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I'm trying to get list of n-gram tokens for text

Ex: 'How to use build_analyzer in sklearn feature extraction '

output :['How', 'use', 'build_analyzer', 'sklearn', 'feature', 'extraction', 'How use', 'use build_analyzer', 'build_analzer sklearn', 'sklearn feature', 'feature extraction']

from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer

vectorizer = TfidfVectorizer(stop_words = 'english',ngram_range=(1, 2), token_pattern=r'\b\w+\b', min_df=1)

 df['Text'].apply(lambda x : vectorizer.build_analyzer(x))

TypeError: build_analyzer() takes 1 positional argument but 2 were given

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build_analyzer() returns a callable that let's you extract the tokenizing step from the transformation pipeline wrapped in the CountVectorizer or TfidfVectorizer. You can do something like this:

analyze = vectorizer.build_analyzer()
df['Text'].apply(lambda x: analyze(x)) #or df['Text'].apply(analyze)
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