geopandas/doc/source/docs/user_guide/interactive_mapping.ipynb

Interactive mapping

Alongside static plots, geopandas can create interactive maps based on the folium library.

Creating maps for interactive exploration mirrors the API of static plots in an explore() method of a GeoSeries or GeoDataFrame.

Loading some example data:

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import geopandas

nybb = geopandas.read_file(geopandas.datasets.get_path('nybb'))
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
cities = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))

The simplest option is to use GeoDataFrame.explore():

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nybb.explore()

Interactive plotting offers largely the same customisation as static one plus some features on top of that. Check the code below which plots a customised choropleth map. You can use "BoroName" column with NY boroughs names as an input of the choropleth, show (only) its name in the tooltip on hover but show all values on click. You can also pass custom background tiles (either a name supported by folium, a name recognized by xyzservices.providers.query_name(), XYZ URL or xyzservices.TileProvider object), specify colormap (all supported by matplotlib) and specify black outline.

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nybb.explore( 
     column="BoroName", # make choropleth based on "BoroName" column
     tooltip="BoroName", # show "BoroName" value in tooltip (on hover)
     popup=True, # show all values in popup (on click)
     tiles="CartoDB positron", # use "CartoDB positron" tiles
     cmap="Set1", # use "Set1" matplotlib colormap
     style_kwds=dict(color="black") # use black outline
    )

The explore() method returns a folium.Map object, which can also be passed directly (as you do with ax in plot()). You can then use folium functionality directly on the resulting map. In the example below, you can plot two GeoDataFrames on the same map and add layer control using folium. You can also add additional tiles allowing you to change the background directly in the map.

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import folium

m = world.explore(
     column="pop_est",  # make choropleth based on "BoroName" column
     scheme="naturalbreaks",  # use mapclassify's natural breaks scheme
     legend=True, # show legend
     k=10, # use 10 bins
     legend_kwds=dict(colorbar=False), # do not use colorbar
     name="countries" # name of the layer in the map
)

cities.explore(
     m=m, # pass the map object
     color="red", # use red color on all points
     marker_kwds=dict(radius=10, fill=True), # make marker radius 10px with fill
     tooltip="name", # show "name" column in the tooltip
     tooltip_kwds=dict(labels=False), # do not show column label in the tooltip
     name="cities" # name of the layer in the map
)

folium.TileLayer('Stamen Toner', control=True).add_to(m)  # use folium to add alternative tiles
folium.LayerControl().add_to(m)  # use folium to add layer control

m  # show map