错误或功能:使用切片克隆一个numpy数组 [英] Bug or feature: cloning a numpy array w/ slicing

问题描述

按照David Morrissey对""中的列表的回答进行操作?'我正在运行一些性能测试，并且在使用numpy数组时遇到意外行为.我知道可以/应该克隆一个numpy数组w/

Following up to David Morrissey's answer on 'How to clone a list in python?' I was running some performance tests and hit unexpected behavior when working w/ numpy arrays. I know that a numpy array can/ should be cloned w/

clone = numpy.array(original)

或

clone = numpy.copy(original)

，但是错误地认为切片也可以解决问题.但是:

but have incorrectly assumed that slicing would do the trick too. However:

In [11]: original = numpy.arange(4)

In [12]: original
Out[12]: array([0, 1, 2, 3])

In [13]: clone = original[:]

In [14]: clone
Out[14]: array([0, 1, 2, 3])

In [15]: clone[0] = 1

In [16]: clone
Out[16]: array([1, 1, 2, 3])

In [17]: original
Out[17]: array([1, 1, 2, 3])

是否存在这种轻微不一致的充分原因，或者我应该提交错误?

Is there a good reason for this slight inconsistency or should I file a bug?

推荐答案

在numpy中，切片是原始数组上的引用或视图"，因此它们不是副本.那是设计使然，而不是错误.原因是副本不如视图有用.

In numpy, slices are references or "views" on the original array, so they are not copies. That is by design, not a bug. The reason is that a copy is not as useful as a view.

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