Set Up Your Environment
00:00 You are now in the first section of this course, where you’ll get started with a couple of video lessons that’ll show you how to set up Jupyter notebooks and then also how to create and explored the example dataset, which will have something to do with tomatoes.
00:17 To set up your environment, you’ll first create a virtual environment, activate it, and then install a couple of packages before starting a Jupyter notebook.
00:27 I will show you how to do this in a Bash console, and these are the relevant commands. If you’re on a Windows machine, the commands will be slightly different, but the process will be the same. So if you’re on Windows, just pause, take a screenshot of this, or come back to this slide and punch in the relevant commands, and you’ll be set up in no time. Let’s get started.
00:52 To get set up, you should first create a new folder for your project …
00:59 move in there, and then create a virtual environment.
01:11 You then activate this environment.
And once you can see that it’s successfully been activated by the prepended prompt your virtual environment’s name, then you can install
pandas interpreter into this environment.
These are the only two dependencies that you will need. And we can skip to the end of this install. Once everything has been installed, you can go ahead and start the Jupyter notebook by typing
01:54 This will open up a browser window for you, and in there you can start a new notebook. Your browser should open up with a window that looks similar to this one.
02:06 You see you have your virtual environment folder in there and so far nothing else. So go ahead and create a new notebook by going to the New dropdown and selecting Python 3 (ipykernel).
02:20 And if you see this screen, you’re ready to get going. Here’s another quick view of the commands that you used, first for Linux and macOS, and then here’s the commands on a Windows machine.
02:40 And that wraps up setting up your Jupyter notebook environment. And in the next lesson, you will create and explore the example dataset.
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