Parser for Column with Date

Question

How can I parser all the sales and date column in the dataframe below

import pandas as pd 

data = [['30-06-2021', 43578, '31-01-2022', 5000, '28-02-2022', 78564, '31-03-2022', 52353, '30-04-2022'], 
        ['14-06-2021', 4475, '14-01-2022', 2546, '05-02-2022', 5757, '28-03-2022', 2352, '01-04-2022']]
ds = pd.DataFrame(data, columns = ['Start', 'Sales_Jan-22', 'Jan-22', 'Sales_Feb-22', 'Feb-22', 'Sales_Mar-22', 'Mar-22',
                                   'Sales_Apr-22', 'Apr-22'])

    Start    Sales_Jan-22 Jan-22  Sales_Feb-22 Feb-22 Sales_Mar-22 Mar-22 Sales_Apr-22 Apr-22
0   30-06-2021  43578   31-01-2022  5000    28-02-2022  78564   31-03-2022  52353   30-04-2022
1   14-06-2021  4475    14-01-2022  2546    05-02-2022  5757    28-03-2022  2352    01-04-2022

So far, this is what I have done. However I received an error for the Sales_%b-%y and %b-%y

from dateutil import parser

gross_date = parser.parse(ds['Start'][0])
sales_col = parser.parse(ds['Sales_%b-%y'][0])
monthly_date = parser.parse(ds['%b-%y'][0])

How should I do the parser for date format? My desired output should be like this

    Start     Sales_Jan-22 Jan-22 Sales_Feb-22 Feb-22 Sales_Mar-22 Mar-22 Sales_Apr-22  Apr-22 Total
0   30-06-2021  43578   31-01-2022  5000    28-02-2022  78564   31-03-2022  52353   30-04-2022  3525
1   14-06-2021  4475    14-01-2022  2546    05-02-2022  5757    28-03-2022  2352    01-04-2022  63747

Because later I'm going to use is create a formula to get the Total column as shown above.

Answer 1

IIUC, you can use pd.to_datetime to force convert all the date values and leave the numbers unchanged:

ds = ds.apply(pd.to_datetime, errors="ignore", format="%d-%m-%Y")

To check the values have been converted:

>>> ds["Start"].iat[1]
Timestamp('2021-06-14 00:00:00')
>>> ds["Jan-22"].iat[0]
Timestamp('2022-01-31 00:00:00')