从2D Numpy数组中删除NaN [英] Remove NaN from 2D numpy array

问题描述

例如，如果我具有如下2D数组.

For example, if I have the 2D array as follows.

[[1,2,3,NAN],
 [4,5,NAN,NAN],
 [6,NAN,NAN,NAN]
]

所需的结果是

[[1,2,3],
 [4,5],
 [6]
]

我应该如何转变?

我发现使用 x = x[~numpy.isnan(x)]只能生成被压缩为一维数组的[1,2,3,4,5,6].

I find using x = x[~numpy.isnan(x)] can only generate [1,2,3,4,5,6], which has been squeezed into one dimensional array.

谢谢！

推荐答案

只需逐行应用isnan

In [135]: [row[~np.isnan(row)] for row in arr]
Out[135]: [array([1., 2., 3.]), array([4., 5.]), array([6.])]

如x[~numpy.isnan(x)]所述的布尔蒙版会产生平坦的结果，因为通常情况下，结果将像这样参差不齐，并且无法形成2d数组.

Boolean masking as in x[~numpy.isnan(x)] produces a flattened result because, in general, the result will be ragged like this, and can't be formed into a 2d array.

源数组必须是float dtype-因为np.nan是float:

The source array must be float dtype - because np.nan is a float:

In [138]: arr = np.array([[1,2,3,np.nan],[4,5,np.nan,np.nan],[6,np.nan,np.nan,np.nan]])
In [139]: arr
Out[139]: 
array([[ 1.,  2.,  3., nan],
       [ 4.,  5., nan, nan],
       [ 6., nan, nan, nan]])

如果是object dtype，则数字可以是整数，但np.isnan(arr)无效.

If object dtype, the numbers can be integer, but np.isnan(arr) won't work.

如果原始文件是列表而不是数组:

If the original is a list, rather than an array:

In [146]: alist = [[1,2,3,np.nan],[4,5,np.nan,np.nan],[6,np.nan,np.nan,np.nan]]
In [147]: alist
Out[147]: [[1, 2, 3, nan], [4, 5, nan, nan], [6, nan, nan, nan]]
In [148]: [[i for i in row if ~np.isnan(i)] for row in alist]
Out[148]: [[1, 2, 3], [4, 5], [6]]

---

使用split可以将平面数组转换为数组列表:

---

The flat array could be turned into a list of arrays with split:

In [152]: np.split(arr[~np.isnan(arr)],(3,5))
Out[152]: [array([1., 2., 3.]), array([4., 5.]), array([6.])]

其中(3,5)拆分参数可以通过对每行中的非nan进行计数来确定，但这需要更多工作，并且不能保证比行迭代要快.

where the (3,5) split parameter could be determined by counting the non-nan in each row, but that's more work and doesn't promise to be faster than than the row iteration.

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