Pandas DataFrame 用 None 替换 NaT
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Pandas DataFrame Replace NaT with None
提问by Haipeng Su
I have been struggling with this question for a long while, and I tried different methods.
我一直在为这个问题苦苦挣扎，我尝试了不同的方法。
I have a simple DataFrame as shown,
我有一个简单的 DataFrame，如图所示，
I can use code to replace NaNwith None(Not String "None"),
我可以使用代码替换NaN为None(Not String "None")，
[![dfTest2 = dfTest.where(pd.notnull(dfTest), None)][2]][2]
[![dfTest2 = dfTest.where(pd.notnull(dfTest), None)][2]][2]
I support that NaTis also classified as 'Null' because the following,
我支持也NaT被归类为“空”，因为以下，
However, NaTis not replaced with None.
但是，NaT没有替换为None。
I have been searching for answers but got no luck. Anyone could Help?
我一直在寻找答案，但没有运气。任何人都可以帮助？
Thank you in advance.
先感谢您。
回答by piRSquared
Make the dtypeobject
使 dtypeobject
dfTest2 = pd.DataFrame(dict(InvoiceDate=pd.to_datetime(['2017-06-01', pd.NaT])))
dfTest2.InvoiceDate.astype(object).where(dfTest2.InvoiceDate.notnull(), None)
0 2017-06-01 00:00:00
1 None
Name: InvoiceDate, dtype: object
回答by Neeraj Yadav
Make the column type as str first
首先将列类型设为 str
dfTest2.InvoiceDate = dfTest2.InvoiceDate.astype(str)
then compare it directly with "NaT" and replace with None
然后直接与“NaT”进行比较并替换为None
dfTest2.InvoiceDate = dfTest2.InvoiceDate.apply(lambda x : None if x=="NaT" else x)
回答by dshefman
The simplest solution I found that worked for me is...
我发现对我有用的最简单的解决方案是......
Input:
输入：
dfTest = pd.DataFrame(dict(InvoiceDate=pd.to_datetime(['2017-06-01', pd.NaT])))
dfTest.replace({pd.np.nan: None})
Output:
输出：
Note: pd.np.nan is provided by pandas, so that you don't have to import numpy to use np.nan.
注意：pd.np.nan 是由pandas 提供的，所以你不必导入numpy 来使用np.nan。
