If you're converting a list into an array, you'll need to make a new copy anyway, so

arr = np.array(my_list, dtype='float32')

also works.

One use case is when my_list is not actually a list but some other list-like object; in which case explicitly casting to list beforehand might be helpful.

arr = np.array(list(my_list), dtype='float32')

For example,

my_list = pd.Series([[1], [2], [3]]).values

np.array(my_list)             # jagged array; not OK
np.array(list(my_list))       # OK
np.array(my_list.tolist())    # OK


my_list = {'a': [1], 'b': [2]}.values()
np.array(my_list)             # jagged array; not OK
np.array(list(my_list))       # OK

To cast nested list into an array, the shapes of the sublists must match. If they don't maybe you want to concatenate the sublists along some axis. Try np.concatenate/np.r_/np.c_ etc. instead.