Matplotlib Data Visualization & Styling

Map data types (nominal, ordinal, quantitative) to visual encodings (position, length, color, etc.) and rank them by perceptual accuracy.

Select an appropriate chart type to answer questions about comparison, distribution, composition, or relationships in a dataset.

Identify and correct common chart misuses, such as 3D pie charts, dual y-axes, and truncated baselines.

Apply Tufte's data-ink ratio principle to declutter charts by removing redundant spines, gridlines, and labels.

Choose and apply appropriate categorical, sequential, and diverging color palettes from Matplotlib's colormaps.

Evaluate color palettes for colorblind safety and sufficient contrast.

Improve readability by applying typography best practices (hierarchy, size, weight) to titles, labels, and annotations.

Use annotations and direct labeling to highlight key insights, as a powerful alternative to legends.

Create and interpret statistical plots for distributions (histogram, KDE) and categories (boxplot, violinplot) using Seaborn.

Create and interpret relationship plots (e.g., scatterplot with a regression line) using Seaborn.

Access the underlying Matplotlib Axes object of a Seaborn plot to perform fine-grained customizations not available in the Seaborn API.

Customize Matplotlib globally using rcParams and stylesheets to enforce a consistent visual identity.

Construct small-multiples (faceted plots) to compare patterns across different subsets of the data.

Convert descriptive chart titles into active, takeaway-style headlines that state the main finding.

Assemble a sequence of 2–3 charts into a cohesive data narrative, using annotations and titles to guide the reader.

Export publication-quality figures, choosing appropriate vector or raster formats and resolutions for different media (print, web, slides).