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 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 GeoDataFrames 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

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

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

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

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

In [10]: eurasia = europe.append(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 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 pandas DataFrame.

# `country_shapes` is GeoDataFrame with country shapes and iso codes
In [11]: country_shapes.head()
Out[11]: 
                                            geometry iso_a3
0  MULTIPOLYGON (((180.00000 -16.06713, 180.00000...    FJI
1  POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...    TZA
2  POLYGON ((-8.66559 27.65643, -8.66512 27.58948...    ESH
3  MULTIPOLYGON (((-122.84000 49.00000, -122.9742...    CAN
4  MULTIPOLYGON (((-122.84000 49.00000, -120.0000...    USA

# `country_names` is DataFrame with country names and iso codes
In [12]: country_names.head()
Out[12]: 
                       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()
Out[14]: 
                                            geometry  ...                      name
0  MULTIPOLYGON (((180.00000 -16.06713, 180.00000...  ...                      Fiji
1  POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...  ...                  Tanzania
2  POLYGON ((-8.66559 27.65643, -8.66512 27.58948...  ...                 W. Sahara
3  MULTIPOLYGON (((-122.84000 49.00000, -122.9742...  ...                    Canada
4  MULTIPOLYGON (((-122.84000 49.00000, -120.0000...  ...  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()
Out[15]: 
                                            geometry                   country
0  MULTIPOLYGON (((180.00000 -16.06713, 180.00000...                      Fiji
1  POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...                  Tanzania
2  POLYGON ((-8.66559 27.65643, -8.66512 27.58948...                 W. Sahara
3  MULTIPOLYGON (((-122.84000 49.00000, -122.9742...                    Canada
4  MULTIPOLYGON (((-122.84000 49.00000, -120.0000...  United States of America

In [16]: cities.head()
Out[16]: 
           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)

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

In [18]: cities_with_country.head()
Out[18]: 
             name                   geometry  index_right  country
0    Vatican City  POINT (12.45339 41.90328)          141    Italy
1      San Marino  POINT (12.44177 43.93610)          141    Italy
192          Rome  POINT (12.48131 41.89790)          141    Italy
2           Vaduz   POINT (9.51667 47.13372)          114  Austria
184        Vienna  POINT (16.36469 48.20196)          114  Austria

Sjoin Arguments

sjoin() has two core arguments: how and op.

op

The op 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 op 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 op:

  • intersects

  • contains

  • within

  • touches

  • crosses

  • overlaps

You can read more about each join type in the Shapely documentation.

how

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 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 geodataframes; 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.