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 to 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'})

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 [7]: country_shapes.head()
Out[7]: 
                                            geometry iso_a3
0  (POLYGON ((180 -16.06713266364245, 180 -16.555...    FJI
1  POLYGON ((33.90371119710453 -0.950000000000000...    TZA
2  POLYGON ((-8.665589565454809 27.65642588959236...    ESH
3  (POLYGON ((-122.84 49.00000000000011, -122.974...    CAN
4  (POLYGON ((-122.84 49.00000000000011, -120 49....    USA

# `country_names` is DataFrame with country names and iso codes
In [8]: country_names.head()
Out[8]: 
                       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 [9]: country_shapes = country_shapes.merge(country_names, on='iso_a3')

In [10]: country_shapes.head()
Out[10]: 
                                            geometry iso_a3                      name
0  (POLYGON ((180 -16.06713266364245, 180 -16.555...    FJI                      Fiji
1  POLYGON ((33.90371119710453 -0.950000000000000...    TZA                  Tanzania
2  POLYGON ((-8.665589565454809 27.65642588959236...    ESH                 W. Sahara
3  (POLYGON ((-122.84 49.00000000000011, -122.974...    CAN                    Canada
4  (POLYGON ((-122.84 49.00000000000011, -120 49....    USA  United States of America

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 [11]: countries.head()
Out[11]: 
                                            geometry                   country
0  (POLYGON ((180 -16.06713266364245, 180 -16.555...                      Fiji
1  POLYGON ((33.90371119710453 -0.950000000000000...                  Tanzania
2  POLYGON ((-8.665589565454809 27.65642588959236...                 W. Sahara
3  (POLYGON ((-122.84 49.00000000000011, -122.974...                    Canada
4  (POLYGON ((-122.84 49.00000000000011, -120 49....  United States of America

In [12]: cities.head()
Out[12]: 
           name                                     geometry
0  Vatican City  POINT (12.45338654497177 41.90328217996012)
1    San Marino    POINT (12.44177015780014 43.936095834768)
2         Vaduz  POINT (9.516669472907267 47.13372377429357)
3    Luxembourg  POINT (6.130002806227083 49.61166037912108)
4       Palikir  POINT (158.1499743237623 6.916643696007725)

# Execute spatial join
In [13]: cities_with_country = geopandas.sjoin(cities, countries, how="inner", op='intersects')

In [14]: cities_with_country.head()
Out[14]: 
             name                                     geometry  index_right  country
0    Vatican City  POINT (12.45338654497177 41.90328217996012)          141    Italy
1      San Marino    POINT (12.44177015780014 43.936095834768)          141    Italy
192          Rome    POINT (12.481312562874 41.89790148509894)          141    Italy
2           Vaduz  POINT (9.516669472907267 47.13372377429357)          114  Austria
184        Vienna  POINT (16.36469309674374 48.20196113681686)          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. There are three different join options as follows:

  • intersects: The attributes will be joined if the boundary and interior of the object intersect in any way with the boundary and/or interior of the other object.
  • within: The attributes will be joined if the object’s boundary and interior intersect only with the interior of the other object (not its boundary or exterior).
  • contains: The attributes will be joined if the object’s interior contains the boundary and interior of the other object and their boundaries do not touch at all.

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.