for main repo see: https://github.com/textstat/textstat
Modifications from main repo:
Textstat is an easy to use library to calculate statistics from text. It helps determine readability, complexity, and grade level.
Photo by Patrick Tomasso on Unsplash
>>> import textstat
>>> test_data = (
"Playing games has always been thought to be important to "
"the development of well-balanced and creative children; "
"however, what part, if any, they should play in the lives "
"of adults has never been researched that deeply. I believe "
"that playing games is every bit as important for adults "
"as for children. Not only is taking time out to play games "
"with our children and other adults valuable to building "
"interpersonal relationships but is also a wonderful way "
"to release built up tension."
)
>>> textstat.flesch_reading_ease(test_data)
>>> textstat.flesch_kincaid_grade(test_data)
>>> textstat.smog_index(test_data)
>>> textstat.coleman_liau_index(test_data)
>>> textstat.automated_readability_index(test_data)
>>> textstat.dale_chall_readability_score(test_data)
>>> textstat.difficult_words(test_data)
>>> textstat.linsear_write_formula(test_data)
>>> textstat.gunning_fog(test_data)
>>> textstat.text_standard(test_data)
>>> textstat.fernandez_huerta(test_data)
>>> textstat.szigriszt_pazos(test_data)
>>> textstat.gutierrez_polini(test_data)
>>> textstat.crawford(test_data)
>>> textstat.gulpease_index(test_data)
>>> textstat.osman(test_data)
The argument (text) for all the defined functions remains the same - i.e the text for which statistics need to be calculated.
You can install textstat either via the Python Package Index (PyPI) or from source.
pip install textstat
easy_install textstat
git clone https://github.com/textstat/textstat.git
cd textstat
pip install .
Download the latest version of textstat from http://pypi.python.org/pypi/textstat/
You can install it by doing the following:
tar xfz textstat-*.tar.gz
cd textstat-*/
python setup.py build
python setup.py install # as root
By default functions implement algorithms for english language. To change language, use:
textstat.set_lang(lang)
The language will be used for syllable calculation and to choose variant of the formula.
All functions implement en_US language. Some of them has also variants
for other languages listed below.
| Function | en | de | es | fr | it | nl | pl | ru |
|---|---|---|---|---|---|---|---|---|
| flesch_reading_ease | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| gunning_fog | ✔ | ✔ |
The following functions are specifically designed for spanish language. They can be used on non-spanish texts, even though that use case is not recommended.
>>> textstat.fernandez_huerta(test_data)
>>> textstat.szigriszt_pazos(test_data)
>>> textstat.gutierrez_polini(test_data)
>>> textstat.crawford(test_data)
Additional information on the formula they implement can be found in their respective docstrings.
textstat.flesch_reading_ease(text)
Returns the Flesch Reading Ease Score.
The following table can be helpful to assess the ease of readability in a document.
The table is an example of values. While the maximum score is 121.22, there is no limit on how low the score can be. A negative score is valid.
| Score | Difficulty |
|---|---|
| 90-100 | Very Easy |
| 80-89 | Easy |
| 70-79 | Fairly Easy |
| 60-69 | Standard |
| 50-59 | Fairly Difficult |
| 30-49 | Difficult |
| 0-29 | Very Confusing |
Further reading on Wikipedia
textstat.flesch_kincaid_grade(text)
Returns the Flesch-Kincaid Grade of the given text. This is a grade formula in that a score of 9.3 means that a ninth grader would be able to read the document.
Further reading on Wikipedia
textstat.gunning_fog(text)
Returns the FOG index of the given text. This is a grade formula in that a score of 9.3 means that a ninth grader would be able to read the document.
Further reading on Wikipedia
textstat.smog_index(text)
Returns the SMOG index of the given text. This is a grade formula in that a score of 9.3 means that a ninth grader would be able to read the document.
Texts of fewer than 30 sentences are statistically invalid, because the SMOG formula was normed on 30-sentence samples. textstat requires at least 3 sentences for a result.
Further reading on Wikipedia
textstat.automated_readability_index(text)
Returns the ARI (Automated Readability Index) which outputs a number that approximates the grade level needed to comprehend the text.
For example if the ARI is 6.5, then the grade level to comprehend the text is 6th to 7th grade.
Further reading on Wikipedia
textstat.coleman_liau_index(text)
Returns the grade level of the text using the Coleman-Liau Formula. This is a grade formula in that a score of 9.3 means that a ninth grader would be able to read the document.
Further reading on Wikipedia
textstat.linsear_write_formula(text)
Returns the grade level using the Linsear Write Formula. This is a grade formula in that a score of 9.3 means that a ninth grader would be able to read the document.
Further reading on Wikipedia
textstat.dale_chall_readability_score(text)
Different from other tests, since it uses a lookup table of the most commonly used 3000 English words. Thus it returns the grade level using the New Dale-Chall Formula.
| Score | Understood by |
|---|---|
| 4.9 or lower | average 4th-grade student or lower |
| 5.0–5.9 | average 5th or 6th-grade student |
| 6.0–6.9 | average 7th or 8th-grade student |
| 7.0–7.9 | average 9th or 10th-grade student |
| 8.0–8.9 | average 11th or 12th-grade student |
| 9.0–9.9 | average 13th to 15th-grade (college) student |
Further reading on Wikipedia
textstat.text_standard(text, float_output=False)
Based upon all the above tests, returns the estimated school grade level required to understand the text.
Optional float_output allows the score to be returned as a
float. Defaults to False.
textstat.spache_readability(text)
Returns grade level of english text.
Intended for text written for children up to grade four.
Further reading on Wikipedia
textstat.mcalpine_eflaw(text)
Returns a score for the readability of an english text for a foreign learner or English, focusing on the number of miniwords and length of sentences.
It is recommended to aim for a score equal to or lower than 25.
Further reading on This blog post
textstat.reading_time(text, ms_per_char=14.69)
Returns the reading time of the given text.
Assumes 14.69ms per character.
Further reading in This academic paper
textstat.fernandez_huerta(text)
Reformulation of the Flesch Reading Ease Formula specifically for spanish. The results can be interpreted similarly
Further reading on This blog post
textstat.szigriszt_pazos(text)
Adaptation of Flesch Reading Ease formula for spanish-based texts.
Attempts to quantify how understandable a text is.
Further reading on This blog post
textstat.gutierrez_polini(text)
Returns the Guttiérrez de Polini understandability index.
Specifically designed for the texts in spanish, not an adaptation. Conceived for grade-school level texts.
Scores for more complex text are not reliable.
Further reading on This blog post
textstat.crawford(text)
Returns the Crawford score for the text.
Returns an estimate of the years of schooling required to understand the text.
The text is only valid for elementary school level texts.
Further reading on This blog post
textstat.osman(text)
Returns OSMAN score for text.
Designed for Arabic, an adaption of Flesch and Fog Formula. Introduces a new factor called "Faseeh".
Further reading in This academic paper
textstat.gulpease_index(text)
Returns the Gulpease index of Italian text, which translates to level of education completed.
Lower scores require higher level of education to read with ease.
Further reading on Wikipedia
textstat.wiener_sachtextformel(text, variant)
Returns a grade level score for the given text.
A value of 4 means very easy text, whereas 15 means very difficult text.
Further reading on Wikipedia
textstat.syllable_count(text)
Returns the number of syllables present in the given text.
Uses the Python module syllapy for syllable calculation.
textstat.lexicon_count(text, removepunct=True)
Calculates the number of words present in the text.
Optional removepunct specifies whether we need to take
punctuation symbols into account while counting lexicons.
Default value is True, which removes the punctuation
before counting lexicon items.
textstat.sentence_count(text)
Returns the number of sentences present in the given text.
textstat.char_count(text, ignore_spaces=True)
Returns the number of characters present in the given text.
textstat.letter_count(text, ignore_spaces=True)
Returns the number of characters present in the given text without punctuation.
textstat.polysyllabcount(text)
Returns the number of words with a syllable count greater than or equal to 3.
textstat.monosyllabcount(text)
Returns the number of words with a syllable count equal to one.
If you find any problems, you should open an issue.
If you can fix an issue you've found, or another issue, you should open a pull request.
It is recommended you use a virtual environment, or Pipenv to keep your development work isolated from your systems Python installation.
$ git clone https://github.com/<yourname>/textstat.git # Clone the repo from your fork
$ cd textstat
$ pip install -r requirements.txt # Install all dependencies
$ # Make changes
$ python -m pytest test.py # Run tests
FAQs
Calculate statistical features from text
We found that whylabs-textstat demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
PyPI confirms no security flaws were exploited in the Ultralytics supply chain attack and highlights improvements for safer package publishing.
Security News
Research
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.