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Creating a bag-of-words in scikit-learn

# Import CountVectorizer
from sklearn.feature_extraction.text import CountVectorizer

# Create the token pattern: TOKENS_ALPHANUMERIC
TOKENS_ALPHANUMERIC = '[A-Za-z0-9]+(?=s+)'

# Fill missing values in df.Position_Extra
df.Position_Extra.fillna('', inplace=True)

# Instantiate the CountVectorizer: vec_alphanumeric
vec_alphanumeric = CountVectorizer(token_pattern=TOKENS_ALPHANUMERIC)

# Fit to the data
vec_alphanumeric.fit(df.Position_Extra)

# Print the number of tokens and first 15 tokens
msg = "There are {} tokens in Position_Extra if we split on non-alpha numeric"
print(msg.format(len(vec_alphanumeric.get_feature_names())))
print(vec_alphanumeric.get_feature_names()[:15])

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Creating a bag-of-words in scikit-learn

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