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!