- of coursean easier and faster way to do this is using resample (much more on this later)in [ ]ts resample(' 'how='mean'out[ ]- - freqm time zone handling working with time zones is generally considered one of the most unpleasant parts of time series manipulation in particulardaylight savings time (dsttransitions are common source of complication as suchmany time series users choose to work with time series in coordinated universal time or utcwhich is the successor to greenwich mean time and is the current international standard time zones are expressed as offsets from utcfor examplenew york is four hours behind utc during daylight savings time and hours the rest of the year in pythontime zone information comes from the rd party pytz librarywhich exposes the olson databasea compilation of world time zone information this is especially important for historical data because the dst transition dates (and even utc offsetshave been changed numerous times depending on the whims of local governments in the united states,the dst transition times have been changed many times since for detailed information about pytz libraryyou'll need to look at that library' documentation as far as this book is concernedpandas wraps pytz' functionality so you can ignore its api outside of the time zone names time zone names can be found interactively and in the docsin [ ]import pytz in [ ]pytz common_timezones[- :out[ ]['us/eastern''us/hawaii''us/mountain''us/pacific''utc'to get time zone object from pytzuse pytz timezonein [ ]tz pytz timezone('us/eastern'in [ ]tz out[ ]methods in pandas will accept either time zone names or these objects recommend just using the names time zone handling www it-ebooks info