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# scipy.sparse.csr_matrix

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

  Compressed Sparse Row matrix

  This can be instantiated in several ways:
    csr_matrix(D)
      with a dense matrix or rank-2 ndarray D
    csr_matrix(S)
      with another sparse matrix S (equivalent to S.tocsr())
    csr_matrix((M, N), [dtype])
      to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype=’d’.
    csr_matrix((data, ij), [shape=(M, N)])
      where `data` and `ij` satisfy the relationship `a[ij[0, k], ij[1, k]] = data[k]`
    csr_matrix((data, indices, indptr), [shape=(M, N)])
      is the standard CSR representation where the column indices for row 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 CSR format
    - efficient arithmetic operations CSR + CSR, CSR * CSR, etc.
    - efficient row slicing
    - fast matrix vector products
  Disadvantages of the CSR format
    - slow column slicing operations (consider CSC)
    - changes to the sparsity structure are expensive (consider LIL or DOK)

  Examples

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

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

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

  Methods

  `asformat` |
  `asfptype` |
  `astype` |
  `check_format` |
  `conj` |
  `conjugate` |
  `copy` | Generic (shallow and deep) copying operations.
  `diagonal` |
  `dot` |
  `eliminate_zeros` |
  `getH` |
  `get_shape` |
  `getcol` |
  `getformat` |
  `getmaxprint` |
  `getnnz` |
  `getrow` |
  `mean` |
  `multiply` |
  `nonzero` |
  `prune` |
  `reshape` |
  `set_shape` |
  `setdiag` |
  `sort_indices` |
  `sorted_indices` |
  `sum` |
  `sum_duplicates` |
  `toarray` |
  `tobsr` |
  `tocoo` |
  `tocsc` |
  `tocsr` |
  `todense` |
  `todia` |
  `todok` |
  `tolil` |
  `transpose` |

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