geopandas_spatial_joins.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples: GeoPandas Spatial Joins\n",
    "A *spatial join* uses [binary predicates](http://toblerity.org/shapely/manual.html#binary-predicates) such as `intersects` and `crosses` to combine two `GeoDataFrames` based on the  spatial relationship between their geometries.\n",
    "\n",
    "A common use case might be a spatial join between a point layer and a polygon layer where you want to retain the point geometries and grab the attributes of the intersecting polygons.\n",
    "\n",
    "-------------------------------------------------------\n",
    "*From https://github.com/geopandas/geopandas/blob/master/examples/spatial_joins.ipynb. Re-ran and tweaked by [Emilio Mayorga](https://github.com/emiliom), 12/10/2015, on my geopandas conda environment.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://dl.dropboxusercontent.com/u/6769420/sjoin_test.png\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import Image \n",
    "Image(url=\"https://dl.dropboxusercontent.com/u/6769420/sjoin_test.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Types of spatial joins\n",
    "\n",
    "We currently support the following methods of spatial joins. We refer to the *left_df* and *right_df* which are the correspond to the two dataframes passed in as args.\n",
    "\n",
    "### Left outer join\n",
    "\n",
    "In a LEFT OUTER JOIN (`how='left'`), we keep *all* rows from the left and duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the right if they intersect and lose right rows that don't intersect. A left outer join implies that we are interested in retaining the geometries of the left. \n",
    "\n",
    "This is equivalent to the PostGIS query:\n",
    "```\n",
    "SELECT pts.geom, pts.id as ptid, polys.id as polyid  \n",
    "FROM pts\n",
    "LEFT OUTER JOIN polys\n",
    "ON ST_Intersects(pts.geom, polys.geom);\n",
    "\n",
    "                    geom                    | ptid | polyid \n",
    "--------------------------------------------+------+--------\n",
    " 010100000040A9FBF2D88AD03F349CD47D796CE9BF |    4 |     10\n",
    " 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF |    3 |     10\n",
    " 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF |    3 |     20\n",
    " 0101000000F0D88AA0E1A4EEBF7052F7E5B115E9BF |    2 |     20\n",
    " 0101000000818693BA2F8FF7BF4ADD97C75604E9BF |    1 |       \n",
    "(5 rows)\n",
    "```\n",
    "\n",
    "### Right outer join\n",
    "\n",
    "In a RIGHT OUTER JOIN (`how='right'`), we keep *all* rows from the right and duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the left if they intersect and lose left rows that don't intersect. A right outer join implies that we are interested in retaining the geometries of the right. \n",
    "\n",
    "This is equivalent to the PostGIS query:\n",
    "```\n",
    "SELECT polys.geom, pts.id as ptid, polys.id as polyid  \n",
    "FROM pts\n",
    "RIGHT OUTER JOIN polys\n",
    "ON ST_Intersects(pts.geom, polys.geom);\n",
    "\n",
    "  geom    | ptid | polyid \n",
    "----------+------+--------\n",
    " 01...9BF |    4 |     10\n",
    " 01...9BF |    3 |     10\n",
    " 02...7BF |    3 |     20\n",
    " 02...7BF |    2 |     20\n",
    " 00...5BF |      |     30\n",
    "(5 rows)\n",
    "```\n",
    "\n",
    "### Inner join\n",
    "\n",
    "In an INNER JOIN (`how='inner'`), we keep rows from the right and left only where their binary predicate is `True`. We duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the right and left only if they intersect and lose all rows that do not. An inner join implies that we are interested in retaining the geometries of the left. \n",
    "\n",
    "This is equivalent to the PostGIS query:\n",
    "```\n",
    "SELECT pts.geom, pts.id as ptid, polys.id as polyid  \n",
    "FROM pts\n",
    "INNER JOIN polys\n",
    "ON ST_Intersects(pts.geom, polys.geom);\n",
    "\n",
    "                    geom                    | ptid | polyid \n",
    "--------------------------------------------+------+--------\n",
    " 010100000040A9FBF2D88AD03F349CD47D796CE9BF |    4 |     10\n",
    " 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF |    3 |     10\n",
    " 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF |    3 |     20\n",
    " 0101000000F0D88AA0E1A4EEBF7052F7E5B115E9BF |    2 |     20\n",
    "(4 rows) \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial Joins between two GeoDataFrames\n",
    "\n",
    "Let's take a look at how we'd implement these using `GeoPandas`. First, load up the NYC test data into `GeoDataFrames`:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Downloaded sample NYC Boroughs shape file from http://www.nyc.gov/html/dcp/download/bytes/nybb_14aav.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "from shapely.geometry import Point\n",
    "from geopandas import GeoDataFrame, read_file\n",
    "from geopandas.tools import overlay, sjoin\n",
    "\n",
    "# NYC Boros\n",
    "#zippath = os.path.abspath('../examples/nybb_14aav.zip')\n",
    "zippath = os.path.abspath('/home/mayorga/Desktop/MySciPythonApps/nybb_14aav.zip')\n",
    "polydf = read_file('/nybb_14a_av/nybb.shp', vfs='zip://' + zippath)\n",
    "\n",
    "# Generate some points\n",
    "b = [int(x) for x in polydf.total_bounds]\n",
    "N = 8\n",
    "pointdf = GeoDataFrame([\n",
    "    {'geometry' : Point(x, y), 'value1': x + y, 'value2': x - y}\n",
    "    for x, y in zip(range(b[0], b[2], int((b[2]-b[0])/N)),\n",
    "                    range(b[1], b[3], int((b[3]-b[1])/N)))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>geometry</th>\n",
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       "                 geometry   value1  value2\n",
       "0   POINT (913175 120121)  1033296  793054\n",
       "1   POINT (932450 139211)  1071661  793239\n",
       "2   POINT (951725 158301)  1110026  793424\n",
       "3   POINT (971000 177391)  1148391  793609\n",
       "4   POINT (990275 196481)  1186756  793794\n",
       "5  POINT (1009550 215571)  1225121  793979\n",
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pointdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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CCXII8bzVZmaDgHuOdsLxZfXQ5MvqY+TkaDY0+LLazGwQcHI0Myvh5GhmVsLJ0cyshJOj\nmVkJJ0czsxJOjmZmJZwczcxKODmamZVwcjQzK+HkaGZWwsnRzKyEk6OZWQknRzOzEk6OZmYlnBzN\nzEp0mxwl3S5pn6Qthdj7JW2Q9LSkjZIuLixbJGmXpB2SphXikyRtycuWFeLDJa3K8Q2SxheWzZe0\nM7/m9c0hm5l1ryc9xzuAGZ1i/wwsjogLgRvz90iaCMwBJuZ1bs1TsQIsBxZExARggqTaNhcAHTl+\nM7A0b2tk3vbk/FosqeG4jtLM7Bh1mxwj4kfAy53Ch4ARudwAtOfyLGBlRBzI06vuBqZIOg84IyJa\nc727gdm5PBOoTbn6AHBZLk8H1kXE/jzt63remqTNzOpi2HGu93ngEUnfICXYv87xUcCGQr02YDRw\nIJdr2nOc/HUPQEQclPSKpLPyttpKtmVmVnfHmxybgM9HxL9K+u/A7cDlfbdbx6a5uflwuVKpUKlU\nBmpXzKyPtLS00NLSMmDt92j2QUnnA/8WERfk7/dHREMuC9gfESMkXQ8QEV/Ly9YCi4GfA09ExLtz\n/OPAhyLiqlynOSI2SBoG/CIi3i5pLlCJiIV5ne8Aj0fEqk775tkHzYaAE2X2wRclXZLLlwI7c/lh\nYK6kUyU1AhOA1ojYC7wqaUpOplcCDxXWmZ/LVwCP5fI6YJqkBklnknqmjxzn/lqdVavVw/NJm50M\nur2slrQSuAQ4W9Ie0gjyPwDLck/v98BnACJim6TVwDbgINBU6NY1AXcCpwFrImJtjt8G3CNpF9AB\nzM3beknSTcDGXG9JHpixQaZarXLpim8C8PjC62hsbBzgPTLrvW6TY0R8/CiLPnCU+l8FvloS3wRc\nUBL/I/Cxo2zrDtKjRGZm/apH9xwHM99zHBxql9TuNVq99Pc9RydHMzshnCgDMmZmJzUnRzOzEk6O\nZmYlnBzNzEo4OZqZlXByNDMr4eRoZlbCydHMrISTo5lZCSdHM7MSTo5mZiWcHM3MSjg5mpmVcHI0\nMyvh5GhmVsLJ0cyshJOjmVmJbpOjpNsl7ZO0pVP8c5K2S9oqaWkhvkjSLkk7JE0rxCdJ2pKXLSvE\nh0taleMbJI0vLJsvaWd+zev94ZqZ9UxPeo53ADOKAUkfBmYC74uI9wLfyPGJwBxgYl7n1jwVK8By\nYEFETAAmSKptcwHQkeM3A0vztkaSZjqcnF+LJTUc74GamR2LbpNjRPwIeLlT+CrgnyLiQK7zqxyf\nBayMiAMR8TywG5gi6TzgjIhozfXuBmbn8kzgrlx+ALgsl6cD6yJif56SdT2dkrSZWb0c7z3HCcCH\n8mVwi6TaNK2jgLZCvTZgdEm8PcfJX/cARMRB4BVJZ3WxLTOzuut23uou1jszIj4o6WJgNfCOvtut\nY9Pc3Hy4XKlUqFQqA7Urg4qnS7UTWUtLCy0tLQPW/vEmxzbgewARsVHSIUlnk3qEYwv1xuS67bnc\nOU5eNg54UdIwYEREdEhqByqFdcYCj5ftTDE5WlKtVpl61QoAnly+0AnSTjidOzpLlizp1/aP97L6\nQeBSAEnvAk6NiF8DDwNzJZ0qqZF0+d0aEXuBVyVNyQM0VwIP5W09DMzP5SuAx3J5HTBNUoOkM4HL\ngUeOc3/NzI6JIqLrCtJK4BLgLOCXpBHke4HbgfcDrwPXRURLrn8D8GngIHBNRDyS45OAO4HTgDUR\ncXWODwfuAS4EOoC5eTAHSZ8Cbsi78o8RURu4Ke5fdHcMQ5Uvq+1kIomIUPc1+6i9Ez2xODmaDQ39\nnRz9CRkzsxJOjmZmJZwczcxKODmamZVwcjQzK+HkaGZWwsnRzKyEk6OZWQknRzOzEk6OZmYlnBzN\nzEo4OZqZlXByNDMr4eRoZlbCydHMrISTo5lZCSdHM7MSTo5mZiW6TY6Sbpe0T9KWkmXX5ZkHRxZi\niyTtkrRD0rRCfJKkLXnZskJ8uKRVOb5B0vjCsvmSdubXvN4dqplZz/Wk53gHMKNzUNJY0oyAPy/E\nJgJzgIl5nVvzbIMAy4EFETEBmCCpts0FQEeO3wwszdsaSZrMa3J+LZbUcMxHaGZ2HLpNjhHxI+Dl\nkkXfAr7UKTYLWBkRB/IMgruBKZLOA86IiNZc725gdi7PBGqzCj4AXJbL04F1EbE/IvYD6ylJ0mZm\n9XBc9xwlzQLaIuKZTotGAW2F79uA0SXx9hwnf90DEBEHgVckndXFtszM6m7Ysa4g6c9Ic0lfXgz3\n2R6d5DyXtNmJ4ZiTI/BO4Hzgp/l24hhgk6QppB7h2ELdMaQeX3sud46Tl40DXpQ0DBgRER2S2oFK\nYZ2xwONlO9Tc3Hy4XKlUqFQqZdUGXLVaZerUFQA8+eRCJ0izLrS0tNDS0jJg7Ssiuq8knQ/8W0Rc\nULKsCkyKiJfygMx9pAGU0cCjwF9EREh6CrgaaAW+D9wSEWslNQEXRMRVkuYCsyNibh6Q+QlwEaln\nugm4KN9/LLYfPTmGwcDJ0ez4SSIi+u0qtdueo6SVwCXAWZL2ADdGxB2FKoczU0Rsk7Qa2AYcBJoK\nmasJuBM4DVgTEWtz/DbgHkm7gA5gbt7WS5JuAjbmeks6J8YTTWNjI08+ufBw2cwGrx71HAezE6nn\naGbHr797jv6EjJlZCSdHM7MSTo5mZiWcHM3MSjg5mpmVcHI0Myvh5GhmVsLJ0cyshJOjmVkJJ0cz\nsxJOjmZmJZwczcxKODmamZVwcjQzK+HkaGZWwsnRzKyEk6OZWQknRzOzEk6OZmYluk2Okm6XtE/S\nlkLs65K2S/qppO9JGlFYtkjSLkk7JE0rxCdJ2pKXLSvEh0taleMbJI0vLJsvaWd+zeubQzYz615P\neo53ADM6xdYB74mIvwJ2AosA8tSsc4CJeZ1blSe3BpYDCyJiAjBBUm2bC4COHL8ZWJq3NRK4kTTN\n62RgsaSG4zrKPtTf8+i6vROzLbd34us2OUbEj4CXO8XWR8Sh/O1TwJhcngWsjIgDEfE8sBuYIuk8\n4IyIaM317gZm5/JM4K5cfgC4LJenA+siYn+eknU9b03S/e5kPwFP5vZO5mMbCu31t7645/hpYE0u\njwLaCsvagNEl8fYcJ3/dAxARB4FXJJ3Vxbb6XLVapVqt1mPTZnaC6lVylPQV4PWIuK+P9qffVatV\npk69iqlTr3KCNLMjIqLbF3A+sKVT7JPAk8DbCrHrgesL368FpgDnAtsL8Y8Dywt1PpjLw4Bf5fJc\nYEVhne8Ac0r2Lfzyy6+h8epJvuqr1zCOQx5M+SJwSUT8obDoYeA+Sd8iXQJPAFojIiS9KmkK0Apc\nCdxSWGc+sAG4Angsx9cBX82DMAIuB77ceV8iQp1jZma91W1ylLQSuAQ4W9IeYDFpdPpUYH0ejP5x\nRDRFxDZJq4FtwEGgKXL3DmgC7gROA9ZExNocvw24R9IuoIPUYyQiXpJ0E7Ax11uSB2bMzOpOR3KX\nmZkd1p/X8CX3C68BtgBbgWtybCTpsZ2dpEvrhkL9RcAuYAcwrRCflLezC1hWiA8HVuX483m9rbnd\n+cBLwB+BF4DvASP6qK0NQHPh2O7Nx7MTmAdcBxwCRvZDe3cD23N8aZ3b+znp8a2nST3+i3vR3p3A\nH/L7swEYXzg39gK/zW3N66PjWQXsy3Xn5+PZnY+vdi6+j/ToWhvp3HkG+Anw4Tq3twv4LvAO4DXg\nun5o7wd5va35OE+tc3vV3M423jxu0dP2xheW1drbWTs/cryx08/zlC7z0wAmxvfmg34b8Kekk/6d\nwD8DX8p1vgx8LZcnApuBU0gDRLs50vNtBSbn8hpgRi43Abfmtl4AVue2niD9Is8GzgSeIz2A3uu2\ncvl/AfvzsZ0N/A54P9BAStKP5pNhZJ3b+6+5fHau9/Y6t/c48GI+zo8AT/SivW/m92sL6YMF3yWd\nG4vz+7U4v2fPkQb9enM8c/J7ciHpl/O5fAzLSLd6RpDOxe3Ax/Kx3gksBN4DtBXO6z5vL6+zHPh/\npIRwXT3bI91u+zXwv/P6ZwJ/Usf2/p6UsBaSbrtVgXHH2N53c3lkob2GXB6Rl63u9PNc2FWOGsjP\nVv8l8FRE/CEi3gD+D/BR3vxQ+F0ceVi8Nw+Y/yUp+X44t/VLoD0iHoyIl/OyQ/Tdw+wvAMMjDVZd\nTvoFvzzSPdM3SH81i+rSHmng61HgbwEi4ld1bu+PpJ7BDNKJ2d6L9t4D1B4Rq304YCapN7KO9PTC\nR0jv3ed7eTwPAH9F+rDD6eQPH5A+iPDvuZ27SAOM90fEZmBF3tY24DRJp9SrvbxONf9Mt+XvqWN7\n00g9/8kAEfFyRByqY3uHSFcDs4E/B14HXu3DD498JH9S78OFn2cxt5QayOS4FfgvkkZK+jPg70jJ\n6ZyI2Jfr7APOyeXePGC+Ffgb4DeSxpB6GkVtpF/oPnmYnXR58KeS3km6HDwXGCtpFukkeKNT+3Vp\nD3gX6eRcKqlF0gfq1N55pPfuWtIv1HLg6+SPlfaivRcBIn84IB/Xn+d1audGW2671x8+ICWfUwrr\nnEPq0YwGDpB6o4c6beujwKaIOJC/7/P2JJ1O6mH9jjerS3uk8+a3wFRJmyR9sc7t3U+6TfG3pCur\nr+fEdkztdfPhkZHA/pL376iO61GevhAROyQtJfUCfku6LHqjU52Q1OsRo0Jbt5IuS9o7VbkEeCP6\n6GH23N7LpDf9PwG/IN0jWUT6S1bTJ48hHaW9N0jv79tIj039kHRZ8Y46tPci6a//VcD3SZd/LwC3\nk3qUfa6vzo2eNneU+HDga/T9MXZurxn4F+Cz9NE50017w4CLScnnb4DHJG0iJbx6tDeFdL5uBy4F\nfiTpsbes1Xft9ciA/suyiLg9Ij4QEZeQuuA7gX2SzoXDlw2/zNXbSb2hmlpvoZ0jl8PFeG2dcbl8\nN/CbiJiat/lGbuOTwAWk+1kU1jvutiQNI92juRD4Cumxp2dJN4SvId1vGQNsknROndrbmev+mnRP\nbCNwSNLZdWxvPmnAoI2UOCf38uc5utDeCFKv+zVSL7x2bowl/RL39nhGkO6jHihsax+pF9WejzEk\n1X5nLsz7d2VEVAvbrEd7k0n/hOWdpPPnBklNHOk193V7tautFyLi96QrqovqeHx/T7oaac+3fp4k\nDcQc0/FFRAdvPdfG5thLQEPh/RvDWztJb9bVDcl6v4D/nL+OI/3VGEFKUl/O8et56yDJqaQk8xxH\nbro/RfrrI95607b2SZzPkG7qjwP+g3QP56O53ed586h4b9uaC/xrLl9AuocylnRj+2eky42yAZm+\nbu9a0h+dBtJJ+UId2xtHGimuDchcBmzsZXv3ku5nzuXIgExz/hk2kwZkfsaRAZneHM93SYM52wrv\nUW0AoYF0Lm4n3fxvIP3RWV5yTvd5e3mdFaQBi8XAF+rZHuk8/RXwOVIvcj3wkTq29yXSubOQdNvk\nWeC9x9peHBmQqbV3+PctjgzIvOnn2WV+GuDk+MP8g9hMfhwiH9yjlD/KcwPpZvsOYHohXhvu3w3c\nUogPzz+QXcCreZubSTdmP0X6pT5AGrl+mjz61Qdtbchvau3Ylub4LmB+rv8z3vwoT73aezKvvwmo\n1Lm9PaSkvxn4MXBhL9pbBfyedEn0R9IIee3cqD3K81zh59nb43mYlNhfJ/0C78vbf5a3Pnry63ze\nPF14nV3H9nbln8cpvDU51qu9H5N6j1vIHZQ6trebdBtma15e9qhSd+2dX1j2KTr9vuV48VGeVXTz\nKI8fAjczK+FpEszMSjg5mpmVcHI0Myvh5GhmVsLJ0cyshJOjmVkJJ0czsxJOjmZmJf4/gGlPTVWz\nIV4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e3c3ef710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pointdf.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>BoroCode</th>\n",
       "      <th>BoroName</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>1.623847e+09</td>\n",
       "      <td>330454.175933</td>\n",
       "      <td>(POLYGON ((970217.0223999023 145643.3322143555...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1.937810e+09</td>\n",
       "      <td>741227.337073</td>\n",
       "      <td>(POLYGON ((1021176.479003906 151374.7969970703...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Queens</td>\n",
       "      <td>3.045079e+09</td>\n",
       "      <td>896875.396449</td>\n",
       "      <td>(POLYGON ((1029606.076599121 156073.8142089844...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>6.364308e+08</td>\n",
       "      <td>358400.912836</td>\n",
       "      <td>(POLYGON ((981219.0557861328 188655.3157958984...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>1.186822e+09</td>\n",
       "      <td>464475.145651</td>\n",
       "      <td>(POLYGON ((1012821.805786133 229228.2645874023...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BoroCode       BoroName    Shape_Area     Shape_Leng  \\\n",
       "0         5  Staten Island  1.623847e+09  330454.175933   \n",
       "1         3       Brooklyn  1.937810e+09  741227.337073   \n",
       "2         4         Queens  3.045079e+09  896875.396449   \n",
       "3         1      Manhattan  6.364308e+08  358400.912836   \n",
       "4         2          Bronx  1.186822e+09  464475.145651   \n",
       "\n",
       "                                            geometry  \n",
       "0  (POLYGON ((970217.0223999023 145643.3322143555...  \n",
       "1  (POLYGON ((1021176.479003906 151374.7969970703...  \n",
       "2  (POLYGON ((1029606.076599121 156073.8142089844...  \n",
       "3  (POLYGON ((981219.0557861328 188655.3157958984...  \n",
       "4  (POLYGON ((1012821.805786133 229228.2645874023...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polydf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MwM/Pz1CvkelRxlHx3jB71kxqly9AmSK5E9Qt++s0AC2rVMElk6YiCA4LY+T69Zy9fp2n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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e106dae90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "polydf.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>geometry</th>\n",
       "      <th>value1</th>\n",
       "      <th>value2</th>\n",
       "      <th>index_right</th>\n",
       "      <th>BoroCode</th>\n",
       "      <th>BoroName</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>Shape_Leng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POINT (913175 120121)</td>\n",
       "      <td>1033296</td>\n",
       "      <td>793054</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>POINT (932450 139211)</td>\n",
       "      <td>1071661</td>\n",
       "      <td>793239</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>1.623847e+09</td>\n",
       "      <td>330454.175933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POINT (951725 158301)</td>\n",
       "      <td>1110026</td>\n",
       "      <td>793424</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>1.623847e+09</td>\n",
       "      <td>330454.175933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POINT (971000 177391)</td>\n",
       "      <td>1148391</td>\n",
       "      <td>793609</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>4</th>\n",
       "      <td>POINT (990275 196481)</td>\n",
       "      <td>1186756</td>\n",
       "      <td>793794</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>5</th>\n",
       "      <td>POINT (1009550 215571)</td>\n",
       "      <td>1225121</td>\n",
       "      <td>793979</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Queens</td>\n",
       "      <td>3.045079e+09</td>\n",
       "      <td>896875.396449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>POINT (1028825 234661)</td>\n",
       "      <td>1263486</td>\n",
       "      <td>794164</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>1.186822e+09</td>\n",
       "      <td>464475.145651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>POINT (1048100 253751)</td>\n",
       "      <td>1301851</td>\n",
       "      <td>794349</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>8</th>\n",
       "      <td>POINT (1067375 272841)</td>\n",
       "      <td>1340216</td>\n",
       "      <td>794534</td>\n",
       "      <td>NaN</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": [
       "                 geometry   value1  value2  index_right  BoroCode  \\\n",
       "0   POINT (913175 120121)  1033296  793054          NaN       NaN   \n",
       "1   POINT (932450 139211)  1071661  793239            0         5   \n",
       "2   POINT (951725 158301)  1110026  793424            0         5   \n",
       "3   POINT (971000 177391)  1148391  793609          NaN       NaN   \n",
       "4   POINT (990275 196481)  1186756  793794          NaN       NaN   \n",
       "5  POINT (1009550 215571)  1225121  793979            2         4   \n",
       "6  POINT (1028825 234661)  1263486  794164            4         2   \n",
       "7  POINT (1048100 253751)  1301851  794349          NaN       NaN   \n",
       "8  POINT (1067375 272841)  1340216  794534          NaN       NaN   \n",
       "\n",
       "        BoroName    Shape_Area     Shape_Leng  \n",
       "0            NaN           NaN            NaN  \n",
       "1  Staten Island  1.623847e+09  330454.175933  \n",
       "2  Staten Island  1.623847e+09  330454.175933  \n",
       "3            NaN           NaN            NaN  \n",
       "4            NaN           NaN            NaN  \n",
       "5         Queens  3.045079e+09  896875.396449  \n",
       "6          Bronx  1.186822e+09  464475.145651  \n",
       "7            NaN           NaN            NaN  \n",
       "8            NaN           NaN            NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_left_df = sjoin(pointdf, polydf, how=\"left\")\n",
    "join_left_df\n",
    "# Note the NaNs where the point did not intersect a boro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A/suyiLg9Ij4QEZeQuuA7gX2SzoXDlw2/zNXbSb2hmlpvoZ0jl8PFeG2dcbl8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\nIV4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e1047fc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "join_left_df.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>value1</th>\n",
       "      <th>value2</th>\n",
       "      <th>index_left</th>\n",
       "      <th>BoroCode</th>\n",
       "      <th>BoroName</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index_right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1071661</td>\n",
       "      <td>793239</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>1.623847e+09</td>\n",
       "      <td>330454.175933</td>\n",
       "      <td>(POLYGON ((970217.0223999023 145643.3322143555...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1110026</td>\n",
       "      <td>793424</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>1.623847e+09</td>\n",
       "      <td>330454.175933</td>\n",
       "      <td>(POLYGON ((970217.0223999023 145643.3322143555...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1225121</td>\n",
       "      <td>793979</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Queens</td>\n",
       "      <td>3.045079e+09</td>\n",
       "      <td>896875.396449</td>\n",
       "      <td>(POLYGON ((1029606.076599121 156073.8142089844...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1263486</td>\n",
       "      <td>794164</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>1.186822e+09</td>\n",
       "      <td>464475.145651</td>\n",
       "      <td>(POLYGON ((1012821.805786133 229228.2645874023...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1.937810e+09</td>\n",
       "      <td>741227.337073</td>\n",
       "      <td>(POLYGON ((1021176.479003906 151374.7969970703...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>6.364308e+08</td>\n",
       "      <td>358400.912836</td>\n",
       "      <td>(POLYGON ((981219.0557861328 188655.3157958984...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              value1  value2  index_left  BoroCode       BoroName  \\\n",
       "index_right                                                         \n",
       "0            1071661  793239           1         5  Staten Island   \n",
       "0            1110026  793424           2         5  Staten Island   \n",
       "2            1225121  793979           5         4         Queens   \n",
       "4            1263486  794164           6         2          Bronx   \n",
       "1                NaN     NaN         NaN         3       Brooklyn   \n",
       "3                NaN     NaN         NaN         1      Manhattan   \n",
       "\n",
       "               Shape_Area     Shape_Leng  \\\n",
       "index_right                                \n",
       "0            1.623847e+09  330454.175933   \n",
       "0            1.623847e+09  330454.175933   \n",
       "2            3.045079e+09  896875.396449   \n",
       "4            1.186822e+09  464475.145651   \n",
       "1            1.937810e+09  741227.337073   \n",
       "3            6.364308e+08  358400.912836   \n",
       "\n",
       "                                                      geometry  \n",
       "index_right                                                     \n",
       "0            (POLYGON ((970217.0223999023 145643.3322143555...  \n",
       "0            (POLYGON ((970217.0223999023 145643.3322143555...  \n",
       "2            (POLYGON ((1029606.076599121 156073.8142089844...  \n",
       "4            (POLYGON ((1012821.805786133 229228.2645874023...  \n",
       "1            (POLYGON ((1021176.479003906 151374.7969970703...  \n",
       "3            (POLYGON ((981219.0557861328 188655.3157958984...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_right_df = sjoin(pointdf, polydf, how=\"right\")\n",
    "join_right_df\n",
    "# Note Staten Island is repeated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tj6xZo9RtPHYMgA49q5KvYNT6lIDfLX/aN5zFgX8u8PjxYwDOnT+DQ0Zr1iw9wtJ5e7h1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\nSIL/B7AiPtKxsomYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e108242d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "join_right_df.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>geometry</th>\n",
       "      <th>value1</th>\n",
       "      <th>value2</th>\n",
       "      <th>index_right</th>\n",
       "      <th>BoroCode</th>\n",
       "      <th>BoroName</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>Shape_Leng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POINT (932450 139211)</td>\n",
       "      <td>1071661</td>\n",
       "      <td>793239</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>1.623847e+09</td>\n",
       "      <td>330454.175933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POINT (951725 158301)</td>\n",
       "      <td>1110026</td>\n",
       "      <td>793424</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>1.623847e+09</td>\n",
       "      <td>330454.175933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>POINT (1009550 215571)</td>\n",
       "      <td>1225121</td>\n",
       "      <td>793979</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Queens</td>\n",
       "      <td>3.045079e+09</td>\n",
       "      <td>896875.396449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>POINT (1028825 234661)</td>\n",
       "      <td>1263486</td>\n",
       "      <td>794164</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>1.186822e+09</td>\n",
       "      <td>464475.145651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 geometry   value1  value2  index_right  BoroCode  \\\n",
       "1   POINT (932450 139211)  1071661  793239            0         5   \n",
       "2   POINT (951725 158301)  1110026  793424            0         5   \n",
       "5  POINT (1009550 215571)  1225121  793979            2         4   \n",
       "6  POINT (1028825 234661)  1263486  794164            4         2   \n",
       "\n",
       "        BoroName    Shape_Area     Shape_Leng  \n",
       "1  Staten Island  1.623847e+09  330454.175933  \n",
       "2  Staten Island  1.623847e+09  330454.175933  \n",
       "5         Queens  3.045079e+09  896875.396449  \n",
       "6          Bronx  1.186822e+09  464475.145651  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_inner_df = sjoin(pointdf, polydf, how=\"inner\")\n",
    "join_inner_df\n",
    "# Note the lack of NaNs; dropped anything that didn't intersect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e0ddf6b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "join_inner_df.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're not limited to using the `intersection` binary predicate. Any of the `Shapely` geometry methods that return a Boolean can be used by specifying the `op` kwarg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>geometry</th>\n",
       "      <th>value1</th>\n",
       "      <th>value2</th>\n",
       "      <th>index_right</th>\n",
       "      <th>BoroCode</th>\n",
       "      <th>BoroName</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>Shape_Leng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POINT (913175 120121)</td>\n",
       "      <td>1033296</td>\n",
       "      <td>793054</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>POINT (932450 139211)</td>\n",
       "      <td>1071661</td>\n",
       "      <td>793239</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>1.623847e+09</td>\n",
       "      <td>330454.175933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POINT (951725 158301)</td>\n",
       "      <td>1110026</td>\n",
       "      <td>793424</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>1.623847e+09</td>\n",
       "      <td>330454.175933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POINT (971000 177391)</td>\n",
       "      <td>1148391</td>\n",
       "      <td>793609</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>4</th>\n",
       "      <td>POINT (990275 196481)</td>\n",
       "      <td>1186756</td>\n",
       "      <td>793794</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>5</th>\n",
       "      <td>POINT (1009550 215571)</td>\n",
       "      <td>1225121</td>\n",
       "      <td>793979</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Queens</td>\n",
       "      <td>3.045079e+09</td>\n",
       "      <td>896875.396449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>POINT (1028825 234661)</td>\n",
       "      <td>1263486</td>\n",
       "      <td>794164</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>1.186822e+09</td>\n",
       "      <td>464475.145651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>POINT (1048100 253751)</td>\n",
       "      <td>1301851</td>\n",
       "      <td>794349</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>8</th>\n",
       "      <td>POINT (1067375 272841)</td>\n",
       "      <td>1340216</td>\n",
       "      <td>794534</td>\n",
       "      <td>NaN</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": [
       "                 geometry   value1  value2  index_right  BoroCode  \\\n",
       "0   POINT (913175 120121)  1033296  793054          NaN       NaN   \n",
       "1   POINT (932450 139211)  1071661  793239            0         5   \n",
       "2   POINT (951725 158301)  1110026  793424            0         5   \n",
       "3   POINT (971000 177391)  1148391  793609          NaN       NaN   \n",
       "4   POINT (990275 196481)  1186756  793794          NaN       NaN   \n",
       "5  POINT (1009550 215571)  1225121  793979            2         4   \n",
       "6  POINT (1028825 234661)  1263486  794164            4         2   \n",
       "7  POINT (1048100 253751)  1301851  794349          NaN       NaN   \n",
       "8  POINT (1067375 272841)  1340216  794534          NaN       NaN   \n",
       "\n",
       "        BoroName    Shape_Area     Shape_Leng  \n",
       "0            NaN           NaN            NaN  \n",
       "1  Staten Island  1.623847e+09  330454.175933  \n",
       "2  Staten Island  1.623847e+09  330454.175933  \n",
       "3            NaN           NaN            NaN  \n",
       "4            NaN           NaN            NaN  \n",
       "5         Queens  3.045079e+09  896875.396449  \n",
       "6          Bronx  1.186822e+09  464475.145651  \n",
       "7            NaN           NaN            NaN  \n",
       "8            NaN           NaN            NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sjoin(pointdf, polydf, how=\"left\", op=\"within\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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