Python Memory Management and Tips Transcripts
Chapter: Efficient data structures
Lecture: Container sizes, NumPy
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Last but not least, let's say NumPy arrays. so NumPy, the foundation of Pandas
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as well, and much of the data science stuff,
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Let's see how it compares. Now,
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NumPy is really for numbers.
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It might be able to store strings, but I don't know if we're gonna get any advantage.
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So I'm just gonna, like the array,
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just have a NumPy ages and go with that.
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So we'll say "np" and of course
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we gotta do our trick up here as well.
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Then we'll have an array. It's gonna be from ages,
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and the "dtype = int8" as well.
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And let's just print out one more time. Let's first print
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"np_ages" just to make sure we got what we're expecting.
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Yeah, there's a bunch of ages local.
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Now let's print out the size.
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Here goes. How's NumPy treating us?
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It's doing better than Pandas DataFrames,
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but you know, it's certainly nowhere near this.
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So quite a powerful library here,
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but also not as efficient. Anyway,
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I think that probably is a good place to call it done.
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All the things we've compared, I think that's enough.
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And the goal really is not even to tell you "these are better, these are worse".
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The goal here is to give you a sense of scale,
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right? If I store it as a list relative to storing it in,
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say, a list of classes,
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how much bigger does it get? If I store it in a number like one of
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these typed arrays versus stored as a pandas DataFrame,
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how much bigger does that get?
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And you can see, there's quite a bit of variability here.
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So keep this, you know,
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add on the own data types that you want to play with.
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Like, what if you're gonna store stuff in say a set instead of a list
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or in a dictionary instead of a list,
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you could have those kinds of things as well.
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Here it is, some sizes of containers compared for a whole bunch of items.