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abhishek-shrm/eta_on_text_data-16.py

Last active April 1, 2021 10:11
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eta_on_text_data-16.py
# Creating Document Term Matrix
from sklearn.feature_extraction.text
import CountVectorizer
cv=CountVectorizer(analyzer='word')
data=cv.fit_transform(df_grouped['lemmatized'])
df_dtm = pd.DataFrame(data.toarray(), columns=cv.get_feature_names())
df_dtm.index=df_grouped.index
df_dtm.head(3)
@leidmarfesta
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leidmarfesta commented Mar 31, 2021 •
edited

just a note about this code: in Python you need to separate it (two lines)

from sklearn.feature_extraction.text import CountVectorizer
cv=CountVectorizer(analyzer='word')

@abhishek-shrm
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Thank you for pointing it out. I have updated the code.

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