python-scikit-learnHow to use CountVectorizer

Usage example

from sklearn import feature_extraction

docs = [
  'Programming languahe is python.',
  'Programming in python and javascript is good.',
  'Programming also in lua as well as javaascripipt is ok.',
  'Programming in no language is bad',
]

cv = feature_extraction.text.CountVectorizer()
X = cv.fit_transform(docs)

print(cv.get_feature_names_out())
print(X.toarray())

output

['also' 'and' 'as' 'bad' 'good' 'in' 'is' 'javaascripipt' 'javascript'
 'language' 'languahe' 'lua' 'no' 'ok' 'programming' 'python' 'well']
[[0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0]
 [0 1 0 0 1 1 1 0 1 0 0 0 0 0 1 1 0]
 [1 0 2 0 0 1 1 1 0 0 0 1 0 1 1 0 1]
 [0 0 0 1 0 1 1 0 0 1 0 0 1 0 1 0 0]]