Pandas Performance
Analyzes Pandas code to identify performance bottlenecks and suggest optimizations for faster data processing.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add pandas-performance npx -- -y @trustedskills/pandas-performance
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"pandas-performance": {
"command": "npx",
"args": [
"-y",
"@trustedskills/pandas-performance"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
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.
Tags
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