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Martina OefeleinandMartin Fleischmann
86b0b9dec6
* Document full capabilities of aggfunc argument Resolves #2302 * Apply black formatting Co-authored-by: Martin Fleischmann <martin@martinfleischmann.net> Co-authored-by: Martin Fleischmann <martin@martinfleischmann.net>
99 lines
3.7 KiB
ReStructuredText
99 lines
3.7 KiB
ReStructuredText
.. ipython:: python
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:suppress:
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import geopandas
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import matplotlib
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orig = matplotlib.rcParams['figure.figsize']
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matplotlib.rcParams['figure.figsize'] = [orig[0] * 1.5, orig[1]]
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Aggregation with dissolve
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=============================
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Spatial data are often more granular than we need. For example, we might have data on sub-national units, but we're actually interested in studying patterns at the level of countries.
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In a non-spatial setting, when all we need are summary statistics of the data, we aggregate our data using the :meth:`~pandas.DataFrame.groupby` function. But for spatial data, we sometimes also need to aggregate geometric features. In the *geopandas* library, we can aggregate geometric features using the :meth:`~geopandas.GeoDataFrame.dissolve` function.
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:meth:`~geopandas.GeoDataFrame.dissolve` can be thought of as doing three things:
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(a) it dissolves all the geometries within a given group together into a single geometric feature (using the :attr:`~geopandas.GeoSeries.unary_union` method), and
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(b) it aggregates all the rows of data in a group using :ref:`groupby.aggregate <groupby.aggregate>`, and
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(c) it combines those two results.
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:meth:`~geopandas.GeoDataFrame.dissolve` Example
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Suppose we are interested in studying continents, but we only have country-level data like the country dataset included in *geopandas*. We can easily convert this to a continent-level dataset.
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First, let's look at the most simple case where we just want continent shapes and names. By default, :meth:`~geopandas.GeoDataFrame.dissolve` will pass ``'first'`` to :ref:`groupby.aggregate <groupby.aggregate>`.
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.. ipython:: python
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world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
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world = world[['continent', 'geometry']]
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continents = world.dissolve(by='continent')
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@savefig continents1.png
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continents.plot();
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continents.head()
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If we are interested in aggregate populations, however, we can pass different functions to the :meth:`~geopandas.GeoDataFrame.dissolve` method to aggregate populations using the ``aggfunc =`` argument:
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.. ipython:: python
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world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
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world = world[['continent', 'geometry', 'pop_est']]
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continents = world.dissolve(by='continent', aggfunc='sum')
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@savefig continents2.png
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continents.plot(column = 'pop_est', scheme='quantiles', cmap='YlOrRd');
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continents.head()
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.. ipython:: python
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:suppress:
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matplotlib.rcParams['figure.figsize'] = orig
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.. toctree::
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:maxdepth: 2
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Dissolve Arguments
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~~~~~~~~~~~~~~~~~~
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The ``aggfunc =`` argument defaults to 'first' which means that the first row of attributes values found in the dissolve routine will be assigned to the resultant dissolved geodataframe.
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However it also accepts other summary statistic options as allowed by :meth:`pandas.groupby <pandas.DataFrame.groupby>` including:
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* 'first'
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* 'last'
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* 'min'
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* 'max'
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* 'sum'
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* 'mean'
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* 'median'
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* function
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* string function name
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* list of functions and/or function names, e.g. [np.sum, 'mean']
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* dict of axis labels -> functions, function names or list of such.
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For example, to get the number of contries on each continent,
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as well as the populations of the largest and smallest country of each,
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we can aggregate the ``'name'`` column using ``'count'``,
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and the ``'pop_est'`` column using ``'min'`` and ``'max'``:
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.. ipython:: python
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world = geopandas.read_file(geopandas.datasets.get_path("naturalearth_lowres"))
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continents = world.dissolve(
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by="continent",
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aggfunc={
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"name": "count",
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"pop_est": ["min", "max"],
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},
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)
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continents.head() |