pandas

Pandas data manipulation with DataFrames. Use for data analysis.

7 stars

byG1Joshi

View on GitHub Installation ↓

Best use case

pandas is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Pandas data manipulation with DataFrames. Use for data analysis.

Teams using pandas should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/pandas/SKILL.md --create-dirs "https://raw.githubusercontent.com/G1Joshi/Agent-Skills/main/skills/ai-ml/pandas/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/pandas/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How pandas Compares

Feature / Agent	pandas	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Pandas data manipulation with DataFrames. Use for data analysis.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Pandas

Pandas is the Excel of Python. v3.0 (2025/2026) enforces **Copy-on-Write (CoW)**, finally fixing the `SettingWithCopyWarning` confusion.

## When to Use

- **Data Cleaning**: Loading CSV/Excel/SQL and cleaning it.
- **Time Series**: Unmatched datetime indexing capabilities.
- **Small/Medium Data**: Features that fit in RAM.

## Core Concepts

### DataFrame / Series

2D tables and 1D arrays.

### Copy-on-Write (CoW)

Views are always views, copies are always copies. Modifying a view triggers a copy _only if necessary_.

### PyArrow Backend

Using Arrow memory format for speed and string handling (`dtype="string[pyarrow]"`).

## Best Practices (2025)

**Do**:

- **Use PyArrow Strings**: `pd.options.future.infer_string = True` (Default in 3.0).
- **Use `.query()`**: For cleaner filtering syntax.
- **Migrate to CoW**: Ensure your code doesn't rely on side-effects of views.

**Don't**:

- **Don't iterate rows**: Use vectorization (`df['a'] + df['b']`).

## References

- [Pandas Documentation](https://pandas.pydata.org/)