From the course: Python Data Visualization: Create Impactful Visuals, Animations, and Dashboards by Pearson

Python data visualization: Introduction - Python Tutorial

From the course: Python Data Visualization: Create Impactful Visuals, Animations, and Dashboards by Pearson

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Python data visualization: Introduction

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Welcome to Python Data Visualization for Everyone. In this course, we're going to learn how to craft impactful and useful visualizations using Matplotlib, Seaborn, Plotly, and Bokeh. My name is Bruno Goncalves and I'm an author, public speaker, trainer, and consultant with a background in physics and computer science, but I've always had a strong passion for visualization and making sure that the visualizations and the figures I produce are meaningful, impactful, and essentially convey the message that I'm trying to convey, and this highlights the most important aspects of the data that's being plotted. In this course, we're going to cover the entire pipeline from the design and initial concept of visualization all the way to the final product. We're going to start by exploring human perception, how do we actually perceive colors and shapes, and how we can leverage that, and understand also a little bit about analytical design, better to craft and design a visualization before diving into data exploration and cleaning using pandas. And all of this will be the preparation for the meat of the course, where we're essentially going to be diving in some depth on the details of the Matplotlib visualization package. Matplotlib is of course the base layer for most visualizations in Python, especially non-interactive ones, so we're going to look at it in some detail. We're also going to explore how we can add a degree of interactivity to static Matplotlib visualizations using IPython widgets. We're going to see how we can generate static animations, so essentially movie files directly from Matplotlib, before diving into and exploring Seaborn, which is a statistical visualization package that essentially generates Matplotlib objects in the background, that then you can customize to our hearts content. Finally, in the final third of the course, we're going to be exploring in detail two powerful interactive visualization packages, Bokeh and Plotly. Bokeh and Plotly were built from the ground up to craft interactive visualizations. They rely essentially on the JavaScript backend and build everything using this interactive layer. Even though they don't rely directly and explicitly on Matplotlib, they still use similar ideas and concepts and approaches. So let's dive in.

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