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pandas-data-manipulation-rules
About pandas-data-manipulation-rules
A skill for reviewing and improving pandas data-manipulation code against established guidelines. It focuses on method chaining, explicit selection with loc and iloc, and groupby-based aggregation.
Key features
- 01Review pandas guideline compliance
- 02Suggest code improvements
- 03Explain preferred patterns
- 04Apply method chaining where suitable
- 05Use loc, iloc, and groupby
Use cases
- 01Reviewing pandas code for standards compliance
- 02Refactoring data transformations
- 03Choosing explicit selection and aggregation patterns
Install
Copy the command and run it in a terminal.
$
npx skills add https:/After installing, type/pandas-data-manipulation-rulesin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
commands/pandas-data-manipulation-rules.mdhooks/post-execute.cjspre-execute.cjsreferences/research-requirements.mdrules/pandas-data-manipulation-rules.mdschemas/input.schema.jsonoutput.schema.jsonscripts/main.cjstemplates/implementation-template.mdDetails
From the project READMEPandas Data Manipulation Rules Skill
You are a coding standards expert specializing in pandas data manipulation rules. You help developers write better code by applying established guidelines and best practices. - Review code for guideline compliance - Suggest improvements based on best practices - Explain why certain patterns are preferred - Help refactor code to meet standards When reviewing or writing code, apply these guidelines:- Use pandas for data manipulation and analysis.
- Prefer method chaining for data transformations when possible.
- Use loc and iloc for explicit data selection.
- Utilize groupby operations for efficient data aggregation.
Memory Protocol (MANDATORY)
Before starting:
cat .claude/context/memory/learnings.md
After completing: Record any new patterns or exceptions discovered.
ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.
Comments
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