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