问题描述
我正在使用np.arrays.我正在尝试删除最后n个元素，其中n也可以是1.
I am working with np.arrays. I am trying to remove the last n elements, where n can be also 1.
n=5
corr=np.full(10,10)
通常，我将这种方法用于数组切片:
Usually I use this approach with array slicing:
corr=corr[:-n]
但是我正在考虑使用np.delete来提高性能:
But I was thinking of using np.delete to increase the performance:
np.delete(corr,range(-n,0))
但是它不起作用，与数组切片相比，还有更好的解决方案吗?(该方法还可以处理n = 0的情况，这将是一个优势)
But it doesn't work, is there any better solution compare with array slicing?(method able to deal also with case in which n=0, would be a point of advantage)
推荐答案
数组是具有诸如 shape ， dtype 和数据缓冲区之类的属性的对象.像 A [:-5] 之类的视图是另一个具有自己的 shape 等形状但具有共享数据缓冲区的数组.它正在查看相同的数据，但只看到一个切片.
An array is an object with attributes like shape, dtype, and a data buffer. A view like A[:-5] is another array with its own shape, etc, but with a shared data buffer. It's looking at the same data, but only sees a slice.
A [:-5] .copy()看起来是相同的，但是将具有自己的数据缓冲区，即从 A 中选择的元素的副本.
A[:-5].copy() will appear to be the same, but will have its own data buffer, a copy of selected elements from A.
无法更改 A 的数据缓冲区的大小.
There's no way of changing the size of the data buffer of A.
np.delete 返回具有其自己的数据缓冲区的新数组.它根据形状和删除图案使用各种方法.在所有情况下，它都是副本，并且比切片要慢.
np.delete returns a new array with its own data buffer. It uses various methods depending on the shape and delete pattern. It all cases it is a copy, and slower than slicing.
这篇关于更有效的方法来删除np.array中的最后N个值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！