geopandas.clip(gdf, mask, keep_geom_type=False)[source]#

Clip points, lines, or polygon geometries to the mask extent.

Both layers must be in the same Coordinate Reference System (CRS). The gdf will be clipped to the full extent of the clip object.

If there are multiple polygons in mask, data from gdf will be clipped to the total boundary of all polygons in mask.

If the mask is list-like with four elements (minx, miny, maxx, maxy), a faster rectangle clipping algorithm will be used. Note that this can lead to slightly different results in edge cases, e.g. if a line would be reduced to a point, this point might not be returned. The geometry is clipped in a fast but possibly dirty way. The output is not guaranteed to be valid. No exceptions will be raised for topological errors.

gdfGeoDataFrame or GeoSeries

Vector layer (point, line, polygon) to be clipped to mask.

maskGeoDataFrame, GeoSeries, (Multi)Polygon, list-like

Polygon vector layer used to clip gdf. The mask’s geometry is dissolved into one geometric feature and intersected with gdf. If the mask is list-like with four elements (minx, miny, maxx, maxy), clip will use a faster rectangle clipping (clip_by_rect()), possibly leading to slightly different results.

keep_geom_typeboolean, default False

If True, return only geometries of original type in case of intersection resulting in multiple geometry types or GeometryCollections. If False, return all resulting geometries (potentially mixed-types).

GeoDataFrame or GeoSeries

Vector data (points, lines, polygons) from gdf clipped to polygon boundary from mask.

See also


equivalent GeoDataFrame method


equivalent GeoSeries method


Clip points (grocery stores) with polygons (the Near West Side community):

>>> import geodatasets
>>> chicago = geopandas.read_file(
...     geodatasets.get_path("geoda.chicago_health")
... )
>>> near_west_side = chicago[chicago["community"] == "NEAR WEST SIDE"]
>>> groceries = geopandas.read_file(
...     geodatasets.get_path("geoda.groceries")
... ).to_crs(
>>> groceries.shape
(148, 8)
>>> nws_groceries = geopandas.clip(groceries, near_west_side)
>>> nws_groceries.shape
(7, 8)