`textstat <https://pypi.python.org/pypi/textstat/>`__ΒΆ
pip install textstat
Python package to calculate statistics from text, which helps to decide readability, complexity and grade level of a particular corpus.
[1]:
from articles import *; from pandas import *; o = __name__ == '__main__'
if o:
%load_ext literacy
[2]:
from textstat.textstat import textstat
measures = a[{
k: f for k in dir(textstat) if not k.startswith('_') for f in [getattr(textstat, k)] if callable(f)
}]
from textstat.textstat import textstat
measures = a[{
k: f for k in dir(textstat) if not k.startswith('_') for f in [getattr(textstat, k)] if callable(f)
}]
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.
[3]:
if o:
df = a.Path().read_text().loads()[this()['cells']][DataFrame](
__import__('Untitled183').__file__)
df.source = df.source.apply('\n'.join)
df = df[df.cell_type == 'markdown'].source.apply(measures).apply(Series).pipe(lambda df_: concat([df, df_], axis=1))
o and df.T
[3]:
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| cell_type | markdown | code | code | markdown | code | code |
| execution_count | NaN | 11 | 2 | NaN | 10 | NaN |
| metadata | {} | {} | {} | {} | {} | {'collapsed': True} |
| outputs | NaN | [{'metadata': {}, 'output_type': 'display_data... | [{'metadata': {}, 'output_type': 'display_data... | NaN | [{'execution_count': 10, 'metadata': {}, 'outp... | [] |
| source | # [`textstat`](https://pypi.python.org/pypi/te... | from articles import *; from pandas import... | from textstat.textstat import textstat\n\n... | Playing games has always been thought to be im... | \n\n if o: \n\n df = a.Path().re... | |
| automated_readability_index | 29.1 | NaN | NaN | 15.5 | NaN | NaN |
| avg_letter_per_word | 8.17 | NaN | NaN | 4.93 | NaN | NaN |
| avg_sentence_length | 24 | NaN | NaN | 27.3 | NaN | NaN |
| avg_sentence_per_word | 0.04 | NaN | NaN | 0.04 | NaN | NaN |
| avg_syllables_per_word | 2.2 | NaN | NaN | 1.5 | NaN | NaN |
| char_count | 196 | NaN | NaN | 404 | NaN | NaN |
| coleman_liau_index | 30.4 | NaN | NaN | 11.61 | NaN | NaN |
| dale_chall_readability_score | 13.38 | NaN | NaN | 10.57 | NaN | NaN |
| difficult_words | 13 | NaN | NaN | 29 | NaN | NaN |
| flesch_kincaid_grade | 19.7 | NaN | NaN | 12.8 | NaN | NaN |
| flesch_reading_ease | -3.64 | NaN | NaN | 52.23 | NaN | NaN |
| gunning_fog | 33.2667 | NaN | NaN | 27.0663 | NaN | NaN |
| lexicon_count | 24 | NaN | NaN | 82 | NaN | NaN |
| linsear_write_formula | 17 | NaN | NaN | 15 | NaN | NaN |
| polysyllabcount | 4 | NaN | NaN | 4 | NaN | NaN |
| sentence_count | 1 | NaN | NaN | 3 | NaN | NaN |
| smog_index | 0 | NaN | NaN | 9.7 | NaN | NaN |
| syllable_count | 53.1 | NaN | NaN | 122.4 | NaN | NaN |
| text_standard | 29th and 30th grade | NaN | NaN | 9th and 10th grade | NaN | NaN |