create-vizlisted

# /create-viz - Create Visualizations > If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../CONNECTORS-data.md). Create publication-quality data visualizations using Python. Generates charts from data with best practices for clarity, accuracy, and design. ## Usage ``` /create-viz <data source> [chart type] [additional instructions] ``` ## 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) |