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textstat — Readability & SEO Text Scoring
MIT-licensed Python library computing 12 readability scores (Flesch, Gunning Fog, SMOG, Dale-Chall, etc.) for content optimization and SEO.
textstat — Readability & SEO Text Scoring
textstat is a Python library with no heavy dependencies that computes a comprehensive set of readability and text complexity metrics. For content marketers and SEO specialists, it provides instant objective scores for any piece of copy — blog posts, landing pages, email subjects, ad copy — enabling data-driven content optimization and readability targeting by audience.
Key Features
- 12+ readability formulas: Flesch Reading Ease, Flesch-Kincaid Grade, Gunning Fog, SMOG Index, Automated Readability Index, Coleman-Liau, Linsear Write, Dale-Chall, and more
- Grade-level estimates for audience targeting
- Reading time estimation
- Syllable, word, sentence, and lexicon counting
- Multi-language support for international content teams
Quick Start
pip install textstat
import textstat
text = "Marketing teams use data analytics to optimize conversion funnels and improve customer retention across digital channels."
print(textstat.flesch_reading_ease(text)) # 0-100, higher = easier
print(textstat.flesch_kincaid_grade(text)) # US grade level
print(textstat.gunning_fog(text)) # Fog index
print(textstat.text_standard(text)) # Consensus grade
print(textstat.reading_time(text, ms_per_char=14.69)) # seconds
npx ai-supply add textstat-readability-scoring
Curated mirror of the open-source textstat (MIT). Get it from the source.