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# scipy.sparse.csc_matrix

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

  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, ij), [shape=(M, N)])
      where `data` and `ij` satisfy `a[ij[0, k], ij[1, 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]:indices[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

  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)

  Examples

      >>> from scipy.sparse import *
      >>> from scipy import *
      >>> csc_matrix( (3,4), dtype=int8 ).todense()
      matrix([[0, 0, 0, 0],
              [0, 0, 0, 0],
              [0, 0, 0, 0]], dtype=int8)

      >>> row = array([0,2,2,0,1,2])
      >>> col = array([0,0,1,2,2,2])
      >>> data = array([1,2,3,4,5,6])
      >>> csc_matrix( (data,(row,col)), shape=(3,3) ).todense()
      matrix([[1, 0, 4],
              [0, 0, 5],
              [2, 3, 6]])

      >>> indptr = array([0,2,3,6])
      >>> indices = array([0,2,2,0,1,2])
      >>> data = array([1,2,3,4,5,6])
      >>> csc_matrix( (data,indices,indptr), shape=(3,3) ).todense()
      matrix([[1, 0, 4],
              [0, 0, 5],
              [2, 3, 6]])

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