#100DaysOfCode in Python Transcripts
Chapter: Days 49-51: Measuring performance
Lecture: PyCharm's profiling
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Alright, so all of this was really
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useful and helpful and I think we did a lot
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of good stuff with it, but this text view,
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while it is technically helpful, you really can do better.
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In this simple program, what I'm going to show you
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doesn't come out really that great
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because there's so much overhead,
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like I said, a sort of programmed start-up and stuff.
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But in a real complex application,
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you would really be able to make great use
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of what I'm going to show you.
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So, we saw that we can come over here
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and run the profiler externally like this.
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And that works fine,
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or we can even use the API internally.
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Let me show you one other option.
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Now for this to work, we need to go back,
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we need to take a bit of a step back into this mode here
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where we're running the profiler from the command line.
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Just the whole program basically.
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So let's drop in this program PyCharm bit.
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Let's drop this enabling and disabling and printing
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and we can still leave everything else the same.
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But we're going to take away the profiler API internally.
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And we're going to run this just like normal, and it runs.
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There's no output that is anything special.
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But once you have a run configuration--
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now this is only for those of you
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who care about PyCharm and have the Pro Edition.
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If you're using something else like Visual Studio Code
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or something, you're going to have to do
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what we've already seen, alright?
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There are ways to implement these tools
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outside of PyCharm, but this is pretty nice.
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Once we create this,
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we can run it here but if you go over there,
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it'll say profile that. We click it, wait a second.
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First of all, if you look up at the top, way at the top,
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it is running the cProfiler.
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And this list here is the list that you already saw.
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But we can click on, say 'time'
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and see, here's main, here's the
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research py stuff we're doing,
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here's the Hot Days, all that kind of stuff.
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Here's the init that we're calling.
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Same thing, but you can quickly jump around.
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You can even say, "Show this on the call graph."
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Well, of course, you see it right there.
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This is a visualization of that result.
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Let me come down here and zoom in, this will become useful.
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Notice, here's our program, it's calling main,
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it's calling "Hot Days, Wet Days, Cold Days."
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These are pretty quick. Now we're calling this "Init."
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We're calling this one 99 times,
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but we're only going through the parse row
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365 times. Remember, that's one year's worth of data,
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365 rows, so even though we call this 99 times,
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we're not actually parsing it 99 times,
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we're just doing that for one round through the file.
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So here you can see where you're spending your time.
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You can actually visually go through it.
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Like I said, in a real app, this is actually more helpful
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because the overall program start-up is not
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so significantly shown in the graph.
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It's where your app's doing most of its work.
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This is so simple that it kind of
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gets lost in the noise, but this graphical view
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is really, really nice as well.
