Python create an empty sparse matrix

Question

I am trying to parse some real data into a .mat object to be loaded in my matlab script.

I am getting this error:

TypeError: \'co

Answer 1

Use a sparse format, which supports efficient indexing, like dok_matrix

This is an efficient structure for constructing sparse matrices incrementally.

...

Allows for efficient O(1) access of individual elements. Duplicates are not allowed. Can be efficiently converted to a coo_matrix once constructed.

The last sentence can be generalized to: can be efficiently converted to all the other common formats if needed.

from scipy.sparse import dok_matrix

M = dok_matrix((100, 100))  # extra brackets needed as mentioned in comments
                            # thanks Daniel!
M[0,3] = 5

Answer 2

Constructing this matrix with coo_matrix, using the (data, (rows, cols))` parameter format:

In [2]: from scipy import sparse
In [3]: from scipy import io
In [4]: data=np.array([[10,45],[11,12],[4,1]])
In [5]: data
Out[5]: 
array([[10, 45],
       [11, 12],
       [ 4,  1]])
In [6]: rows = data[:,0]
In [7]: cols = data[:,1]
In [8]: data = np.ones(rows.shape, dtype=int)
In [9]: M = sparse.coo_matrix((data, (rows, cols)), shape=(100,100))
In [10]: M
Out[10]: 
<100x100 sparse matrix of type '<class 'numpy.int32'>'
    with 3 stored elements in COOrdinate format>
In [11]: print(M)
  (10, 45)  1
  (11, 12)  1
  (4, 1)    1

If you save it to a .mat file for use in MATLAB, it will save it in csc format (having converted it from the coo):

In [13]: io.savemat('test.mat',{'M':M})
In [14]: d = io.loadmat('test.mat')
In [15]: d
Out[15]: 
{'M': <100x100 sparse matrix of type '<class 'numpy.int32'>'
    with 3 stored elements in Compressed Sparse Column format>,
 '__globals__': [],
 '__header__': b'MATLAB 5.0 MAT-file Platform: posix, Created on: Mon Aug  7 08:45:12 2017',
 '__version__': '1.0'}

coo format does not implement item assignment. csr and csc do implement it, but will complain. But they are the normal formats for calculation. lil and dok are the best formats for iterative assignment.