4py

4py is an open-source Python toolkit designed to streamline the development of data-processing pipelines and modular applications. It emphasizes a four-stage processing model, enabling developers to define input, transformation, aggregation, and output stages within a single, cohesive API. The project aims to be lightweight, portable, and easy to integrate with existing Python codebases.

Core features include a minimal runtime that handles task scheduling and error propagation, a collection of composable operators for data transformation, support for streaming data sources, and utilities for testing and debugging. The library is designed to be dependency-light and compatible with CPython and PyPy.

Implementation: 4py is primarily written in Python, with critical performance paths implemented in Cython and optional bindings to NumPy. It follows a functional, pipelined programming model and supports type hints. The API favors declarative expressions over imperative boilerplate, enabling clearer pipeline definitions. It provides a small standard library of operators and allows user-defined functions to be integrated as first-class pipeline components.

Usage and ecosystem: Users typically install via pip, import 4py, and build pipelines by chaining operators. Data can flow from various sources, including files, streams, or in-memory collections. The project includes basic documentation, examples, and a test suite. It has a growing community with contributions on a public repository and issue tracker.

Limitations and reception: As a lightweight toolkit, 4py may not match the performance of optimized, lower-level frameworks for heavy numerical workloads. It prioritizes readability and rapid development over raw speed. The project is suitable for prototyping and smaller-scale data processing, with larger projects often combining it with other libraries.