AnswerDotAI/fastcore

What it solves

It addresses the limitations and "rough edges" of the Python standard library by introducing features inspired by other languages (like Ruby and Haskell) and NumPy. It aims to make Python more flexible and expressive for developers by providing missing utilities for data manipulation, class extension, and parallel processing.

How it works

fastcore extends Python's dynamic nature through several mechanisms:

• Enhanced Data Structures: It replaces the standard list with L, which supports advanced indexing and method chaining.
• Metaprogramming: It uses the @patch decorator to add methods to existing classes (including built-ins) without needing to subclassing.
• Functional Tools: It provides utilities for currying, binding, composition, and mapping to enable a functional programming style.
• Developer Utilities: It includes tools like store_attr() to automate attribute assignment in __init__ methods and @delegates to improve keyword argument handling and IDE support.

Who it’s for

Python developers who want to write more concise, readable, and functional code, as well as those looking for more powerful alternatives to standard Python collections and concurrency tools.

Highlights

• L Class: A drop-in replacement for list with superpowers like collection-based indexing and built-in .map() and .filter() methods.
• Monkey Patching: The @patch decorator allows for seamless extension of existing classes.
• Boilerplate Reduction: store_attr() simplifies class initialization.
• Parallelism: Enhanced ThreadPoolExecutor and ProcessPoolExecutor for easier concurrent execution.
• Extended Pathlib: Adds useful methods like ls() and read_json() to the standard Path class.
• CLI Tooling: fastcore.script quickly converts Python functions into command-line interfaces.