timestamp( : :'tz='pytz fixedoffset(- )')timestamp( : : + 'tz='pytz fixedoffset( )')timestamp( : : + 'tz='pytz fixedoffset( )')]dtype=objectyou can clearly see from the temporal values that we have multiple components for each timestamp object which include datetimeand even time based offsetwhich can be used to identify the time zone also of course there is no way we can directly ingest or use these features in any machine learning model hence we need specific strategies to extract meaningful features from this data in the following sectionswe cover some of these strategies that you can start using on your own temporal data in the future date-based features each temporal value has date component that can be used to extract useful information and features pertaining to the date these include features and components like yearmonthdayquarterday of the weekday nameday and week of the yearand many more the following code depicts how we can obtain some of these features from our temporal data in [ ]df['year'df['ts_obj'apply(lambda dd yeardf['month'df['ts_obj'apply(lambda dd monthdf['day'df['ts_obj'apply(lambda dd daydf['dayofweek'df['ts_obj'apply(lambda dd dayofweekdf['dayname'df['ts_obj'apply(lambda dd weekday_namedf['dayofyear'df['ts_obj'apply(lambda dd dayofyeardf['weekofyear'df['ts_obj'apply(lambda dd weekofyeardf['quarter'df['ts_obj'apply(lambda dd quarterdf[['time''year''month''day''quarter''dayofweek''dayname''dayofyear''weekofyear']figure - date based features in temporal data the features depicted in figure - show some of the attributes we talked about earlier and have been derived purely from the date segment of each temporal value each of these features can be used as categorical features and further feature engineering can be done like one hot encodingaggregationsbinningand more