How to add time as a feature into clustering algorithm? I've wrote here about my problem. And now I have a new one.
How to use time as a feature? I have a pandas DataFrame with date-time column('date') and I don't know how to use it as a feature. For now I'm using  TfidfVectorizer.

from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(min_df=5, max_features=10000, ngram_range=(1, 2))
vz = vectorizer.fit_transform(df['word'])


Where 'word' column is tokens on my text. 
I'm using MiniBatchKMeans. Here is code example:

from sklearn.cluster import MiniBatchKMeans

num_clusters = 32
kmeans_model = MiniBatchKMeans(n_clusters=num_clusters, init='k-means++',     n_init=1,  init_size=1000, batch_size=1000, verbose=False, max_iter=1000)
kmeans = kmeans_model.fit(vz)
kmeans_clusters = kmeans.predict(vz)
kmeans_distances = kmeans.transform(vz)


I'm interested at how to add my time feature(my time column) to clusterizator and is there any possibility to make some kind of "weight" factor for time features and for different tokens.
I've decided to try LDA:

cvectorizer = CountVectorizer(min_df=4, max_features=10000, ngram_range(1,2))
cvz = cvectorizer.fit_transform(df['word'])
n_topics = 32
n_iter = 2000
lda_model = lda.LDA(n_topics=n_topics, n_iter=n_iter)
X_topics = lda_model.fit_transform(cvz)


Then I've concatenated time feature to the LDA vector:

 X_all  = np.hstack((X_topics, X_time))


Where X_time was my time feature column.