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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, (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.

  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 coo_matrix
      >>> coo_matrix((3, 4), dtype=np.int8).toarray()
      array([[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)).toarray()
      array([[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)).toarray()
      array([[3, 0, 1, 0],
             [0, 2, 0, 0],
             [0, 0, 0, 0],
             [0, 0, 0, 1]])

  Attributes

  `nnz` | Get the count of explicitly-stored values (nonzeros) :Parameters: axis : None, 0, or 1 Select between the number of values across the whole matrix, in each column, or in each row.

  dtype | (dtype) Data type of the matrix
  shape | (2-tuple) Shape of the matrix
  ndim | (int) Number of dimensions (this is always 2)
  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) | Ordinary dot product ..
  `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 matrix (column vector).
  `getformat`() |
  `getmaxprint`() |
  `getnnz`([axis]) | Get the count of explicitly-stored values (nonzeros) :Parameters: axis : None, 0, or 1 Select between the number of values across the whole matrix, in each column, or in each row.
  `getrow`(i) | Returns a copy of row i of the matrix, as a (1 x n) sparse matrix (row vector).
  `log1p`() | Element-wise log1p.
  `max`([axis]) | Maximum of the elements of this matrix.
  `maximum`(other) |
  `mean`([axis]) | Average the matrix over the given axis.
  `min`([axis]) | Minimum of the elements of this matrix.
  `minimum`(other) |
  `multiply`(other) | Point-wise multiplication by another matrix
  `nonzero`() | nonzero indices
  `power`(n[, dtype]) | This function performs element-wise power.
  `rad2deg`() | Element-wise rad2deg.
  `reshape`(shape) |
  `rint`() | Element-wise rint.
  `set_shape`(shape) |
  `setdiag`(values[, k]) | Set diagonal or off-diagonal elements of the array.
  `sign`() | Element-wise sign.
  `sin`() | Element-wise sin.
  `sinh`() | Element-wise sinh.
  `sqrt`() | Element-wise sqrt.
  `sum`([axis]) | Sum the matrix over the given axis.
  `sum_duplicates`() | Eliminate duplicate matrix entries by adding them together
  `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 Duplicate entries will be summed together.
  `tocsr`() | Return a copy of this matrix in Compressed Sparse Row format Duplicate entries will be summed together.
  `todense`([order, out]) | Return a dense matrix representation of this matrix.
  `todia`() |
  `todok`() |
  `tolil`() |
  `transpose`([copy]) |
  `trunc`() | Element-wise trunc.