data-visualization

# Data Visualization This skill enables an AI agent to transform structured data into meaningful visual representations. The agent selects appropriate chart types based on the data and the question being asked, builds publication-quality static charts with matplotlib and seaborn, and creates interactive visualizations with plotly. It follows established data visualization principles to ensure clarity, accuracy, and visual appeal. ## Workflow 1. **Understand the data and the question.** Examine the dataset's structure — how many variables, what types (numeric, categorical, temporal), and what relationship or comparison the user wants to highlight. The question drives chart selection more than the data alone. 2. **Select the appropriate chart type.** Match the analytical goal to the right visual form. Use bar charts for categorical comparisons, line charts for trends over time, scatter plots for relationships between two continuous variables, histograms for distributions, box plots for spread and outliers, and heatmaps for correlation matrices or dense categorical grids. 3. **Prepare the data for plotting.** Aggregate, pivot, or reshape the data as needed. Sort categorical axes by value for bar charts. Resample time-series to the right granularity. Ensure no NaN values leak into the plot that would create gaps or errors. 4. **Build the visualization with appropriate styling.** Apply consistent color palettes, readable axis labels, descriptive titles, and proper legends. R...