Compressed Sparse Column matrix
This can be instantiated in several ways:
- csc_matrix(D)
- with a dense matrix or rank-2 ndarray D
- csc_matrix(S)
- with another sparse matrix S (equivalent to S.tocsc())
- csc_matrix((M, N), [dtype])
- to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype=’d’.
- csc_matrix((data, ij), [shape=(M, N)])
- where data and ij satisfy the relationship a[ij[0, k], ij[1, k]] = data[k]
- csc_matrix((data, indices, indptr), [shape=(M, N)])
- is the standard CSC representation where the row indices for column 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
Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power.
Examples
>>> from scipy.sparse import *
>>> from scipy import *
>>> csc_matrix( (3,4), dtype=int8 ).todense()
matrix([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]], dtype=int8)
>>> row = array([0,2,2,0,1,2])
>>> col = array([0,0,1,2,2,2])
>>> data = array([1,2,3,4,5,6])
>>> csc_matrix( (data,(row,col)), shape=(3,3) ).todense()
matrix([[1, 0, 4],
[0, 0, 5],
[2, 3, 6]])
>>> indptr = array([0,2,3,6])
>>> indices = array([0,2,2,0,1,2])
>>> data = array([1,2,3,4,5,6])
>>> csc_matrix( (data,indices,indptr), shape=(3,3) ).todense()
matrix([[1, 0, 4],
[0, 0, 5],
[2, 3, 6]])
Attributes
dtype | |
shape | |
ndim | |
nnz | |
has_sorted_indices |
data | Data array of the matrix |
indices | CSC format index array |
indptr | CSC format index pointer array |
Methods