geopandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import fiona as fi\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "\n",
    "cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))\n",
    "\n",
    "# For attribute join\n",
    "country_shapes = world[['geometry', 'iso_a3']]\n",
    "\n",
    "country_names = world[['name', 'iso_a3']]\n",
    "\n",
    "# For spatial join\n",
    "countries = world[['geometry', 'name']]\n",
    "\n",
    "countries = countries.rename(columns={'name':'country'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>28400000.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>22270.0</td>\n",
       "      <td>POLYGON ((61.21081709172574 35.65007233330923,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12799293.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>110300.0</td>\n",
       "      <td>(POLYGON ((16.32652835456705 -5.87747039146621...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3639453.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>21810.0</td>\n",
       "      <td>POLYGON ((20.59024743010491 41.85540416113361,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4798491.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>184300.0</td>\n",
       "      <td>POLYGON ((51.57951867046327 24.24549713795111,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40913584.0</td>\n",
       "      <td>South America</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "      <td>573900.0</td>\n",
       "      <td>(POLYGON ((-65.50000000000003 -55.199999999999...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pop_est      continent                  name iso_a3  gdp_md_est  \\\n",
       "0  28400000.0           Asia           Afghanistan    AFG     22270.0   \n",
       "1  12799293.0         Africa                Angola    AGO    110300.0   \n",
       "2   3639453.0         Europe               Albania    ALB     21810.0   \n",
       "3   4798491.0           Asia  United Arab Emirates    ARE    184300.0   \n",
       "4  40913584.0  South America             Argentina    ARG    573900.0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((61.21081709172574 35.65007233330923,...  \n",
       "1  (POLYGON ((16.32652835456705 -5.87747039146621...  \n",
       "2  POLYGON ((20.59024743010491 41.85540416113361,...  \n",
       "3  POLYGON ((51.57951867046327 24.24549713795111,...  \n",
       "4  (POLYGON ((-65.50000000000003 -55.199999999999...  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>geometry</th>\n",
       "      <th>iso_a3</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((61.21081709172574 35.65007233330923,...</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(POLYGON ((16.32652835456705 -5.87747039146621...</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLYGON ((20.59024743010491 41.85540416113361,...</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POLYGON ((51.57951867046327 24.24549713795111,...</td>\n",
       "      <td>ARE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(POLYGON ((-65.50000000000003 -55.199999999999...</td>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry iso_a3  \\\n",
       "0  POLYGON ((61.21081709172574 35.65007233330923,...    AFG   \n",
       "1  (POLYGON ((16.32652835456705 -5.87747039146621...    AGO   \n",
       "2  POLYGON ((20.59024743010491 41.85540416113361,...    ALB   \n",
       "3  POLYGON ((51.57951867046327 24.24549713795111,...    ARE   \n",
       "4  (POLYGON ((-65.50000000000003 -55.199999999999...    ARG   \n",
       "\n",
       "                   name  \n",
       "0           Afghanistan  \n",
       "1                Angola  \n",
       "2               Albania  \n",
       "3  United Arab Emirates  \n",
       "4             Argentina  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_shapes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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</th>\n",
       "      <th>iso_a3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name iso_a3\n",
       "0           Afghanistan    AFG\n",
       "1                Angola    AGO\n",
       "2               Albania    ALB\n",
       "3  United Arab Emirates    ARE\n",
       "4             Argentina    ARG"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_names.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>geometry</th>\n",
       "      <th>iso_a3</th>\n",
       "      <th>name_x</th>\n",
       "      <th>name_y</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((61.21081709172574 35.65007233330923,...</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(POLYGON ((16.32652835456705 -5.87747039146621...</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Angola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLYGON ((20.59024743010491 41.85540416113361,...</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POLYGON ((51.57951867046327 24.24549713795111,...</td>\n",
       "      <td>ARE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(POLYGON ((-65.50000000000003 -55.199999999999...</td>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Argentina</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry iso_a3  \\\n",
       "0  POLYGON ((61.21081709172574 35.65007233330923,...    AFG   \n",
       "1  (POLYGON ((16.32652835456705 -5.87747039146621...    AGO   \n",
       "2  POLYGON ((20.59024743010491 41.85540416113361,...    ALB   \n",
       "3  POLYGON ((51.57951867046327 24.24549713795111,...    ARE   \n",
       "4  (POLYGON ((-65.50000000000003 -55.199999999999...    ARG   \n",
       "\n",
       "                 name_x                name_y                  name  \n",
       "0           Afghanistan           Afghanistan           Afghanistan  \n",
       "1                Angola                Angola                Angola  \n",
       "2               Albania               Albania               Albania  \n",
       "3  United Arab Emirates  United Arab Emirates  United Arab Emirates  \n",
       "4             Argentina             Argentina             Argentina  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_shapes = country_shapes.merge(country_names, on='iso_a3')\n",
    "country_shapes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>geometry</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((61.21081709172574 35.65007233330923,...</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(POLYGON ((16.32652835456705 -5.87747039146621...</td>\n",
       "      <td>Angola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLYGON ((20.59024743010491 41.85540416113361,...</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POLYGON ((51.57951867046327 24.24549713795111,...</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(POLYGON ((-65.50000000000003 -55.199999999999...</td>\n",
       "      <td>Argentina</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry               country\n",
       "0  POLYGON ((61.21081709172574 35.65007233330923,...           Afghanistan\n",
       "1  (POLYGON ((16.32652835456705 -5.87747039146621...                Angola\n",
       "2  POLYGON ((20.59024743010491 41.85540416113361,...               Albania\n",
       "3  POLYGON ((51.57951867046327 24.24549713795111,...  United Arab Emirates\n",
       "4  (POLYGON ((-65.50000000000003 -55.199999999999...             Argentina"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One GeoDataFrame of countries, one of Cities.\n",
    "# Want to merge so we can get each city's country.\n",
    "countries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Vatican City</td>\n",
       "      <td>POINT (12.45338654497177 41.90328217996012)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Marino</td>\n",
       "      <td>POINT (12.44177015780014 43.936095834768)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vaduz</td>\n",
       "      <td>POINT (9.516669472907267 47.13372377429357)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>POINT (6.130002806227083 49.61166037912108)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Palikir</td>\n",
       "      <td>POINT (158.1499743237623 6.916643696007725)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name                                     geometry\n",
       "0  Vatican City  POINT (12.45338654497177 41.90328217996012)\n",
       "1    San Marino    POINT (12.44177015780014 43.936095834768)\n",
       "2         Vaduz  POINT (9.516669472907267 47.13372377429357)\n",
       "3    Luxembourg  POINT (6.130002806227083 49.61166037912108)\n",
       "4       Palikir  POINT (158.1499743237623 6.916643696007725)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Vatican City</td>\n",
       "      <td>POINT (12.45338654497177 41.90328217996012)</td>\n",
       "      <td>79</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Marino</td>\n",
       "      <td>POINT (12.44177015780014 43.936095834768)</td>\n",
       "      <td>79</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Rome</td>\n",
       "      <td>POINT (12.481312562874 41.89790148509894)</td>\n",
       "      <td>79</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vaduz</td>\n",
       "      <td>POINT (9.516669472907267 47.13372377429357)</td>\n",
       "      <td>9</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Vienna</td>\n",
       "      <td>POINT (16.36469309674374 48.20196113681686)</td>\n",
       "      <td>9</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name                                     geometry  index_right  \\\n",
       "0    Vatican City  POINT (12.45338654497177 41.90328217996012)           79   \n",
       "1      San Marino    POINT (12.44177015780014 43.936095834768)           79   \n",
       "192          Rome    POINT (12.481312562874 41.89790148509894)           79   \n",
       "2           Vaduz  POINT (9.516669472907267 47.13372377429357)            9   \n",
       "184        Vienna  POINT (16.36469309674374 48.20196113681686)            9   \n",
       "\n",
       "     country  \n",
       "0      Italy  \n",
       "1      Italy  \n",
       "192    Italy  \n",
       "2    Austria  \n",
       "184  Austria  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_with_country = gpd.sjoin(cities, countries, how=\"inner\", op='intersects')\n",
    "cities_with_country.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f93e49c91d0>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAACiCAYAAABCpHYhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cities_with_country.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['naturalearth_cities', 'naturalearth_lowres', 'nybb']"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpd.datasets.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "boros = gpd.read_file(gpd.datasets.get_path('nybb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>BoroCode</th>\n",
       "      <th>BoroName</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</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>330470.010332</td>\n",
       "      <td>1.623820e+09</td>\n",
       "      <td>(POLYGON ((970217.0223999023 145643.3322143555...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>Queens</td>\n",
       "      <td>896344.047763</td>\n",
       "      <td>3.045213e+09</td>\n",
       "      <td>(POLYGON ((1029606.076599121 156073.8142089844...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>741080.523166</td>\n",
       "      <td>1.937479e+09</td>\n",
       "      <td>(POLYGON ((1021176.479003906 151374.7969970703...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>359299.096471</td>\n",
       "      <td>6.364715e+08</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>464392.991824</td>\n",
       "      <td>1.186925e+09</td>\n",
       "      <td>(POLYGON ((1012821.805786133 229228.2645874023...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BoroCode       BoroName     Shape_Leng    Shape_Area  \\\n",
       "0         5  Staten Island  330470.010332  1.623820e+09   \n",
       "1         4         Queens  896344.047763  3.045213e+09   \n",
       "2         3       Brooklyn  741080.523166  1.937479e+09   \n",
       "3         1      Manhattan  359299.096471  6.364715e+08   \n",
       "4         2          Bronx  464392.991824  1.186925e+09   \n",
       "\n",
       "                                            geometry  \n",
       "0  (POLYGON ((970217.0223999023 145643.3322143555...  \n",
       "1  (POLYGON ((1029606.076599121 156073.8142089844...  \n",
       "2  (POLYGON ((1021176.479003906 151374.7969970703...  \n",
       "3  (POLYGON ((981219.0557861328 188655.3157958984...  \n",
       "4  (POLYGON ((1012821.805786133 229228.2645874023...  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boros.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "In [3]: boro_locations = geopandas.tools.geocode(boros.BoroName, provider=\"google\")\n",
    "\n",
    "In [4]: boro_locations\n",
    "Out[4]: \n",
    "                                geometry                       address\n",
    "0         POINT (-74.1502007 40.5795317)        Staten Island, NY, USA\n",
    "1         POINT (-73.7948516 40.7282239)               Queens, NY, USA\n",
    "2  POINT (-73.94415789999999 40.6781784)             Brooklyn, NY, USA\n",
    "3         POINT (-73.9712488 40.7830603)  Manhattan, New York, NY, USA\n",
    "4         POINT (-73.8648268 40.8447819)                Bronx, NY, USA\n",
    "\n",
    "In [5]: import matplotlib.pyplot as plt\n",
    "\n",
    "In [6]: fig, ax = plt.subplots()\n",
    "\n",
    "In [7]: boros.to_crs({\"init\": \"epsg:4326\"}).plot(ax=ax, color=\"white\", edgecolor=\"black\");\n",
    "\n",
    "In [8]: boro_locations.plot(ax=ax, color=\"red\");"
   ]
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}