在 pandas 查询NaN和其他名称
问题说明
说我有一个数据框df，其中的列value包含一些浮点值和一些NaN.
Say I have a dataframe df with a column value holding some float values and some NaN. How can I get the part of the dataframe where we have NaN using the query syntax?
例如，以下内容不起作用:
The following, for example, does not work:
df.query( '(value < 10) or (value == NaN)' )
我得到name NaN is not defined(与df.query('value ==NaN')相同)
一般来讲，有什么方法可以在查询中使用numpy名称，例如inf，nan，pi，e等吗?
Generally speaking, is there any way to use numpy names in query, such as inf, nan, pi, e, etc.?
正确答案
通常，您可以使用@local_variable_name，因此类似
In general, you could use @local_variable_name, so something like
>>> pi = np.pi; nan = np.nan
>>> df = pd.DataFrame({"value": [3,4,9,10,11,np.nan,12]})
>>> df.query("(value < 10) and (value > @pi)")
value
1 4
2 9
可以工作，但是nan不等于它本身，因此value == NaN始终为false.解决这个问题的一种方法是使用该事实，然后将value != value用作isnan检查.我们有
would work, but nan isn't equal to itself, so value == NaN will always be false. One way to hack around this is to use that fact, and use value != value as an isnan check. We have
>>> df.query("(value < 10) or (value == @nan)")
value
0 3
1 4
2 9
但是
>>> df.query("(value < 10) or (value != value)")
value
0 3
1 4
2 9
5 NaN
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