Python for decision makers and business leaders Transcripts
Chapter: Data science in Python
Lecture: Counting the domains
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We're so close to having the answer.
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We've gone and downloaded the data
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we parsed it apart.
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As XML we started working through it
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and then we said well each one of has embedded html
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which is all sorts of yucky
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but we can use this Beautiful Soup to pull the pieces out.
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And now we've found there's 799 unique domains
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and 2,824 total. What do we do now?
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Well, the last thing to do is figure out
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how many times each one appears.
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That may sound complicated
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and in some languages it is, but watch this.
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We talked about Python's batteries included
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well one of those batteries
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one of those things in the standard library
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is something called the collections module.
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Like this, and it has this thing called a Counter
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and to the counter we can give the things
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we want it, well to count. What are we getting?
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Well it has, oh, looks like it's already got some stuff here
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like GitHub this many and so on
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but what I'd like is to sort it.
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So we can say the most common is going to be counter
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not you guessed it most common.
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And if we just print that out, there you go.
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GitHub 447 references, Twitter 202
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Python Bytes which maybe exclude ourselves maybe not
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YouTube, Python.org and so on.
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Maybe we only want the top 25
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'cause you don't want to graph all of them
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you just want to see the most important ones.
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So we can come down here and do one
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literally one more line say give me all the items
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from 0 to 25, and we just show the top 25
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and there they are. Those are the top 25.
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This is the kind of stuff that makes Python so useful
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it's just like a couple of steps
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a couple of lines. You don't...
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There's no algorithmic thinking here
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I don't have to come up with a algorithm
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where I could make mistakes
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or I have to spend time working on it.
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No, I just grab the right thing
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ask the right question, and boom out comes the answer.
