{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hvplot.pandas  # noqa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using hvplot with geopandas is as simple as loading a geopandas dataframe and calling `hvplot` on it with `geo=True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "\n",
    "cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))\n",
    "cities.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities.hvplot(geo=True, tiles=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can easily change the tiles, add coastlines, or which fields show up in the hover text:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities.hvplot(tiles='EsriTerrain', coastline=True, hover_cols='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also alter the projection of the data using cartopy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cartopy.crs as ccrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities.hvplot(coastline=True, projection=ccrs.Geostationary(central_longitude=-30), global_extent=True)"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python",
   "pygments_lexer": "ipython3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
