Data Visualization in Python - Building Blocks of Altair - Tutorial Series Part 2

Explore the fundamental building blocks of Altair in this comprehensive tutorial, the second part of a Data Visualization in Python series. Dive deep into data types, graphical marks, and visual encoding channels essential for creating powerful visualizations. Learn about nominal, ordinal, and quantitative data types, and master techniques such as customizing scales, ranges, and colors. Discover how to enhance your plots with tooltips, faceting, and legend positioning. Gain hands-on experience with various graphical marks including bar, line, and area charts, and explore different stacking strategies. Based on the University of Washington's data visualization curriculum, this tutorial provides practical insights and code examples to elevate your Altair skills.

Intro to Building Blocks of Altair
Data Types in Altair
Nominal :N vs Ordinal :O data
Quantitative :Q data types
Turning off zero-baselining and nice-snapping with alt.Scale
alt.X, alt.Y, alt.Size w/ custom ranges
alt.Color for continuous vs discreet variables
mark_circle is just mark_pointfilled=True
alt.OpacityValue
alt.Shape
Altair tooltips!
Using order in Altair encoding
Tooltip in Altair plots
Faceting w/row and columns
Chart's width and height
Legend positioning & orientation
selection_single with binding_range and initialized value
Graphical marks: mark_bar
Graphical marks: mark_line
Graphical marks: mark_area w/ different stacking strategies