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 '/home/runner/.cache/geodatasets'.
Extracting 'chicago_commpop/chicago_commpop.geojson' from '/home/runner/.cache/geodatasets/chicago_commpop.zip' to '/home/runner/.cache/geodatasets/chicago_commpop.zip.unzip'
| community | NID | POP2010 | POP2000 | POPCH | POPPERCH | popplus | popneg | geometry | |
|---|---|---|---|---|---|---|---|---|---|
| 58 | NEW CITY | 61 | 44377 | 51721 | -7344 | -14.199261 | 0 | 1 | MULTIPOLYGON (((-87.63546 41.79448, -87.63599 ... |
| 53 | ARCHER HEIGHTS | 57 | 13393 | 12644 | 749 | 5.923758 | 1 | 0 | MULTIPOLYGON (((-87.71437 41.82604, -87.71436 ... |
| 60 | GAGE PARK | 63 | 39894 | 39193 | 701 | 1.788585 | 1 | 0 | MULTIPOLYGON (((-87.67882 41.79387, -87.67882 ... |
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',
)
This web page was generated from a Jupyter notebook and not all
interactivity will work on this website.