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textstat


Textstat

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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

Usage

>>> 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.

Install

You can install textstat either via the Python Package Index (PyPI) or from source.

Install using pip


pip install textstat

Install using easy_install


easy_install textstat

Install latest version from GitHub


git clone https://github.com/shivam5992/textstat.git
cd textstat
pip install .

Install from PyPI

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

Language support

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.

Language variants

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

Spanish-specific tests

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.

List of Functions

Formulas

The Flesch Reading Ease formula


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

The Flesch-Kincaid Grade Level


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

The Fog Scale (Gunning FOG Formula)


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

The SMOG Index


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

Automated Readability Index


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

The Coleman-Liau Index


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

Linsear Write Formula


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

Dale-Chall Readability Score


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

Readability Consensus based upon all the above tests


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.

Spache Readability Formula


textstat.spache_readability(text)

Returns grade level of english text.

Intended for text written for children up to grade four.

Further reading on
Wikipedia

McAlpine EFLAW Readability Score


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

Reading Time


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

Language Specific Formulas

Índice de lecturabilidad Fernandez-Huerta (Spanish)


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

Índice de perspicuidad de Szigriszt-Pazos (Spanish)


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

Fórmula de comprensibilidad de Gutiérrez de Polini (Spanish)


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

Fórmula de Crawford (Spanish)


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

Osman (Arabic)


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

Gulpease Index (Italian)


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

Wiener Sachtextformel (German)


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

Aggregates and Averages

Syllable Count


textstat.syllable_count(text)

Returns the number of syllables present in the given text.

Uses the Python module Pyphen
for syllable calculation.

Lexicon Count


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.

Sentence Count


textstat.sentence_count(text)

Returns the number of sentences present in the given text.

Character Count


textstat.char_count(text, ignore_spaces=True)

Returns the number of characters present in the given text.

Letter Count


textstat.letter_count(text, ignore_spaces=True)

Returns the number of characters present in the given text without punctuation.

Polysyllable Count


textstat.polysyllabcount(text)

Returns the number of words with a syllable count greater than or equal to 3.

Monosyllable Count


textstat.monosyllabcount(text)

Returns the number of words with a syllable count equal to one.

Contributing

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.

  1. Fork this repository on GitHub to start making your changes to the master
    branch (or branch off of it).
  2. Write a test which shows that the bug was fixed or that the feature works as expected.
  3. Send a pull request!

Development setup

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

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