I wanted to have a look at how much PyPy might speed up the task so I generated a one million line x 20, 287M, big-data.csv of my own with the following. i

I wanted to have a look at how much PyPy might speed up the task so I generated a one million line x 20, 287M, big-data.csv of my own with the following. import random with open("big-data.csv","w") as f: for b in range(1000000): f.write(','.join([str(random.random()) for x in range(20)])+"\n") Each line looks like: "0.47509825737,0.525866136528,0.167956183215...0.888687040645". The script from the blog takes 7.3 seconds to chew through it on my machine with Python 2.7.8, and 4.9-5.5 seconds with PyPy, a reasonable improvement. Out of interest, taking all the boilerplate and checking off, the following is 7 lines versus the 24 lines or so in the blog and runs in a similar 4.9-5.4 second range on PyPy (although performance blows out under Python 2.7.8 to 13 seconds or ~6 seconds slower than the blog). Somewhat more Pythonic, and more likely do be done live in the REPL without even bothering to write a script, to my mind. from collections import Counter c = Counter() lines = (rawline.strip().split(",") for rawline in open("big-data.csv")) for line in lines: for colname, colval in enumerate(line): c[colname]+=float(colval) print c Above on PyPy. time pypy sum5.py ./big-data.csv real 0m4.972s user 0m4.946s sys 0m0.024s wc on the same machine. time wc big-data.csv real 0m3.701s user 0m3.672s sys 0m0.028s Bonus one-liner (6.6 seconds under PyPy). print map(sum,zip(*([float(x) for x in rawline.strip().split(",")] for rawline in open("big-data.csv"))))