Visualization

The two mental models behind Python's plotting libraries - declarative grammar-of-graphics mapping versus imperative figure/axes drawing - and how to choose a chart and a library for the question and the audience, the mental model behind every other page in this section.

Learn data visualization basics with 8 Python examples using Matplotlib, Seaborn, and Pandas. Create line, bar, and histogram charts to analyze data.

Learn to use Matplotlib for Python to create publication-quality static charts. Control figures, axes, and plot elements with fine-grained precision.

Learn how to use Seaborn for statistical graphics, exploratory data analysis, and creating various plots like histograms, box plots, and bar plots.

Build interactive, web-ready Plotly charts with Python. Learn to export HTML, embed in Dash apps, and create custom hover and combo plots.

Learn to create declarative, grammar-of-graphics charts with Altair. Generate Vega-Lite JSON specs for version control, diffing, and web embedding.

Learn to build interactive data dashboards with Streamlit, Dash, and Gradio. Turn analyses into shareable apps for stakeholders and ML teams.

Learn best practices for creating honest, readable, and accessible data visualizations. Improve charts by matching types to questions, using colorblind-safe palettes

A single-page roundup of every highlight bullet from the 9 pages in the Visualization section, grouped by source page so you can scan all 36 takeaways without opening each article individually.