flupy
Fluent data processing in Python - a chainable stream processing library for expressive data manipulation using method chaining
Decision gist · record as of 2026-08-14
Yes. Flupy is a low-friction, well-maintained library with zero dependencies, permissive licensing, and no known vulnerabilities. Install it if you want readable, chainable lazy evaluation for iterables without the overhead of larger frameworks. It's particularly useful for memory-constrained scenarios or when you prefer functional-style pipelines over imperative loops.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Installation is straightforward with no runtime dependencies.
- The package is actively maintained with a recent release (27 days ago) and has been stable since its initial 2018 release, supporting Python 3.10 through 3.13.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it safe to adopt in most projects without licensing concerns.
last release 2026-07-18 (27 days) · last repo commit 2026-07-26 · 195 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,835,286 downloads/mo, #3,503 on PyPI
Alternatives
Verify before relying
from flupy import flu
from itertools import count
pipeline = (
flu(count())
.map(lambda x: x**2)
.filter(lambda x: x % 517 == 0)
.take(3)
)
for item in pipeline:
print(item)- Whether the CLI tool is included in the standard pip installation or requires additional setup.
- Performance characteristics on very large datasets compared to alternatives like itertools or pandas.
- Community adoption and real-world usage patterns beyond the documented examples.
What it is and what it does
Flupy is a lightweight, zero-dependency Python library that implements a fluent interface for working with iterables. It lets you chain operations like map, filter, chunk, and take in a readable, method-chaining style while maintaining lazy evaluation—all transformations are generators that process data on-demand rather than loading everything into memory at once.
The library is designed as a pure-Python alternative to Apache Spark for scenarios where you need expressive, memory-efficient data pipelines without external dependencies. It supports both library usage (importing `flu` and chaining methods) and command-line usage through a `flu` shell command that applies the same lazy pipeline syntax to file or stdin input.
Use it for
- Process infinite or very large sequences (like itertools.count) without exhausting memory by chaining transformations.
- Build readable, chainable data transformation pipelines in place of nested loops or complex list comprehensions.
- Filter, map, and chunk streaming data in a shell script using the flupy CLI tool.
- Prototype data processing logic that could later scale to Spark without rewriting the pipeline structure.
- Combine multiple lazy operations (e.g., map → filter → chunk → take) while keeping intermediate results as generators.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Flupy is a low-friction, well-maintained library with zero dependencies, permissive licensing, and no known vulnerabilities. Install it if you want readable, chainable lazy evaluation for iterables without the overhead of larger frameworks. It's particularly useful for memory-constrained scenarios or when you prefer functional-style pipelines over imperative loops.
Install
flupy on PyPI
Before you install
Installation is straightforward with no runtime dependencies. The package is actively maintained with a recent release (27 days ago) and has been stable since its initial 2018 release, supporting Python 3.10 through 3.13.
Requires Python 3.10 or later.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it safe to adopt in most projects without licensing concerns.
Quickstart
from flupy import flu
from itertools import count
pipeline = (
flu(count())
.map(lambda x: x**2)
.filter(lambda x: x % 517 == 0)
.take(3)
)
for item in pipeline:
print(item)Verify before relying
- Whether the CLI tool is included in the standard pip installation or requires additional setup.
- Performance characteristics on very large datasets compared to alternatives like itertools or pandas.
- Community adoption and real-world usage patterns beyond the documented examples.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 27 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,835,286 / month, #3,503 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/StableNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: flupy-1.2.4-py3-none-any.whl
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