6.2 Set up Anaconda in Jupyter - Video Tutorials & Practice Problems

<v ->Okay.</v> So the first thing we're gonna do is set your computer up with Anaconda and Jupyter Notebooks. So, you can download it here. And download and run the installer. So, when you open it up, it should look something like this. This is called the Anaconda Navigator. So, remember that, like, Anaconda is a distribution of Python. It includes a lot of libraries, but this is the app that comes with it, that allows you to use different tools and also, like, add other libraries that it didn't come with. So, in Home, we can launch various tools. We're gonna launch this Jupyter Notebook. And this is the one we're gonna be using in this lesson. And it should open up on, in a new browser. And going back to Anaconda, just look over, there's these, this Environments tab. And you can see in the base environment, there are a number of libraries already downloaded and these are common ones for using with data science. You can also create new environments and also search for packages and like add new libraries. Yeah. There's also a number of tutorials that you can view, like this Python tutorial and ones for different libraries that are already downloaded. Going through this Jupyter tutorial would be great because I'm just gonna cover a small portion of it today. And then also this Community tab. So, once we're here, we can navigate to our Code. So mine is in here, intro-to-python. And then Challenges. And then I've got this challenge_4_data_analysis.ipynb. Dot-I-P-Y-N-B. Which stands for I Python Notebook. Okay, so you open it up and it should look something like this. We have a bunch of texts that I've put in here already and this is formatted text, which is in something called Markdown. So if you haven't seen Markdown before, you can format text by, like, you know, putting pound signs for different headings. Like this is a third level heading. I can add some tabs. You can bold and italicize and lots of stuff. So this already better than just using .pie files and running those Python files in something, like PyCharm. Because it can support all of this formatted text and you can give more information to people who are looking at your notebooks about what's going on. Why are you doing this step? Etc. So, you can read the intro here but the main thing is that notebooks contain either text, formatted text, or code. And a code block, a code cell looks like this. So I can run some Python in here. Press run and then everything inside of here is evaluated and the output is down here. You can have multiple lines and hit run. And, if you wanted to add more text, I can say, here's another heading and some text and then change this from code to Markdown and then run this. And so I can intersperse code and descriptive text. So this is great for sharing with people and showing your process, in like a linear order. You read it from top to bottom. And people can then, look at your code and rerun it using the same data set. Maybe expand on it. Maybe just like, duplicate your results. Find any bugs. Or use your process for their own data sets. Alright, in the rest of the lessons, we're gonna be doing a sample data analysis in this file, using the tips.csv data set.