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scipy.sparse.

# coo_array

class scipy.sparse.coo_array(arg1, shape=None, dtype=None, copy=False, *, maxprint=None)

  A sparse array in COOrdinate format.

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

  This can be instantiated in several ways:
    coo_array(D)

      where D is an ndarray

    coo_array(S)

      with another sparse array or matrix S (equivalent to S.tocoo())

    coo_array(shape, [dtype])

      to construct an empty sparse array with shape `shape` dtype is optional, defaulting to dtype=’d’.

    coo_array((data, coords), [shape])
      to construct from existing data and index arrays:

        1. data[:] the entries of the sparse array, in any order

        2. coords[i][:] the axis-i coordinates of the data entries

      Where `A[coords] = data`, and coords is a tuple of index arrays. When shape is not specified, it is inferred from the index arrays.

  Attributes:
    dtype dtype

      Data type of the sparse array

    shape tuple of integers

      Shape of the sparse array

    ndim int

      Number of dimensions of the sparse array

    `nnz`

      Number of stored values, including explicit zeros.

    `size`

      Number of stored values.

    data

      COO format data array of the sparse array

    coords

      COO format tuple of index arrays

    has_canonical_format bool

      Whether the matrix has sorted coordinates and no duplicates

    `format`

      Format string for matrix.

    `T`

      Transpose.

  Methods

  `__len__`() |
  `arcsin`() | Element-wise arcsin.
  `arcsinh`() | Element-wise arcsinh.
  `arctan`() | Element-wise arctan.
  `arctanh`() | Element-wise arctanh.
  `argmax`([axis, out, explicit]) | Return indices of maximum elements along an axis.
  `argmin`([axis, out, explicit]) | Return indices of minimum elements along an axis.
  `asformat`(format[, copy]) | Return this array/matrix in the passed format.
  `astype`(dtype[, casting, copy]) | Cast the array/matrix elements to a specified type.
  `ceil`() | Element-wise ceil.
  `conj`([copy]) | Element-wise complex conjugation.
  `conjugate`([copy]) | Element-wise complex conjugation.
  `copy`() | Returns a copy of this array/matrix.
  `count_nonzero`([axis]) | Number of non-zero entries, equivalent to
  `deg2rad`() | Element-wise deg2rad.
  `diagonal`([k]) | Returns the kth diagonal of the array/matrix.
  `dot`(other) | Return the dot product of two arrays.
  `eliminate_zeros`() | Remove zero entries from the array/matrix
  `expm1`() | Element-wise expm1.
  `floor`() | Element-wise floor.
  `log1p`() | Element-wise log1p.
  `max`([axis, out, explicit]) | Return the maximum of the array/matrix or maximum along an axis.
  `maximum`(other) | Element-wise maximum between this and another array/matrix.
  `mean`([axis, dtype, out]) | Compute the arithmetic mean along the specified axis.
  `min`([axis, out, explicit]) | Return the minimum of the array/matrix or maximum along an axis.
  `minimum`(other) | Element-wise minimum between this and another array/matrix.
  `multiply`(other) | Element-wise multiplication by another array/matrix.
  `nanmax`([axis, out, explicit]) | Return the maximum, ignoring any Nans, along an axis.
  `nanmin`([axis, out, explicit]) | Return the minimum, ignoring any Nans, along an axis.
  `nonzero`() | Nonzero indices of the array/matrix.
  `power`(n[, dtype]) | This function performs element-wise power.
  `rad2deg`() | Element-wise rad2deg.
  `reshape`(self, shape[, order, copy]) | Gives a new shape to a sparse array/matrix without changing its data.
  `resize`(*shape) | Resize the array/matrix in-place to dimensions given by `shape`
  `rint`() | Element-wise rint.
  `setdiag`(values[, k]) | Set diagonal or off-diagonal elements of the array/matrix.
  `sign`() | Element-wise sign.
  `sin`() | Element-wise sin.
  `sinh`() | Element-wise sinh.
  `sqrt`() | Element-wise sqrt.
  `sum`([axis, dtype, out]) | Sum the array/matrix elements over a given axis.
  `sum_duplicates`() | Eliminate duplicate entries by adding them together
  `tan`() | Element-wise tan.
  `tanh`() | Element-wise tanh.
  `tensordot`(other[, axes]) | Return the tensordot product with another array along the given axes.
  `toarray`([order, out]) | Return a dense ndarray representation of this sparse array/matrix.
  `tobsr`([blocksize, copy]) | Convert this array/matrix to Block Sparse Row format.
  `tocoo`([copy]) | Convert this array/matrix to COOrdinate format.
  `tocsc`([copy]) | Convert this array/matrix to Compressed Sparse Column format
  `tocsr`([copy]) | Convert this array/matrix to Compressed Sparse Row format
  `todense`([order, out]) | Return a dense representation of this sparse array.
  `todia`([copy]) | Convert this array/matrix to sparse DIAgonal format.
  `todok`([copy]) | Convert this array/matrix to Dictionary Of Keys format.
  `tolil`([copy]) | Convert this array/matrix to List of Lists format.
  `trace`([offset]) | Returns the sum along diagonals of the sparse array/matrix.
  `transpose`([axes, copy]) | Reverses the dimensions of the sparse array/matrix.
  `trunc`() | Element-wise trunc.

  __mul__ |

  Notes

  Sparse arrays 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 arrays

    - Once a COO array 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)

  Canonical format

    - Entries and coordinates sorted by row, then column.

    - There are no duplicate entries (i.e. duplicate (i,j) locations)

    - Data arrays MAY have explicit zeros.

  Examples

      >>> # Constructing an empty sparse array
      >>> import numpy as np
      >>> from scipy.sparse import coo_array
      >>> coo_array((3, 4), dtype=np.int8).toarray()
      array([[0, 0, 0, 0],
             [0, 0, 0, 0],
             [0, 0, 0, 0]], dtype=int8)

      >>> # Constructing a sparse array using ijv format
      >>> row  = np.array([0, 3, 1, 0])
      >>> col  = np.array([0, 3, 1, 2])
      >>> data = np.array([4, 5, 7, 9])
      >>> coo_array((data, (row, col)), shape=(4, 4)).toarray()
      array([[4, 0, 9, 0],
             [0, 7, 0, 0],
             [0, 0, 0, 0],
             [0, 0, 0, 5]])

      >>> # Constructing a sparse array with duplicate coordinates
      >>> 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 = coo_array((data, (row, col)), shape=(4, 4))
      >>> # Duplicate coordinates are maintained until implicitly or explicitly summed
      >>> np.max(coo.data)
      1
      >>> coo.toarray()
      array([[3, 0, 1, 0],
             [0, 2, 0, 0],
             [0, 0, 0, 0],
             [0, 0, 0, 1]])