To avoid zero division, TfidfVectorizer uses smoothing for document and term frequencies:

• smooth_idf: Add 1 to document frequency, as if an extra document contained every token in the vocabulary, to prevent zero divisions
• sublinear_tf: Apply sublinear tf scaling; in other words, replace tf with 1 + log(tf)

In combination with normed weights, the results differ slightly:

vect = TfidfVectorizer(smooth_idf=True,
                      norm='l2',  # squared weights sum to 1 by 
                                    document
                      sublinear_tf=False,  # if True, use 1+log(tf)
                      binary=False)
pd.DataFrame(vect.fit_transform(sample_docs).todense(),
            columns=vect.get_feature_names())

   call   me  please  taxi  tomorrow  you
0  0.39 0.00    0.00  0.00      0.65 0.65
1  0.43 0.55    0.00  0.72      0.00 0.00
2  0.27 0.34    0.90  0.00      0.00 0.00