Understand the typical workflow of installing Python packages on the fly while working locally and see how this process integrates with running code in a Jupyter Notebook. Follow along as the article demonstrates data formatting, plotting, and the practical use of notebooks for learning and prototyping.
Key Insights
- Installing packages during development is a normal part of the local workflow, as it's common to realize a dependency is missing only when running the code.
- Jupyter Notebooks are valuable for prototyping, practicing, and educational purposes, though they may have limitations in performance and usability for more complex tasks.
- Noble Desktop uses this workflow to demonstrate real-world Python data formatting and visualization, preparing learners for more advanced topics like Lambda functions.
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So that took a while to install, but here's the thing. That was actually a fairly common workflow when you're working locally. You go to import something, you realize, oh, I haven't actually installed that, and you go to install it.
There's no reason to install everything up front because you don't necessarily know what you'll need. And I actually thought, hey, I don't think we need in this one, but we do. So I went and installed it, and now we can go back to our notebook, run this, and this time it should work.
Great. Well, it's actually taking a moment. Well, probably great.
We'll see. This is taking quite a while. Oh, it's building the font cache.
Give it a moment. And if this takes too long, I will. It didn't take too long.
Let's take a look now. Now that we've run that successfully, you can go to the bottom, and I should be able to run this without an error. And here we go.
Here's our top five. Now, this was really good practice for what we're going to be doing a lot of, formatting our data and plotting it. Also, I think a pretty good demonstration of some of the limitations of writing things in a notebook.
But your notebook's great for practice, for prototyping, and for education. And we wanted to do all three of those. All right.
In our next part, we'll be jumping into learning some Python, making sure that our Lambda skills are up to par.