Data visualization involves exploring data through visual representations. It’s closely associated with data analysis, which uses code to explore the patterns and connections in a data set. A data set can be made up of a small list of numbers that fits in one line of code or it can be many gigabytes of data. Making beautiful data representations is about more than pretty pictures.

When a representation of a data set is simple and visually appealing, its meaning becomes clear to viewers. People will see patterns and significance in your data sets that they never knew existed.

Fortunately, you don’t need a supercomputer to visualize complex data. With Python’s efficiency, you can quickly explore data sets made of millions of individual data points on just a laptop. Also, the data points don’t have to be numbers. With the basics you learned in the first part of this book, you can analyze nonnumerical data as well. People use Python for data-intensive work in genetics, climate research, political and economic analysis, and much more. Data scientists have written an impressive array of visualization and analysis tools in Python, many of which are available to you as well. One of the most popular tools is Matplotlib, a mathematical plotting library. We’ll use Matplotlib to make simple plots, such as line graphs and scatter plots. Then we’ll create a more interesting data set based on the concept of a random walk—a visualization generated from a series of random decisions.

We’ll also use a package called Plotly, which creates visualizations that work well on digital devices. Plotly generates visualizations that automatically resize to fit a variety of display devices. These visualizations can also include a number of interactive features, such as emphasizing particular aspects of the data set when users hover over different parts of the visualization. We’ll use Plotly to analyze the results of rolling dice.

This is comprised of the following parts

1. matplot101 This covers the basics of using matplotlib and how to draw a simple line chart using matplotlib
2. matplot102 This covers how to draw a scatter plot, coloring a plot and saving a plot
3. matplot103 This covers on how to create a random walk plot and depends on the random walk class
4. plotly Covers data visualization using the plotly library - Roll some dice

Source Python Crash Course 2nd Edition A Hands-On, Project-Based Introduction to Programming by Eric Matthes