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name: 'pandas-best-practices' description: 'Standards for efficient, readable, and performant data manipulation using Python''s Pandas library.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command - write_file

Pandas Best Practices

This skill provides guidelines for working with tabular data in Python. It focuses on vectorization, memory management, and method chaining to write "Modern Pandas" code.

When to Use This Skill

• Data Cleaning: Preprocessing clinical or genomic datasets.
• Analysis: Performing aggregations, merges, or statistical summaries.
• Performance: Optimizing slow-running scripts that process large CSVs/DataFrames.

Core Capabilities

1. Vectorization: Replacing for loops with vectorized array operations.
2. Method Chaining: Writing readable, fluent data transformation pipelines.
3. Memory Optimization: Using appropriate dtypes (Categoricals, Nullable Ints) to reduce RAM usage.
4. Modern Indexing: Using .loc and .iloc correctly; avoiding SettingWithCopyWarning.

Workflow

1. Inspect Data: Check df.info() and df.head().
2. Define Pipeline: Plan transformations (filter -> group -> aggregate).
3. Implement Chain: Write the logic as a chain of methods.
4. Optimize: Check for loops or apply calls that can be vectorized.

Example Usage

User: "Calculate the mean age by patient group, but exclude patients with missing IDs."

Agent Action:

1. Reads references/rules.md.
2. Generates:

   result = (
       df
       .dropna(subset=['patient_id'])
       .groupby('patient_group')['age']
       .mean()
       .reset_index()
   )
   

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