examples/spatial_joins.ipynb

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      "#Spatial Joins\n",
      "\n",
      "A *spatial join* uses [binary predicates](http://toblerity.org/shapely/manual.html#binary-predicates) \n",
      "such as `intersects` and `crosses` to combine two `GeoDataFrames` based on the spatial relationship \n",
      "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"
     ]
    },
    {
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      "from IPython.core.display import Image \n",
      "Image(url=\"https://dl.dropboxusercontent.com/u/6769420/sjoin_test.png\") "
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      "\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",
      "```"
     ]
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      "## 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`:"
     ]
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     "input": [
      "import os\n",
      "from shapely.geometry import Point\n",
      "from geopandas import GeoDataFrame, read_file\n",
      "from geopandas.tools import overlay\n",
      "\n",
      "# NYC Boros\n",
      "zippath = os.path.abspath('../examples/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)))])"
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        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
        "    </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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  POINT (932450 139211)</td>\n",
        "      <td> 1071661</td>\n",
        "      <td> 793239</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  POINT (951725 158301)</td>\n",
        "      <td> 1110026</td>\n",
        "      <td> 793424</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  POINT (971000 177391)</td>\n",
        "      <td> 1148391</td>\n",
        "      <td> 793609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>  POINT (990275 196481)</td>\n",
        "      <td> 1186756</td>\n",
        "      <td> 793794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> POINT (1009550 215571)</td>\n",
        "      <td> 1225121</td>\n",
        "      <td> 793979</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> POINT (1028825 234661)</td>\n",
        "      <td> 1263486</td>\n",
        "      <td> 794164</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> POINT (1048100 253751)</td>\n",
        "      <td> 1301851</td>\n",
        "      <td> 794349</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> POINT (1067375 272841)</td>\n",
        "      <td> 1340216</td>\n",
        "      <td> 794534</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\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",
        "6  POINT (1028825 234661)  1263486  794164\n",
        "7  POINT (1048100 253751)  1301851  794349\n",
        "8  POINT (1067375 272841)  1340216  794534"
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        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
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        "   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...  "
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       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fe17c8344d0>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "polydf.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7fe17c48f8d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CgizNyZMncXZ2Zninqj8kgAFBn3gV8IHyDkUIe/8Sl3kd0NOTBkC3vF6x8dAd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\nJCQkJCQ+Pv4PeOABo584nw0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe17c7a93d0>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Joins"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from geopandas.tools import sjoin\n",
      "join_left_df = sjoin(pointdf, polydf, how=\"left\")\n",
      "join_left_df\n",
      "# Note the NaNs where the point did not intersect a boro"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>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>          None</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>  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>  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>          None</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>          None</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>  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>  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>          None</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>          None</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>           NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "                 geometry   value1  value2  BoroCode       BoroName  \\\n",
        "0   POINT (913175 120121)  1033296  793054       NaN           None   \n",
        "1   POINT (932450 139211)  1071661  793239         5  Staten Island   \n",
        "2   POINT (951725 158301)  1110026  793424         5  Staten Island   \n",
        "3   POINT (971000 177391)  1148391  793609       NaN           None   \n",
        "4   POINT (990275 196481)  1186756  793794       NaN           None   \n",
        "5  POINT (1009550 215571)  1225121  793979         4         Queens   \n",
        "6  POINT (1028825 234661)  1263486  794164         2          Bronx   \n",
        "7  POINT (1048100 253751)  1301851  794349       NaN           None   \n",
        "8  POINT (1067375 272841)  1340216  794534       NaN           None   \n",
        "\n",
        "     Shape_Area     Shape_Leng  \n",
        "0           NaN            NaN  \n",
        "1  1.623847e+09  330454.175933  \n",
        "2  1.623847e+09  330454.175933  \n",
        "3           NaN            NaN  \n",
        "4           NaN            NaN  \n",
        "5  3.045079e+09  896875.396449  \n",
        "6  1.186822e+09  464475.145651  \n",
        "7           NaN            NaN  \n",
        "8           NaN            NaN  "
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "join_right_df = sjoin(pointdf, polydf, how=\"right\")\n",
      "join_right_df\n",
      "# Note Staten Island is repeated"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
        "      <th>value1</th>\n",
        "      <th>value2</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",
        "      <td> 1071661</td>\n",
        "      <td> 793239</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</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",
        "      <td> 1110026</td>\n",
        "      <td> 793424</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</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",
        "      <td>     NaN</td>\n",
        "      <td>    NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</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",
        "      <td> 1225121</td>\n",
        "      <td> 793979</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</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",
        "      <td>     NaN</td>\n",
        "      <td>    NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</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",
        "      <td> 1263486</td>\n",
        "      <td> 794164</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "   BoroCode       BoroName    Shape_Area     Shape_Leng  \\\n",
        "0         5  Staten Island  1.623847e+09  330454.175933   \n",
        "1         5  Staten Island  1.623847e+09  330454.175933   \n",
        "2         3       Brooklyn  1.937810e+09  741227.337073   \n",
        "3         4         Queens  3.045079e+09  896875.396449   \n",
        "4         1      Manhattan  6.364308e+08  358400.912836   \n",
        "5         2          Bronx  1.186822e+09  464475.145651   \n",
        "\n",
        "                                            geometry   value1  value2  \n",
        "0  (POLYGON ((970217.0223999023 145643.3322143555...  1071661  793239  \n",
        "1  (POLYGON ((970217.0223999023 145643.3322143555...  1110026  793424  \n",
        "2  (POLYGON ((1021176.479003906 151374.7969970703...      NaN     NaN  \n",
        "3  (POLYGON ((1029606.076599121 156073.8142089844...  1225121  793979  \n",
        "4  (POLYGON ((981219.0557861328 188655.3157958984...      NaN     NaN  \n",
        "5  (POLYGON ((1012821.805786133 229228.2645874023...  1263486  794164  "
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "join_inner_df = sjoin(pointdf, polydf, how=\"inner\")\n",
      "join_inner_df\n",
      "# Note the lack of NaNs; dropped anything that didn't intersect"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>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 (932450 139211)</td>\n",
        "      <td> 1071661</td>\n",
        "      <td> 793239</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>1</th>\n",
        "      <td>  POINT (951725 158301)</td>\n",
        "      <td> 1110026</td>\n",
        "      <td> 793424</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 (1009550 215571)</td>\n",
        "      <td> 1225121</td>\n",
        "      <td> 793979</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>3</th>\n",
        "      <td> POINT (1028825 234661)</td>\n",
        "      <td> 1263486</td>\n",
        "      <td> 794164</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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "                 geometry   value1  value2  BoroCode       BoroName  \\\n",
        "0   POINT (932450 139211)  1071661  793239         5  Staten Island   \n",
        "1   POINT (951725 158301)  1110026  793424         5  Staten Island   \n",
        "2  POINT (1009550 215571)  1225121  793979         4         Queens   \n",
        "3  POINT (1028825 234661)  1263486  794164         2          Bronx   \n",
        "\n",
        "     Shape_Area     Shape_Leng  \n",
        "0  1.623847e+09  330454.175933  \n",
        "1  1.623847e+09  330454.175933  \n",
        "2  3.045079e+09  896875.396449  \n",
        "3  1.186822e+09  464475.145651  "
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "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",
     "collapsed": false,
     "input": [
      "sjoin(pointdf, polydf, how=\"left\", op=\"within\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>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>          None</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>  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>  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>          None</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>          None</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>  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>  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>          None</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>          None</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>           NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "                 geometry   value1  value2  BoroCode       BoroName  \\\n",
        "0   POINT (913175 120121)  1033296  793054       NaN           None   \n",
        "1   POINT (932450 139211)  1071661  793239         5  Staten Island   \n",
        "2   POINT (951725 158301)  1110026  793424         5  Staten Island   \n",
        "3   POINT (971000 177391)  1148391  793609       NaN           None   \n",
        "4   POINT (990275 196481)  1186756  793794       NaN           None   \n",
        "5  POINT (1009550 215571)  1225121  793979         4         Queens   \n",
        "6  POINT (1028825 234661)  1263486  794164         2          Bronx   \n",
        "7  POINT (1048100 253751)  1301851  794349       NaN           None   \n",
        "8  POINT (1067375 272841)  1340216  794534       NaN           None   \n",
        "\n",
        "     Shape_Area     Shape_Leng  \n",
        "0           NaN            NaN  \n",
        "1  1.623847e+09  330454.175933  \n",
        "2  1.623847e+09  330454.175933  \n",
        "3           NaN            NaN  \n",
        "4           NaN            NaN  \n",
        "5  3.045079e+09  896875.396449  \n",
        "6  1.186822e+09  464475.145651  \n",
        "7           NaN            NaN  \n",
        "8           NaN            NaN  "
       ]
      }
     ],
     "prompt_number": 32
    }
   ],
   "metadata": {}
  }
 ]
}