- - - - - - as before you can pass either string datedatetimeor timestamp remember that slicing in this manner produces views on the source time series just like slicing numpy arrays there is an equivalent instance method truncate which slices timeseries between two datesin [ ]ts truncate(after=''out[ ] - - all of the above holds true for dataframe as wellindexing on its rowsin [ ]dates pd date_range(''periods= freq=' -wed'in [ ]long_df dataframe(np random randn( )index=datescolumns=['colorado''texas''new york''ohio']in [ ]long_df ix[' - 'out[ ]colorado texas new york ohio - - - - - - - - - - time series with duplicate indices in some applicationsthere may be multiple data observations falling on particular timestamp here is an examplein [ ]dates pd datetimeindex(['''''''''']in [ ]dup_ts series(np arange( )index=datesin [ ]dup_ts out[ ] we can tell that the index is not unique by checking its is_unique property time series www it-ebooks info