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Python Data Visualization: Create impactful visuals, animations and dashboards (Video Course)
- By Bruno Goncalves
- Published Apr 30, 2025 by Addison-Wesley Professional.
Online Video
- Your Price: $239.99
- List Price: $299.99
- About this video
Video accessible from your Account page after purchase.
Description
- Copyright 2025
- Edition: 1st
- Online Video
- ISBN-10: 0-13-542660-X
- ISBN-13: 978-0-13-542660-9
6+ Hours of Video Instruction
Unlock the power of data with Pythons cutting-edge visualization tools--transform complex insights into compelling, interactive visuals that drive better business decisions.
Python Data Visualization teaches you the basic principles of data visualization and how to implement them using various Python data visualization platforms. Information visualization, as David McCandless aptly puts it, is a form of "knowledge compression." Our highly evolved visual processing system allows us to efficiently handle vast amounts of information. Visualization's power lies in its ability to encode data intuitively, making complex data accessible. As data grows in volume and complexity, the importance of effective visualization increases. This video explores how the human visual cortex processes colors and shapes and how we can utilize these mechanisms for effective visualization using Pythons powerful visualization libraries.
Starting with pandas and matplotlib--two core Python libraries--you learn the basics of Python data pre-processing and visualization before moving on to more advanced packages. Seaborn, built on top of matplotlib, simplifies common tasks and enhances productivity. Interactive visualizations using bokeh and plotly are also explored. You will use jupyter notebooks to craft our visualizations.
Learn How To:
- Utilize an understanding of the fundamentals of human perception in designing data visualizations
- Identify the best visualization for a specific dataset
- Make effective use of matplotlib and seaborn
- Use seaborn and matplotlib together for best results
- Use bokeh and plotly to produce interactive plots
- Generate animations with matplotlib and plotly
Who Should Take This Course:
The typical participant will be a data scientist who wants to be able to take advantage of state-of-the-art Python visualization packages. That person will have an understanding of Python and basic familiarity with jupyter notebooks and be interested in developing their visualization skills using state of the art Python visualization packages.
Course Requirements:
- Knowledge of basic Python
- Familiarity with Jupyter notebooks
Lesson Descriptions
Lesson 1: Human Perception
Lesson 1 explores human perception, focusing on how we perceive colors and shapes and how our brain processes them. This includes a look at color theory, how human vision works, and also how to choose the proper color scheme for your visualization.
Lesson 2: Analytical Design
In Lesson 2, you learn analytical design, including how to understand the fundamental principles of analytical design, how to properly design visualizations, and what the fundamental tools of visualization are. The lesson also explains the advantages and disadvantages of some different common chart types.
Lesson 3: Data Cleaning and Visualization with Pandas
Data cleaning is one of the fundamental steps in crafting a good and successful visualization. In Lesson 3, we explore pandas and how we can use it to slice and dice our data to make sure we have the proper data set that we're trying to visualize. We start by exploring Dataframes and Series, which are the fundamental data structures of pandas, before looking into different ways of aggregating the data using group by and pivot tables, how to merge and combine different data frames using join and merge, before finally generating some quick and dirty visualizations using the plot function.
Lesson 4: Matplotlib
Matplotlib is the workhorse of non-interactive visualizations in Python. Lesson 4 covers the fundamental components of a Matplotlib plot, including the Matplotlib API and how you can leverage it to create custom-made, impactful and effective visualizations. It also covers style sheets and geographical mappings for more customized and advanced results.
Lesson 5: Matploltib Animations
One of the least known functionalities of matplotlib is its ability to create animations. In Lesson 5 you learn how you can use the matplotlib animation API to generate and create animation videos and GIFs that can add a temporal component to your visualizations.
Lesson 6: Jupyter Widgets
Lesson 6 shows you how you can add a degree of interactivity to your static Matplotlib visualizations through the use of jupyter ipywidgets. ipywidgets are essentially browser controls you can add to your notebook that allow you to interact directly with your visualization. In Lesson 6, you learn how you can define, leverage, and customize IPY widgets and also how you can craft and customize the layout of your visualization to better take advantage of this functionality.
Lesson 7: Seaborn
Seaborn is a statistical visualization package that leverages all the power and functionality of matplotlib and makes it easier for us to generate high-quality visualizations easily. In Lesson 7, you learn the structure and API of seaborn, how it builds on top of matplotlib, and how to use matplotlib to further customize the visualizations that seaborn generates for you.
Lesson 8: Bokeh
Bokeh is one of the most popular JavaScript-based visualization packages for Python. Because it leverages a JavaScript backend, it makes it easy to generate interactive visualizations that allow you to explore your data in a more intuitive way. In Lesson 8, we look not just at the basic structure of bokeh but also how you can build some advanced visualizations, including how to visualize networks and graphs.
Lesson 9: Plotly
Plotly is perhaps the most powerful and popular JavaScript-based visualization framework for Python. It makes it extremely easy for you to design appealing, powerful, sophisticated, interactive visualizations of our data in a way that is simple and intuitive. Lesson 9 shows you the wide range of functionality that plotly makes available to you, ranging from simple line and scatter plots all the way to more advanced topics, such as three-dimensional plots and animated plots.
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Sample Content
Table of Contents
Lesson 1: Human Perception
Lesson 2: Analytical Design
Lesson 3: Data Cleaning and Visualization with Pandas
Lesson 4: Matplotlib
Lesson 5: Matploltib Animations
Lesson 6: Jupyter Widgets
Lesson 7: Seaborn
Lesson 8: Bokeh
Lesson 9: Plotly
More Information
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