python - 简单的 imputer 删除 nan 而不是插补

Question

我尝试使用 SimpleImputer 均值策略来插补 NaN 值，但它不是插补它，而是删除了 NaN 值，我阅读了如何使用它 here和 the documentation ，它根本不适用于 numpy 数组或 python 列表，出了什么问题？解决办法是什么？

import numpy as np
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split

X = np.array([[2,3,6,5,4, np.nan],[2,3,6,15,4, np.nan]])

SI = SimpleImputer(strategy='mean')
X = SI.fit_transform(X)
print(X)

输出

runfile('D:/python projects/untitled0.py', wdir='D:/python projects')
[[ 2.  3.  6.  5.  4.]
 [ 2.  3.  6. 15.  4.]]

Answer 1

In [239]: SI=SimpleImputer(verbose=1)                                                          
In [240]: SI.fit_transform(X)                                                                  
/usr/local/lib/python3.6/dist-packages/sklearn/impute/_base.py:403: UserWarning: Deleting features without observed values: [5]
  "observed values: %s" % missing)
Out[240]: 
array([[ 2.,  3.,  6.,  5.,  4.],
       [ 2.,  3.,  6., 15.,  4.]])

调整X:

In [241]: X = np.array([[2,3,6,5,4, np.nan],[2,3,6,15,np.nan, 4]])                             
In [242]: SI.fit_transform(X)                                                                  
Out[242]: 
array([[ 2.,  3.,  6.,  5.,  4.,  4.],
       [ 2.,  3.,  6., 15.,  4.,  4.]])