Polygons#
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 geodatasets
import geopandas as gpd
chicago = gpd.read_file(geodatasets.get_path("geoda.chicago_commpop"))
chicago.sample(3)
Downloading file 'chicago_commpop.zip' from 'https://geodacenter.github.io/data-and-lab//data/chicago_commpop.zip' to '/Users/runner/Library/Caches/geodatasets'.
Extracting 'chicago_commpop/chicago_commpop.geojson' from '/Users/runner/Library/Caches/geodatasets/chicago_commpop.zip' to '/Users/runner/Library/Caches/geodatasets/chicago_commpop.zip.unzip'
| community | NID | POP2010 | POP2000 | POPCH | POPPERCH | popplus | popneg | geometry | |
|---|---|---|---|---|---|---|---|---|---|
| 51 | HEGEWISCH | 55 | 9426 | 9781 | -355 | -3.629486 | 0 | 1 | MULTIPOLYGON (((-87.52462 41.6918, -87.52465 4... |
| 24 | WEST TOWN | 24 | 81432 | 87435 | -6003 | -6.865672 | 0 | 1 | MULTIPOLYGON (((-87.65686 41.91078, -87.65685 ... |
| 27 | EAST GARFIELD PARK | 27 | 20567 | 20881 | -314 | -1.503759 | 0 | 1 | MULTIPOLYGON (((-87.69157 41.8882, -87.68968 4... |
chicago.hvplot(geo=True)
Control the color of the elements using the c option.
chicago.hvplot.polygons(geo=True, c='POP2010', hover_cols='all')
You can even color by another series, such as population density:
chicago.hvplot.polygons(
geo=True,
c=chicago.POP2010/chicago.to_crs('EPSG:32616').area,
clabel='pop density',
)
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