Pandas引入了一个将所有数据类型转换为缺失值的选项。

https://pandas.pydata.org/docs/user_guide/missing_data.html

np.isnan()函数不兼容所有的数据类型，例如:

>>> import numpy as np
>>> values = [np.nan, "x", "y"]
>>> np.isnan(values)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

pd.isna()和pd.notna()函数与许多数据类型兼容，pandas引入了pd.notna()函数。NA值:

>>> import numpy as np
>>> import pandas as pd

>>> values = pd.Series([np.nan, "x", "y"])
>>> values
0    NaN
1      x
2      y
dtype: object
>>> values.loc[pd.isna(values)]
0    NaN
dtype: object
>>> values.loc[pd.isna(values)] = pd.NA
>>> values.loc[pd.isna(values)]
0    <NA>
dtype: object
>>> values
0    <NA>
1       x
2       y
dtype: object

#
# using map with lambda, or a list comprehension
#

>>> values = [np.nan, "x", "y"]
>>> list(map(lambda x: pd.NA if pd.isna(x) else x, values))
[<NA>, 'x', 'y']
>>> [pd.NA if pd.isna(x) else x for x in values]
[<NA>, 'x', 'y']

如果你使用numpy

# first get the indices where the values are finite
ii = np.isfinite(x)

# second get the values
x = x[ii]

对我来说，@jmetz的答案不工作，但是使用pandas isnull()做到了。

x = x[~pd.isnull(x)]

接受的答案改变了2d数组的形状。 我在这里提供了一个解决方案，使用Pandas dropna()功能。 它适用于1D和2D数组。在2D情况下，可以选择weather删除包含np.nan的行或列。

import pandas as pd
import numpy as np

def dropna(arr, *args, **kwarg):
    assert isinstance(arr, np.ndarray)
    dropped=pd.DataFrame(arr).dropna(*args, **kwarg).values
    if arr.ndim==1:
        dropped=dropped.flatten()
    return dropped

x = np.array([1400, 1500, 1600, np.nan, np.nan, np.nan ,1700])
y = np.array([[1400, 1500, 1600], [np.nan, 0, np.nan] ,[1700,1800,np.nan]] )


print('='*20+' 1D Case: ' +'='*20+'\nInput:\n',x,sep='')
print('\ndropna:\n',dropna(x),sep='')

print('\n\n'+'='*20+' 2D Case: ' +'='*20+'\nInput:\n',y,sep='')
print('\ndropna (rows):\n',dropna(y),sep='')
print('\ndropna (columns):\n',dropna(y,axis=1),sep='')

print('\n\n'+'='*20+' x[np.logical_not(np.isnan(x))] for 2D: ' +'='*20+'\nInput:\n',y,sep='')
print('\ndropna:\n',x[np.logical_not(np.isnan(x))],sep='')

结果:

==================== 1D Case: ====================
Input:
[1400. 1500. 1600.   nan   nan   nan 1700.]

dropna:
[1400. 1500. 1600. 1700.]


==================== 2D Case: ====================
Input:
[[1400. 1500. 1600.]
 [  nan    0.   nan]
 [1700. 1800.   nan]]

dropna (rows):
[[1400. 1500. 1600.]]

dropna (columns):
[[1500.]
 [   0.]
 [1800.]]


==================== x[np.logical_not(np.isnan(x))] for 2D: ====================
Input:
[[1400. 1500. 1600.]
 [  nan    0.   nan]
 [1700. 1800.   nan]]

dropna:
[1400. 1500. 1600. 1700.]

@jmetz的答案可能是大多数人需要的;然而，它会产生一个一维数组，例如，使其无法用于删除矩阵中的整行或整列。

为此，应该将逻辑数组缩减为一维，然后对目标数组进行索引。例如，下面将删除至少有一个NaN值的行:

x = x[~numpy.isnan(x).any(axis=1)]

点击这里查看更多细节。

Pandas引入了一个将所有数据类型转换为缺失值的选项。

https://pandas.pydata.org/docs/user_guide/missing_data.html

np.isnan()函数不兼容所有的数据类型，例如:

>>> import numpy as np
>>> values = [np.nan, "x", "y"]
>>> np.isnan(values)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

pd.isna()和pd.notna()函数与许多数据类型兼容，pandas引入了pd.notna()函数。NA值:

>>> import numpy as np
>>> import pandas as pd

>>> values = pd.Series([np.nan, "x", "y"])
>>> values
0    NaN
1      x
2      y
dtype: object
>>> values.loc[pd.isna(values)]
0    NaN
dtype: object
>>> values.loc[pd.isna(values)] = pd.NA
>>> values.loc[pd.isna(values)]
0    <NA>
dtype: object
>>> values
0    <NA>
1       x
2       y
dtype: object

#
# using map with lambda, or a list comprehension
#

>>> values = [np.nan, "x", "y"]
>>> list(map(lambda x: pd.NA if pd.isna(x) else x, values))
[<NA>, 'x', 'y']
>>> [pd.NA if pd.isna(x) else x for x in values]
[<NA>, 'x', 'y']