切出一部分numpy数组的Pythonic方法

Suppose you have the following numpy array,

假设您有以下numpy数组，

>>> x = numpy.array([0,1,2,3,4,5,6,7,8,9,10])

and you want to extract a new numpy array consisting of only the first three (3) and last four (4) elements, i.e.,

并且你想提取一个新的numpy数组，只包含前三（3）和后四（4）个元素，即

>>> y = x[something]
>>> print y
[0 1 2 7 8 9 10]

Is this possible? I know that to extract the first three numbers you simply do x[:3] and to extract the last four you do x[-4:], but is there a simple way of extracting all that in a simple slice? I know this can be done by, e.g., appending both calls,

这可能吗？我知道要提取前三个数字你只需做x [：3]并提取最后四个你做x [-4：]，但是有一种简单的方法可以在一个简单的切片中提取所有这些吗？我知道这可以通过例如附加两个电话来完成，

 >>> y = numpy.append(x[:3],x[-4:])

but I was wondering if there is some simple little trick to do it in a more direct, pythonic way, without having to reference x again (i.e., I first thought that maybe x[-4:3] could work but I realized immediately that it didn't made sense).

但是我想知道是否有一些简单的小技巧可以用更直接，更pythonic的方式来做，而不必再次引用x（即，我首先想到也许x [-4：3]可以工作但我立即意识到它没有意义）。

3 个解决方案

#1

I think you should use index arrays.

我认为你应该使用索引数组。

indices = list(range(3))+list(range(-4,0))
y = x[indices]

You can probably drop list casts (not sure because python 3 changed the behaviour a bit). Or you could use numpy range genreators.

您可以删除列表转换（不确定，因为python 3稍微改变了行为）。或者你可以使用numpy range genreators。

Edit: not sure why the downvote, cause it works:

编辑：不确定为什么downvote，导致它工作：

import numpy
x = numpy.array([0,1,2,3,4,5,6,7,8,9,10])
indices = list(range(3))+list(range(-4,0))
y = x[indices]
print(y)

#2

A simple slice probably won't work. You can use numpy's extended slicing:

一个简单的切片可能无法正常工作。你可以使用numpy的扩展切片：

>>> import numpy as np
>>> a = np.arange(10)
>>> something = [0, 1, 2, -4, -3, -2, -1]
>>> a[something] 
array([0, 1, 2, 6, 7, 8, 9])

Notice I passed in a list of indices that I wanted to take from the original array ...

注意我传入了一个我想从原始数组中获取的索引列表...

Frankly though, your solution with np.append is likely just as good...

坦率地说，你的np.append解决方案可能同样好......

#3

np.arange(11)[np.r_[0:3,7:11]]
# array([ 0,  1,  2,  7,  8,  9, 10])

np.r_ is a function, actually an indexable object, in numpy/lib/index_tricks. It takes multiple slices, or indexes, and concatenates them into one indexing array.

np.r_是一个函数，实际上是numpy / lib / index_tricks中的可索引对象。它需要多个切片或索引，并将它们连接成一个索引数组。

np.r_[0:3,7:11]
# array([ 0,  1,  2,  7,  8,  9, 10])

Or borrowing from luk32s answer, a negative slice works:

或者借用luk32s的答案，一个负片可行：

a[np.r_[:3,-4:0]]

An alternative to splicing the 2 parts is to delete the middle. s_ lets you use slice notation inside the function.

拼接两部分的另一种方法是删除中间部分。 s_允许您在函数内部使用切片表示法。

np.delete(np.arange(11),np.s_[3:7])
# array([ 0,  1,  2,  7,  8,  9, 10])

Look at the code for the .lib.index_tricks, delete, and insert functions for more ideas on how to construct index arrays from multiple pieces.

查看.lib.index_tricks，delete和insert函数的代码，以获取有关如何从多个部分构造索引数组的更多想法。

Sometimes a boolean index is convenient. Make it all True or False, and flip selected elements.

有时布尔索引很方便。使其全部为真或假，并翻转所选元素。

i = np.ones(a.shape,dtype='bool')
i[3:7] = False
a[i]

#1