filter(lambda v: v==v, x)

既适用于列表和numpy数组 因为v !=v仅用于NaN

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']

试试这个:

import math
print [value for value in x if not math.isnan(value)]

要了解更多，请阅读列表推导式。

做到以上几点:

x = x[~numpy.isnan(x)]

or

x = x[numpy.logical_not(numpy.isnan(x))]

我发现重置到相同的变量(x)并没有删除实际的nan值，必须使用不同的变量。将其设置为不同的变量删除了nan。 如。

y = x[~numpy.isnan(x)]

最简单的方法是:

numpy.nan_to_num(x)

文档:https://docs.scipy.org/doc/numpy/reference/generated/numpy.nan_to_num.html

简单地填充

 x = numpy.array([
 [0.99929941, 0.84724713, -0.1500044],
 [-0.79709026, numpy.NaN, -0.4406645],
 [-0.3599013, -0.63565744, -0.70251352]])

x[numpy.isnan(x)] = .555

print(x)

# [[ 0.99929941  0.84724713 -0.1500044 ]
#  [-0.79709026  0.555      -0.4406645 ]
#  [-0.3599013  -0.63565744 -0.70251352]]