NLP Text Similarity on python

import sys
# Define the documents
doc_trump = "How do I make a Directory entry?"

doc_election = "I make a Directory submit"

#doc_putin = "i am feeling very very bad"

documents = [doc_trump, doc_election]

# Scikit Learn
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd

# Create the Document Term Matrix
count_vectorizer = CountVectorizer(stop_words='english')
count_vectorizer = CountVectorizer()
sparse_matrix = count_vectorizer.fit_transform(documents)

# OPTIONAL: Convert Sparse Matrix to Pandas Dataframe if you want to see the word frequencies.
doc_term_matrix = sparse_matrix.todense()
df = pd.DataFrame(doc_term_matrix,
                  columns=count_vectorizer.get_feature_names(),
                  index=['doc_trump', 'doc_election'])
df
# Compute Cosine Similarity
from sklearn.metrics.pairwise import cosine_similarity
print(cosine_similarity(df, df))

Output:

[[1.     0.51639778]

 [0.51639778 1.    ]]