我进入这篇文章，因为我在功能方面遇到了一些问题：

math.isnan()

运行此代码时出现问题：

a = "hello"
math.isnan(a)

它引发了异常。我的解决方案是再做一次检查：

def is_nan(x):
    return isinstance(x, float) and math.isnan(x)

math.isnan（）

或将数字与自身进行比较。NaN总是！=NaN，否则（例如，如果是数字），比较应成功。

判断变量是NaN还是None的所有方法：

无类型

In [1]: from numpy import math

In [2]: a = None
In [3]: not a
Out[3]: True

In [4]: len(a or ()) == 0
Out[4]: True

In [5]: a == None
Out[5]: True

In [6]: a is None
Out[6]: True

In [7]: a != a
Out[7]: False

In [9]: math.isnan(a)
Traceback (most recent call last):
  File "<ipython-input-9-6d4d8c26d370>", line 1, in <module>
    math.isnan(a)
TypeError: a float is required

In [10]: len(a) == 0
Traceback (most recent call last):
  File "<ipython-input-10-65b72372873e>", line 1, in <module>
    len(a) == 0
TypeError: object of type 'NoneType' has no len()

NaN类型

In [11]: b = float('nan')
In [12]: b
Out[12]: nan

In [13]: not b
Out[13]: False

In [14]: b != b
Out[14]: True

In [15]: math.isnan(b)
Out[15]: True

似乎检查它是否等于自身（x！=x）是最快的。

import pandas as pd 
import numpy as np 
import math 

x = float('nan')

%timeit x != x
44.8 ns ± 0.152 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

%timeit math.isnan(x)
94.2 ns ± 0.955 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

%timeit pd.isna(x)
281 ns ± 5.48 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

%timeit np.isnan(x)
1.38 µs ± 15.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

用于浮球类型

>>> import pandas as pd
>>> value = float(nan)
>>> type(value)
>>> <class 'float'>
>>> pd.isnull(value)
True
>>>
>>> value = 'nan'
>>> type(value)
>>> <class 'str'>
>>> pd.isnull(value)
False

使用math.isnan：

>>> import math
>>> x = float('nan')
>>> math.isnan(x)
True