What is the difference between CountVectorizer token counts and TfidfTransformer with use_idf set to False? 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:



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:



We can see the representation is different. The TF is shown as 0.15523. Why is this different than the token count using CountVectorizer?
Actually, the documentation was pretty clear. I'll keep it posted in case someone else searches before reading:
The TfidfTransformer transforms a count matrix to a normalized tf or tf-idf representation. So although both the CountVectorizer and TfidfTransformer (with use_idf=False) produce term frequencies, TfidfTransformer is normalizing the count.