Pandas Pro

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.

12,146 stars 1,162 forksData & Analytics

Install

skills CLI (Claude Code, Cursor, Codex and more)
npx skills add Jeffallan/claude-skills --skill pandas-pro
Manual install
git clone --depth 1 https://github.com/Jeffallan/claude-skills.git /tmp/claude-skills
mkdir -p ~/.claude/skills
cp -r /tmp/claude-skills/skills/pandas-pro ~/.claude/skills/pandas-pro

Personal skills live in ~/.claude/skills. Use .claude/skills in a project folder to share a skill with your team.

From the SKILL.md

# 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 |
Read the full skill on GitHub

Details

Skill name
pandas-pro
Repository
Jeffallan/claude-skills
Path
skills/pandas-pro/SKILL.md
License
MIT
Repo updated
30 October 2026
Listed in
BehiSecc/awesome-claude-skills, hesreallyhim/awesome-claude-code, karanb192/awesome-claude-skills

FAQ

What does the Pandas Pro skill do?

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.

How do I install the Pandas Pro skill?

skills CLI (Claude Code, Cursor, Codex and more): npx skills add Jeffallan/claude-skills --skill pandas-pro. Skills go in ~/.claude/skills (personal) or .claude/skills (project), and Claude loads them automatically when a task matches the description.

Is Pandas Pro popular?

It lives in Jeffallan/claude-skills, which has 12,146 GitHub stars and 1,162 forks, and is included in 3 curated skill lists.

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