Defining Data Visualization

Name: Defining Data Visualization
Author: Bill Hood

Collect, organize, and plot your first data set

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Course: On-Demand

This is the first plot. As we went through in the introduction, we're going to follow a three-step process. The first step is to prepare. In this case, we're going to introduce two Python imports, NumPy, which stands for numeric Python, and matplotlib, which is, as the name suggests, a plotter. We're also going to just hard code data. And a lot of times, hand inputting data is the best answer, the fastest answer, and certainly a good approach. So we'll introduce that in this lesson. In the spirit of organizing that data, we are first going to just organize the data itself and make sure we understand what data we want to put on what axis. We are going to label these and introduce how you do that. We're going to assign titles and choose styles. Our first plot is going to be a simple XY one-dimensional plot. Let's get started. This is my Jupyter Notebook environment. Jupyter Notebook is a virtual machine that creates a virtual programming environment. Import numpy as np. As np is traditional, most people will do that and then it allows us to refer to this library just like we do right here where we say np.random. That's where we actually use numpy to generate random number. Second import is matplotlib. pyplot, And that's a really long name, so we shorten that to as PLT. Also a very basic import. In fact, these two imports right here, we'll never talk about again, and we'll use them almost every single time. So now that you know what they are, just put them in your bag of tricks and feel free to use them going forward. This actually imports a style function for us from the matplotlib library. So we have to import this first and then we can get the style library. And in fact, we'll just use that first. And we will go ahead and print out the styles that are available. So if you'll notice right here, plotter is what we're pointing to. So that's matplotlib and the style library. And now what we want to see is what's available. So if we click Run, that little segment of code will execute. And you will see that these are the names of the styles that are available. And if we pick, we got Seaborne Dark Grid, if we pick BMH, all we have to do is to grab that, copy it, and change it here. Run this again, and we will get a different looking graph. So let's go ahead and select VMH. And now let's go into our second code block. And that's the thing about a Jupyter Notebook that really is handy for teaching coding. Because we really, this is step one, let's get these imports lined up. This is the first part that I was talking about. Now that we have, we need to organize ourselves and make sure we know how we're going to get this data and what we're going to do. Now in this case, what we're going to do is we're going to generate it. And if you look here, we have a variable called random number, and we are going to create a NumPy function that assigns 100 random numbers to random numbers. You also know Python pretty well. You know that means it's a list. And so why don't we go ahead and print that out right here and just take a look at what numbers we get. So we'll run that again. And here's our 100 numbers. And you can see that they're within a bracket. And that means that is a Python list. So here's our numbers. Now, if we run this again, we'll get a different set of numbers because they're truly random. Pretty close. Close as a computer can make it. Alright, so now we have our data. And we're completed step two, and all we need to do now is plot that. Now, the thing to think about in this particular example, however, is that you only have one variable. There aren't two variables. There's only one variable, the random number. So we do need two dimensions to plot anything. So in that scenario, you always would use either time or sequence. We're gonna choose sequence. We could use time. How could we choose time? Well, this number was generated at a very unique time. Same with this number, same with this number. Or we'll just use sequence, as in this was the first number, the second number, the third number, the fourth number. So we'll plot this first, this second, this third and so on. So we're gonna just say plot random number. That's really all we need to do. And then this plotter by default, we'll put these in sequential order. So in this case, very simple, point to the plotter, plot out my random number please. And that's exactly what that codes does or sets you up for. It also, what this says is, on that plotter, make my x label range. If we wanted to call it range x, we would just simply type that in, and that's what's going to be displayed. Ditto for the y label, which we labeled random number. And ditto for title, which we've named my first plot. Now all of these are applicable really to all future plots. So we're not going to talk about doing labels too much in the future because we've really laid it out right here. Final command is plot show, and that is what will run the code to execute show plot. Let's go ahead and run that now. There's our plot. It's obviously a weird wiggly number, but it's because it's these random numbers. And if you look at our list up here you'll see 0.18. Well yeah, that looks like 0.18. And then the second number looks like to me 0.7 something. Oops, and there it is, 0.78. We run this again, as we demonstrated earlier. So let's regenerate our data, and then we regenerate our plot, and it will look different. It does look different.

Intermediate Provider : Bill Hood 3 Lessons · 22m in Arabic, German, English, Spanish, French, Portuguese, Chinese

Course Description

In these lessons, we introduce our virtual environment, Python 3.x, our first plot, our first 2D plot, and we’ll explore the Jupyter notebook platform. We’ll also introduce our formula for data visualization, which includes collecting, organizing, and then plotting the results. You’ll learn about how to control lines, patterns, colors, annotations, and transparency.

We’ll focus on the basics and extend your general knowledge of Python and programming, including how to import and use a new set of libraries focused on data visualization, and the three-step process of obtaining, organizing, and plotting data, or the “collect-organize-present” model of data visualization.

What You'll Learn

Import and use data visualization libraries, including matplotlib.pyplot
Build your first plot and 2D line plot in the Jupyter notebook platform
Collect data and organize it for an XY plot, including data from a CSV
Apply the collect-organize-present model to plot results
Control lines, patterns, colors, annotations, and transparency in plots
Set up and work within the Python 3.x virtual environment

Key Takeaways

The course introduces a virtual environment, Python 3.x, a first plot, a first 2D plot, and the Jupyter notebook platform.
Data visualization is presented through a three-step formula of collecting, organizing, and then plotting the results, described as the collect-organize-present model.
Learners import and use a new set of libraries focused on data visualization, including matplotlib.pyplot.
The course covers controlling lines, patterns, colors, annotations, and transparency in plots.
It focuses on the basics while extending general knowledge of Python and programming.

Frequently Asked Questions

What will I learn in this course?

You will learn to import and use data visualization libraries including matplotlib.pyplot, collect and organize data for an XY plot, collect data from a CSV, and create plots using the collect-organize-present model. The course also covers controlling lines, patterns, colors, annotations, and transparency.

What tools and platform does this course use?

The course uses a virtual environment, Python 3.x, and the Jupyter notebook platform.

What topics do the lessons cover?

The lessons cover First Plot, 2D Line Plot, and Building a Foundation.

What skills does this course build?

It builds skills in Application Data, File Handling, and Shared Libraries, while extending your general knowledge of Python and programming.

How is data visualization approached in this course?

Data visualization is approached through a three-step process of obtaining, organizing, and plotting data, referred to as the collect-organize-present model.