Web Class: A Bite-Sized Guide to Data Visualization

Web Class: A Bite-Sized Guide to Data Visualization

You can access the full course here: Bite-Sized Python Data Visualization

Transcript 1

Hello, world, my name is Mohit Deshpande. In this course, we’ll be learning all about plotting. So plotting is a fundamental aspect of doing any kind of data science, or really just science in general. It’s the ability to take your data and present it in a nice, clean way that’s easy for people to understand.

So, you see here we have different kinds of plots, and these are all using different plotting libraries that we’re going to be learning about. So the three big plotting libraries that are out there that we’re gonna be discussing is Matplotlib, Seaborn, and Bokeh, and all of them provide different advantages as to how you want to display your data, and they provide very nice APIs for you. They just consume your data and then present a very nice-looking plot that’s completely customizable. So this is what we’re gonna be learning, we’re gonna be learning the APIs of these libraries, as well as how to create beautiful plots with all of these libraries.

We’ve been making courses since 2012, and we’re super-excited to have you onboard. Online courses are a fantastic way to learn new skills, and I take a lot of online courses myself. Zenva courses consist mainly of video lessons that you can watch at your own pace, as many times as you want, you can always go back and rewatch videos as many times as you want. We also have downloadable source code, and project files, and data, and they contain everything that we’ll be building in the lessons.

It’s highly, highly recommended that you code along with me. In my experience, it’s the best way to learn something is to get your feet wet, get your hands dirty with the code, so coding along with me will really help you get a good understanding of the code and what’s going on. And lastly, we’ve seen that we notice the students who get the most of these online courses are the same students who make some kind of weekly planner or schedule and stick with it, depending, of course, on your own availability and learning style.

So over the past eight years or so, Zenva has taught all different kinds of topics on programming and game development to over 300,000 students, and this is across about 100 courses. And the skills that they’ve learned in these courses are completely transferable to other domains as well. In fact, some of these students have used the skills that they’ve learned in these courses to advance their own careers, to start a company, or publish their own content from the skills that they’ve learned. Thanks for joining, and I look forward to seeing all the cool stuff that you’ll be building.

Now without further ado, let’s get started.

Transcript 2

So let’s get started with some imports. And so the first thing we need to import is of course Matplotlib and all of the plotting functionality is in a submodule called pyplot. We want to load our data with open, and then inside here we want the file name. And our data’s stored in a file called fruit-sales.pickle. And this would just open it as a text file, but for efficiency, I’ve saved the data in a binary format, so we have to tell Python to read from a binary format, so that’s what this RB stands for.

So we’re now reading binary data and then as F links this, it creates a variable F that represents this file. Okay and then I use a colon here to set up an indentation block. So inside of this block, now I can do anything with F, this file, that I want, and then after I get out of this indentation block it will automatically close the file for me. So then I’ll just load my data. So let’s see our data, let’s see our data somewhere on this guy.

As you can see we have, it’s actually a list of tuples. Each tuple has a name of a fruit, and then the quantity that sold. So it’s in this format, but what we can do to make our lives easier to work with, is we can split this list of tuples out into two separate lists. So essentially, we’ll have one list that has all of the fruit, and another list that has all of the numerical values.

So using a Python function called zip, so I can say fruit and num_sold equals zip, and then there’s a special operator that we have to use here, there’s a special syntax. It’s star, or asterisk, and then data, and what this does is this is going to split our list of tuples into two separate lists. So fruit will have all of our fruit here, and then num sold will have all of our numerical data. So this is what we want the result to kinda look like.

We need to tell Matplotlib where to put these bars. so we need to create a list where we say zero, one, two, three, and then we can give that to Matplotlib, and Matplotlib will know where to position it. Bar coords, range, length of fruit. So what this will do is this will create essentially a list that goes from starts at zero and goes up to however many fruit we have.

Plt.bar, and the first input to this guy is going to be these coordinates, so I have to tell Matplotlib hey here are the coordinates. And then the second argument is how tall do I want the bars? And that’s just the num_sold here. Now this is going to set up our bar plot, and then to show it we have to call plot.show. so I’m gonna call this guy and now that I have this, I can actually run this and we should be able to see our plot.

So here is our plot. So you see we have these bars here, and their positioned at zero, one, two, three and four because we told Matplotlib to do that.

Transcript 3

In this video, we are going to make our plot look a bit better and then add things like labels on the axes, and the title. So we can have a look at a function on plot called title that I can add, and you can look at the documentation for there’s a bunch of other arguments that you can go with this. But we will just say, let’s give it a simple title. So number of fruit sold 2017. So here in the figure that shows up, we have a title at the very top here. There are other configuration options as to where do you wanna put the title.

The other thing that we want to do is set a title or an axis label for the y-axis, y-label, and that will label the y-axis for us. So I can say number of fruit, and this will be in the millions. And we see that on the left, you see that there is the Number of fruit (millions) label on the y-axis.

The one last thing that we need to do is set up the x-axis so that instead of saying 0, 1, 2, 3, 4, we use the actual fruit x-ticks. We are essentially going to take the numbers and replace them with ticks. So we need to tell Matplotlib which of the numbers we wanna replace and what we wanna replace them with. So we wanna replace these r-coordinates here with the fruit here. So if I run this guy again, you see that now, we have replaced the 0, 1, 2, 3, 4 with the actual names of the fruits, and we have a completed chart here.

So you can see that apples sold the most and grapefruits apparently didn’t quite sell as much, and oranges and bananas were very close, pears were a little bit more, but not quite as much as apples. So using this bar chart, we can look at different kinds of information and gain some insights out of this. So these kinds of column charts are great for categorical data, so here in the x-axis, we have categories, and then we have a numerical value assigned to each category. So these kinds of bar column charts are great for this kind of categorical data.

And then I’ll just make one last subtle point is that these are technically called column charts, but you’ll hear people call them bar charts all the time. They’re really just interchangeable depending on how you wanna show the bars.

Interested in continuing? Check out the full Bite-Sized Python Data Visualization course, which is part of our Bite-Sized Coding Academy.