TextDescriptives
TextDescriptives is a free Python library that calculates text statistics and other metrics through spaCy v.3 pipeline components and extensions.
What TextDescriptives is
TextDescriptives is a free Python library that calculates text statistics and other metrics through spaCy v.3 pipeline components and extensions. Its extractors cover descriptive statistics, readability, dependency distance, part-of-speech proportions, coherence, text quality, and information theory. Statistics include token and character counts, unique-token proportions, sentence lengths, syllable counts, and token lengths. Readability components provide Flesch, SMOG, Gunning-Fog, Automated Readability Index, Coleman-Liau, LIX, and RIX measures. Quality checks can examine stop-word and out-of-vocabulary ratios, repetition, bullet points, and ellipses with configurable thresholds. Extractors accept raw text or spaCy documents and return a Pandas DataFrame or list of dictionaries. Documentation shows spaCy pipeline and scikit-learn pipeline featurizer use, plus an online demo. Install it with `pip install textdescriptives`. The project is Apache-2.0 licensed and listed for API and Linux use with on-premise deployment. Some hyphenation-dependent readability results may be NaN for languages unsupported by Pyphen.
Good
- Free Apache-2.0 Python library
- Broad statistics and readability metrics
- Works with raw text or spaCy documents
- DataFrame or dictionary output
- Online demo available
Watch out
- Some readability results may return NaN
- Requires spaCy v.3 pipeline components
- Usage support is through GitHub Discussions
Who it suits
TextDescriptives suits Python users building spaCy or scikit-learn text-analysis pipelines. It is useful when you need structured statistics, readability measures, or configurable text-quality checks.
Verdict
TextDescriptives provides a broad, installable metric toolkit for spaCy-based analysis. Review the Pyphen language limitation if your readability workflow depends on hyphenation.
The facts
Each one read from TextDescriptives's own pages, with the date it was checked.
What it does
- Demo
- The documentation links to an online demo for trying the package.checked 4 Oct 2026
- Install
- The documentation says to install the package with `pip install textdescriptives`.checked 4 Oct 2026
- Integrations
- The documentation shows integration with spaCy pipelines and a scikit-learn pipeline featurizer.checked 4 Oct 2026
- License
- The project’s GitHub page identifies its license as Apache-2.0.checked 4 Oct 2026
- Metrics
- Its components cover descriptive statistics, readability, dependency distance, part-of-speech proportions, coherence, text quality, and information theory.checked 4 Oct 2026
- Notable limitation
- Readability metrics that require hyphenation may return NaN for languages unsupported by Pyphen.checked 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.checked 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.checked 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.checked 4 Oct 2026
- Readability
- Readability measures include Flesch, SMOG, Gunning-Fog, Automated Readability Index, Coleman-Liau, LIX, and RIX.checked 4 Oct 2026
- Support
- The documentation directs bug reports and feature requests to GitHub Issues and usage questions to GitHub Discussions.checked 4 Oct 2026
- Text statistics
- Descriptive statistics include token and character counts, unique-token proportions, sentence lengths, syllable counts, and token lengths.checked 4 Oct 2026
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