python-scikit-learnHow to use CountVectorizer
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)
words = cv.get_feature_names_out()
vectors = X.toarray()ctrl + cfrom sklearn importimport module from scikit-learn | docs = [sample set of text docs to vectorize |
.CountVectorizer(creates count vectorizer which creates vectors based on words counts | .fit_transform(train and process vectorizer to get vectors |
wordswill contain list of words used for vectors | vectorswill contain final vectors |
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]]
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