Pandas DataFrame Replace NaT with None

Question

I have been struggling with this question for a long while, and I tried different methods.

I have a simple DataFrame as shown,

I can use code to rep

Answer 1

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})

Output of dfTest:

Answer 2

Similar approach as suggested by @neerajYadav but without the apply:

dfTest2['InvoiceDate'] = (dfTest2['InvoiceDate']
                          .astype(str) # <- cast to string to simplify
                                       #    .replace() in newer versions
                          .replace({'NaT': None} # <- replace with None
                         )

Answer 3

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

Answer 4

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)