timestamps and periods are represented as -bit integers using numpy' date time dtype this means that for each data pointthere is an associated bytes of memory per timestamp thusa time series with million float data points has memory footprint of approximately megabytes since pandas makes every effort to share indexes among time seriescreating views on existing time series do not cause any more memory to be used additionallyindexes for lower frequencies (daily and upare stored in central cacheso that any fixed-frequency index is view on the date cache thusif you have large collection of low-frequency time seriesthe memory footprint of the indexes will not be as significant performance-wisepandas has been highly optimized for data alignment operations (the behind-the-scenes work of differently indexed ts ts and resampling here is an example of aggregating mm data points to ohlcin [ ]rng pd date_range(''periods= freq=' ms'in [ ]ts series(np random randn(len(rng))index=rngin [ ]ts out[ ] : : - : : : : - : : - : : : : : : : : freq llength in [ ]ts resample(' min'how='ohlc'out[ ]datetimeindex entries : : to : : freq data columnsopen non-null values high non-null values low non-null values close non-null values dtypesfloat ( in [ ]%timeit ts resample(' min'how='ohlc' loopsbest of ms per loop the runtime may depend slightly on the relative size of the aggregated resulthigher frequency aggregates unsurprisingly take longer to computein [ ]rng pd date_range(''periods= freq=' 'performance and memory usage notes www it-ebooks info