---
id: textstat
version: "0.7.13"
license: MIT
license_treatment: permissive
maintenance: active
---

# textstat — Calculate statistical features from text

License: permissive · Maintenance: active · Downloads: 1.4M/mo

## What it is and what it does

Textstat is a text analysis library that computes readability and complexity metrics using established linguistic formulas. It provides functions like Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning FOG, SMOG Index, Coleman-Liau Index, Dale-Chall Readability Score, and others—each returning a numerical score or grade level that estimates the difficulty of comprehending a given text.

The library supports multiple languages (en_US, de, es, fr, it, nl, pl, ru) with language-specific formula variants, and includes Spanish-specific readability tests. It depends on pyphen for hyphenation and nltk for linguistic processing. Most functions accept plain text and return a single metric; text_standard() synthesizes multiple formulas into a consensus grade estimate.

Use it for:

- Evaluate readability of educational content, documentation, or web copy before publication.
- Assess whether written material matches its intended audience's reading level.
- Analyze text complexity in academic or publishing workflows to guide revision.
- Compare readability across multiple documents or versions to track clarity improvements.
- Generate readability reports for content management or accessibility compliance.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Textstat calculates readability and complexity metrics from text using multiple established formulas, returning grade-level estimates and readability scores.

Yes. Textstat is actively maintained, has no known vulnerabilities, installs with low friction, and offers a straightforward API for a well-defined task. It is suitable for anyone needing to measure text readability or grade level, whether for educational, editorial, or accessibility purposes. The MIT license removes licensing concerns.

## Install

pip install textstat
uv add textstat
poetry add textstat

## Installing textstat

Before you install: Low friction install with three runtime dependencies (pyphen, nltk, setuptools). Active maintenance with recent release on 2026-02-18 and 1377 repository stars.

License in practice: MIT license permits unrestricted use, modification, and distribution with minimal obligations—suitable for commercial and proprietary projects.

Quickstart:

pip install textstat

import textstat

text = "Playing games has always been thought to be important to the development of well-balanced and creative children."
print(textstat.flesch_reading_ease(text))
print(textstat.flesch_kincaid_grade(text))

nltk may require downloading language data on first use; see nltk documentation for setup if tokenization fails.

Verify before relying:

- Whether nltk requires additional data downloads (e.g., corpora) beyond the package install for full functionality.
- Performance characteristics on very large texts or batch processing workloads.
- Accuracy comparison between the multiple readability formulas for specific use cases.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 1.4M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

readability scoring, text complexity analysis, grade level estimation, flesch kincaid readability, text statistics calculation, reading difficulty metrics, gunning fog index, text-analysis, readability-metrics, nlp

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