geopandas-play.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to Parallelize GeoPandas (maybe)\n",
    "====================================\n",
    "\n",
    "This notebook gives examples of how to break apart a geopandas dataset into a GeoDataFrame of GeoDataFrames.  It performs a region subselection and a spatial join on the full dataset by manipulating the sub-datasets.\n",
    "\n",
    "There is no actual parallelism here, but everything here is amenable to being wrapped with [dask.delayed](http://dask.pydata.org/en/latest/delayed.html), which should be able to handle all of the actual execution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A simple dataset cut into two triangular pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import shapely\n",
    "import random\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f16deb8dc88>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16deb8d240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = [shapely.geometry.Point(random.random(), random.random()) for i in range(100)]\n",
    "gdf = gpd.GeoDataFrame({'geometry': points, 'value': np.random.random(len(points))})\n",
    "gdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POINT (0.93541533352915 0.09510387753660587)</td>\n",
       "      <td>0.354677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POINT (0.0682664027026203 0.6300355077103446)</td>\n",
       "      <td>0.068994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POINT (0.6111960159676231 0.4762759727847248)</td>\n",
       "      <td>0.490330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POINT (0.1918998115120579 0.8120888506748247)</td>\n",
       "      <td>0.760044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POINT (0.8891961071955971 0.07229128951183272)</td>\n",
       "      <td>0.889795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         geometry     value\n",
       "0    POINT (0.93541533352915 0.09510387753660587)  0.354677\n",
       "1   POINT (0.0682664027026203 0.6300355077103446)  0.068994\n",
       "2   POINT (0.6111960159676231 0.4762759727847248)  0.490330\n",
       "3   POINT (0.1918998115120579 0.8120888506748247)  0.760044\n",
       "4  POINT (0.8891961071955971 0.07229128951183272)  0.889795"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.04 -0.04 1.08 1.08\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,1.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.0216\" opacity=\"0.6\" d=\"M 0.0,0.0 L 1.0,0.0 L 0.0,1.0 L 0.0,0.0 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x7f16deb82940>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = shapely.geometry.Polygon([(0, 0), (1, 0), (0, 1)])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.04 -0.04 1.08 1.08\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,1.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.0216\" opacity=\"0.6\" d=\"M 1.0,0.0 L 1.0,1.0 L 0.0,1.0 L 1.0,0.0 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x7f16db366978>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = shapely.geometry.Polygon([(1, 0), (1, 1), (0, 1)])\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we separate the data among the two geometries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = gdf[gdf.intersects(a)]\n",
    "bdf = gdf[gdf.intersects(b)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### And arrange them into a GeoDataFrame of GeoDataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((0 0, 1 0, 0 1, 0 0))</td>\n",
       "      <td>ge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((1 0, 1 1, 0 1, 1 0))</td>\n",
       "      <td>geome...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         geometry  \\\n",
       "0  POLYGON ((0 0, 1 0, 0 1, 0 0))   \n",
       "1  POLYGON ((1 0, 1 1, 0 1, 1 0))   \n",
       "\n",
       "                                              region  \n",
       "0                                              ge...  \n",
       "1                                           geome...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dgdf = gpd.GeoDataFrame({'geometry': [a, b], 'region': [adf, bdf]})\n",
    "dgdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f16db5e5ef0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8BKwuYK7k+TJfA24HXi/ZbNcBeyLiVYCIeKVk8wXw/uz22UBh/w5ZRDxK6/M95rIFuDda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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16deb82978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dgdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f16db232dd8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16db271f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dgdf.region[0].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parallel Operation 1: Subset with polygon\n",
    "\n",
    "Lets select out a region from our super-dataframe.  This will force us to think about how to break down a macro-scopic operation into many small operations.\n",
    "\n",
    "Lets subset out a square from the center of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"0.17600000000000002 0.17600000000000002 0.648 0.648\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,1.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.012960000000000001\" opacity=\"0.6\" d=\"M 0.2,0.2 L 0.8,0.2 L 0.8,0.8 L 0.2,0.8 L 0.2,0.2 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x7f16db13ad30>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = shapely.geometry.Polygon([(0.2, 0.2), (0.8, 0.2), (0.8, 0.8), (0.2, 0.8)])\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f16db0f5d68>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16db106080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "geometries = []\n",
    "regions = []\n",
    "\n",
    "for index, (geo, region) in dgdf.iterrows():\n",
    "    if geo.intersects(c):\n",
    "        geometry = geo.intersection(c)\n",
    "        region = region[region.intersects(c)]\n",
    "        \n",
    "        geometries.append(geometry)\n",
    "        regions.append(region)\n",
    "        \n",
    "result = gpd.GeoDataFrame({'geometry': geometries, 'region': regions})\n",
    "result.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f16db142fd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16db15a0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.region[0].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operation 2: Spatial Join with GeoPandas dataset\n",
    "\n",
    "Here we do a simple spatial join, where the other side is a normal GeoPandas GeoDataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a grid of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f16daf2a400>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UX6jBX/gi4D/7b6gb+/fdTO87LPSS+EvAPPAt4H1T1NvXgf8GHuuvuWl67YZqJxYEDV+7\nAH8DHAC+A2yfpteO3k8KHuqHxGPAb0+wt7uAHwKv0vvufyXwMeBjA6/dzn7v31nO19VPFkqammsE\nklaQQSDJIJBkEEjCIJCEQSAJg0ASBoEk4P8AnsO5/8TMv2UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16dafa2c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "delta = 0.1\n",
    "grid = [shapely.geometry.Polygon([(x, y), (x + delta, y), (x + delta, y + delta), (x, y + delta)])\n",
    "        for x in np.arange(0, 1, 0.1)\n",
    "        for y in np.arange(0, 1, 0.1)]\n",
    "\n",
    "grid = gpd.GeoDataFrame({'geometry': grid, 'x': np.arange(len(grid)), 'y': np.arange(len(grid)) ** 2})\n",
    "grid.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((0 0, 0.1 0, 0.1 0.1, 0 0.1, 0 0))</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((0 0.1, 0.1 0.1, 0.1 0.2, 0 0.2, 0 0.1))</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLYGON ((0 0.2, 0.1 0.2, 0.1 0.3, 0 0.3, 0 0.2))</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POLYGON ((0 0.3, 0.1 0.3, 0.1 0.4, 0 0.4, 0 0.3))</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POLYGON ((0 0.4, 0.1 0.4, 0.1 0.5, 0 0.5, 0 0.4))</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry  x   y\n",
       "0        POLYGON ((0 0, 0.1 0, 0.1 0.1, 0 0.1, 0 0))  0   0\n",
       "1  POLYGON ((0 0.1, 0.1 0.1, 0.1 0.2, 0 0.2, 0 0.1))  1   1\n",
       "2  POLYGON ((0 0.2, 0.1 0.2, 0.1 0.3, 0 0.3, 0 0.2))  2   4\n",
       "3  POLYGON ((0 0.3, 0.1 0.3, 0.1 0.4, 0 0.4, 0 0.3))  3   9\n",
       "4  POLYGON ((0 0.4, 0.1 0.4, 0.1 0.5, 0 0.5, 0 0.4))  4  16"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform Spatial Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometries</th>\n",
       "      <th>regions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((0 0, 1 0, 0 1, 0 0))</td>\n",
       "      <td>ge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((1 0, 1 1, 0 1, 1 0))</td>\n",
       "      <td>geome...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       geometries  \\\n",
       "0  POLYGON ((0 0, 1 0, 0 1, 0 0))   \n",
       "1  POLYGON ((1 0, 1 1, 0 1, 1 0))   \n",
       "\n",
       "                                             regions  \n",
       "0                                              ge...  \n",
       "1                                           geome...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geometries = []\n",
    "regions = []\n",
    "\n",
    "for index, (geo, region) in dgdf.iterrows():\n",
    "    intersection = grid.intersects(geo)\n",
    "    if intersection.any():\n",
    "        geometry = geo  # old geometry\n",
    "        subset = grid[intersection]\n",
    "        region = gpd.sjoin(region, subset, how='inner')\n",
    "        \n",
    "        geometries.append(geometry)\n",
    "        regions.append(region)\n",
    "        \n",
    "result = gpd.GeoDataFrame({'geometries': geometries, 'regions': regions})\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>value</th>\n",
       "      <th>index_right</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POINT (0.0682664027026203 0.6300355077103446)</td>\n",
       "      <td>0.068994</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>POINT (0.07428419722196156 0.6224502621273443)</td>\n",
       "      <td>0.839670</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POINT (0.8891961071955971 0.07229128951183272)</td>\n",
       "      <td>0.889795</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>6400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>POINT (0.8397928154621241 0.009835043023548429)</td>\n",
       "      <td>0.198468</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>6400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>POINT (0.2207359963462852 0.4475341217122858)</td>\n",
       "      <td>0.737915</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           geometry     value  index_right  \\\n",
       "1     POINT (0.0682664027026203 0.6300355077103446)  0.068994            6   \n",
       "70   POINT (0.07428419722196156 0.6224502621273443)  0.839670            6   \n",
       "4    POINT (0.8891961071955971 0.07229128951183272)  0.889795           80   \n",
       "27  POINT (0.8397928154621241 0.009835043023548429)  0.198468           80   \n",
       "5     POINT (0.2207359963462852 0.4475341217122858)  0.737915           24   \n",
       "\n",
       "     x     y  \n",
       "1    6    36  \n",
       "70   6    36  \n",
       "4   80  6400  \n",
       "27  80  6400  \n",
       "5   24   576  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.regions[0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f16dab9b128>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16dabe1c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.regions[0].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial Join with other DaskGeoPandas dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First lets make another parallelizable dataset by pulling apart our grid into two sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f16daa4f940>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAD8CAYAAACcoKqNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16daa59198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = shapely.geometry.Polygon([(0, 0), (.5, 0), (.5, 1), (0, 1)])\n",
    "e = shapely.geometry.Polygon([(.5, 0), (1, 0), (1, 1), (.5, 1)])\n",
    "\n",
    "ddf = grid[grid.geometry.centroid.intersects(d)]\n",
    "edf = grid[grid.geometry.centroid.intersects(e)]\n",
    "\n",
    "other = gpd.GeoDataFrame({'geometry': [d, e], 'region': [ddf, edf]})\n",
    "other.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((0 0, 0.5 0, 0.5 1, 0 1, 0 0))</td>\n",
       "      <td>g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((0.5 0, 1 0, 1 1, 0.5 1, 0.5 0))</td>\n",
       "      <td>g...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    geometry  \\\n",
       "0    POLYGON ((0 0, 0.5 0, 0.5 1, 0 1, 0 0))   \n",
       "1  POLYGON ((0.5 0, 1 0, 1 1, 0.5 1, 0.5 0))   \n",
       "\n",
       "                                              region  \n",
       "0                                               g...  \n",
       "1                                               g...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f16daa53710>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJUAAAD8CAYAAAB+WebdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16daa59fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "other.region[0].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we perform a dask-dask spatial join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = dgdf  # triangle dask-geodataframe from earlier\n",
    "B = other # half-split grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f16da7cab70>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8BKwuYK7k+TJfA24HXi/ZbNcBeyLiVYCIeKVk8wXw/uz22UBh/w5ZRDxK6/M95rIFuDda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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16da7dfc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f16da730828>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAD8CAYAAACcoKqNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16dafdf7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "B.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f16da656048>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ynO0eSYclPTvJ/ZJ0VzL7XknnzfjBIqLnv2j89eb/BJYB84CfAIMta64D7k5ubwC+W6DZ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\nkaYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16da721c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "geos = []\n",
    "regions = []\n",
    "\n",
    "for a_index, (a_geo, a_region) in A.iterrows():\n",
    "    for b_index, (b_geo, b_region) in B.iterrows():\n",
    "        if a_geo.intersects(b_geo):\n",
    "            geo = a_geo.intersection(b_geo)\n",
    "            region = gpd.sjoin(a_region, b_region, how='inner')\n",
    "            \n",
    "            geos.append(geo)\n",
    "            regions.append(region)\n",
    "            \n",
    "result = gpd.GeoDataFrame({'geometry': geos, 'region': regions})\n",
    "result.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.04 -0.04 0.5800000000000001 1.08\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,1.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.0216\" opacity=\"0.6\" d=\"M 0.5,0.0 L 0.0,0.0 L 0.0,1.0 L 0.5,0.5 L 0.5,0.0 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x7f16da70f630>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.geometry[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>value</th>\n",
       "      <th>index_right</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POINT (0.0682664027026203 0.6300355077103446)</td>\n",
       "      <td>0.068994</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>POINT (0.07428419722196156 0.6224502621273443)</td>\n",
       "      <td>0.839670</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>POINT (0.2207359963462852 0.4475341217122858)</td>\n",
       "      <td>0.737915</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>POINT (0.03510484030883243 0.7511655248137925)</td>\n",
       "      <td>0.669927</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>POINT (0.1822252097249891 0.2151233784401591)</td>\n",
       "      <td>0.357155</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          geometry     value  index_right   x  \\\n",
       "1    POINT (0.0682664027026203 0.6300355077103446)  0.068994            6   6   \n",
       "70  POINT (0.07428419722196156 0.6224502621273443)  0.839670            6   6   \n",
       "5    POINT (0.2207359963462852 0.4475341217122858)  0.737915           24  24   \n",
       "10  POINT (0.03510484030883243 0.7511655248137925)  0.669927            7   7   \n",
       "11   POINT (0.1822252097249891 0.2151233784401591)  0.357155           12  12   \n",
       "\n",
       "      y  \n",
       "1    36  \n",
       "70   36  \n",
       "5   576  \n",
       "10   49  \n",
       "11  144  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.region[0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f16da600710>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16da9719e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.region[0].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next steps\n",
    "\n",
    "So, none of this was actually parallelized.  To do that we need to wrap the operations on the sub-geodataframes in dask.delayed while keeping the operations on the metadata concrete."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}