ak.values_astype#

ak.values_astype(array, to, *, including_unknown=False, highlevel=True, behavior=None, attrs=None)#

Parameters:

array – Array-like data (anything ak.to_layout recognizes).
to (dtype or dtype specifier) – Type to convert the numbers into.
including_unknown (bool) – If True, the unknown type is considered a value type and is converted to the specified dtype; if False, unknown will remain unknown.
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.

Converts all numbers in the array to a new type, leaving the structure untouched.

For example,

>>> array = ak.Array([1.1, 2.2, 3.3, 4.4, 5.5])
>>> ak.values_astype(array, np.int32)
<Array [1, 2, 3, 4, 5] type='5 * int32'>

and

>>> array = ak.Array([[1.1, 2.2, 3.3], [], [4.4, 5.5]])
>>> ak.values_astype(array, np.int32)
<Array [[1, 2, 3], [], [4, 5]] type='3 * var * int32'>

Note, when converting values to a np.datetime64 type that is unitless, a default ‘[us]’ unit is assumed - until further specified as numpy dtypes.

For example,

>>> array = ak.Array([1567416600000])
>>> ak.values_astype(array, "datetime64[ms]")
<Array [2019-09-02T09:30:00.000] type='1 * datetime64[ms]'>

or

>>> array = ak.Array([1567416600000])
>>> ak.values_astype(array, np.dtype("M8[ms]"))
<Array [2019-09-02T09:30:00.000] type='1 * datetime64[ms]['>