CODEWITHSUNDEEP
pandasnlpspacy
Programmer_nltk
Programmer_nltk

Reputation: 915

Percentage Count Verb, Noun using Spacy?

I want to count percentage split of POS in a sentence using spacy, similiar to

Count verbs, nouns, and other parts of speech with python's NLTK

Currently able to detect and count POS. How to find percentage split.

from __future__ import unicode_literals
import spacy,en_core_web_sm
from collections import Counter
nlp = en_core_web_sm.load()
print Counter(([token.pos_ for token in nlp('The cat sat on the mat.')]))

Current output:

Counter({u'NOUN': 2, u'DET': 2, u'VERB': 1, u'ADP': 1, u'PUNCT': 1})

Expected output:

Noun: 28.5%
DET: 28.5%
VERB: 14.28%
ADP: 14.28%
PUNCT: 14.28%

How to write the output to pandas dataframe?

Upvotes: 0

Views: 2823

Answers (2)

Programmer_nltk
Programmer_nltk

Reputation: 915

from __future__ import unicode_literals
import spacy,en_core_web_sm
from collections import Counter
nlp = en_core_web_sm.load()
c = Counter(([token.pos_ for token in nlp('The cat sat on the mat.')]))
sbase = sum(c.values())
for el, cnt in c.items():
    print(el, '{0:2.2f}%'.format((100.0* cnt)/sbase))

Output:

(u'NOUN', u'28.57%')
(u'VERB', u'14.29%')
(u'DET', u'28.57%')
(u'ADP', u'14.29%')
(u'PUNCT', u'14.29%')

Upvotes: 0

sophros
sophros

Reputation: 16728

Something along these lines should give you what you need:

sbase = sum(c.values())

for el, cnt in c.items():
    print(el, '{0:2.2f}%'.format((100.0* cnt)/sbase))


NOUN 28.57%
DET 28.57%
VERB 14.29%
ADP 14.29%
PUNCT 14.29%

Upvotes: 1

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