'longitude': -7...  ... -73.996277   \n",
       "72     {'latitude': 40.71789, 'longitude': -73.99692}  ... -73.996920   \n",
       "\n",
       "                      geometry index_right  boro_code  boro_name  county_fip  \\\n",
       "0   POINT (-73.99622 40.71917)          94        1.0  Manhattan         061   \n",
       "16  POINT (-73.99978 40.71789)          94        1.0  Manhattan         061   \n",
       "29  POINT (-74.00068 40.71897)          94        1.0  Manhattan         061   \n",
       "60  POINT (-73.99628 40.71923)          94        1.0  Manhattan         061   \n",
       "72  POINT (-73.99692 40.71789)          94        1.0  Manhattan         061   \n",
       "\n",
       "    ntacode                                 ntaname    shape_area  \\\n",
       "0      MN24  SoHo-TriBeCa-Civic Center-Little Italy  2.503268e+07   \n",
       "16     MN24  SoHo-TriBeCa-Civic Center-Little Italy  2.503268e+07   \n",
       "29     MN24  SoHo-TriBeCa-Civic Center-Little Italy  2.503268e+07   \n",
       "60     MN24  SoHo-TriBeCa-Civic Center-Little Italy  2.503268e+07   \n",
       "72     MN24  SoHo-TriBeCa-Civic Center-Little Italy  2.503268e+07   \n",
       "\n",
       "      shape_leng  \n",
       "0   26855.031527  \n",
       "16  26855.031527  \n",
       "29  26855.031527  \n",
       "60  26855.031527  \n",
       "72  26855.031527  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_df = gpd.sjoin(gdf, \n",
    "                    neighborhoods, \n",
    "                    op='intersects') \n",
    "\n",
    "join_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "independent-throat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ntaname</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>counts</th>\n",
       "      <th>counts_squaremile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Flushing</td>\n",
       "      <td>3.786424e+07</td>\n",
       "      <td>40</td>\n",
       "      <td>29.450898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chinatown</td>\n",
       "      <td>1.450188e+07</td>\n",
       "      <td>28</td>\n",
       "      <td>53.827184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sunset Park East</td>\n",
       "      <td>2.711007e+07</td>\n",
       "      <td>23</td>\n",
       "      <td>23.651849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hunters Point-Sunnyside-West Maspeth</td>\n",
       "      <td>1.024191e+08</td>\n",
       "      <td>19</td>\n",
       "      <td>5.171787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Elmhurst</td>\n",
       "      <td>3.267480e+07</td>\n",
       "      <td>17</td>\n",
       "      <td>14.504535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                ntaname    shape_area  counts  \\\n",
       "0                              Flushing  3.786424e+07      40   \n",
       "1                             Chinatown  1.450188e+07      28   \n",
       "2                      Sunset Park East  2.711007e+07      23   \n",
       "3  Hunters Point-Sunnyside-West Maspeth  1.024191e+08      19   \n",
       "4                              Elmhurst  3.267480e+07      17   \n",
       "\n",
       "   counts_squaremile  \n",
       "0          29.450898  \n",
       "1          53.827184  \n",
       "2          23.651849  \n",
       "3           5.171787  \n",
       "4          14.504535  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_df = join_df.groupby(by=['ntaname', 'shape_area'])['id'].count().sort_values(ascending=False)\n",
    "join_df = join_df.reset_index()\n",
    "\n",
    "join_df = join_df.rename(columns={'id':'counts'})\n",
    "join_df['counts_squaremile'] = join_df['counts'] / (join_df['shape_area'] / 27878400)\n",
    "\n",
    "join_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "unable-vatican",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "data = join_df.sort_values(by='counts', ascending=False).head(20)\n",
    "\n",
    "sns.barplot(x=\"counts\", \n",
    "            y=\"ntaname\", \n",
    "            data=data, \n",
    "            ax=ax)\n",
    "\n",
    "plt.title('Most bubble tea locations per neighborhood in NYC', fontsize=15)\n",
    "plt.ylabel('neighborhood')\n",
    "plt.xlabel('count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('busineses-per-neighborhood.png', dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "special-leonard",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "data = join_df.sort_values(by='counts_squaremile', ascending=False).head(20)\n",
    "\n",
    "sns.barplot(x=\"counts_squaremile\", \n",
    "            y=\"ntaname\", \n",
    "            data=data, \n",
    "            ax=ax)\n",
    "\n",
    "plt.suptitle('Most bubble tea locations per square mile by neighborhood in NYC', \n",
    "             fontsize=15,\n",
    "             y=.96, x=.60)\n",
    "plt.ylabel('neighborhood')\n",
    "plt.xlabel('count per square mile')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('busineses-per-neighborhood.png', dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "according-scroll",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}