while used it previously without explanationyou may have guessed that pan das date_range is responsible for generating datetimeindex with an indicated length according to particular frequencyin [ ]index pd date_range(''''in [ ]index out[ ] : : : : length freqdtimezonenone by defaultdate_range generates daily timestamps if you pass only start or end dateyou must pass number of periods to generatein [ ]pd date_range(start=''periods= out[ ] : : : : length freqdtimezonenone in [ ]pd date_range(end=''periods= out[ ] : : : : length freqdtimezonenone the start and end dates define strict boundaries for the generated date index for exampleif you wanted date index containing the last business day of each monthyou would pass the 'bmfrequency (business end of monthand only dates falling on or inside the date interval will be includedin [ ]pd date_range(''''freq='bm'out[ ] : : : : length freqbmtimezonenone date_range by default preserves the time (if anyof the start or end timestampin [ ]pd date_range( : : 'periods= out[ ] : : : : length freqdtimezonenone sometimes you will have start or end dates with time information but want to generate set of timestamps normalized to midnight as convention to do thisthere is normalize optionin [ ]pd date_range( : : 'periods= normalize=trueout[ ] time series www it-ebooks info