geopandas/doc/source/docs/user_guide/mergingdata.rst

.. currentmodule:: geopandas

.. ipython:: python
   :suppress:

   import geopandas


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:

.. ipython:: python

   world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
   cities = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))

   # For attribute join
   country_shapes = world[['geometry', 'iso_a3']]
   country_names = world[['name', 'iso_a3']]

   # For spatial join
   countries = world[['geometry', 'name']]
   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.

.. ipython:: python

    # Appending GeoSeries
    joined = world.geometry.append(cities.geometry)

    # Appending GeoDataFrames
    europe = world[world.continent == 'Europe']
    asia = world[world.continent == 'Asia']
    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``.

.. ipython:: python

   # `country_shapes` is GeoDataFrame with country shapes and iso codes
   country_shapes.head()

   # `country_names` is DataFrame with country names and iso codes
   country_names.head()

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



Spatial Joins
----------------

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

.. ipython:: python


   # One GeoDataFrame of countries, one of Cities.
   # Want to merge so we can get each city's country.
   countries.head()
   cities.head()

   # Execute spatial join

   cities_with_country = geopandas.sjoin(cities, countries, how="inner", op='intersects')
   cities_with_country.head()


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 <http://shapely.readthedocs.io/en/latest/manual.html#binary-predicates>`__.

**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.