M4. MTA Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NYC BLOCKS AND MTA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from random import randint, uniform\n",
    "from datetime import datetime\n",
    "from urllib import urlretrieve\n",
    "from zipfile import ZipFile\n",
    "import fiona \n",
    "import geopandas as gpd\n",
    "from geopandas import GeoDataFrame \n",
    "import shapefile\n",
    "\n",
    "from shapely.geometry import Point\n",
    "from shapely.geometry import Polygon\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('bmh')\n",
    "plt.rcParams['figure.figsize'] = (10.0, 10.0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": 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>admin_fire</th>\n",
       "      <th>assemdist</th>\n",
       "      <th>atomic_num</th>\n",
       "      <th>atomicid</th>\n",
       "      <th>borough</th>\n",
       "      <th>censusbl_1</th>\n",
       "      <th>censusbl_2</th>\n",
       "      <th>censusbl_3</th>\n",
       "      <th>censusbloc</th>\n",
       "      <th>censustr_1</th>\n",
       "      <th>...</th>\n",
       "      <th>sb_1_page</th>\n",
       "      <th>sb_1_volum</th>\n",
       "      <th>sb_2_page</th>\n",
       "      <th>sb_2_volum</th>\n",
       "      <th>sb_3_page</th>\n",
       "      <th>sb_3_volum</th>\n",
       "      <th>schooldist</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>water_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E 151</td>\n",
       "      <td>62</td>\n",
       "      <td>None</td>\n",
       "      <td>5024401430</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>1020</td>\n",
       "      <td>None</td>\n",
       "      <td>4023</td>\n",
       "      <td>024401</td>\n",
       "      <td>...</td>\n",
       "      <td>533</td>\n",
       "      <td>05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>31</td>\n",
       "      <td>244110.620576</td>\n",
       "      <td>2424.560416</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E 151</td>\n",
       "      <td>62</td>\n",
       "      <td>None</td>\n",
       "      <td>5024401439</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>2004</td>\n",
       "      <td>None</td>\n",
       "      <td>4030</td>\n",
       "      <td>024401</td>\n",
       "      <td>...</td>\n",
       "      <td>533</td>\n",
       "      <td>05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>31</td>\n",
       "      <td>177042.348067</td>\n",
       "      <td>1722.931212</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E 151</td>\n",
       "      <td>62</td>\n",
       "      <td>None</td>\n",
       "      <td>5024402223</td>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "      <td>1005</td>\n",
       "      <td>None</td>\n",
       "      <td>2002</td>\n",
       "      <td>024402</td>\n",
       "      <td>...</td>\n",
       "      <td>530</td>\n",
       "      <td>05</td>\n",
       "      <td>529</td>\n",
       "      <td>05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>31</td>\n",
       "      <td>33790.843893</td>\n",
       "      <td>770.085650</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E 151</td>\n",
       "      <td>62</td>\n",
       "      <td>None</td>\n",
       "      <td>5024401441</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>2005</td>\n",
       "      <td>None</td>\n",
       "      <td>4031</td>\n",
       "      <td>024401</td>\n",
       "      <td>...</td>\n",
       "      <td>533</td>\n",
       "      <td>05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>31</td>\n",
       "      <td>171623.335762</td>\n",
       "      <td>1697.908522</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E 151</td>\n",
       "      <td>62</td>\n",
       "      <td>None</td>\n",
       "      <td>5024401440</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>1018</td>\n",
       "      <td>None</td>\n",
       "      <td>4015</td>\n",
       "      <td>024401</td>\n",
       "      <td>...</td>\n",
       "      <td>532</td>\n",
       "      <td>05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>31</td>\n",
       "      <td>78855.828376</td>\n",
       "      <td>1243.106869</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  admin_fire assemdist atomic_num    atomicid borough censusbl_1 censusbl_2  \\\n",
       "0      E 151        62       None  5024401430       5       None       1020   \n",
       "1      E 151        62       None  5024401439       5       None       2004   \n",
       "2      E 151        62       None  5024402223       5          B       1005   \n",
       "3      E 151        62       None  5024401441       5       None       2005   \n",
       "4      E 151        62       None  5024401440       5       None       1018   \n",
       "\n",
       "  censusbl_3 censusbloc censustr_1    ...     sb_1_page sb_1_volum sb_2_page  \\\n",
       "0       None       4023     024401    ...           533         05      None   \n",
       "1       None       4030     024401    ...           533         05      None   \n",
       "2       None       2002     024402    ...           530         05       529   \n",
       "3       None       4031     024401    ...           533         05      None   \n",
       "4       None       4015     024401    ...           532         05      None   \n",
       "\n",
       "  sb_2_volum sb_3_page sb_3_volum schooldist     shape_area   shape_leng  \\\n",
       "0       None      None       None         31  244110.620576  2424.560416   \n",
       "1       None      None       None         31  177042.348067  1722.931212   \n",
       "2         05      None       None         31   33790.843893   770.085650   \n",
       "3       None      None       None         31  171623.335762  1697.908522   \n",
       "4       None      None       None         31   78855.828376  1243.106869   \n",
       "\n",
       "  water_flag  \n",
       "0          2  \n",
       "1          2  \n",
       "2          2  \n",
       "3          2  \n",
       "4          2  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BRING IN BLOCK DATA\n",
    "blocshapes = gpd.GeoDataFrame.from_file('mappy.dbf')\n",
    "blocshapes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": 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>subway line</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>county_name</th>\n",
       "      <th>position</th>\n",
       "      <th>income2011</th>\n",
       "      <th>income2000</th>\n",
       "      <th>stop_name</th>\n",
       "      <th>county</th>\n",
       "      <th>tract</th>\n",
       "      <th>tract_lookup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>40.701961</td>\n",
       "      <td>-74.013397</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>124048.0</td>\n",
       "      <td>105456.0</td>\n",
       "      <td>South Ferry</td>\n",
       "      <td>61.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>40.708107</td>\n",
       "      <td>-74.013445</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>2.0</td>\n",
       "      <td>114412.0</td>\n",
       "      <td>81609.0</td>\n",
       "      <td>Rector St.</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>1300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>40.715576</td>\n",
       "      <td>-74.009031</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>3.0</td>\n",
       "      <td>205192.0</td>\n",
       "      <td>128384.0</td>\n",
       "      <td>Chambers St.</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>2100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>40.719918</td>\n",
       "      <td>-74.006620</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>4.0</td>\n",
       "      <td>164656.0</td>\n",
       "      <td>113332.0</td>\n",
       "      <td>Franklin St.</td>\n",
       "      <td>61.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>3300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subway line        lat       long county_name  position  income2011  \\\n",
       "0         NaN        NaN        NaN         NaN       NaN         NaN   \n",
       "1           1  40.701961 -74.013397   Manhattan       1.0    124048.0   \n",
       "2           1  40.708107 -74.013445   Manhattan       2.0    114412.0   \n",
       "3           1  40.715576 -74.009031   Manhattan       3.0    205192.0   \n",
       "4           1  40.719918 -74.006620   Manhattan       4.0    164656.0   \n",
       "\n",
       "   income2000     stop_name  county   tract  tract_lookup  \n",
       "0         NaN           NaN     NaN     NaN           NaN  \n",
       "1    105456.0   South Ferry    61.0   900.0         900.0  \n",
       "2     81609.0    Rector St.    61.0  1300.0        1300.0  \n",
       "3    128384.0  Chambers St.    61.0  2100.0        2100.0  \n",
       "4    113332.0  Franklin St.    61.0  3300.0        3300.0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BRING IN MTA DATA\n",
    "import pandas as pd\n",
    "import csv\n",
    "mta2 = pd.read_csv(\"income_by_subway_stops.csv\")\n",
    "mta2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# CLEAN COLUMNS\n",
    "mta2 = mta2.drop(['county_name', 'position','income2011','income2000','tract','tract_lookup','county'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# CLEAN ROWS\n",
    "mta2 = mta2.drop(0,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": 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>subway line</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>stop_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>40.701961</td>\n",
       "      <td>-74.013397</td>\n",
       "      <td>South Ferry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>40.708107</td>\n",
       "      <td>-74.013445</td>\n",
       "      <td>Rector St.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>40.715576</td>\n",
       "      <td>-74.009031</td>\n",
       "      <td>Chambers St.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>40.719918</td>\n",
       "      <td>-74.006620</td>\n",
       "      <td>Franklin St.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>40.722497</td>\n",
       "      <td>-74.005994</td>\n",
       "      <td>Canal St.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subway line        lat       long     stop_name\n",
       "1           1  40.701961 -74.013397   South Ferry\n",
       "2           1  40.708107 -74.013445    Rector St.\n",
       "3           1  40.715576 -74.009031  Chambers St.\n",
       "4           1  40.719918 -74.006620  Franklin St.\n",
       "5           1  40.722497 -74.005994     Canal St."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mta2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# CLEAN DATAFRAME\n",
    "mta2 = mta2.rename(columns = {'subway line':'subway_line'})\n",
    "mta2 = mta2.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(701, 4)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mta2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": 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>subway_line</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>stop_name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>40.701961</td>\n",
       "      <td>-74.013397</td>\n",
       "      <td>South Ferry</td>\n",
       "      <td>POINT (-74.013397 40.701961)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>40.708107</td>\n",
       "      <td>-74.013445</td>\n",
       "      <td>Rector St.</td>\n",
       "      <td>POINT (-74.01344499999999 40.708107)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>40.715576</td>\n",
       "      <td>-74.009031</td>\n",
       "      <td>Chambers St.</td>\n",
       "      <td>POINT (-74.00903100000001 40.715576)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>40.719918</td>\n",
       "      <td>-74.006620</td>\n",
       "      <td>Franklin St.</td>\n",
       "      <td>POINT (-74.00662 40.719918)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>40.722497</td>\n",
       "      <td>-74.005994</td>\n",
       "      <td>Canal St.</td>\n",
       "      <td>POINT (-74.005994 40.722497)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subway_line        lat       long     stop_name  \\\n",
       "1           1  40.701961 -74.013397   South Ferry   \n",
       "2           1  40.708107 -74.013445    Rector St.   \n",
       "3           1  40.715576 -74.009031  Chambers St.   \n",
       "4           1  40.719918 -74.006620  Franklin St.   \n",
       "5           1  40.722497 -74.005994     Canal St.   \n",
       "\n",
       "                               geometry  \n",
       "1          POINT (-74.013397 40.701961)  \n",
       "2  POINT (-74.01344499999999 40.708107)  \n",
       "3  POINT (-74.00903100000001 40.715576)  \n",
       "4           POINT (-74.00662 40.719918)  \n",
       "5          POINT (-74.005994 40.722497)  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CONVERT STATION DATAFRAME TO GEODATAFRAME\n",
    "crs = {'init' :'epsg:4326'}\n",
    "geometry = [Point(xy) for xy in zip(mta2.long, mta2.lat)]\n",
    "mta2_gdf = GeoDataFrame(mta2, geometry=geometry)\n",
    "mta2_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40.69, 40.81)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAIDMKBwAAEBmFA4AACAzCgcAAJAZhQMAAMjMCReOxYsXx2233dbxcW1tbSxatCgmT54c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\n2l+CAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11687be50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mta2_gdf.plot('subway_line')\n",
    "plt.xlim(-74.03, -73.92)\n",
    "plt.ylim(40.69, 40.81)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40.65, 40.81)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z/Phx6tWrx4YNG6hQoYLF+/H09CQhIcHi7ToLSdSEEHYlJCSEESNG0Lx5c5o0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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116467c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zips.plot()\n",
    "plt.rcParams['figure.figsize'] = (10.0, 10.0)\n",
    "plt.scatter(x=mta2['long'], y=mta2['lat'], c='b', s=10)\n",
    "plt.xlim(-74.03, -73.92)\n",
    "plt.ylim(40.65, 40.81)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# FUNCTION TO GET DISTANCE TO THE 3 CLOSEST MTA STATIONS TO BLOCK\n",
    "def closest_mtas(x):\n",
    "    distances=[]\n",
    "    for i,j in enumerate(mta2_gdf.geometry):    \n",
    "        distance = x.distance(j)\n",
    "        distances.append(distance) \n",
    "    distances = sorted(distances)        \n",
    "    return distances[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "blocshapes['closest_mtastations'] = blocshapes.geometry.apply(closest_mtas) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": 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>admin_fire</th>\n",
       "      <th>assemdist</th>\n",
       "      <th>atomic_num</th>\n",
       "      <th>atomicid</th>\n",
       "      <th>borough</th>\n",
       "      <th>censusbl_1</th>\n",
       "      <th>censusbl_2</th>\n",
       "      <th>censusbl_3</th>\n",
       "      <th>censusbloc</th>\n",
       "      <th>censustr_1</th>\n",
       "      <th>...</th>\n",
       "      <th>sb_1_volum</th>\n",
       "      <th>sb_2_page</th>\n",
       "      <th>sb_2_volum</th>\n",
       "      <th>sb_3_page</th>\n",
       "      <th>sb_3_volum</th>\n",
       "      <th>schooldist</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>water_flag</th>\n",
       "      <th>closest_mtastations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E 151</td>\n",
       "      <td>62</td>\n",
       "      <td>None</td>\n",
       "      <td>5024401430</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>1020</td>\n",
       "      <td>None</td>\n",
       "      <td>4023</td>\n",
       "      <td>024401</td>\n",
       "      <td>...</td>\n",
       "      <td>05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>31</td>\n",
       "      <td>244110.620576</td>\n",
       "      <td>2424.560416</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.242428389554, 0.247227281305, 0.25252207981...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E 151</td>\n",
       "      <td>62</td>\n",
       "      <td>None</td>\n",
       "      <td>5024401439</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>2004</td>\n",
       "      <td>None</td>\n",
       "      <td>4030</td>\n",
       "      <td>024401</td>\n",
       "      <td>...</td>\n",
       "      <td>05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>31</td>\n",
       "      <td>177042.348067</td>\n",
       "      <td>1722.931212</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.236859586008, 0.241711596108, 0.24705439816...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E 151</td>\n",
       "      <td>62</td>\n",
       "      <td>None</td>\n",
       "      <td>5024402223</td>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "      <td>1005</td>\n",
       "      <td>None</td>\n",
       "      <td>2002</td>\n",
       "      <td>024402</td>\n",
       "      <td>...</td>\n",
       "      <td>05</td>\n",
       "      <td>529</td>\n",
       "      <td>05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>31</td>\n",
       "      <td>33790.843893</td>\n",
       "      <td>770.085650</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.229135059429, 0.234059704395, 0.23946790353...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E 151</td>\n",
       "      <td>62</td>\n",
       "      <td>None</td>\n",
       "      <td>5024401441</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>2005</td>\n",
       "      <td>None</td>\n",
       "      <td>4031</td>\n",
       "      <td>024401</td>\n",
       "      <td>...</td>\n",
       "      <td>05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>31</td>\n",
       "      <td>171623.335762</td>\n",
       "      <td>1697.908522</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.235713559318, 0.240572355528, 0.24592139642...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E 151</td>\n",
       "      <td>62</td>\n",
       "      <td>None</td>\n",
       "      <td>5024401440</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>1018</td>\n",
       "      <td>None</td>\n",
       "      <td>4015</td>\n",
       "      <td>024401</td>\n",
       "      <td>...</td>\n",
       "      <td>05</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>31</td>\n",
       "      <td>78855.828376</td>\n",
       "      <td>1243.106869</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.247370128834, 0.252118772915, 0.25736810009...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  admin_fire assemdist atomic_num    atomicid borough censusbl_1 censusbl_2  \\\n",
       "0      E 151        62       None  5024401430       5       None       1020   \n",
       "1      E 151        62       None  5024401439       5       None       2004   \n",
       "2      E 151        62       None  5024402223       5          B       1005   \n",
       "3      E 151        62       None  5024401441       5       None       2005   \n",
       "4      E 151        62       None  5024401440       5       None       1018   \n",
       "\n",
       "  censusbl_3 censusbloc censustr_1  \\\n",
       "0       None       4023     024401   \n",
       "1       None       4030     024401   \n",
       "2       None       2002     024402   \n",
       "3       None       4031     024401   \n",
       "4       None       4015     024401   \n",
       "\n",
       "                         ...                         sb_1_volum sb_2_page  \\\n",
       "0                        ...                                 05      None   \n",
       "1                        ...                                 05      None   \n",
       "2                        ...                                 05       529   \n",
       "3                        ...                                 05      None   \n",
       "4                        ...                                 05      None   \n",
       "\n",
       "  sb_2_volum sb_3_page sb_3_volum schooldist     shape_area   shape_leng  \\\n",
       "0       None      None       None         31  244110.620576  2424.560416   \n",
       "1       None      None       None         31  177042.348067  1722.931212   \n",
       "2         05      None       None         31   33790.843893   770.085650   \n",
       "3       None      None       None         31  171623.335762  1697.908522   \n",
       "4       None      None       None         31   78855.828376  1243.106869   \n",
       "\n",
       "  water_flag                                closest_mtastations  \n",
       "0          2  [0.242428389554, 0.247227281305, 0.25252207981...  \n",
       "1          2  [0.236859586008, 0.241711596108, 0.24705439816...  \n",
       "2          2  [0.229135059429, 0.234059704395, 0.23946790353...  \n",
       "3          2  [0.235713559318, 0.240572355528, 0.24592139642...  \n",
       "4          2  [0.247370128834, 0.252118772915, 0.25736810009...  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blocshapes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ORGANIZE / SEPERATE STATIONS\n",
    "blocshapes['mta1_distance'] = blocshapes.closest_mtastations.apply(lambda x: x[0])\n",
    "blocshapes['mta2_distance'] = blocshapes.closest_mtastations.apply(lambda x: x[1])\n",
    "blocshapes['mta3_distance'] = blocshapes.closest_mtastations.apply(lambda x: x[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": 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>admin_fire</th>\n",
       "      <th>assemdist</th>\n",
       "      <th>atomic_num</th>\n",
       "      <th>atomicid</th>\n",
       "      <th>borough</th>\n",
       "      <th>censusbl_1</th>\n",
       "      <th>censusbl_2</th>\n",
       "      <th>censusbl_3</th>\n",
       "      <th>censusbloc</th>\n",
       "      <th>censustr_1</th>\n",
       "      <th>...</th>\n",
       "      <th>sb_3_page</th>\n",
       "      <th>sb_3_volum</th>\n",
       "      <th>schooldist</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>water_flag</th>\n",
       "      <th>closest_mtastations</th>\n",
       "      <th>mta1_distance</th>\n",
       "      <th>mta2_distance</th>\n",
       "      <th>mta3_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E 151</td>\n",
       "      <td>62</td>\n",
       "      <td>None</td>\n",
       "      <td>5024401430</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>1020</td>\n",
       "      <td>None</td>\n",
       "      <td>4023</td>\n",
       "      <td>024401</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>31</td>\n",
       "      <td>244110.620576</td>\n",
       "      <td>2424.560416</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.242428389554, 0.247227281305, 0.25252207981...</td>\n",
       "      <td>0.242428</td>\n",
       "      <td>0.247227</td>\n",
       "      <td>0.252522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E 151</td>\n",
       "      <td>62</td>\n",
       "      <td>None</td>\n",
       "      <td>5024401439</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>2004</td>\n",
       "      <td>None</td>\n",
       "      <td>4030</td>\n",
       "      <td>024401</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>31</td>\n",
       "      <td>177042.348067</td>\n",
       "      <td>1722.931212</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.236859586008, 0.241711596108, 0.24705439816...</td>\n",
       "      <td>0.236860</td>\n",
       "      <td>0.241712</td>\n",
       "      <td>0.247054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E 151</td>\n",
       "      <td>62</td>\n",
       "      <td>None</td>\n",
       "      <td>5024402223</td>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "      <td>1005</td>\n",
       "      <td>None</td>\n",
       "      <td>2002</td>\n",
       "      <td>024402</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>31</td>\n",
       "      <td>33790.843893</td>\n",
       "      <td>770.085650</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.229135059429, 0.234059704395, 0.23946790353...</td>\n",
       "      <td>0.229135</td>\n",
       "      <td>0.234060</td>\n",
       "      <td>0.239468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E 151</td>\n",
       "      <td>62</td>\n",
       "      <td>None</td>\n",
       "      <td>5024401441</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>2005</td>\n",
       "      <td>None</td>\n",
       "      <td>4031</td>\n",
       "      <td>024401</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>31</td>\n",
       "      <td>171623.335762</td>\n",
       "      <td>1697.908522</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.235713559318, 0.240572355528, 0.24592139642...</td>\n",
       "      <td>0.235714</td>\n",
       "      <td>0.240572</td>\n",
       "      <td>0.245921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E 151</td>\n",
       "      <td>62</td>\n",
       "      <td>None</td>\n",
       "      <td>5024401440</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>1018</td>\n",
       "      <td>None</td>\n",
       "      <td>4015</td>\n",
       "      <td>024401</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>31</td>\n",
       "      <td>78855.828376</td>\n",
       "      <td>1243.106869</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.247370128834, 0.252118772915, 0.25736810009...</td>\n",
       "      <td>0.247370</td>\n",
       "      <td>0.252119</td>\n",
       "      <td>0.257368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  admin_fire assemdist atomic_num    atomicid borough censusbl_1 censusbl_2  \\\n",
       "0      E 151        62       None  5024401430       5       None       1020   \n",
       "1      E 151        62       None  5024401439       5       None       2004   \n",
       "2      E 151        62       None  5024402223       5          B       1005   \n",
       "3      E 151        62       None  5024401441       5       None       2005   \n",
       "4      E 151        62       None  5024401440       5       None       1018   \n",
       "\n",
       "  censusbl_3 censusbloc censustr_1      ...      sb_3_page sb_3_volum  \\\n",
       "0       None       4023     024401      ...           None       None   \n",
       "1       None       4030     024401      ...           None       None   \n",
       "2       None       2002     024402      ...           None       None   \n",
       "3       None       4031     024401      ...           None       None   \n",
       "4       None       4015     024401      ...           None       None   \n",
       "\n",
       "  schooldist     shape_area   shape_leng water_flag  \\\n",
       "0         31  244110.620576  2424.560416          2   \n",
       "1         31  177042.348067  1722.931212          2   \n",
       "2         31   33790.843893   770.085650          2   \n",
       "3         31  171623.335762  1697.908522          2   \n",
       "4         31   78855.828376  1243.106869          2   \n",
       "\n",
       "                                 closest_mtastations mta1_distance  \\\n",
       "0  [0.242428389554, 0.247227281305, 0.25252207981...      0.242428   \n",
       "1  [0.236859586008, 0.241711596108, 0.24705439816...      0.236860   \n",
       "2  [0.229135059429, 0.234059704395, 0.23946790353...      0.229135   \n",
       "3  [0.235713559318, 0.240572355528, 0.24592139642...      0.235714   \n",
       "4  [0.247370128834, 0.252118772915, 0.25736810009...      0.247370   \n",
       "\n",
       "  mta2_distance mta3_distance  \n",
       "0      0.247227      0.252522  \n",
       "1      0.241712      0.247054  \n",
       "2      0.234060      0.239468  \n",
       "3      0.240572      0.245921  \n",
       "4      0.252119      0.257368  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blocshapes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# DROP COLUMNS\n",
    "blocshapes = blocshapes.drop(['admin_fire', 'assemdist', 'atomic_num', 'borough',\n",
    "       'censusbl_1', 'censusbl_2', 'censusbl_3', 'censusbloc',\n",
    "       'censustr_1', 'censustr_2', 'censustrac', 'commdist', 'electdist',\n",
    "       'geometry', 'hurricane', 'sb_1_page', 'sb_1_volum', 'sb_2_page',\n",
    "       'sb_2_volum', 'sb_3_page', 'sb_3_volum', 'schooldist',\n",
    "       'shape_area', 'shape_leng', 'water_flag', \n",
    "                             'closest_mtastations' ], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": 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>atomicid</th>\n",
       "      <th>mta1_distance</th>\n",
       "      <th>mta2_distance</th>\n",
       "      <th>mta3_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5024401430</td>\n",
       "      <td>0.242428</td>\n",
       "      <td>0.247227</td>\n",
       "      <td>0.252522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5024401439</td>\n",
       "      <td>0.236860</td>\n",
       "      <td>0.241712</td>\n",
       "      <td>0.247054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5024402223</td>\n",
       "      <td>0.229135</td>\n",
       "      <td>0.234060</td>\n",
       "      <td>0.239468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5024401441</td>\n",
       "      <td>0.235714</td>\n",
       "      <td>0.240572</td>\n",
       "      <td>0.245921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5024401440</td>\n",
       "      <td>0.247370</td>\n",
       "      <td>0.252119</td>\n",
       "      <td>0.257368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     atomicid  mta1_distance  mta2_distance  mta3_distance\n",
       "0  5024401430       0.242428       0.247227       0.252522\n",
       "1  5024401439       0.236860       0.241712       0.247054\n",
       "2  5024402223       0.229135       0.234060       0.239468\n",
       "3  5024401441       0.235714       0.240572       0.245921\n",
       "4  5024401440       0.247370       0.252119       0.257368"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blocshapes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65788, 4)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blocshapes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "blocshapes.to_csv(\"Blocs_mtadistances.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: CRS does not match!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>admin_fire</th>\n",
       "      <th>assemdist</th>\n",
       "      <th>atomic_num</th>\n",
       "      <th>atomicid</th>\n",
       "      <th>borough</th>\n",
       "      <th>censusbl_1</th>\n",
       "      <th>censusbl_2</th>\n",
       "      <th>censusbl_3</th>\n",
       "      <th>censusbloc</th>\n",
       "      <th>censustr_1</th>\n",
       "      <th>...</th>\n",
       "      <th>sb_3_volum</th>\n",
       "      <th>schooldist</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>water_flag</th>\n",
       "      <th>index_right</th>\n",
       "      <th>subway_line</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>stop_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1641</th>\n",
       "      <td>E 245</td>\n",
       "      <td>46</td>\n",
       "      <td>None</td>\n",
       "      <td>3036001402</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>1001</td>\n",
       "      <td>None</td>\n",
       "      <td>2003</td>\n",
       "      <td>036001</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>21</td>\n",
       "      <td>114560.458077</td>\n",
       "      <td>1410.439523</td>\n",
       "      <td>2</td>\n",
       "      <td>604</td>\n",
       "      <td>Q</td>\n",
       "      <td>40.576234</td>\n",
       "      <td>-73.967561</td>\n",
       "      <td>Ocean Parkway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2885</th>\n",
       "      <td>E 253</td>\n",
       "      <td>47</td>\n",
       "      <td>None</td>\n",
       "      <td>3029800204</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>2003</td>\n",
       "      <td>None</td>\n",
       "      <td>2003</td>\n",
       "      <td>029800</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>21</td>\n",
       "      <td>150481.414973</td>\n",
       "      <td>1765.263739</td>\n",
       "      <td>2</td>\n",
       "      <td>359</td>\n",
       "      <td>D</td>\n",
       "      <td>40.597889</td>\n",
       "      <td>-73.986958</td>\n",
       "      <td>25 Ave.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3165</th>\n",
       "      <td>E 245</td>\n",
       "      <td>46</td>\n",
       "      <td>None</td>\n",
       "      <td>3035400133</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>1006</td>\n",
       "      <td>None</td>\n",
       "      <td>1008</td>\n",
       "      <td>035400</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>21</td>\n",
       "      <td>551517.164145</td>\n",
       "      <td>3116.691608</td>\n",
       "      <td>2</td>\n",
       "      <td>416</td>\n",
       "      <td>F</td>\n",
       "      <td>40.580576</td>\n",
       "      <td>-73.974443</td>\n",
       "      <td>Neptune Ave.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3205</th>\n",
       "      <td>L 166</td>\n",
       "      <td>45</td>\n",
       "      <td>None</td>\n",
       "      <td>3030800120</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>1000</td>\n",
       "      <td>None</td>\n",
       "      <td>1001</td>\n",
       "      <td>030800</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>21</td>\n",
       "      <td>914201.746256</td>\n",
       "      <td>4291.565078</td>\n",
       "      <td>2</td>\n",
       "      <td>358</td>\n",
       "      <td>D</td>\n",
       "      <td>40.588066</td>\n",
       "      <td>-73.983498</td>\n",
       "      <td>Bay 50 St.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3380</th>\n",
       "      <td>E 265</td>\n",
       "      <td>23</td>\n",
       "      <td>None</td>\n",
       "      <td>4099200324</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>2015</td>\n",
       "      <td>None</td>\n",
       "      <td>3007</td>\n",
       "      <td>099200</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>27</td>\n",
       "      <td>59246.476648</td>\n",
       "      <td>2061.958701</td>\n",
       "      <td>2</td>\n",
       "      <td>246</td>\n",
       "      <td>A</td>\n",
       "      <td>40.595469</td>\n",
       "      <td>-73.767919</td>\n",
       "      <td>Beach 36 St.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     admin_fire assemdist atomic_num    atomicid borough censusbl_1  \\\n",
       "1641      E 245        46       None  3036001402       3       None   \n",
       "2885      E 253        47       None  3029800204       3       None   \n",
       "3165      E 245        46       None  3035400133       3       None   \n",
       "3205      L 166        45       None  3030800120       3       None   \n",
       "3380      E 265        23       None  4099200324       4       None   \n",
       "\n",
       "     censusbl_2 censusbl_3 censusbloc censustr_1      ...       sb_3_volum  \\\n",
       "1641       1001       None       2003     036001      ...             None   \n",
       "2885       2003       None       2003     029800      ...             None   \n",
       "3165       1006       None       1008     035400      ...             None   \n",
       "3205       1000       None       1001     030800      ...             None   \n",
       "3380       2015       None       3007     099200      ...             None   \n",
       "\n",
       "     schooldist     shape_area   shape_leng water_flag index_right  \\\n",
       "1641         21  114560.458077  1410.439523          2         604   \n",
       "2885         21  150481.414973  1765.263739          2         359   \n",
       "3165         21  551517.164145  3116.691608          2         416   \n",
       "3205         21  914201.746256  4291.565078          2         358   \n",
       "3380         27   59246.476648  2061.958701          2         246   \n",
       "\n",
       "     subway_line        lat       long      stop_name  \n",
       "1641           Q  40.576234 -73.967561  Ocean Parkway  \n",
       "2885           D  40.597889 -73.986958        25 Ave.  \n",
       "3165           F  40.580576 -73.974443   Neptune Ave.  \n",
       "3205           D  40.588066 -73.983498     Bay 50 St.  \n",
       "3380           A  40.595469 -73.767919   Beach 36 St.  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# JOIN BLOCKS AND STATION DATA (INNER JOIN)\n",
    "blocs_mta_inner = gpd.sjoin(blocshapes, mta2_gdf, how=\"inner\", op='intersects') #used to be left\n",
    "blocs_mta_inner.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(701, 31)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blocs_mta_inner.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# DROP COLUMNS\n",
    "blocs_mta_inner = blocs_mta_inner.drop(['admin_fire', 'assemdist', 'atomic_num', 'borough',\n",
    "       'censusbl_1', 'censusbl_2', 'censusbl_3', 'censusbloc',\n",
    "       'censustr_1', 'censustr_2', 'censustrac', 'commdist', 'electdist',\n",
    "       'geometry', 'hurricane', 'sb_1_page', 'sb_1_volum', 'sb_2_page',\n",
    "       'sb_2_volum', 'sb_3_page', 'sb_3_volum', 'schooldist',\n",
    "       'shape_area', 'shape_leng', 'water_flag', 'subway_line', 'lat', 'long', 'stop_name'], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": 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>atomicid</th>\n",
       "      <th>index_right</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1641</th>\n",
       "      <td>3036001402</td>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2885</th>\n",
       "      <td>3029800204</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3165</th>\n",
       "      <td>3035400133</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3205</th>\n",
       "      <td>3030800120</td>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3380</th>\n",
       "      <td>4099200324</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3617</th>\n",
       "      <td>4097202125</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4165</th>\n",
       "      <td>3044600101</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4228</th>\n",
       "      <td>3052000106</td>\n",
       "      <td>614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4252</th>\n",
       "      <td>3152200119</td>\n",
       "      <td>615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4469</th>\n",
       "      <td>3024200207</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4592</th>\n",
       "      <td>3024000301</td>\n",
       "      <td>366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4710</th>\n",
       "      <td>3021400207</td>\n",
       "      <td>578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5027</th>\n",
       "      <td>3047800101</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5056</th>\n",
       "      <td>3024800107</td>\n",
       "      <td>576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5308</th>\n",
       "      <td>3043200304</td>\n",
       "      <td>574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5616</th>\n",
       "      <td>3058000205</td>\n",
       "      <td>608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5777</th>\n",
       "      <td>3078600202</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5777</th>\n",
       "      <td>3078600202</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5995</th>\n",
       "      <td>3040800306</td>\n",
       "      <td>573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6413</th>\n",
       "      <td>3043800203</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6617</th>\n",
       "      <td>3038600127</td>\n",
       "      <td>571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6773</th>\n",
       "      <td>3076800103</td>\n",
       "      <td>610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6907</th>\n",
       "      <td>3052000212</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6907</th>\n",
       "      <td>3052000212</td>\n",
       "      <td>613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6974</th>\n",
       "      <td>3058600107</td>\n",
       "      <td>607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        atomicid  index_right\n",
       "1641  3036001402          604\n",
       "2885  3029800204          359\n",
       "3165  3035400133          416\n",
       "3205  3030800120          358\n",
       "3380  4099200324          246\n",
       "3617  4097202125          247\n",
       "4165  3044600101          421\n",
       "4228  3052000106          614\n",
       "4252  3152200119          615\n",
       "4469  3024200207          365\n",
       "4592  3024000301          366\n",
       "4710  3021400207          578\n",
       "5027  3047800101          425\n",
       "5056  3024800107          576\n",
       "5308  3043200304          574\n",
       "5616  3058000205          608\n",
       "5777  3078600202           38\n",
       "5777  3078600202          149\n",
       "5995  3040800306          573\n",
       "6413  3043800203          420\n",
       "6617  3038600127          571\n",
       "6773  3076800103          610\n",
       "6907  3052000212          283\n",
       "6907  3052000212          613\n",
       "6974  3058600107          607"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blocs_mta_inner.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(701, 2)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blocs_mta_inner.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "blocs_mta_inner.to_csv(\"BlocsMTA.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
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   "display_name": "Python [conda root]",
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  "language_info": {
   "codemirror_mode": {
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