Data Visualization and Data Science

Name: Data Visualization and Data Science
Author: Bill Hood

Get acquainted with multiple plots and interpreting data

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Course: On-Demand

Our subject is subplots. We're going to introduce plots and subplots. And we'll start by reusing our old friends matplotlib.pyplot and NumPy. We'll introduce a new function called line space, which is a NumPy function. And essentially what line space does, it creates a list of data by programmatic means. So for example, the way line space works is it's start, stop, and number of samples. So we're going to say create me a line space starting at zero going to five times pi, which is five times 3.14, and give me 400 increments. And so that's really what that does and it is just handy for us to create that X space. And then this is a Y is a function of X and what that means you may have heard of this is pretty basic math idea But what that means is that Y is evaluated every space along the line. So in other words, we've got 400 increments of X. We're going to evaluate Y at each of those points. And then what we're going to do is we're going to take the square of that. So for example, if X is 2, then we would square that and call it 4 and Y would be equal to 4 in that case. So Y is directly related to X, so as X progresses, Y is recalculated. We'll print those out and then plot it. Notice here we're going to change our plot format a little bit and in this case we're going to use a figure format and our axis is going to be the subplots and in this case we're not going to provide an argument, which means one subplot. We'll then plot x and y, those variables right there, and call it a single plot. And you can see here is our line space and that is I see starting at zero and going to you know three times pi and then here's the Y the value and Pretty quickly easy to figure out that zero squared is going to be zero so that certainly looks right so here's our values of Y and then we plotted it out and this is our single plot right here and I like to use this plot because we can do some easy fun things with this like reverse the Y data or the X data to make it look different and look at four subplots now. So notice this statement here is a little bit different now because before we didn't have an argument. Now we're gonna have this argument two, two. And what that means is that we're gonna have four and they're gonna be stacked on top of each other. If you think about English speaking rules, it's always left to right and always top to bottom. And that's the way this would lay out too. And what this implies is these are going to be stacked two on top of each other and the values will be from left to right and top to bottom. So for example this position right here will be top most left most. This will be top line right most, left most second row, right most second row, and so on. And then what we do that's just handy for us is we're reversing the Y value to reverse the graph, thereby making it easy to see. And you can see here, we've got four plots all on a single layout in English speaking order, right to left, top to bottom. And that's how you would have four plots. Now the reason we're doing this is they're applicable to any plot you might create, and you're gonna find that a lot of people are gonna wanna see more plots in less space. And the way you would accomplish that is with this technique. All right, now let's do another example. And in this case, we're going to just vertically stack it. So the change here is two subplots, is how we would refer to that, and vertically stacked. And we'll do it like this. And we'll also reverse the data just so we can see it like before. And here's our single plot and then here's our vertically stacked version of that. One more quick sample we can run that also does a horizontal stacking of these vertical. So we'll demonstrate a single plot, vertically stacked, and then horizontally stacked. And once again we're really just altering the data. But each of these is a different plot and that's the technique we're trying to show. So let's run that and there's our single plot, there's our vertically stacked and here's our horizontally stacked. All right, thank you. See you in the next lesson.

Intermediate Provider : Bill Hood 3 Lessons · 15m in Arabic, German, English, Spanish, French, Portuguese, Chinese

Course Description

In these lessons, we explore working with multiple variables, multiple plots, subplots, and how to interpret data. We’ll begin with an exploration of multiple variables in a simple plot, such as an XY plot. You’ll learn the reason an XY plot can have a large number of values for Y, but only one for X. We’ll also introduce Numpy, another primary tool for data visualization. Numpy is a fundamental package for scientific computing, and can enable n-dimension arrays and make computations on these arrays quickly.

We will also explore higher image density through plotting multiple images in condensed spaces. You’ll also begin the learning process for interpreting data. As a data visualization specialist, you will be challenged with interpreting all kinds of data and trying to understand where it fits and how you might visualize it. In these lessons you’ll learn to present a large 3D array containing some 12,000 data points, all between 0 and 255. Using Python, we’ll explore another data file containing text and numeric data separated by commas and look at the contents.

What You'll Learn

Work with multiple variables in a simple plot such as an XY plot
Explain why an XY plot can have many Y values but only one X value
Use Numpy to generate random numbers and work with n-dimensional arrays
Create subplots and plot multiple images in condensed spaces
Interpret data and determine how it fits and how to visualize it
Recognize a JPG and examine data files containing text and numeric data separated by commas

Key Takeaways

The lessons cover working with multiple variables, multiple plots, subplots, and interpreting data.
Numpy is a fundamental package for scientific computing that enables n-dimension arrays and fast computations on them.
Higher image density can be achieved by plotting multiple images in condensed spaces.
A data visualization specialist is challenged with interpreting all kinds of data and understanding where it fits and how to visualize it.
Using Python, the course presents a large 3D array of about 12,000 data points, all between 0 and 255, and explores a data file containing text and numeric data separated by commas.

Frequently Asked Questions

What topics does this course cover?

It explores working with multiple variables, multiple plots, subplots, and how to interpret data, beginning with multiple variables in a simple XY plot and introducing Numpy as a primary tool for data visualization.

What is Numpy and why is it used in this course?

Numpy is a fundamental package for scientific computing that can enable n-dimension arrays and make computations on these arrays quickly. The course shows how to use Numpy to generate random numbers.

Who is this course for?

It is aimed at data visualization specialists, who are challenged with interpreting all kinds of data and trying to understand where it fits and how to visualize it.

What hands-on work does the course include?

Using Python, you will present a large 3D array containing about 12,000 data points all between 0 and 255, and explore a data file containing text and numeric data separated by commas to look at its contents.

What lessons are included?

The course includes Multiple Plots, Interpreting Data, and Multiple Plots and Subplots, and builds skills in data analysis, data science, data visualization, graphing, and information visualization.