pyochain ⛓️¶

Functional-style method chaining for Python data structures.

pyochain brings a fluent, declarative API inspired by Rust's Iterator and DataFrame libraries like Polars to your everyday Python iterables and dictionaries.

Manipulate data through composable chains of operations, enhancing readability and reducing boilerplate.

Installation¶

uv add pyochain

Overview¶

Philosophy¶

Declarative over Imperative: Replace explicit for and while loops with sequences of high-level operations (map, filter, group, join...).
Fluent Chaining: Each method transforms the data and returns a new wrapper instance, allowing for seamless chaining.
Lazy and Eager: Iter operates lazily for efficiency on large or infinite sequences, while Seq represents materialized collections for eager operations.
100% Type-safe: Extensive use of generics and overloads ensures type safety and improves developer experience.
Documentation-first: Each method is thoroughly documented with clear explanations, and usage examples.
Functional paradigm: Design encourages building complex data transformations by composing simple, reusable functions.

Core Components¶

`Iter[T]`¶

Wraps a Python Iterator or Generator. All operations are lazy, consuming the underlying iterator on demand.

See full documentation →

`Dict[K, V]`¶

Wraps a Python dict with chainable methods specific to dictionaries.

See full documentation →

Quick Examples¶

import pyochain as pc

# Chain operations on iterables
result = (
    pc.Iter.from_(range(10))
    .filter(lambda x: x % 2 == 0)
    .map(lambda x: x ** 2)
    .collect()
    .unwrap()
)
# [0, 4, 16, 36, 64]

Contributing¶

See CONTRIBUTING.md

License¶

MIT License - see LICENSE.md