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Pandas Performance

Checked
tondevrel·vlatest·data·MIT

Analyzes Pandas code to identify performance bottlenecks and suggest optimizations for faster data processing.

Repository

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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 tondevrel/scientific-agent-skills --skill pandas-performance -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

tondevrel

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Live repository

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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.

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    Makes no network calls.

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    Scanned tondevrel/scientific-agent-skills at 6e89d9e8c8.

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Commit
6e89d9e8c8
Manifest
skills/pandas-performance/SKILL.md
Scanned
1 file, 197 lines
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About this skill

What it does

This skill optimizes pandas DataFrame operations for improved performance. It analyzes code and suggests vectorized alternatives to slow loops, identifies inefficient memory usage, and helps reduce overall execution time when working with tabular data. The goal is to make your pandas workflows significantly faster and more resource-efficient.

When to use it

  • Slow Data Processing: When you have a pandas DataFrame operation taking an unexpectedly long time to complete.
  • Large Datasets: When dealing with datasets that are too large to comfortably fit in memory or whose processing is becoming a bottleneck.
  • Iterative Operations: If your code relies heavily on apply, iterrows, or other row-wise operations within a DataFrame.
  • Resource Constraints: When running pandas code on systems with limited CPU or memory resources.

Key capabilities

  • Vectorization suggestions
  • Memory usage optimization
  • Performance bottleneck identification
  • Code analysis for inefficient loops

Example prompts

  • "Analyze this pandas code and suggest performance improvements: [paste code snippet]"
  • "How can I vectorize this loop in my DataFrame processing?"
  • "Identify memory bottlenecks in this pandas script."

Tips & gotchas

This skill is most effective when provided with specific pandas code snippets. It assumes a basic understanding of pandas DataFrames and operations.