# dgilland/pydash

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

Repository: https://github.com/dgilland/pydash
Canonical: https://ross.abutalabs.com/products/pydash
Homepage: http://pydash.readthedocs.io
Language: Python
License: MIT
License Family: permissive
Topics: lodash, utility, functional, python, python3
Last push: 2026-08-22T01:31:57+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 35, longevity 100
- inputs: {"age_days": 4859, "days_push": 12, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1448, forks 101 (observed 2026-08-28T04:04:45.562360+00:00)

## What it is
pydash is a Python utility library providing a large set of functional programming helpers, modeled on the Lo-Dash JavaScript library. It offers functions for manipulating lists, dictionaries, strings, and objects, with support for chaining.

## Use cases
- manipulate lists and dicts with functional helpers in Python
- use lodash-style utilities like chunk, flatten, and pluck in Python
- chain collection transformations in a functional style
- clean and transform nested dictionaries
- replace hand-rolled utility functions with a standard toolkit

## When to choose
- you want a lodash/underscore-like utility belt in Python
- you frequently transform lists, dicts, and strings functionally
- you prefer a batteries-included utility library over writing helpers yourself

## When to avoid
- you need memory-efficient processing of large datasets (the author recommends fnc instead)
- you prefer native Python idioms like comprehensions and itertools
- you need a minimal dependency footprint

## Facets
- artifact type: library
- maturity: stable
- function: developer-tools
- domain: developer-tools
- platform: python, cross-platform
- tags: lodash, functional-programming, utility-library, collections, chaining, algorithms

## Member repositories
- dgilland/pydash (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:45.562360+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 2026-08-30T04:36:03.305804+00:00, confidence not recorded.
  - readme: https://github.com/dgilland/pydash (fetched 2026-08-28T04:04:45.562360+00:00, sha 5a3f6aa696dd)
  - registry_pypi: https://pypi.org/pypi/pydash/json (fetched 2026-08-29T11:46:17.382697+00:00, sha 3cb0a4afbfdb)
- Data as of 2026-08-30T08:39:29.467469+00:00.