class_docstr: 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.
  
  Attributes
  ----------
  dtype : dtype
      Data type of the matrix
  shape : 2-tuple
      Shape of the matrix
  ndim : int
      Number of dimensions (this is always 2)
  nnz
      Number of stored values, including explicit zeros
  data
      Data array of the matrix
  indices
      CSC format index array
  indptr
      CSC format index pointer array
  has_sorted_indices
      Whether indices are sorted
  
  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]])
code: def nonzero(self):
      # CSC can't use _cs_matrix's .nonzero method because it
      # returns the indices sorted for self transposed.
  
      # Get row and col indices, from _cs_matrix.tocoo
      major_dim, minor_dim = self._swap(self.shape)
      minor_indices = self.indices
      major_indices = np.empty(
          len(minor_indices), dtype=self.indices.dtype
      )
      expandptr(major_dim, self.indptr, major_indices)
      row, col = self._swap((major_indices, minor_indices))
  
      # Remove explicit zeros
      nz_mask = self.data != 0
      row = row[nz_mask]
      col = col[nz_mask]
  
      # Sort them to be in C-style order
      ind = np.argsort(row, kind="mergesort")
      row = row[ind]
      col = col[ind]
  
      return row, col
  
imports: import numpy
  from .base import spmatrix
  from ._sparsetools import csc_tocsr, expandptr
  from .sputils import upcast, get_index_dtype
  from .compressed import _cs_matrix
label_desc: Use image node_id 5 for calling the csc_matrix obj's underlying member method code with example usage: obj.nonzero() and returns: row,  col