sedona.spark.geopandas.tools package

Submodules

sedona.spark.geopandas.tools.sjoin module

sedona.spark.geopandas.tools.sjoin.sjoin(left_df: GeoDataFrame, right_df: GeoDataFrame, how='inner', predicate='intersects', lsuffix='left', rsuffix='right', distance=None, on_attribute=None, **kwargs) → GeoDataFrame[source]

Spatial join of two GeoDataFrames.

Parameters:

  • left_df (GeoDataFrames)

  • right_df (GeoDataFrames)

  • how (string, 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

  • predicate (string, 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.

  • lsuffix (string, default 'left') – Suffix to apply to overlapping column names (left GeoDataFrame).

  • rsuffix (string, default 'right') – Suffix to apply to overlapping column names (right GeoDataFrame).

  • distance (number 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_attribute (string, 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.

Returns:

The joined GeoDataFrame.

Return type:

GeoDataFrame

Examples

>>> 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]

Notes

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

Module contents