Interactive Data Visualization with Python
Published by Pearson
Use matplotlib, Bokeh, and Plotly to Explore Your Data Inside Jupyter Notebooks
- Create interactive visualizations to best highlight your message
- Use animations to bring your visualizations to life
- Learn with practical application to real-world datasets
Our brains are highly developed visual processing machines. We’ve evolved sophisticated visual systems to quickly process large quantities of information. The power of visualization rests on its ability to encode data in a way that can be processed intuitively and accurately.
As the quantity and complexity of data increases, the correct use of visualization will only become more important. In this course, we survey a range of techniques and tools to produce visually appealing and effective interactive visualizations using Jupyter notebooks, Bokeh, and Plotly to make your data shine.
What you’ll learn and how you can apply it
- How to create interactive visualization with Jupyter
- How to generate animation with matplotlib
- The fundamentals of Bokeh for geographic plots
- How to use Plotly to produce 3D plots
And you’ll be able to:
- Identify the best visualization for a specific dataset
- Use Bokeh and Plotly to produce interactive plots
- Generate animations with matplotlib and Plotly
- Build simple dashboards with Jupyter widgets
This live event is for you because...
The typical participant will be a data scientist who wants to be able to take advantage of state-of-the-art Python visualization packages. They’ll have an understanding of basic plotting with matplotlib and will be ready to take the next step toward more effective and appealing visualizations.
Prerequisites
- Basic Python
- Fundamentals of matplotlib
- Familiarity with Jupyter notebooks
Course Set-up
Have access to the following if you want to follow along with the demos:
- Python
- Pandas
- Maplotlib
- Bokeh
- plotly
- GitHub link
Recommended Preparation
- Watch: Use Matplotlib to Create Informative 2D Visualizations by Nimrita Koul
- Watch: Data Visualization in Jupyter Using matplotlib and Seaborn by Bruno Gonçalves
Recommended Follow-up
- Read: Interactive Dashboards and Data Apps with Plotly and Dash by Elias Dabbas
- Read: How Charts Work: Understand and Explain Data with Confidence by Alan Smith
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Segment 1 – Data Cleaning and Visualization with Pandas Duration: 40 minutes
- Data Frames and Series
- GroupBy
- Pivot Tables
- The plot() function
- Customizing pandas plots with matplotlib
- Interactive Pandas plots
Q&A 5 minutes
Break 5 minutes
Segment 2 – Matplotlib Animations Duration: 25 minutes
- The Matplotlib animation API
- FuncAnimation
- Animation writers
Q&A 5 minutes
Segment 3 – Jupyter ipywidgets Duration: 30 minutes
- Ipywidgets as interactive browser controls
- Simple Widget use
- Widget customizing
- Layout
Q&A 5 minutes
Break 5 minutes
Segment 4 – Bokeh Duration: 50 minutes
- Basic Plotting
- Styling and Annotations
- Networks
- Geographic Plots
Q&A 5 minutes
Break 5 minutes
Segment 4 – Plotly Duration: 55 minutes
- Basic Plotly
- Importing Data
- 3D plotting
- Animated Plots
Q&A 5 minutes
Your Instructor
Bruno Gonçalves
Bruno Gonçalves is an author, public speaker, corporate trainer, and consultant specializing in Generative AI, Blockchain Analytics, and Machine Learning. He has a diverse background that spans academia and industry, having previously served as a Data Science fellow at NYU's Center for Data Science while on leave from his tenured faculty position at Aix-Marseille Université. Bruno earned his PhD in the Physics of Complex Systems in 2008. He later focused his research on applying Data Science and Machine Learning to the large-scale analysis of online human behavior.