create-viz

To generate publication-quality data visualizations from a pandas DataFrame, provide your data source and desired chart type to receive reproducible Python code templates using matplotlib, seaborn, or Plotly with consistent professional styling.

Choosing chart types for CSV data analysis reports requires evaluating data relationships like trend, distribution, or comparison. The skill provides chart selection guidance to recommend appropriate visuals that communicate insights clearly.

Yes, you can use Plotly and seaborn to create dashboards from query results. The skill processes query outputs and generates publication-ready charts suitable for reports, dashboards, and presentations using these Python libraries.

This approach works directly with raw CSV files, DataFrames, and query results. It handles data processing using pandas DataFrames to produce visuals, ensuring you can turn varied data sources into charts without separate preprocessing.

The difference between matplotlib, seaborn, and Plotly involves styling and interactivity. The skill generates code-driven visuals across all three libraries, ensuring professional styling and reproducibility while guiding design choices based on your chart requirements.

Generated charts lack professional styling when default settings are applied. This skill resolves that by applying consistent style templates and design choices to matplotlib, seaborn, and Plotly outputs, ensuring publication-quality aesthetics for presentations.