Other Suggestions

For more insight, try modifying the repetition counts used by these modules, or explore the alternative timeit module in Python’s standard library, which automates timing of code, supports command-line usage modes, and finesses some platform-specific issues. Python’s manuals document its use.

You might also want to look at the profile standard library module for a complete source code profiler tool—we’ll learn more about it in Chapter 35 in the context of development tools for large projects. In general, you should profile code to isolate bottlenecks before recoding and timing alternatives as we’ve done here.

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It might be useful to experiment with the new str.format method in Python 2.6 and 3.0 instead of the % formatting expression (which could potentially be deprecated in the future!), by changing the timing script’s formatted print lines as follows:

print('<%s>' % tester.__name__)            # From expression

print('<{0}>'.format(tester.__name__))     # To method call

        print ('%-9s: %.5f => [%s...%s]' %
               (test.__name__, elapsed, result[0], result[-1]))

        print('{0:<9}: {1:.5f} => [{2}...{3}]'.format(
                test.__name__, elapsed, result[0], result[-1]))

You can judge the difference between these techniques yourself.

You might try modifying or emulating the timing script to measure the speed of the 3.0 set and dictionary comprehensions shown in this chapter, and their for loop equivalents. Using them is less common in Python programs than building lists of results, so we’ll leave this task in the suggested exercise column (and please, no wagering...).

Finally, keep the timing module we wrote here filed away for future reference—we’ll repurpose it to measure performance of alternative numeric square root operations in an exercise at the end of this chapter. If you’re interested in pursuing this topic further, we’ll also experiment with techniques for timing dictionary comprehensions versus for loops interactively.[46]

 

[45] Also note how we must pass functions in to the timer manually here. In Chapters 38 and 39 we'll see decorator-based timer alternatives with which timed functions are called normally.

[46] For more fun, apply a simple user-defined function like def f(I): return(I) in all five iteration techniques timed. The results are similar to using a built-in function like abs: among the five iteration techniques, map is fastest today if all five call a function, built-in or not, but slowest when the others do not. That is, map appears to be slower simply because it requires function calls, and function calls are relatively slow in general. Since map can't avoid calling functions, it can lose by association.