Interactive Visualization With Python

Code and slides to accompany the online series of webinars: https://data4sci.com/interactive-data-visualization by Data For Science.

Run the code in Binder:

Our brains are highly developed visual processing machines. We’ve evolved highly 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.

Schedule

1. Data Cleaning and Visualization with Pandas

• Data Frames and Series
• GroupBy
• Pivot Tables
• The plot() function
• Customizing pandas plots with matplotlib
• Interactive Pandas plots Break (10 min) Q&A (5 min)

2. matplotlib animations

• The Matplotlib animation API
• FuncAnimation
• Animation writers

3. Jupyter ipywidgets

• Ipywidgets as interactive browser controls
• Simple Widget use
• Widget customizing
• Layout Break (10 min) Q&A (5 min)

4. Bokeh

• Basic Plotting
• Styling and Annotations
• Networks
• Geographic Plots

5. Plotly

• Basic Plotly
• Importing Data
• 3D plotting
• Animated Plots
• Course wrap-up, Q&A, and Next Steps (15 min)

Slides: https://data4sci.com/landing/interactiveviz