geopandas.GeoDataFrame.sjoin#

GeoDataFrame.sjoin(df, *args, **kwargs)[source]#

Spatial join of two GeoDataFrames.

See the User Guide page Merging data for details.

Parameters
dfGeoDataFrame
howstring, default ‘inner’

The type of join:

  • ‘left’: use keys from left_df; retain only left_df geometry column

  • ‘right’: use keys from right_df; retain only right_df geometry column

  • ‘inner’: use intersection of keys from both dfs; retain only left_df geometry column

predicatestring, default ‘intersects’

Binary predicate. Valid values are determined by the spatial index used. You can check the valid values in left_df or right_df as left_df.sindex.valid_query_predicates or right_df.sindex.valid_query_predicates

lsuffixstring, default ‘left’

Suffix to apply to overlapping column names (left GeoDataFrame).

rsuffixstring, default ‘right’

Suffix to apply to overlapping column names (right GeoDataFrame).

See also

GeoDataFrame.sjoin_nearest

nearest neighbor join

sjoin

equivalent top-level function

Notes

Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account.

Examples

>>> import geodatasets
>>> chicago = geopandas.read_file(
...     geodatasets.get_path("geoda.chicago_commpop")
... )
>>> groceries = geopandas.read_file(
...     geodatasets.get_path("geoda.groceries")
... ).to_crs(chicago.crs)

>>> chicago.head()  
         community  ...                                           geometry
0          DOUGLAS  ...  MULTIPOLYGON (((-87.60914 41.84469, -87.60915 ...
1          OAKLAND  ...  MULTIPOLYGON (((-87.59215 41.81693, -87.59231 ...
2      FULLER PARK  ...  MULTIPOLYGON (((-87.62880 41.80189, -87.62879 ...
3  GRAND BOULEVARD  ...  MULTIPOLYGON (((-87.60671 41.81681, -87.60670 ...
4          KENWOOD  ...  MULTIPOLYGON (((-87.59215 41.81693, -87.59215 ...

[5 rows x 9 columns]

>>> groceries.head()  
   OBJECTID     Ycoord  ...  Category                         geometry
0        16  41.973266  ...       NaN  MULTIPOINT (-87.65661 41.97321)
1        18  41.696367  ...       NaN  MULTIPOINT (-87.68136 41.69713)
2        22  41.868634  ...       NaN  MULTIPOINT (-87.63918 41.86847)
3        23  41.877590  ...       new  MULTIPOINT (-87.65495 41.87783)
4        27  41.737696  ...       NaN  MULTIPOINT (-87.62715 41.73623)
[5 rows x 8 columns]

>>> groceries_w_communities = groceries.sjoin(chicago)
>>> groceries_w_communities[["OBJECTID", "community", "geometry"]].head()
     OBJECTID    community                         geometry
0          16       UPTOWN  MULTIPOINT (-87.65661 41.97321)
87        365       UPTOWN  MULTIPOINT (-87.65465 41.96138)
90        373       UPTOWN  MULTIPOINT (-87.65598 41.96297)
140       582       UPTOWN  MULTIPOINT (-87.67417 41.96977)
1          18  MORGAN PARK  MULTIPOINT (-87.68136 41.69713)