Pandas DataFrame Replace NaT with None

Make the dtype object

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

Make the column type as str first

 dfTest2.InvoiceDate =  dfTest2.InvoiceDate.astype(str)

then compare it directly with "NaT" and replace with None

dfTest2.InvoiceDate = dfTest2.InvoiceDate.apply(lambda x : None if x=="NaT" else x)

The simplest solution I found that worked for me is...

Input:

import pandas as pd
import numpy as np
dfTest = pd.DataFrame(dict(InvoiceDate=pd.to_datetime(['2017-06-01', pd.NaT]), CorpId=[2997373, np.nan], TestName=[1,1]))
dfTest.replace({np.nan: None}, inplace = True)

Output of dfTest: