Equivalent of R's removeSparseTerms in Python

If your data is plain text, you can use CountVectorizer in order to get this job done.

For example:

from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(min_df=2)
corpus = [
    'This is the first document.',
    'This is the second second document.',
    'And the third one.',
    'Is this the first document?',
]
vectorizer = vectorizer.fit(corpus)
print vectorizer.vocabulary_ 
#prints {u'this': 4, u'is': 2, u'the': 3, u'document': 0, u'first': 1}
X = vectorizer.transform(corpus)

Now X is the document-term matrix. (If you are into information retrieval you want to consider also Tf–idf term weighting.

It can help you get the document-term matrix easily with a few lines.

Regarding the sparsity - you can control these parameters:

• min_df - the minimum document frequency allowed for a term in the document-term matrix.
• max_features - the maximum number of features allowed in the document-term matrix

Alternatively, If you already have the document-term matrix or Tf-idf matrix, and you have the notion of what is sparse, define MIN_VAL_ALLOWED, and then do the following:

import numpy as np
from scipy.sparse import csr_matrix
MIN_VAL_ALLOWED = 2

X = csr_matrix([[7,8,0],
                [2,1,1],
                [5,5,0]])

z = np.squeeze(np.asarray(X.sum(axis=0) > MIN_VAL_ALLOWED)) #z is the non-sparse terms 

print X[:,z].toarray()
#prints X without the third term (as it is sparse)
[[7 8]
[2 1]
[5 5]]

(use X = X[:,z] so X remains a csr_matrix.)

If it is the minimum document frequency you wish to set a threshold on, binarize the matrix first, and than use it the same way:

import numpy as np
from scipy.sparse import csr_matrix

MIN_DF_ALLOWED = 2

X = csr_matrix([[7, 1.3, 0.9, 0],
                [2, 1.2, 0.8  , 1],
                [5, 1.5, 0  , 0]])

#Creating a copy of the data
B = csr_matrix(X, copy=True)
B[B>0] = 1
z = np.squeeze(np.asarray(X.sum(axis=0) > MIN_DF_ALLOWED))
print  X[:,z].toarray()
#prints
[[ 7.   1.3]
[ 2.   1.2]
[ 5.   1.5]]

In this example, the third and fourth term (or columns) are gone, since they only appear in two documents (rows). Use MIN_DF_ALLOWED to set the threshold.