geopandas.sjoin#

geopandas.sjoin(left_df, right_df, how='inner', predicate='intersects', lsuffix='left', rsuffix='right', distance=None, on_attribute=None, **kwargs)[source]#

Spatial join of two GeoDataFrames.

See the User Guide page Merging data for details.

Parameters:

left_df, right_dfGeoDataFrames

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 Replaces deprecated op parameter.

lsuffixstring, default ‘left’

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

rsuffixstring, default ‘right’

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

distancenumber or array_like, optional

Distance(s) around each input geometry within which to query the tree for the ‘dwithin’ predicate. If array_like, must be one-dimesional with length equal to length of left GeoDataFrame. Required if predicate='dwithin'.

on_attributestring, list or tuple

Column name(s) to join on as an additional join restriction on top of the spatial predicate. These must be found in both DataFrames. If set, observations are joined only if the predicate applies and values in specified columns match.

See also

overlay: overlay operation resulting in a new geometry
GeoDataFrame.sjoin: equivalent method

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

>>> chicago.head()
    ComAreaID  ...                                           geometry
0         35  ...  POLYGON ((-87.60914 41.84469, -87.60915 41.844...
1         36  ...  POLYGON ((-87.59215 41.81693, -87.59231 41.816...
2         37  ...  POLYGON ((-87.62880 41.80189, -87.62879 41.801...
3         38  ...  POLYGON ((-87.60671 41.81681, -87.60670 41.816...
4         39  ...  POLYGON ((-87.59215 41.81693, -87.59215 41.816...
[5 rows x 87 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 = geopandas.sjoin(groceries, chicago)
>>> groceries_w_communities.head()
   OBJECTID       community                           geometry
0        16          UPTOWN  MULTIPOINT ((-87.65661 41.97321))
1        18     MORGAN PARK  MULTIPOINT ((-87.68136 41.69713))
2        22  NEAR WEST SIDE  MULTIPOINT ((-87.63918 41.86847))
3        23  NEAR WEST SIDE  MULTIPOINT ((-87.65495 41.87783))
4        27         CHATHAM  MULTIPOINT ((-87.62715 41.73623))
[5 rows x 95 columns]

geopandas.sjoin#

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