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scipy.sparse.

# csc_array

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

  Compressed Sparse Column array.

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

      where D is a 2-D ndarray

    csc_array(S)

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

    csc_array((M, N), [dtype])

      to construct an empty array with shape (M, N) dtype is optional, defaulting to dtype=’d’.

    csc_array((data, (row_ind, col_ind)), [shape=(M, N)])

      where `data`, `row_ind` and `col_ind` satisfy the relationship `a[row_ind[k], col_ind[k]] = data[k]`.

    csc_array((data, indices, indptr), [shape=(M, N)])

      is the standard CSC representation where the row indices for column i are stored in `indices[indptr[i]:indptr[i+1]]` and their corresponding values are stored in `data[indptr[i]:indptr[i+1]]`. If the shape parameter is not supplied, the array dimensions are inferred from the index arrays.

  Attributes:
    dtype dtype

      Data type of the array

    shape 2-tuple

      Shape of the array

    ndim int

      Number of dimensions (this is always 2)

    `nnz`

      Number of stored values, including explicit zeros.

    `size`

      Number of stored values.

    data

      CSC format data array of the array

    indices

      CSC format index array of the array

    indptr

      CSC format index pointer array of the array

    `has_sorted_indices`

      Whether the indices are sorted

    `has_canonical_format`

      Whether the array/matrix has sorted indices and no duplicates

    `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.
  `check_format`([full_check]) | Check whether the array/matrix respects the CSR or CSC format.
  `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) | Ordinary dot product
  `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.
  `prune`() | Remove empty space after all non-zero elements.
  `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.
  `sort_indices`() | Sort the indices of this array/matrix in place
  `sorted_indices`() | Return a copy of this array/matrix with sorted indices
  `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.
  `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.

  __getitem__ |
  __mul__ |

  Notes

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

  Advantages of the CSC format

    - efficient arithmetic operations CSC + CSC, CSC * CSC, etc.

    - efficient column slicing

    - fast matrix vector products (CSR, BSR may be faster)

  Disadvantages of the CSC format

    - slow row slicing operations (consider CSR)

    - changes to the sparsity structure are expensive (consider LIL or DOK)

  Canonical format

    - Within each column, indices are sorted by row.

    - There are no duplicate entries.

  Examples

      >>> import numpy as np
      >>> from scipy.sparse import csc_array
      >>> csc_array((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, 2, 2, 0, 1, 2])
      >>> col = np.array([0, 0, 1, 2, 2, 2])
      >>> data = np.array([1, 2, 3, 4, 5, 6])
      >>> csc_array((data, (row, col)), shape=(3, 3)).toarray()
      array([[1, 0, 4],
             [0, 0, 5],
             [2, 3, 6]])

      >>> indptr = np.array([0, 2, 3, 6])
      >>> indices = np.array([0, 2, 2, 0, 1, 2])
      >>> data = np.array([1, 2, 3, 4, 5, 6])
      >>> csc_array((data, indices, indptr), shape=(3, 3)).toarray()
      array([[1, 0, 4],
             [0, 0, 5],
             [2, 3, 6]])