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]:indptr[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

arcsin()	Element-wise arcsin.
arcsinh()	Element-wise arcsinh.
arctan()	Element-wise arctan.
arctanh()	Element-wise arctanh.
asformat(format)	Return this matrix in a given sparse format
asfptype()	Upcast matrix to a floating point format (if necessary)
astype(t)
ceil()	Element-wise ceil.
check_format([full_check])	check whether the matrix format is valid
conj()
conjugate()
copy()
deg2rad()	Element-wise deg2rad.
diagonal()	Returns the main diagonal of the matrix
dot(other)
eliminate_zeros()	Remove zero entries from the matrix
expm1()	Element-wise expm1.
floor()	Element-wise floor.
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
log1p()	Element-wise log1p.
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.
rad2deg()	Element-wise rad2deg.
reshape(shape)
rint()	Element-wise rint.
set_shape(shape)
setdiag(values[, k])	Fills the diagonal elements {a_ii} with the values from the given sequence.
sign()	Element-wise sign.
sin()	Element-wise sin.
sinh()	Element-wise sinh.
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
tan()	Element-wise tan.
tanh()	Element-wise tanh.
toarray([order, out])	See the docstring for spmatrix.toarray.
tobsr([blocksize])
tocoo([copy])	Return a COOrdinate representation of this matrix
tocsc([copy])
tocsr()
todense([order, out])	Return a dense matrix representation of this matrix.
todia()
todok()
tolil()
transpose([copy])
trunc()	Element-wise trunc.