Microseconds which will give you a much larger range. OR you can give up the nanosecond resolution and go with.It in nanoseconds which is similar to pandas. if the date falls in the above mentioned range you can store.In case of numpy (pandas is built on top of numpy) datetime64 data type, With pandas timestamp we have higher accuracy but lower date range.
Since the space is limited(64 bits), its a matter of range vs accuracy.
SQL server stores date in nanosecond resolution but only up to a accuracy of 100 ns(as opposed to 1 ns in pandas).
Also maximum of 64 bits are used in most of the cases to store the date. This is surprising because databases like sql server and libraries like numpy allows to store date beyond this range. There is no option in pandas to store a timestamp outside of the above mentioned range. Which means the date values have to be in the range pd.Timestamp.min( 00:12:43.145225) andĮven if you only want the date with resolution of seconds or microseconds, pandas will still store it internally in nanoseconds. "OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 00:00:00" is because pandas timestamp data type stores date in nanosecond resolution( from the docs). The reason you are seeing this error message