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# 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

  Advantages of the COO format
    - facilitates fast conversion among sparse formats
    - permits duplicate entries (see example)
    - very fast conversion to and from CSR/CSC formats
  Disadvantages of the COO format
    - 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]])

  Methods

  `asformat` |
  `asfptype` |
  `astype` |
  `conj` |
  `conjugate` |
  `copy` | Generic (shallow and deep) copying operations.
  `diagonal` |
  `dot` |
  `getH` |
  `get_shape` |
  `getcol` |
  `getformat` |
  `getmaxprint` |
  `getnnz` |
  `getrow` |
  `mean` |
  `multiply` |
  `nonzero` |
  `reshape` |
  `set_shape` |
  `setdiag` |
  `sum` |
  `toarray` |
  `tobsr` |
  `tocoo` |
  `tocsc` |
  `tocsr` |
  `todense` |
  `todia` |
  `todok` |
  `tolil` |
  `transpose` |

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