API Reference#

Functions#

groupby_reduce(array, *by, func[, ...])

GroupBy reductions using tree reductions for dask.array

xarray.xarray_reduce(obj, *by, func[, ...])

GroupBy reduce operations on xarray objects using numpy-groupies

Rechunking#

rechunk_for_blockwise(array, axis, labels)

Rechunks array so that group boundaries line up with chunk boundaries, allowing embarrassingly parallel group reductions.

rechunk_for_cohorts(array, axis, labels, ...)

Rechunks array so that each new chunk contains groups that always occur together.

xarray.rechunk_for_blockwise(obj, dim, labels)

Rechunks array so that group boundaries line up with chunk boundaries, allowing embarrassingly parallel group reductions.

xarray.rechunk_for_cohorts(obj, dim, labels, ...)

Rechunks array so that each new chunk contains groups that always occur together.

Visualization#

visualize.draw_mesh(nrow, ncol, *[, ...])

visualize.visualize_groups_1d(array, labels)

Visualize group distribution for a 1D array of group labels.

visualize.visualize_cohorts_2d(by, chunks)

Aggregation Objects#

Aggregation(name, *[, numpy, preprocess, ...])

Attributes:

aggregations.sum_

aggregations.nansum