old_contents: """
  Sparse matrix
  =============
  
  Scipy 2D sparse matrix module.
  
  Original code by Travis Oliphant.
  Modified and extended by Ed Schofield and Robert Cimrman.
  
  There are four available sparse matrix types:
      (1) csc_matrix: Compressed Sparse Column format
      (2) csr_matrix: Compressed Sparse Row format
      (3) lil_matrix: List of Lists format
      (4) dok_matrix: Dictionary of Keys format
  
  To construct a matrix efficiently, use either lil_matrix (recommended) or
  dok_matrix. The lil_matrix class supports basic slicing and fancy
  indexing with a similar syntax to NumPy arrays.
  
  To perform manipulations such as multiplication or inversion, first
  convert the matrix to either CSC or CSR format. The lil_matrix format is
  row-based, so conversion to CSR is efficient, whereas conversion to CSC
  is less so.
  
  Example:
      Construct a 10x1000 lil_matrix and add some values to it:
      >>> from scipy import sparse, linsolve
      >>> from numpy import rand, linalg
      >>> A = sparse.lil_matrix((1000, 1000))
      >>> A[0, :100] = rand(100)
      >>> A[1, 100:200] = A[0, :100]
      >>> A.setdiag(rand(1000))
      
      Now convert it to CSR format and solve (A A^T) x = b for x:
      >>> A = A.tocsr()
      >>> b = rand(1000)
      >>> x = linsolve.spsolve(A * A.T, b)
  
      Convert it to a dense matrix and solve, and check that the result
      is the same:
      >>> A_ = A.todense()
      >>> x_ = linalg.solve(A_ * A_.T, b)
      >>> err = linalg.norm(x-x_)
  
      Now we can print the error norm with:
          print "Norm error =", err
      It should be small :)
  
  """
  
  postpone_import = 1
  
new_contents: """
  Sparse matrix
  =============
  
  Scipy 2D sparse matrix module.
  
  Original code by Travis Oliphant.
  Modified and extended by Ed Schofield and Robert Cimrman.
  
  There are four available sparse matrix types:
      (1) csc_matrix: Compressed Sparse Column format
      (2) csr_matrix: Compressed Sparse Row format
      (3) lil_matrix: List of Lists format
      (4) dok_matrix: Dictionary of Keys format
  
  To construct a matrix efficiently, use either lil_matrix (recommended) or
  dok_matrix. The lil_matrix class supports basic slicing and fancy
  indexing with a similar syntax to NumPy arrays.
  
  To perform manipulations such as multiplication or inversion, first
  convert the matrix to either CSC or CSR format. The lil_matrix format is
  row-based, so conversion to CSR is efficient, whereas conversion to CSC
  is less so.
  
  Example:
      Construct a 10x1000 lil_matrix and add some values to it:
      >>> from scipy import sparse, linsolve
      >>> from numpy import linalg
      >>> from numpy.random import rand
      >>> A = sparse.lil_matrix((1000, 1000))
      >>> A[0, :100] = rand(100)
      >>> A[1, 100:200] = A[0, :100]
      >>> A.setdiag(rand(1000))
  
      Now convert it to CSR format and solve (A A^T) x = b for x:
      >>> A = A.tocsr()
      >>> b = rand(1000)
      >>> x = linsolve.spsolve(A * A.T, b)
  
      Convert it to a dense matrix and solve, and check that the result
      is the same:
      >>> A_ = A.todense()
      >>> x_ = linalg.solve(A_ * A_.T, b)
      >>> err = linalg.norm(x-x_)
  
      Now we can print the error norm with:
          print "Norm error =", err
      It should be small :)
  
  """
  
  postpone_import = 1
  
subject: Fix sparse.info docstring.