Inconsistent Numpy array aliasing behavior

Question

The following behavior is expected and is what I get. This is consistent with how aliasing works for native Python objects like lists.

>>> x = np.array([1, 2, 3])
>>> y = x
>>> x
array([1, 2, 3])
>>> y
array([1, 2, 3])
>>> x = x + np.array([2, 3, 4])
>>> x
array([3, 5, 7])
>>> y
array([1, 2, 3])

But the following behavior is unexpected by changing x = x + np.array([2, 3, 4]) to x += np.array([2, 3, 4])

>>> x += np.array([2, 3, 4])
>>> x
array([3, 5, 7])
>>> y
array([3, 5, 7])

The Numpy version is 1.16.4 on my machine. Is this a bug or feature? If it is a feature how x = x + np.array([2, 3, 4]) differs from x += np.array([2, 3, 4])

Randy · Accepted Answer · 2020-05-17 17:06:28Z

2

Your line y = x doesn't create a copy of the array; it simply tells y to point to the same data as x, which you can see if you look at their ids:

x = np.array([1,2,3])
y = x
print(id(x), id(y))

(140644627505280, 140644627505280)

x = x + np.array([2, 3, 4]) will do a reassignment of x to a new id, while x += np.array([2, 3, 4]) will modify it in place. Thus, the += will also modify y, while x = x + ... won't.

x += np.array([2, 3, 4])
print(id(x))
print(x, y)

x = x + np.array([2, 3, 4])
print(id(x))
print(x, y)

140644627505280
[3 5 7] [3 5 7]
140644627175744
[ 5  8 11] [3 5 7]

answered May 17, 2020 at 17:06

Randy

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1 Comment

SethMMorton Over a year ago

The main takeaway from this: whenever you are confused about "aliasing" (as the OP refers to it), make generous use of print(id(x)) to see what is going on, and that typically clears things up.

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