scipy.sparse.csr_matrix¶

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

Compressed Sparse Row matrix

This can be instantiated in several ways:
csr_matrix(D)
with a dense matrix or rank-2 ndarray D
csr_matrix(S)
with another sparse matrix S (equivalent to S.tocsr())
csr_matrix((M, N), [dtype])
to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype=’d’.
csr_matrix((data, ij), [shape=(M, N)])
where data and ij satisfy the relationship a[ij[0, k], ij[1, k]] = data[k]
csr_matrix((data, indices, indptr), [shape=(M, N)])
is the standard CSR representation where the column indices for row 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.

• efficient arithmetic operations CSR + CSR, CSR * CSR, etc.
• efficient row slicing
• fast matrix vector products
• slow column slicing operations (consider CSC)
• changes to the sparsity structure are expensive (consider LIL or DOK)

Examples

>>> from scipy.sparse import *
>>> from scipy import *
>>> csr_matrix( (3,4), dtype=int8 ).todense()
matrix([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]], dtype=int8)
>>> row = array([0,0,1,2,2,2])
>>> col = array([0,2,2,0,1,2])
>>> data = array([1,2,3,4,5,6])
>>> csr_matrix( (data,(row,col)), shape=(3,3) ).todense()
matrix([[1, 0, 2],
[0, 0, 3],
[4, 5, 6]])
>>> indptr = array([0,2,3,6])
>>> indices = array([0,2,2,0,1,2])
>>> data = array([1,2,3,4,5,6])
>>> csr_matrix( (data,indices,indptr), shape=(3,3) ).todense()
matrix([[1, 0, 2],
[0, 0, 3],
[4, 5, 6]])

Attributes

 dtype shape ndim int(x[, base]) -> integer nnz has_sorted_indices Determine whether the matrix has sorted indices
 data CSR format data array of the matrix indices CSR format index array of the matrix indptr CSR format index pointer array of the matrix

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, copy]) tocoo([copy]) Return a COOrdinate representation of this matrix tocsc() tocsr([copy]) todense() todia() todok() tolil() transpose([copy])

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