ak.local_index — Awkward Array documentation

# ak.local_index

Defined in awkward.operations.structure on line 2515.

ak.local_index(array, axis=-1, highlevel=True, behavior=None)
  Parameters:

    - array – Array to index.

    - 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.

For example,

    >>> array = ak.Array([
    ...     [[0.0, 1.1, 2.2], []],
    ...     [[3.3, 4.4]],
    ...     [],
    ...     [[5.5], [], [6.6, 7.7, 8.8, 9.9]]])
    >>> ak.local_index(array, axis=0)
    <Array [0, 1, 2, 3] type='4 * int64'>
    >>> ak.local_index(array, axis=1)
    <Array [[0, 1], [0], [], [0, 1, 2]] type='4 * var * int64'>
    >>> ak.local_index(array, axis=2)
    <Array [[[0, 1, 2], []], ... [], [0, 1, 2, 3]]] type='4 * var * var * int64'>

Note that you can make a Pandas-style MultiIndex by calling this function on every axis.

    >>> multiindex = ak.zip([ak.local_index(array, i) for i in range(array.ndim)])
    >>> multiindex
    <Array [[[(0, 0, 0), (0, 0, ... ), (3, 2, 3)]]] type='4 * var * var * (int64, in...'>
    >>> ak.to_list(multiindex)
    [[[(0, 0, 0), (0, 0, 1), (0, 0, 2)], []],
     [[(1, 0, 0), (1, 0, 1)]],
     [],
     [[(3, 0, 0)], [], [(3, 2, 0), (3, 2, 1), (3, 2, 2), (3, 2, 3)]]]
    >>> ak.to_list(ak.flatten(ak.flatten(multiindex)))
    [(0, 0, 0),
     (0, 0, 1),
     (0, 0, 2),
     (1, 0, 0),
     (1, 0, 1),
     (3, 0, 0),
     (3, 2, 0),
     (3, 2, 1),
     (3, 2, 2),
     (3, 2, 3)]

But if you’re interested in Pandas, you may want to use `ak.to_pandas` directly.

    >>> ak.to_pandas(array)
                                values
    entry subentry subsubentry
    0     0        0               0.0
                   1               1.1
                   2               2.2
    1     0        0               3.3
                   1               4.4
    3     0        0               5.5
          2        0               6.6
                   1               7.7
                   2               8.8
                   3               9.9