# amueller/word_cloud

A little word cloud generator in Python

Repository: https://github.com/amueller/word_cloud
Canonical: https://ross.abutalabs.com/products/word_cloud
Homepage: https://amueller.github.io/word_cloud
Language: Python
License: MIT
License Family: permissive
Last push: 2026-01-22T01:57:37+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 63, release rhythm 34, longevity 100
- inputs: {"age_days": 5050, "days_push": 224, "days_rel": 224, "gap_med": 218.0, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 10536, forks 2317 (observed 2026-08-28T04:10:42.261518+00:00)

## What it is
A Python library for generating word clouds from text, with support for arbitrary masks, custom colors, and space-filling layout. It also ships a wordcloud_cli command-line tool for generating clouds directly from text files.

## Use cases
- generate a word cloud from a text file in python
- create a wordcloud image from the command line
- make a word cloud shaped like a custom mask
- visualize word frequency in a document
- generate word clouds for Arabic or other non-Latin text
- turn a pdf into a word cloud

## When to choose
- you need word cloud images in a Python project or notebook
- you want masked or color-customized word clouds
- you want a simple CLI to pipe text into a word cloud

## When to avoid
- you need interactive or web-based visualizations rather than static images
- you need general text analytics beyond word frequency visualization

## Facets
- artifact type: library
- maturity: stable
- function: image-processing, nlp, data-visualization, cli
- domain: data-visualization, developer-tools
- platform: python, cli, cross-platform
- tags: word-cloud, text-visualization, matplotlib, pillow, numpy, natural-language-processing

## Member repositories
- amueller/word_cloud (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:42.261518+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 2026-08-29T17:18:33.820297+00:00, confidence not recorded.
  - readme: https://github.com/amueller/word_cloud (fetched 2026-08-28T04:10:42.261518+00:00, sha 51be76a53931)
  - homepage: https://amueller.github.io/word_cloud (fetched 2026-08-29T08:17:52.920639+00:00, sha 41b32119a416)
- Data as of 2026-08-30T08:39:29.467469+00:00.