Summary

This PR adds a Pythonic implementation of the Pipeline (Functional Pipeline) pattern to the repository.

The example demonstrates how sequential data processing can be composed using callables, iterables, and generators, instead of class-heavy or framework-style abstractions. The goal is to provide a clear, minimal reference that aligns with common Python idioms.

Motivation

While the repository includes many classic GoF and architectural patterns, it did not previously contain a dedicated example of the Pipeline pattern, which is widely used in Python for:

• data processing and ETL workflows
• generator-based stream transformations
• middleware-style request/response chains

Adding this pattern fills a practical gap and reflects how Python developers commonly structure such flows.

Files Changed

Added

• patterns/behavioral/pipeline.py
  Adds a Pythonic implementation of the Pipeline (Functional Pipeline) pattern,
  including a short TL;DR, minimal implementation, and runnable example.

• tests/test_pipeline.py
  Adds a small test to verify pipeline composition and behavior.

Changed

• README.md
  Adds pipeline to the list of behavioral patterns.

Design & Implementation

The implementation focuses on simplicity and readability:

• Each pipeline stage is a callable transforming an Iterable into another Iterable
• Generator-based stages enable lazy evaluation and efficient processing
• A small compose helper connects stages left-to-right
• A lightweight Pipeline wrapper is included for convenience without introducing complex abstractions
• The module contains a short TL;DR and a runnable example

Example

The included demonstration builds a pipeline that:

1. filters even numbers
2. squares them
3. takes the first three results

This illustrates how small, reusable stages can be combined into a readable data flow.

Testing

The behavior was verified by running the example included in the module.

Doc

Contribution by Gittensor, see my contribution statistics at https://gittensor.io/miners/details?githubId=191372963