geopandas.overlay#
- geopandas.overlay(df1, df2, how='intersection', keep_geom_type=None, make_valid=True)[source]#
Perform spatial overlay between two GeoDataFrames.
Currently only supports data GeoDataFrames with uniform geometry types, i.e. containing only (Multi)Polygons, or only (Multi)Points, or a combination of (Multi)LineString and LinearRing shapes. Implements several methods that are all effectively subsets of the union.
See the User Guide page Set operations with overlay for details.
- Parameters:
- df1GeoDataFrame
- df2GeoDataFrame
- howstring
Method of spatial overlay: ‘intersection’, ‘union’, ‘identity’, ‘symmetric_difference’ or ‘difference’.
- keep_geom_typebool
If True, return only geometries of the same geometry type as df1 has, if False, return all resulting geometries. Default is None, which will set keep_geom_type to True but warn upon dropping geometries.
- make_validbool, default True
If True, any invalid input geometries are corrected with a call to make_valid(), if False, a ValueError is raised if any input geometries are invalid.
- Returns:
- dfGeoDataFrame
GeoDataFrame with new set of polygons and attributes resulting from the overlay
See also
sjoin
spatial join
GeoDataFrame.overlay
equivalent method
Notes
Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account.
Examples
>>> from shapely.geometry import Polygon >>> polys1 = geopandas.GeoSeries([Polygon([(0,0), (2,0), (2,2), (0,2)]), ... Polygon([(2,2), (4,2), (4,4), (2,4)])]) >>> polys2 = geopandas.GeoSeries([Polygon([(1,1), (3,1), (3,3), (1,3)]), ... Polygon([(3,3), (5,3), (5,5), (3,5)])]) >>> df1 = geopandas.GeoDataFrame({'geometry': polys1, 'df1_data':[1,2]}) >>> df2 = geopandas.GeoDataFrame({'geometry': polys2, 'df2_data':[1,2]})
>>> geopandas.overlay(df1, df2, how='union') df1_data df2_data geometry 0 1.0 1.0 POLYGON ((2 2, 2 1, 1 1, 1 2, 2 2)) 1 2.0 1.0 POLYGON ((2 2, 2 3, 3 3, 3 2, 2 2)) 2 2.0 2.0 POLYGON ((4 4, 4 3, 3 3, 3 4, 4 4)) 3 1.0 NaN POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0)) 4 2.0 NaN MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4... 5 NaN 1.0 MULTIPOLYGON (((2 3, 2 2, 1 2, 1 3, 2 3)), ((3... 6 NaN 2.0 POLYGON ((3 5, 5 5, 5 3, 4 3, 4 4, 3 4, 3 5))
>>> geopandas.overlay(df1, df2, how='intersection') df1_data df2_data geometry 0 1 1 POLYGON ((2 2, 2 1, 1 1, 1 2, 2 2)) 1 2 1 POLYGON ((2 2, 2 3, 3 3, 3 2, 2 2)) 2 2 2 POLYGON ((4 4, 4 3, 3 3, 3 4, 4 4))
>>> geopandas.overlay(df1, df2, how='symmetric_difference') df1_data df2_data geometry 0 1.0 NaN POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0)) 1 2.0 NaN MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4... 2 NaN 1.0 MULTIPOLYGON (((2 3, 2 2, 1 2, 1 3, 2 3)), ((3... 3 NaN 2.0 POLYGON ((3 5, 5 5, 5 3, 4 3, 4 4, 3 4, 3 5))
>>> geopandas.overlay(df1, df2, how='difference') geometry df1_data 0 POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0)) 1 1 MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4... 2
>>> geopandas.overlay(df1, df2, how='identity') df1_data df2_data geometry 0 1.0 1.0 POLYGON ((2 2, 2 1, 1 1, 1 2, 2 2)) 1 2.0 1.0 POLYGON ((2 2, 2 3, 3 3, 3 2, 2 2)) 2 2.0 2.0 POLYGON ((4 4, 4 3, 3 3, 3 4, 4 4)) 3 1.0 NaN POLYGON ((2 0, 0 0, 0 2, 1 2, 1 1, 2 1, 2 0)) 4 2.0 NaN MULTIPOLYGON (((3 4, 3 3, 2 3, 2 4, 3 4)), ((4...