ak.to_packed#

Defined in awkward.operations.ak_to_packed on line 15.

ak.to_packed(array, *, highlevel=True, behavior=None, attrs=None)#

Parameters:

array – Array-like data (anything ak.to_layout recognizes).
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 the same type and values as the input, but with packed inner structures:

ak.contents.NumpyArray becomes C-contiguous (if it isn’t already)
ak.contents.RegularArray trims unreachable content
ak.contents.ListArray becomes ak.contents.ListOffsetArray, making all list data contiguous
ak.contents.ListOffsetArray starts at offsets[0] == 0, trimming unreachable content
ak.contents.RecordArray trims unreachable contents
ak.contents.IndexedArray gets projected
ak.contents.IndexedOptionArray remains an ak.contents.IndexedOptionArray (with simplified index) if it contains records, becomes ak.contents.ByteMaskedArray otherwise
ak.contents.ByteMaskedArray becomes an ak.contents.IndexedOptionArray if it contains records, stays a ak.contents.ByteMaskedArray otherwise
ak.contents.BitMaskedArray becomes an ak.contents.IndexedOptionArray if it contains records, stays a ak.contents.BitMaskedArray otherwise
ak.contents.UnionArray gets projected contents
ak.record.Record becomes a record over a single-item ak.contents.RecordArray

Example

>>> a = ak.Array([[1, 2, 3], [], [4, 5], [6], [7, 8, 9, 10]])
>>> b = a[::-1]
>>> b.layout
<ListArray len='5'>
    <starts><Index dtype='int64' len='5'>
        [6 5 3 3 0]
    </Index></starts>
    <stops><Index dtype='int64' len='5'>
        [10  6  5  3  3]
    </Index></stops>
    <content><NumpyArray dtype='int64' len='10'>
        [ 1  2  3  4  5  6  7  8  9 10]
    </NumpyArray></content>
</ListArray>

>>> c = ak.to_packed(b)
>>> c.layout
<ListOffsetArray len='5'>
    <offsets><Index dtype='int64' len='6'>[ 0  4  5  7  7 10]</Index></offsets>
    <content><NumpyArray dtype='int64' len='10'>
        [ 7  8  9 10  6  4  5  1  2  3]
    </NumpyArray></content>
</ListOffsetArray>

Performing these operations will minimize the output size of data sent to ak.to_buffers (though conversions through Arrow, ak.to_arrow and ak.to_parquet, do not need this because packing is part of that conversion).