Filter Dataframe based on two columns using OR

Question

I need to filter a dataframe down to reduce time updating user attributes.

+----------+------------+------------+
| userCol1 |  dateCol1  |  dateCol2  |
+----------+------------+------------+
| user1    | 2020-01-16 | 2019-12-30 |
| user2    | 2019-10-31 | 2020-01-12 |
| user3    | 2019-08-15 | 2019-09-30 |
| user4    | 2019-08-25 | NaN        |
+----------+------------+------------+

above is an example of the dataframe. I need to filter it for any user that the LATEST date of either datecol1 or datecol2 is <= today-90 days . In the above example the above dataframe should result in user2 and user4 left in the dataframe for processing.

The code I wrote (and haven't tested so I do not know if it works) doesn't filter the dataframe and instead tries to loop over the entire thing; here is the code.

 for row in df3.itertuples() :
     print(row.username)
     print(row.Password_Last_Set)
     print(row.Password_Last_forgot)
     if row.Password_Last_Forgot is 'NaN' and row.Password_Last_Set <= today.timedelta(days=90) :
         print('password expired based on last set, no forgot passwords')
     elif row.Password_Last_Forgot is not 'NaN' and row.Password_Last_Forgot > row.Password_Last_Set and row.Password_Last_Forgot <= today.timedelta(days=90) :
         print('password expired based on last forgot')
      elif row.Password_Last_Forgot is not 'NaN' and row.Password_Last_Forgot < row.Password_Last_Set and row.Password_Last_Set <= today.timedelta(days=90) :
         print('password expired based on last set')

How can I filter prior to looping over the users to perform an action on the remaining users?

Answer 1

Use boolean indexing with max for latest datetime:

df[['dateCol1','dateCol2']] = df[['dateCol1','dateCol2']].apply(pd.to_datetime)

cols = ['dateCol1','dateCol2']
df1 = df.loc[df[cols].max(axis=1)<=pd.Timestamp.now() - pd.Timedelta(90, unit='d'), 'userCol1']
print (df1)
2    user3
3    user4
Name: userCol1, dtype: object