beautiful-data-vizlisted

# Beautiful Data Viz Create polished, publication-ready visualizations in Python/Jupyter with strong typography, clean layout, accessible color choices, and high data-ink. The default style is restrained: show the data, remove non-data decoration, label directly when possible, and add only the context needed to interpret the finding. ## Instructions 1. Clarify the message, comparison context, audience, and medium (notebook/paper/slides). If the data is one or two values, prefer a sentence; if it is a short lookup list, prefer a table. 2. Choose the simplest chart type that answers the question. Prefer horizontal bars for ranked categories, small multiples for >4 series or dual-axis temptations, slopegraphs for before/after changes, and sparklines for compact trend context. 3. Start gray-first: neutral series by default, one accent for the finding, and no rainbow palettes. Select an appropriate palette type only when color is carrying real information. 4. Remove chart junk before styling: no 3D, pie charts only if explicitly requested, no decorative borders, no heavy grids, no gradient fills, no dual y-axes. 5. Use direct labels instead of legends when series count and space allow. Keep legends only when direct labels would collide or obscure data. 6. For manuscript/paper figures, do not add in-plot titles or subtitles; use axis labels, legends/direct labels, panel letters, and the manuscript caption instead. 7. Apply the shared style helpers, then build the plot. 8. Valida