Generating a dense matrix from a sparse matrix in numpy python
Question: I have a Sqlite database that contains following type of schema:

    
    
    termcount(doc_num, term , count)
    

This table contains terms with their respective counts in the document. like

    
    
    (doc1 , term1 ,12)
    (doc1, term 22, 2)
    .
    .
    (docn,term1 , 10)
    

This matrix can be considered as sparse matrix as each documents contains very
few terms that will have a non-zero value.

How would I create a dense matrix from this sparse matrix using numpy as I
have to calculate the similarity among documents using cosine similarity.

This dense matrix will look like a table that have docid as the first column
and all the terms will be listed as the first row.and remaining cells will
contain counts.


Answer: 
     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](http://www.scipy.org/SciPyPackages/Sparse)

This example is now listed in the stackoverflow docs on SciPy
[here](http://stackoverflow.com/documentation/scipy/2128/introduction-to-
scipy/9963/convert-a-sparse-matrix-to-a-dense-matrix-using-
scipy#t=201607242031316475931)

