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TextDescriptives

On the record

Demo
The documentation links to an online demo for trying the package.hlasse.github.io · read 4 Oct 2026
Install
The documentation says to install the package with `pip install textdescriptives`.hlasse.github.io · read 4 Oct 2026
Integrations
The documentation shows integration with spaCy pipelines and a scikit-learn pipeline featurizer.hlasse.github.io · read 4 Oct 2026
License
The project’s GitHub page identifies its license as Apache-2.0.github.com · read 4 Oct 2026
Metrics
Its components cover descriptive statistics, readability, dependency distance, part-of-speech proportions, coherence, text quality, and information theory.hlasse.github.io · read 4 Oct 2026
Notable limitation
The quality component is designed for usability and spaCy integration rather than speed, and the docs suggest another implementation for large-corpus filtering.hlasse.github.io · read 4 Oct 2026
Output
The extractors can return metrics in a Pandas DataFrame or a list of dictionaries, and can process raw text or spaCy documents.hlasse.github.io · read 4 Oct 2026
Purpose
TextDescriptives is a Python library for calculating statistics and other metrics from text using spaCy v.3 pipeline components and extensions.hlasse.github.io · read 4 Oct 2026
Quality filtering
Quality metrics include stop-word and out-of-vocabulary ratios, text repetition, bullet points, ellipses, and configurable thresholds for checks.hlasse.github.io · read 4 Oct 2026
Readability
Readability measures include Flesch, SMOG, Gunning-Fog, Automated Readability Index, Coleman-Liau, LIX, and RIX.hlasse.github.io · read 4 Oct 2026
Support
The documentation directs bug reports and feature requests to GitHub Issues and usage questions to GitHub Discussions.hlasse.github.io · read 4 Oct 2026
Text statistics
Descriptive statistics include token and character counts, unique-token proportions, sentence lengths, syllable counts, and token lengths.hlasse.github.io · read 4 Oct 2026

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