5.2.1.1.3. scipy.sparse.csc_matrix

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

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, (row_ind, col_ind)), [shape=(M, N)])
where data, row_ind and col_ind satisfy the relationship a[row_ind[k], col_ind[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

>>> import numpy as np
>>> from scipy.sparse import csc_matrix
>>> csc_matrix((3, 4), dtype=np.int8).toarray()
array([[0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0]], dtype=int8)

>>> row = np.array([0, 2, 2, 0, 1, 2])
>>> col = np.array([0, 0, 1, 2, 2, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csc_matrix((data, (row, col)), shape=(3, 3)).toarray()
array([[1, 0, 4],
       [0, 0, 5],
       [2, 3, 6]])

>>> indptr = np.array([0, 2, 3, 6])
>>> indices = np.array([0, 2, 2, 0, 1, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csc_matrix((data, indices, indptr), shape=(3, 3)).toarray()
array([[1, 0, 4],
       [0, 0, 5],
       [2, 3, 6]])

Attributes

nnz Get the count of explicitly-stored values (nonzeros)
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)
data Data array of the matrix
indices CSC format index array
indptr CSC format index pointer array
__init__(arg1, shape=None, dtype=None, copy=False)

Methods

__init__(arg1[, shape, dtype, copy])
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) Ordinary dot product
eliminate_zeros() Remove zero entries from the matrix
expm1() Element-wise expm1.
floor() Element-wise floor.
getH()
get_shape()
getcol(i) Returns a copy of column i of the matrix, as a (m x 1) CSC matrix (column vector).
getformat()
getmaxprint()
getnnz([axis]) Get the count of explicitly-stored values (nonzeros)
getrow(i) Returns a copy of row i of the matrix, as a (1 x n) CSR matrix (row vector).
log1p() Element-wise log1p.
max([axis]) Maximum of the elements of this matrix.
maximum(other)
mean([axis]) Average the matrix over the given axis.
min([axis]) Minimum of the elements of this matrix.
minimum(other)
multiply(other) Point-wise multiplication by another matrix, vector, or scalar.
nonzero() nonzero indices
power(n[, dtype]) This function performs element-wise power.
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]) Set diagonal or off-diagonal elements of the array.
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.
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.

Attributes

dtype
has_canonical_format Determine whether the matrix has sorted indices and no duplicates
has_sorted_indices Determine whether the matrix has sorted indices
ndim
nnz Get the count of explicitly-stored values (nonzeros)
shape