Data Visualization and Plotting

Name: Data Visualization and Plotting
Author: Bill Hood

Learn layout and spacing, and get introduced to Pandas

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Course: On-Demand

We're going to do a quick dive into layout and spacing, and we'll use subplots and some of the subplot things we've learned already. No new libraries, matplotlib as PLT is all we need, numpy, which we're familiar with. We're also going to introduce something new here, too, that I consider best practice. And that's a definition statement or a subroutine, some might call it. Essentially, it's a repetitive function that we're going to repeat over and over again. So in our case we are going to do more than one plot. So we're going to write a definition that actually will provide a way to create a plot for us with some variables in the plot itself and then the font size. And you can see here is our basic sample chart. And notice that when we change the font size, like we'll change it to 12 and run this again, Basically that changes these titles. That's all we need to know for right now. What we are going to do is we're going to call this multiple times and play with some layout and spacing. So now what if we were to put these in close order? And so what this next segment of code's going to do is to, using that figure format we talked about earlier, create a two by two array, two columns, two rows, four plots, and then we will use our example plot and just call it four times. Hence why we use the definition. So let's run that one right there. And now what you can see is that we've got our four plots together, but they do overlap a bit right here. And that is something that you're going to find that happens. However, there's a really, actually a nice easy fit. And if you look at this code right here, one of the things that we can do is to just specify that this plot's going to be a tight layout. And then tight layout will calculate how much space this needs, including the labels, and re-space this. So let's run this and see what it looks like. And there you are. You see that there are no more overlaps. And that's what the type layout does. That's it for layout and spacing. Coming next pandas.

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

Course Description

In these lessons, we start by taking a deep dive into layout and spacing and exploring different ways to develop and plot using a figure with the axis. Then you’ll begin an exploration of Pandas, one of the most important visualization tools. You’ll discover how Pandas and the Pandas library provide all-purpose solutions for visualization and, in some cases, is the only library you will need.

Pandas is an open-source library specifically for data analysis and data science. Pandas is fast, powerful, flexible, and easy to use. As with other libraries used in this course, Pandas is intended to be used in conjunction with Python 3.x, and is one of the few libraries that can provide an entire visualization solution within the Pandas libraries. We’ll also introduce you to a number of associated tools, such as CSV readers, that you’ll continue to use throughout the course.

Learning Objectives:

• Understand layout and spacing

• Describe the use of Numpy to generate random data

• Explain Pandas and a DataSeries and DataFrame

What You'll Learn

Explore layout and spacing when developing and plotting with a figure and its axis
Use Numpy to generate random data for visualization
Work with Pandas, a DataSeries, and a DataFrame for data analysis and visualization
Plot data using Pandas DataFrames
Apply associated tools such as CSV readers used throughout the course

Key Takeaways

Pandas is an open-source library specifically built for data analysis and data science.
Pandas is fast, powerful, flexible, and easy to use, and is intended to be used with Python 3.x.
Pandas can provide an entire visualization solution within its own libraries, and in some cases is the only library you will need.
The course introduces associated tools such as CSV readers that are used throughout the lessons.
The lessons cover layout and spacing, the use of Numpy to generate random data, and Pandas DataSeries and DataFrames.

Frequently Asked Questions

What topics does this course cover?

The course covers layout and spacing, developing and plotting with a figure and its axis, using Numpy to generate random data, and working with Pandas, including DataSeries and DataFrames, to plot and visualize data.

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

Pandas is an open-source library specifically for data analysis and data science. It is fast, powerful, flexible, and easy to use, and in some cases is the only visualization library you will need, providing an entire visualization solution within its libraries.

What programming language is required for this course?

Pandas, like the other libraries used in this course, is intended to be used in conjunction with Python 3.x.

What lessons are included in this course?

The course includes three lessons: Layout and Spacing; Introducing Pandas and Dataframes; and Plotting with Panda DataFrames.

What skills will I gain from this course?

You will gain skills in data visualization, graphing, information visualization, Pandas (Python package), statistical graphics, and visualization.