I have a pandas dataframe column named disbursal_date which is a datetime:

disbursal_date
2009-01-28
2008-01-03
2008-07-15

and so on...

I want to keep the date and month part and replace the years by 2022 for all values. I tried using df['disbursal_date'].map(lambda x: x.replace(year=2022)) but this didn't work for me.

CodePudding user response：

1. You need to use apply not map to run a python function on a dataframe columns.
2. We need to make sure that the dtype is datetime of pandas and not object or string.

Below is the sample code I tried and it works fine, it replaces the year to 2022.

df = pd.DataFrame(['2009-01-28', '2008-01-03', '2008-07-15'],columns=['disbursal_old'])
df['disbursal_old'] = df['disbursal_old'].astype('datetime64[ns]')
df['disbursal_new'] = df['disbursal_old'].apply(lambda x : x.replace(year=2022))
print(df['disbursal_new'])

0   2022-01-28
1   2022-01-03
2   2022-07-15
Name: disbursal_new, dtype: datetime64[ns]

The below code gives the difference between the years.

df['disbursal_diff_year'] = df['disbursal_new'].dt.year - df['disbursal_old'].dt.year
print(df)

disbursal_old disbursal_new  disbursal_diff_year
0    2009-01-28    2022-01-28                   13
1    2008-01-03    2022-01-03                   14
2    2008-07-15    2022-07-15                   14