Numpy: Division-assignment operator raises error, separate division and assignment work

[MareinK]

Sorry if this is a known issue, I searched for it but there are so many results...

This works:

import numpy as np
a = np.array([1, 2, 3])
a = a / 4

But this raises an error:

import numpy as np
a = np.array([1, 2, 3])
a /= 4

Namely:

TypeError: No loop matching the specified signature and casting
was found for ufunc true_divide

This is surprising since I would expect them to do the same.

Using numpy version 1.14.0.

[rkern]

I assume that you are on Python 3. This is expected behavior on Python 3. The result of a / 4 will not be an integer array but a floating point array. When you simply reassign that result with the line a = a / 4, that's fine. It essentially converts the code to a = np.true_divide(a, 4) which creates a new floating point array and reassigns the name a to it.

When you try to do in-place assignment, the true_divide ufunc is being asked to output back into the existing a integer array: np.true_divide(a, 4, out=a). There is no implementation of true_divide that takes two integer arguments and outputs another integer, so you get the exception.

[MareinK]

Thanks for the clear explanation, that makes sense. The below helped me to understand the difference:

>>> a = [1 ,2, 3]
>>> b = a
>>> id(a) == id(b)
True
>>> a += [4, 5, 6]  # list object is changed, no new object created
>>> id(a) == id(b)
True
>>> a = a + [4, 5, 6]  # new object created, and assigned to `a`
>>> id(a) == id(b)
False

If I understand correctly, the fact that no new object is created with +=, is the analogy to out=a.

One way around the issue is to use dtype=float upon array creation.

[rkern]

That's right.