Generating a dense matrix from a sparse matrix in numpy python

 from scipy.sparse import csr_matrix A = csr_matrix([[1,0,2],[0,3,0]]) >>>A <2x3 sparse matrix of type '<type 'numpy.int64'>'    with 3 stored elements in Compressed Sparse Row format> >>> A.todense()   matrix([[1, 0, 2],           [0, 3, 0]]) >>> A.toarray()      array([[1, 0, 2],            [0, 3, 0]])

this is an example of how to convert a sparse matrix to a dense matrix taken from scipy

I solved this problem using Pandas. Because we want to keep the document ids and term ids.

from pandas import DataFrame # A sparse matrix in dictionary form (can be a SQLite database). Tuples contains doc_id        and term_id. doc_term_dict={('d1','t1'):12, ('d2','t3'):10, ('d3','t2'):5}#extract all unique documents and terms ids and intialize a empty dataframe.rows = set([d for (d,t) in doc_term_dict.keys()])  cols = set([t for (d,t) in doc_term_dict.keys()])df = DataFrame(index = rows, columns = cols )df = df.fillna(0)#assign all nonzero values in dataframefor key, value in doc_term_dict.items():    df[key[1]][key[0]] = value   print df

Output:

    t2  t3  t1d2  0  10   0d3  5   0   0d1  0   0  12