TextDescriptives

What TextDescriptives is

TextDescriptives is a free Python library for measuring text through spaCy v.3 pipeline components and extensions. It calculates descriptive statistics, readability scores, dependency distance, part-of-speech proportions, coherence, text quality, and information-theory metrics. Its statistics include token and character totals, unique-token proportions, sentence and token lengths, and syllable counts. Readability options include Flesch, SMOG, Gunning-Fog, Automated Readability Index, Coleman-Liau, LIX, and RIX. Quality checks cover stop-word and out-of-vocabulary ratios, repeated text, bullet points, and ellipses, with configurable thresholds. You can analyze raw text or spaCy documents, then receive results as a Pandas DataFrame or a list of dictionaries. The documentation describes use in spaCy pipelines and a scikit-learn pipeline featurizer, and links to an online demo. Installation is through `pip install textdescriptives`. The project is licensed under Apache-2.0 and is listed for API and Linux use, with on-premise deployment. Some readability calculations that rely on hyphenation can return NaN for languages Pyphen does not support.

Who it suits

TextDescriptives suits Python users who need to calculate text statistics, readability, quality, or linguistic metrics. It is also relevant to people building spaCy workflows or scikit-learn pipeline features.

Limits

Hyphenation-dependent readability metrics may return NaN for languages unsupported by Pyphen.

Getting started

1. Open the TextDescriptives documentation website.
2. Install the package with `pip install textdescriptives`.
3. Use it with raw text or spaCy documents.
4. Try the online demo linked from the documentation.

Verdict

Choose TextDescriptives if you need a free Python library that brings a broad range of text measurements into spaCy or scikit-learn workflows. Look elsewhere if your required readability measures depend on hyphenation for a language Pyphen does not support.