scipy.sparse.csr_matrix

class scipy.sparse.csr_matrix(arg1, shape=None, dtype=None, copy=False, dims=None, nzmax=None)

Compressed Sparse Row matrix

This can be instantiated in several ways:

csr_matrix(D)

with a dense matrix or rank-2 ndarray D

csr_matrix(S)

with another sparse matrix S (equivalent to S.tocsr())

csr_matrix((M, N), [dtype])

to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype=’d’.

csr_matrix((data, ij), [shape=(M, N)])

where data and ij satisfy a[ij[0, k], ij[1, k]] = data[k]

csr_matrix((data, indices, indptr), [shape=(M, N)])

is the standard CSR representation where the column indices for row i are stored in indices[indptr[i]:indices[i+1]] and their corresponding values are stored in data[indptr[i]:indptr[i+1]]. If the shape parameter is not supplied, the matrix dimensions are inferred from the index arrays.

Notes

Advantages of the CSR format

• efficient arithmetic operations CSR + CSR, CSR * CSR, etc.
• efficient row slicing
• fast matrix vector products

Disadvantages of the CSR format

• slow column slicing operations (consider CSC)
• changes to the sparsity structure are expensive (consider LIL or DOK)

Examples

>>> from scipy.sparse import *
>>> from scipy import *
>>> csr_matrix( (3,4), dtype=int8 ).todense()
matrix([[0, 0, 0, 0],
        [0, 0, 0, 0],
        [0, 0, 0, 0]], dtype=int8)

>>> row = array([0,0,1,2,2,2])
>>> col = array([0,2,2,0,1,2])
>>> data = array([1,2,3,4,5,6])
>>> csr_matrix( (data,(row,col)), shape=(3,3) ).todense()
matrix([[1, 0, 2],
        [0, 0, 3],
        [4, 5, 6]])

>>> indptr = array([0,2,3,6])
>>> indices = array([0,2,2,0,1,2])
>>> data = array([1,2,3,4,5,6])
>>> csr_matrix( (data,indices,indptr), shape=(3,3) ).todense()
matrix([[1, 0, 2],
        [0, 0, 3],
        [4, 5, 6]])