ak.pad_none#

Defined in awkward.operations.ak_pad_none on line 21.

ak.pad_none(array, target, axis=1, *, clip=False, highlevel=True, behavior=None, attrs=None)#
Parameters:

  • array – Array-like data (anything ak.to_layout recognizes).

  • target (int) – The intended length of the lists. If clip=True, the output lists will have exactly this length; otherwise, they will have at least this length.

  • 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.

  • clip (bool) – If True, the output lists will have regular lengths (ak.types.RegularType) of exactly target; otherwise the output lists will have in-principle variable lengths (ak.types.ListType) of at least target.

  • 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.

Increase the lengths of lists to a target length by adding None values.

Consider the following

>>> array = ak.Array([[[1.1, 2.2, 3.3],
...                    [],
...                    [4.4, 5.5],
...                    [6.6]],
...                   [],
...                   [[7.7],
...                    [8.8, 9.9]
...                   ]])

At axis=0, this operation pads the whole array, adding None at the outermost level:

>>> ak.pad_none(array, 5, axis=0).show()
[[[1.1, 2.2, 3.3], [], [4.4, 5.5], [6.6]],
 [],
 [[7.7], [8.8, 9.9]],
 None,
 None]

At axis=1, this operation pads the first nested level:

>>> ak.pad_none(array, 3, axis=1).show()
[[[1.1, 2.2, 3.3], [], [4.4, 5.5], [6.6]],
 [None, None, None],
 [[7.7], [8.8, 9.9], None]]

And so on for higher values of axis:

>>> ak.pad_none(array, 2, axis=2).show()
[[[1.1, 2.2, 3.3], [None, None], [4.4, 5.5], [6.6, None]],
 [],
 [[7.7, None], [8.8, 9.9]]]

Note that the clip parameter not only determines whether the lengths are at least target or exactly target, it also determines the type of the output:

The in-principle variable-length lists might, in fact, all have the same length, but the type difference is significant, for instance in broadcasting rules (see ak.broadcast_arrays).

The difference between

>>> ak.pad_none(array, 2, axis=2)
<Array [[[1.1, 2.2, 3.3], ..., [...]], ...] type='3 * var * var * ?float64'>

and

>>> ak.pad_none(array, 2, axis=2, clip=True)
<Array [[[1.1, 2.2], ..., [6.6, None]], ...] type='3 * var * 2 * ?float64'>

is not just in the length of [1.1, 2.2, 3.3] vs [1.1, 2.2], but also in the distinction between the following types.

>>> ak.pad_none(array, 2, axis=2).type.show()
3 * var * var * ?float64
>>> ak.pad_none(array, 2, axis=2, clip=True).type.show()
3 * var *   2 * ?float64