pyfunctional
Package for creating data pipelines with chain functional programming
Decision gist · record as of 2026-08-14
Yes, if you prefer functional composition over imperative loops and want a lightweight alternative for file-based data pipelines. The low install friction, permissive MIT license, and stable API make it a reasonable choice for small to medium data tasks. However, the aging maintenance status (last release 884 days ago) means you should verify that the feature set meets your needs before adopting it for new projects.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later (supports 3.8, 3.9, 3.10, 3.11); CPython and PyPy implementations supported.
- Low friction installation with only two runtime dependencies (dill and tabulate).
- Last release was 884 days ago; the repository remains active with recent commits and 2489 stars, though maintenance appears aging rather than actively developed.
License · maintenance · safety
MIT (permissive) — MIT license is permissive, allowing commercial and private use with minimal restrictions—suitable for most projects that can include attribution.
last release 2024-03-13 (884 days) · last repo commit 2025-03-13 · 2,489 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 601,443 downloads/mo, #5,818 on PyPI
Alternatives
Verify before relying
pip install pyfunctional
from functional import seq
result = seq(1, 2, 3, 4).map(lambda x: x * 2).filter(lambda x: x > 4).reduce(lambda x, y: x + y)- Whether parallelization is production-ready and what performance gains are typical for embarrassingly parallel workloads.
- Current test coverage claim of 100% and whether it remains accurate in version 1.5.0.
- Compatibility and performance with large datasets or streaming scenarios beyond the documented examples.
What it is and what it does
PyFunctional is a Python library that brings functional programming patterns to data transformation tasks. It lets you build data pipelines by chaining operations like map, filter, and reduce on sequences, with lazy evaluation so transformations only run when you call an action method like reduce or to_list.
The library handles I/O for common formats: it can read and write CSV, JSON, JSONL, SQLite, and compressed files (gzip, bz2, lzma/xz). It also supports grouping, joining, and aggregating data, and offers optional parallelization for map-like operations. Runtime dependencies are dill and tabulate.
Use it for
- Process CSV or JSON files with filtering and aggregation (e.g., sum expenses by category, find outliers).
- Join data from multiple sources (e.g., match user records with transaction logs) and extract insights.
- Build word-count or frequency-analysis pipelines from text or log files using map and reduce.
- Parallelize embarrassingly parallel transformations across large datasets without manual multiprocessing code.
- Read from and write to SQLite databases with chainable transformations in between.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you prefer functional composition over imperative loops and want a lightweight alternative for file-based data pipelines.
The low install friction, permissive MIT license, and stable API make it a reasonable choice for small to medium data tasks. However, the aging maintenance status (last release 884 days ago) means you should verify that the feature set meets your needs before adopting it for new projects.
Install
pyfunctional on PyPI
Before you install
Low friction installation with only two runtime dependencies (dill and tabulate). Last release was 884 days ago; the repository remains active with recent commits and 2489 stars, though maintenance appears aging rather than actively developed.
Requires Python 3.8 or later (supports 3.8, 3.9, 3.10, 3.11); CPython and PyPy implementations supported.
License in practice
MIT license is permissive, allowing commercial and private use with minimal restrictions—suitable for most projects that can include attribution.
Quickstart
pip install pyfunctional
from functional import seq
result = seq(1, 2, 3, 4).map(lambda x: x * 2).filter(lambda x: x > 4).reduce(lambda x, y: x + y)Verify before relying
- Whether parallelization is production-ready and what performance gains are typical for embarrassingly parallel workloads.
- Current test coverage claim of 100% and whether it remains accurate in version 1.5.0.
- Compatibility and performance with large datasets or streaming scenarios beyond the documented examples.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8.0,<4.0.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesdilltabulate |
| Maintenance | Aging 884 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 601,443 / month, #5,818 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTopic :: Software Development :: Libraries :: Python Modules |
Evidence: pyfunctional-1.5.0-py3-none-any.whl
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