Often we want to bin or group data, produce statistics (mean, variance) on the groups, and then return a reduced data set. To do this, 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~xarray.Dataset and :py~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:
python
import numpy as np import pandas as pd import xarray as xr
np.random.seed(123456)
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:
python
ds.groupby("letters")
This object works very similarly to a pandas GroupBy object. You can view the group indices with the groups attribute:
python
ds.groupby("letters").groups
You can also iterate over groups in (label, group) pairs:
python
list(ds.groupby("letters"))
You can index out a particular group:
python
ds.groupby("letters")["b"]
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~xarray.Dataset.groupby_bins method.
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:
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~xarray.core.groupby.DatasetGroupBy.map method. The resulting objects are automatically concatenated back together along the group axis:
python
- def standardize(x):
return (x - x.mean()) / x.std()
arr.groupby("letters").map(standardize)
GroupBy objects also have a :py~xarray.core.groupby.DatasetGroupBy.reduce method and methods like :py~xarray.core.groupby.DatasetGroupBy.mean as shortcuts for applying an aggregation function:
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:
python
ds.groupby("x").std(...)
Note
We use an ellipsis (...) here to indicate we want to reduce over all other dimensions
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:
python
ds.groupby("letters").first(...)
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:
python
alt = arr.groupby("letters").mean(...) 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(letters=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:
python
next(iter(arr.groupby("x")))
python
next(iter(arr.groupby("x", squeeze=False)))
Although xarray will attempt to automatically :py~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~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:
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(...) da.groupby("lon").map(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~xarray.Dataset.groupby_bins may be desirable:
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:
python
stacked = da.stack(gridcell=["ny", "nx"]) stacked.groupby("gridcell").sum(...).unstack("gridcell")