Some guidelines I like for Python optimization (and on loops specifically): - Rule 1: profile first! Only optimize when there is a proven speed bottleneck; I l

Some guidelines I like for Python optimization (and on loops specifically): - Rule 1: profile first! Only optimize when there is a proven speed bottleneck; I like using kernprof to profile. When you do go to optimize, test it in a tight loop with time.clock(). - Go for built-in functions; you can't beat a loop written in C. - Use intrinsic operations. An implied loop is faster than an explicit for loop, and a while loop with an explicit loop counter is even slower. - Avoid calling functions written in Python in your inner loop. This includes lambdas. In-lining the inner loop can save a lot of time. - Local variables are faster than globals; if you use a global constant in a loop, copy it to a local variable before the loop. And in Python, function names (global or built-in) are also global constants! - Try to use map(), filter() or reduce() to replace an explicit for loop, but only if you can use a built-in function: map with a built-in function beats for loop, but a for loop with in-line code beats map with a lambda function! - Check your algorithms for quadratic behavior, but notice that a more complex algorithm only pays off for large N. More at https://www.python.org/doc/essays/list2str/ https://www.python.org/doc/essays/list2str/