toarray(self, order=None, out=None)

Parameters

order : {'C', 'F'}, optional

Whether to store multidimensional data in C (row-major) or Fortran (column-major) order in memory. The default is 'None', which provides no ordering guarantees. Cannot be specified in conjunction with the :None:None:`out` argument.

out : ndarray, 2-D, optional

If specified, uses this array as the output buffer instead of allocating a new array to return. The provided array must have the same shape and dtype as the sparse matrix on which you are calling the method. For most sparse types, :None:None:`out` is required to be memory contiguous (either C or Fortran ordered).

Returns

arr : ndarray, 2-D

An array with the same shape and containing the same data represented by the sparse matrix, with the requested memory order. If :None:None:`out` was passed, the same object is returned after being modified in-place to contain the appropriate values.

Return a dense ndarray representation of this matrix.

Examples

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.sparse.csgraph._flow.maximum_flow scipy.sparse._construct.vstack scipy.sparse._construct.hstack scipy.sparse._extract.triu scipy.sparse.linalg._dsolve.linsolve.spsolve scipy.sparse._coo.coo_matrix.tocsr scipy.sparse._csr.csr_matrix scipy.sparse._matrix_io.load_npz scipy.sparse._csc.csc_matrix scipy.sparse.linalg._onenormest.onenormest scipy.sparse.linalg._eigen._svds.svds scipy.sparse._construct.bmat scipy.sparse.linalg._expm_multiply.expm_multiply scipy.sparse.csgraph._matching.min_weight_full_bipartite_matching scipy.sparse.csgraph._matching.maximum_bipartite_matching scipy.sparse._construct.eye scipy.sparse._coo.coo_matrix.tocsc scipy.sparse._matrix_io.save_npz networkx.linalg.bethehessianmatrix.bethe_hessian_matrix scipy.sparse._construct.identity scipy.sparse.linalg._matfuncs.expm scipy.sparse._extract.tril scipy.sparse._arrays.csr_array scipy.sparse._arrays.csc_array networkx.convert_matrix.to_scipy_sparse_array

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GitHub : /scipy/sparse/_compressed.py#1048
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