pydash
The kitchen sink of Python utility libraries for doing "stuff" in a functional way. Based on the Lo-Dash Javascript library.
Decision gist · record as of 2026-08-14
Yes. Pydash is actively maintained, carries no known vulnerabilities, has a permissive MIT license, and imposes minimal install friction. It's well-established and in the top 5000 PyPI packages by download volume. Install it if you prefer functional programming patterns and want a comprehensive utility library; skip it if you need memory efficiency for large datasets or prefer Python's built-in functional tools.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later.
- Low install friction with a single lightweight dependency (typing-extensions).
- The package is actively maintained with a recent commit on 2026-07-26, classified as Production/Stable, and supports current Python versions.
License · maintenance · safety
MIT (permissive) — MIT license permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
last release 2026-01-17 (209 days) · last repo commit 2026-07-26 · 1,450 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 10,469,398 downloads/mo, #1,451 on PyPI
Alternatives
Verify before relying
pip install pydash
from pydash import map_, filter_
data = [1, 3, 5]
result = map_(data, lambda x: x * 10)- Whether the package's memory efficiency or performance characteristics are documented for large datasets.
- Specific functional programming paradigms or patterns the library emphasizes beyond general utility functions.
- Which specific utility functions are included and their exact behavior compared to the original Lo-Dash library.
What it is and what it does
Pydash is a Python port of the Lo-Dash JavaScript utility library, providing a collection of functions for functional-style programming. It offers utilities for common tasks like data transformation, filtering, mapping, and composition in a functional paradigm. The package is designed as a general-purpose toolkit—"the kitchen sink"—for developers who prefer functional approaches to procedural code.
The library depends only on typing-extensions and requires Python 3.9 or later. It's actively maintained (last commit 2026-07-26) and classified as Production/Stable. The authors note that alternative libraries may be better suited for memory-intensive work with large datasets, suggesting pydash is optimized for general utility rather than streaming or memory efficiency.
Use it for
- Apply functional utilities for data transformation and filtering across collections.
- Compose data processing pipelines with functional helpers for common operations.
- Port JavaScript code using Lo-Dash to Python while maintaining similar function names.
- Build utilities for functional-style data manipulation in Python projects.
- Simplify repeated utility operations across a codebase with a shared functional toolkit.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Pydash is actively maintained, carries no known vulnerabilities, has a permissive MIT license, and imposes minimal install friction. It's well-established and in the top 5000 PyPI packages by download volume. Install it if you prefer functional programming patterns and want a comprehensive utility library; skip it if you need memory efficiency for large datasets or prefer Python's built-in functional tools.
Install
pydash on PyPI
Before you install
Low install friction with a single lightweight dependency (typing-extensions). The package is actively maintained with a recent commit on 2026-07-26, classified as Production/Stable, and supports current Python versions.
Requires Python 3.9 or later.
License in practice
MIT license permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
Quickstart
pip install pydash
from pydash import map_, filter_
data = [1, 3, 5]
result = map_(data, lambda x: x * 10)Verify before relying
- Whether the package's memory efficiency or performance characteristics are documented for large datasets.
- Specific functional programming paradigms or patterns the library emphasizes beyond general utility functions.
- Which specific utility functions are included and their exact behavior compared to the original Lo-Dash library.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagetyping-extensions |
| Maintenance | Actively maintained 209 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 10,469,398 / month, #1,451 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 :: DevelopersOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9Topic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python ModulesTopic :: Utilities |
Evidence: pydash-8.0.6-py3-none-any.whl
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