Numpy surprises

When multiplying int32*uint64 numpy 'promotes' the result to float (!):

>>> np.int32(1)*np.uint64(1)
np.float64(1.0)
>>> np.int32(1)*np.int64(1)
np.int64(1)

New in numpy version 2: numpy int types aren't promoted to bigger types in operations that involve a python int:

>>> np.__version__
'2.2.4'
#
np.uint8(1) + 255
<python-input-21>:1: RuntimeWarning: overflow encountered in scalar add
np.uint8(0)
#
# When the value of the int is bigger than would fit in the numpy type, an error is thrown:
np.uint8(0)+256
Traceback (most recent call last):
  File "<python-input-29>", line 1, in <module>
    np.uint8(0)+256
    17:13, 11 December 2025 (CET)17:13, 11 December 2025 (CET)~^Joosteto (talk)
OverflowError: Python integer 256 out of bounds for uint8

The old numpy 1.24 used to promote numpy int types to 64-bit:

np.__version__
'1.24.2'
#
type(np.uint8(1) + 1)
<class 'numpy.int64'>
#or to bigger types, if needed
type(np.uint8(1)+2**63)
<class 'numpy.ulonglong'>

Also funny: the old 1.24 numpy 'promoted' the uint64 type to float when confronted with python int:

np.uint64(1)+1
2.0
type(np.uint64(1)+1)
<class 'numpy.float64'>
#
#But... uin32 isn't promoted to float, but to... int64(!)
type(np.uint32(1)+1)
<class 'numpy.int64'>
#
# Same as uint8:
type(np.uint8(1)+1)
<class 'numpy.int64'>
#
# However, the signed versions, numpy.int64, numpy.int32 are just always converted to numpy.int64:
type(np.int32(1)+1)
<class 'numpy.int64'>
#
np.__version__
'1.24.2'

This is all improved a lot in numpy 2:

type(np.uint64(1)+1)
<class 'numpy.uint64'>
#
type(np.uint32(1)+1)
<class 'numpy.uint32'>
#
type(np.int32(1)+1)
<class 'numpy.int32'>
#
np.__version__
'2.2.4'

And without warning/exception:

2**np.int8(8)
np.int8(0)
#
np.int8(2)**8
np.int8(0)
#
np.__version__
'2.2.4'