Create publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, generating a plot for a report or presentation, or needing an interactive chart with hover and zoom.

/create viz Create Visualizations If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md). Create publication quality data visualizations using Python. Generates charts from data with best practices for clarity, accuracy, and design. Usage Workflow 1. Understand the Request Determine: Data source : Query results, pasted data, CSV/Excel file, or data to be queried Chart type : Explicitly requested or needs to be recommended Purpose : Exploration, presentation, report, dashboard component Audience : Technical team, executives, external stakeholders 2. Get the Data If data warehouse is connected and data needs querying: 1. Write and execute the query 2. Load results into a pandas DataFrame If data is pasted or uploaded: 1. Parse the data into a pandas DataFrame 2. Clean and prepare as needed (type conversions, null handling) If data is from a previous analysis in the conversation: 1. Reference the existing data 3. Select Chart Type If the user didn't specify a chart type, recommend one based on the data and question: Data Relationship Recommended Chart Trend over time Line chart Comparison across categories Bar chart (horizontal if many categories) Part to whole composition Stacked bar or area chart (avoid pie charts unless <6 categories) Distribution of values Histogram or box plot Correlation between two variables Scatter plot Two variable comparison over time Dual axis line or grouped bar Geographic data Choropleth map Ranking Horizontal bar chart Flow or process Sankey diagram Matrix of relationships Heatmap Explain the recommendation briefly if the user didn't specify. 4. Generate the Visualization Write Python code using one of these libraries based on the need: matplotlib + seaborn : Best for static, publication quality charts. Default choice. plotly : Best for interactive charts or when the user requests interactivity. Code requirements: 5. Apply Design Best Practices Color: Use a consistent, colorblind friendly palette Use color meaningfully (not decoratively) Highlight the key data point or trend with a contrasting color Grey out less important reference data Typography: Descriptive title that states the insight, not just the metric (e.g., "Revenue grew 23% YoY" not "Revenue by Month") Readable axis labels (not rotated 90 degrees if avoidable) Data labels on key points when they add clarity Layout: Appropriate whitespace and margins Legend placement that doesn't obscure data Sorted categories by value (not alphabetically) unless there's a natural order Accuracy: Y axis starts at zero for bar charts No misleading axis breaks without clear notation Consistent scales when comparing panels Appropriate precision (don't show 10 decimal places) 6. Save and Present 1. Save the chart as a PNG file with descriptive name 2. Display the chart to the user 3. Provide the code used so they can modify it 4. Suggest variations (different chart type, different grouping, zoomed time range) Examples Tips If you want interactive charts (hover, zoom, filter), mention "interactive" and Claude will use plotly Specify "presentation" if you need larger fonts and higher contrast You can request multiple charts at once (e.g., "create a 2x2 grid of charts showing...") Charts are saved to your current directory as PNG files

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