Google_Maps_Direction_API.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NYC Neighborhood Commute Time: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Buying a Home in NYC: What Neighborhoods are the Best Value?\n",
    "### Applying Data Science Tools to Understand NYC's Residential Real Estate Fundamentals\n",
    "\n",
    "    Josh Grasso | joshgrasso@gmail.com\n",
    "This project seeks to understand the fundamental factors that explain differences in residential real estate prices across NYC. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neighborhood Commute Time: via Public Transit to Midtown Manhattan using Google Maps Directions API\n",
    "Manhattan is the obvious cultural and commercial epicenter of all of New York City, and the region. And a neighborhoods proximity to the epicenter is a key feature in understanding its own real estate development, the mix of professionals and the income of its residents. When choosing between neighborhoods within Manhattan, commute time to work is an important consideration – working on the West-side and living on the East-side is an inconvenience.  And when choosing between one neighborhood in an outer borough, or another, a very important consideration is how long does it take to get to Manhattan – for work, for restaurants, for museums and theater. The analogy holds even traveling out to nearby states - in Connecticut, Greenwich has a premium to Stamford, which has a premium to Norwalk and Fairfield. \n",
    "\n",
    "To capture this information across neighborhoods, I chose to focus on commuting to work, where midtown is the commercial epicenter of Manhattan, and pretty close to the geographic center as well. Grand Central is an obvious destination, and a comparable destination with which to compare commute times for outer suburbs as well – for a future iteration of the analysis. To remove any bias to the East-side, and neighborhoods along the green subway line, I also include the commute time to Times Square-42nd Street – which adds the red and yellow subway lines, and is even connected to Grand Central by shuttle. \n",
    "\n",
    "Thus, I asked the Google Maps Directions API for all the routes, via public transportation, from the center of each neighborhood to both Grand Central and Times Square subway stations. Public transit includes the ferry – for Staten Island – and bus routes – Oakland Gardens, Queen is a great example. I then selected the route(s) to either station with the fewest amount of transfers, and among those chose the fastest-route as the final commute time for the neighborhood. \n",
    "\n",
    "\n",
    "The end result is that single-family home prices in NYC, as measured in the Zillow dataset, were -50% correlated to the commute time to Midtown Manhattan, overall a strong result. This was the strongest correlation among the commute metrics I extracted from Google Maps Directions – as intended. The correlation falls to 28% between prices to the number of commute transfers, and falls to 17% between prices and the amount of walking that is included in the commute route. Neighborhoods further from a subway station will have more walking time/distance included in the fastest route. For simplicity, I only included the travel time (minutes) in the regression analysis. However, I think there is additional information in the other variables as well: how many transfers the commute requires, if the commute requires taking the ferry or a bus, and how far away the nearest subway station is located.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "from pathlib import Path\n",
    "home_path = Path.home() / 'Jupyter' / 'Real_Estate'\n",
    "Image(filename= home_path / 'Manhattan_Workers_Transit_Mode2.png')\n",
    "# NYC Department of Planning, The Ins and Outs of NYC Commuting, September 2019\n",
    "# Source: https://www1.nyc.gov/assets/planning/download/pdf/planning-level/housing-economy/nyc-ins-and-out-of-commuting.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The NYC Department of Planning has a great presentation detailing all as aspects of NYC commuting patterns. The data says that of the 2.6 mm individuals who work in Manhattan for the year 2017, 30% or 750k actually live in Manhattan, followed by 468k or 18% that live in Brooklyn, 396k or 15% live in Queens, 338k or 13% live in Northern New Jersey (think Hoboken, Jersey City, and suburbs), 216k or 8% live in the Bronx, and 50k, or 2% live in Staten Island. Manhattan is the only borough or surrounding region with a higher worker-to-resident ratio at 2.9x – all other regions have more residents than workers/local jobs. As a percentage of residents, 39% of Brooklyn residents commute to Manhattan for work, similar to 38% of Bronx residents, followed by 36% of Queens’ residents, and 24% of Staten Island residents. \n",
    "\n",
    "Further, the slide below details how the individuals working in the “hub” region of Manhattan – Midtown, south through the Financial District – get to work. The subway is the clear leader at 2.1 mm workers, followed by the 222k who take NJ Transit or Path trains, 215k take the bus from NJ, 118k take the Long Island Rail-Road, 109k take the Metro-North railroad, 63k take the local MTA bus, and 41k take the Staten Island ferry. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Commute Times\n",
    "# https://www.nytimes.com/interactive/2019/07/08/upshot/nyc-subway-variability-calculator.html\n",
    "# https://streeteasy.com/blog/nyc-subway-neighborhoods-best-worst-access/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#plt.style.use('seaborn')  \n",
    "sns.set()\n",
    "\n",
    "from datetime import datetime\n",
    "today = datetime.now()\n",
    "month,day,year = today.month,today.day,today.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from pathlib import Path\n",
    "# home_path = Path.home() / 'Jupyter' / 'Real_Estate'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Neighborhood Coordinates: \n",
    "# Neighborhoods Names\n",
    "# https://www1.nyc.gov/site/planning/data-maps/open-data.page\n",
    "\n",
    "neighborhood_url = 'https://services5.arcgis.com/GfwWNkhOj9bNBqoJ/arcgis/rest/services/Neighborhood_Names/FeatureServer/0/query?where=1=1&outFields=*&outSR=4326&f=pgeojson'\n",
    "resp = requests.get(neighborhood_url)\n",
    "neighborhood_json = resp.json()\n",
    "\n",
    "neighborhood_ids_list = []\n",
    "neighborhood_details_list = []\n",
    "\n",
    "for neighborhood_dict in neighborhood_json['features']:\n",
    "    neighborhood_ids_list.append(neighborhood_dict['id']) \n",
    "    \n",
    "    d = {}\n",
    "    d['ID'] = neighborhood_dict['id']\n",
    "    d['Name'] = neighborhood_dict['properties']['Name']\n",
    "    d['Borough'] = neighborhood_dict['properties']['Borough']\n",
    "    d['Lat'] = neighborhood_dict['geometry']['coordinates'][1]\n",
    "    d['Long'] = neighborhood_dict['geometry']['coordinates'][0]\n",
    "    \n",
    "    neighborhood_details_list.append(d)\n",
    "\n",
    "neighborhood_df = pd.DataFrame.from_dict(neighborhood_details_list)\n",
    "neighborhood_df['Borough'] = neighborhood_df['Borough'].replace({'Staten Island': 'Staten_Island'})\n",
    "neighborhood_df = neighborhood_df.set_index(['Name','Borough'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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></th>\n",
       "      <th>ID</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th>Borough</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Wakefield</th>\n",
       "      <th>Bronx</th>\n",
       "      <td>1</td>\n",
       "      <td>40.894713</td>\n",
       "      <td>-73.847202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Co-op City</th>\n",
       "      <th>Bronx</th>\n",
       "      <td>2</td>\n",
       "      <td>40.874302</td>\n",
       "      <td>-73.829941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eastchester</th>\n",
       "      <th>Bronx</th>\n",
       "      <td>3</td>\n",
       "      <td>40.887564</td>\n",
       "      <td>-73.827808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fieldston</th>\n",
       "      <th>Bronx</th>\n",
       "      <td>4</td>\n",
       "      <td>40.895446</td>\n",
       "      <td>-73.905644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Riverdale</th>\n",
       "      <th>Bronx</th>\n",
       "      <td>5</td>\n",
       "      <td>40.890843</td>\n",
       "      <td>-73.912587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stapleton</th>\n",
       "      <th>Staten_Island</th>\n",
       "      <td>302</td>\n",
       "      <td>40.626936</td>\n",
       "      <td>-74.077903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rosebank</th>\n",
       "      <th>Staten_Island</th>\n",
       "      <td>303</td>\n",
       "      <td>40.615313</td>\n",
       "      <td>-74.069807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Brighton</th>\n",
       "      <th>Staten_Island</th>\n",
       "      <td>304</td>\n",
       "      <td>40.631887</td>\n",
       "      <td>-74.107183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grymes Hill</th>\n",
       "      <th>Staten_Island</th>\n",
       "      <td>305</td>\n",
       "      <td>40.624193</td>\n",
       "      <td>-74.087250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Todt Hill</th>\n",
       "      <th>Staten_Island</th>\n",
       "      <td>306</td>\n",
       "      <td>40.597077</td>\n",
       "      <td>-74.111330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>306 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              ID        Lat       Long\n",
       "Name          Borough                                 \n",
       "Wakefield     Bronx            1  40.894713 -73.847202\n",
       "Co-op City    Bronx            2  40.874302 -73.829941\n",
       "Eastchester   Bronx            3  40.887564 -73.827808\n",
       "Fieldston     Bronx            4  40.895446 -73.905644\n",
       "Riverdale     Bronx            5  40.890843 -73.912587\n",
       "...                          ...        ...        ...\n",
       "Stapleton     Staten_Island  302  40.626936 -74.077903\n",
       "Rosebank      Staten_Island  303  40.615313 -74.069807\n",
       "West Brighton Staten_Island  304  40.631887 -74.107183\n",
       "Grymes Hill   Staten_Island  305  40.624193 -74.087250\n",
       "Todt Hill     Staten_Island  306  40.597077 -74.111330\n",
       "\n",
       "[306 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhood_df  # .set_index(['Name','Borough'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Google Maps:\n",
    "# Call My Google API Key:  Next-step: Write and save into a .py file and call the file\n",
    "MyGoogleAPIKey_path = Path.home() / 'Jupyter' / Path('My_Google_API_Key.txt')\n",
    "if MyGoogleAPIKey_path.is_file():\n",
    "    MyGoogleAPIKey_file = open(MyGoogleAPIKey_path, 'rt')\n",
    "    my_gmaps_key = MyGoogleAPIKey_file.read()\n",
    "    MyGoogleAPIKey_file.close()\n",
    "else:\n",
    "    print('Error: File not found')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.offsets import BDay\n",
    "\n",
    "def gmaps_directions(my_gmaps_key, start_lat, start_long, destination_id='ChIJhRwB-yFawokRi0AhGH87UTc'):\n",
    "    # 'https://maps.googleapis.com/maps/api/directions/json?parameters'\n",
    "    \n",
    "    # Build URL:\n",
    "    gmaps_url = 'https://maps.googleapis.com/maps/api/directions/json?'\n",
    "    \n",
    "    gmaps_url += \"&origin={},{}\".format(str(start_lat), str(start_long))\n",
    "    gmaps_url += \"&destination=place_id:{}\".format(destination_id)\n",
    "    gmaps_url += \"&mode=transit\"\n",
    "    gmaps_url += \"&transit_mode=subway\"\n",
    "    gmaps_url += \"&transit_routing_preference=fewer_transfers\"\n",
    "    \n",
    "    # You can specify the time as an integer in seconds since midnight, January 1, 1970 UTC\n",
    "    today = datetime.now()\n",
    "    search_time = (pd.Timestamp('{}-{}-{} 8:30'.format(today.year, today.month, (today + BDay(1)).day), \n",
    "                                tz='America/New_York'))  \n",
    "    departure_time = str(search_time.value)[:-9]\n",
    "    gmaps_url += \"&departure_time={}\".format(departure_time)\n",
    "    \n",
    "    gmaps_url += \"&alternatives=true\"\n",
    "    gmaps_url += \"&key={}\".format(my_gmaps_key)\n",
    "    \n",
    "    return requests.get(gmaps_url).json()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#i=0\n",
    "#j=1\n",
    "#_directions_json['routes'][i]['legs'][0]['steps'][j]['transit_details'] #['line']['short_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_directions(gmaps_directions_json):\n",
    "    master_list = []\n",
    "    # Loop through each Alternative Route:\n",
    "    for route in gmaps_directions_json['routes']:\n",
    "        columns_list = ['Entry_station', 'Station_Lat', 'Station_Long',\n",
    "                        'Travel_time', 'Transit_transfers',  # 'Duration_txt', \n",
    "                        'Walking_time', 'Walking_distance', 'Subway_lines'] # 'Neighborhood', \n",
    "        route_dict = dict.fromkeys(columns_list)\n",
    "        \n",
    "        # For routes that contain no waypoints, the route will consist of a single \"leg,\"\n",
    "        #route_dict['Duration_txt'] =  route['legs'][0]['duration']['text']\n",
    "        route_dict['Travel_time'] = route['legs'][0]['duration']['value'] / 60  # seconds to minutes\n",
    "        \n",
    "        # Loop through each Step:\n",
    "        walking_time = 0\n",
    "        walking_distance = 0\n",
    "        subway_string = ''\n",
    "        transit_transfers = 0\n",
    "        for step in route['legs'][0]['steps']:\n",
    "            if step['travel_mode'] == 'WALKING':\n",
    "                walking_time += step['duration']['value'] / 60  # seconds to minutes\n",
    "                walking_distance += step['distance']['value'] / 1610  # meters to miles\n",
    "            elif step['travel_mode'] == 'TRANSIT':\n",
    "                transit_transfers += 1\n",
    "                if transit_transfers == 1:\n",
    "                    route_dict['Entry_station'] = step['transit_details']['departure_stop']['name']\n",
    "                    route_dict['Station_Lat'] = step['transit_details']['departure_stop']['location']['lat']\n",
    "                    route_dict['Station_Long'] = step['transit_details']['departure_stop']['location']['lng']\n",
    "                subway_string += step['transit_details']['line']['name'] + ';'  # short_name\n",
    "            else:\n",
    "                pass\n",
    "            route_dict['Walking_time'] = walking_time\n",
    "            route_dict['Walking_distance'] = walking_distance\n",
    "            route_dict['Transit_transfers'] = transit_transfers\n",
    "            route_dict['Subway_lines'] = subway_string\n",
    "        master_list.append(route_dict)\n",
    "    return pd.DataFrame(master_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Destinations: \n",
    "destination_id_dict = {'Grand Central Terminal': 'ChIJhRwB-yFawokRi0AhGH87UTc',\n",
    "                      'Times Sq - 42 St': 'ChIJ10bnS6tZwokROfmCPOetmk0'}\n",
    "# - or - Penn Station? \n",
    "# Penn Station Place ID: ChIJI2HvCK5ZwokRDzoIZCfLoPc  \n",
    "# New York Penn Station Place ID: ChIJZ278179ZwokRi6_g2g7zoG0\n",
    "# Penn Station LIRR Place ID: ChIJ30bGgq5ZwokR4NkG2EK7qN8\n",
    "\n",
    "# Source: https://developers.google.com/maps/documentation/javascript/examples/places-placeid-finder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Main Loop: \n",
    "# Build df with all routes for all neighborhoods\n",
    "#i = 0\n",
    "all_routes_df = pd.DataFrame()\n",
    "for x, y in zip(neighborhood_df.reset_index(drop=False)['Name'], \n",
    "                neighborhood_df.reset_index(drop=False)['Borough']):\n",
    "    #print(neighborhood_df['Lat'].loc[x, y])\n",
    "    #print(neighborhood_df['Long'].loc[x, y])\n",
    "    for destination in destination_id_dict.keys():    \n",
    "        _directions_json = gmaps_directions(my_gmaps_key,\n",
    "                                            neighborhood_df['Lat'].loc[x, y], neighborhood_df['Long'].loc[x, y],\n",
    "                                            destination_id=destination_id_dict[destination])\n",
    "        _directions_df = parse_directions(_directions_json)\n",
    "        _directions_df['Destination_station'] = destination\n",
    "        _directions_df['Neighborhood'] = x\n",
    "        _directions_df['Borough'] = y\n",
    "        all_routes_df = all_routes_df.append(_directions_df)\n",
    "        \n",
    "    #i += 1\n",
    "    #if i == 5:\n",
    "        #break\n",
    "all_routes_df = all_routes_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Entry_station</th>\n",
       "      <th>Station_Lat</th>\n",
       "      <th>Station_Long</th>\n",
       "      <th>Travel_time</th>\n",
       "      <th>Transit_transfers</th>\n",
       "      <th>Walking_time</th>\n",
       "      <th>Walking_distance</th>\n",
       "      <th>Subway_lines</th>\n",
       "      <th>Destination_station</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>233 St</td>\n",
       "      <td>40.8932</td>\n",
       "      <td>-73.8575</td>\n",
       "      <td>68.300000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.283333</td>\n",
       "      <td>0.664596</td>\n",
       "      <td>2 Line;4 Line;</td>\n",
       "      <td>Grand Central Terminal</td>\n",
       "      <td>Wakefield</td>\n",
       "      <td>Bronx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>233 St</td>\n",
       "      <td>40.8932</td>\n",
       "      <td>-73.8575</td>\n",
       "      <td>76.300000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.283333</td>\n",
       "      <td>0.664596</td>\n",
       "      <td>2 Line;5 Line;</td>\n",
       "      <td>Grand Central Terminal</td>\n",
       "      <td>Wakefield</td>\n",
       "      <td>Bronx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>233 St</td>\n",
       "      <td>40.8932</td>\n",
       "      <td>-73.8575</td>\n",
       "      <td>68.300000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.283333</td>\n",
       "      <td>0.664596</td>\n",
       "      <td>2 Line;4 Line;</td>\n",
       "      <td>Grand Central Terminal</td>\n",
       "      <td>Wakefield</td>\n",
       "      <td>Bronx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nereid Av</td>\n",
       "      <td>40.8984</td>\n",
       "      <td>-73.8544</td>\n",
       "      <td>80.800000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.483333</td>\n",
       "      <td>0.796894</td>\n",
       "      <td>2 Line;42 Street Shuttle;</td>\n",
       "      <td>Grand Central Terminal</td>\n",
       "      <td>Wakefield</td>\n",
       "      <td>Bronx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nereid Av</td>\n",
       "      <td>40.8984</td>\n",
       "      <td>-73.8544</td>\n",
       "      <td>72.616667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.616667</td>\n",
       "      <td>0.676398</td>\n",
       "      <td>2 Line;</td>\n",
       "      <td>Times Sq - 42 St</td>\n",
       "      <td>Wakefield</td>\n",
       "      <td>Bronx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2387</th>\n",
       "      <td>Richmond Rd/Seaview Av</td>\n",
       "      <td>40.5913</td>\n",
       "      <td>-74.1009</td>\n",
       "      <td>107.933333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29.483333</td>\n",
       "      <td>1.503106</td>\n",
       "      <td>St. George - Bricktown Mall;College of Staten ...</td>\n",
       "      <td>Grand Central Terminal</td>\n",
       "      <td>Todt Hill</td>\n",
       "      <td>Staten_Island</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2388</th>\n",
       "      <td>Richmond Rd/Seaview Av</td>\n",
       "      <td>40.5913</td>\n",
       "      <td>-74.1009</td>\n",
       "      <td>103.316667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.683333</td>\n",
       "      <td>1.410559</td>\n",
       "      <td>St. George - Oakwood;Staten Island Ferry;1 Line;</td>\n",
       "      <td>Times Sq - 42 St</td>\n",
       "      <td>Todt Hill</td>\n",
       "      <td>Staten_Island</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2389</th>\n",
       "      <td>Richmond Rd/Seaview Av</td>\n",
       "      <td>40.5913</td>\n",
       "      <td>-74.1009</td>\n",
       "      <td>104.366667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.416667</td>\n",
       "      <td>1.186335</td>\n",
       "      <td>St. George - Bricktown Mall;College of Staten ...</td>\n",
       "      <td>Times Sq - 42 St</td>\n",
       "      <td>Todt Hill</td>\n",
       "      <td>Staten_Island</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2390</th>\n",
       "      <td>Richmond Rd/Seaview Av</td>\n",
       "      <td>40.5913</td>\n",
       "      <td>-74.1009</td>\n",
       "      <td>112.866667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.416667</td>\n",
       "      <td>1.186335</td>\n",
       "      <td>St. George - Bricktown Mall;College of Staten ...</td>\n",
       "      <td>Times Sq - 42 St</td>\n",
       "      <td>Todt Hill</td>\n",
       "      <td>Staten_Island</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2391</th>\n",
       "      <td>Richmond Rd/Four Corners Rd</td>\n",
       "      <td>40.5905</td>\n",
       "      <td>-74.1009</td>\n",
       "      <td>117.633333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.366667</td>\n",
       "      <td>1.623602</td>\n",
       "      <td>St. George - Bricktown Mall;Staten Island Rail...</td>\n",
       "      <td>Times Sq - 42 St</td>\n",
       "      <td>Todt Hill</td>\n",
       "      <td>Staten_Island</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2392 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Entry_station Station_Lat Station_Long  Travel_time  \\\n",
       "0                          233 St     40.8932     -73.8575    68.300000   \n",
       "1                          233 St     40.8932     -73.8575    76.300000   \n",
       "2                          233 St     40.8932     -73.8575    68.300000   \n",
       "3                       Nereid Av     40.8984     -73.8544    80.800000   \n",
       "4                       Nereid Av     40.8984     -73.8544    72.616667   \n",
       "...                           ...         ...          ...          ...   \n",
       "2387       Richmond Rd/Seaview Av     40.5913     -74.1009   107.933333   \n",
       "2388       Richmond Rd/Seaview Av     40.5913     -74.1009   103.316667   \n",
       "2389       Richmond Rd/Seaview Av     40.5913     -74.1009   104.366667   \n",
       "2390       Richmond Rd/Seaview Av     40.5913     -74.1009   112.866667   \n",
       "2391  Richmond Rd/Four Corners Rd     40.5905     -74.1009   117.633333   \n",
       "\n",
       "      Transit_transfers  Walking_time  Walking_distance  \\\n",
       "0                   2.0     15.283333          0.664596   \n",
       "1                   2.0     15.283333          0.664596   \n",
       "2                   2.0     15.283333          0.664596   \n",
       "3                   2.0     17.483333          0.796894   \n",
       "4                   1.0     13.616667          0.676398   \n",
       "...                 ...           ...               ...   \n",
       "2387                4.0     29.483333          1.503106   \n",
       "2388                3.0     25.683333          1.410559   \n",
       "2389                4.0     21.416667          1.186335   \n",
       "2390                3.0     21.416667          1.186335   \n",
       "2391                4.0     30.366667          1.623602   \n",
       "\n",
       "                                           Subway_lines  \\\n",
       "0                                        2 Line;4 Line;   \n",
       "1                                        2 Line;5 Line;   \n",
       "2                                        2 Line;4 Line;   \n",
       "3                             2 Line;42 Street Shuttle;   \n",
       "4                                               2 Line;   \n",
       "...                                                 ...   \n",
       "2387  St. George - Bricktown Mall;College of Staten ...   \n",
       "2388   St. George - Oakwood;Staten Island Ferry;1 Line;   \n",
       "2389  St. George - Bricktown Mall;College of Staten ...   \n",
       "2390  St. George - Bricktown Mall;College of Staten ...   \n",
       "2391  St. George - Bricktown Mall;Staten Island Rail...   \n",
       "\n",
       "         Destination_station Neighborhood        Borough  \n",
       "0     Grand Central Terminal    Wakefield          Bronx  \n",
       "1     Grand Central Terminal    Wakefield          Bronx  \n",
       "2     Grand Central Terminal    Wakefield          Bronx  \n",
       "3     Grand Central Terminal    Wakefield          Bronx  \n",
       "4           Times Sq - 42 St    Wakefield          Bronx  \n",
       "...                      ...          ...            ...  \n",
       "2387  Grand Central Terminal    Todt Hill  Staten_Island  \n",
       "2388        Times Sq - 42 St    Todt Hill  Staten_Island  \n",
       "2389        Times Sq - 42 St    Todt Hill  Staten_Island  \n",
       "2390        Times Sq - 42 St    Todt Hill  Staten_Island  \n",
       "2391        Times Sq - 42 St    Todt Hill  Staten_Island  \n",
       "\n",
       "[2392 rows x 11 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_routes_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#all_routes_df = all_routes_df.set_index(['Neighborhood','Borough'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Borough</th>\n",
       "      <th>ID</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Howland Hook</td>\n",
       "      <td>Staten_Island</td>\n",
       "      <td>146</td>\n",
       "      <td>40.638441</td>\n",
       "      <td>-74.186225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name        Borough   ID        Lat       Long\n",
       "145  Howland Hook  Staten_Island  146  40.638441 -74.186225"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhood_df.reset_index()[neighborhood_df.reset_index()['Name'] == 'Howland Hook']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Entry_station</th>\n",
       "      <th>Station_Lat</th>\n",
       "      <th>Station_Long</th>\n",
       "      <th>Travel_time</th>\n",
       "      <th>Transit_transfers</th>\n",
       "      <th>Walking_time</th>\n",
       "      <th>Walking_distance</th>\n",
       "      <th>Subway_lines</th>\n",
       "      <th>Destination_station</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Entry_station, Station_Lat, Station_Long, Travel_time, Transit_transfers, Walking_time, Walking_distance, Subway_lines, Destination_station, Neighborhood, Borough]\n",
       "Index: []"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_routes_df[all_routes_df['Neighborhood'] == 'Howland Hook']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-39-313443ce9c48>:11: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  _neighborhood_directions_df = all_routes_df.set_index(['Neighborhood','Borough']).loc[x, y]\n"
     ]
    }
   ],
   "source": [
    "# Find Single Best Route\n",
    "# Fastest route, with fewest transfers, for each neighborhood\n",
    "#single_route_df = pd.DataFrame(index=neighborhood_df.index, columns=all_routes_df.columns)\n",
    "single_route_df = pd.DataFrame()\n",
    "missing_route_list = []\n",
    "#i = 0\n",
    "for x, y in zip(neighborhood_df.reset_index(drop=False)['Name'], \n",
    "                neighborhood_df.reset_index(drop=False)['Borough']):\n",
    "    #print(neighborhood_df['Lat'].loc[x, y])\n",
    "    #print(neighborhood_df['Long'].loc[x, y])\n",
    "    try:\n",
    "        _neighborhood_directions_df = all_routes_df.set_index(['Neighborhood','Borough']).loc[x, y]\n",
    "    except:\n",
    "        missing_route_list.append((x, y))\n",
    "        continue\n",
    "    \n",
    "    _min_transfers_df = (_neighborhood_directions_df[_neighborhood_directions_df['Transit_transfers'] == \n",
    "                                                     _neighborhood_directions_df['Transit_transfers'].min()])\n",
    "    \n",
    "    if isinstance(_min_transfers_df, pd.Series):\n",
    "        single_route_df = single_route_df.append(_min_transfers_df)\n",
    "    if isinstance(_min_transfers_df, pd.DataFrame):\n",
    "        #single_route_df.append(all_routes_df.iloc[_min_transfers_df['Travel_time'].idxmin()])\n",
    "        _min_transfers_df = _min_transfers_df.reset_index(drop=False)\n",
    "        single_route_df = single_route_df.append(_min_transfers_df\n",
    "                                                 .iloc[_min_transfers_df['Travel_time'].idxmin()])\n",
    "    #i += 1\n",
    "    #if i == 5:\n",
    "        #break"
   ]
  },
  {
   "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>Borough</th>\n",
       "      <th>Destination_station</th>\n",
       "      <th>Entry_station</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Station_Lat</th>\n",
       "      <th>Station_Long</th>\n",
       "      <th>Subway_lines</th>\n",
       "      <th>Transit_transfers</th>\n",
       "      <th>Travel_time</th>\n",
       "      <th>Walking_distance</th>\n",
       "      <th>Walking_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>Times Sq - 42 St</td>\n",
       "      <td>Nereid Av</td>\n",
       "      <td>Wakefield</td>\n",
       "      <td>40.898380</td>\n",
       "      <td>-73.854378</td>\n",
       "      <td>2 Line;</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.116667</td>\n",
       "      <td>0.676398</td>\n",
       "      <td>13.616667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>Grand Central Terminal</td>\n",
       "      <td>Baychester Avenue Station</td>\n",
       "      <td>Co-op City</td>\n",
       "      <td>40.878662</td>\n",
       "      <td>-73.838593</td>\n",
       "      <td>5 Line;</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.683333</td>\n",
       "      <td>0.859006</td>\n",
       "      <td>19.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>Grand Central Terminal</td>\n",
       "      <td>Eastchester - Dyre Av</td>\n",
       "      <td>Eastchester</td>\n",
       "      <td>40.888302</td>\n",
       "      <td>-73.830833</td>\n",
       "      <td>5 Line;</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.983333</td>\n",
       "      <td>0.362112</td>\n",
       "      <td>7.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>Times Sq - 42 St</td>\n",
       "      <td>242 Street Station</td>\n",
       "      <td>Fieldston</td>\n",
       "      <td>40.889248</td>\n",
       "      <td>-73.898582</td>\n",
       "      <td>1 Line;</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.916667</td>\n",
       "      <td>1.118634</td>\n",
       "      <td>19.383333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>Times Sq - 42 St</td>\n",
       "      <td>238th Street Station</td>\n",
       "      <td>Riverdale</td>\n",
       "      <td>40.884666</td>\n",
       "      <td>-73.900871</td>\n",
       "      <td>1 Line;</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.350000</td>\n",
       "      <td>0.934783</td>\n",
       "      <td>18.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Staten_Island</td>\n",
       "      <td>Times Sq - 42 St</td>\n",
       "      <td>Bay St/Water St</td>\n",
       "      <td>Stapleton</td>\n",
       "      <td>40.627083</td>\n",
       "      <td>-74.075821</td>\n",
       "      <td>St. George - West Shore Plaza;Staten Island Fe...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>68.350000</td>\n",
       "      <td>0.550311</td>\n",
       "      <td>10.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Staten_Island</td>\n",
       "      <td>Grand Central Terminal</td>\n",
       "      <td>Tompkins Ave/St Mary's Av</td>\n",
       "      <td>Rosebank</td>\n",
       "      <td>40.613789</td>\n",
       "      <td>-74.072304</td>\n",
       "      <td>St. George - Staten Island Univ. Hospital;Stat...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65.650000</td>\n",
       "      <td>0.300621</td>\n",
       "      <td>7.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Staten_Island</td>\n",
       "      <td>Times Sq - 42 St</td>\n",
       "      <td>Castleton Ave/Bard Av</td>\n",
       "      <td>West Brighton</td>\n",
       "      <td>40.634644</td>\n",
       "      <td>-74.106163</td>\n",
       "      <td>St. George - West Shore Plaza LTD;Staten Islan...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>77.483333</td>\n",
       "      <td>0.696894</td>\n",
       "      <td>12.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Staten_Island</td>\n",
       "      <td>Times Sq - 42 St</td>\n",
       "      <td>Broad St/Gordon St</td>\n",
       "      <td>Grymes Hill</td>\n",
       "      <td>40.623623</td>\n",
       "      <td>-74.082390</td>\n",
       "      <td>St. George - Bricktown Mall;Staten Island Ferr...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>78.483333</td>\n",
       "      <td>0.851553</td>\n",
       "      <td>15.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Staten_Island</td>\n",
       "      <td>Times Sq - 42 St</td>\n",
       "      <td>Richmond Rd/Seaview Av</td>\n",
       "      <td>Todt Hill</td>\n",
       "      <td>40.591282</td>\n",
       "      <td>-74.100853</td>\n",
       "      <td>St. George - Oakwood;Staten Island Ferry;1 Line;</td>\n",
       "      <td>3.0</td>\n",
       "      <td>103.316667</td>\n",
       "      <td>1.410559</td>\n",
       "      <td>25.683333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>304 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Borough     Destination_station              Entry_station  \\\n",
       "2           Bronx        Times Sq - 42 St                  Nereid Av   \n",
       "1           Bronx  Grand Central Terminal  Baychester Avenue Station   \n",
       "0           Bronx  Grand Central Terminal      Eastchester - Dyre Av   \n",
       "1           Bronx        Times Sq - 42 St         242 Street Station   \n",
       "1           Bronx        Times Sq - 42 St       238th Street Station   \n",
       "..            ...                     ...                        ...   \n",
       "3   Staten_Island        Times Sq - 42 St            Bay St/Water St   \n",
       "1   Staten_Island  Grand Central Terminal  Tompkins Ave/St Mary's Av   \n",
       "2   Staten_Island        Times Sq - 42 St      Castleton Ave/Bard Av   \n",
       "2   Staten_Island        Times Sq - 42 St         Broad St/Gordon St   \n",
       "1   Staten_Island        Times Sq - 42 St     Richmond Rd/Seaview Av   \n",
       "\n",
       "     Neighborhood  Station_Lat  Station_Long  \\\n",
       "2       Wakefield    40.898380    -73.854378   \n",
       "1      Co-op City    40.878662    -73.838593   \n",
       "0     Eastchester    40.888302    -73.830833   \n",
       "1       Fieldston    40.889248    -73.898582   \n",
       "1       Riverdale    40.884666    -73.900871   \n",
       "..            ...          ...           ...   \n",
       "3       Stapleton    40.627083    -74.075821   \n",
       "1        Rosebank    40.613789    -74.072304   \n",
       "2   West Brighton    40.634644    -74.106163   \n",
       "2     Grymes Hill    40.623623    -74.082390   \n",
       "1       Todt Hill    40.591282    -74.100853   \n",
       "\n",
       "                                         Subway_lines  Transit_transfers  \\\n",
       "2                                             2 Line;                1.0   \n",
       "1                                             5 Line;                1.0   \n",
       "0                                             5 Line;                1.0   \n",
       "1                                             1 Line;                1.0   \n",
       "1                                             1 Line;                1.0   \n",
       "..                                                ...                ...   \n",
       "3   St. George - West Shore Plaza;Staten Island Fe...                3.0   \n",
       "1   St. George - Staten Island Univ. Hospital;Stat...                3.0   \n",
       "2   St. George - West Shore Plaza LTD;Staten Islan...                3.0   \n",
       "2   St. George - Bricktown Mall;Staten Island Ferr...                3.0   \n",
       "1    St. George - Oakwood;Staten Island Ferry;1 Line;                3.0   \n",
       "\n",
       "    Travel_time  Walking_distance  Walking_time  \n",
       "2     67.116667          0.676398     13.616667  \n",
       "1     67.683333          0.859006     19.166667  \n",
       "0     57.983333          0.362112      7.966667  \n",
       "1     57.916667          1.118634     19.383333  \n",
       "1     55.350000          0.934783     18.350000  \n",
       "..          ...               ...           ...  \n",
       "3     68.350000          0.550311     10.033333  \n",
       "1     65.650000          0.300621      7.033333  \n",
       "2     77.483333          0.696894     12.583333  \n",
       "2     78.483333          0.851553     15.900000  \n",
       "1    103.316667          1.410559     25.683333  \n",
       "\n",
       "[304 rows x 11 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_route_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Howland Hook', 'Staten_Island'), ('Breezy Point', 'Queens')]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_route_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to CSV:\n",
    "all_routes_df.to_csv(path_or_buf= home_path / 'Google_Maps_All_Routes.csv')\n",
    "single_route_df.to_csv(path_or_buf= home_path / 'Google_Maps_Single_Route.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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></th>\n",
       "      <th>Avg_Price_2005_2019</th>\n",
       "      <th>Annual_Growth_2005_2019</th>\n",
       "      <th>Growth_%_2005_2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Queens</th>\n",
       "      <th>Hunters Point</th>\n",
       "      <td>333275.455556</td>\n",
       "      <td>29088.598191</td>\n",
       "      <td>0.087281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manhattan</th>\n",
       "      <th>Manhattanville</th>\n",
       "      <td>503046.054264</td>\n",
       "      <td>42159.332200</td>\n",
       "      <td>0.083808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Avg_Price_2005_2019  Annual_Growth_2005_2019  \\\n",
       "Borough   Neighborhood                                                   \n",
       "Queens    Hunters Point         333275.455556             29088.598191   \n",
       "Manhattan Manhattanville        503046.054264             42159.332200   \n",
       "\n",
       "                          Growth_%_2005_2019  \n",
       "Borough   Neighborhood                        \n",
       "Queens    Hunters Point             0.087281  \n",
       "Manhattan Manhattanville            0.083808  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare Commute Features to Prices: \n",
    "growth_zillow_2005_2019_df = pd.read_csv(home_path / 'Zillow' / 'Zillow_NYC_SFR_2005_2019.csv',\n",
    "                                         index_col=[0,1])\n",
    "growth_zillow_2005_2019_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "transit_anlysis = (growth_zillow_2005_2019_df.join(\n",
    "    single_route_df[['Borough', 'Neighborhood', 'Transit_transfers', 'Travel_time', \n",
    "                     'Walking_distance', 'Walking_time']].set_index(['Borough','Neighborhood'])))\n",
    "# transit_anlysis[transit_anlysis.isnull().any(axis=1)]\n",
    "transit_anlysis = transit_anlysis.drop(index=transit_anlysis[transit_anlysis.isnull().any(axis=1)].index)\n",
    "transit_anlysis = transit_anlysis.sort_values(by='Avg_Price_2005_2019', ascending=False)\n",
    "transit_anlysis['Avg_Price'] = transit_anlysis['Avg_Price_2005_2019'] /1000 # .apply(lambda x: x/1000/1000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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></th>\n",
       "      <th>Transit_transfers</th>\n",
       "      <th>Travel_time</th>\n",
       "      <th>Walking_distance</th>\n",
       "      <th>Walking_time</th>\n",
       "      <th>Avg_Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Manhattan</th>\n",
       "      <th>Carnegie Hill</th>\n",
       "      <td>1.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8044.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper East Side</th>\n",
       "      <td>1.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>6311.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Little Italy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>16.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>5957.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greenwich Village</th>\n",
       "      <td>1.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>4930.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Village</th>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4922.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queens</th>\n",
       "      <th>Rochdale</th>\n",
       "      <td>2.0</td>\n",
       "      <td>53.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>311.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <th>Parkchester</th>\n",
       "      <td>1.0</td>\n",
       "      <td>46.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10.2</td>\n",
       "      <td>300.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staten_Island</th>\n",
       "      <th>Port Ivory</th>\n",
       "      <td>3.0</td>\n",
       "      <td>95.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>17.3</td>\n",
       "      <td>296.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Bronx</th>\n",
       "      <th>Westchester Square</th>\n",
       "      <td>1.0</td>\n",
       "      <td>44.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>266.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unionport</th>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>156.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>234 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Transit_transfers  Travel_time  \\\n",
       "Borough       Neighborhood                                         \n",
       "Manhattan     Carnegie Hill                     1.0         17.2   \n",
       "              Upper East Side                   1.0         14.3   \n",
       "              Little Italy                      1.0         16.7   \n",
       "              Greenwich Village                 1.0         15.5   \n",
       "              West Village                      1.0         13.0   \n",
       "...                                             ...          ...   \n",
       "Queens        Rochdale                          2.0         53.9   \n",
       "Bronx         Parkchester                       1.0         46.7   \n",
       "Staten_Island Port Ivory                        3.0         95.3   \n",
       "Bronx         Westchester Square                1.0         44.2   \n",
       "              Unionport                         1.0         45.0   \n",
       "\n",
       "                                  Walking_distance  Walking_time  Avg_Price  \n",
       "Borough       Neighborhood                                                   \n",
       "Manhattan     Carnegie Hill                    0.3           6.7     8044.2  \n",
       "              Upper East Side                  0.3           5.8     6311.3  \n",
       "              Little Italy                     0.4           7.2     5957.6  \n",
       "              Greenwich Village                0.4           8.5     4930.8  \n",
       "              West Village                     0.3           6.0     4922.5  \n",
       "...                                            ...           ...        ...  \n",
       "Queens        Rochdale                         0.7          14.6      311.3  \n",
       "Bronx         Parkchester                      0.5          10.2      300.4  \n",
       "Staten_Island Port Ivory                       0.9          17.3      296.2  \n",
       "Bronx         Westchester Square               0.1           3.2      266.6  \n",
       "              Unionport                        0.4           8.5      156.3  \n",
       "\n",
       "[234 rows x 5 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transit_anlysis[['Transit_transfers', 'Travel_time', 'Walking_distance', 'Walking_time', 'Avg_Price']].round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature Correlation:\n",
    "corr = (transit_anlysis[['Transit_transfers', 'Travel_time', 'Walking_distance', 'Walking_time', 'Avg_Price']]\n",
    "        .corr().round(2))\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "cmap=sns.color_palette(\"viridis\", as_cmap=True) # 'PuOr' 'viridis'\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, square=True, linewidths=.5, center=0, vmax=1, annot=True, ax=ax);\n",
    "plt.title(\"NYC Commute Feature Correlations\", fontsize=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map: Walking Distance to Nearest Subway \n",
    "\n",
    "# http://faculty.baruch.cuny.edu/geoportal/data/nyc_gdb/sample_maps/subway_ac.pdf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map MTA Turnstile Data\n",
    "\n",
    "\"\"\"\n",
    "subway_stations_url = 'http://web.mta.info/developers/data/nyct/subway/Stations.csv'\n",
    "# Resource: 'https://qri.cloud/nyc-transit-data/stationscsv'\n",
    "\n",
    "resp = requests.get(subway_stations_url)\n",
    "local_path = home_path / 'MTA_Subway_Stations.csv'\n",
    "with open(local_path, 'wb') as output:\n",
    "    output.write(resp.content)\n",
    "\n",
    "subway_stations_df = pd.read_csv(local_path)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}