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

Compressed Sparse Column matrix

This can be instantiated in several ways:

with a dense matrix or rank-2 ndarray D
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.


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)


>>> 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]])


has_sorted_indices Determine whether the matrix has sorted indices
dtype (dtype) Data type of the matrix
shape (2-tuple) Shape of the matrix
ndim (int) Number of dimensions (this is always 2)
nnz Number of nonzero elements
data Data array of the matrix
indices CSC format index array
indptr CSC format index pointer array


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)
ceil() Element-wise ceil.
check_format([full_check]) check whether the matrix format is valid
deg2rad() Element-wise deg2rad.
diagonal() Returns the main diagonal of the matrix
dot(other) Ordinary dot product ..
eliminate_zeros() Remove zero entries from the matrix
expm1() Element-wise expm1.
floor() Element-wise floor.
getcol(i) Returns a copy of column i of the matrix, as a (m x 1)
getrow(i) Returns a copy of row i of the matrix, as a (1 x n)
log1p() Element-wise log1p.
max() Maximum of the elements of this matrix.
mean([axis]) Average the matrix over the given axis.
min() Minimum of the elements of this matrix.
multiply(other) Point-wise multiplication by another matrix, vector, or
nonzero() nonzero indices
prune() Remove empty space after all non-zero elements.
rad2deg() Element-wise rad2deg.
rint() Element-wise rint.
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
sqrt() Element-wise sqrt.
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.
tocoo([copy]) Return a COOrdinate representation of this matrix
todense([order, out]) Return a dense matrix representation of this matrix.
trunc() Element-wise trunc.