I have trouble properly understanding numpy.where() despite reading the doc, this post and this other post.
我无法正确理解numpy.where（）尽管阅读了文档，这篇文章和其他帖子。
Can someone provide step-by-step commented examples with 1D and 2D arrays?
有人可以提供一维和二维阵列的逐步注释示例吗？
2 个解决方案
#1
126
After fiddling around for a while, I figured things out, and am posting them here hoping it will help others.
在摆弄了一段时间后，我想出了一些事情，并将它们发布在这里，希望它能帮助别人。
Intuitively, np.where is like asking "tell me where in this array, entries satisfy a given condition".
直觉上，np.where就像问“告诉我在这个数组中哪些条目满足给定条件”。
>>> a = np.arange(5,10)
>>> np.where(a < 8) # tell me where in a, entries are < 8
(array([0, 1, 2]),) # answer: entries indexed by 0, 1, 2
It can also be used to get entries in array that satisfy the condition:
它还可用于获取满足条件的数组条目：
>>> a[np.where(a < 8)]
array([5, 6, 7]) # selects from a entries 0, 1, 2
When a is a 2d array, np.where() returns an array of row idx's, and an array of col idx's:
当a是2d数组时，np.where（）返回一个行idx的数组，以及一个col idx的数组：
>>> a = np.arange(4,10).reshape(2,3)
array([[4, 5, 6],
[7, 8, 9]])
>>> np.where(a > 8)
(array(1), array(2))
So that as in the 1d case, we can use np.where() to get entries in the 2d array that satisfy the condition:
因此，与1d情况一样，我们可以使用np.where（）来获取满足条件的2d数组中的条目：
>>> a[np.where(a > 8)] # selects from a entries 0, 1, 2
array([9])
阵列（[9]）
Note, when a is 1d, np.where() still returns an array of row idx's and an array of col idx's but columns are of length 1, so latter is empty array.
注意，当a为1d时，np.where（）仍返回行idx的数组和col idx的数组，但列的长度为1，因此后者为空数组。
#2
4
Here is a little more fun. I've found that very often NumPy does exactly what I wish it would do - sometimes it's faster for me to just try things than it is to read the docs. Actually a mixture of both is best.
这里更有趣。我发现NumPy经常完全按照我的意愿行事 - 有时我尝试的东西比阅读文档更快。实际上两者的混合是最好的。
I think your answer is fine (and it's OK to accept it if you like). This is just "extra".
我认为你的答案很好（如果你愿意，可以接受它）。这只是“额外的”。
import numpy as np
a = np.arange(4,10).reshape(2,3)
wh = np.where(a>7)
gt = a>7
x = np.where(gt)
print "wh: ", wh
print "gt: ", gt
print "x: ", x
gives:
得到：
wh: (array([1, 1]), array([1, 2]))
gt: [[False False False]
[False True True]]
x: (array([1, 1]), array([1, 2]))
... but:
......但是：
print "a[wh]: ", a[wh]
print "a[gt] ", a[gt]
print "a[x]: ", a[x]
gives:
得到：
a[wh]: [8 9]
a[gt] [8 9]
a[x]: [8 9]
#1
126
After fiddling around for a while, I figured things out, and am posting them here hoping it will help others.
在摆弄了一段时间后，我想出了一些事情，并将它们发布在这里，希望它能帮助别人。
Intuitively, np.where is like asking "tell me where in this array, entries satisfy a given condition".
直觉上，np.where就像问“告诉我在这个数组中哪些条目满足给定条件”。
>>> a = np.arange(5,10)
>>> np.where(a < 8) # tell me where in a, entries are < 8
(array([0, 1, 2]),) # answer: entries indexed by 0, 1, 2
It can also be used to get entries in array that satisfy the condition:
它还可用于获取满足条件的数组条目：
>>> a[np.where(a < 8)]
array([5, 6, 7]) # selects from a entries 0, 1, 2
When a is a 2d array, np.where() returns an array of row idx's, and an array of col idx's:
当a是2d数组时，np.where（）返回一个行idx的数组，以及一个col idx的数组：
>>> a = np.arange(4,10).reshape(2,3)
array([[4, 5, 6],
[7, 8, 9]])
>>> np.where(a > 8)
(array(1), array(2))
So that as in the 1d case, we can use np.where() to get entries in the 2d array that satisfy the condition:
因此，与1d情况一样，我们可以使用np.where（）来获取满足条件的2d数组中的条目：
>>> a[np.where(a > 8)] # selects from a entries 0, 1, 2
array([9])
阵列（[9]）
Note, when a is 1d, np.where() still returns an array of row idx's and an array of col idx's but columns are of length 1, so latter is empty array.
注意，当a为1d时，np.where（）仍返回行idx的数组和col idx的数组，但列的长度为1，因此后者为空数组。
#2
4
Here is a little more fun. I've found that very often NumPy does exactly what I wish it would do - sometimes it's faster for me to just try things than it is to read the docs. Actually a mixture of both is best.
这里更有趣。我发现NumPy经常完全按照我的意愿行事 - 有时我尝试的东西比阅读文档更快。实际上两者的混合是最好的。
I think your answer is fine (and it's OK to accept it if you like). This is just "extra".
我认为你的答案很好（如果你愿意，可以接受它）。这只是“额外的”。
import numpy as np
a = np.arange(4,10).reshape(2,3)
wh = np.where(a>7)
gt = a>7
x = np.where(gt)
print "wh: ", wh
print "gt: ", gt
print "x: ", x
gives:
得到：
wh: (array([1, 1]), array([1, 2]))
gt: [[False False False]
[False True True]]
x: (array([1, 1]), array([1, 2]))
... but:
......但是：
print "a[wh]: ", a[wh]
print "a[gt] ", a[gt]
print "a[x]: ", a[x]
gives:
得到：
a[wh]: [8 9]
a[gt] [8 9]
a[x]: [8 9]