Points#
Download this notebook from GitHub (right-click to download).
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'))
cities.sample(5)
| name | geometry | |
|---|---|---|
| 153 | Warsaw | POINT (21.00535 52.23087) |
| 98 | Guatemala City | POINT (-90.52891 14.62308) |
| 228 | Nairobi | POINT (36.81471 -1.28140) |
| 241 | Singapore | POINT (103.85387 1.29498) |
| 6 | Majuro | POINT (171.38000 7.10300) |
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)
This web page was generated from a Jupyter notebook and not all interactivity will work on this website. Right click to download and run locally for full Python-backed interactivity.
Download this notebook from GitHub (right-click to download).