geopandas.sjoin#

geopandas.sjoin(left_df, right_df, how='inner', predicate='intersects', lsuffix='left', rsuffix='right', **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).

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

>>> 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       Lobamba  POINT (31.20000 -26.46667)
4    Luxembourg    POINT (6.13000 49.61166)

>>> cities_w_country_data = geopandas.sjoin(cities, 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    Austria    AUT    416600.0
184        Vienna  POINT (16.36469 48.20196)          114   8754413    Europe    Austria    AUT    416600.0