存在多个条件时替换numpy数组中的元素 [英] Replacing elements in a numpy array when there are multiple conditions

问题描述

此问题与以下帖子相关:如果条件为，则替换Numpy元素遇见.

This question is related to the following post: Replacing Numpy elements if condition is met.

假设我有两个一维的numpy数组a和b，每个数组有50行.

Suppose i have two, one-dimensional numpy arrays a and b, with 50 rows each.

我想创建一个包含50行的数组c，每行将根据是否满足条件采用0-4的值:

I would like to create an array c of 50 rows, each of which will take the values 0-4 depending on whether a condition is met:

if a > 0 the value in the corresponding row of c should be 0
if a < 0 the value in the corresponding row of c should be 1
if a > 0 and b < 0 the value in the corresponding row of c should be 2
if b > 0 the value in the corresponding row of c should be 3

我想这是一个更广泛的问题，我如何才能将特定的值分配给 有多个条件时的数组.我已经尝试了上面提到的帖子的各种变体，但没有成功.

I suppose the broader question here is how can i assign specific values to an array when there are multiple conditions. I have tried variations from the post i referenced above but i have not been successful.

关于如何实现此目标的任何想法，最好不使用 for循环?

Any ideas of how i could achieve this, preferably without using a for-loop?

推荐答案

直接解决方案是按顺序应用分配.

A straight forward solution would be to apply the assignments in sequence.

In [18]: a = np.random.choice([-1,1],size=(10,))
In [19]: b = np.random.choice([-1,1],size=(10,))
In [20]: a
Out[20]: array([-1,  1, -1, -1,  1, -1, -1,  1,  1, -1])
In [21]: b
Out[21]: array([-1,  1,  1,  1, -1,  1, -1,  1,  1,  1])

从具有默认值的数组开始:

Start off with an array with the 'default' value:

In [22]: c = np.zeros_like(a)

应用第二个条件:

In [23]: c[a<0] = 1

第三个需要注意一点，因为它结合了两个测试. ()很重要:

The third requires a little care since it combines 2 tests. () matter here:

In [25]: c[(a>0)&(b<0)] = 2

最后一个:

In [26]: c[b>0] = 3
In [27]: c
Out[27]: array([1, 3, 3, 3, 2, 3, 1, 3, 3, 3])

看起来所有开头的0都被覆盖了.

Looks like all of the initial 0s are overwritten.

在数组中有许多元素，仅进行了几次测试，我就不必担心速度.专注于清晰度和表现力，而不是紧凑性.

With many elements in the arrays, and just a few tests, I wouldn't worry about speed. Focus on clarity and expressiveness, not compactness.

where有3个参数的版本，可以在值或数组之间进行选择.但是我很少使用它，也没有看到很多关于它的问题.

There is a 3 argument version of where that can choose between values or arrays. But I rarely use it, and don't see many questions about it either.

In [28]: c = np.where(a>0, 0, 1)
In [29]: c
Out[29]: array([1, 0, 1, 1, 0, 1, 1, 0, 0, 1])
In [30]: c = np.where((a>0)&(b<0), 2, c)
In [31]: c
Out[31]: array([1, 0, 1, 1, 2, 1, 1, 0, 0, 1])
In [32]: c = np.where(b>0, 3, c)
In [33]: c
Out[33]: array([1, 3, 3, 3, 2, 3, 1, 3, 3, 3])

这些where可以链接在一行上.

These wheres could be chained on one line.

c = np.where(b>0, 3, np.where((a>0)&(b<0), 2, np.where(a>0, 0, 1)))

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