---
id: pydash
version: "8.0.6"
license: MIT
license_treatment: permissive
maintenance: active
---

# pydash — The kitchen sink of Python utility libraries for doing "stuff" in a functional way. Based on the Lo-Dash Javascript library.

License: permissive · Maintenance: active · Downloads: 10.5M/mo

## 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 above — verify before relying.

Pydash provides a collection of functional utility functions for common programming tasks, ported from the Lo-Dash JavaScript library to Python.

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

pip install pydash
uv add pydash
poetry add pydash

## Installing pydash

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.

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)

Requires Python 3.9 or later.

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_current
- Install friction: low
- Maintenance: active
- Downloads: 10.5M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

functional utility library, lodash python equivalent, utility functions, functional programming helpers, python utility toolkit, data transformation utilities, functional composition, functional-programming, utility-library, lodash-port

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