routes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import glob\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trips = pd.read_excel(\"data/EXCEL/UNLINKED_Public.xlsx\")\n",
    "taz = gpd.read_file(\"data/BPM_TAZ2010.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.options.display.max_columns = None"
   ]
  },
  {
   "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>ID</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AREA1</th>\n",
       "      <th>TAZ_ID1</th>\n",
       "      <th>BPZ3_ID</th>\n",
       "      <th>REGION</th>\n",
       "      <th>COUNTYCODE</th>\n",
       "      <th>geometry</th>\n",
       "      <th>COUNTY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1009</td>\n",
       "      <td>0.196601</td>\n",
       "      <td>0.196601</td>\n",
       "      <td>1009</td>\n",
       "      <td>3257</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((-73.8236 40.89089999999999, -73.8235...</td>\n",
       "      <td>Bronx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>903</td>\n",
       "      <td>0.551513</td>\n",
       "      <td>0.551513</td>\n",
       "      <td>903</td>\n",
       "      <td>3151</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((-73.921145 40.887094, -73.9207009999...</td>\n",
       "      <td>Bronx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>901</td>\n",
       "      <td>0.100066</td>\n",
       "      <td>0.100066</td>\n",
       "      <td>901</td>\n",
       "      <td>3149</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((-73.9041 40.9083, -73.9036 40.9099, ...</td>\n",
       "      <td>Bronx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>902</td>\n",
       "      <td>0.194815</td>\n",
       "      <td>0.194815</td>\n",
       "      <td>902</td>\n",
       "      <td>3150</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((-73.90649999999999 40.90159999999999...</td>\n",
       "      <td>Bronx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>900</td>\n",
       "      <td>0.169347</td>\n",
       "      <td>0.169347</td>\n",
       "      <td>900</td>\n",
       "      <td>3148</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((-73.9084 40.8993, -73.90729999999999...</td>\n",
       "      <td>Bronx</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID      AREA     AREA1  TAZ_ID1  BPZ3_ID  REGION  COUNTYCODE  \\\n",
       "0  1009  0.196601  0.196601     1009     3257       1           3   \n",
       "1   903  0.551513  0.551513      903     3151       1           3   \n",
       "2   901  0.100066  0.100066      901     3149       1           3   \n",
       "3   902  0.194815  0.194815      902     3150       1           3   \n",
       "4   900  0.169347  0.169347      900     3148       1           3   \n",
       "\n",
       "                                            geometry COUNTY  \n",
       "0  POLYGON ((-73.8236 40.89089999999999, -73.8235...  Bronx  \n",
       "1  POLYGON ((-73.921145 40.887094, -73.9207009999...  Bronx  \n",
       "2  POLYGON ((-73.9041 40.9083, -73.9036 40.9099, ...  Bronx  \n",
       "3  POLYGON ((-73.90649999999999 40.90159999999999...  Bronx  \n",
       "4  POLYGON ((-73.9084 40.8993, -73.90729999999999...  Bronx  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "COUNTIES_TAZ = {1: \"Manhattan\",\n",
    "                2: \"Queens\",\n",
    "                3: \"Bronx\",\n",
    "                4: \"Brooklyn\",\n",
    "                5: \"Staten Island\"}\n",
    "nyc_taz = taz[taz['REGION'] == 1].reset_index(drop=True)\n",
    "nyc_taz[\"COUNTY\"] = nyc_taz[\"COUNTYCODE\"].apply(lambda x: COUNTIES_TAZ[x])\n",
    "nyc_taz.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trips[\"OTAZ\"]\n",
    "trips_with_origin = trips.merge(nyc_taz, left_on=\"OTAZ\", right_on=\"TAZ_ID1\")\n",
    "trips_with_od = trips_with_origin.merge(nyc_taz, left_on=\"DTAZ\", right_on=\"TAZ_ID1\", suffixes=(\"_O\", \"_D\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "to_manhattan = trips_with_od[\"COUNTY_D\"] == \"Manhattan\"\n",
    "from_manhattan = trips_with_od[\"COUNTY_O\"] == \"Manhattan\"\n",
    "manhattan_only = trips_with_od[to_manhattan | from_manhattan]\n",
    "manhattan_gdf = gpd.GeoDataFrame(manhattan_only.reset_index(drop=True))\n",
    "manhattan_gdf[\"geometry\"] = manhattan_gdf[\"geometry_D\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6f864a9e10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAIMCAYAAAD7M2fRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "night_only = manhattan_gdf[(manhattan_gdf[\"TRP_DEP_HR\"] < 6) | (manhattan_gdf[\"TRP_DEP_HR\"] == 24 )]\n",
    "\n",
    "# from travel survey docs\n",
    "SUBWAY_MODE = 15\n",
    "subway_night_trips = night_only[night_only[\"ULTMODE\"] == SUBWAY_MODE]\n",
    "survey_cols = [\"PLSAM\",  u'TRP_DEP_HR',\n",
    " u'TRP_ARR_HR',\n",
    " u'TRP_DEP_MIN',\n",
    " u'TRP_ARR_MIN',\n",
    " u'TRPDUR',]\n",
    "subway_night_trips = subway_night_trips[survey_cols + list(subway_night_trips.columns[-12:])].reset_index(drop=True)\n",
    "subway_night_trips.plot(figsize=(16, 9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(subway_night_trips)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find likely subway path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -73.898583\n",
       "1      -73.898583\n",
       "2      -73.898583\n",
       "3      -73.900870\n",
       "4      -73.900870\n",
       "5      -73.900870\n",
       "6      -73.904834\n",
       "7      -73.904834\n",
       "8      -73.904834\n",
       "9      -73.909831\n",
       "10     -73.909831\n",
       "11     -73.909831\n",
       "12     -73.915279\n",
       "13     -73.915279\n",
       "14     -73.915279\n",
       "15     -73.918822\n",
       "16     -73.918822\n",
       "17     -73.918822\n",
       "18     -73.925536\n",
       "19     -73.925536\n",
       "20     -73.925536\n",
       "21     -73.929412\n",
       "22     -73.929412\n",
       "23     -73.929412\n",
       "24     -73.933596\n",
       "25     -73.933596\n",
       "26     -73.933596\n",
       "27     -73.940133\n",
       "28     -73.940133\n",
       "29     -73.940133\n",
       "          ...    \n",
       "1473   -74.117210\n",
       "1474   -74.117210\n",
       "1475   -74.117210\n",
       "1476   -74.109704\n",
       "1477   -74.109704\n",
       "1478   -74.109704\n",
       "1479   -74.103338\n",
       "1480   -74.103338\n",
       "1481   -74.103338\n",
       "1482   -74.096090\n",
       "1483   -74.096090\n",
       "1484   -74.096090\n",
       "1485   -74.087368\n",
       "1486   -74.087368\n",
       "1487   -74.087368\n",
       "1488   -74.084087\n",
       "1489   -74.084087\n",
       "1490   -74.084087\n",
       "1491   -74.071402\n",
       "1492   -74.071402\n",
       "1493   -74.071402\n",
       "1494   -74.075162\n",
       "1495   -74.075162\n",
       "1496   -74.075162\n",
       "1497   -74.074835\n",
       "1498   -74.074835\n",
       "1499   -74.074835\n",
       "1500   -74.073643\n",
       "1501   -74.073643\n",
       "1502   -74.073643\n",
       "Name: geometry, Length: 1503, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stops[\"geometry\"].apply(lambda x: x.x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6f87a3f650>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJEAAAVBCAYAAAAzBxopAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkxAJAAAAgJqESAAAAADU9P8DTGXXF6PGkZAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x2880 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shapely.geometry\n",
    "\n",
    "stops = pd.read_csv(\"data/mta_gtfs/stops.txt\")\n",
    "stops[\"geometry\"] = stops.apply(lambda x: shapely.geometry.Point((x[\"stop_lon\"], x[\"stop_lat\"])), axis=1)\n",
    "stops = gpd.GeoDataFrame(stops)\n",
    "filters = stops[\"geometry\"].apply(lambda x: x.x) > -74.05\n",
    "\n",
    "ax = stops[filters].plot(c='red', figsize=(20, 40))\n",
    "subway_night_trips.plot(ax=ax)\n",
    "stops[\"base_geom\"] = stops[\"geometry\"]\n",
    "subway_night_trips[\"geometry\"] = subway_night_trips[\"geometry_O\"]\n",
    "subway_night_trips[subway_night_trips[\"PLSAM\"] == 41193320204].plot(ax=ax, color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TODO\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Expand buffer zone to make sure every origin TAZ has a list of candidate stops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Repeat the code below to get destination candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "old_stops = stops\n",
    "stops = stops.dropna(subset=[\"parent_station\"]).reset_index(drop=True)\n",
    "\n",
    "subway_night_trips[\"geometry\"] = subway_night_trips[\"geometry_O\"]\n",
    "stops[\"geometry\"] = stops[\"base_geom\"]\n",
    "stops[\"buffered\"] = stops.buffer(.002)\n",
    "stops[\"geometry\"] = stops[\"buffered\"]\n",
    "joined_o = gpd.sjoin(subway_night_trips, stops, op='intersects', how='left')\\\n",
    "              .reset_index(drop=True)\n",
    "#ax = stops.plot(figsize=(16, 16))\n",
    "no_candidate = joined_o[\"stop_lat\"].isnull()\n",
    "interesting = joined_o[no_candidate]\n",
    "\n",
    "to_join = interesting[subway_night_trips.columns]\n",
    "stops['geometry'] = stops['base_geom']\n",
    "stops['buffered'] = stops.buffer(.003)\n",
    "stops['geometry'] = stops['buffered']\n",
    "expanded_buffer = gpd.sjoin(to_join, stops, op='intersects', how='left')\n",
    "del joined_o[\"index_right\"]\n",
    "joined_o = joined_o[~no_candidate].reset_index(drop=True)\n",
    "joined_o = pd.concat([joined_o, expanded_buffer]).reset_index(drop=True)\n",
    "assert len(joined_o[joined_o[\"stop_lat\"].isnull()]) == 0\n",
    "\n",
    "\n",
    "joined_o[\"geometry\"] = joined_o[\"geometry_D\"]\n",
    "stops[\"geometry\"] = stops[\"base_geom\"]\n",
    "stops[\"buffered\"] = stops.buffer(.002)\n",
    "stops[\"geometry\"] = stops[\"buffered\"]\n",
    "\n",
    "joined_d = gpd.sjoin(joined_o, stops, op='intersects', how='left', lsuffix=\"O\", rsuffix=\"D\").reset_index(drop=True)\n",
    "\n",
    "#ax = stops.plot(figsize=(16, 16))\n",
    "no_candidate = joined_d[\"stop_lat_D\"].isnull()\n",
    "interesting = joined_d[no_candidate]\n",
    "\n",
    "to_join = interesting\n",
    "del to_join[\"index_D\"]\n",
    "for c in stops.columns:\n",
    "    del to_join[c + \"_D\"]\n",
    "stops['geometry'] = stops['base_geom']\n",
    "stops['buffered'] = stops.buffer(.0115)\n",
    "stops['geometry'] = stops['buffered']\n",
    "expanded_buffer = gpd.sjoin(to_join, stops, op='intersects', how='left', lsuffix=\"O\", rsuffix=\"D\")\n",
    "stops_cols = set(stops.columns)\n",
    "expanded_buffer.columns = [(c if c not in stops_cols else c + \"_D\") for c in expanded_buffer.columns]\n",
    "\n",
    "expanded_buffer = expanded_buffer.reset_index(drop=True)\n",
    "joined_d = joined_d[~no_candidate].reset_index(drop=True)\n",
    "joined_d = pd.concat([joined_d, expanded_buffer]).reset_index(drop=True)\n",
    "joined_d = joined_d.dropna(subset=[\"parent_station_D\"])\n",
    "assert len(joined_d[joined_d[\"stop_lat_D\"].isnull()]) == 0\n",
    "assert set(joined_o.PLSAM.unique()) - set(joined_d.PLSAM.unique()) == set()\n",
    "assert set(subway_night_trips.PLSAM.unique()) - set(joined_o.PLSAM.unique()) == set()\n",
    "stops = old_stops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AREA1_D</th>\n",
       "      <th>AREA_D</th>\n",
       "      <th>BPZ3_ID_D</th>\n",
       "      <th>COUNTYCODE_D</th>\n",
       "      <th>COUNTY_D</th>\n",
       "      <th>COUNTY_O</th>\n",
       "      <th>ID_D</th>\n",
       "      <th>PLSAM</th>\n",
       "      <th>REGION_D</th>\n",
       "      <th>TAZ_ID1_D</th>\n",
       "      <th>TRPDUR</th>\n",
       "      <th>TRP_ARR_HR</th>\n",
       "      <th>TRP_ARR_MIN</th>\n",
       "      <th>TRP_DEP_HR</th>\n",
       "      <th>TRP_DEP_MIN</th>\n",
       "      <th>base_geom_D</th>\n",
       "      <th>base_geom_O</th>\n",
       "      <th>buffered_D</th>\n",
       "      <th>buffered_O</th>\n",
       "      <th>geometry</th>\n",
       "      <th>geometry_D</th>\n",
       "      <th>geometry_O</th>\n",
       "      <th>index_D</th>\n",
       "      <th>index_right</th>\n",
       "      <th>location_type_D</th>\n",
       "      <th>location_type_O</th>\n",
       "      <th>parent_station_D</th>\n",
       "      <th>parent_station_O</th>\n",
       "      <th>stop_code_D</th>\n",
       "      <th>stop_code_O</th>\n",
       "      <th>stop_desc_D</th>\n",
       "      <th>stop_desc_O</th>\n",
       "      <th>stop_id_D</th>\n",
       "      <th>stop_id_O</th>\n",
       "      <th>stop_lat_D</th>\n",
       "      <th>stop_lat_O</th>\n",
       "      <th>stop_lon_D</th>\n",
       "      <th>stop_lon_O</th>\n",
       "      <th>stop_name_D</th>\n",
       "      <th>stop_name_O</th>\n",
       "      <th>stop_url_D</th>\n",
       "      <th>stop_url_O</th>\n",
       "      <th>zone_id_D</th>\n",
       "      <th>zone_id_O</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.98207600000001 40.746081)</td>\n",
       "      <td>POINT (-73.90483399999999 40.878856)</td>\n",
       "      <td>POLYGON ((-73.98007600000001 40.746081, -73.98...</td>\n",
       "      <td>POLYGON ((-73.902834 40.878856, -73.9028436305...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>292.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>632</td>\n",
       "      <td>104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>632N</td>\n",
       "      <td>104N</td>\n",
       "      <td>40.746081</td>\n",
       "      <td>40.878856</td>\n",
       "      <td>-73.982076</td>\n",
       "      <td>-73.904834</td>\n",
       "      <td>33 St</td>\n",
       "      <td>231 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.98207600000001 40.746081)</td>\n",
       "      <td>POINT (-73.90483399999999 40.878856)</td>\n",
       "      <td>POLYGON ((-73.98007600000001 40.746081, -73.98...</td>\n",
       "      <td>POLYGON ((-73.902834 40.878856, -73.9028436305...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>293.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>632</td>\n",
       "      <td>104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>632S</td>\n",
       "      <td>104N</td>\n",
       "      <td>40.746081</td>\n",
       "      <td>40.878856</td>\n",
       "      <td>-73.982076</td>\n",
       "      <td>-73.904834</td>\n",
       "      <td>33 St</td>\n",
       "      <td>231 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.984264 40.74307)</td>\n",
       "      <td>POINT (-73.90483399999999 40.878856)</td>\n",
       "      <td>POLYGON ((-73.982264 40.74307, -73.98227363054...</td>\n",
       "      <td>POLYGON ((-73.902834 40.878856, -73.9028436305...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>295.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>633</td>\n",
       "      <td>104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>633S</td>\n",
       "      <td>104N</td>\n",
       "      <td>40.743070</td>\n",
       "      <td>40.878856</td>\n",
       "      <td>-73.984264</td>\n",
       "      <td>-73.904834</td>\n",
       "      <td>28 St</td>\n",
       "      <td>231 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.984264 40.74307)</td>\n",
       "      <td>POINT (-73.90483399999999 40.878856)</td>\n",
       "      <td>POLYGON ((-73.982264 40.74307, -73.98227363054...</td>\n",
       "      <td>POLYGON ((-73.902834 40.878856, -73.9028436305...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>294.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>633</td>\n",
       "      <td>104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>633N</td>\n",
       "      <td>104N</td>\n",
       "      <td>40.743070</td>\n",
       "      <td>40.878856</td>\n",
       "      <td>-73.984264</td>\n",
       "      <td>-73.904834</td>\n",
       "      <td>28 St</td>\n",
       "      <td>231 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.98207600000001 40.746081)</td>\n",
       "      <td>POINT (-73.90483399999999 40.878856)</td>\n",
       "      <td>POLYGON ((-73.98007600000001 40.746081, -73.98...</td>\n",
       "      <td>POLYGON ((-73.902834 40.878856, -73.9028436305...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>292.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>632</td>\n",
       "      <td>104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>632N</td>\n",
       "      <td>104S</td>\n",
       "      <td>40.746081</td>\n",
       "      <td>40.878856</td>\n",
       "      <td>-73.982076</td>\n",
       "      <td>-73.904834</td>\n",
       "      <td>33 St</td>\n",
       "      <td>231 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.98207600000001 40.746081)</td>\n",
       "      <td>POINT (-73.90483399999999 40.878856)</td>\n",
       "      <td>POLYGON ((-73.98007600000001 40.746081, -73.98...</td>\n",
       "      <td>POLYGON ((-73.902834 40.878856, -73.9028436305...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>293.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>632</td>\n",
       "      <td>104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>632S</td>\n",
       "      <td>104S</td>\n",
       "      <td>40.746081</td>\n",
       "      <td>40.878856</td>\n",
       "      <td>-73.982076</td>\n",
       "      <td>-73.904834</td>\n",
       "      <td>33 St</td>\n",
       "      <td>231 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.984264 40.74307)</td>\n",
       "      <td>POINT (-73.90483399999999 40.878856)</td>\n",
       "      <td>POLYGON ((-73.982264 40.74307, -73.98227363054...</td>\n",
       "      <td>POLYGON ((-73.902834 40.878856, -73.9028436305...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>295.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>633</td>\n",
       "      <td>104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>633S</td>\n",
       "      <td>104S</td>\n",
       "      <td>40.743070</td>\n",
       "      <td>40.878856</td>\n",
       "      <td>-73.984264</td>\n",
       "      <td>-73.904834</td>\n",
       "      <td>28 St</td>\n",
       "      <td>231 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.984264 40.74307)</td>\n",
       "      <td>POINT (-73.90483399999999 40.878856)</td>\n",
       "      <td>POLYGON ((-73.982264 40.74307, -73.98227363054...</td>\n",
       "      <td>POLYGON ((-73.902834 40.878856, -73.9028436305...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>294.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>633</td>\n",
       "      <td>104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>633N</td>\n",
       "      <td>104S</td>\n",
       "      <td>40.743070</td>\n",
       "      <td>40.878856</td>\n",
       "      <td>-73.984264</td>\n",
       "      <td>-73.904834</td>\n",
       "      <td>28 St</td>\n",
       "      <td>231 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.98207600000001 40.746081)</td>\n",
       "      <td>POINT (-73.90087 40.884667)</td>\n",
       "      <td>POLYGON ((-73.98007600000001 40.746081, -73.98...</td>\n",
       "      <td>POLYGON ((-73.89887 40.884667, -73.89887963054...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>292.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>632</td>\n",
       "      <td>103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>632N</td>\n",
       "      <td>103S</td>\n",
       "      <td>40.746081</td>\n",
       "      <td>40.884667</td>\n",
       "      <td>-73.982076</td>\n",
       "      <td>-73.900870</td>\n",
       "      <td>33 St</td>\n",
       "      <td>238 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.98207600000001 40.746081)</td>\n",
       "      <td>POINT (-73.90087 40.884667)</td>\n",
       "      <td>POLYGON ((-73.98007600000001 40.746081, -73.98...</td>\n",
       "      <td>POLYGON ((-73.89887 40.884667, -73.89887963054...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>293.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>632</td>\n",
       "      <td>103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>632S</td>\n",
       "      <td>103S</td>\n",
       "      <td>40.746081</td>\n",
       "      <td>40.884667</td>\n",
       "      <td>-73.982076</td>\n",
       "      <td>-73.900870</td>\n",
       "      <td>33 St</td>\n",
       "      <td>238 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.984264 40.74307)</td>\n",
       "      <td>POINT (-73.90087 40.884667)</td>\n",
       "      <td>POLYGON ((-73.982264 40.74307, -73.98227363054...</td>\n",
       "      <td>POLYGON ((-73.89887 40.884667, -73.89887963054...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>295.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>633</td>\n",
       "      <td>103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>633S</td>\n",
       "      <td>103S</td>\n",
       "      <td>40.743070</td>\n",
       "      <td>40.884667</td>\n",
       "      <td>-73.984264</td>\n",
       "      <td>-73.900870</td>\n",
       "      <td>28 St</td>\n",
       "      <td>238 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.984264 40.74307)</td>\n",
       "      <td>POINT (-73.90087 40.884667)</td>\n",
       "      <td>POLYGON ((-73.982264 40.74307, -73.98227363054...</td>\n",
       "      <td>POLYGON ((-73.89887 40.884667, -73.89887963054...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>294.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>633</td>\n",
       "      <td>103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>633N</td>\n",
       "      <td>103S</td>\n",
       "      <td>40.743070</td>\n",
       "      <td>40.884667</td>\n",
       "      <td>-73.984264</td>\n",
       "      <td>-73.900870</td>\n",
       "      <td>28 St</td>\n",
       "      <td>238 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.98207600000001 40.746081)</td>\n",
       "      <td>POINT (-73.90087 40.884667)</td>\n",
       "      <td>POLYGON ((-73.98007600000001 40.746081, -73.98...</td>\n",
       "      <td>POLYGON ((-73.89887 40.884667, -73.89887963054...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>292.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>632</td>\n",
       "      <td>103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>632N</td>\n",
       "      <td>103N</td>\n",
       "      <td>40.746081</td>\n",
       "      <td>40.884667</td>\n",
       "      <td>-73.982076</td>\n",
       "      <td>-73.900870</td>\n",
       "      <td>33 St</td>\n",
       "      <td>238 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.98207600000001 40.746081)</td>\n",
       "      <td>POINT (-73.90087 40.884667)</td>\n",
       "      <td>POLYGON ((-73.98007600000001 40.746081, -73.98...</td>\n",
       "      <td>POLYGON ((-73.89887 40.884667, -73.89887963054...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>293.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>632</td>\n",
       "      <td>103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>632S</td>\n",
       "      <td>103N</td>\n",
       "      <td>40.746081</td>\n",
       "      <td>40.884667</td>\n",
       "      <td>-73.982076</td>\n",
       "      <td>-73.900870</td>\n",
       "      <td>33 St</td>\n",
       "      <td>238 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.984264 40.74307)</td>\n",
       "      <td>POINT (-73.90087 40.884667)</td>\n",
       "      <td>POLYGON ((-73.982264 40.74307, -73.98227363054...</td>\n",
       "      <td>POLYGON ((-73.89887 40.884667, -73.89887963054...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>295.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>633</td>\n",
       "      <td>103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>633S</td>\n",
       "      <td>103N</td>\n",
       "      <td>40.743070</td>\n",
       "      <td>40.884667</td>\n",
       "      <td>-73.984264</td>\n",
       "      <td>-73.900870</td>\n",
       "      <td>28 St</td>\n",
       "      <td>238 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.984264 40.74307)</td>\n",
       "      <td>POINT (-73.90087 40.884667)</td>\n",
       "      <td>POLYGON ((-73.982264 40.74307, -73.98227363054...</td>\n",
       "      <td>POLYGON ((-73.89887 40.884667, -73.89887963054...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>294.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>633</td>\n",
       "      <td>103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>633N</td>\n",
       "      <td>103N</td>\n",
       "      <td>40.743070</td>\n",
       "      <td>40.884667</td>\n",
       "      <td>-73.984264</td>\n",
       "      <td>-73.900870</td>\n",
       "      <td>28 St</td>\n",
       "      <td>238 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.98207600000001 40.746081)</td>\n",
       "      <td>POINT (-73.898583 40.88924799999999)</td>\n",
       "      <td>POLYGON ((-73.98007600000001 40.746081, -73.98...</td>\n",
       "      <td>POLYGON ((-73.89658300000001 40.88924799999999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>292.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>632</td>\n",
       "      <td>101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>632N</td>\n",
       "      <td>101S</td>\n",
       "      <td>40.746081</td>\n",
       "      <td>40.889248</td>\n",
       "      <td>-73.982076</td>\n",
       "      <td>-73.898583</td>\n",
       "      <td>33 St</td>\n",
       "      <td>Van Cortlandt Park - 242 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.98207600000001 40.746081)</td>\n",
       "      <td>POINT (-73.898583 40.88924799999999)</td>\n",
       "      <td>POLYGON ((-73.98007600000001 40.746081, -73.98...</td>\n",
       "      <td>POLYGON ((-73.89658300000001 40.88924799999999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>293.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>632</td>\n",
       "      <td>101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>632S</td>\n",
       "      <td>101S</td>\n",
       "      <td>40.746081</td>\n",
       "      <td>40.889248</td>\n",
       "      <td>-73.982076</td>\n",
       "      <td>-73.898583</td>\n",
       "      <td>33 St</td>\n",
       "      <td>Van Cortlandt Park - 242 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.984264 40.74307)</td>\n",
       "      <td>POINT (-73.898583 40.88924799999999)</td>\n",
       "      <td>POLYGON ((-73.982264 40.74307, -73.98227363054...</td>\n",
       "      <td>POLYGON ((-73.89658300000001 40.88924799999999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>295.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>633</td>\n",
       "      <td>101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>633S</td>\n",
       "      <td>101S</td>\n",
       "      <td>40.743070</td>\n",
       "      <td>40.889248</td>\n",
       "      <td>-73.984264</td>\n",
       "      <td>-73.898583</td>\n",
       "      <td>28 St</td>\n",
       "      <td>Van Cortlandt Park - 242 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.984264 40.74307)</td>\n",
       "      <td>POINT (-73.898583 40.88924799999999)</td>\n",
       "      <td>POLYGON ((-73.982264 40.74307, -73.98227363054...</td>\n",
       "      <td>POLYGON ((-73.89658300000001 40.88924799999999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>294.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>633</td>\n",
       "      <td>101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>633N</td>\n",
       "      <td>101S</td>\n",
       "      <td>40.743070</td>\n",
       "      <td>40.889248</td>\n",
       "      <td>-73.984264</td>\n",
       "      <td>-73.898583</td>\n",
       "      <td>28 St</td>\n",
       "      <td>Van Cortlandt Park - 242 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.98207600000001 40.746081)</td>\n",
       "      <td>POINT (-73.898583 40.88924799999999)</td>\n",
       "      <td>POLYGON ((-73.98007600000001 40.746081, -73.98...</td>\n",
       "      <td>POLYGON ((-73.89658300000001 40.88924799999999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>292.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>632</td>\n",
       "      <td>101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>632N</td>\n",
       "      <td>101N</td>\n",
       "      <td>40.746081</td>\n",
       "      <td>40.889248</td>\n",
       "      <td>-73.982076</td>\n",
       "      <td>-73.898583</td>\n",
       "      <td>33 St</td>\n",
       "      <td>Van Cortlandt Park - 242 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.98207600000001 40.746081)</td>\n",
       "      <td>POINT (-73.898583 40.88924799999999)</td>\n",
       "      <td>POLYGON ((-73.98007600000001 40.746081, -73.98...</td>\n",
       "      <td>POLYGON ((-73.89658300000001 40.88924799999999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>293.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>632</td>\n",
       "      <td>101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>632S</td>\n",
       "      <td>101N</td>\n",
       "      <td>40.746081</td>\n",
       "      <td>40.889248</td>\n",
       "      <td>-73.982076</td>\n",
       "      <td>-73.898583</td>\n",
       "      <td>33 St</td>\n",
       "      <td>Van Cortlandt Park - 242 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.984264 40.74307)</td>\n",
       "      <td>POINT (-73.898583 40.88924799999999)</td>\n",
       "      <td>POLYGON ((-73.982264 40.74307, -73.98227363054...</td>\n",
       "      <td>POLYGON ((-73.89658300000001 40.88924799999999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>295.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>633</td>\n",
       "      <td>101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>633S</td>\n",
       "      <td>101N</td>\n",
       "      <td>40.743070</td>\n",
       "      <td>40.889248</td>\n",
       "      <td>-73.984264</td>\n",
       "      <td>-73.898583</td>\n",
       "      <td>28 St</td>\n",
       "      <td>Van Cortlandt Park - 242 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>0.066257</td>\n",
       "      <td>0.066257</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>93</td>\n",
       "      <td>41193320204</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>POINT (-73.984264 40.74307)</td>\n",
       "      <td>POINT (-73.898583 40.88924799999999)</td>\n",
       "      <td>POLYGON ((-73.982264 40.74307, -73.98227363054...</td>\n",
       "      <td>POLYGON ((-73.89658300000001 40.88924799999999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.978245 40.746614, -73.9787039999...</td>\n",
       "      <td>POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...</td>\n",
       "      <td>294.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>633</td>\n",
       "      <td>101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>633N</td>\n",
       "      <td>101N</td>\n",
       "      <td>40.743070</td>\n",
       "      <td>40.889248</td>\n",
       "      <td>-73.984264</td>\n",
       "      <td>-73.898583</td>\n",
       "      <td>28 St</td>\n",
       "      <td>Van Cortlandt Park - 242 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      AREA1_D    AREA_D  BPZ3_ID_D  COUNTYCODE_D   COUNTY_D COUNTY_O  ID_D  \\\n",
       "964  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "965  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "966  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "967  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "968  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "969  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "970  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "971  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "972  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "973  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "974  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "975  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "976  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "977  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "978  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "979  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "980  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "981  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "982  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "983  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "984  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "985  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "986  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "987  0.066257  0.066257       1095             1  Manhattan    Bronx    93   \n",
       "\n",
       "           PLSAM  REGION_D  TAZ_ID1_D  TRPDUR  TRP_ARR_HR  TRP_ARR_MIN  \\\n",
       "964  41193320204         1         93      45           6         20.0   \n",
       "965  41193320204         1         93      45           6         20.0   \n",
       "966  41193320204         1         93      45           6         20.0   \n",
       "967  41193320204         1         93      45           6         20.0   \n",
       "968  41193320204         1         93      45           6         20.0   \n",
       "969  41193320204         1         93      45           6         20.0   \n",
       "970  41193320204         1         93      45           6         20.0   \n",
       "971  41193320204         1         93      45           6         20.0   \n",
       "972  41193320204         1         93      45           6         20.0   \n",
       "973  41193320204         1         93      45           6         20.0   \n",
       "974  41193320204         1         93      45           6         20.0   \n",
       "975  41193320204         1         93      45           6         20.0   \n",
       "976  41193320204         1         93      45           6         20.0   \n",
       "977  41193320204         1         93      45           6         20.0   \n",
       "978  41193320204         1         93      45           6         20.0   \n",
       "979  41193320204         1         93      45           6         20.0   \n",
       "980  41193320204         1         93      45           6         20.0   \n",
       "981  41193320204         1         93      45           6         20.0   \n",
       "982  41193320204         1         93      45           6         20.0   \n",
       "983  41193320204         1         93      45           6         20.0   \n",
       "984  41193320204         1         93      45           6         20.0   \n",
       "985  41193320204         1         93      45           6         20.0   \n",
       "986  41193320204         1         93      45           6         20.0   \n",
       "987  41193320204         1         93      45           6         20.0   \n",
       "\n",
       "     TRP_DEP_HR  TRP_DEP_MIN                           base_geom_D  \\\n",
       "964           5         35.0  POINT (-73.98207600000001 40.746081)   \n",
       "965           5         35.0  POINT (-73.98207600000001 40.746081)   \n",
       "966           5         35.0           POINT (-73.984264 40.74307)   \n",
       "967           5         35.0           POINT (-73.984264 40.74307)   \n",
       "968           5         35.0  POINT (-73.98207600000001 40.746081)   \n",
       "969           5         35.0  POINT (-73.98207600000001 40.746081)   \n",
       "970           5         35.0           POINT (-73.984264 40.74307)   \n",
       "971           5         35.0           POINT (-73.984264 40.74307)   \n",
       "972           5         35.0  POINT (-73.98207600000001 40.746081)   \n",
       "973           5         35.0  POINT (-73.98207600000001 40.746081)   \n",
       "974           5         35.0           POINT (-73.984264 40.74307)   \n",
       "975           5         35.0           POINT (-73.984264 40.74307)   \n",
       "976           5         35.0  POINT (-73.98207600000001 40.746081)   \n",
       "977           5         35.0  POINT (-73.98207600000001 40.746081)   \n",
       "978           5         35.0           POINT (-73.984264 40.74307)   \n",
       "979           5         35.0           POINT (-73.984264 40.74307)   \n",
       "980           5         35.0  POINT (-73.98207600000001 40.746081)   \n",
       "981           5         35.0  POINT (-73.98207600000001 40.746081)   \n",
       "982           5         35.0           POINT (-73.984264 40.74307)   \n",
       "983           5         35.0           POINT (-73.984264 40.74307)   \n",
       "984           5         35.0  POINT (-73.98207600000001 40.746081)   \n",
       "985           5         35.0  POINT (-73.98207600000001 40.746081)   \n",
       "986           5         35.0           POINT (-73.984264 40.74307)   \n",
       "987           5         35.0           POINT (-73.984264 40.74307)   \n",
       "\n",
       "                              base_geom_O  \\\n",
       "964  POINT (-73.90483399999999 40.878856)   \n",
       "965  POINT (-73.90483399999999 40.878856)   \n",
       "966  POINT (-73.90483399999999 40.878856)   \n",
       "967  POINT (-73.90483399999999 40.878856)   \n",
       "968  POINT (-73.90483399999999 40.878856)   \n",
       "969  POINT (-73.90483399999999 40.878856)   \n",
       "970  POINT (-73.90483399999999 40.878856)   \n",
       "971  POINT (-73.90483399999999 40.878856)   \n",
       "972           POINT (-73.90087 40.884667)   \n",
       "973           POINT (-73.90087 40.884667)   \n",
       "974           POINT (-73.90087 40.884667)   \n",
       "975           POINT (-73.90087 40.884667)   \n",
       "976           POINT (-73.90087 40.884667)   \n",
       "977           POINT (-73.90087 40.884667)   \n",
       "978           POINT (-73.90087 40.884667)   \n",
       "979           POINT (-73.90087 40.884667)   \n",
       "980  POINT (-73.898583 40.88924799999999)   \n",
       "981  POINT (-73.898583 40.88924799999999)   \n",
       "982  POINT (-73.898583 40.88924799999999)   \n",
       "983  POINT (-73.898583 40.88924799999999)   \n",
       "984  POINT (-73.898583 40.88924799999999)   \n",
       "985  POINT (-73.898583 40.88924799999999)   \n",
       "986  POINT (-73.898583 40.88924799999999)   \n",
       "987  POINT (-73.898583 40.88924799999999)   \n",
       "\n",
       "                                            buffered_D  \\\n",
       "964  POLYGON ((-73.98007600000001 40.746081, -73.98...   \n",
       "965  POLYGON ((-73.98007600000001 40.746081, -73.98...   \n",
       "966  POLYGON ((-73.982264 40.74307, -73.98227363054...   \n",
       "967  POLYGON ((-73.982264 40.74307, -73.98227363054...   \n",
       "968  POLYGON ((-73.98007600000001 40.746081, -73.98...   \n",
       "969  POLYGON ((-73.98007600000001 40.746081, -73.98...   \n",
       "970  POLYGON ((-73.982264 40.74307, -73.98227363054...   \n",
       "971  POLYGON ((-73.982264 40.74307, -73.98227363054...   \n",
       "972  POLYGON ((-73.98007600000001 40.746081, -73.98...   \n",
       "973  POLYGON ((-73.98007600000001 40.746081, -73.98...   \n",
       "974  POLYGON ((-73.982264 40.74307, -73.98227363054...   \n",
       "975  POLYGON ((-73.982264 40.74307, -73.98227363054...   \n",
       "976  POLYGON ((-73.98007600000001 40.746081, -73.98...   \n",
       "977  POLYGON ((-73.98007600000001 40.746081, -73.98...   \n",
       "978  POLYGON ((-73.982264 40.74307, -73.98227363054...   \n",
       "979  POLYGON ((-73.982264 40.74307, -73.98227363054...   \n",
       "980  POLYGON ((-73.98007600000001 40.746081, -73.98...   \n",
       "981  POLYGON ((-73.98007600000001 40.746081, -73.98...   \n",
       "982  POLYGON ((-73.982264 40.74307, -73.98227363054...   \n",
       "983  POLYGON ((-73.982264 40.74307, -73.98227363054...   \n",
       "984  POLYGON ((-73.98007600000001 40.746081, -73.98...   \n",
       "985  POLYGON ((-73.98007600000001 40.746081, -73.98...   \n",
       "986  POLYGON ((-73.982264 40.74307, -73.98227363054...   \n",
       "987  POLYGON ((-73.982264 40.74307, -73.98227363054...   \n",
       "\n",
       "                                            buffered_O  \\\n",
       "964  POLYGON ((-73.902834 40.878856, -73.9028436305...   \n",
       "965  POLYGON ((-73.902834 40.878856, -73.9028436305...   \n",
       "966  POLYGON ((-73.902834 40.878856, -73.9028436305...   \n",
       "967  POLYGON ((-73.902834 40.878856, -73.9028436305...   \n",
       "968  POLYGON ((-73.902834 40.878856, -73.9028436305...   \n",
       "969  POLYGON ((-73.902834 40.878856, -73.9028436305...   \n",
       "970  POLYGON ((-73.902834 40.878856, -73.9028436305...   \n",
       "971  POLYGON ((-73.902834 40.878856, -73.9028436305...   \n",
       "972  POLYGON ((-73.89887 40.884667, -73.89887963054...   \n",
       "973  POLYGON ((-73.89887 40.884667, -73.89887963054...   \n",
       "974  POLYGON ((-73.89887 40.884667, -73.89887963054...   \n",
       "975  POLYGON ((-73.89887 40.884667, -73.89887963054...   \n",
       "976  POLYGON ((-73.89887 40.884667, -73.89887963054...   \n",
       "977  POLYGON ((-73.89887 40.884667, -73.89887963054...   \n",
       "978  POLYGON ((-73.89887 40.884667, -73.89887963054...   \n",
       "979  POLYGON ((-73.89887 40.884667, -73.89887963054...   \n",
       "980  POLYGON ((-73.89658300000001 40.88924799999999...   \n",
       "981  POLYGON ((-73.89658300000001 40.88924799999999...   \n",
       "982  POLYGON ((-73.89658300000001 40.88924799999999...   \n",
       "983  POLYGON ((-73.89658300000001 40.88924799999999...   \n",
       "984  POLYGON ((-73.89658300000001 40.88924799999999...   \n",
       "985  POLYGON ((-73.89658300000001 40.88924799999999...   \n",
       "986  POLYGON ((-73.89658300000001 40.88924799999999...   \n",
       "987  POLYGON ((-73.89658300000001 40.88924799999999...   \n",
       "\n",
       "                                              geometry  \\\n",
       "964  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "965  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "966  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "967  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "968  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "969  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "970  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "971  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "972  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "973  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "974  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "975  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "976  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "977  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "978  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "979  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "980  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "981  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "982  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "983  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "984  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "985  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "986  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "987  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "\n",
       "                                            geometry_D  \\\n",
       "964  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "965  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "966  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "967  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "968  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "969  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "970  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "971  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "972  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "973  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "974  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "975  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "976  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "977  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "978  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "979  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "980  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "981  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "982  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "983  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "984  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "985  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "986  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "987  POLYGON ((-73.978245 40.746614, -73.9787039999...   \n",
       "\n",
       "                                            geometry_O  index_D  index_right  \\\n",
       "964  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    292.0          NaN   \n",
       "965  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    293.0          NaN   \n",
       "966  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    295.0          NaN   \n",
       "967  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    294.0          NaN   \n",
       "968  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    292.0          NaN   \n",
       "969  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    293.0          NaN   \n",
       "970  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    295.0          NaN   \n",
       "971  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    294.0          NaN   \n",
       "972  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    292.0          NaN   \n",
       "973  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    293.0          NaN   \n",
       "974  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    295.0          NaN   \n",
       "975  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    294.0          NaN   \n",
       "976  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    292.0          NaN   \n",
       "977  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    293.0          NaN   \n",
       "978  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    295.0          NaN   \n",
       "979  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    294.0          NaN   \n",
       "980  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    292.0          NaN   \n",
       "981  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    293.0          NaN   \n",
       "982  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    295.0          NaN   \n",
       "983  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    294.0          NaN   \n",
       "984  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    292.0          NaN   \n",
       "985  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    293.0          NaN   \n",
       "986  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    295.0          NaN   \n",
       "987  POLYGON ((-73.8985 40.8901, -73.899 40.8892, -...    294.0          NaN   \n",
       "\n",
       "     location_type_D  location_type_O parent_station_D parent_station_O  \\\n",
       "964              0.0              0.0              632              104   \n",
       "965              0.0              0.0              632              104   \n",
       "966              0.0              0.0              633              104   \n",
       "967              0.0              0.0              633              104   \n",
       "968              0.0              0.0              632              104   \n",
       "969              0.0              0.0              632              104   \n",
       "970              0.0              0.0              633              104   \n",
       "971              0.0              0.0              633              104   \n",
       "972              0.0              0.0              632              103   \n",
       "973              0.0              0.0              632              103   \n",
       "974              0.0              0.0              633              103   \n",
       "975              0.0              0.0              633              103   \n",
       "976              0.0              0.0              632              103   \n",
       "977              0.0              0.0              632              103   \n",
       "978              0.0              0.0              633              103   \n",
       "979              0.0              0.0              633              103   \n",
       "980              0.0              0.0              632              101   \n",
       "981              0.0              0.0              632              101   \n",
       "982              0.0              0.0              633              101   \n",
       "983              0.0              0.0              633              101   \n",
       "984              0.0              0.0              632              101   \n",
       "985              0.0              0.0              632              101   \n",
       "986              0.0              0.0              633              101   \n",
       "987              0.0              0.0              633              101   \n",
       "\n",
       "     stop_code_D  stop_code_O  stop_desc_D  stop_desc_O stop_id_D stop_id_O  \\\n",
       "964          NaN          NaN          NaN          NaN      632N      104N   \n",
       "965          NaN          NaN          NaN          NaN      632S      104N   \n",
       "966          NaN          NaN          NaN          NaN      633S      104N   \n",
       "967          NaN          NaN          NaN          NaN      633N      104N   \n",
       "968          NaN          NaN          NaN          NaN      632N      104S   \n",
       "969          NaN          NaN          NaN          NaN      632S      104S   \n",
       "970          NaN          NaN          NaN          NaN      633S      104S   \n",
       "971          NaN          NaN          NaN          NaN      633N      104S   \n",
       "972          NaN          NaN          NaN          NaN      632N      103S   \n",
       "973          NaN          NaN          NaN          NaN      632S      103S   \n",
       "974          NaN          NaN          NaN          NaN      633S      103S   \n",
       "975          NaN          NaN          NaN          NaN      633N      103S   \n",
       "976          NaN          NaN          NaN          NaN      632N      103N   \n",
       "977          NaN          NaN          NaN          NaN      632S      103N   \n",
       "978          NaN          NaN          NaN          NaN      633S      103N   \n",
       "979          NaN          NaN          NaN          NaN      633N      103N   \n",
       "980          NaN          NaN          NaN          NaN      632N      101S   \n",
       "981          NaN          NaN          NaN          NaN      632S      101S   \n",
       "982          NaN          NaN          NaN          NaN      633S      101S   \n",
       "983          NaN          NaN          NaN          NaN      633N      101S   \n",
       "984          NaN          NaN          NaN          NaN      632N      101N   \n",
       "985          NaN          NaN          NaN          NaN      632S      101N   \n",
       "986          NaN          NaN          NaN          NaN      633S      101N   \n",
       "987          NaN          NaN          NaN          NaN      633N      101N   \n",
       "\n",
       "     stop_lat_D  stop_lat_O  stop_lon_D  stop_lon_O stop_name_D  \\\n",
       "964   40.746081   40.878856  -73.982076  -73.904834       33 St   \n",
       "965   40.746081   40.878856  -73.982076  -73.904834       33 St   \n",
       "966   40.743070   40.878856  -73.984264  -73.904834       28 St   \n",
       "967   40.743070   40.878856  -73.984264  -73.904834       28 St   \n",
       "968   40.746081   40.878856  -73.982076  -73.904834       33 St   \n",
       "969   40.746081   40.878856  -73.982076  -73.904834       33 St   \n",
       "970   40.743070   40.878856  -73.984264  -73.904834       28 St   \n",
       "971   40.743070   40.878856  -73.984264  -73.904834       28 St   \n",
       "972   40.746081   40.884667  -73.982076  -73.900870       33 St   \n",
       "973   40.746081   40.884667  -73.982076  -73.900870       33 St   \n",
       "974   40.743070   40.884667  -73.984264  -73.900870       28 St   \n",
       "975   40.743070   40.884667  -73.984264  -73.900870       28 St   \n",
       "976   40.746081   40.884667  -73.982076  -73.900870       33 St   \n",
       "977   40.746081   40.884667  -73.982076  -73.900870       33 St   \n",
       "978   40.743070   40.884667  -73.984264  -73.900870       28 St   \n",
       "979   40.743070   40.884667  -73.984264  -73.900870       28 St   \n",
       "980   40.746081   40.889248  -73.982076  -73.898583       33 St   \n",
       "981   40.746081   40.889248  -73.982076  -73.898583       33 St   \n",
       "982   40.743070   40.889248  -73.984264  -73.898583       28 St   \n",
       "983   40.743070   40.889248  -73.984264  -73.898583       28 St   \n",
       "984   40.746081   40.889248  -73.982076  -73.898583       33 St   \n",
       "985   40.746081   40.889248  -73.982076  -73.898583       33 St   \n",
       "986   40.743070   40.889248  -73.984264  -73.898583       28 St   \n",
       "987   40.743070   40.889248  -73.984264  -73.898583       28 St   \n",
       "\n",
       "                     stop_name_O  stop_url_D  stop_url_O  zone_id_D  zone_id_O  \n",
       "964                       231 St         NaN         NaN        NaN        NaN  \n",
       "965                       231 St         NaN         NaN        NaN        NaN  \n",
       "966                       231 St         NaN         NaN        NaN        NaN  \n",
       "967                       231 St         NaN         NaN        NaN        NaN  \n",
       "968                       231 St         NaN         NaN        NaN        NaN  \n",
       "969                       231 St         NaN         NaN        NaN        NaN  \n",
       "970                       231 St         NaN         NaN        NaN        NaN  \n",
       "971                       231 St         NaN         NaN        NaN        NaN  \n",
       "972                       238 St         NaN         NaN        NaN        NaN  \n",
       "973                       238 St         NaN         NaN        NaN        NaN  \n",
       "974                       238 St         NaN         NaN        NaN        NaN  \n",
       "975                       238 St         NaN         NaN        NaN        NaN  \n",
       "976                       238 St         NaN         NaN        NaN        NaN  \n",
       "977                       238 St         NaN         NaN        NaN        NaN  \n",
       "978                       238 St         NaN         NaN        NaN        NaN  \n",
       "979                       238 St         NaN         NaN        NaN        NaN  \n",
       "980  Van Cortlandt Park - 242 St         NaN         NaN        NaN        NaN  \n",
       "981  Van Cortlandt Park - 242 St         NaN         NaN        NaN        NaN  \n",
       "982  Van Cortlandt Park - 242 St         NaN         NaN        NaN        NaN  \n",
       "983  Van Cortlandt Park - 242 St         NaN         NaN        NaN        NaN  \n",
       "984  Van Cortlandt Park - 242 St         NaN         NaN        NaN        NaN  \n",
       "985  Van Cortlandt Park - 242 St         NaN         NaN        NaN        NaN  \n",
       "986  Van Cortlandt Park - 242 St         NaN         NaN        NaN        NaN  \n",
       "987  Van Cortlandt Park - 242 St         NaN         NaN        NaN        NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_d[joined_d[\"PLSAM\"] == 41193320204]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Develop the graph of the subway, where weights are distances between stops?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "times = pd.read_csv(\"data/mta_gtfs/stop_times.txt\")\n",
    "trips = pd.read_csv(\"data/mta_gtfs/trips.txt\")\n",
    "transfers = pd.read_csv(\"data/mta_gtfs/transfers.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "times_with_line = times.merge(trips, on=\"trip_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2018-02-15 06:01:00\n",
       "1   2018-02-15 06:04:00\n",
       "2   2018-02-15 06:05:00\n",
       "3   2018-02-15 06:06:00\n",
       "4   2018-02-15 06:07:00\n",
       "Name: timestamp, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from datetime import datetime, time\n",
    "\n",
    "basic = times_with_line.groupby([\"route_id\", \"direction_id\"]).first()[\"trip_id\"].reset_index(drop=True)\n",
    "weird = stops.merge(times_with_line, on=\"stop_id\")\n",
    "times_trip_indexed = times_with_line.set_index(\"trip_id\")\n",
    "basic_stop_ids = times_trip_indexed.loc[basic].stop_id.unique()\n",
    "missing = weird[~weird[\"stop_id\"].isin(basic_stop_ids)].stop_id.unique()\n",
    "\n",
    "extra_trip_ids = times_with_line.groupby(\"stop_id\").first().loc[missing].trip_id.reset_index(drop=True)\n",
    "final_trip_ids = pd.concat([basic, extra_trip_ids]).reset_index(drop=True)\n",
    "final_trips = times_trip_indexed.loc[final_trip_ids].reset_index()\n",
    "final_trips[\"arrival_hour\"] = final_trips[\"arrival_time\"].apply(lambda x: int(x.split(\":\")[0]))\n",
    "final_trips[\"arrival_minute\"] = final_trips[\"arrival_time\"].apply(lambda x: int(x.split(\":\")[1]))\n",
    "final_trips[\"arrival_hour\"].max()\n",
    "final_trips[\"timestamp\"] = final_trips.apply(lambda x: datetime.combine(datetime.now(), \n",
    "                                                                        time(x[\"arrival_hour\"], \n",
    "                                                                             x[\"arrival_minute\"])),\n",
    "                                             axis=1)\n",
    "final_trips[\"timestamp\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_trips[\"our_id\"] = final_trips.apply(lambda x: x[\"stop_id\"] + \"-\" + x[\"route_id\"], axis=1)\n",
    "final_trips[\"our_id\"].head()\n",
    "our_id_by_stop = final_trips.set_index(\"stop_id\").our_id\n",
    "stop_by_our_id = final_trips.drop_duplicates(subset=[\"our_id\"]).set_index(\"our_id\").stop_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "graph = nx.Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for _, t in final_trips.groupby(\"trip_id\"):\n",
    "    prev_stop = None\n",
    "    route = t.route_id.iloc[0]\n",
    "    for _, stop in t.sort_values(\"stop_sequence\").iterrows():\n",
    "        if prev_stop is not None:\n",
    "            diff = (stop[\"timestamp\"] - prev_stop[\"timestamp\"]).total_seconds()\n",
    "            graph.add_edge(stop.our_id, prev_stop.our_id, attr_dict={\"weight\": diff})\n",
    "        prev_stop = stop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fails = set()\n",
    "for parent, stop_group in stops.dropna(subset=[\"parent_station\"]).groupby(\"parent_station\"):\n",
    "    for _, s_1 in stop_group.iterrows():\n",
    "        for _, s_2 in stop_group.iterrows():\n",
    "            try:\n",
    "                our_s_1 = our_id_by_stop.loc[s_1.stop_id]\n",
    "                our_s_2 = our_id_by_stop.loc[s_2.stop_id]\n",
    "            except KeyError:\n",
    "                fails.add(s_1.stop_id)\n",
    "                fails.add(s_2.stop_id)\n",
    "                continue\n",
    "\n",
    "            our_s_1 = our_s_1 if isinstance(our_s_1, pd.Series) else pd.Series([our_s_1])\n",
    "            our_s_2 = our_s_2 if isinstance(our_s_2, pd.Series) else pd.Series([our_s_2])\n",
    "            for our_1 in our_s_1:\n",
    "                for our_2 in our_s_2:\n",
    "                    graph.add_edge(our_1, our_2, attr_dict={\"weight\": 900.})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fails = list()\n",
    "stops_parent_index = stops.set_index(\"parent_station\")\n",
    "for _, transfer in transfers.iterrows():\n",
    "    from_stops = stops_parent_index.loc[transfer.from_stop_id]\n",
    "    to_stops = stops_parent_index.loc[transfer.to_stop_id]\n",
    "    try:\n",
    "        our_from = our_id_by_stop.loc[from_stops.stop_id.as_matrix()]\n",
    "        our_to = our_id_by_stop.loc[to_stops.stop_id.as_matrix()]\n",
    "    except:\n",
    "        fails.append(our_from)\n",
    "        fails.append(our_to)\n",
    "        \n",
    "    for stop_1 in our_from:\n",
    "        for stop_2 in our_to:\n",
    "            graph.add_edge(stop_1, stop_2, attr_dict={\"weight\": 900.})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# child_stops = stops.dropna(subset=[\"parent_station\"]).stop_id.unique()\n",
    "# from collections import defaultdict\n",
    "# our_by_parent = defaultdict(set)\n",
    "# for n in graph.nodes():\n",
    "#     our_by_parent[n.split(\"-\")[0]].add(n)\n",
    "\n",
    "# for main, by_line in our_by_parent.iteritems():\n",
    "#     for line in by_line:\n",
    "#         graph.add_edge(main, line, attr_dict={\"weight\": 0.})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Dijkstra out each pair of origin/destination candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# with_id = stops.set_index(\"stop_id\")\n",
    "# dudes = []\n",
    "# for component_list in nx.algorithms.connected_components(graph):\n",
    "#     try:\n",
    "#         dudes.append(pd.DataFrame([with_id.loc[s] for s in component_list]))\n",
    "#     except:\n",
    "#         break\n",
    "# dudes[7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Take the one that is minimal overall travel distance, i suppose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "grouped_ods = joined_d.groupby(\"PLSAM\")\n",
    "o = None\n",
    "pairs = []\n",
    "potentials = []\n",
    "irrelevant = set([\"140N\", \"140S\"])\n",
    "for gix, group in grouped_ods:\n",
    "    min_distance = 9999999999\n",
    "    shortest_path = None\n",
    "    for _, item in group.iterrows():\n",
    "        o_id = item.stop_id_O\n",
    "        d_id = item.stop_id_D\n",
    "        if o_id in irrelevant or d_id in irrelevant:\n",
    "            continue\n",
    "        o_potentials = our_id_by_stop.loc[o_id]\n",
    "        d_potentials = our_id_by_stop.loc[d_id]\n",
    "        o_potentials = o_potentials if isinstance(o_potentials, pd.Series) else pd.Series(o_potentials)\n",
    "        d_potentials = d_potentials if isinstance(d_potentials, pd.Series) else pd.Series(d_potentials)\n",
    "        for o_p in o_potentials:\n",
    "            for d_p in d_potentials:      \n",
    "                distance = nx.algorithms.dijkstra_path_length(graph, o_p,  d_p)\n",
    "                if gix == 41193320204:\n",
    "                    potentials.append((distance, nx.algorithms.dijkstra_path(graph, o_p,  d_p)))\n",
    "                if distance < min_distance:\n",
    "                    shortest_path = nx.algorithms.dijkstra_path(graph, o_p,  d_p)\n",
    "                    min_distance = distance\n",
    "                    \n",
    "    pairs.append((gix, shortest_path, min_distance))\n",
    "len(grouped_ods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fixed = pd.DataFrame(pairs, columns=[\"PLSAM\", \"path\", \"total_seconds\"])\n",
    "fixed[\"stop_id_O\"] = fixed[\"path\"].apply(lambda x: stop_by_our_id.loc[x[0]])\n",
    "fixed[\"stop_id_D\"] = fixed[\"path\"].apply(lambda x: stop_by_our_id.loc[x[-1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A51S'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stop_by_our_id.loc[\"A51S-A\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optimal_stops = joined_d.merge(fixed, on=[\"PLSAM\", \"stop_id_O\", \"stop_id_D\"])\n",
    "optimal_stops[\"num_stations\"] = optimal_stops[\"path\"].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stop_code</th>\n",
       "      <th>stop_name</th>\n",
       "      <th>stop_desc</th>\n",
       "      <th>stop_lat</th>\n",
       "      <th>stop_lon</th>\n",
       "      <th>zone_id</th>\n",
       "      <th>stop_url</th>\n",
       "      <th>location_type</th>\n",
       "      <th>parent_station</th>\n",
       "      <th>geometry</th>\n",
       "      <th>base_geom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A22S</th>\n",
       "      <td>NaN</td>\n",
       "      <td>72 St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.775594</td>\n",
       "      <td>-73.976410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>A22</td>\n",
       "      <td>POINT (-73.97641 40.775594)</td>\n",
       "      <td>POINT (-73.97641 40.775594)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A24S</th>\n",
       "      <td>NaN</td>\n",
       "      <td>59 St - Columbus Circle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.768296</td>\n",
       "      <td>-73.981736</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>A24</td>\n",
       "      <td>POINT (-73.981736 40.768296)</td>\n",
       "      <td>POINT (-73.981736 40.768296)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      stop_code                stop_name  stop_desc   stop_lat   stop_lon  \\\n",
       "A22S        NaN                    72 St        NaN  40.775594 -73.976410   \n",
       "A24S        NaN  59 St - Columbus Circle        NaN  40.768296 -73.981736   \n",
       "\n",
       "      zone_id  stop_url  location_type parent_station  \\\n",
       "A22S      NaN       NaN              0            A22   \n",
       "A24S      NaN       NaN              0            A24   \n",
       "\n",
       "                          geometry                     base_geom  \n",
       "A22S   POINT (-73.97641 40.775594)   POINT (-73.97641 40.775594)  \n",
       "A24S  POINT (-73.981736 40.768296)  POINT (-73.981736 40.768296)  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi4AAAIXCAYAAAC7P9W6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5208be2bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stops[\"geometry\"] = stops[\"base_geom\"]\n",
    "stops_g = gpd.GeoDataFrame(stops).set_index(\"stop_id\")\n",
    "ax = stops_g.plot(figsize=(16, 9))\n",
    "loc = 17\n",
    "p = optimal_stops.iloc[loc][\"path\"]\n",
    "things = []\n",
    "for i, stop in enumerate(p):\n",
    "    l = stops_g.loc[stop.split(\"-\")[0]]\n",
    "    things.append(l)\n",
    "    mpmp = gpd.GeoDataFrame(l).swapaxes(0, 1)\n",
    "    mpmp.plot(ax=ax, color='red')\n",
    "\n",
    "print optimal_stops.iloc[loc][\"total_seconds\"]\n",
    "pd.DataFrame(things)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stop_name_O</th>\n",
       "      <th>stop_name_D</th>\n",
       "      <th>total_minutes</th>\n",
       "      <th>TRPDUR</th>\n",
       "      <th>minutes_diff</th>\n",
       "      <th>path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Fulton St</td>\n",
       "      <td>190 St</td>\n",
       "      <td>35.0</td>\n",
       "      <td>103</td>\n",
       "      <td>6.800000e+01</td>\n",
       "      <td>[A38S-A, A36S-A, A34S-A, A32S-A, A31S-A, A28S-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>51 St</td>\n",
       "      <td>Fulton St</td>\n",
       "      <td>16.0</td>\n",
       "      <td>60</td>\n",
       "      <td>4.400000e+01</td>\n",
       "      <td>[630N-4, 631N-4, 632N-4, 633N-4, 634N-4, 635N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Newkirk Av</td>\n",
       "      <td>59 St - Columbus Circle</td>\n",
       "      <td>51.0</td>\n",
       "      <td>83</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>[246N-2, 245N-2, 244N-2, 243N-2, 242N-2, 241N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Brooklyn Bridge - City Hall</td>\n",
       "      <td>68 St - Hunter College</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>[640S-6X, 639S-6X, 638S-6X, 637S-6X, 636S-6X, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>116 St</td>\n",
       "      <td>34 St - Penn Station</td>\n",
       "      <td>15.0</td>\n",
       "      <td>45</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>[A16S-C, A17S-C, A18S-C, A19S-C, A20S-C, A21S-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sheepshead Bay</td>\n",
       "      <td>34 St - Herald Sq</td>\n",
       "      <td>36.0</td>\n",
       "      <td>59</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>[D39N-B, D35N-B, D31N-B, D28N-B, D26N-B, D25N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Sutphin Blvd</td>\n",
       "      <td>47-50 Sts - Rockefeller Ctr</td>\n",
       "      <td>28.0</td>\n",
       "      <td>50</td>\n",
       "      <td>2.200000e+01</td>\n",
       "      <td>[F04N-F, F05N-F, F06N-F, F07N-F, G08N-F, G14N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>3 Av</td>\n",
       "      <td>Livonia Av</td>\n",
       "      <td>26.0</td>\n",
       "      <td>48</td>\n",
       "      <td>2.200000e+01</td>\n",
       "      <td>[L05S-L, L06S-L, L08S-L, L10S-L, L11S-L, L12S-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>34 St - Penn Station</td>\n",
       "      <td>Pelham Pkwy</td>\n",
       "      <td>43.0</td>\n",
       "      <td>65</td>\n",
       "      <td>2.200000e+01</td>\n",
       "      <td>[128N-2, 127N-2, 126N-2, 125N-2, 124N-2, 123N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>47-50 Sts - Rockefeller Ctr</td>\n",
       "      <td>75 Av</td>\n",
       "      <td>23.0</td>\n",
       "      <td>43</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>[D15N-F, B10N-F, B08N-F, B06N-F, B04N-F, G14N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Atlantic Av - Barclays Ctr</td>\n",
       "      <td>14 St - Union Sq</td>\n",
       "      <td>14.0</td>\n",
       "      <td>33</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>[D24S-Q, R30S-Q, Q01S-Q, R22S-Q, R21S-Q, R20S-Q]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>42 St - Port Authority Bus Terminal</td>\n",
       "      <td>Utica Av</td>\n",
       "      <td>26.0</td>\n",
       "      <td>45</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>[A27S-A, A28S-A, A31S-A, A32S-A, A34S-A, A36S-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>5 Av/59 St</td>\n",
       "      <td>Queensboro Plaza</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>[R13S-N, R11S-N, R09S-N]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>14 St</td>\n",
       "      <td>168 St</td>\n",
       "      <td>22.0</td>\n",
       "      <td>40</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>[A31S-A, A28S-A, A27S-A, A24S-A, A15S-A, A12S-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Canal St</td>\n",
       "      <td>Inwood - 207 St</td>\n",
       "      <td>35.0</td>\n",
       "      <td>49</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>[A34S-A, A32S-A, A31S-A, A28S-A, A27S-A, A24S-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>47-50 Sts - Rockefeller Ctr</td>\n",
       "      <td>W 4 St</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>[D15S-B, D16S-B, D17S-B, D20S-B]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Dyckman St</td>\n",
       "      <td>Chambers St</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>[A03S-A, A05S-A, A06S-A, A07S-A, A09S-A, A12S-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>125 St</td>\n",
       "      <td>168 St</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>[A15S-C, A14S-C, A12S-C, A11S-C, A10S-C, A09S-C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7 Av</td>\n",
       "      <td>42 St - Bryant Pk</td>\n",
       "      <td>20.0</td>\n",
       "      <td>33</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>[D25N-B, D24N-B, R30N-B, D22N-B, D21N-B, D20N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Times Sq - 42 St</td>\n",
       "      <td>30 Av</td>\n",
       "      <td>18.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>[R16S-N, R15S-N, R14S-N, R13S-N, R11S-N, R09S-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>125 St</td>\n",
       "      <td>86 St</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>[621S-4, 626S-4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Canal St</td>\n",
       "      <td>Cortlandt St</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>[135N-1, 136N-1, 137N-1, 138N-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34 St - Herald Sq</td>\n",
       "      <td>Roosevelt Island</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>[D17S-F, D16S-F, D15S-F, B10S-F, B08S-F, B06S-F]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>34 St - Penn Station</td>\n",
       "      <td>103 St</td>\n",
       "      <td>14.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>[128N-1, 127N-1, 126N-1, 125N-1, 124N-1, 123N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Broadway-Lafayette St</td>\n",
       "      <td>125 St</td>\n",
       "      <td>19.0</td>\n",
       "      <td>29</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>[D21S-D, D20S-D, D17S-D, D16S-D, D15S-D, D14S-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14 St - Union Sq</td>\n",
       "      <td>Times Sq - 42 St</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>[R20S-N, R19S-N, R18S-N, R17S-N, R16S-N]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Parkchester</td>\n",
       "      <td>68 St - Hunter College</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>[608N-6X, 613N-6X, 619N-6X, 621N-6X, 622N-6X, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Flushing - Main St</td>\n",
       "      <td>Grand Central - 42 St</td>\n",
       "      <td>25.0</td>\n",
       "      <td>35</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>[701S-7X, 702S-7X, 707S-7X, 712S-7X, 718S-7X, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Middletown Rd</td>\n",
       "      <td>23 St</td>\n",
       "      <td>41.0</td>\n",
       "      <td>51</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>[603S-6X, 604S-6X, 606S-6X, 607S-6X, 608S-6X, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Broadway Jct</td>\n",
       "      <td>Union Sq - 14 St</td>\n",
       "      <td>24.0</td>\n",
       "      <td>34</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>[L22S-L, L21S-L, L20S-L, L19S-L, L17S-L, L16S-...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>231 St</td>\n",
       "      <td>215 St</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>[104N-1, 106N-1, 107N-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kew Gardens - Union Tpke</td>\n",
       "      <td>5 Av/53 St</td>\n",
       "      <td>29.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>[F06N-E, F07N-E, G08N-E, G09N-E, G10N-E, G11N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Times Sq - 42 St</td>\n",
       "      <td>Pelham Pkwy</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>[902N-GS, 901N-GS, 631N-5, 629N-5, 626N-5, 621...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Bowery</td>\n",
       "      <td>Myrtle Av</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>[M19S-J, M18S-J, M16S-J, M14S-J, M13S-J, M12S-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>28 St</td>\n",
       "      <td>191 St</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>[129S-1, 128S-1, 127S-1, 126S-1, 125S-1, 124S-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>81 St - Museum of Natural History</td>\n",
       "      <td>72 St</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.220446e-16</td>\n",
       "      <td>[A21N-B, A22N-B]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>86 St</td>\n",
       "      <td>81 St - Museum of Natural History</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.220446e-16</td>\n",
       "      <td>[A20N-C, A21N-C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>96 St</td>\n",
       "      <td>86 St</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.220446e-16</td>\n",
       "      <td>[A19N-B, A20N-B]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>47-50 Sts - Rockefeller Ctr</td>\n",
       "      <td>34 St - Herald Sq</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>-4.440892e-16</td>\n",
       "      <td>[D15S-B, D16S-B, D17S-B]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42 St - Port Authority Bus Terminal</td>\n",
       "      <td>Lexington Av/53 St</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6</td>\n",
       "      <td>-8.881784e-16</td>\n",
       "      <td>[A27N-E, A25N-E, D14N-E, F12N-E, F11N-E]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Jefferson St</td>\n",
       "      <td>6 Av</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16</td>\n",
       "      <td>-3.552714e-15</td>\n",
       "      <td>[L15S-L, L14S-L, L13S-L, L12S-L, L11S-L, L10S-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Hunts Point Av</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>52.0</td>\n",
       "      <td>51</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>[613S-6X, 619S-6X, 621S-6X, 622S-6X, 623S-6X, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>23 St</td>\n",
       "      <td>90 St - Elmhurst Av</td>\n",
       "      <td>36.0</td>\n",
       "      <td>35</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>[D18N-F, D17N-F, D16N-F, D15N-F, B10N-F, B08N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>72 St</td>\n",
       "      <td>59 St - Columbus Circle</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>[A22S-B, A24S-B]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>125 St</td>\n",
       "      <td>96 St</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>[116N-1, 117N-1, 118N-1, 119N-1, 120N-1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Houston St</td>\n",
       "      <td>79 St</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>[134S-1, 133S-1, 132S-1, 131S-1, 130S-1, 129S-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Newkirk Av</td>\n",
       "      <td>Chambers St</td>\n",
       "      <td>32.0</td>\n",
       "      <td>30</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>[246N-2, 245N-2, 244N-2, 243N-2, 242N-2, 241N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Jamaica - Van Wyck</td>\n",
       "      <td>7 Av</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30</td>\n",
       "      <td>-3.000000e+00</td>\n",
       "      <td>[G07S-E, F05S-E, F06S-E, F07S-E, G08S-E, G09S-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Rockaway Av</td>\n",
       "      <td>125 St</td>\n",
       "      <td>65.0</td>\n",
       "      <td>60</td>\n",
       "      <td>-5.000000e+00</td>\n",
       "      <td>[253N-4, 252N-4, 251N-4, 250N-4, 249N-4, 248N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>191 St</td>\n",
       "      <td>135 St</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20</td>\n",
       "      <td>-5.000000e+00</td>\n",
       "      <td>[110S-1, 111S-1, 112S-1, A09N-C, A10N-C, A11N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Kew Gardens - Union Tpke</td>\n",
       "      <td>Lexington Av/53 St</td>\n",
       "      <td>27.0</td>\n",
       "      <td>22</td>\n",
       "      <td>-5.000000e+00</td>\n",
       "      <td>[F06N-E, F07N-E, G08N-E, G09N-E, G10N-E, G11N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>8 Av</td>\n",
       "      <td>2 Av</td>\n",
       "      <td>21.0</td>\n",
       "      <td>15</td>\n",
       "      <td>-6.000000e+00</td>\n",
       "      <td>[L01S-L, L02S-L, D19N-F, D20N-F, D21N-F, F14N-F]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Broadway Jct</td>\n",
       "      <td>Fulton St</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12</td>\n",
       "      <td>-6.000000e+00</td>\n",
       "      <td>[A51S-A, A48S-A, A46S-A, A42S-A, A41S-A, A40S-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Burnside Av</td>\n",
       "      <td>Fulton St</td>\n",
       "      <td>35.0</td>\n",
       "      <td>28</td>\n",
       "      <td>-7.000000e+00</td>\n",
       "      <td>[409S-4, 410S-4, 411S-4, 412S-4, 413S-4, 414S-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8 Av</td>\n",
       "      <td>Lexington Av/59 St</td>\n",
       "      <td>53.0</td>\n",
       "      <td>44</td>\n",
       "      <td>-9.000000e+00</td>\n",
       "      <td>[N02N-N, R41N-N, R40N-N, R39N-N, R36N-N, R35N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>East Broadway</td>\n",
       "      <td>Jefferson St</td>\n",
       "      <td>39.0</td>\n",
       "      <td>30</td>\n",
       "      <td>-9.000000e+00</td>\n",
       "      <td>[F16N-F, F15N-F, F14N-F, D21N-F, D20N-F, D19N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Myrtle - Willoughby Avs</td>\n",
       "      <td>Brooklyn Bridge - City Hall</td>\n",
       "      <td>44.0</td>\n",
       "      <td>35</td>\n",
       "      <td>-9.000000e+00</td>\n",
       "      <td>[G32N-G, G33N-G, G34N-G, G35N-G, G36N-G, A42N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Avenue X</td>\n",
       "      <td>59 St</td>\n",
       "      <td>63.0</td>\n",
       "      <td>52</td>\n",
       "      <td>-1.100000e+01</td>\n",
       "      <td>[F38N-F, F36N-F, F35N-F, F34N-F, F33N-F, F32N-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>135 St</td>\n",
       "      <td>96 St</td>\n",
       "      <td>34.0</td>\n",
       "      <td>20</td>\n",
       "      <td>-1.400000e+01</td>\n",
       "      <td>[A14N-C, A12N-C, A11N-C, A10N-C, A09N-C, 112S-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>231 St</td>\n",
       "      <td>33 St</td>\n",
       "      <td>68.0</td>\n",
       "      <td>45</td>\n",
       "      <td>-2.300000e+01</td>\n",
       "      <td>[104S-1, 106S-1, 107S-1, 108S-1, 109S-1, 110S-...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             stop_name_O                        stop_name_D  \\\n",
       "86                             Fulton St                             190 St   \n",
       "58                                 51 St                          Fulton St   \n",
       "19                            Newkirk Av            59 St - Columbus Circle   \n",
       "43           Brooklyn Bridge - City Hall             68 St - Hunter College   \n",
       "11                                116 St               34 St - Penn Station   \n",
       "7                         Sheepshead Bay                  34 St - Herald Sq   \n",
       "30                          Sutphin Blvd        47-50 Sts - Rockefeller Ctr   \n",
       "102                                 3 Av                         Livonia Av   \n",
       "55                  34 St - Penn Station                        Pelham Pkwy   \n",
       "112          47-50 Sts - Rockefeller Ctr                              75 Av   \n",
       "15            Atlantic Av - Barclays Ctr                   14 St - Union Sq   \n",
       "75   42 St - Port Authority Bus Terminal                           Utica Av   \n",
       "85                            5 Av/59 St                   Queensboro Plaza   \n",
       "65                                 14 St                             168 St   \n",
       "80                              Canal St                    Inwood - 207 St   \n",
       "28           47-50 Sts - Rockefeller Ctr                             W 4 St   \n",
       "27                            Dyckman St                        Chambers St   \n",
       "97                                125 St                             168 St   \n",
       "21                                  7 Av                  42 St - Bryant Pk   \n",
       "77                      Times Sq - 42 St                              30 Av   \n",
       "73                                125 St                              86 St   \n",
       "70                              Canal St                       Cortlandt St   \n",
       "3                      34 St - Herald Sq                   Roosevelt Island   \n",
       "24                  34 St - Penn Station                             103 St   \n",
       "67                 Broadway-Lafayette St                             125 St   \n",
       "20                      14 St - Union Sq                   Times Sq - 42 St   \n",
       "44                           Parkchester             68 St - Hunter College   \n",
       "33                    Flushing - Main St              Grand Central - 42 St   \n",
       "64                         Middletown Rd                              23 St   \n",
       "16                          Broadway Jct                   Union Sq - 14 St   \n",
       "..                                   ...                                ...   \n",
       "108                               231 St                             215 St   \n",
       "12              Kew Gardens - Union Tpke                         5 Av/53 St   \n",
       "94                      Times Sq - 42 St                        Pelham Pkwy   \n",
       "104                               Bowery                          Myrtle Av   \n",
       "78                                 28 St                             191 St   \n",
       "107    81 St - Museum of Natural History                              72 St   \n",
       "106                                86 St  81 St - Museum of Natural History   \n",
       "72                                 96 St                              86 St   \n",
       "13           47-50 Sts - Rockefeller Ctr                  34 St - Herald Sq   \n",
       "2    42 St - Port Authority Bus Terminal                 Lexington Av/53 St   \n",
       "57                          Jefferson St                               6 Av   \n",
       "53                        Hunts Point Av                           Grand St   \n",
       "99                                 23 St                90 St - Elmhurst Av   \n",
       "17                                 72 St            59 St - Columbus Circle   \n",
       "39                                125 St                              96 St   \n",
       "110                           Houston St                              79 St   \n",
       "35                            Newkirk Av                        Chambers St   \n",
       "59                    Jamaica - Van Wyck                               7 Av   \n",
       "37                           Rockaway Av                             125 St   \n",
       "96                                191 St                             135 St   \n",
       "45              Kew Gardens - Union Tpke                 Lexington Av/53 St   \n",
       "68                                  8 Av                               2 Av   \n",
       "62                          Broadway Jct                          Fulton St   \n",
       "51                           Burnside Av                          Fulton St   \n",
       "1                                   8 Av                 Lexington Av/59 St   \n",
       "101                        East Broadway                       Jefferson St   \n",
       "88               Myrtle - Willoughby Avs        Brooklyn Bridge - City Hall   \n",
       "93                              Avenue X                              59 St   \n",
       "40                                135 St                              96 St   \n",
       "22                                231 St                              33 St   \n",
       "\n",
       "     total_minutes  TRPDUR  minutes_diff  \\\n",
       "86            35.0     103  6.800000e+01   \n",
       "58            16.0      60  4.400000e+01   \n",
       "19            51.0      83  3.200000e+01   \n",
       "43            18.0      50  3.200000e+01   \n",
       "11            15.0      45  3.000000e+01   \n",
       "7             36.0      59  2.300000e+01   \n",
       "30            28.0      50  2.200000e+01   \n",
       "102           26.0      48  2.200000e+01   \n",
       "55            43.0      65  2.200000e+01   \n",
       "112           23.0      43  2.000000e+01   \n",
       "15            14.0      33  1.900000e+01   \n",
       "75            26.0      45  1.900000e+01   \n",
       "85             6.0      25  1.900000e+01   \n",
       "65            22.0      40  1.800000e+01   \n",
       "80            35.0      49  1.400000e+01   \n",
       "28             6.0      20  1.400000e+01   \n",
       "27            36.0      49  1.300000e+01   \n",
       "97             7.0      20  1.300000e+01   \n",
       "21            20.0      33  1.300000e+01   \n",
       "77            18.0      30  1.200000e+01   \n",
       "73             4.0      15  1.100000e+01   \n",
       "70             3.0      14  1.100000e+01   \n",
       "3              9.0      20  1.100000e+01   \n",
       "24            14.0      25  1.100000e+01   \n",
       "67            19.0      29  1.000000e+01   \n",
       "20             5.0      15  1.000000e+01   \n",
       "44            27.0      37  1.000000e+01   \n",
       "33            25.0      35  1.000000e+01   \n",
       "64            41.0      51  1.000000e+01   \n",
       "16            24.0      34  1.000000e+01   \n",
       "..             ...     ...           ...   \n",
       "108            3.0       4  1.000000e+00   \n",
       "12            29.0      30  1.000000e+00   \n",
       "94            50.0      50  0.000000e+00   \n",
       "104           15.0      15  0.000000e+00   \n",
       "78            30.0      30  0.000000e+00   \n",
       "107            1.0       1 -2.220446e-16   \n",
       "106            1.0       1 -2.220446e-16   \n",
       "72             1.0       1 -2.220446e-16   \n",
       "13             3.0       3 -4.440892e-16   \n",
       "2              6.0       6 -8.881784e-16   \n",
       "57            16.0      16 -3.552714e-15   \n",
       "53            52.0      51 -1.000000e+00   \n",
       "99            36.0      35 -1.000000e+00   \n",
       "17             2.0       1 -1.000000e+00   \n",
       "39             6.0       5 -1.000000e+00   \n",
       "110           16.0      15 -1.000000e+00   \n",
       "35            32.0      30 -2.000000e+00   \n",
       "59            33.0      30 -3.000000e+00   \n",
       "37            65.0      60 -5.000000e+00   \n",
       "96            25.0      20 -5.000000e+00   \n",
       "45            27.0      22 -5.000000e+00   \n",
       "68            21.0      15 -6.000000e+00   \n",
       "62            18.0      12 -6.000000e+00   \n",
       "51            35.0      28 -7.000000e+00   \n",
       "1             53.0      44 -9.000000e+00   \n",
       "101           39.0      30 -9.000000e+00   \n",
       "88            44.0      35 -9.000000e+00   \n",
       "93            63.0      52 -1.100000e+01   \n",
       "40            34.0      20 -1.400000e+01   \n",
       "22            68.0      45 -2.300000e+01   \n",
       "\n",
       "                                                  path  \n",
       "86   [A38S-A, A36S-A, A34S-A, A32S-A, A31S-A, A28S-...  \n",
       "58   [630N-4, 631N-4, 632N-4, 633N-4, 634N-4, 635N-...  \n",
       "19   [246N-2, 245N-2, 244N-2, 243N-2, 242N-2, 241N-...  \n",
       "43   [640S-6X, 639S-6X, 638S-6X, 637S-6X, 636S-6X, ...  \n",
       "11   [A16S-C, A17S-C, A18S-C, A19S-C, A20S-C, A21S-...  \n",
       "7    [D39N-B, D35N-B, D31N-B, D28N-B, D26N-B, D25N-...  \n",
       "30   [F04N-F, F05N-F, F06N-F, F07N-F, G08N-F, G14N-...  \n",
       "102  [L05S-L, L06S-L, L08S-L, L10S-L, L11S-L, L12S-...  \n",
       "55   [128N-2, 127N-2, 126N-2, 125N-2, 124N-2, 123N-...  \n",
       "112  [D15N-F, B10N-F, B08N-F, B06N-F, B04N-F, G14N-...  \n",
       "15    [D24S-Q, R30S-Q, Q01S-Q, R22S-Q, R21S-Q, R20S-Q]  \n",
       "75   [A27S-A, A28S-A, A31S-A, A32S-A, A34S-A, A36S-...  \n",
       "85                            [R13S-N, R11S-N, R09S-N]  \n",
       "65   [A31S-A, A28S-A, A27S-A, A24S-A, A15S-A, A12S-...  \n",
       "80   [A34S-A, A32S-A, A31S-A, A28S-A, A27S-A, A24S-...  \n",
       "28                    [D15S-B, D16S-B, D17S-B, D20S-B]  \n",
       "27   [A03S-A, A05S-A, A06S-A, A07S-A, A09S-A, A12S-...  \n",
       "97    [A15S-C, A14S-C, A12S-C, A11S-C, A10S-C, A09S-C]  \n",
       "21   [D25N-B, D24N-B, R30N-B, D22N-B, D21N-B, D20N-...  \n",
       "77   [R16S-N, R15S-N, R14S-N, R13S-N, R11S-N, R09S-...  \n",
       "73                                    [621S-4, 626S-4]  \n",
       "70                    [135N-1, 136N-1, 137N-1, 138N-1]  \n",
       "3     [D17S-F, D16S-F, D15S-F, B10S-F, B08S-F, B06S-F]  \n",
       "24   [128N-1, 127N-1, 126N-1, 125N-1, 124N-1, 123N-...  \n",
       "67   [D21S-D, D20S-D, D17S-D, D16S-D, D15S-D, D14S-...  \n",
       "20            [R20S-N, R19S-N, R18S-N, R17S-N, R16S-N]  \n",
       "44   [608N-6X, 613N-6X, 619N-6X, 621N-6X, 622N-6X, ...  \n",
       "33   [701S-7X, 702S-7X, 707S-7X, 712S-7X, 718S-7X, ...  \n",
       "64   [603S-6X, 604S-6X, 606S-6X, 607S-6X, 608S-6X, ...  \n",
       "16   [L22S-L, L21S-L, L20S-L, L19S-L, L17S-L, L16S-...  \n",
       "..                                                 ...  \n",
       "108                           [104N-1, 106N-1, 107N-1]  \n",
       "12   [F06N-E, F07N-E, G08N-E, G09N-E, G10N-E, G11N-...  \n",
       "94   [902N-GS, 901N-GS, 631N-5, 629N-5, 626N-5, 621...  \n",
       "104  [M19S-J, M18S-J, M16S-J, M14S-J, M13S-J, M12S-...  \n",
       "78   [129S-1, 128S-1, 127S-1, 126S-1, 125S-1, 124S-...  \n",
       "107                                   [A21N-B, A22N-B]  \n",
       "106                                   [A20N-C, A21N-C]  \n",
       "72                                    [A19N-B, A20N-B]  \n",
       "13                            [D15S-B, D16S-B, D17S-B]  \n",
       "2             [A27N-E, A25N-E, D14N-E, F12N-E, F11N-E]  \n",
       "57   [L15S-L, L14S-L, L13S-L, L12S-L, L11S-L, L10S-...  \n",
       "53   [613S-6X, 619S-6X, 621S-6X, 622S-6X, 623S-6X, ...  \n",
       "99   [D18N-F, D17N-F, D16N-F, D15N-F, B10N-F, B08N-...  \n",
       "17                                    [A22S-B, A24S-B]  \n",
       "39            [116N-1, 117N-1, 118N-1, 119N-1, 120N-1]  \n",
       "110  [134S-1, 133S-1, 132S-1, 131S-1, 130S-1, 129S-...  \n",
       "35   [246N-2, 245N-2, 244N-2, 243N-2, 242N-2, 241N-...  \n",
       "59   [G07S-E, F05S-E, F06S-E, F07S-E, G08S-E, G09S-...  \n",
       "37   [253N-4, 252N-4, 251N-4, 250N-4, 249N-4, 248N-...  \n",
       "96   [110S-1, 111S-1, 112S-1, A09N-C, A10N-C, A11N-...  \n",
       "45   [F06N-E, F07N-E, G08N-E, G09N-E, G10N-E, G11N-...  \n",
       "68    [L01S-L, L02S-L, D19N-F, D20N-F, D21N-F, F14N-F]  \n",
       "62   [A51S-A, A48S-A, A46S-A, A42S-A, A41S-A, A40S-...  \n",
       "51   [409S-4, 410S-4, 411S-4, 412S-4, 413S-4, 414S-...  \n",
       "1    [N02N-N, R41N-N, R40N-N, R39N-N, R36N-N, R35N-...  \n",
       "101  [F16N-F, F15N-F, F14N-F, D21N-F, D20N-F, D19N-...  \n",
       "88   [G32N-G, G33N-G, G34N-G, G35N-G, G36N-G, A42N-...  \n",
       "93   [F38N-F, F36N-F, F35N-F, F34N-F, F33N-F, F32N-...  \n",
       "40   [A14N-C, A12N-C, A11N-C, A10N-C, A09N-C, 112S-...  \n",
       "22   [104S-1, 106S-1, 107S-1, 108S-1, 109S-1, 110S-...  \n",
       "\n",
       "[114 rows x 6 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEACAYAAACj0I2EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAE9hJREFUeJzt3W+MXGd1x/HvMUv+ERIb2nhLDV4QqklD6YKqFBpVjBoK\nKahJRCU3lKJso76CNlGpUJxQKe2biiC1MWrVFwgau5SWhKCQRELKH5krqkoIUpISkpAiUZtA8SYQ\ncIUCJqlPX8zY3gSvd2dmr+/c5/l+pJX3Xt/Ze46f6+O7v9kZR2YiSeq/TV0XIEnaGA50SSqEA12S\nCuFAl6RCONAlqRAOdEkqxNx6DoqI/cAh4AjwTGZeGBFbgFuA7cB+YGdmHmqpTknSGtZ7h34EGGTm\n6zPzwtG+XcB9mbkD2Adc10aBkqT1We9AjxMcexmwd/T5XuDyjSpKkjS+9Q70BO6NiC9HxB+P9m3N\nzGWAzDwInNdGgZKk9VlXhg5clJnfjYifB+6JiMcYDvmVfA8BSerQugZ6Zn539OuTEfFZ4EJgOSK2\nZuZyRMwDT5zosRHhoJekCWRmjHP8mpFLRJwVEWePPn8R8FbgIeBOYGl02JXAHScpqtiPG264ofMa\n7M/e7K+8j0ms5w59K3D76E57DvhkZt4TEfcDt0bEVcABYOdEFfTc/v37uy6hVSX3V3JvYH81WnOg\nZ+Z/A4sn2P8U8JY2ipIkjc9Xik5paWmp6xJaVXJ/JfcG9lejmDSrWfcJIrLtc0hSaSKC3OgnRXVy\nTdN0XUKrSu6v5N7A/mrkQJekQhi5SNIMMnLRqubnF4iI1j/m5xe6blWqlgN9Sn3J8ZaXDzB8d4Zx\nPz4/1vHD8/RDX9ZuUvZXHwe6JBXCDL0SEcGpef+0mPhly5KOM0OXpIo50KdUfo7XdF1Aa0pfO/ur\njwNdkgphhl4JM3SpX8zQJaliDvQplZ/jNV0X0JrS187+6uNAl6RCmKFXwgxd6hczdEmqmAN9SuXn\neE3XBbSm9LWzv/o40CWpEGbolTBDl/rFDF2SKuZAn1L5OV7TdQGtKX3t7K8+DnRJKoQZeiXM0KV+\nMUOXpIo50KdUfo7XdF1Aa0pfO/urjwNdkgphhl4JM3SpX8zQJaliDvQplZ/jNV0X0JrS187+6uNA\nl6RCmKFXwgxd6hczdEmqmAN9SuXneE3XBbSm9LWzv/o40CWpEOvO0CNiE3A/8O3MvDQitgC3ANuB\n/cDOzDx0gseZoc8AM3SpX9rO0K8BHlmxvQu4LzN3APuA68Y5sSRpY61roEfENuDtwMdW7L4M2Dv6\nfC9w+caW1g/l53hN1wW0pvS1s7/6rPcO/SbgAzz3e/atmbkMkJkHgfM2uDZJ0hjm1jogIt4BLGfm\ngxExOMmhqwanS0tLLCwsALB582YWFxcZDIZf6ui/sn3dPrpvVupZbfu4o9uDdW4f3bf+4/vw5zEY\nDBgMBjNVj/3V3V/TNOzZswfg2Lwc15pPikbEXwN/CDwLnAm8GLgd+DVgkJnLETEPfD4zzz/B431S\ndAb4pKjUL608KZqZ12fmKzLzVcAVwL7MfA9wF7A0OuxK4I4x6y3Cz94Bl6bpuoDWlL529lefaX4O\n/UPAb0fEY8DFo21JUkd8L5dKGLlI/eJ7uUhSxRzoUyo/x2u6LqA1pa+d/dXHgS5JhTBDr4QZutQv\nZuiaAacTEa1+zM8vdN2kNJMc6FMqP8drxjz+MMPvBNr7WF4+ME1Dx5S+dvZXHwe6JBXCDL0SpzJD\nb/885vQqnxm6JFXMgT6l8nO8pusCWlP62tlffRzoklQIM/RKmKFL/WKGLkkVc6BPqfwcr+m6gNaU\nvnb2Vx8HuiQVwgy9EmboUr+YoUtSxRzoUyo/x2u6LqA1pa+d/dXHgS5JhTBDr4QZutQvZuiSVDEH\n+pTKz/GargtoTelrZ3/1caBLUiHM0Cthhi71ixm6JFXMgT6l8nO8pusCWlP62tlffRzoklQIM/RK\nmKFL/WKGLkkVc6BPqfwcr+m6gNaUvnb2Vx8HuiQVwgy9EmboUr+YoUtSxRzoUyo/x2u6LqA1pa+d\n/dXHgS5JhVgzQ4+I04EvAKcBc8BtmflXEbEFuAXYDuwHdmbmoRM83gx9BpihS/0ySYa+ridFI+Ks\nzHw6Il4A/DtwNfB7wPcz88MRcS2wJTN3neCxDvQZ4ECX+qW1J0Uz8+nRp6czvEtP4DJg72j/XuDy\ncU5civJzvKbrAlpT+trZX33WNdAjYlNEPAAcBO7NzC8DWzNzGSAzDwLntVemJGktY/0cekScA9zO\nMHL5t8x8yYrf+35mvvQEjzFymQFGLlK/TBK5zI1zcGb+b0Q0wCXAckRszczliJgHnljtcUtLSyws\nLACwefNmFhcXGQwGwPFvm9xud/u4o9uDlraP7mvr6x/dHm3NyJ+v225Pu900DXv27AE4Ni/HtZ6f\ncvk54JnMPBQRZwJ3Ax8C3gw8lZk31vykaNM0xxZnlk1+h97w3GG95pkmPM84NuYOvS9rNyn767e2\n7tB/AdgbEZsYZu63ZObnIuKLwK0RcRVwANg5dsWSpA3je7lUwgxd6hffy0WSKuZAn9LPPulYmqbr\nAlpT+trZX30c6JJUCDP0SpihS/1ihi5JFXOgT6n8HK/puoDWlL529lcfB7okFcIMvRJm6FK/mKFL\nUsUc6FMqP8drui6gNaWvnf3Vx4EuSYUwQ6+EGbrUL2boklQxB/qUys/xmq4LaE3pa2d/9XGgS1Ih\nzNArYYYu9YsZuiRVzIE+pfJzvKbrAlpT+trZX30c6JJUCDP0SpihS/1ihi5JFXOgT6n8HK/puoDW\nlL529lcfB7okFcIMvRJm6FK/mKFLUsUc6FMqP8drui6gNaWvnf3Vx4EuSYUwQ6+EGbrUL2boklQx\nB/qUys/xmq4LaE3pa2d/9XGgS1IhzNArYYYu9YsZuiRVzIE+pfJzvKbrAlpT+trZX30c6JJUCDP0\nSpihS/3SSoYeEdsiYl9EPBwRD0XE1aP9WyLinoh4LCLujohzJy1ckjS99UQuzwLvz8wLgDcB74uI\n1wC7gPsycwewD7iuvTJnV/k5XtN1Aa0pfe3srz5rDvTMPJiZD44+/xHwKLANuAzYOzpsL3B5W0VK\nktY2VoYeEQsMb9leCzyemVtW/N5TmfmSEzzGDH0GmKFL/dLqz6FHxNnAbcA1ozv15/+N8m+YJHVo\nbj0HRcQcw2H+icy8Y7R7OSK2ZuZyRMwDT6z2+KWlJRYWFgDYvHkzi4uLDAYD4HgO1tft3bt396Kf\n445uD9a5vRtYHOP4o/vWe/yk26OtKf58Vv7ZdL0+bWzbX7+2m6Zhz549AMfm5bjWFblExD8B38vM\n96/YdyPwVGbeGBHXAlsyc9cJHlt05NI0zbHFmWWTRy4Nzx3Wa55pwvOMY2Mil76s3aTsr98miVzW\nHOgRcRHwBeAhhn9TE7ge+BJwK/By4ACwMzN/eILHFz3Q+8IMXeqXVgb6tBzos8GBLvWLb87VgZ/N\nqEvTdF1Aa0pfO/urjwNdkgph5FIJIxepX4xcJKliDvQplZ/jNV0X0JrS187+6uNAl6RCmKFXwgxd\n6hczdEmqmAN9SuXneE3XBbSm9LWzv/o40CWpEGbolTBDl/rFDF2SKuZAn1L5OV7TdQGtKX3t7K8+\nDnRJKoQZeiXM0KV+MUOXpIo50KdUfo7XdF1Aa0pfO/urjwNdkgphhl4JM3SpX8zQJaliDvQplZ/j\nNV0X0JrS187+6uNAl6RCmKFXwgxd6hczdEmqmAN9SuXneE3XBbSm9LWzv/o40CWpEGbolTBDl/rF\nDF2SKuZAn1L5OV7TdQGtKX3t7K8+DnRJKoQZeiXM0KV+MUOXpIo50KdUfo7XdF1Aa0pfO/urjwNd\nkgphhl4JM3SpX8zQJaliaw70iPh4RCxHxFdX7NsSEfdExGMRcXdEnNtumbOr/Byv6bqA1pS+dvZX\nn/Xcod8MvO15+3YB92XmDmAfcN1GFyZJGs+6MvSI2A7clZmvG21/HXhzZi5HxDzQZOZrVnmsGfoM\nMEOX+uVUZujnZeYyQGYeBM6b8OtIkjbI3AZ9nZPeLi0tLbGwsADA5s2bWVxcZDAYAMdzsL5u7969\ne+p+3vnOK/jBD5Y5NZrRr4N1bu8GFsc4/ui+9R4/yfYLR99xtGfr1u0cPLi/8+trmu2VGfMs1GN/\na/ezZ88egGPzclyTRi6PAoMVkcvnM/P8VR5bdOTSNM2xxZnUqYlDJj1Hw3OHdVvnGcdGnaNh9d76\nH+tsxLU5y0rvb5LIZb0DfYHhQP+V0faNwFOZeWNEXAtsycxdqzy26IG+EWZ7oM/ieczpVb5WBnpE\n/AvD25iXAsvADcBngU8DLwcOADsz84erPN6BvgYH+myew+tWXWrlSdHM/IPMfFlmnp6Zr8jMmzPz\nB5n5lszckZlvXW2Y16D8n4Vtui6gRU3XBbSq9Guz9P4m4StFJakQvpfLDDBymc1zeN2qS76XiyRV\nzIE+pfJzvKbrAlrUdF1Aq0q/NkvvbxIOdEkqhBn6DDBDn81zeN2qS2boklQxB/qUys/xmq4LaFHT\ndQGtKv3aLL2/STjQJakQZugzwAx9Ns/hdasumaFLUsUc6FMqP8drui6gRU3XBbSq9Guz9P4m4UCX\npEKYoc8AM/TZPIfXrbpkhi5JFXOgT6n8HK/puoAWNV0X0KrSr83S+5uEA12SCmGGPgPM0GfzHF63\n6pIZuiRVzIE+pfJzvKbrAlrUdF1Aq0q/NkvvbxIOdEkqhBn6DDBDn81zeN2qS2boklQxB/qUys/x\nmq4LaFHTdQGtKv3aLL2/STjQJakQZugzwAx9Ns/hdasumaFLUsUc6FMqP8drui6gRU3XBbSq9Guz\n9P4m4UCXpEKYoc8AM/TZPIfXrbo0SYY+11YxfffTn/6Uq6++lieffKrV85xxxmmtfn1J9fAOfRUH\nDhxgx443cPjw365x5KPA+ROf58wzP8yPf/wIs3tX2wCDU3CecWzUORpW7+0M4PAGnOPkNm06iyNH\nnm71HFu3bufgwf2tnqMLTdMwGAy6LqM13qFvsLm5szl8+Mo1jmoYb+A912mn/fNooGu2HOZURFRH\njrT5D2ADDFheHmsmqMd8UnRqg64LaNmg6wJaNOi6gJYNui6gVSXfnU/KgS5JhZhqoEfEJRHx9Yj4\nr4i4dqOK6pem6wJa1nRdQIuargtoWTP69XQiotWP+fmFU9LR/PxCMb20YeKBHhGbgL8H3gZcALwr\nIl6zUYX1x4NdF9CykvsruTc43t/R5wPa+1hePnBKOhqe5+h5b+p1L22Y5g79QuAbmXkgM58BPgVc\ntjFl9ckPuy6gZSX3V3JvYH/1mWag/yLw+Irtb4/2SZI64I8trmJubo7Dh5/knHN+96THPf30A5x1\n1n9MfJ6f/OSBiR97auzvuoAW7e+6gJbt77qAlu3vuoCZM/ELiyLijcBfZuYlo+1dQGbmjc87rn+v\nKpKkGTDuC4umGegvAB4DLga+C3wJeFdmPjrRF5QkTWXiyCUz/y8i/gS4h2EW/3GHuSR1p/X3cpEk\nnRqtvlI0Iv48Io5ExEtW7LsuIr4REY9GxFvbPH9bIuLDo/ofjIjPRMQ5K36v9/1BeS8ai4htEbEv\nIh6OiIci4urR/i0RcU9EPBYRd0fEuV3XOqmI2BQRX4mIO0fbJfV2bkR8evT36uGI+PXC+vuziPha\nRHw1Ij4ZEadN0l9rAz0itgG/DRxYse98YCfDtyf8HeAfYvhm4H1zD3BBZi4C3wCuA4iIX6aA/gp9\n0dizwPsz8wLgTcD7Rj3tAu7LzB3APkZr2VPXACvf6a2k3j4CfC4zzwd+Ffg6hfQXES8D/hR4Q2a+\njmEU/i4m6K/NO/SbgA88b99lwKcy89nM3M9wGF7YYg2tyMz7MvPIaPOLwLbR55dSQH8U+KKxzDyY\nmQ+OPv8Rw/c93sawr72jw/YCl3dT4XRGN1BvBz62YncpvZ0D/GZm3gww+vt1iEL6G3kB8KKImAPO\nBL7DBP21MtAj4lLg8cx86Hm/9fwXI32H/r8Y6Srgc6PPS+mv6BeNRcQCsMjwH+OtmbkMw6EPnNdd\nZVM5egO18kmxUnp7JfC9iLh5FCl9NCLOopD+MvN/gL8BvsVwZhzKzPuYoL+Jf8olIu4Ftq7cxfBi\n+gvgeoZxS2+dpL8PZuZdo2M+CDyTmf/aQYmaQEScDdwGXJOZPzrB6yR691MCEfEOYDkzH4yIwUkO\n7V1vI3PAG4D3Zeb9EXETwzii92sHEBGbGd6NbwcOAZ+OiHczQX/T/NjiCQd2RLwWWAD+c5QfbwO+\nEhEXMvzX5xUrDt822jdzVuvvqIhYYvgt7m+t2P0d4OUrtme2vzX0Zp3GMfp29jbgE5l5x2j3ckRs\nzczliJgHnuiuwoldBFwaEW9n+O36iyPiE8DBAnqD4XeIj2fm/aPtzzAc6CWsHcBbgG9m5lMAEXE7\n8BtM0N+GRy6Z+bXMnM/MV2XmKxkuxusz8wngTuD3R8/gvhJ4NcMXJPVKRFzC8NvbSzNz5f9Tdidw\nRd/7A74MvDoitkfEacAVDHvru38EHsnMj6zYdyewNPr8SuCO5z9o1mXm9Zn5isx8FcO12peZ7wHu\noue9AYxih8cj4pdGuy4GHqaAtRv5FvDGiDhjdBN8McMnt8fu71S8l0syjCvIzEci4laGxT4DvLeX\n/+Eo/B1wGnDv6IdYvpiZ7y2lvxJfNBYRFwHvBh6KiAcYXpfXAzcCt0bEVQx/Imtnd1VuuA9RTm9X\nA5+MiBcC3wT+iOETib3vLzO/FBG3AQ8wnBsPAB8FXsyY/fnCIkkqhP8FnSQVwoEuSYVwoEtSIRzo\nklQIB7okFcKBLkmFcKBLUiEc6JJUiP8Hcvo75uqc9wUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5209161f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimal_stops[\"total_minutes\"] = ((optimal_stops[\"total_seconds\"] / 60.))\n",
    "\n",
    "optimal_stops[\"minutes_diff\"] = (optimal_stops[\"TRPDUR\"] - (optimal_stops[\"total_minutes\"]))\n",
    "optimal_stops[\"minutes_diff\"].hist()\n",
    "optimal_stops.sort_values(\"minutes_diff\", ascending=False)[[\"stop_name_O\", \"stop_name_D\", \"total_minutes\", \"TRPDUR\", \"minutes_diff\", \"path\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.651884264353532"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = np.sqrt(np.mean((optimal_stops[\"TRPDUR\"] - optimal_stops[\"total_minutes\"]) ** 2))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now cut off on manhattan stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!wget -qnc -O data/nybb_18a.zip https://www1.nyc.gov/assets/planning/download/zip/data-maps/open-data/nybb_18a.zip\n",
    "!cd data; unzip -oq nybb_18a.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: CRS does not match!\n"
     ]
    }
   ],
   "source": [
    "boroughs = gpd.read_file(\"data/nybb_18a/nybb.shp\")\n",
    "manhattan_geo = boroughs[boroughs[\"BoroName\"] == \"Manhattan\"]\n",
    "stops[\"geometry\"] = stops[\"base_geom\"]\n",
    "stops_with_borough = gpd.sjoin(stops, boroughs.to_crs(epsg=4326))\n",
    "\n",
    "stops_in_manhattan = stops_with_borough.set_index(\"stop_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def filter_path(path):\n",
    "    return [True if stops_in_manhattan.loc[p.split(\"-\")[0]].BoroName == \"Manhattan\" else False for p in path]\n",
    "def manhattan_stops(path):\n",
    "    return [p for p in path if stops_in_manhattan.loc[p.split(\"-\")[0]].BoroName == \"Manhattan\"]\n",
    "def check_path(path):\n",
    "    last_mn_index = -1\n",
    "    for i, p in enumerate(path):\n",
    "        if p and ((last_mn_index != (i-1)) and (last_mn_index != -1)):\n",
    "            raise Exception(path)\n",
    "        if p:\n",
    "            last_mn_index = i\n",
    "    return True\n",
    "        \n",
    "optimal_stops[\"in_manhattan\"] = optimal_stops[\"path\"].apply(filter_path)\n",
    "optimal_stops[\"manhattan_path\"] = optimal_stops[\"path\"].apply(manhattan_stops)\n",
    "optimal_stops[\"manhattan_geo\"] = optimal_stops[\"manhattan_path\"].apply(lambda x: [y.split(\"-\")[0] for y in x])\n",
    "optimal_stops[\"manhattan_len\"] = optimal_stops[\"manhattan_path\"].apply(len)\n",
    "\n",
    "## SANITY CHECK\n",
    "_ = optimal_stops[\"in_manhattan\"].apply(check_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dump_fields = [\"PLSAM\", \"TRP_ARR_HR\", \"TRP_ARR_MIN\", \"TRP_DEP_HR\", \"TRP_DEP_MIN\", \"TRPDUR\", \"stop_id_O\", \"stop_id_D\", \"path\", \"total_minutes\", \"manhattan_path\", \"manhattan_geo\"]\n",
    "out = optimal_stops[dump_fields]\n",
    "out.to_csv(\"data/nycmtc_final_od.csv\")\n",
    "out.to_pickle(\"data/nycmtc_final_od.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TODOS 2/13\n",
    "- Run this for all trips, to potentially cover trips that go through manhattan as well?\n",
    "- Histogram is still not great, but it's much better. COULD use nighttime schedules rather than the ones i'm specifically using"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Old code - used for census tract geo information (rather than NYMTC TAZ which is more granular)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# stops[\"line_list\"] = stops[\"line\"].apply(lambda x: x.split(\"-\"))\n",
    "# stops.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# tracts = pd.concat(gpd.read_file(f) for f in glob.glob(\"gz*.shp\n",
    "# state_by_tract = {\"36\": \"NY\",\n",
    "#                   \"34\": \"NJ\",\n",
    "#                   \"09\": \"CT\"}\n",
    "# trips_with_destination = trips.merge(tracts, left_on=\"DTRACT\", right_on=\"clean_geo\", how=\"left\",\n",
    "#                                      suffixes=(\"_d\", \"_d\"))\n",
    "# trips_with_od = trips_with_destination.merge(tracts, left_on=\"OTRACT\", right_on=\"clean_geo\", how=\"left\", \n",
    "#                                              suffixes=(\"_o\", \"_d\"))\n",
    "# tracts[\"clean_geo\"] = tracts.GEO_ID.apply(lambda x: int(x.split(\"US\")[1]))\n",
    "# trips_with_od[\"geometry\"] = trips_with_od[\"geometry_o\"]\n",
    "# trips_o_geo = gpd.GeoDataFrame(trips_with_od)\n",
    "# trips_o_geo[trips_o_geo[\"TRP_DEP_HR\"] < 6].plot()\n",
    "# len(trips_with_od[trips_with_od[\"TRP_DEP_HR\"] < 5]\n",
    "\n",
    "\n",
    "# manhattan_counties = {\"061\"}\n",
    "# def o_or_d(row):\n",
    "#     origin =  (row[\"COUNTY_o\"] == \"061\")\n",
    "#     destination = (row[\"COUNTY_d\"] == \"061\")\n",
    "#     if origin and not destination:\n",
    "#         return \"start\"\n",
    "#     if destination and not origin:\n",
    "#         return \"end\"\n",
    "#     if destination and origin:\n",
    "#         return \"both\"\n",
    "#     return \"no\"\n",
    "# trips_o_geo[\"start_or_end\"] = trips_o_geo[[\"COUNTY_o\", \"COUNTY_d\"]].apply(o_or_d, axis=1)\n",
    "\n",
    "# of_interest = trips_o_geo[((trips_o_geo[\"COUNTY_d\"] == \"061\") | \n",
    "#                   (trips_o_geo[\"COUNTY_o\"] == \"061\")) &\n",
    "#                   (trips_o_geo[\"TRP_DEP_HR\"] < 6)]\n",
    "\n",
    "# all_subway_trips = trips_o_geo[trips_o_geo[\"ULTMODE\"] == 15]\n",
    "# interest_subway = of_interest[of_interest[\"ULTMODE\"] == 15]\n",
    "# len(interest_subway) / float(len(all_subway_trips))\n",
    "\n",
    "# import matplotlib.pyplot as plt\n",
    "# fig, axes = plt.subplots(1,  2, figsize=(16, 9))\n",
    "# interest_subway[\"geometry\"] = interest_subway[\"geometry_o\"]\n",
    "# interest_subway.plot('start_or_end', categorical=True, legend=True, ax=axes[0])\n",
    "\n",
    "# interest_subway[\"geometry\"] = interest_subway[\"geometry_d\"]\n",
    "# interest_subway.plot('start_or_end', categorical=True, legend=True, ax=axes[1])\n",
    "\n",
    "# (interest_subway[\"start_or_end\"].value_counts() / float(len(interest_subway)))\n",
    "\n",
    "# import matplotlib\n",
    "# %matplotlib inline\n",
    "\n",
    "\n",
    "# areas_2010 = glob.glob(\"tl*.shp\")\n",
    "# areas_2010\n",
    "\n",
    "# things = [gpd.read_file(s) for s in areas_2010]\n",
    "# thing_1 = pd.concat(things)\n",
    "# gdfs_2010 = gpd.GeoDataFrame(thing_1)\n",
    "\n",
    "# ax = things[1].plot(figsize=(16, 9), color='red')\n",
    "# ax = things[0].plot(ax=ax)\n",
    "# ax = things[2].plot(ax=ax, color='green')\n",
    "\n",
    "# things[0][things[0][\"TAZCE10\"].str.contains(\"2626\")]\n",
    "\n",
    "# trips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# stops_merged"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}