Resolved: In 1-11- Got an error when i tried to convert them into pandas timestamps?
5 answers ( 1 marked as helpful)
using the ,dayfirst = True, seems to have done the trick... .
Only difference now, is that in the final head result, instead of not a number (NaN), position 4 gives us "not a timestamp" (NaT).
But probably this wouldnt be enough in a business setting right? They would ask me to clean the table more thoroughly... .
Hi Panagiotis!
Thanks for sharing this information.
Another way of fixing the issue would be to adjust your format string to match the date data. Like this:
pd.to_datetime(lending_co_data['StartDate'], format="%d/%m/%Y" )
Hope this helps.
Best,
Ivan