Category Archives: software engineering

I recently came across a stack overflow post just perfect for Pi day. The path to knowledge is asking many questions, and it is a strange feature of the days in which we live how steep this path can be: a question that starts “How to determine whether my calculation of pi is accurate? I was trying various methods to implement a program that gives the digits of pi sequentially…” eventually receives an answer that starts “Since I’m the current world record holder for the most digits of pi, I’ll add my two cents…”

All here.

I’ve written before about my search for the way to deal with data in python. It’s time to write again, though because I have a new favorite: pandas, the panel data package.

There is copious, and growing documentation for pandas, but it assumes a level of familiarity with python and numpy. I thought I’d write some little examples calculations that I’ve done with pandas recently to complement the real docs with some “recipes”. You don’t really need to know python to use these, let alone numpy.

To begin, here are the creation and subset routines in pandas that do the same work that my last foray into this subject accomplished with the rec_array:

import pandas
a = ['USA','USA','CAN']
b = [1,6,4]
c = [1990.1,2005.,1995.]
d = ['x','y','z']
df = pandas.DataFrame({'country': a, 'age': b, 'year': c, 'data': d})

This is cooler than a rec_array because you don’t have to dig in the docs for the constructor, and you can use a dictionary to name each column.

You can select the subset of data relevant to a particular country-year-age thusly:

df[(df['country']=='USA') & (df['age']==6) & (df['year']==2005)]

This is not as cool as a rec_array, because writing df['age'] has more characters than df.age, but I feel churlish to complain about it.
It’s good that I complained about my uncool df['age'] business, because I learned that df.age works, too, as long as you are using an up-to-date pandas.

More substantial recipe to come. Is there already a cookbook out there?