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hhristov94 opened this issue Nov 25, 2018 · 3 comments
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Numpy silently assigns zeroes #12449

hhristov94 opened this issue Nov 25, 2018 · 3 comments

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@hhristov94
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hhristov94 commented Nov 25, 2018 •
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Numpy silently assigns zeroes instead of the correct array if the array has a negative fraction.

Reproducing code example:

A = np.array([[3,2,4],
              [2,1,3],
              [1,2,4]])
b = A[1,0:3] - 0.66*A[0,0:3]
A[1,1:3] = b[1:3]

A

Output:

array([[3, 2, 4],
       [2, 0, 0],
       [1, 2, 4]])

Numpy/Python version information:

OS: Windows 10
Numpy version : 1.14.5
Python version : 3.6.6
1.14.5 3.6.6 (v3.6.6:4cf1f54eb7, Jun 27 2018, 03:37:03) [MSC v.1900 64 bit (AMD64)]
@hhristov94
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Updated to Numpy version 1.15.4 . The issue still persists.

@eric-wieser
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eric-wieser commented Nov 25, 2018 •
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This fails for the same reason as:

>>> arr = np.array([1, 2, 3])
>>> arr.dtype
int  # or something
>>> arr[0] = 1.5
>>> arr.dtype
int  # unchanged
>>> arr[0]
1  # 1.5 is not an int, so we had to round

Assigning to an array never changes its type.

You could argue we should emit a warning or error here, rather than rounding. I think there are multiple issues open about that.

@eric-wieser
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Duplicate of #8733

@eric-wieser eric-wieser marked this as a duplicate of #8733 Nov 25, 2018
@anuppari anuppari mentioned this issue Jun 28, 2019
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