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GroupBy: split-apply-combine

xray 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~xray.Dataset and :py~xray.DataArray objects. Currently, you can only group by a single one-dimensional variable (eventually, we hope to remove this limitation).

Split

Let's create a simple example dataset:

python

import numpy as np import pandas as pd import xray np.random.seed(123456)

python

ds = xray.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 :pyxray.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 over groups in (label, group) pairs:

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.

Apply

To apply a function to each group, you can use the flexible :pyxray.GroupBy.apply 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').apply(standardize)

GroupBy objects also have a :py~xray.GroupBy.reduce method and methods like :py~xray.GroupBy.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()

First and last

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).

Grouped arithmetic

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(x=label))
xray.concat(results, dim='x')

Squeezing

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 xray will attempt to automatically :py~xray.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~xray.DataArray.squeeze methods.