# kachayev/fn.py

Functional programming in Python: implementation of missing features to enjoy FP

Repository: https://github.com/kachayev/fn.py
Canonical: https://ross.abutalabs.com/products/fnpy
Language: Python
License: NOASSERTION
License Family: other
Last push: 2022-08-30T14:08:21+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4980, "days_push": 1464, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3368, forks 203 (observed 2026-08-28T04:07:58.343712+00:00)

## What it is
Fn.py is a Python library that adds functional programming utilities missing from the standard library, such as Scala-style lambda syntax via the underscore placeholder, persistent data structures, currying, and composition helpers. It lets developers apply functional style even in mostly imperative Python programs.

## Use cases
- write concise lambda expressions like _ * 2 in Python
- use persistent immutable data structures like skew heaps
- compose and curry functions functionally
- apply functional programming style in imperative Python code
- replace verbose lambda boilerplate with placeholder syntax

## When to avoid
- you need actively developed persistent data structures (marked under active development)
- you work in an interactive shell where _ conflicts with last output
- your team prefers plain Python idioms over FP abstractions

## Facets
- artifact type: library
- maturity: maintenance
- function: developer-tools
- domain: programming-languages, developer-tools
- platform: python, cross-platform
- tags: functional-programming, lambdas, immutable-data-structures, monads, currying

## Member repositories
- kachayev/fn.py (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:58.343712+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:39:58.932178+00:00, confidence not recorded.
  - readme: https://github.com/kachayev/fn.py (fetched 2026-08-28T04:07:58.343712+00:00, sha e87c8c1ce544)
- Data as of 2026-08-30T08:39:29.467469+00:00.