A more obvious optimisation (to me) would be to leverage the native functions and avoid creating a list of ~80 million strings in memory. On my machine, the ba

A more obvious optimisation (to me) would be to leverage the native functions and avoid creating a list of ~80 million strings in memory. On my machine, the base script pretty reliably takes ~10s: Reading : 0.1935129165649414 Processing: 9.955206871032715 Sorting : 0.0067043304443359375 Outputting: 0.01335597038269043 TOTAL : 10.168780088424683 Switching content to a no-op (`content = sys.stdin`) and feeding `Counter` from a native iterators pipeline: counts = collections.Counter(chain.from_iterable(map(str.split, map(str.lower, content)))) is a pretty reliable 10% gain: Reading : 1.1920928955078125e-06 Processing: 8.863707780838013 Sorting : 0.004117012023925781 Outputting: 0.012418985366821289 TOTAL : 8.880244970321655 As far as I can tell, the bottleneck is about half the preprocessing (lowercasing and splitting) and half filling the Counter. You won't get a 10x gain out of that though.