Date Operations
IMHO, two date functions from `SAS` can be very useful and straight forward. I wonder if they can be incorporated in Pandas. 
### [`intck()`](http://support.sas.com/documentation/cdl/en/lefunctionsref/63354/HTML/default/viewer.htm#p1md4mx2crzfaqn14va8kt7qvfhr.htm)

This function computes the difference between 2 dates/datetime with specified units. For example 
Assume: `d1=1992-03-20` and `d2=1992-06-12`. And these are all of `date` type in SAS.
`intck('month', d1, d2)` yields 3 (regardless of what day it is). `intck('qtr', d1, d2)` yields 1. `intck('year', d1, d2)` yields 0.
### [ `intnx()`](http://support.sas.com/documentation/cdl/en/lefunctionsref/63354/HTML/default/viewer.htm#p10v3sa3i4kfxfn1sovhi5xzxh8n.htm)

This function increments a given date by an interval. Assuming `d1=1992-03-20`, `intnx('month', d1, 3, 'end')` yields 1992-06-30 (increase month by 3 and return the last day of that month. `intnx('month', d1, 3, 'same')` yields 1992-06-20 (increase month by 3 and return the same day of that month. 

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As you can see, these two functions are very flexible and simple. The whole `offset` system seems to be a little too cumbersome comparing to these 2. 

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See this [question](http://stackoverflow.com/questions/23840797/convert-a-column-of-timestamps-into-periods-in-pandas) too. Especially the solution offered by @hayd 
