Data Visualization Recipes with Python and Matplotlib 3

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 27m | 677 MB

Become a data visualizations expert with Matplotlib 3 by learning effective and practical data visualization recipes

Creating custom visualizations with Matplotlib on real-world data can be tricky, sometimes with a lot of different features and complex code. As a data analyst or a data scientist, you don’t want to get bogged down. You really want to get those Data visualizations in front of the audience.

This course cuts down all the complexities and unnecessary details. It boils it down to the things you really need to get those visualizations going quickly and efficiently. The course gives you practical recipes to do what exactly needs to be done in the minimum amount of time. All the examples are based on real-world data with practical visualization solutions.

By the end of the course, you’ll be able to get the most out of data visualizations where Matplotlib 3 is concerned.

This course is practical and hands-on, sans all theoretical stuff. Once the setup is done, each video is a standalone practical recipe to help you visualize data using certain Matplotlib functionality. All examples use real-world datasets from lots of different domains.

What You Will Learn

• Draw various kinds of plots using Matplotlib
• Visualize and gain insights into a real-world dataset via different chart types
• Use practical recipes to draw subplots, histograms, heat maps, box plots, and pie charts
• Customize plots and charts for data visualizations in an appealing way
• Build plots from the ground-up with scaffolding
• Manipulate plot, axes, and figures
• Build Matplotlib 3D graphs functionality to visualize data with multiple variables and dimensions
• Use wire framing techniques and plot complex data with Matplotlib 3
• Make your data visualizations animated and interactive
• Use Matplotlib 3’s animation and interactive capabilities to spice up your data visualizations
• Use real-world dataset of stocks to learn the techniques
• Troubleshoot both common and tricky data visualizations while creating plots with Matplotlib 3