# scipy.sparse.coo_matrix¶

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

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, ij), [shape=(M, N)])
The arguments ‘data’ and ‘ij’ represent three arrays:
1. data[:] the entries of the matrix, in any order
2. ij[0][:] the row indices of the matrix entries
3. ij[1][:] the column indices of the matrix entries

Where A[ij[0][k], ij[1][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 *
>>> from scipy import *
>>> coo_matrix( (3,4), dtype=int8 ).todense()
matrix([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]], dtype=int8)
```
```>>> row  = array([0,3,1,0])
>>> col  = array([0,3,1,2])
>>> data = 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  = array([0,0,1,3,1,0,0])
>>> col  = array([0,2,1,3,1,0,0])
>>> data = 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

 asformat(format) Return this matrix in a given sparse format asfptype() Upcast matrix to a floating point format (if necessary) astype(t) conj() conjugate() copy() diagonal() Returns the main diagonal of the matrix dot(other) 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 reshape(shape) set_shape(shape) setdiag(values[, k]) Fills the diagonal elements {a_ii} with the values from the given sequence. sum([axis]) Sum the matrix over the given axis. 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() todia() todok() tolil() transpose([copy])

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