8000 Inconsistent behaviour in array multiplication using *= · Issue #6379 · numpy/numpy · GitHub
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0mar opened this issue Sep 28, 2015 · 1 comment
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Inconsistent behaviour in array multiplication using *= #6379

0mar opened this issue Sep 28, 2015 · 1 comment

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@0mar
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0mar commented Sep 28, 2015

I saw similar reports in #6074 and #4707, but I didn't find any report matching this issue exacly.
When multiplying arrays a and b, a*=b and a=a*b are not equivalent if their types are different.

The same happens with addition.

In Numpy 1.8.2, on python 3.4, different behaviour is observed in the following two cases:

import numpy as np

#1

a1 = np.array([1,2])
c = np.array([0.7,0.7])
a1*= c
print(a1,a1.dtype)

Output:
[0 1] int64
#2

a1 = np.array([1,2])
c = np.array([0.7,0.7])
a1= a1*c
print(a1,a1.dtype)

Output
[ 0.7 1.4] float64

However, I'm not sure whether this is a bug, or just a matter of semantics.
@seberg
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seberg commented Sep 28, 2015

There is no inconsistency here, because the first example corresponds to:

a1[...] = a1 * c

since it is in-place the data type cannot change, so that there is an (unsafe) cast involved. Note that there is a deprecation warning about this, and it may be turned into an error in the future (or maybe it was already in 1.10). Which will force the user to make the cast manually.

If you have any further questions, feel free to ask.

@seberg seberg closed this as completed Sep 28, 2015
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