pd.notnull的奇怪的空检查行为(Weird null checking behaviour by pd.notnull)

 这基本上是对我的答案内容的重新阐述。 
 
 在尝试使用pd.notnull解决这个问题时 ，我遇到了一些奇怪的行为。 
 
 考虑 
 
x = ('A4', nan)
 
 我想检查这些项中的哪些是null。 直接使用np.isnan会抛出一个TypeError（但我已经弄清楚如何解决）。 
 
 使用pd.notnull不起作用。 
 
>>> pd.notnull(x)
True
 
 它将元组视为单个值（而不是可迭代的值）。 此外，将其转换为列表然后测试也会给出错误的答案。 
 
>>> pd.notnull(list(x))
array([ True,  True])
 
 由于第二个值是nan ，我正在寻找的结果应该是[True, False] 。 它最终适用于您预转换为系列时： 
 
>>> pd.Series(x).notnull() 
0     True
1    False
dtype: bool
 
 因此，解决方案是Series-ify，然后测试值。 
 
 沿着类似的路线，另一个（公认的pd.notnull ）解决方案是预先转换为object pd.notnull numpy数组， pd.notnull或np.isnan将直接工作： 
 
>>> pd.notnull(np.array(x, dtype=object))
Out[151]: array([True,  False])
 
 我想pd.notnull直接将x转换为封面下的字符串数组，将NaN渲染为字符串“nan”，因此它不再是“null”值。 
 
 pd.notnull在这里做同样的事情吗？ 或者我应该注意哪些内容正在进行？ 
 
 笔记 
 
In [156]: pd.__version__
Out[156]: '0.22.0'

This is essentially a rehashing of the content of my answer here.
 
I came across some weird behaviour when trying to solve this question, using pd.notnull.
 
Consider 
 
x = ('A4', nan)
 
I want to check which of these items are null. Using np.isnan directly will throw a TypeError (but I've figured out how to solve that).
 
Using pd.notnull does not work.
 
>>> pd.notnull(x)
True
 
It treats the tuple as a single value (rather than an iterable of values). Furthermore, converting this to a list and then testing also gives an incorrect answer.
 
>>> pd.notnull(list(x))
array([ True,  True])
 
Since the second value is nan, the result I'm looking for should be [True, False]. It finally works when you pre-convert to a Series:
 
>>> pd.Series(x).notnull() 
0     True
1    False
dtype: bool
 
So, the solution is to Series-ify it and then test the values. 
 
Along similar lines, another (admittedly roundabout) solution is to pre-convert to an object dtype numpy array, and pd.notnull or np.isnan will work directly:
 
>>> pd.notnull(np.array(x, dtype=object))
Out[151]: array([True,  False])
 
I imagine that pd.notnull directly converts x to a string array under the covers, rendering the NaN as a string "nan", so it is no longer a "null" value.
 
Is pd.notnull doing the same thing here? Or is there something else going on under the covers that I should be aware of?
 
Notes
 
In [156]: pd.__version__
Out[156]: '0.22.0'

原文：https://stackoverflow.com/questions/51035790