We can use CountVectorizer to count the number of times a word occurs in a corpus:

    # Tokenizing text
    from sklearn.feature_extraction.text import CountVectorizer
    count_vect = CountVectorizer()
    X_train_counts = count_vect.fit_transform(twenty_train.data)

If we convert this to a data frame, we can see what the tokens look like:

[![enter image description here][1]][1]

For example, the 35,780th word of the 3rd document occurs twice. 

We can use TfidfTransformer to count the number of times a word occurs in a corpus (only the term frequency and not the inverse) as follows:

    from sklearn.feature_extraction.text import TfidfTransformer
    tf_transformer = TfidfTransformer(use_idf=False).fit(X_train_counts)
    X_train_tf = tf_transformer.transform(X_train_counts)

Converting this to a data frame, we get:

[![enter image description here][2]][2]

  [1]: https://i.sstatic.net/eB4Uh.png
  [2]: https://i.sstatic.net/3VnIe.png

We can see the representation is different. The TF is shown as 0.15523. Why is this different than the token count using CountVectorizer?