ak.local_index

Defined in awkward.operations.ak_local_index on line 22.

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

Parameters:

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

• 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.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.

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], ..., [...]]] 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.show()
[[[(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.flatten(ak.flatten(multiindex)).show()
[(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_dataframe directly.

>>> ak.to_dataframe(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