- ak.to_numpy(array, allow_missing=True)¶
array (many types supported, including all Awkward Arrays and
Records) into a NumPy array, if possible.
If the data are numerical and regular (nested lists have equal lengths in each dimension, as described by the #type), they can be losslessly converted to a NumPy array and this function returns without an error.
Otherwise, the function raises an error. It does not create a NumPy
array with dtype
np.object_ (see the
note on object_ type)
since silent conversions to dtype
"O" arrays would not only be a
significant performance hit, but would also break functionality, since
nested lists in a NumPy
"O" array are severed from the array and
cannot be sliced as dimensions.
array is a scalar, it is converted into a NumPy scalar.
allow_missing is True; NumPy
are a possible result; otherwise, missing values (None) cause this
function to raise an error.