ak.unflatten — Awkward Array documentation

# ak.unflatten

Defined in awkward.operations.structure on line 2023.

ak.unflatten(array, counts, axis=0, highlevel=True, behavior=None)
  Parameters:

    - array – Data to create an array with an additional level from.

    - 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) – 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.

    - highlevel (bool) – If True, return an `ak.Array`; otherwise, return a low-level `ak.layout.Content` subclass.

    - behavior (None or dict) – Custom `ak.behavior` 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], [], ... [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]])
    >>> print(ak.unflatten(original, [2, 2, 1, 2, 1, 1], axis=1))
    [[[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]`.

    >>> print(ak.unflatten(original, [2, 1, 2, 2, 1, 1], axis=1))
    Traceback (most recent call last):
    ...
    ValueError: structure imposed by 'counts' does not fit in the array at axis=1

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

See also `ak.num` and `ak.flatten`.