Adding certain dates to data extracts#
Use a Python expression to add certain dates to a data extract.
Introduction#
Enter a Python expression into the addfieldx transformation to add certain dates to your data extract. For example, use a Python expression to add today’s date or yesterday’s date to a data extract.
The examples below demonstrate how the Python expressions can convert the dates from one format to another.
Configuring the Python expression#
To add certain dates to your data extract, enter the following Python expressions into the transformation:
For today’s date, enter:
__import__('datetime').datetime.today()
For yesterday’s date, enter:
__import__('datetime').datetime.today() - __import__('datetime').timedelta(days=1)
Example 1 - Adding today’s date#
This example uses the addfieldx transformation to add a new column
to your data extract and populate the new column with today’s date
(the day on which the transformation is run). The dates are added in
the following date and time format %Y-%m-%d %H:%M:%S.%f.
In this example the transformation is run at 10 am on 27 July 2022.
Notice that the times are different. This is because the python expression includes microseconds in the output. To remove the microseconds from the values in the column, change the format of the dates and time using another Python expression . For more information on how to convert times and date formats, see Converting dates from one format to another.
For more information on the addfieldx transformation configuration, see the addfieldx transformation reference.
Transformation configuration#
- Field Name
Transformation Date- Python Expression
__import__('datetime').datetime.today()
- Field Index
-1
Data table before transformation#
Conversion ID |
Purchase Date |
|---|---|
4310177778 |
09/29/2021 |
4328218379 |
01/05/2022 |
5260818288 |
03/27/2022 |
2028958545 |
08/07/2021 |
4393034066 |
03/13/2022 |
4989141466 |
09/04/2021 |
4911856467 |
07/11/2022 |
Data table after transformation#
Conversion ID |
Purchase Date |
Transformation Date |
|---|---|---|
4310177778 |
09/29/2021 |
2022-07-27 10:00:00.020131 |
4328218379 |
01/05/2022 |
2022-07-27 10:00:00.020347 |
5260818288 |
03/27/2022 |
2022-07-27 10:00:00.020467 |
2028958545 |
08/07/2021 |
2022-07-27 10:00:00.020578 |
4393034066 |
03/13/2022 |
2022-07-27 10:00:00.020687 |
4989141466 |
09/04/2021 |
2022-07-27 10:00:00.020795 |
4911856467 |
07/11/2022 |
2022-07-27 10:00:00.020903 |
Example 2 - Adding yesterday’s date#
This example uses the addfieldx transformation to add a new column
to your data extract and populate the new column with yesterday’s
date (24 hours before the transformation is run). The dates are
added in the following date and time format %Y-%m-%d %H:%M:%S.%f.
In this example the transformation is run at 10 am on 27 July 2022.
Notice that the times are different. This is because the python expression includes microseconds in the output. To remove the microseconds from the values in the column, change the format of the dates and time using another Python expression . For more information on how to convert times and date formats, see Converting dates from one format to another.
For more information on the addfieldx transformation configuration, see the addfieldx transformation reference.
Transformation configuration#
- Field Name
24 hours ago- Python Expression
__import__('datetime').datetime.today() - __import__('datetime').timedelta(days=1)
- Field Index
-1
Data table before transformation#
Conversion ID |
Purchase Date |
|---|---|
4310177778 |
09/29/2021 |
4328218379 |
01/05/2022 |
5260818288 |
03/27/2022 |
2028958545 |
08/07/2021 |
4393034066 |
03/13/2022 |
4989141466 |
09/04/2021 |
4911856467 |
07/11/2022 |
Data table after transformation#
Conversion ID |
Purchase Date |
24 hours ago |
|---|---|---|
4310177778 |
09/29/2021 |
2022-07-26 10:00:00.020138 |
4328218379 |
01/05/2022 |
2022-07-26 10:00:00.020352 |
5260818288 |
03/27/2022 |
2022-07-26 10:00:00.020469 |
2028958545 |
08/07/2021 |
2022-07-26 10:00:00.020512 |
4393034066 |
03/13/2022 |
2022-07-26 10:00:00.020695 |
4989141466 |
09/04/2021 |
2022-07-26 10:00:00.020802 |
4911856467 |
07/11/2022 |
2022-07-26 10:00:00.020915 |