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Pandas Best Practices

Checked
mindrally·vlatest·data·Apache-2.0

Ensures your pandas code adheres to best practices for efficiency, readability, and maintainability.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add mindrally/skills --skill pandas-best-practices -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

mindrally

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned mindrally/skills at 7682ca7771.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
7682ca7771
Manifest
pandas-best-practices/SKILL.md
Scanned
1 file, 125 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill equips AI agents with optimized workflows for handling, cleaning, and analyzing data using the Python pandas library. It ensures efficient manipulation of structured datasets through best-practice coding patterns and performance tuning techniques.

When to use it

  • Processing large-scale tabular data that requires memory-efficient operations.
  • Automating complex data cleaning pipelines involving missing values or type casting.
  • Generating reproducible analysis scripts for financial or scientific reporting.
  • Integrating pandas workflows into broader AI agent tasks requiring structured insights.

Key capabilities

  • Efficient data loading and saving with optimized I/O methods.
  • Robust handling of missing, duplicate, and inconsistent data entries.
  • Advanced grouping, aggregation, and merging of multiple datasets.
  • Vectorized operations to maximize computational speed over loops.
  • Dynamic column selection and filtering based on complex conditions.

Example prompts

  • "Create a pandas script to load a CSV file, remove rows with missing timestamps, and group sales data by region."
  • "Optimize this slow DataFrame transformation loop using vectorized operations for better performance."
  • "Write a function that merges three datasets on a common ID column and handles conflicting values automatically."

Tips & gotchas

Ensure your AI agent has the pandas library installed in its execution environment before attempting to run data manipulation tasks. For very large datasets, explicitly request chunked reading or memory-mapped files to prevent system crashes.