geopandas.GeoDataFrame.sjoin

GeoDataFrame.sjoin(df, *args, **kwargs)

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

>>> countries = geopandas.read_file(     geopandas.datasets.get_path("naturalearth_lowres"))
>>> cities = geopandas.read_file(     geopandas.datasets.get_path("naturalearth_cities"))
>>> countries.head()  
    pop_est      continent                      name     iso_a3  gdp_md_est                                           geometry
0     920938        Oceania                      Fiji    FJI      8374.0     MULTIPOLYGON (((180.00000 -16.06713, 180.00000...
1   53950935         Africa                  Tanzania    TZA    150600.0     POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...
2     603253         Africa                 W. Sahara    ESH       906.5     POLYGON ((-8.66559 27.65643, -8.66512 27.58948...
3   35623680  North America                    Canada    CAN   1674000.0     MULTIPOLYGON (((-122.84000 49.00000, -122.9742...
4  326625791  North America  United States of America    USA  18560000.0     MULTIPOLYGON (((-122.84000 49.00000, -120.0000...
>>> cities.head()
        name                   geometry
0  Vatican City  POINT (12.45339 41.90328)
1    San Marino  POINT (12.44177 43.93610)
2         Vaduz   POINT (9.51667 47.13372)
3    Luxembourg   POINT (6.13000 49.61166)
4       Palikir  POINT (158.14997 6.91664)

>>> cities_w_country_data = cities.sjoin(countries)
>>> cities_w_country_data.head()  
        name_left                   geometry  index_right   pop_est     continent name_right iso_a3  gdp_md_est
0    Vatican City  POINT (12.45339 41.90328)          141  62137802     Europe    Italy    ITA   2221000.0
1    San Marino  POINT (12.44177 43.93610)          141  62137802     Europe    Italy    ITA   2221000.0
192          Rome  POINT (12.48131 41.89790)          141  62137802     Europe    Italy    ITA   2221000.0
2           Vaduz   POINT (9.51667 47.13372)          114   8754413     Europe    Au    stria    AUT    416600.0
184        Vienna  POINT (16.36469 48.20196)          114   8754413     Europe    Austria    AUT    416600.0
geopandas.GeoDataFrame.explode geopandas.GeoDataFrame.sjoin_nearest