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 (((180.000000000 -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()
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 (((180.000000000 -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()
Out[15]:
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()
Out[16]:
name geometry
0 Vatican City POINT (12.453386545 41.903282180)
1 San Marino POINT (12.441770158 43.936095835)
2 Vaduz POINT (9.516669473 47.133723774)
3 Luxembourg POINT (6.130002806 49.611660379)
4 Palikir POINT (158.149974324 6.916643696)
# 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 (12.453386545 41.903282180) 141 Italy
1 San Marino POINT (12.441770158 43.936095835) 141 Italy
192 Rome POINT (12.481312563 41.897901485) 141 Italy
2 Vaduz POINT (9.516669473 47.133723774) 114 Austria
184 Vienna POINT (16.364693097 48.201961137) 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 tosjoin
; retain only the left_df geometry columnright
: use index from second (or right_df); retain only the right_df geometry columninner
: 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.