geopandas_restaurant_addresses.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import os\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "#for the function: \n",
    "import geopy\n",
    "import requests\n",
    "from shapely.geometry import Point\n",
    "from geopy.geocoders import Nominatim\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"C:\\\\Users\\\\Sanata\\\\Dropbox\\\\01AA_Insight Projects\\\\Restaurants\")\n",
    "pd.options.display.max_rows = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc = gpd.read_file('nyc_shape.shp') #nyc census blocks \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fd18f1b7f0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nyc.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make points \n",
    "address = \"175 5th Avenue NYC\"\n",
    "geolocator = Nominatim(user_agent=\"my_app\")\n",
    "location = geolocator.geocode(address)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location(Flatiron Building, 175, 5th Avenue, Flatiron District, Manhattan, Manhattan Community Board 5, New York County, NYC, New York, 10010, USA, (40.7410861, -73.9896298241625, 0.0))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat = float(location.raw['lat'])\n",
    "lon = float(location.raw['lon'])\n",
    "d = {'lat':lat, 'lon':lon}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.741086</td>\n",
       "      <td>-73.98963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat       lon\n",
       "0  40.741086 -73.98963"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coord = pd.DataFrame(d, index = np.arange(1))\n",
    "coord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_coords(address):\n",
    "    full_address = address + ' ' + 'New York City'\n",
    "    geolocator= Nominatim(user_agent='my_app17', timeout=10000)\n",
    "    location = geolocator.geocode(full_address)\n",
    "    if location is None:\n",
    "        return print('Address not found')\n",
    "    else:\n",
    "        lat = float(location.raw['lat'])\n",
    "        lon = float(location.raw['lon'])\n",
    "        df = pd.DataFrame({'lat':lat, 'lon':lon}, index = np.arange(1))\n",
    "        return df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.66281</td>\n",
       "      <td>-73.953081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lat        lon\n",
       "0  40.66281 -73.953081"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_coords('236 Sterling Street')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "geom = coord.apply(lambda x : Point([x['lon'],x['lat']]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "point = gpd.GeoDataFrame(coord, geometry=geom) #geom is a Series\n",
    "point.crs = nyc.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    POINT (-73.98962982416251 40.7410861)\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>STATEFP10</th>\n",
       "      <th>COUNTYFP10</th>\n",
       "      <th>TRACTCE10</th>\n",
       "      <th>BLKGRPCE10</th>\n",
       "      <th>GEOID10</th>\n",
       "      <th>NAMELSAD10</th>\n",
       "      <th>MTFCC10</th>\n",
       "      <th>FUNCSTAT10</th>\n",
       "      <th>ALAND10</th>\n",
       "      <th>AWATER10</th>\n",
       "      <th>INTPTLAT10</th>\n",
       "      <th>INTPTLON10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.741086</td>\n",
       "      <td>-73.98963</td>\n",
       "      <td>POINT (-73.98962982416251 40.7410861)</td>\n",
       "      <td>1091</td>\n",
       "      <td>36</td>\n",
       "      <td>061</td>\n",
       "      <td>005600</td>\n",
       "      <td>2</td>\n",
       "      <td>360610056002</td>\n",
       "      <td>Block Group 2</td>\n",
       "      <td>G5030</td>\n",
       "      <td>S</td>\n",
       "      <td>62163</td>\n",
       "      <td>0</td>\n",
       "      <td>+40.7414790</td>\n",
       "      <td>-073.9881325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat       lon                               geometry  index_right  \\\n",
       "0  40.741086 -73.98963  POINT (-73.98962982416251 40.7410861)         1091   \n",
       "\n",
       "  STATEFP10 COUNTYFP10 TRACTCE10 BLKGRPCE10       GEOID10     NAMELSAD10  \\\n",
       "0        36        061    005600          2  360610056002  Block Group 2   \n",
       "\n",
       "  MTFCC10 FUNCSTAT10  ALAND10  AWATER10   INTPTLAT10    INTPTLON10  \n",
       "0   G5030          S    62163         0  +40.7414790  -073.9881325  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge with shapefile \n",
    "point_in_nyc = gpd.sjoin(point, nyc, op='within')\n",
    "point_in_nyc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert to function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def address_in_block(address):\n",
    "    geolocator = Nominatim(user_agent=\"my_app\")\n",
    "    location = geolocator.geocode(address)\n",
    "    lat = float(location.raw['lat'])\n",
    "    lon = float(location.raw['lon'])\n",
    "    coord = pd.DataFrame({'lat':lat, 'lon':lon}, index = np.arange(1))\n",
    "    geom = coord.apply(lambda x : Point([x['lon'],x['lat']]), axis=1)\n",
    "    point = gpd.GeoDataFrame(coord, geometry=geom) #geom is a Series\n",
    "    point.crs = nyc.crs\n",
    "    point_in_nyc = gpd.sjoin(point, nyc, op='within')\n",
    "    return point_in_nyc.GEOID10 \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read subway entrances \n",
    "subway = gpd.read_file('geo_export_91732a86-b926-4a56-a45f-dfc72fe53d8a.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23950160c88>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "subway.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Unnamed: 0</th>\n",
       "      <th>Source</th>\n",
       "      <th>License_Permit_Holder</th>\n",
       "      <th>Business_Description</th>\n",
       "      <th>License/Permit_Number</th>\n",
       "      <th>Permit_Type_Description</th>\n",
       "      <th>Permit_Subtype_Description</th>\n",
       "      <th>Permit_Issuance_Date</th>\n",
       "      <th>Permit_Expiration_Date</th>\n",
       "      <th>Permit_Status_Date</th>\n",
       "      <th>...</th>\n",
       "      <th>n_sales_250k</th>\n",
       "      <th>n_sales_500k</th>\n",
       "      <th>n_sales_999k</th>\n",
       "      <th>n_sales_1m</th>\n",
       "      <th>med_block_income</th>\n",
       "      <th>logpop</th>\n",
       "      <th>logpopdens</th>\n",
       "      <th>loginc</th>\n",
       "      <th>ztype__C</th>\n",
       "      <th>ztype__R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32769</td>\n",
       "      <td>DOHMH Permit</td>\n",
       "      <td>PIZZA WORLD 163 ST., LLC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1349148.0</td>\n",
       "      <td>FOOD SERVICE EST.</td>\n",
       "      <td>FOOD SERVICE ESTABL.</td>\n",
       "      <td>2012-05-09</td>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>08/16/2013 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.253968</td>\n",
       "      <td>30471.0</td>\n",
       "      <td>7.602900</td>\n",
       "      <td>8.516120</td>\n",
       "      <td>7.602900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37638</td>\n",
       "      <td>DOHMH Permit</td>\n",
       "      <td>RUNNING COOL II INC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1458006.0</td>\n",
       "      <td>FOOD SERVICE EST.</td>\n",
       "      <td>FOOD SERVICE ESTABL.</td>\n",
       "      <td>2013-02-25</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>02/22/2013 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.253968</td>\n",
       "      <td>30471.0</td>\n",
       "      <td>7.602900</td>\n",
       "      <td>8.516120</td>\n",
       "      <td>7.602900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53108</td>\n",
       "      <td>DOHMH Permit</td>\n",
       "      <td>163 CHICKEN CORP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1372435.0</td>\n",
       "      <td>FOOD SERVICE EST.</td>\n",
       "      <td>FOOD SERVICE ESTABL.</td>\n",
       "      <td>2012-07-24</td>\n",
       "      <td>2013-09-30</td>\n",
       "      <td>07/24/2012 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.253968</td>\n",
       "      <td>30471.0</td>\n",
       "      <td>7.602900</td>\n",
       "      <td>8.516120</td>\n",
       "      <td>7.602900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>54576</td>\n",
       "      <td>DOHMH Permit</td>\n",
       "      <td>TRIPLE A JAMAICAN RESTAURANT INC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1415409.0</td>\n",
       "      <td>FOOD SERVICE EST.</td>\n",
       "      <td>FOOD SERVICE ESTABL.</td>\n",
       "      <td>2011-12-19</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>01/14/2013 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.253968</td>\n",
       "      <td>30471.0</td>\n",
       "      <td>7.602900</td>\n",
       "      <td>8.516120</td>\n",
       "      <td>7.602900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>32770</td>\n",
       "      <td>DOHMH Permit</td>\n",
       "      <td>UNCLE JACK'S STEAKHOUSE MIDTOWN, INC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1270552.0</td>\n",
       "      <td>FOOD SERVICE EST.</td>\n",
       "      <td>FOOD SERVICE ESTABL.</td>\n",
       "      <td>2012-10-03</td>\n",
       "      <td>2014-10-31</td>\n",
       "      <td>11/15/2013 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030075</td>\n",
       "      <td>0.165414</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>58304.0</td>\n",
       "      <td>7.086738</td>\n",
       "      <td>10.118698</td>\n",
       "      <td>7.086738</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 109 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0        Source                 License_Permit_Holder  \\\n",
       "0        32769  DOHMH Permit              PIZZA WORLD 163 ST., LLC   \n",
       "1        37638  DOHMH Permit                   RUNNING COOL II INC   \n",
       "4        53108  DOHMH Permit                      163 CHICKEN CORP   \n",
       "5        54576  DOHMH Permit      TRIPLE A JAMAICAN RESTAURANT INC   \n",
       "11       32770  DOHMH Permit  UNCLE JACK'S STEAKHOUSE MIDTOWN, INC   \n",
       "\n",
       "   Business_Description  License/Permit_Number Permit_Type_Description  \\\n",
       "0                   NaN              1349148.0       FOOD SERVICE EST.   \n",
       "1                   NaN              1458006.0       FOOD SERVICE EST.   \n",
       "4                   NaN              1372435.0       FOOD SERVICE EST.   \n",
       "5                   NaN              1415409.0       FOOD SERVICE EST.   \n",
       "11                  NaN              1270552.0       FOOD SERVICE EST.   \n",
       "\n",
       "   Permit_Subtype_Description Permit_Issuance_Date Permit_Expiration_Date  \\\n",
       "0        FOOD SERVICE ESTABL.           2012-05-09             2014-04-30   \n",
       "1        FOOD SERVICE ESTABL.           2013-02-25             2014-02-28   \n",
       "4        FOOD SERVICE ESTABL.           2012-07-24             2013-09-30   \n",
       "5        FOOD SERVICE ESTABL.           2011-12-19             2012-12-31   \n",
       "11       FOOD SERVICE ESTABL.           2012-10-03             2014-10-31   \n",
       "\n",
       "     Permit_Status_Date  ... n_sales_250k n_sales_500k n_sales_999k  \\\n",
       "0   08/16/2013 00:00:00  ...     0.333333     0.142857     0.111111   \n",
       "1   02/22/2013 00:00:00  ...     0.333333     0.142857     0.111111   \n",
       "4   07/24/2012 00:00:00  ...     0.333333     0.142857     0.111111   \n",
       "5   01/14/2013 00:00:00  ...     0.333333     0.142857     0.111111   \n",
       "11  11/15/2013 00:00:00  ...     0.030075     0.165414     0.157895   \n",
       "\n",
       "   n_sales_1m  med_block_income    logpop  logpopdens    loginc  ztype__C  \\\n",
       "0    0.253968           30471.0  7.602900    8.516120  7.602900         0   \n",
       "1    0.253968           30471.0  7.602900    8.516120  7.602900         0   \n",
       "4    0.253968           30471.0  7.602900    8.516120  7.602900         0   \n",
       "5    0.253968           30471.0  7.602900    8.516120  7.602900         0   \n",
       "11   0.631579           58304.0  7.086738   10.118698  7.086738         0   \n",
       "\n",
       "    ztype__R  \n",
       "0          0  \n",
       "1          0  \n",
       "4          0  \n",
       "5          0  \n",
       "11         0  \n",
       "\n",
       "[5 rows x 109 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get restaurant coords\n",
    "data = pd.read_pickle('restaurant_final_data')\n",
    "data = data.drop_duplicates(['bus_name', 'Building_ID_No'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['License/Permit_Holder_Name', 'DOB_Skilled_Trades_Lic_Num',\n",
       "       'DOB_Skilled_Trades_Lic_Type', 'Permit_Expiration_Date2',\n",
       "       'Permit_Issuance_Date2', 'exp_year', 'iss_year', 'min_iss_year',\n",
       "       'age2010', 'bus_name'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns[20:30]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot restaurants as points \n",
    "coords = data[['Longitude_WGS84','Latitude_WGS84' , 'Id', 'bus_name', 'Building_ID_No']]\n",
    "geom = coords.apply(lambda x : Point([x['Longitude_WGS84'],x['Latitude_WGS84']]), axis=1)\n",
    "points = gpd.GeoDataFrame(coords, geometry=geom) \n",
    "points.crs = \"+init=epsg:4326\" #initialize to WGS84 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change to UTM \n",
    "points = points.to_crs(\"+proj=utm +zone=33 +ellps=WGS84 +datum=WGS84 +units=m +no_defs\")\n",
    "subway = subway.to_crs(\"+proj=utm +zone=33 +ellps=WGS84 +datum=WGS84 +units=m +no_defs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>Longitude_WGS84</th>\n",
       "      <th>Latitude_WGS84</th>\n",
       "      <th>Id</th>\n",
       "      <th>bus_name</th>\n",
       "      <th>Building_ID_No</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>PIZZA WORLD 163 ST</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891596 9857680.653247522)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>RUNNING COOL II</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891596 9857680.653247522)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>163 CHICKEN</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891593 9857680.653247522)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>TRIPLE A JAMAICAN RESTAURANT</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891593 9857680.653247522)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-73.980657</td>\n",
       "      <td>40.779332</td>\n",
       "      <td>1500000US360610157005</td>\n",
       "      <td>UNCLE JACKS STEAKHOUSE MIDTOWN</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (-5810911.415347045 9866637.548673157)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Longitude_WGS84  Latitude_WGS84                     Id  \\\n",
       "0        -73.909377       40.824685  1500000US360050185001   \n",
       "1        -73.909377       40.824685  1500000US360050185001   \n",
       "4        -73.909377       40.824685  1500000US360050185001   \n",
       "5        -73.909377       40.824685  1500000US360050185001   \n",
       "11       -73.980657       40.779332  1500000US360610157005   \n",
       "\n",
       "                          bus_name  Building_ID_No  \\\n",
       "0               PIZZA WORLD 163 ST         2113088   \n",
       "1                  RUNNING COOL II         2113088   \n",
       "4                      163 CHICKEN         2113088   \n",
       "5     TRIPLE A JAMAICAN RESTAURANT         2113088   \n",
       "11  UNCLE JACKS STEAKHOUSE MIDTOWN               0   \n",
       "\n",
       "                                        geometry  \n",
       "0   POINT (-5802974.288891596 9857680.653247522)  \n",
       "1   POINT (-5802974.288891596 9857680.653247522)  \n",
       "4   POINT (-5802974.288891593 9857680.653247522)  \n",
       "5   POINT (-5802974.288891593 9857680.653247522)  \n",
       "11  POINT (-5810911.415347045 9866637.548673157)  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23951a56780>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "points.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "points['buffer500'] = points.geometry.buffer(152) #500-ft buffer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "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>Longitude_WGS84</th>\n",
       "      <th>Latitude_WGS84</th>\n",
       "      <th>Id</th>\n",
       "      <th>bus_name</th>\n",
       "      <th>Building_ID_No</th>\n",
       "      <th>geometry</th>\n",
       "      <th>buffer500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>PIZZA WORLD 163 ST</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-73.90937699994538 40.824685)</td>\n",
       "      <td>POLYGON ((78.09062300005462 40.824685, 77.3587...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>RUNNING COOL II</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-73.90937699994538 40.824685)</td>\n",
       "      <td>POLYGON ((78.09062300005462 40.824685, 77.3587...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>163 CHICKEN</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-73.90937699994538 40.82468500000001)</td>\n",
       "      <td>POLYGON ((78.09062300005462 40.82468500000001,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>TRIPLE A JAMAICAN RESTAURANT</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-73.90937699994538 40.82468500000001)</td>\n",
       "      <td>POLYGON ((78.09062300005462 40.82468500000001,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-73.980657</td>\n",
       "      <td>40.779332</td>\n",
       "      <td>1500000US360610157005</td>\n",
       "      <td>UNCLE JACKS STEAKHOUSE MIDTOWN</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (-73.98065700003644 40.779332)</td>\n",
       "      <td>POLYGON ((78.01934299996356 40.779332, 77.2874...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Longitude_WGS84  Latitude_WGS84                     Id  \\\n",
       "0        -73.909377       40.824685  1500000US360050185001   \n",
       "1        -73.909377       40.824685  1500000US360050185001   \n",
       "4        -73.909377       40.824685  1500000US360050185001   \n",
       "5        -73.909377       40.824685  1500000US360050185001   \n",
       "11       -73.980657       40.779332  1500000US360610157005   \n",
       "\n",
       "                          bus_name  Building_ID_No  \\\n",
       "0               PIZZA WORLD 163 ST         2113088   \n",
       "1                  RUNNING COOL II         2113088   \n",
       "4                      163 CHICKEN         2113088   \n",
       "5     TRIPLE A JAMAICAN RESTAURANT         2113088   \n",
       "11  UNCLE JACKS STEAKHOUSE MIDTOWN               0   \n",
       "\n",
       "                                        geometry  \\\n",
       "0           POINT (-73.90937699994538 40.824685)   \n",
       "1           POINT (-73.90937699994538 40.824685)   \n",
       "4   POINT (-73.90937699994538 40.82468500000001)   \n",
       "5   POINT (-73.90937699994538 40.82468500000001)   \n",
       "11          POINT (-73.98065700003644 40.779332)   \n",
       "\n",
       "                                            buffer500  \n",
       "0   POLYGON ((78.09062300005462 40.824685, 77.3587...  \n",
       "1   POLYGON ((78.09062300005462 40.824685, 77.3587...  \n",
       "4   POLYGON ((78.09062300005462 40.82468500000001,...  \n",
       "5   POLYGON ((78.09062300005462 40.82468500000001,...  \n",
       "11  POLYGON ((78.01934299996356 40.779332, 77.2874...  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>bus_name</th>\n",
       "      <th>Building_ID_No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>PIZZA WORLD 163 ST</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>RUNNING COOL II</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>163 CHICKEN</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>TRIPLE A JAMAICAN RESTAURANT</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1500000US360610157005</td>\n",
       "      <td>UNCLE JACKS STEAKHOUSE MIDTOWN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1500000US360610157005</td>\n",
       "      <td>WINE AND ROSES BAR AND CAFES</td>\n",
       "      <td>1030017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1500000US360610157005</td>\n",
       "      <td>UNCLE JACKS STEAKHOUSE MIDTOWN</td>\n",
       "      <td>1034848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1500000US360610157005</td>\n",
       "      <td>NABUCCO</td>\n",
       "      <td>1030007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1500000US360610033001</td>\n",
       "      <td>DAVIS K PARK ENTERPRISES</td>\n",
       "      <td>1002209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1500000US360610033001</td>\n",
       "      <td>VILPERS</td>\n",
       "      <td>1001658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1500000US360610033001</td>\n",
       "      <td>TRINI GUL ORIGINAL ROTI  WEST INDIAN FOOD</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1500000US360610033001</td>\n",
       "      <td>ANDY JAMES DELI</td>\n",
       "      <td>1001649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1500000US360610033001</td>\n",
       "      <td>SIRING</td>\n",
       "      <td>1079211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1500000US360610033001</td>\n",
       "      <td>SUSHEIN</td>\n",
       "      <td>1079211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1500000US360610033001</td>\n",
       "      <td>SUPER DUPER PIZZA</td>\n",
       "      <td>1001642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1500000US360610033001</td>\n",
       "      <td>FARINELLA</td>\n",
       "      <td>1079211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1500000US360610033001</td>\n",
       "      <td>YAWA JAPANESE CUISINE</td>\n",
       "      <td>1001642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1500000US360610033001</td>\n",
       "      <td>WEST SIDE CAFE</td>\n",
       "      <td>1002703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1500000US360610033001</td>\n",
       "      <td>JULY VENTURES</td>\n",
       "      <td>1001903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1500000US360610033001</td>\n",
       "      <td>BANCONE</td>\n",
       "      <td>1002204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1500000US360610033001</td>\n",
       "      <td>NEW SHEZAN</td>\n",
       "      <td>1001642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1500000US360610033001</td>\n",
       "      <td>339 BROADWAY</td>\n",
       "      <td>1001882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1500000US360610033001</td>\n",
       "      <td>SAM ATOMIC WINGS</td>\n",
       "      <td>1001658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1500000US360610033001</td>\n",
       "      <td>PK FOOD</td>\n",
       "      <td>1001649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1500000US360610033001</td>\n",
       "      <td>MILLAU</td>\n",
       "      <td>1001893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24444</th>\n",
       "      <td>1500000US360810329001</td>\n",
       "      <td>PANORA ELOY V</td>\n",
       "      <td>4000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24445</th>\n",
       "      <td>1500000US360470218001</td>\n",
       "      <td>BORONITZI</td>\n",
       "      <td>3138992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24447</th>\n",
       "      <td>1500000US360470858002</td>\n",
       "      <td>HELENA ERWIN</td>\n",
       "      <td>3109607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24449</th>\n",
       "      <td>1500000US360810352002</td>\n",
       "      <td>NEW YORK FRIED CHICKEN</td>\n",
       "      <td>4279643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24450</th>\n",
       "      <td>1500000US360050435001</td>\n",
       "      <td>LILLYS TAVERN</td>\n",
       "      <td>2084354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24451</th>\n",
       "      <td>1500000US360810339001</td>\n",
       "      <td>EL CARRIEL BAR</td>\n",
       "      <td>4033431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24452</th>\n",
       "      <td>1500000US360470734002</td>\n",
       "      <td>PALAPA MARCOS PEREZ   MUNOZ NICASIO PEREZ</td>\n",
       "      <td>3215970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24453</th>\n",
       "      <td>1500000US360470690001</td>\n",
       "      <td>RIBAUDOS REST</td>\n",
       "      <td>3220432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24455</th>\n",
       "      <td>1500000US360610161003</td>\n",
       "      <td>MAKE FLAGSHIP ONE</td>\n",
       "      <td>1028743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24456</th>\n",
       "      <td>1500000US360470397002</td>\n",
       "      <td>MEMORIAL BAPTIS BETHESDA</td>\n",
       "      <td>3077146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24457</th>\n",
       "      <td>1500000US360470742001</td>\n",
       "      <td>CHARMAINE SMITH</td>\n",
       "      <td>3207294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24459</th>\n",
       "      <td>1500000US360610177006</td>\n",
       "      <td>PESCE PASTA QUATTRO</td>\n",
       "      <td>1031595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24462</th>\n",
       "      <td>1500000US360810483001</td>\n",
       "      <td>NEW DRAGON RESTAURANT</td>\n",
       "      <td>4037785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24464</th>\n",
       "      <td>1500000US360050072001</td>\n",
       "      <td>PARKCHESTER PIZZA PALACE</td>\n",
       "      <td>2024906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24466</th>\n",
       "      <td>1500000US360850020011</td>\n",
       "      <td>SGTMICHAEL JLABETTI POST2159 V</td>\n",
       "      <td>5044138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24467</th>\n",
       "      <td>1500000US360050179023</td>\n",
       "      <td>VALDEZ JUAN</td>\n",
       "      <td>2007981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24470</th>\n",
       "      <td>1500000US360810181022</td>\n",
       "      <td>SALT AND FAT</td>\n",
       "      <td>4002538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24471</th>\n",
       "      <td>1500000US360470433003</td>\n",
       "      <td>TROPICAL BAR</td>\n",
       "      <td>3076148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24472</th>\n",
       "      <td>1500000US360050386008</td>\n",
       "      <td>NACHO PIZZA</td>\n",
       "      <td>2060208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24473</th>\n",
       "      <td>1500000US360811241002</td>\n",
       "      <td>16819  UNION TURNPIKE</td>\n",
       "      <td>4151767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24474</th>\n",
       "      <td>1500000US360470866003</td>\n",
       "      <td>WEST MARIA V</td>\n",
       "      <td>3100917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24476</th>\n",
       "      <td>1500000US360050197005</td>\n",
       "      <td>YUPA PAUCAR MANUEL ALEJANDRO</td>\n",
       "      <td>2002837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24477</th>\n",
       "      <td>1500000US360050076004</td>\n",
       "      <td>CHENG TAI KING</td>\n",
       "      <td>2025611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24478</th>\n",
       "      <td>1500000US360050145001</td>\n",
       "      <td>COMBS JAMES</td>\n",
       "      <td>2004212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24479</th>\n",
       "      <td>1500000US360471164001</td>\n",
       "      <td>LI SHU FANG</td>\n",
       "      <td>3089563</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14336 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Id                                   bus_name  \\\n",
       "0      1500000US360050185001                         PIZZA WORLD 163 ST   \n",
       "1      1500000US360050185001                            RUNNING COOL II   \n",
       "4      1500000US360050185001                                163 CHICKEN   \n",
       "5      1500000US360050185001               TRIPLE A JAMAICAN RESTAURANT   \n",
       "11     1500000US360610157005             UNCLE JACKS STEAKHOUSE MIDTOWN   \n",
       "12     1500000US360610157005               WINE AND ROSES BAR AND CAFES   \n",
       "13     1500000US360610157005             UNCLE JACKS STEAKHOUSE MIDTOWN   \n",
       "16     1500000US360610157005                                    NABUCCO   \n",
       "19     1500000US360610033001                   DAVIS K PARK ENTERPRISES   \n",
       "20     1500000US360610033001                                    VILPERS   \n",
       "23     1500000US360610033001  TRINI GUL ORIGINAL ROTI  WEST INDIAN FOOD   \n",
       "24     1500000US360610033001                            ANDY JAMES DELI   \n",
       "26     1500000US360610033001                                     SIRING   \n",
       "29     1500000US360610033001                                    SUSHEIN   \n",
       "31     1500000US360610033001                          SUPER DUPER PIZZA   \n",
       "32     1500000US360610033001                                  FARINELLA   \n",
       "34     1500000US360610033001                      YAWA JAPANESE CUISINE   \n",
       "35     1500000US360610033001                             WEST SIDE CAFE   \n",
       "36     1500000US360610033001                              JULY VENTURES   \n",
       "37     1500000US360610033001                                    BANCONE   \n",
       "39     1500000US360610033001                                 NEW SHEZAN   \n",
       "40     1500000US360610033001                               339 BROADWAY   \n",
       "42     1500000US360610033001                           SAM ATOMIC WINGS   \n",
       "43     1500000US360610033001                                    PK FOOD   \n",
       "45     1500000US360610033001                                     MILLAU   \n",
       "...                      ...                                        ...   \n",
       "24444  1500000US360810329001                              PANORA ELOY V   \n",
       "24445  1500000US360470218001                                  BORONITZI   \n",
       "24447  1500000US360470858002                               HELENA ERWIN   \n",
       "24449  1500000US360810352002                     NEW YORK FRIED CHICKEN   \n",
       "24450  1500000US360050435001                              LILLYS TAVERN   \n",
       "24451  1500000US360810339001                             EL CARRIEL BAR   \n",
       "24452  1500000US360470734002  PALAPA MARCOS PEREZ   MUNOZ NICASIO PEREZ   \n",
       "24453  1500000US360470690001                              RIBAUDOS REST   \n",
       "24455  1500000US360610161003                          MAKE FLAGSHIP ONE   \n",
       "24456  1500000US360470397002                   MEMORIAL BAPTIS BETHESDA   \n",
       "24457  1500000US360470742001                            CHARMAINE SMITH   \n",
       "24459  1500000US360610177006                        PESCE PASTA QUATTRO   \n",
       "24462  1500000US360810483001                      NEW DRAGON RESTAURANT   \n",
       "24464  1500000US360050072001                   PARKCHESTER PIZZA PALACE   \n",
       "24466  1500000US360850020011             SGTMICHAEL JLABETTI POST2159 V   \n",
       "24467  1500000US360050179023                                VALDEZ JUAN   \n",
       "24470  1500000US360810181022                               SALT AND FAT   \n",
       "24471  1500000US360470433003                               TROPICAL BAR   \n",
       "24472  1500000US360050386008                                NACHO PIZZA   \n",
       "24473  1500000US360811241002                      16819  UNION TURNPIKE   \n",
       "24474  1500000US360470866003                               WEST MARIA V   \n",
       "24476  1500000US360050197005               YUPA PAUCAR MANUEL ALEJANDRO   \n",
       "24477  1500000US360050076004                             CHENG TAI KING   \n",
       "24478  1500000US360050145001                                COMBS JAMES   \n",
       "24479  1500000US360471164001                                LI SHU FANG   \n",
       "\n",
       "       Building_ID_No  \n",
       "0             2113088  \n",
       "1             2113088  \n",
       "4             2113088  \n",
       "5             2113088  \n",
       "11                  0  \n",
       "12            1030017  \n",
       "13            1034848  \n",
       "16            1030007  \n",
       "19            1002209  \n",
       "20            1001658  \n",
       "23                  0  \n",
       "24            1001649  \n",
       "26            1079211  \n",
       "29            1079211  \n",
       "31            1001642  \n",
       "32            1079211  \n",
       "34            1001642  \n",
       "35            1002703  \n",
       "36            1001903  \n",
       "37            1002204  \n",
       "39            1001642  \n",
       "40            1001882  \n",
       "42            1001658  \n",
       "43            1001649  \n",
       "45            1001893  \n",
       "...               ...  \n",
       "24444         4000000  \n",
       "24445         3138992  \n",
       "24447         3109607  \n",
       "24449         4279643  \n",
       "24450         2084354  \n",
       "24451         4033431  \n",
       "24452         3215970  \n",
       "24453         3220432  \n",
       "24455         1028743  \n",
       "24456         3077146  \n",
       "24457         3207294  \n",
       "24459         1031595  \n",
       "24462         4037785  \n",
       "24464         2024906  \n",
       "24466         5044138  \n",
       "24467         2007981  \n",
       "24470         4002538  \n",
       "24471         3076148  \n",
       "24472         2060208  \n",
       "24473         4151767  \n",
       "24474         3100917  \n",
       "24476         2002837  \n",
       "24477         2025611  \n",
       "24478         2004212  \n",
       "24479         3089563  \n",
       "\n",
       "[14336 rows x 3 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points[['Id', 'bus_name', 'Building_ID_No']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = gpd.GeoDataFrame(points[['Id', 'bus_name', 'Building_ID_No']], \n",
    "                       geometry = points.buffer500)\n",
    "poly.crs = '+proj=utm +zone=33 +ellps=WGS84 +datum=WGS84 +units=m +no_defs'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "#join buffers with restaurant points \n",
    "test = gpd.sjoin(subway, poly, op='within')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14336, 7)"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8763, 10)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>line</th>\n",
       "      <th>name</th>\n",
       "      <th>objectid</th>\n",
       "      <th>url</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>Id</th>\n",
       "      <th>bus_name</th>\n",
       "      <th>Building_ID_No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2-5</td>\n",
       "      <td>Morris Park Ave &amp; 180th St at NW corner</td>\n",
       "      <td>1737.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (-5799998.647563174 9853074.772479454)</td>\n",
       "      <td>3179</td>\n",
       "      <td>1500000US360050240003</td>\n",
       "      <td>DEMI RESTAURANT</td>\n",
       "      <td>2110698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2-5</td>\n",
       "      <td>Boston Rd &amp; 178th St at SW corner</td>\n",
       "      <td>1738.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (-5800132.402861771 9853936.433511501)</td>\n",
       "      <td>14635</td>\n",
       "      <td>1500000US360050361003</td>\n",
       "      <td>CHEN BI RONG</td>\n",
       "      <td>2013281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2-5</td>\n",
       "      <td>Boston Rd &amp; E Tremont Ave at NE corner</td>\n",
       "      <td>1740.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (-5800211.204638127 9853961.097033164)</td>\n",
       "      <td>14635</td>\n",
       "      <td>1500000US360050361003</td>\n",
       "      <td>CHEN BI RONG</td>\n",
       "      <td>2013281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2-5</td>\n",
       "      <td>Boston Rd &amp; 178th St at SE corner</td>\n",
       "      <td>1741.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (-5800161.921096674 9853926.809797814)</td>\n",
       "      <td>14635</td>\n",
       "      <td>1500000US360050361003</td>\n",
       "      <td>CHEN BI RONG</td>\n",
       "      <td>2013281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2-5</td>\n",
       "      <td>Boston Rd &amp; 178th St at NW corner</td>\n",
       "      <td>1742.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (-5800087.040727478 9853908.71801807)</td>\n",
       "      <td>14635</td>\n",
       "      <td>1500000US360050361003</td>\n",
       "      <td>CHEN BI RONG</td>\n",
       "      <td>2013281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  line                                     name  objectid  \\\n",
       "3  2-5  Morris Park Ave & 180th St at NW corner    1737.0   \n",
       "4  2-5        Boston Rd & 178th St at SW corner    1738.0   \n",
       "6  2-5   Boston Rd & E Tremont Ave at NE corner    1740.0   \n",
       "7  2-5        Boston Rd & 178th St at SE corner    1741.0   \n",
       "8  2-5        Boston Rd & 178th St at NW corner    1742.0   \n",
       "\n",
       "                                 url  \\\n",
       "3  http://web.mta.info/nyct/service/   \n",
       "4  http://web.mta.info/nyct/service/   \n",
       "6  http://web.mta.info/nyct/service/   \n",
       "7  http://web.mta.info/nyct/service/   \n",
       "8  http://web.mta.info/nyct/service/   \n",
       "\n",
       "                                       geometry  index_right  \\\n",
       "3  POINT (-5799998.647563174 9853074.772479454)         3179   \n",
       "4  POINT (-5800132.402861771 9853936.433511501)        14635   \n",
       "6  POINT (-5800211.204638127 9853961.097033164)        14635   \n",
       "7  POINT (-5800161.921096674 9853926.809797814)        14635   \n",
       "8   POINT (-5800087.040727478 9853908.71801807)        14635   \n",
       "\n",
       "                      Id         bus_name  Building_ID_No  \n",
       "3  1500000US360050240003  DEMI RESTAURANT         2110698  \n",
       "4  1500000US360050361003     CHEN BI RONG         2013281  \n",
       "6  1500000US360050361003     CHEN BI RONG         2013281  \n",
       "7  1500000US360050361003     CHEN BI RONG         2013281  \n",
       "8  1500000US360050361003     CHEN BI RONG         2013281  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ed99cccb38>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "poly.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ed99b43748>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_sub = test.groupby(['bus_name', 'Building_ID_No'])['name'].size().rename('n_subway_ent').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>bus_name</th>\n",
       "      <th>Building_ID_No</th>\n",
       "      <th>n_subway_ent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 AGUAPANELAS</td>\n",
       "      <td>4064657</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 TEA CAFE</td>\n",
       "      <td>3141388</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100 LAFAYETTE STREET LTD</td>\n",
       "      <td>1002329</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101 EAST 161ST RESTAURANT</td>\n",
       "      <td>2002919</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101 W 55 TH ST REST</td>\n",
       "      <td>1023394</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    bus_name  Building_ID_No  n_subway_ent\n",
       "0              1 AGUAPANELAS         4064657             3\n",
       "1                 1 TEA CAFE         3141388             1\n",
       "2   100 LAFAYETTE STREET LTD         1002329             2\n",
       "3  101 EAST 161ST RESTAURANT         2002919             3\n",
       "4        101 W 55 TH ST REST         1023394             4"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2899, 3)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_sub.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "test2 = gpd.sjoin(points[''], poly, op='within')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Longitude_WGS84</th>\n",
       "      <th>Latitude_WGS84</th>\n",
       "      <th>Id_left</th>\n",
       "      <th>bus_name_left</th>\n",
       "      <th>Building_ID_No_left</th>\n",
       "      <th>geometry</th>\n",
       "      <th>buffer500</th>\n",
       "      <th>index_right</th>\n",
       "      <th>Id_right</th>\n",
       "      <th>bus_name_right</th>\n",
       "      <th>Building_ID_No_right</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>PIZZA WORLD 163 ST</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891596 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891596 9857680.653247522...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>RUNNING COOL II</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891596 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891596 9857680.653247522...</td>\n",
       "      <td>0</td>\n",
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       "      <td>PIZZA WORLD 163 ST</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>163 CHICKEN</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891593 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891593 9857680.653247522...</td>\n",
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       "      <td>PIZZA WORLD 163 ST</td>\n",
       "      <td>2113088</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>TRIPLE A JAMAICAN RESTAURANT</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891593 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891593 9857680.653247522...</td>\n",
       "      <td>0</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>PIZZA WORLD 163 ST</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19847</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050195002</td>\n",
       "      <td>FEEDING TREE STYLE</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891596 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891596 9857680.653247522...</td>\n",
       "      <td>0</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>PIZZA WORLD 163 ST</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>PIZZA WORLD 163 ST</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891596 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891596 9857680.653247522...</td>\n",
       "      <td>1</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>RUNNING COOL II</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>RUNNING COOL II</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891596 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891596 9857680.653247522...</td>\n",
       "      <td>1</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>RUNNING COOL II</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>163 CHICKEN</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891593 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891593 9857680.653247522...</td>\n",
       "      <td>1</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>RUNNING COOL II</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>TRIPLE A JAMAICAN RESTAURANT</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891593 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891593 9857680.653247522...</td>\n",
       "      <td>1</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>RUNNING COOL II</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19847</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050195002</td>\n",
       "      <td>FEEDING TREE STYLE</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891596 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891596 9857680.653247522...</td>\n",
       "      <td>1</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>RUNNING COOL II</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>PIZZA WORLD 163 ST</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891596 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891596 9857680.653247522...</td>\n",
       "      <td>4</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>163 CHICKEN</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>RUNNING COOL II</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891596 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891596 9857680.653247522...</td>\n",
       "      <td>4</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>163 CHICKEN</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>163 CHICKEN</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891593 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891593 9857680.653247522...</td>\n",
       "      <td>4</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>163 CHICKEN</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>TRIPLE A JAMAICAN RESTAURANT</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891593 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891593 9857680.653247522...</td>\n",
       "      <td>4</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>163 CHICKEN</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19847</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050195002</td>\n",
       "      <td>FEEDING TREE STYLE</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891596 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891596 9857680.653247522...</td>\n",
       "      <td>4</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>163 CHICKEN</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>PIZZA WORLD 163 ST</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891596 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891596 9857680.653247522...</td>\n",
       "      <td>5</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>TRIPLE A JAMAICAN RESTAURANT</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>RUNNING COOL II</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891596 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891596 9857680.653247522...</td>\n",
       "      <td>5</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>TRIPLE A JAMAICAN RESTAURANT</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>163 CHICKEN</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891593 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891593 9857680.653247522...</td>\n",
       "      <td>5</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>TRIPLE A JAMAICAN RESTAURANT</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>TRIPLE A JAMAICAN RESTAURANT</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891593 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891593 9857680.653247522...</td>\n",
       "      <td>5</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>TRIPLE A JAMAICAN RESTAURANT</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>40.824685</td>\n",
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       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891596 9857680.653247522)</td>\n",
       "      <td>POLYGON ((-5802822.288891596 9857680.653247522...</td>\n",
       "      <td>5</td>\n",
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       "      <td>TRIPLE A JAMAICAN RESTAURANT</td>\n",
       "      <td>2113088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Longitude_WGS84  Latitude_WGS84                Id_left  \\\n",
       "0           -73.909377       40.824685  1500000US360050185001   \n",
       "1           -73.909377       40.824685  1500000US360050185001   \n",
       "4           -73.909377       40.824685  1500000US360050185001   \n",
       "5           -73.909377       40.824685  1500000US360050185001   \n",
       "19847       -73.909377       40.824685  1500000US360050195002   \n",
       "0           -73.909377       40.824685  1500000US360050185001   \n",
       "1           -73.909377       40.824685  1500000US360050185001   \n",
       "4           -73.909377       40.824685  1500000US360050185001   \n",
       "5           -73.909377       40.824685  1500000US360050185001   \n",
       "19847       -73.909377       40.824685  1500000US360050195002   \n",
       "0           -73.909377       40.824685  1500000US360050185001   \n",
       "1           -73.909377       40.824685  1500000US360050185001   \n",
       "4           -73.909377       40.824685  1500000US360050185001   \n",
       "5           -73.909377       40.824685  1500000US360050185001   \n",
       "19847       -73.909377       40.824685  1500000US360050195002   \n",
       "0           -73.909377       40.824685  1500000US360050185001   \n",
       "1           -73.909377       40.824685  1500000US360050185001   \n",
       "4           -73.909377       40.824685  1500000US360050185001   \n",
       "5           -73.909377       40.824685  1500000US360050185001   \n",
       "19847       -73.909377       40.824685  1500000US360050195002   \n",
       "\n",
       "                      bus_name_left  Building_ID_No_left  \\\n",
       "0                PIZZA WORLD 163 ST              2113088   \n",
       "1                   RUNNING COOL II              2113088   \n",
       "4                       163 CHICKEN              2113088   \n",
       "5      TRIPLE A JAMAICAN RESTAURANT              2113088   \n",
       "19847            FEEDING TREE STYLE              2113088   \n",
       "0                PIZZA WORLD 163 ST              2113088   \n",
       "1                   RUNNING COOL II              2113088   \n",
       "4                       163 CHICKEN              2113088   \n",
       "5      TRIPLE A JAMAICAN RESTAURANT              2113088   \n",
       "19847            FEEDING TREE STYLE              2113088   \n",
       "0                PIZZA WORLD 163 ST              2113088   \n",
       "1                   RUNNING COOL II              2113088   \n",
       "4                       163 CHICKEN              2113088   \n",
       "5      TRIPLE A JAMAICAN RESTAURANT              2113088   \n",
       "19847            FEEDING TREE STYLE              2113088   \n",
       "0                PIZZA WORLD 163 ST              2113088   \n",
       "1                   RUNNING COOL II              2113088   \n",
       "4                       163 CHICKEN              2113088   \n",
       "5      TRIPLE A JAMAICAN RESTAURANT              2113088   \n",
       "19847            FEEDING TREE STYLE              2113088   \n",
       "\n",
       "                                           geometry  \\\n",
       "0      POINT (-5802974.288891596 9857680.653247522)   \n",
       "1      POINT (-5802974.288891596 9857680.653247522)   \n",
       "4      POINT (-5802974.288891593 9857680.653247522)   \n",
       "5      POINT (-5802974.288891593 9857680.653247522)   \n",
       "19847  POINT (-5802974.288891596 9857680.653247522)   \n",
       "0      POINT (-5802974.288891596 9857680.653247522)   \n",
       "1      POINT (-5802974.288891596 9857680.653247522)   \n",
       "4      POINT (-5802974.288891593 9857680.653247522)   \n",
       "5      POINT (-5802974.288891593 9857680.653247522)   \n",
       "19847  POINT (-5802974.288891596 9857680.653247522)   \n",
       "0      POINT (-5802974.288891596 9857680.653247522)   \n",
       "1      POINT (-5802974.288891596 9857680.653247522)   \n",
       "4      POINT (-5802974.288891593 9857680.653247522)   \n",
       "5      POINT (-5802974.288891593 9857680.653247522)   \n",
       "19847  POINT (-5802974.288891596 9857680.653247522)   \n",
       "0      POINT (-5802974.288891596 9857680.653247522)   \n",
       "1      POINT (-5802974.288891596 9857680.653247522)   \n",
       "4      POINT (-5802974.288891593 9857680.653247522)   \n",
       "5      POINT (-5802974.288891593 9857680.653247522)   \n",
       "19847  POINT (-5802974.288891596 9857680.653247522)   \n",
       "\n",
       "                                               buffer500  index_right  \\\n",
       "0      POLYGON ((-5802822.288891596 9857680.653247522...            0   \n",
       "1      POLYGON ((-5802822.288891596 9857680.653247522...            0   \n",
       "4      POLYGON ((-5802822.288891593 9857680.653247522...            0   \n",
       "5      POLYGON ((-5802822.288891593 9857680.653247522...            0   \n",
       "19847  POLYGON ((-5802822.288891596 9857680.653247522...            0   \n",
       "0      POLYGON ((-5802822.288891596 9857680.653247522...            1   \n",
       "1      POLYGON ((-5802822.288891596 9857680.653247522...            1   \n",
       "4      POLYGON ((-5802822.288891593 9857680.653247522...            1   \n",
       "5      POLYGON ((-5802822.288891593 9857680.653247522...            1   \n",
       "19847  POLYGON ((-5802822.288891596 9857680.653247522...            1   \n",
       "0      POLYGON ((-5802822.288891596 9857680.653247522...            4   \n",
       "1      POLYGON ((-5802822.288891596 9857680.653247522...            4   \n",
       "4      POLYGON ((-5802822.288891593 9857680.653247522...            4   \n",
       "5      POLYGON ((-5802822.288891593 9857680.653247522...            4   \n",
       "19847  POLYGON ((-5802822.288891596 9857680.653247522...            4   \n",
       "0      POLYGON ((-5802822.288891596 9857680.653247522...            5   \n",
       "1      POLYGON ((-5802822.288891596 9857680.653247522...            5   \n",
       "4      POLYGON ((-5802822.288891593 9857680.653247522...            5   \n",
       "5      POLYGON ((-5802822.288891593 9857680.653247522...            5   \n",
       "19847  POLYGON ((-5802822.288891596 9857680.653247522...            5   \n",
       "\n",
       "                    Id_right                bus_name_right  \\\n",
       "0      1500000US360050185001            PIZZA WORLD 163 ST   \n",
       "1      1500000US360050185001            PIZZA WORLD 163 ST   \n",
       "4      1500000US360050185001            PIZZA WORLD 163 ST   \n",
       "5      1500000US360050185001            PIZZA WORLD 163 ST   \n",
       "19847  1500000US360050185001            PIZZA WORLD 163 ST   \n",
       "0      1500000US360050185001               RUNNING COOL II   \n",
       "1      1500000US360050185001               RUNNING COOL II   \n",
       "4      1500000US360050185001               RUNNING COOL II   \n",
       "5      1500000US360050185001               RUNNING COOL II   \n",
       "19847  1500000US360050185001               RUNNING COOL II   \n",
       "0      1500000US360050185001                   163 CHICKEN   \n",
       "1      1500000US360050185001                   163 CHICKEN   \n",
       "4      1500000US360050185001                   163 CHICKEN   \n",
       "5      1500000US360050185001                   163 CHICKEN   \n",
       "19847  1500000US360050185001                   163 CHICKEN   \n",
       "0      1500000US360050185001  TRIPLE A JAMAICAN RESTAURANT   \n",
       "1      1500000US360050185001  TRIPLE A JAMAICAN RESTAURANT   \n",
       "4      1500000US360050185001  TRIPLE A JAMAICAN RESTAURANT   \n",
       "5      1500000US360050185001  TRIPLE A JAMAICAN RESTAURANT   \n",
       "19847  1500000US360050185001  TRIPLE A JAMAICAN RESTAURANT   \n",
       "\n",
       "       Building_ID_No_right  \n",
       "0                   2113088  \n",
       "1                   2113088  \n",
       "4                   2113088  \n",
       "5                   2113088  \n",
       "19847               2113088  \n",
       "0                   2113088  \n",
       "1                   2113088  \n",
       "4                   2113088  \n",
       "5                   2113088  \n",
       "19847               2113088  \n",
       "0                   2113088  \n",
       "1                   2113088  \n",
       "4                   2113088  \n",
       "5                   2113088  \n",
       "19847               2113088  \n",
       "0                   2113088  \n",
       "1                   2113088  \n",
       "4                   2113088  \n",
       "5                   2113088  \n",
       "19847               2113088  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test2[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_rest500 = (test2\n",
    "             .groupby(['bus_name_right', 'Building_ID_No_right'])['bus_name_right']\n",
    "             .size()\n",
    "             .rename('n_restaurants_500')\n",
    "             .reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "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>bus_name_right</th>\n",
       "      <th>Building_ID_No_right</th>\n",
       "      <th>n_restaurants_500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 AGUAPANELAS</td>\n",
       "      <td>4064657</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 CHIMI SUSHI</td>\n",
       "      <td>1019865</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1 EAST 66TH STREET</td>\n",
       "      <td>1041159</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1 PANDA</td>\n",
       "      <td>1017884</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1 PERRY ST RESTAURANT</td>\n",
       "      <td>1010939</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          bus_name_right  Building_ID_No_right  n_restaurants_500\n",
       "0          1 AGUAPANELAS               4064657                  7\n",
       "1          1 CHIMI SUSHI               1019865                 17\n",
       "2     1 EAST 66TH STREET               1041159                  5\n",
       "3                1 PANDA               1017884                  8\n",
       "4  1 PERRY ST RESTAURANT               1010939                 12"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_rest500.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_rest500.columns = ['bus_name', 'Building_ID_No', 'n_restaurants_500']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14336, 3)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_rest500.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combine \n",
    "rest_buffer = n_rest500.merge(n_sub, how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14336, 4)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_buffer.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>bus_name</th>\n",
       "      <th>Building_ID_No</th>\n",
       "      <th>n_restaurants_500</th>\n",
       "      <th>n_subway_ent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 AGUAPANELAS</td>\n",
       "      <td>4064657</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 CHIMI SUSHI</td>\n",
       "      <td>1019865</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1 EAST 66TH STREET</td>\n",
       "      <td>1041159</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1 PANDA</td>\n",
       "      <td>1017884</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1 PERRY ST RESTAURANT</td>\n",
       "      <td>1010939</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                bus_name  Building_ID_No  n_restaurants_500  n_subway_ent\n",
       "0          1 AGUAPANELAS         4064657                  7           3.0\n",
       "1          1 CHIMI SUSHI         1019865                 17           NaN\n",
       "2     1 EAST 66TH STREET         1041159                  5           NaN\n",
       "3                1 PANDA         1017884                  8           NaN\n",
       "4  1 PERRY ST RESTAURANT         1010939                 12           NaN"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_buffer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "rest_buffer.loc[rest_buffer.n_subway_ent.isnull(), 'n_subway_ent'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "rest_buffer.to_pickle('restaurants_buffer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "rest_buffer.to_csv('restaurants_buffer.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "scrolled": true
   },
   "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>bus_name</th>\n",
       "      <th>Building_ID_No</th>\n",
       "      <th>n_restaurants_500</th>\n",
       "      <th>n_subway_ent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 AGUAPANELAS</td>\n",
       "      <td>4064657</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 CHIMI SUSHI</td>\n",
       "      <td>1019865</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1 EAST 66TH STREET</td>\n",
       "      <td>1041159</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1 PANDA</td>\n",
       "      <td>1017884</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1 PERRY ST RESTAURANT</td>\n",
       "      <td>1010939</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                bus_name  Building_ID_No  n_restaurants_500  n_subway_ent\n",
       "0          1 AGUAPANELAS         4064657                  7           3.0\n",
       "1          1 CHIMI SUSHI         1019865                 17           0.0\n",
       "2     1 EAST 66TH STREET         1041159                  5           0.0\n",
       "3                1 PANDA         1017884                  8           0.0\n",
       "4  1 PERRY ST RESTAURANT         1010939                 12           0.0"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_buffer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(rest_buffer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get rental prices \n",
    "bk = pd.read_csv('bk_condo_comp2010.csv', header=1)\n",
    "bx = pd.read_csv('bx_condo_comp2010.csv', header=1)\n",
    "mn = pd.read_csv('mn_ condo_comp2010.csv', header=1)\n",
    "qn = pd.read_csv('qn_condo_comp2010.csv', header=1)\n",
    "si = pd.read_csv('sicondos2009.csv', header=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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-Block-Lot</th>\n",
       "      <th>Condominium Section</th>\n",
       "      <th>Address</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Building Classification</th>\n",
       "      <th>Total Units</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Gross SqFt</th>\n",
       "      <th>Est. Gross Income</th>\n",
       "      <th>Gross Income per SqFt</th>\n",
       "      <th>...</th>\n",
       "      <th>Neighborhood.2</th>\n",
       "      <th>Building Classification.2</th>\n",
       "      <th>Total Units.2</th>\n",
       "      <th>Year Built.2</th>\n",
       "      <th>Gross SqFt.2</th>\n",
       "      <th>Est. Gross Income.2</th>\n",
       "      <th>Gross Income per SqFt.2</th>\n",
       "      <th>Full Market Value.2</th>\n",
       "      <th>Market Value per SqFt.2</th>\n",
       "      <th>Dist. from Coop in miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3-00027-7501</td>\n",
       "      <td>658-R1</td>\n",
       "      <td>1 MAIN STREET</td>\n",
       "      <td>DOWNTOWN-FULTON FERRY</td>\n",
       "      <td>R4-ELEVATOR</td>\n",
       "      <td>124</td>\n",
       "      <td>1913</td>\n",
       "      <td>227916</td>\n",
       "      <td>4984800</td>\n",
       "      <td>21.87</td>\n",
       "      <td>...</td>\n",
       "      <td>BROOKLYN HEIGHTS</td>\n",
       "      <td>D6-ELEVATOR</td>\n",
       "      <td>137.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>108780.0</td>\n",
       "      <td>2429803.0</td>\n",
       "      <td>22.34</td>\n",
       "      <td>10100000.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3-00028-7501</td>\n",
       "      <td>1370-R1</td>\n",
       "      <td>31 WASHINGTON STREET</td>\n",
       "      <td>DOWNTOWN-FULTON FERRY</td>\n",
       "      <td>R4-ELEVATOR</td>\n",
       "      <td>13</td>\n",
       "      <td>2001</td>\n",
       "      <td>24672</td>\n",
       "      <td>556600</td>\n",
       "      <td>22.56</td>\n",
       "      <td>...</td>\n",
       "      <td>BROOKLYN HEIGHTS</td>\n",
       "      <td>D7-ELEVATOR</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1921.0</td>\n",
       "      <td>31692.0</td>\n",
       "      <td>943532.0</td>\n",
       "      <td>29.80</td>\n",
       "      <td>4400000.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3-00028-7502</td>\n",
       "      <td>2018-R1</td>\n",
       "      <td>133 WATER STREET</td>\n",
       "      <td>DOWNTOWN-FULTON FERRY</td>\n",
       "      <td>R4-ELEVATOR</td>\n",
       "      <td>69</td>\n",
       "      <td>2006</td>\n",
       "      <td>78352</td>\n",
       "      <td>1738800</td>\n",
       "      <td>22.19</td>\n",
       "      <td>...</td>\n",
       "      <td>BROOKLYN HEIGHTS</td>\n",
       "      <td>D7-ELEVATOR</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1926.0</td>\n",
       "      <td>73032.0</td>\n",
       "      <td>1781214.0</td>\n",
       "      <td>24.39</td>\n",
       "      <td>7380000.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3-00031-7502</td>\n",
       "      <td>1181-R1</td>\n",
       "      <td>50 BRIDGE STREET</td>\n",
       "      <td>DOWNTOWN-FULTON FERRY</td>\n",
       "      <td>R4-ELEVATOR</td>\n",
       "      <td>58</td>\n",
       "      <td>1904</td>\n",
       "      <td>52530</td>\n",
       "      <td>1044000</td>\n",
       "      <td>19.87</td>\n",
       "      <td>...</td>\n",
       "      <td>DOWNTOWN-FULTON FERRY</td>\n",
       "      <td>D7-ELEVATOR</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>50457.0</td>\n",
       "      <td>1887773.0</td>\n",
       "      <td>37.40</td>\n",
       "      <td>8790000.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3-00035-7501</td>\n",
       "      <td>1771-R1</td>\n",
       "      <td>4 WATER STREET</td>\n",
       "      <td>DOWNTOWN-FULTON FERRY</td>\n",
       "      <td>R4-ELEVATOR</td>\n",
       "      <td>13</td>\n",
       "      <td>2007</td>\n",
       "      <td>20085</td>\n",
       "      <td>580456</td>\n",
       "      <td>28.90</td>\n",
       "      <td>...</td>\n",
       "      <td>BROOKLYN HEIGHTS</td>\n",
       "      <td>D7-ELEVATOR</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1921.0</td>\n",
       "      <td>31692.0</td>\n",
       "      <td>943532.0</td>\n",
       "      <td>29.80</td>\n",
       "      <td>4400000.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Boro-Block-Lot Condominium Section               Address  \\\n",
       "0   3-00027-7501              658-R1         1 MAIN STREET   \n",
       "1   3-00028-7501             1370-R1  31 WASHINGTON STREET   \n",
       "2   3-00028-7502             2018-R1      133 WATER STREET   \n",
       "3   3-00031-7502             1181-R1      50 BRIDGE STREET   \n",
       "4   3-00035-7501             1771-R1        4 WATER STREET   \n",
       "\n",
       "            Neighborhood Building Classification  Total Units  Year Built  \\\n",
       "0  DOWNTOWN-FULTON FERRY             R4-ELEVATOR          124        1913   \n",
       "1  DOWNTOWN-FULTON FERRY             R4-ELEVATOR           13        2001   \n",
       "2  DOWNTOWN-FULTON FERRY             R4-ELEVATOR           69        2006   \n",
       "3  DOWNTOWN-FULTON FERRY             R4-ELEVATOR           58        1904   \n",
       "4  DOWNTOWN-FULTON FERRY             R4-ELEVATOR           13        2007   \n",
       "\n",
       "   Gross SqFt  Est. Gross Income  Gross Income per SqFt  ...  \\\n",
       "0      227916            4984800                  21.87  ...   \n",
       "1       24672             556600                  22.56  ...   \n",
       "2       78352            1738800                  22.19  ...   \n",
       "3       52530            1044000                  19.87  ...   \n",
       "4       20085             580456                  28.90  ...   \n",
       "\n",
       "          Neighborhood.2  Building Classification.2 Total Units.2  \\\n",
       "0       BROOKLYN HEIGHTS                D6-ELEVATOR         137.0   \n",
       "1       BROOKLYN HEIGHTS                D7-ELEVATOR          42.0   \n",
       "2       BROOKLYN HEIGHTS                D7-ELEVATOR          94.0   \n",
       "3  DOWNTOWN-FULTON FERRY                D7-ELEVATOR          59.0   \n",
       "4       BROOKLYN HEIGHTS                D7-ELEVATOR          42.0   \n",
       "\n",
       "  Year Built.2 Gross SqFt.2 Est. Gross Income.2  Gross Income per SqFt.2  \\\n",
       "0       1900.0     108780.0           2429803.0                    22.34   \n",
       "1       1921.0      31692.0            943532.0                    29.80   \n",
       "2       1926.0      73032.0           1781214.0                    24.39   \n",
       "3       2000.0      50457.0           1887773.0                    37.40   \n",
       "4       1921.0      31692.0            943532.0                    29.80   \n",
       "\n",
       "   Full Market Value.2  Market Value per SqFt.2  Dist. from Coop in miles  \n",
       "0           10100000.0                     93.0                      0.35  \n",
       "1            4400000.0                    139.0                      0.55  \n",
       "2            7380000.0                    101.0                      0.95  \n",
       "3            8790000.0                    174.0                      0.20  \n",
       "4            4400000.0                    139.0                      0.45  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_coord(address):\n",
    "    full_address = address + ' ' + 'New York City'\n",
    "    geolocator = Nominatim(user_agent=\"my_app3\", timeout=7)\n",
    "    try: \n",
    "        location = geolocator.geocode(full_address)\n",
    "    except GeocoderTimedOut:\n",
    "        return get_coord(full_address)\n",
    "    if location is not None:   \n",
    "        lat = float(location.raw['lat'])\n",
    "        lon = float(location.raw['lon'])\n",
    "        coord = {'lat':lat, 'lon':lon}\n",
    "    else:\n",
    "        coord = None\n",
    "        \n",
    "    return coord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_coord('37 East 21 Street New York City')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "ename": "GeocoderTimedOut",
     "evalue": "Service timed out",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mtimeout\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1316\u001b[0m                 h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[1;32m-> 1317\u001b[1;33m                           encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0m\u001b[0;32m   1318\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\http\\client.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1228\u001b[0m         \u001b[1;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1229\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1230\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1274\u001b[0m             \u001b[0mbody\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'body'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1275\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1276\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\http\\client.py\u001b[0m in \u001b[0;36mendheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1223\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1224\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1225\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1015\u001b[0m         \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1016\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1017\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\http\\client.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    955\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 956\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    957\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\http\\client.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1383\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1384\u001b[1;33m             \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1385\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\http\\client.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    927\u001b[0m         self.sock = self._create_connection(\n\u001b[1;32m--> 928\u001b[1;33m             (self.host,self.port), self.timeout, self.source_address)\n\u001b[0m\u001b[0;32m    929\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetsockopt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIPPROTO_TCP\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTCP_NODELAY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address)\u001b[0m\n\u001b[0;32m    726\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 727\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    728\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address)\u001b[0m\n\u001b[0;32m    715\u001b[0m                 \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 716\u001b[1;33m             \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msa\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    717\u001b[0m             \u001b[1;31m# Break explicitly a reference cycle\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mtimeout\u001b[0m: timed out",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mURLError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\site-packages\\geopy\\geocoders\\base.py\u001b[0m in \u001b[0;36m_call_geocoder\u001b[1;34m(self, url, timeout, raw, requester, deserializer, **kwargs)\u001b[0m\n\u001b[0;32m    343\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 344\u001b[1;33m             \u001b[0mpage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequester\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    345\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    524\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 525\u001b[1;33m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    526\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_open\u001b[1;34m(self, req, data)\u001b[0m\n\u001b[0;32m    542\u001b[0m         result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[1;32m--> 543\u001b[1;33m                                   '_open', req)\n\u001b[0m\u001b[0;32m    544\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    502\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 503\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    504\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[1;34m(self, req)\u001b[0m\n\u001b[0;32m   1359\u001b[0m             return self.do_open(http.client.HTTPSConnection, req,\n\u001b[1;32m-> 1360\u001b[1;33m                 context=self._context, check_hostname=self._check_hostname)\n\u001b[0m\u001b[0;32m   1361\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1318\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1319\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1320\u001b[0m             \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mURLError\u001b[0m: <urlopen error timed out>",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mGeocoderTimedOut\u001b[0m                          Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-151-c5a72da484f5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mbk_coords\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAddress\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mget_coord\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'New York City'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[0;32m   3589\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3590\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3591\u001b[1;33m                 \u001b[0mmapped\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3592\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3593\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m<ipython-input-151-c5a72da484f5>\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mbk_coords\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAddress\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mget_coord\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'New York City'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-150-3485cce5449f>\u001b[0m in \u001b[0;36mget_coord\u001b[1;34m(address)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_coord\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maddress\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mgeolocator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNominatim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muser_agent\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"my_app\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mlocation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgeolocator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgeocode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maddress\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlocation\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[0mlat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraw\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'lat'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\site-packages\\geopy\\geocoders\\osm.py\u001b[0m in \u001b[0;36mgeocode\u001b[1;34m(self, query, exactly_one, timeout, limit, addressdetails, language, geometry, extratags)\u001b[0m\n\u001b[0;32m    307\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    308\u001b[0m         return self._parse_json(\n\u001b[1;32m--> 309\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_geocoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexactly_one\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    310\u001b[0m         )\n\u001b[0;32m    311\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\insight\\lib\\site-packages\\geopy\\geocoders\\base.py\u001b[0m in \u001b[0;36m_call_geocoder\u001b[1;34m(self, url, timeout, raw, requester, deserializer, **kwargs)\u001b[0m\n\u001b[0;32m    365\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merror\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mURLError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    366\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;34m\"timed out\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 367\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mGeocoderTimedOut\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Service timed out'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    368\u001b[0m                 \u001b[1;32melif\u001b[0m \u001b[1;34m\"unreachable\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    369\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mGeocoderUnavailable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Service not available'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mGeocoderTimedOut\u001b[0m: Service timed out"
     ]
    }
   ],
   "source": [
    "bk_coords = bk.Address.apply(lambda x: get_coord(x + ' ' + 'New York City'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "coordinates_bk = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in np.arange(500, 595):\n",
    "    coordinates.append(get_coord(bk.Address[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "coordinates_bk = coordinates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find closest restaurant to a given address \n",
    "\n",
    "Need to add this code to the model.py script to use on my website "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial import KDTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Longitude_WGS84</th>\n",
       "      <th>Latitude_WGS84</th>\n",
       "      <th>Id</th>\n",
       "      <th>bus_name</th>\n",
       "      <th>Building_ID_No</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>PIZZA WORLD 163 ST</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891596 9857680.653247522)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>RUNNING COOL II</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891596 9857680.653247522)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>163 CHICKEN</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891593 9857680.653247522)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-73.909377</td>\n",
       "      <td>40.824685</td>\n",
       "      <td>1500000US360050185001</td>\n",
       "      <td>TRIPLE A JAMAICAN RESTAURANT</td>\n",
       "      <td>2113088</td>\n",
       "      <td>POINT (-5802974.288891593 9857680.653247522)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-73.980657</td>\n",
       "      <td>40.779332</td>\n",
       "      <td>1500000US360610157005</td>\n",
       "      <td>UNCLE JACKS STEAKHOUSE MIDTOWN</td>\n",
       "      <td>0</td>\n",
       "      <td>POINT (-5810911.415347045 9866637.548673157)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Longitude_WGS84  Latitude_WGS84                     Id  \\\n",
       "0        -73.909377       40.824685  1500000US360050185001   \n",
       "1        -73.909377       40.824685  1500000US360050185001   \n",
       "4        -73.909377       40.824685  1500000US360050185001   \n",
       "5        -73.909377       40.824685  1500000US360050185001   \n",
       "11       -73.980657       40.779332  1500000US360610157005   \n",
       "\n",
       "                          bus_name  Building_ID_No  \\\n",
       "0               PIZZA WORLD 163 ST         2113088   \n",
       "1                  RUNNING COOL II         2113088   \n",
       "4                      163 CHICKEN         2113088   \n",
       "5     TRIPLE A JAMAICAN RESTAURANT         2113088   \n",
       "11  UNCLE JACKS STEAKHOUSE MIDTOWN               0   \n",
       "\n",
       "                                        geometry  \n",
       "0   POINT (-5802974.288891596 9857680.653247522)  \n",
       "1   POINT (-5802974.288891596 9857680.653247522)  \n",
       "4   POINT (-5802974.288891593 9857680.653247522)  \n",
       "5   POINT (-5802974.288891593 9857680.653247522)  \n",
       "11  POINT (-5810911.415347045 9866637.548673157)  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make a tree with restaurant coordinates \n",
    "tree = KDTree(points[['Longitude_WGS84', 'Latitude_WGS84']]) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_coords(address):\n",
    "    full_address = address + ' ' + 'New York City'\n",
    "    geolocator= Nominatim(user_agent='my_app17', timeout=10000)\n",
    "    location = geolocator.geocode(full_address)\n",
    "    if location is None:\n",
    "        return None \n",
    "    else:\n",
    "        lat = float(location.raw['lat'])\n",
    "        lon = float(location.raw['lon'])\n",
    "        df = pd.DataFrame({'lon':lon, 'lat':lat}, index = np.arange(1))\n",
    "        return df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-73.953081</td>\n",
       "      <td>40.66281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lon       lat\n",
       "0 -73.953081  40.66281"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get outside point \n",
    "address = '236 Sterling Street'\n",
    "outside_coord = get_coords(address)\n",
    "outside_coord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = np.array(outside_coord)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-73.95308098,  40.66281025]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance = tree.query(start, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind = distance[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>Longitude_WGS84</th>\n",
       "      <th>Latitude_WGS84</th>\n",
       "      <th>Id</th>\n",
       "      <th>bus_name</th>\n",
       "      <th>Building_ID_No</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23145</th>\n",
       "      <td>-73.9503</td>\n",
       "      <td>40.660626</td>\n",
       "      <td>1500000US360470804001</td>\n",
       "      <td>1109 NOSTRAND AVE BAKERY</td>\n",
       "      <td>3106637</td>\n",
       "      <td>POINT (-5830938.042101377 9862165.459185204)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Longitude_WGS84  Latitude_WGS84                     Id  \\\n",
       "23145         -73.9503       40.660626  1500000US360470804001   \n",
       "\n",
       "                       bus_name  Building_ID_No  \\\n",
       "23145  1109 NOSTRAND AVE BAKERY         3106637   \n",
       "\n",
       "                                           geometry  \n",
       "23145  POINT (-5830938.042101377 9862165.459185204)  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points.iloc[ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make function \n",
    "def find_nearest_rest_index(address): \n",
    "    '''\n",
    "    Returns the index of the nearest restaurant to the address given \n",
    "    Uses another custom function, get_coords (based on Geopy)\n",
    "    '''\n",
    "    outside_coord = get_coords(address)\n",
    "    start = np.array(outside_coord)\n",
    "    ind = tree.query(start, 1)[1]\n",
    "    if ind is None: \n",
    "        return -1 \n",
    "    else: \n",
    "        return ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13382])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_nearest_rest_index('236 Sterling Street')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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