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scipy.sparse.csc_matrix

class scipy.sparse.csc_matrix(arg1, shape=None, dtype=None, copy=False)

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.

Advantages of the CSC format

• efficient arithmetic operations CSC + CSC, CSC * CSC, etc.
• efficient column slicing
• fast matrix vector products (CSR, BSR may be faster)

Disadvantages of the CSC format

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

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	int(x[, base]) -> integer
nnz
has_sorted_indices	Determine whether the matrix has sorted indices

data	Data array of the matrix
indices	CSC format index array
indptr	CSC format index pointer array

Methods

asformat(format)	Return this matrix in a given sparse format
asfptype()	Upcast matrix to a floating point format (if necessary)
astype(t)
check_format([full_check])	check whether the matrix format is valid
conj()
conjugate()
copy()
diagonal()	Returns the main diagonal of the matrix
dot(other)
eliminate_zeros()	Remove zero entries from the matrix
getH()
get_shape()
getcol(j)	Returns a copy of column j of the matrix, as an (m x 1) sparse
getformat()
getmaxprint()
getnnz()
getrow(i)	Returns a copy of row i of the matrix, as a (1 x n) sparse
mean([axis])	Average the matrix over the given axis.
multiply(other)	Point-wise multiplication by another matrix
nonzero()	nonzero indices
prune()	Remove empty space after all non-zero elements.
reshape(shape)
set_shape(shape)
setdiag(values[, k])	Fills the diagonal elements {a_ii} with the values from the given sequence.
sort_indices()	Sort the indices of this matrix in place
sorted_indices()	Return a copy of this matrix with sorted indices
sum([axis])	Sum the matrix over the given axis.
sum_duplicates()	Eliminate duplicate matrix entries by adding them together
toarray()
tobsr([blocksize])
tocoo([copy])	Return a COOrdinate representation of this matrix
tocsc([copy])
tocsr()
todense()
todia()
todok()
tolil()
transpose([copy])