Merging Data#

There are two ways to combine datasets in geopandas – attribute joins and spatial joins.

In an attribute join, a GeoSeries or GeoDataFrame is combined with a regular pandas.Series or pandas.DataFrame based on a common variable. This is analogous to normal merging or joining in pandas.

In a Spatial Join, observations from two GeoSeries or GeoDataFrame are combined based on their spatial relationship to one another.

In the following examples, we use these datasets:

In [1]: world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))

In [2]: cities = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))

# For attribute join
In [3]: country_shapes = world[['geometry', 'iso_a3']]

In [4]: country_names = world[['name', 'iso_a3']]

# For spatial join
In [5]: countries = world[['geometry', 'name']]

In [6]: countries = countries.rename(columns={'name':'country'})


Appending GeoDataFrame and GeoSeries uses pandas append() methods. Keep in mind, that appended geometry columns needs to have the same CRS.

# Appending GeoSeries
In [7]: joined = pd.concat([world.geometry, cities.geometry])

# Appending GeoDataFrames
In [8]: europe = world[world.continent == 'Europe']

In [9]: asia = world[world.continent == 'Asia']

In [10]: eurasia = pd.concat([europe, asia])

Attribute Joins#

Attribute joins are accomplished using the merge() method. In general, it is recommended to use the merge() method called from the spatial dataset. With that said, the stand-alone pandas.merge() function will work if the GeoDataFrame is in the left argument; if a DataFrame is in the left argument and a GeoDataFrame is in the right position, the result will no longer be a GeoDataFrame.

For example, consider the following merge that adds full names to a GeoDataFrame that initially has only ISO codes for each country by merging it with a DataFrame.

# `country_shapes` is GeoDataFrame with country shapes and iso codes
In [11]: country_shapes.head()
                                            geometry iso_a3
0  MULTIPOLYGON (((180.000000000 -16.067132664, 1...    FJI
1  POLYGON ((33.903711197 -0.950000000, 34.072620...    TZA
2  POLYGON ((-8.665589565 27.656425890, -8.665124...    ESH
3  MULTIPOLYGON (((-122.840000000 49.000000000, -...    CAN
4  MULTIPOLYGON (((-122.840000000 49.000000000, -...    USA

# `country_names` is DataFrame with country names and iso codes
In [12]: country_names.head()
                       name iso_a3
0                      Fiji    FJI
1                  Tanzania    TZA
2                 W. Sahara    ESH
3                    Canada    CAN
4  United States of America    USA

# Merge with `merge` method on shared variable (iso codes):
In [13]: country_shapes = country_shapes.merge(country_names, on='iso_a3')

In [14]: country_shapes.head()
                                            geometry  ...                      name
0  MULTIPOLYGON (((180.000000000 -16.067132664, 1...  ...                      Fiji
1  POLYGON ((33.903711197 -0.950000000, 34.072620...  ...                  Tanzania
2  POLYGON ((-8.665589565 27.656425890, -8.665124...  ...                 W. Sahara
3  MULTIPOLYGON (((-122.840000000 49.000000000, -...  ...                    Canada
4  MULTIPOLYGON (((-122.840000000 49.000000000, -...  ...  United States of America

[5 rows x 3 columns]

Spatial Joins#

In a Spatial Join, two geometry objects are merged based on their spatial relationship to one another.

# One GeoDataFrame of countries, one of Cities.
# Want to merge so we can get each city's country.
In [15]: countries.head()
                                            geometry                   country
0  MULTIPOLYGON (((180.000000000 -16.067132664, 1...                      Fiji
1  POLYGON ((33.903711197 -0.950000000, 34.072620...                  Tanzania
2  POLYGON ((-8.665589565 27.656425890, -8.665124...                 W. Sahara
3  MULTIPOLYGON (((-122.840000000 49.000000000, -...                    Canada
4  MULTIPOLYGON (((-122.840000000 49.000000000, -...  United States of America

In [16]: cities.head()
           name                            geometry
0  Vatican City   POINT (12.453386500 41.903282200)
1    San Marino   POINT (12.441770200 43.936095800)
2         Vaduz    POINT (9.516669500 47.133723800)
3       Lobamba  POINT (31.199997100 -26.466667500)
4    Luxembourg    POINT (6.130002800 49.611660400)

# Execute spatial join
In [17]: cities_with_country = cities.sjoin(countries, how="inner", predicate='intersects')

In [18]: cities_with_country.head()
             name                           geometry  index_right  country
0    Vatican City  POINT (12.453386500 41.903282200)          141    Italy
1      San Marino  POINT (12.441770200 43.936095800)          141    Italy
226          Rome  POINT (12.481312600 41.897901500)          141    Italy
2           Vaduz   POINT (9.516669500 47.133723800)          114  Austria
212        Vienna  POINT (16.364693100 48.201961100)          114  Austria

GeoPandas provides two spatial-join functions:


For historical reasons, both methods are also available as top-level functions sjoin() and sjoin_nearest(). It is recommended to use methods as the functions may be deprecated in the future.

Binary Predicate Joins#

Binary predicate joins are available via GeoDataFrame.sjoin().

GeoDataFrame.sjoin() has two core arguments: how and predicate.


The predicate argument specifies how geopandas decides whether or not to join the attributes of one object to another, based on their geometric relationship.

The values for predicate correspond to the names of geometric binary predicates and depend on the spatial index implementation.

The default spatial index in geopandas currently supports the following values for predicate which are defined in the Shapely documentation:

  • intersects

  • contains

  • within

  • touches

  • crosses

  • overlaps


The how argument specifies the type of join that will occur and which geometry is retained in the resultant GeoDataFrame. It accepts the following options:

  • left: use the index from the first (or left_df) GeoDataFrame that you provide to GeoDataFrame.sjoin(); retain only the left_df geometry column

  • right: use index from second (or right_df); retain only the right_df geometry column

  • inner: use intersection of index values from both GeoDataFrame; retain only the left_df geometry column

Note more complicated spatial relationships can be studied by combining geometric operations with spatial join. To find all polygons within a given distance of a point, for example, one can first use the buffer() method to expand each point into a circle of appropriate radius, then intersect those buffered circles with the polygons in question.

Nearest Joins#

Proximity-based joins can be done via GeoDataFrame.sjoin_nearest().

GeoDataFrame.sjoin_nearest() shares the how argument with GeoDataFrame.sjoin(), and includes two additional arguments: max_distance and distance_col.


The max_distance argument specifies a maximum search radius for matching geometries. This can have a considerable performance impact in some cases. If you can, it is highly recommended that you use this parameter.


If set, the resultant GeoDataFrame will include a column with this name containing the computed distances between an input geometry and the nearest geometry.