Python 检查特定值（在单元格中）是否在 Pandas DataFrame 中为 NaN 无法使用 ix 或 iloc
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Checking if particular value (in cell) is NaN in pandas DataFrame not working using ix or iloc
提问by Cedric Zoppolo
Lets say I have following pandasDataFrame:
可以说我有以下几点pandasDataFrame：
import pandas as pd
df = pd.DataFrame({"A":[1,pd.np.nan,2], "B":[5,6,0]})
Which would look like:
看起来像：
>>> df
A B
0 1.0 5
1 NaN 6
2 2.0 0
First option
第一个选项
I know one way to check if a particular value is NaN, which is as follows:
我知道一种检查特定值是否NaN为 的方法，如下所示：
>>> df.isnull().ix[1,0]
True
Second option (not working)
第二个选项（不工作）
I thought below option, using ix, would work as well, but it's not:
我认为下面的选项，使用ix, 也可以，但事实并非如此：
>>> df.ix[1,0]==pd.np.nan
False
I also tried ilocwith same results:
我也尝试iloc过相同的结果：
>>> df.iloc[1,0]==pd.np.nan
False
However if I check for those values using ixor ilocI get:
但是，如果我使用ix或检查这些值，iloc我会得到：
>>> df.ix[1,0]
nan
>>> df.iloc[1,0]
nan
So, why is the second option not working?Is it possible to check for NaNvalues using ixor iloc?
那么，为什么第二个选项不起作用？是否可以NaN使用ix或检查值iloc？
回答by MaxU
回答by hygull
The above answer is excellent. Here is the same with an example for better understanding.
楼上的回答太好了。为了更好地理解，这里有一个例子。
>>> import pandas as pd
>>>
>>> import numpy as np
>>>
>>> pd.Series([np.nan, 34, 56])
0 NaN
1 34.0
2 56.0
dtype: float64
>>>
>>> s = pd.Series([np.nan, 34, 56])
>>> pd.isnull(s[0])
True
>>>
I also tried couple of times, the following trials did not work. Thanks to @MaxU.
我也试过几次，下面的试验没有奏效。感谢@MaxU.
>>> s[0]
nan
>>>
>>> s[0] == np.nan
False
>>>
>>> s[0] is np.nan
False
>>>
>>> s[0] == 'nan'
False
>>>
>>> s[0] == pd.np.nan
False
>>>
回答by Prateek Kumar Dalbehera
pd.isna(cell_value)can be used to check if a given cell value is nan. Alternatively, pd.notna(cell_value)to check the opposite.
pd.isna(cell_value)可用于检查给定的单元格值是否为 nan。或者，pd.notna(cell_value)检查相反。
From source code of pandas:
来自熊猫的源代码：
def isna(obj):
"""
Detect missing values for an array-like object.
This function takes a scalar or array-like object and indicates
whether values are missing (``NaN`` in numeric arrays, ``None`` or ``NaN``
in object arrays, ``NaT`` in datetimelike).
Parameters
----------
obj : scalar or array-like
Object to check for null or missing values.
Returns
-------
bool or array-like of bool
For scalar input, returns a scalar boolean.
For array input, returns an array of boolean indicating whether each
corresponding element is missing.
See Also
--------
notna : Boolean inverse of pandas.isna.
Series.isna : Detect missing values in a Series.
DataFrame.isna : Detect missing values in a DataFrame.
Index.isna : Detect missing values in an Index.
Examples
--------
Scalar arguments (including strings) result in a scalar boolean.
>>> pd.isna('dog')
False
>>> pd.isna(np.nan)
True
