为什么我们在Numpy中获得双布尔索引的结果？ [英] Why do we get such a result for double boolean indexing in Numpy?

问题描述

我对最后一个表达式的结果感到惊讶？

I was surprised by the result of the last expression?

>>> from numpy import array, arange
>>> a = arange(12).reshape(3,4)
>>> b1 = array([False,True,True])             # first dim selection
>>> b2 = array([True,False,True,False])       # second dim selection
>>>
>>> a[b1,:]                                   # selecting rows
array([[ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>>
>>> a[b1]                                     # same thing
array([[ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>>
>>> a[:,b2]                                   # selecting columns
array([[ 0,  2],
       [ 4,  6],
       [ 8, 10]])
>>>
>>> a[b1,b2]                                  # a weird thing to do
array([ 4, 10])

我预计：

array([[ 4,  6],
       [ 8, 10]])

你有什么解释为什么会这样吗？

Do you have any explanation why it is the case?

推荐答案

让我们从你的数组开始：

Let's start with your array:

a = np.array([[ 0,  1,  2,  3],
              [ 4,  5,  6,  7],
              [ 8,  9, 10, 11]])

您当前的索引逻辑等于以下内容：

Your current indexing logic equates to the following:

a[[1, 2], [0, 2]]  # array([ 4, 10])

坚持2维，NumPy将其解释为索引dim1-indices [1,2] 和dim2-indices [0,2 ] ，或坐标（1,0）和（2,2）。此处不涉及广播。

Sticking to 2 dimensions, NumPy interprets this as indexing dim1-indices [1, 2] and dim2-indices [0, 2], or coordinates (1, 0) and (2, 2). There's no broadcasting involved here.

要允许使用布尔数组进行广播，您可以使用 numpy.ix _ ：

To permit broadcasting with Boolean arrays, you can use numpy.ix_:

res = a[np.ix_(b1, b2)]

print(res)

array([[ 4,  6],
       [ 8, 10]])

魔法 ix _ 执行记录在 docs ：布尔序列将被解释为相应维度的布尔掩码（相当于传入 np.nonzero（boolean_sequence））。

The magic ix_ performs is noted in the docs: "Boolean sequences will be interpreted as boolean masks for the corresponding dimension (equivalent to passing in np.nonzero(boolean_sequence))."

print(np.ix_(b1, b2))

(array([[1],
        [2]], dtype=int64), array([[0, 2]], dtype=int64))

---

作为附注，如果你有整数索引，你可以使用更直接的方法：

---

As a side note, you can use a more direct approach if you have integer indices:

b1 = np.array([1, 2])
b2 = np.array([0, 2])

a[b1[:, None], b2]

参见：相关问题，说明为什么此方法不适用于布尔数组。

See also: related question on why this method does not work with Boolean arrays.

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