#### Previous topic

scipy.sparse.coo_matrix.trunc

# scipy.sparse.csc_matrix

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

  Compressed Sparse Column matrix

  This can be instantiated in several ways:

  > csc_matrix(D)
  >   with a dense matrix or rank-2 ndarray D
  > csc_matrix(S)
  >   with another sparse matrix S (equivalent to S.tocsc())
  > csc_matrix((M, N), [dtype])
  >   to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype=’d’.
  > csc_matrix((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_matrix((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 matrix dimensions are 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 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)

  Examples

      >>> import numpy as np
      >>> from scipy.sparse import csc_matrix
      >>> csc_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, 2, 2, 0, 1, 2])
      >>> col = np.array([0, 0, 1, 2, 2, 2])
      >>> data = np.array([1, 2, 3, 4, 5, 6])
      >>> csc_matrix((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_matrix((data, indices, indptr), shape=(3, 3)).toarray()
      array([[1, 0, 4],
             [0, 0, 5],
             [2, 3, 6]])

  Attributes

  `shape` | Get shape of a matrix.
  `nnz` | Number of stored values, including explicit zeros.
  `has_sorted_indices` | Determine whether the matrix has sorted indices

  dtype | (dtype) Data type of the matrix
  ndim | (int) Number of dimensions (this is always 2)
  data | Data array of the matrix
  indices | CSC format index array
  indptr | CSC format index pointer array

  Methods

  `arcsin`() | Element-wise arcsin.
  `arcsinh`() | Element-wise arcsinh.
  `arctan`() | Element-wise arctan.
  `arctanh`() | Element-wise arctanh.
  `argmax`([axis, out]) | Return indices of minimum elements along an axis.
  `argmin`([axis, out]) | Return indices of minimum elements along an axis.
  `asformat`(format) | Return this matrix in a given sparse format
  `asfptype`() | Upcast matrix to a floating point format (if necessary)
  `astype`(t) | Cast the matrix elements to a specified type.
  `ceil`() | Element-wise ceil.
  `check_format`([full_check]) | check whether the matrix format is valid
  `conj`() | Element-wise complex conjugation.
  `conjugate`() | Element-wise complex conjugation.
  `copy`() | Returns a copy of this matrix.
  `count_nonzero`() | Number of non-zero entries, equivalent to
  `deg2rad`() | Element-wise deg2rad.
  `diagonal`() | Returns the main diagonal of the matrix
  `dot`(other) | Ordinary dot product
  `eliminate_zeros`() | Remove zero entries from the matrix
  `expm1`() | Element-wise expm1.
  `floor`() | Element-wise floor.
  `getH`() | Return the Hermitian transpose of this matrix.
  `get_shape`() | Get shape of a matrix.
  `getcol`(i) | Returns a copy of column i of the matrix, as a (m x 1) CSC matrix (column vector).
  `getformat`() | Format of a matrix representation as a string.
  `getmaxprint`() | Maximum number of elements to display when printed.
  `getnnz`([axis]) | Number of stored values, including explicit zeros.
  `getrow`(i) | Returns a copy of row i of the matrix, as a (1 x n) CSR matrix (row vector).
  `log1p`() | Element-wise log1p.
  `max`([axis, out]) | Return the maximum of the matrix or maximum along an axis.
  `maximum`(other) | Element-wise maximum between this and another matrix.
  `mean`([axis, dtype, out]) | Compute the arithmetic mean along the specified axis.
  `min`([axis, out]) | Return the minimum of the matrix or maximum along an axis.
  `minimum`(other) | Element-wise minimum between this and another matrix.
  `multiply`(other) | Point-wise multiplication by another matrix, vector, or scalar.
  `nonzero`() | nonzero indices
  `power`(n[, dtype]) | This function performs element-wise power.
  `prune`() | Remove empty space after all non-zero elements.
  `rad2deg`() | Element-wise rad2deg.
  `reshape`(shape[, order]) | Gives a new shape to a sparse matrix without changing its data.
  `rint`() | Element-wise rint.
  `set_shape`(shape) | See `reshape`.
  `setdiag`(values[, k]) | Set diagonal or off-diagonal elements of the array.
  `sign`() | Element-wise sign.
  `sin`() | Element-wise sin.
  `sinh`() | Element-wise sinh.
  `sort_indices`() | Sort the indices of this matrix in place
  `sorted_indices`() | Return a copy of this matrix with sorted indices
  `sqrt`() | Element-wise sqrt.
  `sum`([axis, dtype, out]) | Sum the matrix elements over a 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, copy]) | Convert this matrix to Block Sparse Row format.
  `tocoo`([copy]) | Convert this matrix to COOrdinate format.
  `tocsc`([copy]) | Convert this matrix to Compressed Sparse Column format.
  `tocsr`([copy]) | Convert this matrix to Compressed Sparse Row format.
  `todense`([order, out]) | Return a dense matrix representation of this matrix.
  `todia`([copy]) | Convert this matrix to sparse DIAgonal format.
  `todok`([copy]) | Convert this matrix to Dictionary Of Keys format.
  `tolil`([copy]) | Convert this matrix to LInked List format.
  `transpose`([axes, copy]) | Reverses the dimensions of the sparse matrix.
  `trunc`() | Element-wise trunc.