What problem does it solve?
Turn raw data and ambiguous chart choices into clear, comparable, and accessible visuals by providing chart selection guidance, reusable Python patterns, and design rules that reduce misleading representations and speed up analysis-to-presentation workflows.
Core Features & Use Cases
- Chart selection guidance for trends, comparisons, distributions, rankings, geospatial displays, and part-to-whole relationships so you pick the most interpretable visual for your data.
- Reusable Python patterns for matplotlib, seaborn, and Plotly including line charts, bar charts, histograms, heatmaps, small multiples, and interactive exports.
- Design and accessibility rules such as palette recommendations, axis conventions, labeling best practices, colorblind-safe alternatives, and alt-text guidance to make visuals publication-ready.
- Use Case: Convert an exploration notebook into a polished dashboard by applying ranked bar charts for comparisons, line charts for trends, and accessible palettes for stakeholder reports.
Quick Start
Generate a clear, accessible time-series line chart from my dataframe showing revenue over date, highlight the main series, and save the output as trend_chart.png.
