Learn more about Jupyter notebooks in our dedicated course: Using Jupyter Notebooks
Overview & Use Cases
00:00 Welcome to this final section that’s going to cover Jupyter Notebooks as an example for data science related editors that are—well, of the Notebook type, there’s a couple of them, and I would say that Jupyter’s probably the most common one, and it’s also free and open-source, so it’s a great choice.
00:18 I’m going to show you how Jupyter Notebooks work and what’s a good way of using them. Keep in mind that there’s also a separate course that we have on Jupyter Notebooks that goes a bit more in-depth than we do here. Okay, let’s get started with a quick overview and the use cases.
00:36 A Jupyter environment, the so-called Notebook, looks pretty much like this. We have cells in here that you can execute. You have up here a menu of different features that you can do, and you have up here a menu with the different menu items, things you can do.
01:16 And you can see that I’m executing each cell separately, which means that I can write code in one cell, and then continue with the output, the variables that got generated in a different cell, but I can have the execution happen all in one cell and then a separate execution in the next cell. So in this case, I imported a couple of data science related libraries, then created this random set of numbers, and then created a plot.
01:43 And you can see how useful it is to get the output right here underneath a code block. This is where the Notebook idea comes from. You can basically read over this code very simply and see “What does the code do?” There’s also the option of integrating Markdown right in here.
notebooks, there you go. And you might see that this is a code block at the moment, but instead, I can turn it into a Markdown block, and then we already see that this recognizes Markdown correctly.
02:24 We can execute this cell and have just a normal, nicely-formatted HTML Markdown code sitting in here. And this allows you to create very, very nice and easily-readable documents that combine both code and explanations.
02:40 You can convert those to PDF, print them out, you can share them online, et cetera. And this is a very popular way for people that work in data science to create their work and also share it. You can see it’s interactive.
02:52 You can play around with the data. You can figure out things, quickly change it. And it’s just a very dynamic process that, at the same time, allows people to be possible to keep track of what’s been happening in here and retrace the steps that someone took to come to a certain conclusion.
03:20 We’re going to dive a little bit deeper in there and walk through creating this little code sample. A little bit of debugging, and then I’ll tell you how you can learn more about using Jupyter Notebooks.
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