Spatial join and sum aggregation of two polygon GeoDataFrames in GeoPandas

Question

I would like to join two GeoDataFrames with polygon geometries (purple and green, see screenshot below). However, I would like to get the sum of all intersecting green areas per purple feature they are intersecting with.

I tried
import geopandas as gpd

gpd.sjoin(purple, green, how="inner", op='intersects')

but that will just tell me which features are intersecting rather than returning the sum of the intersecting green areas. How would I do this in Python?

BERA · Accepted Answer

Intersect and merge results back to the green polygons:
import geopandas as gpd

dfpurple = gpd.read_file(r"C:GISdatatestdatageopandas_sjoin_interaerabuffer.shp")

dfgreen = gpd.read_file(r"C:GISdatatestdatageopandas_sjoin_interaeracounties.shp")
id_col = 'kkod' #Unique identifier to tell the green areas apart

dfpurp_diss = gpd.GeoDataFrame(gpd.GeoSeries([geom for geom in dfpurple.unary_union.geoms])) #Dissolve the purple overlaps
dfpurp_diss.geometry = dfpurp_diss[0] #Set a geometry column
dfpurp_diss.crs = dfgreen.crs #My crs was undefined

intersect = gpd.overlay(dfgreen, dfpurp_diss, how='intersection') #Intersect
intersect['area'] = intersect.geometry.area #Calculate area column

interarea = intersect.groupby(id_col)['area'].sum() #Sum per id_col

dfgreen = dfgreen.merge(right=interarea, how='left', left_on=id_col, right_index=True) #Merge/Join the data back to the original data

Spatial join and sum aggregation of two polygon GeoDataFrames in GeoPandas

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