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Welcome to PipeFrame! 🔄

Pipe Your Data Naturally

PipeFrame is a modern, intuitive data manipulation library for Python that makes your data workflows read like natural language. Built on pandas' robust foundation with a clean, pipe-based syntax inspired by R's dplyr and tidyverse.

Quick

Links

• 📦 Installation - Get started in seconds
• 🚀 Quick Start - Your first pipeline in 5 minutes
• 📚 API Reference - Complete function documentation
• 💡 Examples - Real-world use cases
• 🤝 Contributing - Join the community
• ❓ FAQ - Common questions answered

Installation

pip install pipeframe

Example

from pipeframe import *

# Read like a story!
result = (df
    >> filter('age > 21')
    >> group_by('city')
    >> summarize(avg_income='mean(income)', count='count()')
    >> arrange('-avg_income')
)

💡 How to read >>: Read the >> operator as "pipe to" or "then".

Key Features

• 🔗 Intuitive Pipe Operator (>>) - Chain operations naturally
• 📊 dplyr-Style Verbs - filter(), select(), mutate(), and more
• 🐼 100% Pandas Compatible - Built on pandas, works with pandas
• 🎯 Readable Syntax - Code that reads like English
• 🚀 Production Ready - Tested, documented, type-hinted

Resources

• GitHub: https://github.com/Yasser03/pipeframe
• PyPI: https://pypi.org/project/pipeframe/
• Issues: https://github.com/Yasser03/pipeframe/issues
• Discussions: https://github.com/Yasser03/pipeframe/discussions

Community

Join our growing community of data scientists and engineers using PipeFrame!

• Share your pipelines
• Ask questions
• Contribute code
• Report bugs
• Suggest features

Start Building Better Pipelines Today! 🚀