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, 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.

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

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