# EntilZha/PyFunctional

Python library for creating data pipelines with chain functional programming

Repository: https://github.com/EntilZha/PyFunctional
Canonical: https://ross.abutalabs.com/products/pyfunctional
Homepage: http://pyfunctional.pedro.ai
Language: Python
License: MIT
License Family: permissive
Topics: python, datascience, pipeline, data, functional-programming
Last push: 2025-03-13T23:26:42+00:00

## Health v2 (maintenance only)
Score: 28/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 11, release rhythm 8, longevity 100
- inputs: {"age_days": 4227, "days_push": 538, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2488, forks 134 (observed 2026-08-28T04:06:55.664628+00:00)

## What it is
PyFunctional is a Python library for creating data pipelines using chained functional operators like map, filter, and reduce. Its API is inspired by Scala collections, Apache Spark RDDs, and Microsoft LINQ, and it can read and write many file formats including csv, , sqlite, and compressed archives.

## Use cases
- chain map filter and reduce over collections in python
- build data pipelines with functional programming
- read and transform csv  or sqlite data
- parallelize embarrassingly parallel map operations
- lazy evaluation of large data transformations
- linq-style collection manipulation in python

## When to choose
- you want Spark-like or LINQ-style chained transformations in pure Python
- you need to read/write multiple file formats in a single pipeline
- you want lazy evaluation and easy parallel map without heavy dependencies

## When to avoid
- you need distributed processing across a cluster (use Spark/Dask)
- you prefer pandas for tabular analytics
- your project avoids third-party dependencies for simple loops

## Facets
- artifact type: library
- maturity: stable
- function: etl, data-science, developer-tools
- domain: data-science, developer-tools
- platform: python, cross-platform
- tags: functional-programming, data-pipeline, linq, lazy-evaluation, collections, data-engineering

## Member repositories
- EntilZha/PyFunctional (main) score 28

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:55.664628+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 2026-08-30T02:27:51.241159+00:00, confidence not recorded.
  - readme: https://github.com/EntilZha/PyFunctional (fetched 2026-08-28T04:06:55.664628+00:00, sha 2a9299f9f115)
  - homepage: http://pyfunctional.pedro.ai (fetched 2026-08-29T10:10:38.144754+00:00, sha de4f90883875)
  - registry_pypi: https://pypi.org/pypi/pyfunctional/json (fetched 2026-08-29T10:10:38.150602+00:00, sha 0496006aad07)
- Data as of 2026-08-30T08:39:29.467469+00:00.