Daiyue Weng wrote: > Hi, I need to compare the years in a Series. The values in the Series is > like '1996', '2015', '2006-01-02' or '20130101' etc. The code I created > is, > > col_value_series = pd.to_datetime(col_value_series, > infer_datetime_format=True) min_year = col_value_series.min().year > max_year = col_value_series.max().year > > current_year = datetime.date.today().year > > res1 = min_year > 1970 > res2 = max_year < current_year > return min_year > 1970 and max_year < current_year > > the code is working fine on the values like '20030101' and '2006-01-02', > which are converted to datetime, i.e. '2003-01-01'. But it converted > values '1996' or '2015' to '1970-01-01 00:00:00.000001996' and '1970-01-01 > 00:00:00.000002015', which are completely wrong (meaning the years are all > 1970 now). So how to resolve the issue.
This seems to happen when the year-only dates are integers. Compare: >>> import pandas as pd >>> pd.to_datetime(pd.Series(["2010-10-20", 2000])) 0 2010-10-20 00:00:00 1 1970-01-01 00:00:00.000002 dtype: datetime64[ns] >>> pd.to_datetime(pd.Series(["2010-10-20", "2000"])) 0 2010-10-20 1 2000-01-01 dtype: datetime64[ns] How is your series created? Perhaps you can ensure that you start out with strings only. -- https://mail.python.org/mailman/listinfo/python-list
