hvPlot 0.10 has just been released! Checkout the blog post and support hvPlot by giving it a 🌟 on Github.


import hvplot.pandas  # noqa

Using hvplot with geopandas is as simple as loading a geopandas dataframe and calling hvplot on it with geo=True.

import geopandas as gpd

cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
/tmp/ipykernel_2361/4227810846.py:3: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_cities' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.
  cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
name geometry
180 Chicago POINT (-87.63524 41.84796)
103 Bamako POINT (-8.00198 12.65196)
186 Geneva POINT (6.14003 46.21001)
126 Thimphu POINT (89.63901 27.47299)
121 Phnom Penh POINT (104.91469 11.55198)
cities.hvplot(geo=True, tiles=True)

You can easily change the tiles, add coastlines, or which fields show up in the hover text:

cities.hvplot(tiles='EsriTerrain', coastline=True, hover_cols='all')

We can also alter the projection of the data using cartopy:

import cartopy.crs as ccrs
cities.hvplot(coastline=True, projection=ccrs.Geostationary(central_longitude=-30), global_extent=True)