ak.unflatten#

Defined in awkward.operations.ak_unflatten on line 26.

ak.unflatten(array, counts, axis=0, *, highlevel=True, behavior=None, attrs=None)#

Parameters:

array – Array-like data (anything ak.to_layout recognizes).
counts (int or array) – Number of elements the new level should have. If an integer, the new level will be regularly sized; otherwise, it will consist of variable-length lists with the given lengths.
axis (int or str) – The dimension at which this operation is applied. The outermost dimension is 0, followed by 1, etc., and negative values count backward from the innermost: -1 is the innermost dimension, -2 is the next level up, etc. If a str, it is interpreted as the name of the axis which maps to an int if named axes are present. Named axes are attached to an array using ak.with_named_axis and removed with ak.without_named_axis; also see the Named axes user guide.
highlevel (bool) – If True, return an ak.Array; otherwise, return a low-level ak.contents.Content subclass.
behavior (None or dict) – Custom ak.behavior for the output array, if high-level.
attrs (None or dict) – Custom attributes for the output array, if high-level.

Returns an array with an additional level of nesting. This is roughly the inverse of ak.flatten, where counts were obtained by ak.num (both with axis=1).

For example,

>>> original = ak.Array([[0, 1, 2], [], [3, 4], [5], [6, 7, 8, 9]])
>>> counts = ak.num(original)
>>> array = ak.flatten(original)
>>> counts
<Array [3, 0, 2, 1, 4] type='5 * int64'>
>>> array
<Array [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] type='10 * int64'>
>>> ak.unflatten(array, counts)
<Array [[0, 1, 2], [], [3, ...], [5], [6, 7, 8, 9]] type='5 * var * int64'>

An inner dimension can be unflattened by setting the axis parameter, but operations like this constrain the counts more tightly.

For example, we can subdivide an already divided list:

>>> original = ak.Array([[1, 2, 3, 4], [], [5, 6, 7], [8, 9]])
>>> ak.unflatten(original, [2, 2, 1, 2, 1, 1], axis=1).show()
[[[1, 2], [3, 4]],
 [],
 [[5], [6, 7]],
 [[8], [9]]]

But the counts have to add up to the lengths of those lists. We can’t mix values from the first [1, 2, 3, 4] with values from the next [5, 6, 7].

>>> ak.unflatten(original, [2, 1, 2, 2, 1, 1], axis=1).show()
ValueError: while calling
    ak.unflatten(
        array = <Array [[1, 2, 3, 4], [], ..., [8, 9]] type='4 * var * int64'>
        counts = [2, 1, 2, 2, 1, 1]
        axis = 1
        highlevel = True
        behavior = None
    )
Error details: structure imposed by 'counts' does not fit in the array or partition at axis=1

Also note that new lists created by this function cannot cross partitions (which is only possible at axis=0, anyway).