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After fighting with NumPy and dateutil for days, I recently discovered the amazing Pandas library. I've been poring through the documentation and source code, but I can't figure out how to get date_range() to generate indices at the right breakpoints. from datetime import date import pandas as pd start = date('2012-01-15') end = date('2012-09-20') # 'M' is month-end, instead I need same-day-of-month date_range(start, end, freq='M') What I want: 2012-01-15 2012-02-15 2012-03-15 ... 2012-09-15 What I get: 2012-01-31 2012-02-29 2012-03-31 ... 2012-08-31 I need month-sized chunks that account for the variable number of days in a month. This is possible with dateutil.rrule: rrule(freq=MONTHLY, dtstart=start, bymonthday=(start.day, -1), bysetpos=1) Ugly and illegible, but it works. How can do I this with pandas? I've played with both date_range() and period_range() , so far with no luck. My actual goal is to use groupby , crosstab and/or resample to calculate values for each period based on sums/means/etc of individual entries within the period. In other words, I want to transform data from: total 2012-01-10 00:01 50 2012-01-15 01:01 55 2012-03-11 00:01 60 2012-04-28 00:01 80 #Hypothetical usage dataframe.resample('total', how='sum', freq='M', start='2012-01-09', end='2012-04-15') to total 2012-01-09 105 # Values summed 2012-02-09 0 # Missing from dataframe 2012-03-09 60 2012-04-09 0 # Data past end date, not counted Given that Pandas originated as a financial analysis tool, I'm virtually certain that there's a simple and fast way to do this. Help appreciated!

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