Plotting clustered sentences in Python
It can be made in many ways. My starting approach would be (1) to apply the tf-idf as in your code snippet, then (2) reducing the output matrix to a lower dimensionality (say 2D for seeing it in a scatterplot) with a decomposition method (or with topic modelling approach), and finally (3) apply a clustering algorithm to visualize the samples/documents of each cluster. 

However, this may have many flaws, because I don't know if the decomposition results would make any sense and I'm not aware of how many clusters are good enough, but anyway it would be useful as an exploratory analysis. 

The logic would be something like that: (didn't tested)

# 1
tfidf_vect = TfidfVectorizer(tokenizer= sent_tokenization)
tfidf = tfidf_vect.fit_transform(df['Sentences'])

# 2
decomp_method = NMF(n_components=2)
tf_2d = decomp_method.fit_transform(tfidf) # output shape: [N, 2]

# 3
kmeans = KMeans(n_clusters=2)
tf_kmeans_labels = kmeans.fit(tf_2d).labels_ # array

# visualization
data = pd.DataFrame(tf_2d, columns=['c1', 'c2'])
data['kmeans_labels'] = tf_kmeans_labels
scatterplot(x='c1', y='c2', hue='kmeans_labels',data=dat) 
```