numpy.array's have bizarre behavior with /= operator?

Question

I'm trying to normalize an array of numbers to the range (0, 1] so that I can use them as weights for a weighted random.choice(), so I entered this line:

# weights is a nonzero numpy.array
weights /= weights.max()

However, Pycharm said there's an unfilled parameter to the max() function ( Parameter 'initial' unfilled ). I tried this in the REPL with the /= operator and with "regular" division ( a = a / b ) and got different results for both and a different error than Pycharm thought:

>>> a = numpy.array([1,2,3])
>>> a.max()
3
>>> a /= a.max()
Traceback (most recent call last):
  File "<pyshell#4>", line 1, in <module>
    a /= a.max()
TypeError: No loop matching the specified signature and casting was found for ufunc true_divide
>>> a = a/a.max()
>>> a
array([0.33333333, 0.66666667, 1.        ])

I also realized that for a weighted random, the weights needed to sum to one rather than be normalized to it. But dividing it by the sum yielded the exact same TypeError using the /= operation (but Pycharm thought this was okay):

>>> a = numpy.array([1,2,3])
>>> sum(a)
6
>>> a
array([1, 2, 3])
>>> a /= sum(a)
Traceback (most recent call last):
  File "<pyshell#13>", line 1, in <module>
    a /= sum(a)
TypeError: No loop matching the specified signature and casting was found for ufunc true_divide
>>> a = a / sum(a)
>>> a
array([0.16666667, 0.33333333, 0.5       ])

What have I come across here? Is this some bizarre bug in Numpy or does the /= operator have a different use or something? I know they use __truediv__ and __itruediv__ but I can't see why one has a problem and the other doesn't. I have confirmed this behavior with the latest version of Numpy from pip (1.19.2 on Windows x64).

Answer 1

It's because numpy cares the type. When you apply the division, you're changing int to float . But numpy won't let you do that! That's why your values should be already in float . Try this:

>>> a = np.array([1.0,2.0,3.0])
>>> a /= sum(a)
>>> a
array([0.16666667, 0.33333333, 0.5       ])

But why did the other one work? It's because that's not an "in-place" operation. Hence a new memory location is being created. New variable, new type, hence numpy doesn't care here.