out[ ]false indexing into this time series will now either produce scalar values or slices depending on whether timestamp is duplicatedin [ ]dup_ts[''not duplicated out[ ] in [ ]dup_ts[''duplicated out[ ] suppose you wanted to aggregate the data having non-unique timestamps one way to do this is to use groupby and pass level= (the only level of indexing!)in [ ]grouped dup_ts groupby(level= in [ ]grouped mean(out[ ] in [ ]grouped count(out[ ] date rangesfrequenciesand shifting generic time series in pandas are assumed to be irregularthat isthey have no fixed frequency for many applications this is sufficient howeverit' often desirable to work relative to fixed frequencysuch as dailymonthlyor every minuteseven if that means introducing missing values into time series fortunately pandas has full suite of standard time series frequencies and tools for resamplinginferring frequenciesand generating fixed frequency date ranges for examplein the example time seriesconverting it to be fixed daily frequency can be accomplished by calling resamplein [ ]ts out[ ] - - - - in [ ]ts resample(' 'out[ ] nan nan nan - - nan - nan - freqd conversion between frequencies or resampling is big enough topic to have its own section later here 'll show you how to use the base frequencies and multiples thereof date rangesfrequenciesand shifting www it-ebooks info