xarray supports "group by" operations with the same API as pandas to implement the split-apply-combine strategy:
- Split your data into multiple independent groups.
- Apply some function to each group.
- Combine your groups back into a single data object.
Group by operations work on both :py:class:`~xarray.Dataset` and :py:class:`~xarray.DataArray` objects. Most of the examples focus on grouping by a single one-dimensional variable, although support for grouping over a multi-dimensional variable has recently been implemented. Note that for one-dimensional data, it is usually faster to rely on pandas' implementation of the same pipeline.
Let's create a simple example dataset:
.. ipython:: python
:suppress:
import numpy as np
import pandas as pd
import xarray as xr
np.random.seed(123456)
.. ipython:: python
ds = xr.Dataset({'foo': (('x', 'y'), np.random.rand(4, 3))},
coords={'x': [10, 20, 30, 40],
'letters': ('x', list('abba'))})
arr = ds['foo']
ds
If we groupby the name of a variable or coordinate in a dataset (we can also
use a DataArray directly), we get back a GroupBy object:
.. ipython:: python
ds.groupby('letters')
This object works very similarly to a pandas GroupBy object. You can view
the group indices with the groups attribute:
.. ipython:: python
ds.groupby('letters').groups
You can also iterate over groups in (label, group) pairs:
.. ipython:: python
list(ds.groupby('letters'))
Just like in pandas, creating a GroupBy object is cheap: it does not actually split the data until you access particular values.
Sometimes you don't want to use all the unique values to determine the groups but instead want to "bin" the data into coarser groups. You could always create a customized coordinate, but xarray facilitates this via the :py:meth:`~xarray.Dataset.groupby_bins` method.
.. ipython:: python
x_bins = [0,25,50]
ds.groupby_bins('x', x_bins).groups
The binning is implemented via pandas.cut, whose documentation details how the bins are assigned. As seen in the example above, by default, the bins are labeled with strings using set notation to precisely identify the bin limits. To override this behavior, you can specify the bin labels explicitly. Here we choose float labels which identify the bin centers:
.. ipython:: python
x_bin_labels = [12.5,37.5]
ds.groupby_bins('x', x_bins, labels=x_bin_labels).groups
To apply a function to each group, you can use the flexible :py:meth:`~xarray.DatasetGroupBy.apply` method. The resulting objects are automatically concatenated back together along the group axis:
.. ipython:: python
def standardize(x):
return (x - x.mean()) / x.std()
arr.groupby('letters').apply(standardize)
GroupBy objects also have a :py:meth:`~xarray.DatasetGroupBy.reduce` method and methods like :py:meth:`~xarray.DatasetGroupBy.mean` as shortcuts for applying an aggregation function:
.. ipython:: python
arr.groupby('letters').mean(dim='x')
Using a groupby is thus also a convenient shortcut for aggregating over all dimensions other than the provided one:
.. ipython:: python
ds.groupby('x').std(xr.ALL_DIMS)
There are two special aggregation operations that are currently only found on groupby objects: first and last. These provide the first or last example of values for group along the grouped dimension:
.. ipython:: python
ds.groupby('letters').first(xr.ALL_DIMS)
By default, they skip missing values (control this with skipna).
GroupBy objects also support a limited set of binary arithmetic operations, as
a shortcut for mapping over all unique labels. Binary arithmetic is supported
for (GroupBy, Dataset) and (GroupBy, DataArray) pairs, as long as the
dataset or data array uses the unique grouped values as one of its index
coordinates. For example:
.. ipython:: python
alt = arr.groupby('letters').mean(xr.ALL_DIMS)
alt
ds.groupby('letters') - alt
This last line is roughly equivalent to the following:
results = []
for label, group in ds.groupby('letters'):
results.append(group - alt.sel(x=label))
xr.concat(results, dim='x')
When grouping over a dimension, you can control whether the dimension is
squeezed out or if it should remain with length one on each group by using
the squeeze parameter:
.. ipython:: python
next(iter(arr.groupby('x')))
.. ipython:: python
next(iter(arr.groupby('x', squeeze=False)))
Although xarray will attempt to automatically
:py:attr:`~xarray.DataArray.transpose` dimensions back into their original order
when you use apply, it is sometimes useful to set squeeze=False to
guarantee that all original dimensions remain unchanged.
You can always squeeze explicitly later with the Dataset or DataArray :py:meth:`~xarray.DataArray.squeeze` methods.
Many datasets have a multidimensional coordinate variable (e.g. longitude) which is different from the logical grid dimensions (e.g. nx, ny). Such variables are valid under the CF conventions. Xarray supports groupby operations over multidimensional coordinate variables:
.. ipython:: python
da = xr.DataArray([[0,1],[2,3]],
coords={'lon': (['ny','nx'], [[30,40],[40,50]] ),
'lat': (['ny','nx'], [[10,10],[20,20]] ),},
dims=['ny','nx'])
da
da.groupby('lon').sum(xr.ALL_DIMS)
da.groupby('lon').apply(lambda x: x - x.mean(), shortcut=False)
Because multidimensional groups have the ability to generate a very large number of bins, coarse-binning via :py:meth:`~xarray.Dataset.groupby_bins` may be desirable:
.. ipython:: python
da.groupby_bins('lon', [0,45,50]).sum()
These methods group by lon values. It is also possible to groupby each cell in a grid, regardless of value, by stacking multiple dimensions, applying your function, and then unstacking the result:
.. ipython:: python
stacked = da.stack(gridcell=['ny', 'nx'])
stacked.groupby('gridcell').sum().unstack('gridcell')