Python for Decision Makers and Business Leaders Transcripts
Chapter: Data science in Python
Lecture: Getting started with JupyterLab demo

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0:00 It's one thing to hear about Jupyter. And Jupyter notebooks but to see them in action well that's something entirely different.
0:07 So the next demo that we're going to do in this course, may be the very last one actually is going to be working through a small analysis
0:14 of real data out on the internet using Jupyter. Over here, I've already set up an isolated environment with Jupyter.
0:22 All I have to do over here is type jupyterlab after installing those things. Just going to get started and launch a browser here.
0:28 The browser's going to log my into my Jupyter server. So what I want to do is I want to create a new notebook, and I have these different environments
0:36 this is the one that I've set up for this course. And it's all ready to go. Click here, it creates this not named, which
0:43 is kind of annoying so we're going to call this reference counter, something like that. Now, what is this thing that we're in here?
0:51 So, what we can do is we can type in different types. We can either type out raw stuff we can type in markdown, which is a way
0:58 to write in formatted text. If we go over here I can type something like this and what were going to do is, we're going to go to
1:04 a site and were going to analyze what is the relative reference counter or authority of other sites, according to this one?
1:13 What I mean by that is let's take all the links to the site and figure out where it's looking out to in the world. Figure out the domain names.
1:20 Maybe Github or Twitter, or How many times do each of those appear and then draw a graph, say we'll this one
1:27 is the most popular, so it has the most authority. We refer back to it the most. So we can type markdown, that's in markdown.
1:35 We can type a little title here. We're going to use a place called Python Bytes. So, hit a button to execute that markdown
1:43 and over here, on Python Bytes this is one of the podcasts that I run. You maybe remember that from the beginning.
1:48 And what we do, is we have a whole bunch of episodes here. And if you go into them you can see that we refer back to many things
1:55 this one's referring to Real Python. And if we go down this one's referring over to, and so on. If you look across all these episodes
2:05 we have an RSS feed here at the bottom. You can pull that out, which is in XML format. And basically this is what the podcast players use
2:12 to subscribe to the show, get the show notes and all that kind of stuff. So we're going to use that as an easy way to get all of these show notes
2:19 across all of the different episodes. Instead of trying to go to each page separately. So over here, we can, see that, we can add a new
2:28 markdown section and say we will download the RSS feed, analyze all the lengths and extract the domain names and plot them by popularity.
2:38 Let's fix that spelling, also. Here we go, so the next thing now this is a little bit of a description, we could
2:43 have pictures and whatever, but the next thing we want to do is add some code. So over here I could just come up with some variables.
2:53 If I run it, you can see, it executes this code. X and Y, if we add them together and don't assign it it'll just print out the value, like this.
3:00 Obviously, that's useless to us. But that's what this Jupyter Lab is all about. We're going to write a little bit
3:05 of Python, a little bit of a description. Little bit of Python, little bit of a picture. Little bit of Python and as you go through it
3:12 you can poke at the data, you can look at it this way and that way and transform it. And it's a very exploratory type of experience
3:18 that we're going to have.

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