#100DaysOfCode in Python Transcripts
Chapter: Days 49-51: Measuring performance
Lecture: Concepts: Profiling
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Let's review some of the concepts around measuring performance with profiling and Python's cProfile module.
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Now from any project, regardless of your editor, if you just want to measure the overall performance
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of your app, this is probably the best thing you can do. Python -m cProfile, so run Python, make sure that's the right version.
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Instruct it to run the module cProfile, capital P, and while you're at it, the most useful one is sorting by cumtime.
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So do a -S, cumtime and then give it to a entry point into your Python program to run. Here program notqi, and you automatically get
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that nice output that you can start working with it, iterating on. One, to run just a section of code
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there's two options, there's one I'm showing you here and you could also create a unit test that just run that code and then
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run the unit test with profiling. But we're going to focus on the more general case here. So what you do is you import the profiler,
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and tell it to start up disabled stop tracking anything, just import yourself and go from there. Run whatever startup code you got to do
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to get into the state you want to profile, and then enable profiling, run your code and go back and disable it.
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And in our example you saw we actually moved the reporting outside of this block and the data generation
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within it so that we could measure really precisely just the part we were working on. Some other stuff here at the end you probably don't care about.
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And at some point you want to see the stats so you'll say, profiler.print_stats.