to_datetime Value Error: at least that [year, month, day] must be specified Pandas

For me works apply function to_datetime:

print (dtd)
            1           2           3           4           5           6
0                                                                        
0  2004-01-02  2004-01-02  2004-01-09  2004-01-16  2004-01-23  2004-01-30
1  2004-01-05  2004-01-09  2004-01-16  2004-01-23  2004-01-30  2004-02-06
2  2004-01-06  2004-01-09  2004-01-16  2004-01-23  2004-01-30  2004-02-06
3  2004-01-07  2004-01-09  2004-01-16  2004-01-23  2004-01-30  2004-02-06
4  2004-01-08  2004-01-09  2004-01-16  2004-01-23  2004-01-30  2004-02-06


dtd = dtd.apply(pd.to_datetime)

print (dtd)
           1          2          3          4          5          6
0                                                                  
0 2004-01-02 2004-01-02 2004-01-09 2004-01-16 2004-01-23 2004-01-30
1 2004-01-05 2004-01-09 2004-01-16 2004-01-23 2004-01-30 2004-02-06
2 2004-01-06 2004-01-09 2004-01-16 2004-01-23 2004-01-30 2004-02-06
3 2004-01-07 2004-01-09 2004-01-16 2004-01-23 2004-01-30 2004-02-06
4 2004-01-08 2004-01-09 2004-01-16 2004-01-23 2004-01-30 2004-02-06

You can stack / pd.to_datetime / unstack

pd.to_datetime(dte.stack()).unstack()

explanation
pd.to_datetime works on a string, list, or pd.Series. dte is a pd.DataFrame and is why you are having issues. dte.stack() produces a a pd.Series where all rows are stacked on top of each other. However, in this stacked form, because it is a pd.Series, I can get a vectorized pd.to_datetime to work on it. the subsequent unstack simply reverses the initial stack to get the original form of dte

It works for me:

dtd.apply(lambda x: pd.to_datetime(x,errors = 'coerce', format = '%Y-%m-%d'))

This way you can use function attributes like above (errors and format). See more https://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html