Lets go with the example at the geopandas Documentation. Say we have:

from shapely.geometry import Polygon

polys1 = gpd.GeoSeries([Polygon([(0,0), (2,0), (2,2), (0,2)]),Polygon([(2,2), (4,2), (4,4), (2,4)])])

polys2 = gpd.GeoSeries([Polygon([(1,1), (3,1), (3,3), (1,3)]),Polygon([(3,3), (5,3), (5,5), (3,5)])])

df1 = gpd.GeoDataFrame([[1, 'A'], [2, 'B']], columns=['df1', 'name'], geometry = polys1)
df2 = gpd.GeoDataFrame([[1, 'C'], [2, 'B']], columns=['df2', 'name'], geometry = polys2)

ax = df1.plot(color='red', alpha=0.5);
df1.loc[[1],'geometry'].plot(ax=ax, facecolor='none', edgecolor='black');
df2.plot(ax=ax, color='green', alpha=0.5);
df2.loc[[1],'geometry'].plot(ax=ax, facecolor='none', edgecolor='blue');
ax.annotate('B', xy=(3.4,3.4), size=15, c='yellow');

enter image description here

How would I execute res_symdiff = df1.overlay(df2, how='symmetric_difference') to only perform the operation on the matching name (B) rows? [i.e.: where the yellow B is; only that block would be gone].

1 Answer 1


A simple solution is to select the rows of the GeoDataFrames before overlay:

df1 = df1[df1['name'] == 'B']
df2 = df2[df2['name'] == 'B']
result = df1.overlay(df2,how='symmetric_difference')
ax = result.plot(color='red', alpha=0.5)

enter image description here

  • Thank you @gene. How would I replace result back into the original df1 at the index of the (originally) selected rows?
    – arkriger
    Jun 29 at 15:09
  • Sorry, but it is another question
    – gene
    Jun 29 at 16:25

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