本文介绍了如何分别填写NaT和NaN值的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我的数据框同时包含NaT和NaN值
My dataframe contains both NaT and NaN values
Date/Time_entry Entry Date/Time_exit Exit
0 2015-11-11 10:52:00 19.9900 2015-11-11 11:30:00 20.350
1 2015-11-11 11:36:00 20.4300 2015-11-11 11:38:00 20.565
2 2015-11-11 11:44:00 21.0000 NaT NaN
3 2009-04-20 10:28:00 13.7788 2009-04-20 10:46:00 13.700
我想用日期填充NaT，用数字填充NaN。 Fillna（4）方法将NaT和NaN都替换为4。是否可以通过某种方式区分NaT和NaN？
I want to fill NaT with dates and NaN with numbers. Fillna(4) method replaces both NaT and NaN with 4. Is it possible to differentiate between NaT and NaN somehow?
我当前的解决方法是df [column] .fillna （）
My current workaround is to df[column].fillna()
推荐答案
由于NaT与日期时间列有关，因此您可以在应用填充操作时排除它们。
Since NaTs pertain to datetime columns, you can exclude them when applying your filling operation.
u = df.select_dtypes(exclude=['datetime'])
df[u.columns] = u.fillna(4)
df
Date/Time_entry Entry Date/Time_exit Exit
0 2015-11-11 10:52:00 19.9900 2015-11-11 11:30:00 20.350
1 2015-11-11 11:36:00 20.4300 2015-11-11 11:38:00 20.565
2 2015-11-11 11:44:00 21.0000 NaT 4.000
3 2009-04-20 10:28:00 13.7788 2009-04-20 10:46:00 13.700
$ b同样，仅填充NaT值，在上面的代码中将 exclude更改为 include。
Similarly, to fill NaT values only, change "exclude" to "include" in the code above.
u = df.select_dtypes(include=['datetime'])
df[u.columns] = u.fillna(pd.to_datetime('today'))
df
Date/Time_entry Entry Date/Time_exit Exit
0 2015-11-11 10:52:00 19.9900 2015-11-11 11:30:00.000000 20.350
1 2015-11-11 11:36:00 20.4300 2015-11-11 11:38:00.000000 20.565
2 2015-11-11 11:44:00 21.0000 2019-02-17 16:11:09.407466 4.000
3 2009-04-20 10:28:00 13.7788 2009-04-20 10:46:00.000000 13.700
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