geopandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GeoPandas\n",
    "\n",
    "https://geopandas.org/ A python package for reading, writing and processing geographical data. It has useful helper functions to query, index and manipulate geometric objects. Specifically those related to geographic objects.\n",
    "\n",
    "Other useful packages which geopandas uses:\n",
    "* pandas https://pandas.pydata.org/\n",
    "* shapely https://github.com/Toblerity/Shapely\n",
    "* pyproj https://pyproj4.github.io/pyproj/stable/\n",
    "* geopy https://github.com/geopy/geopy\n",
    "* rtree https://github.com/Toblerity/rtree\n",
    "* ...\n",
    "\n",
    "## Installation\n",
    "\n",
    "According to GeoPandas, you might have issues using pip on Windows. To be safe, use conda for installing binaries directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip3 install geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!conda install -c conda-forge geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "Some options for reading data into GeoPandas DataFrames (the main difference is specifying the geometry column)\n",
    "\n",
    "* Just like Pandas. A GeoPandas DataFrame is a Pandas DataFrame with a geometry (column)\n",
    "* Has some helpful built-in data\n",
    "* SQL\n",
    "* ...\n",
    "\n",
    "### Using Pandas DataFrames\n",
    "\n",
    "* First, build a Pandas DataFrame\n",
    "* Second, use it to create a GeoDataFrame while specifying the geometry column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First create a Pandas DataFrame\n",
    "df = pd.DataFrame({\n",
    "    'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],\n",
    "    'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],\n",
    "    'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],\n",
    "    'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Then create a GeoPandas DataFrame from the Pandas DataFrame\n",
    "\n",
    "# From lat lng\n",
    "gdf = gpd.GeoDataFrame(\n",
    "    df, geometry=gpd.points_from_xy(df.Longitude, df.Latitude)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>-34.58</td>\n",
       "      <td>-58.66</td>\n",
       "      <td>POINT (-58.66000 -34.58000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>-15.78</td>\n",
       "      <td>-47.91</td>\n",
       "      <td>POINT (-47.91000 -15.78000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City    Country  Latitude  Longitude                     geometry\n",
       "0  Buenos Aires  Argentina    -34.58     -58.66  POINT (-58.66000 -34.58000)\n",
       "1      Brasilia     Brazil    -15.78     -47.91  POINT (-47.91000 -15.78000)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Built-in Data\n",
    "\n",
    "* There are a few available datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "nybb = gpd.read_file(gpd.datasets.get_path('nybb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['naturalearth_cities', 'naturalearth_lowres', 'nybb']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpd.datasets.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop_est</th>\n",
       "      <th>continent</th>\n",
       "      <th>name</th>\n",
       "      <th>iso_a3</th>\n",
       "      <th>gdp_md_est</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>920938</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>FJI</td>\n",
       "      <td>8374.0</td>\n",
       "      <td>MULTIPOLYGON (((180.00000 -16.06713, 180.00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53950935</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>TZA</td>\n",
       "      <td>150600.0</td>\n",
       "      <td>POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    pop_est continent      name iso_a3  gdp_md_est  \\\n",
       "0    920938   Oceania      Fiji    FJI      8374.0   \n",
       "1  53950935    Africa  Tanzania    TZA    150600.0   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((180.00000 -16.06713, 180.00000...  \n",
       "1  POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data from the internet\n",
    "* One can read GeoJSON or shape files directly from the internet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "world_high_res = gpd.read_file(\"https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/cultural/ne_50m_admin_0_countries.zip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = world_high_res[world_high_res.CONTINENT == \"South America\"].plot(color=\"white\", edgecolor=\"black\")\n",
    "gdf.plot(ax=ax, color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GeoDataFrame\n",
    "\n",
    "* A Class which inherits from the Pandas DataFrame Class\n",
    "* Has a predefined geometry column (points, polygons, multipolygons)\n",
    "* Exposes geographic methods and properties:\n",
    "  * intersects\n",
    "  * touches\n",
    "  * sindex\n",
    "  * crs\n",
    "  * ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geopandas.geodataframe.GeoDataFrame"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(world)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geopandas.geoseries.GeoSeries"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(world.geometry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CRS (Coordinate Reference System)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: EPSG:4326>\n",
       "Name: WGS 84\n",
       "Axis Info [ellipsoidal]:\n",
       "- Lat[north]: Geodetic latitude (degree)\n",
       "- Lon[east]: Geodetic longitude (degree)\n",
       "Area of Use:\n",
       "- name: World.\n",
       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
       "Datum: World Geodetic System 1984 ensemble\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      MULTIPOLYGON (((180.00000 -16.06713, 180.00000...\n",
       "1      POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...\n",
       "2      POLYGON ((-8.66559 27.65643, -8.66512 27.58948...\n",
       "3      MULTIPOLYGON (((-122.84000 49.00000, -122.9742...\n",
       "4      MULTIPOLYGON (((-122.84000 49.00000, -120.0000...\n",
       "                             ...                        \n",
       "172    POLYGON ((18.82982 45.90887, 18.82984 45.90888...\n",
       "173    POLYGON ((20.07070 42.58863, 19.80161 42.50009...\n",
       "174    POLYGON ((20.59025 41.85541, 20.52295 42.21787...\n",
       "175    POLYGON ((-61.68000 10.76000, -61.10500 10.890...\n",
       "176    POLYGON ((30.83385 3.50917, 29.95350 4.17370, ...\n",
       "Name: geometry, Length: 177, dtype: geometry"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world.geometry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with Geo Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Area of Polygons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 54009 is a reference system that preserves area\n",
    "world[\"geometry_ESRI:54009\"] = world[\"geometry\"].to_crs(\"ESRI:54009\")\n",
    "world[\"area\"] = world[\"geometry_ESRI:54009\"].area // 1e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop_est</th>\n",
       "      <th>continent</th>\n",
       "      <th>name</th>\n",
       "      <th>iso_a3</th>\n",
       "      <th>gdp_md_est</th>\n",
       "      <th>geometry</th>\n",
       "      <th>geometry_ESRI:54009</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>80594017</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DEU</td>\n",
       "      <td>3979000.0</td>\n",
       "      <td>POLYGON ((14.11969 53.75703, 14.35332 53.24817...</td>\n",
       "      <td>POLYGON ((1018674.630 6261050.121, 1043430.938...</td>\n",
       "      <td>356928.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pop_est continent     name iso_a3  gdp_md_est  \\\n",
       "121  80594017    Europe  Germany    DEU   3979000.0   \n",
       "\n",
       "                                              geometry  \\\n",
       "121  POLYGON ((14.11969 53.75703, 14.35332 53.24817...   \n",
       "\n",
       "                                   geometry_ESRI:54009      area  \n",
       "121  POLYGON ((1018674.630 6261050.121, 1043430.938...  356928.0  "
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world[world.name == \"Germany\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop_est</th>\n",
       "      <th>continent</th>\n",
       "      <th>name</th>\n",
       "      <th>iso_a3</th>\n",
       "      <th>gdp_md_est</th>\n",
       "      <th>geometry</th>\n",
       "      <th>geometry_ESRI:54009</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>142257519</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Russia</td>\n",
       "      <td>RUS</td>\n",
       "      <td>3745000.0</td>\n",
       "      <td>MULTIPOLYGON (((178.725 71.099, 180.000 71.516...</td>\n",
       "      <td>MULTIPOLYGON (((8769033.872 7865244.997, 87094...</td>\n",
       "      <td>16953324.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>4050</td>\n",
       "      <td>Antarctica</td>\n",
       "      <td>Antarctica</td>\n",
       "      <td>ATA</td>\n",
       "      <td>810.0</td>\n",
       "      <td>MULTIPOLYGON (((-48.661 -78.047, -48.151 -78.0...</td>\n",
       "      <td>MULTIPOLYGON (((-1784201.372 -8394731.809, -17...</td>\n",
       "      <td>11990339.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35623680</td>\n",
       "      <td>North America</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1674000.0</td>\n",
       "      <td>MULTIPOLYGON (((-122.840 49.000, -122.974 49.0...</td>\n",
       "      <td>MULTIPOLYGON (((-9464830.276 5768352.350, -947...</td>\n",
       "      <td>9996089.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>326625791</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>18560000.0</td>\n",
       "      <td>MULTIPOLYGON (((-122.840 49.000, -120.000 49.0...</td>\n",
       "      <td>MULTIPOLYGON (((-9464830.276 5768352.350, -924...</td>\n",
       "      <td>9526449.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1379302771</td>\n",
       "      <td>Asia</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>21140000.0</td>\n",
       "      <td>MULTIPOLYGON (((109.475 18.198, 108.655 18.508...</td>\n",
       "      <td>MULTIPOLYGON (((10629598.599 2235514.570, 1053...</td>\n",
       "      <td>9428688.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pop_est      continent                      name iso_a3  gdp_md_est  \\\n",
       "18    142257519         Europe                    Russia    RUS   3745000.0   \n",
       "159        4050     Antarctica                Antarctica    ATA       810.0   \n",
       "3      35623680  North America                    Canada    CAN   1674000.0   \n",
       "4     326625791  North America  United States of America    USA  18560000.0   \n",
       "139  1379302771           Asia                     China    CHN  21140000.0   \n",
       "\n",
       "                                              geometry  \\\n",
       "18   MULTIPOLYGON (((178.725 71.099, 180.000 71.516...   \n",
       "159  MULTIPOLYGON (((-48.661 -78.047, -48.151 -78.0...   \n",
       "3    MULTIPOLYGON (((-122.840 49.000, -122.974 49.0...   \n",
       "4    MULTIPOLYGON (((-122.840 49.000, -120.000 49.0...   \n",
       "139  MULTIPOLYGON (((109.475 18.198, 108.655 18.508...   \n",
       "\n",
       "                                   geometry_ESRI:54009        area  \n",
       "18   MULTIPOLYGON (((8769033.872 7865244.997, 87094...  16953324.0  \n",
       "159  MULTIPOLYGON (((-1784201.372 -8394731.809, -17...  11990339.0  \n",
       "3    MULTIPOLYGON (((-9464830.276 5768352.350, -947...   9996089.0  \n",
       "4    MULTIPOLYGON (((-9464830.276 5768352.350, -924...   9526449.0  \n",
       "139  MULTIPOLYGON (((10629598.599 2235514.570, 1053...   9428688.0  "
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world.sort_values(\"area\", ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting more than just geo data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "GDP = (world.gdp_md_est * 100 / world.pop_est).clip(0, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "world.plot(column=GDP, ax=ax, legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing\n",
    "\n",
    "* Indexing like in Pandas is possible\n",
    "* Using indexing like using a geometric index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polygons bordering others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop_est</th>\n",
       "      <th>continent</th>\n",
       "      <th>name</th>\n",
       "      <th>iso_a3</th>\n",
       "      <th>gdp_md_est</th>\n",
       "      <th>geometry</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>8754413</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Austria</td>\n",
       "      <td>AUT</td>\n",
       "      <td>416600.0</td>\n",
       "      <td>POLYGON ((16.97967 48.12350, 16.90375 47.71487...</td>\n",
       "      <td>85033.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>9850845</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>HUN</td>\n",
       "      <td>267600.0</td>\n",
       "      <td>POLYGON ((22.08561 48.42226, 22.64082 48.15024...</td>\n",
       "      <td>92435.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>4292095</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>HRV</td>\n",
       "      <td>94240.0</td>\n",
       "      <td>POLYGON ((16.56481 46.50375, 16.88252 46.38063...</td>\n",
       "      <td>57543.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>62137802</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Italy</td>\n",
       "      <td>ITA</td>\n",
       "      <td>2221000.0</td>\n",
       "      <td>MULTIPOLYGON (((10.44270 46.89355, 11.04856 46...</td>\n",
       "      <td>315263.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pop_est continent     name iso_a3  gdp_md_est  \\\n",
       "114   8754413    Europe  Austria    AUT    416600.0   \n",
       "115   9850845    Europe  Hungary    HUN    267600.0   \n",
       "126   4292095    Europe  Croatia    HRV     94240.0   \n",
       "141  62137802    Europe    Italy    ITA   2221000.0   \n",
       "\n",
       "                                              geometry      area  \n",
       "114  POLYGON ((16.97967 48.12350, 16.90375 47.71487...   85033.0  \n",
       "115  POLYGON ((22.08561 48.42226, 22.64082 48.15024...   92435.0  \n",
       "126  POLYGON ((16.56481 46.50375, 16.88252 46.38063...   57543.0  \n",
       "141  MULTIPOLYGON (((10.44270 46.89355, 11.04856 46...  315263.0  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world[world.touches(slovenia)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polygons within a box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop_est</th>\n",
       "      <th>continent</th>\n",
       "      <th>name</th>\n",
       "      <th>iso_a3</th>\n",
       "      <th>gdp_md_est</th>\n",
       "      <th>geometry</th>\n",
       "      <th>geometry_ESRI:54009</th>\n",
       "      <th>area</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>67106161</td>\n",
       "      <td>Europe</td>\n",
       "      <td>France</td>\n",
       "      <td>-99</td>\n",
       "      <td>2699000.0</td>\n",
       "      <td>MULTIPOLYGON (((-51.65780 4.15623, -52.24934 3...</td>\n",
       "      <td>MULTIPOLYGON (((-5168883.083 513724.093, -5231...</td>\n",
       "      <td>645246.0</td>\n",
       "      <td>104.000894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>48958159</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Spain</td>\n",
       "      <td>ESP</td>\n",
       "      <td>1690000.0</td>\n",
       "      <td>POLYGON ((-7.45373 37.09779, -7.53711 37.42890...</td>\n",
       "      <td>POLYGON ((-649206.283 4462492.961, -654668.610...</td>\n",
       "      <td>502774.0</td>\n",
       "      <td>97.376076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>62137802</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Italy</td>\n",
       "      <td>ITA</td>\n",
       "      <td>2221000.0</td>\n",
       "      <td>MULTIPOLYGON (((10.44270 46.89355, 11.04856 46...</td>\n",
       "      <td>MULTIPOLYGON (((825535.862 5544385.378, 874885...</td>\n",
       "      <td>315263.0</td>\n",
       "      <td>197.098302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>8236303</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>CHE</td>\n",
       "      <td>496300.0</td>\n",
       "      <td>POLYGON ((9.59423 47.52506, 9.63293 47.34760, ...</td>\n",
       "      <td>POLYGON ((752798.109 5611883.754, 757441.420 5...</td>\n",
       "      <td>46156.0</td>\n",
       "      <td>178.444904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>594130</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>LUX</td>\n",
       "      <td>58740.0</td>\n",
       "      <td>POLYGON ((6.04307 50.12805, 6.24275 49.90223, ...</td>\n",
       "      <td>POLYGON ((458882.538 5886875.084, 475453.027 5...</td>\n",
       "      <td>2413.0</td>\n",
       "      <td>246.220472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>11491346</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>BEL</td>\n",
       "      <td>508600.0</td>\n",
       "      <td>POLYGON ((6.15666 50.80372, 6.04307 50.12805, ...</td>\n",
       "      <td>POLYGON ((463309.557 5957377.168, 458882.538 5...</td>\n",
       "      <td>30086.0</td>\n",
       "      <td>381.949943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>80594017</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DEU</td>\n",
       "      <td>3979000.0</td>\n",
       "      <td>POLYGON ((14.11969 53.75703, 14.35332 53.24817...</td>\n",
       "      <td>POLYGON ((1018674.630 6261050.121, 1043430.938...</td>\n",
       "      <td>356928.0</td>\n",
       "      <td>225.799088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pop_est continent         name iso_a3  gdp_md_est  \\\n",
       "43   67106161    Europe       France    -99   2699000.0   \n",
       "132  48958159    Europe        Spain    ESP   1690000.0   \n",
       "141  62137802    Europe        Italy    ITA   2221000.0   \n",
       "127   8236303    Europe  Switzerland    CHE    496300.0   \n",
       "128    594130    Europe   Luxembourg    LUX     58740.0   \n",
       "129  11491346    Europe      Belgium    BEL    508600.0   \n",
       "121  80594017    Europe      Germany    DEU   3979000.0   \n",
       "\n",
       "                                              geometry  \\\n",
       "43   MULTIPOLYGON (((-51.65780 4.15623, -52.24934 3...   \n",
       "132  POLYGON ((-7.45373 37.09779, -7.53711 37.42890...   \n",
       "141  MULTIPOLYGON (((10.44270 46.89355, 11.04856 46...   \n",
       "127  POLYGON ((9.59423 47.52506, 9.63293 47.34760, ...   \n",
       "128  POLYGON ((6.04307 50.12805, 6.24275 49.90223, ...   \n",
       "129  POLYGON ((6.15666 50.80372, 6.04307 50.12805, ...   \n",
       "121  POLYGON ((14.11969 53.75703, 14.35332 53.24817...   \n",
       "\n",
       "                                   geometry_ESRI:54009      area     density  \n",
       "43   MULTIPOLYGON (((-5168883.083 513724.093, -5231...  645246.0  104.000894  \n",
       "132  POLYGON ((-649206.283 4462492.961, -654668.610...  502774.0   97.376076  \n",
       "141  MULTIPOLYGON (((825535.862 5544385.378, 874885...  315263.0  197.098302  \n",
       "127  POLYGON ((752798.109 5611883.754, 757441.420 5...   46156.0  178.444904  \n",
       "128  POLYGON ((458882.538 5886875.084, 475453.027 5...    2413.0  246.220472  \n",
       "129  POLYGON ((463309.557 5957377.168, 458882.538 5...   30086.0  381.949943  \n",
       "121  POLYGON ((1018674.630 6261050.121, 1043430.938...  356928.0  225.799088  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world.loc[world.geometry.sindex.query(box(-1, 40, 8, 50), predicate=\"overlaps\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polygons within a radius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(509363.5489684087, 5774328.95621065, 707649.5489684087, 5972614.95621065)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a radius around Frankfurt as a polygon\n",
    "Frankfurt = gpd.GeoSeries([Point(8, 50)], crs=\"EPSG:4326\")\n",
    "\n",
    "Frankfurt = Frankfurt.to_crs(\"ESRI:54009\") \\\n",
    "                     .buffer(99143) \\\n",
    "                     .squeeze()\n",
    "\n",
    "Frankfurt_geo = gpd.GeoSeries([Frankfurt], crs=\"ESRI:54009\").to_crs(\"EPSG:4326\").squeeze()\n",
    "Frankfurt.bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19.1 ms, sys: 2.69 ms, total: 21.8 ms\n",
      "Wall time: 20.6 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop_est</th>\n",
       "      <th>continent</th>\n",
       "      <th>name</th>\n",
       "      <th>iso_a3</th>\n",
       "      <th>gdp_md_est</th>\n",
       "      <th>geometry</th>\n",
       "      <th>geometry_ESRI:54009</th>\n",
       "      <th>area</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>67106161</td>\n",
       "      <td>Europe</td>\n",
       "      <td>France</td>\n",
       "      <td>-99</td>\n",
       "      <td>2699000.0</td>\n",
       "      <td>MULTIPOLYGON (((-51.65780 4.15623, -52.24934 3...</td>\n",
       "      <td>MULTIPOLYGON (((-5168883.083 513724.093, -5231...</td>\n",
       "      <td>645246.0</td>\n",
       "      <td>104.000894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>80594017</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DEU</td>\n",
       "      <td>3979000.0</td>\n",
       "      <td>POLYGON ((14.11969 53.75703, 14.35332 53.24817...</td>\n",
       "      <td>POLYGON ((1018674.630 6261050.121, 1043430.938...</td>\n",
       "      <td>356928.0</td>\n",
       "      <td>225.799088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pop_est continent     name iso_a3  gdp_md_est  \\\n",
       "43   67106161    Europe   France    -99   2699000.0   \n",
       "121  80594017    Europe  Germany    DEU   3979000.0   \n",
       "\n",
       "                                              geometry  \\\n",
       "43   MULTIPOLYGON (((-51.65780 4.15623, -52.24934 3...   \n",
       "121  POLYGON ((14.11969 53.75703, 14.35332 53.24817...   \n",
       "\n",
       "                                   geometry_ESRI:54009      area     density  \n",
       "43   MULTIPOLYGON (((-5168883.083 513724.093, -5231...  645246.0  104.000894  \n",
       "121  POLYGON ((1018674.630 6261050.121, 1043430.938...  356928.0  225.799088  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "world.loc[world.geometry.sindex.query(Frankfurt_geo, predicate=\"intersects\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joining\n",
    "\n",
    "* joining on geometric columns could be slow if using conventional matching\n",
    "* sjoin using spatial indexing to speed the process up and allow for complicated geometric joining."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from geopandas.tools import sjoin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj = sjoin(world[[\"name\", \"geometry\"]],\n",
    "            world[[\"name\", \"geometry\"]],\n",
    "            op=\"touches\", how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name_right</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name_left</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>[Pakistan, China, Tajikistan, Turkmenistan, Uz...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>[Greece, Macedonia, Kosovo, Montenegro]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>[Mauritania, W. Sahara, Libya, Morocco, Tunisi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>[Namibia, Zambia, Dem. Rep. Congo, Congo]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antarctica</th>\n",
       "      <td>[nan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W. Sahara</th>\n",
       "      <td>[Mauritania, Algeria, Morocco]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>[Oman, Saudi Arabia]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>[Namibia, Botswana, Zimbabwe, Mozambique, Mala...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>[South Africa, Botswana, Mozambique, Zambia]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eSwatini</th>\n",
       "      <td>[South Africa, Mozambique]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    name_right\n",
       "name_left                                                     \n",
       "Afghanistan  [Pakistan, China, Tajikistan, Turkmenistan, Uz...\n",
       "Albania                [Greece, Macedonia, Kosovo, Montenegro]\n",
       "Algeria      [Mauritania, W. Sahara, Libya, Morocco, Tunisi...\n",
       "Angola               [Namibia, Zambia, Dem. Rep. Congo, Congo]\n",
       "Antarctica                                               [nan]\n",
       "...                                                        ...\n",
       "W. Sahara                       [Mauritania, Algeria, Morocco]\n",
       "Yemen                                     [Oman, Saudi Arabia]\n",
       "Zambia       [Namibia, Botswana, Zimbabwe, Mozambique, Mala...\n",
       "Zimbabwe          [South Africa, Botswana, Mozambique, Zambia]\n",
       "eSwatini                            [South Africa, Mozambique]\n",
       "\n",
       "[177 rows x 1 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj.groupby(\"name_left\") \\\n",
    "   .agg({\n",
    "       \"name_right\": list\n",
    "   })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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