pandas out of bounds nanosecond timestamp after offset rollforward plus adding a month offset

Since pandas represents timestamps in nanosecond resolution, the timespan that can be represented using a 64-bit integer is limited to approximately 584 years

pd.Timestamp.min
Out[54]: Timestamp('1677-09-22 00:12:43.145225')

In [55]: pd.Timestamp.max
Out[55]: Timestamp('2262-04-11 23:47:16.854775807')

And your value is out of this range 2262-05-01 00:00:00 and hence the outofbounds error

Straight out of: http://pandas-docs.github.io/pandas-docs-travis/user_guide/timeseries.html#timeseries-timestamp-limits

Workaround:

This will force the dates which are outside the bounds to NaT

pd.to_datetime(date_col_to_force, errors = 'coerce')

Setting the errors parameter in pd.to_datetime to 'coerce' causes replacement of out of bounds values with NaT. Quoting the docs:

If ‘coerce’, then invalid parsing will be set as NaT

E.g.:

datetime_variable = pd.to_datetime(datetime_variable, errors = 'coerce')

This does not fix the data (obviously), but still allows processing the non-NaT data points.

None of above are so good, because it will delete your data. But, you can only mantain and edit your conversion:

# convertin from epoch to datatime mantainig the nanoseconds timestamp
xbarout= pd.to_datetime(xbarout.iloc[:,0],unit='ns')