pirrtools Documentation#

A powerful collection of tools for data analysis, featuring enhanced pandas functionality, Rich table styling, and intelligent file system navigation.

Quick Start#

Install pirrtools via pip:

pip install pirrtools

Basic usage:

import pandas as pd
import pirrtools
from rich.console import Console

# Create a DataFrame
df = pd.DataFrame({
    'Sales': [150, 230, 180, 340],
    'Profit': [25, 45, 32, 68]
})

# Convert to styled Rich table
console = Console()
table = df.pirr.to_rich(bg="viridis", title="Sales Report")
console.print(table)

Key Features#

🎨 Rich Table Styling: Convert pandas DataFrames and Series to beautifully styled Rich tables with gradients, custom headers, and professional theming.
💾 Pandas Caching: Efficient caching system for pandas objects using feather format, perfect for large datasets and complex preprocessing.
📁 AttrPath Navigation: Navigate file systems using dot notation with intelligent file viewing and organized directory structures.
🔧 Development Utilities: Module reloading, path management, and development workflow enhancements.

Documentation Contents#

API Reference

Development

Interactive Tutorial#

Get started quickly with the interactive tutorial:

cd examples/
python tutor.py

This hands-on tutorial walks through all to_rich features with live examples and step-by-step guidance.

Examples Directory#

The examples/ directory contains comprehensive demonstrations:

tutor.py - Interactive tutorial (recommended starting point)
to_rich_examples.py - All styling features demonstrated
example_to_rich_styling.py - Focus on gradient styling
gradient_example.py - Simple gradient examples
pandas_rich_styling_research.py - Technical implementation details

Core Modules#

pirrtools.pandas: Enhanced pandas functionality with caching and Rich table conversion via the .pirr accessor.
pirrtools.structures.attrpath: File system navigation using attribute access with intelligent file viewing.
pirrtools.structures.attrdict: Dictionary with attribute access capabilities.
pirrtools utilities: Path management, module reloading, and development helpers.

Installation#

Requirements#

Python 3.8+
pandas
numpy
rich
feather-format
ipython
pygments
jinja2
matplotlib

Development Installation#

For development work:

git clone https://github.com/pirsquared/pirrtools.git
cd pirrtools
pip install -e .[dev]

This installs development dependencies including pytest, black, pylint, and documentation tools.

Docker Development#

Use the provided Docker environment:

docker-compose up -d
docker-compose exec pirrtools-dev bash

Or with VS Code Dev Containers - open the project and select “Reopen in Container”.

Contributing#

Contributions are welcome! Please see the contributing guidelines for development setup, testing procedures, and code style requirements.

License#

This project is licensed under the MIT License. See the LICENSE file for details.

Support#

GitHub Issues: pirsquared/pirrtools#issues
PyPI Package: https://pypi.org/project/pirrtools/
Documentation: https://pirrtools.readthedocs.io/ (coming soon)

pirrtools Documentation

Contents