Changing dates and substracting to filter out

user_13024918 0
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I have df1 as:

Acc_id   Acc_name   Acc_end_date
qqq-1    test1      12/31/2021
www-2    test2      05/28/2022
yyy-6    test3      06/15/2022
zzz-6    test4      06/17/2022
kkk-6    test5      03/16/2022

The Acc_end_date is in mm/dd/yy format.

I am trying to get only those accounts where Acc_end_date - todays date >30.

Meaning, 30 days should pass as of today for the Acc_id to be selected.

I tried the below:

# changing the column to datetime format
df1['Acc_end_date']= pd.to_datetime(df1['Acc_end_date'])

# getting todays date and creating an array with those number of Acc_id and substracting
todays_date = []
for i in range(df1.shape[0]):
    temp = date.today()
    todays_date.append(temp)

np.array(todays_date)

df2 = df1[df1['Acc_end_date'] - todays_date>30]

I am getting the error as:

TypeError: unsupported operand type(s) for -: 'DatetimeArray' and 'list'

Gives error as:

TypeError: unsupported operand type(s) for -: 'DatetimeArray' and 'datetime.date'

Trying this:

df2 = df1[only_inactive['Acc_end_date'] - date.today()>30]

I am unable to get the date right and in the right format for the substraction.

pandas dataframe python
1 Answers

IIUC, you can use:

df2 = df1[pd.to_datetime(df1['Acc_end_date'], dayfirst=False)
            .rsub(pd.Timestamp('today')).gt('30d')]

output:

  Acc_id Acc_name Acc_end_date
0  qqq-1    test1   12/31/2021
4  kkk-6    test5   03/16/2022
user_163434640
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