# scipy.sparse.coo_matrix¶

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

A sparse matrix in COOrdinate format.

Also known as the ‘ijv’ or ‘triplet’ format.

This can be instantiated in several ways:
coo_matrix(D)
with a dense matrix D
coo_matrix(S)
with another sparse matrix S (equivalent to S.tocoo())
coo_matrix((M, N), [dtype])
to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype=’d’.
coo_matrix((data, (i, j)), [shape=(M, N)])
to construct from three arrays:
1. data[:] the entries of the matrix, in any order
2. i[:] the row indices of the matrix entries
3. j[:] the column indices of the matrix entries

Where A[i[k], j[k]] = data[k]. When shape is not specified, it is inferred from the index arrays

Notes

Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power.

• facilitates fast conversion among sparse formats
• permits duplicate entries (see example)
• very fast conversion to and from CSR/CSC formats
• does not directly support:
• arithmetic operations
• slicing
Intended Usage
• COO is a fast format for constructing sparse matrices
• Once a matrix has been constructed, convert to CSR or CSC format for fast arithmetic and matrix vector operations
• By default when converting to CSR or CSC format, duplicate (i,j) entries will be summed together. This facilitates efficient construction of finite element matrices and the like. (see example)

Examples

```>>> from scipy.sparse import coo_matrix
>>> coo_matrix((3,4), dtype=np.int8).todense()
matrix([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]], dtype=int8)
```
```>>> row  = np.array([0,3,1,0])
>>> col  = np.array([0,3,1,2])
>>> data = np.array([4,5,7,9])
>>> coo_matrix((data,(row,col)), shape=(4,4)).todense()
matrix([[4, 0, 9, 0],
[0, 7, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 5]])
```
```>>> # example with duplicates
>>> row  = np.array([0,0,1,3,1,0,0])
>>> col  = np.array([0,2,1,3,1,0,0])
>>> data = np.array([1,1,1,1,1,1,1])
>>> coo_matrix((data, (row,col)), shape=(4,4)).todense()
matrix([[3, 0, 1, 0],
[0, 2, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 1]])
```

Attributes

 dtype shape ndim int(x[, base]) -> integer nnz
 data COO format data array of the matrix row COO format row index array of the matrix col COO format column index array of the matrix

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. conj() conjugate() copy() deg2rad() Element-wise deg2rad. diagonal() Returns the main diagonal of the matrix dot(other) 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 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. sum([axis]) Sum the matrix over the given axis. tan() Element-wise tan. tanh() Element-wise tanh. toarray([order, out]) See the docstring for spmatrix.toarray. tobsr([blocksize]) tocoo([copy]) tocsc() Return a copy of this matrix in Compressed Sparse Column format tocsr() Return a copy of this matrix in Compressed Sparse Row format todense([order, out]) Return a dense matrix representation of this matrix. todia() todok() tolil() transpose([copy]) trunc() Element-wise trunc.

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