Bag of word scikit learn

import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer

docs = ['Tea is an aromatic beverage..',
        'After water, it is the most widely consumed drink in the world',
        'There are many different types of tea.',
        'Tea has a stimulating effect in humans.',
        'Tea originated in Southwest China during the Shang dynasty'] 

df = pd.DataFrame({'sms_message': docs, 'label': np.random.choice([0, 1], size=5)})

cv = CountVectorizer()
counts = cv.fit_transform(df['sms_message'])

df_counts = pd.DataFrame(counts.A, columns=cv.get_feature_names())
df_counts['label'] = df['label']

Source: stackoverflow.com

Similar codes

0 vote332 views1 answer
Pandas filter rows by column value regex
Python
0 vote345 views1 answer
Terminal commands for install python on cpanel
Python
0 vote361 views1 answer
Python create a program that runs through all possible combinations
Python
1 vote337 views1 answer
File methods in python
Python
2 votes368 views2 answers
Densenet python keras
Python
0 vote375 views1 answer
Bulk m4a to wav ffmepeg
Python
3 votes339 views3 answers
Python check if variable has value
Python
0 vote347 views1 answer
Pandas removing outliers from dataframe
Python
0 vote362 views1 answer
Python remove suffix
Python
0 vote339 views1 answer
K-means clustering and disabling clusters
Python