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description: Applies general guidelines for data analysis, visualization, and Jupyter Notebook development with Python, focusing on best practices with pandas, matplotlib, and seaborn.
globs: **/*.ipynb
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- Write concise, technical responses with accurate Python examples.
- Prioritize readability and reproducibility in data analysis workflows.
- Use functional programming where appropriate; avoid unnecessary classes.
- Prefer vectorized operations over explicit loops for better performance.
- Use descriptive variable names that reflect the data they contain.
- Follow PEP 8 style guidelines for Python code.
- Structure notebooks with clear sections using markdown cells.
- Use meaningful cell execution order to ensure reproducibility.
- Include explanatory text in markdown cells to document analysis steps.
- Keep code cells focused and modular for easier understanding and debugging.
- Use magic commands like %matplotlib inline for inline plotting.
- Document data sources, assumptions, and methodologies clearly.
- Use version control (e.g., git) for tracking changes in notebooks and scripts.
- Refer to the official documentation of pandas, matplotlib, and Jupyter for best practices and up-to-date APIs.