# Pandas Pro

Performs pandas DataFrame operations for data analysis, manipulation, and transformation.

- Repository: https://github.com/Jeffallan/claude-skills (12164 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Jeffallan/claude-skills --skill pandas-pro -g -a claude-code -y`
Codex: `npx skills add Jeffallan/claude-skills --skill pandas-pro -g -a codex -y`
Cursor: `npx skills add Jeffallan/claude-skills --skill pandas-pro -g -a cursor -y`
Gemini CLI: `npx skills add Jeffallan/claude-skills --skill pandas-pro -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Jeffallan/claude-skills`

## Description

Performs pandas DataFrame operations for data analysis, manipulation, and transformation. Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation tasks such as joining DataFrames on multiple keys, pivoting tables, resampling time series, handling NaN values with interpolation or forward-fill, groupby aggregations, type conversion, or performance optimization of large datasets.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Pandas Pro

Expert pandas developer specializing in efficient data manipulation, analysis, and transformation workflows with production-grade performance patterns.

## Core Workflow

1. **Assess data structure**, Examine dtypes, memory usage, missing values, data quality:
   ```python
   print(df.dtypes)
   print(df.memory_usage(deep=True).sum() / 1e6, "MB")
   print(df.isna().sum())
   print(df.describe(include="all"))
   ```
2. **Design transformation**, Plan vectorized operations, avoid loops, identify indexing strategy
3. **Implement efficiently**, Use vectorized methods, method chaining, proper indexing
4. **Validate results**, Check dtypes, shapes, null counts, and row counts:
   ```python
   assert result.shape[0] == expected_rows, f"Row count mismatch: {result.shape[0]}"
   assert result.isna().sum().sum() == 0, "Unexpected nulls after transform"
   assert set(result.columns) == expected_cols
   ```
5. **Optimize**, Profile memory, apply categorical types, use chunking if needed

## Reference Guide

Load detailed guidance based on context:

| Topic | Reference | Load When |
|-------|-----------|-----------|
| DataFrame Operations | `references/dataframe-operations.md` | Indexing, selection, filtering, sorting |
| Data Cleaning | `references/data-cleaning.md` | Missing values, duplicates, type conversion |
| Aggregation & GroupBy | `references/aggregation-groupby.md` | GroupBy, pivot, crosstab, aggregation |
| Merging & Joining | `references/merging-joining.md` | Merge, join, concat, combine strategies |
| Performance Optimization | `references/performance-optimization.md` | Memory usage, vectorization, chunking |

## Code Patterns

### Vectorized Operations (before/after)

```python
# ❌ AVOID: row-by-row iteration
for i, row in df.iterrows():
    df.at[i, 'tax'] = row['price'] * 0.2

# ✅ USE: vectorized assignment
df['tax'] = df['price'] * 0.2
```

### Safe Subsetting with `.copy()`

The rest: https://github.com/Jeffallan/claude-skills/blob/HEAD/skills/pandas-pro/SKILL.md