Python 如何获取numpy数组中所有NaN值的索引列表？

How to get the indices list of all NaN value in numpy array?

提问by xxx222

Say now I have a numpy array which is defined as,

现在说我有一个 numpy 数组，它被定义为，

[[1,2,3,4],
[2,3,NaN,5],
[NaN,5,2,3]]

Now I want to have a list that contains all the indices of the missing values, which is [(1,2),(2,0)]at this case.

现在我想要一个包含缺失值的所有索引的列表，[(1,2),(2,0)]在这种情况下。

Is there any way I can do that?

有什么办法可以做到吗？

回答by michael_j_ward

np.isnancombined with np.argwhere

np.isnan结合np.argwhere

x = np.array([[1,2,3,4],
              [2,3,np.nan,5],
              [np.nan,5,2,3]])
np.argwhere(np.isnan(x))

output:

输出：

array([[1, 2],
       [2, 0]])

回答by Nickil Maveli

You can use np.whereto match the boolean conditions corresponding to Nanvalues of the array and mapeach outcome to generate a list of tuples.

您可以使用np.where匹配与Nan数组值和map每个结果对应的布尔条件来生成tuples.

>>>list(map(tuple, np.where(np.isnan(x))))
[(1, 2), (2, 0)]

回答by johnnyasd12

Since x!=xreturns the same boolean array with np.isnan(x)(because np.nan!=np.nanwould return True), you could also write:

由于x!=x返回与np.isnan(x)（因为np.nan!=np.nan会返回True）相同的布尔数组，您还可以编写：

np.argwhere(x!=x)

However, I still recommend writing np.argwhere(np.isnan(x))since it is more readable. I just try to provide another way to write the code in this answer.

但是，我仍然建议写作，np.argwhere(np.isnan(x))因为它更具可读性。我只是尝试提供另一种方法来编写此答案中的代码。