How to apply python code for all the cells in the column?

Question

Currently I'm running this below code for single record but I want run this code for all the cells in the column. Please help on this request.

from datetime import datetime
import xlrd

Date= 44774.45833333
New_date = datetime (*xlrd.xldate_as_tuple(excel_date,0))
print(New_date)

#rsult is 2022-08-01 11:00:00

Now I have a column called RETA where I want to apply this code to all the cells in this column and change the numeric format to datetime format.

sample data:

Theater |         RETA                 TPM SLACycletime
----------------------------------------------------------
US      |   2022-09-22 15:33:00    | Invalid Data
US      |   44774.45833            | 558:19:30
US      |   2022-09-2022 18:03:00  | 111:44:26
US      |                          | 15:44:26
US      |   1/8/2022  10:00:00 AM  | Invalid Data

Answer 1

As you want a datetime, use xlrd.xldate_as_datetime directly.

The easy (but non-vectorized) answer is:

df = pd.DataFrame({'RETA': [None, 0, 1, 10, 61, 10000, 44774.45833333, 44774.5]})

df['date'] = df['RETA'].apply(lambda date: xlrd.xldate_as_datetime(date, 0)
                                           if pd.notna(date) else date)

Output:

           RETA                date
0           NaN                 NaT
1      0.000000 1899-12-31 00:00:00
2      1.000000 1900-01-01 00:00:00
3     10.000000 1900-01-10 00:00:00
4     60.000000 1900-02-28 00:00:00
5     61.000000 1900-03-01 00:00:00
6  10000.000000 1927-05-18 00:00:00
7  44774.458333 2022-08-01 11:00:00
8  44774.510000 2022-08-01 12:14:24

For a vectorized version, let's rewite xlrd.xldate_as_datetime's code to work on a Series:

def vectorized_xldate(date, mode=0):
    epoch = pd.Timestamp('1970-01-01')
    if mode:
        epoch -= pd.Timestamp('1904-01-01')
    else:
        epoch = (pd.Series(pd.Timestamp('1899-12-31'), index=date.index)
                   .where(date<60, pd.Timestamp('1899-12-30'))
                   .rsub(epoch)
                )
    return (pd.to_datetime(date.mul(86400).round(3), unit='s')
              .sub(epoch).round('us')
            )

df['date_vectorized'] = vectorized_xldate(df['RETA'], mode=0)

# or
# df['date_vectorized'] = vectorized_xldate(df['RETA'])

Example with the two modes:

# mode=0
            RETA              date_mode0   date_mode0_vectorized
0            NaN                     NaT                     NaT
1       0.000000 1899-12-31 00:00:00.000 1899-12-31 00:00:00.000
2       1.000000 1900-01-01 00:00:00.000 1900-01-01 00:00:00.000
3      10.000000 1900-01-10 00:00:00.000 1900-01-10 00:00:00.000
4      59.000000 1900-02-28 00:00:00.000 1900-02-28 00:00:00.000
5      59.999999 1900-02-28 23:59:59.914 1900-02-28 23:59:59.914
6      60.000000 1900-02-28 00:00:00.000 1900-02-28 00:00:00.000
7      61.000000 1900-03-01 00:00:00.000 1900-03-01 00:00:00.000
8   10000.000000 1927-05-18 00:00:00.000 1927-05-18 00:00:00.000
9   44774.458333 2022-08-01 11:00:00.000 2022-08-01 11:00:00.000
10  44774.123457 2022-08-01 02:57:46.667 2022-08-01 02:57:46.667

# mode=1
            RETA              date_mode1   date_mode1_vectorized
0            NaN                     NaT                     NaT
1       0.000000 1904-01-01 00:00:00.000 1904-01-01 00:00:00.000
2       1.000000 1904-01-02 00:00:00.000 1904-01-02 00:00:00.000
3      10.000000 1904-01-11 00:00:00.000 1904-01-11 00:00:00.000
4      59.000000 1904-02-29 00:00:00.000 1904-02-29 00:00:00.000
5      59.999999 1904-02-29 23:59:59.914 1904-02-29 23:59:59.914
6      60.000000 1904-03-01 00:00:00.000 1904-03-01 00:00:00.000
7      61.000000 1904-03-02 00:00:00.000 1904-03-02 00:00:00.000
8   10000.000000 1931-05-19 00:00:00.000 1931-05-19 00:00:00.000
9   44774.458333 2026-08-02 11:00:00.000 2026-08-02 11:00:00.000
10  44774.123457 2026-08-02 02:57:46.667 2026-08-02 02:57:46.667

handling mixed formats

def vectorized_xldate(date, mode=0, errors='raise'):
    epoch = pd.Timestamp('1970-01-01')
    if errors == 'coerce':
        dt = pd.to_datetime(date, format='mixed', errors='coerce')
        date = pd.to_numeric(date, errors='coerce')
    else:
        dt = float('nan')

    if mode:
        epoch -= pd.Timestamp('1904-01-01')
    else:
        epoch = (pd.Series(pd.Timestamp('1899-12-31'), index=date.index)
                   .where(date<60, pd.Timestamp('1899-12-30'))
                   .rsub(epoch)
                )
    return (pd.to_datetime(date.mul(86400).round(3), unit='s')
              .sub(epoch).round('us')
              .fillna(dt)
            )

df['date'] = vectorized_xldate(df['RETA'], errors='coerce').dt.round('S')

Output:

  Theater                   RETA TPM SLACycletime                date
0      US    2022-09-22 15:33:00     Invalid Data 2022-09-22 15:33:00
1      US            44774.45833        558:19:30 2022-08-01 11:00:00
2      US  2022-09-2022 18:03:00        111:44:26                 NaT
3      US               15:44:26             None 2023-08-04 15:44:26
4      US   1/8/2022 10:00:00 AM     Invalid Data 2022-01-08 10:00:00