ak.flatten

Defined in awkward.operations.ak_flatten on line 17.

ak.flatten(array, axis=1, *, highlevel=True, behavior=None, attrs=None)

Parameters:

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

• axis (None or int) – If None, the operation flattens all levels of nesting, returning a 1-dimensional array. Otherwise, it flattens at a specified depth. 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.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 one level of nesting removed by erasing the boundaries between consecutive lists. Since this operates on a level of nesting, axis=0 is a special case that only removes values at the top level that are equal to None.

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=1, the outer lists (length 4, length 0, length 2) become a single list (of length 6).

>>> ak.flatten(array, axis=1).show()
[[1.1, 2.2, 3.3],
 [],
 [4.4, 5.5],
 [6.6],
 [7.7],
 [8.8, 9.9]]

At axis=2, the inner lists (lengths 3, 0, 2, 1, 1, and 2) become three lists (of lengths 6, 0, and 3).

>>> ak.flatten(array, axis=2).show()
[[1.1, 2.2, 3.3, 4.4, 5.5, 6.6],
 [],
 [7.7, 8.8, 9.9]]

There’s also an option to completely flatten the array with axis=None. This is useful for passing the data to a function that doesn’t care about nested structure, such as a plotting routine.

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

Missing values are eliminated by flattening: there is no distinction between an empty list and a value of None at the level of flattening.

>>> array = ak.Array([[1.1, 2.2, 3.3], None, [4.4], [], [5.5]])
>>> ak.flatten(array, axis=1)
<Array [1.1, 2.2, 3.3, 4.4, 5.5] type='5 * float64'>

As a consequence, flattening at axis=0 does only one thing: it removes None values from the top level.

>>> ak.flatten(array, axis=0)
<Array [[1.1, 2.2, 3.3], [4.4], [], [5.5]] type='4 * var * float64'>

As a technical detail, the flattening operation can be trivial in a common case, ak.contents.ListOffsetArray in which the first offset is 0. In that case, the flattened data is simply the array node’s content.

>>> array = ak.Array([[0.0, 1.1, 2.2], [], [3.3, 4.4], [5.5], [6.6, 7.7, 8.8, 9.9]])
>>> array.layout
<ListOffsetArray len='5'>
    <offsets><Index dtype='int64' len='6'>
        [ 0  3  3  5  6 10]
    </Index></offsets>
    <content><NumpyArray dtype='float64' len='10'>
        [0.  1.1 2.2 3.3 4.4 5.5 6.6 7.7 8.8 9.9]
    </NumpyArray></content>
</ListOffsetArray>

>>> ak.flatten(array).layout
<NumpyArray dtype='float64' len='10'>
    [0.  1.1 2.2 3.3 4.4 5.5 6.6 7.7 8.8 9.9]
</NumpyArray>

>>> array.layout.content
<NumpyArray dtype='float64' len='10'>
    [0.  1.1 2.2 3.3 4.4 5.5 6.6 7.7 8.8 9.9]
</NumpyArray>

However, it is important to keep in mind that this is a special case: ak.flatten and content are not interchangeable!

>>> array = ak.Array(
...     ak.contents.ListArray(
...         ak.index.Index64(np.array([ 9, 100, 5, 8, 1])),
...         ak.index.Index64(np.array([12, 100, 7, 9, 5])),
...         ak.contents.NumpyArray(
...             np.array([999, 6.6, 7.7, 8.8, 9.9, 3.3, 4.4, 999, 5.5, 0., 1.1, 2.2, 999])
...         ),
...     )
... )
>>> array.show()
[[0, 1.1, 2.2],
 [],
 [3.3, 4.4],
 [5.5],
 [6.6, 7.7, 8.8, 9.9]]

>>> ak.flatten(array).show()
[0,
 1.1,
 2.2,
 3.3,
 4.4,
 5.5,
 6.6,
 7.7,
 8.8,
 9.9]

>>> ak.Array(array.layout.content).show()
[999,
 6.6,
 7.7,
 8.8,
 9.9,
 3.3,
 4.4,
 999,
 5.5,
 0,
 1.1,
 2.2,
 999]