Date ranges in Pandas

Question

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!

Answer 1

freq='M' is for month-end frequencies (see here). But you can use .shift to shift it by any number of days (or any frequency for that matter):

pd.date_range(start, end, freq='M').shift(15, freq=pd.datetools.day)