I have a CSV file that contains information about the period August 22, 2022 up to September 21, 2022. I loaded to CSV into Python using the Pandas library. The timestamps in the CSV file are in a Dutch format (and are strings), i.e., %d-%m-%Y. When I use pd.to_datetime() for the timestamps, not all data points are converted correctly. For example:
| Old (in %d-%m-%Y) | New |
|---|---|
| 22-02-22 | 2022-08-22 (%Y-%m-%d) |
| 31-08-22 | 2022-08-31 (%Y-%m-%d) |
| 01-09-22 | 2022-01-09 (%Y-%d-%m) |
| 06-09-22 | 2022-06-09 (%Y-%d-%m) |
| 12-09-22 | 2022-12-09 (%Y-%d-%m) |
| 13-09-22 | 2022-09-13 (%Y-%m-%d) |
| 21-09-22 | 2022-09-21 (%Y-%m-%d) |
So, for some data points the months and days are interchanged. I want to convert the strings into the right datetime format. How to solve this?
Thanks in advance!
CodePudding user response：
You have to specify the format during conversation because the dates here can be confusing to pandas. The default format is %Y-%m-%d.
So try this instead:
df.<your_date_col> = pd.to_datetime(df.<your_date_col>, format='%d-%m-%y')
CodePudding user response：
If you read the docs https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html you will see that pd.to_datetime can take yearfirst and dayfirst as parameters.
In your case, just pass dayfirst=True to it and that's it.