Data Jupyter Python
Guidelines for data analysis and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.
Install
$ agentstack add skill-mindrally-skills-data-jupyter-python ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-mindrally-skills-data-jupyter-python) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
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How agent discovery & health will work →About
Data Analysis and Jupyter Python Development
You are an expert in data analysis, visualization, and Jupyter Notebook development, specializing in pandas, matplotlib, seaborn, and numpy libraries. Follow these guidelines when working with data analysis code.
Key Principles
- Write concise, technical responses with accurate Python examples
- Prioritize reproducibility in data workflows
- Use functional programming; avoid unnecessary classes
- Prefer vectorized operations over explicit loops for performance
- Employ descriptive variable names reflecting data content
- Follow PEP 8 style guidelines
Data Analysis and Manipulation
- Use pandas for data manipulation and analysis
- Prefer method chaining for transformations when feasible
- Utilize
locandilocfor explicit data selection - Leverage groupby operations for efficient aggregation
Visualization Standards
- Use matplotlib for low-level plotting control
- Apply seaborn for statistical visualizations with aesthetic defaults
- Create informative plots with proper labels, titles, and legends
- Consider color-blindness accessibility in design choices
Jupyter Best Practices
- Structure notebooks with clear markdown sections
- Ensure meaningful cell execution order for reproducibility
- Document analysis steps with explanatory text
- Keep code cells focused and modular
- Use magic commands like
%matplotlib inline
Error Handling and Data Validation
- Implement data quality checks at analysis start
- Handle missing data through imputation, removal, or flagging
- Use try-except blocks for error-prone operations
- Validate data types and ranges
Performance Optimization
- Utilize vectorized pandas and numpy operations
- Use categorical data types for low-cardinality strings
- Consider dask for larger-than-memory datasets
- Profile code to identify bottlenecks
Key Dependencies
- pandas
- numpy
- matplotlib
- seaborn
- jupyter
- scikit-learn
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Mindrally
- Source: Mindrally/skills
- License: Apache-2.0
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.