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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]: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 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]])

  Attributes

  `has_sorted_indices` | Determine whether the matrix has sorted indices

  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 nonzero elements
  data | CSR format data array of the matrix
  indices | CSR format index array of the matrix
  indptr | CSR format index pointer array of the matrix

  Methods

  `arcsin`() | Element-wise arcsin.
  `arcsinh`() | Element-wise arcsinh.
  `arctan`() | Element-wise arctan.
  `arctanh`() | Element-wise arctanh.
  `asformat`(format) | Return this matrix in a given sparse format
  `asfptype`() | Upcast matrix to a floating point format (if necessary)
  `astype`(t) |
  `ceil`() | Element-wise ceil.
  `check_format`([full_check]) | check whether the matrix format is valid
  `conj`() |
  `conjugate`() |
  `copy`() |
  `deg2rad`() | Element-wise deg2rad.
  `diagonal`() | Returns the main diagonal of the matrix
  `dot`(other) |
  `eliminate_zeros`() | Remove zero entries from the matrix
  `expm1`() | Element-wise expm1.
  `floor`() | Element-wise floor.
  `getH`() |
  `get_shape`() |
  `getcol`(i) | Returns a copy of column i of the matrix, as a (m x 1)
  `getformat`() |
  `getmaxprint`() |
  `getnnz`() |
  `getrow`(i) | Returns a copy of row i of the matrix, as a (1 x n)
  `log1p`() | Element-wise log1p.
  `mean`([axis]) | Average the matrix over the given axis.
  `multiply`(other) | Point-wise multiplication by another matrix
  `nonzero`() | nonzero indices
  `prune`() | Remove empty space after all non-zero elements.
  `rad2deg`() | Element-wise rad2deg.
  `reshape`(shape) |
  `rint`() | Element-wise rint.
  `set_shape`(shape) |
  `setdiag`(values[, k]) | Fills the diagonal elements {a_ii} with the values from the given sequence.
  `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
  `sum`([axis]) | Sum the matrix over the 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]) |
  `tocoo`([copy]) | Return a COOrdinate representation of this matrix
  `tocsc`() |
  `tocsr`([copy]) |
  `todense`([order, out]) | Return a dense matrix representation of this matrix.
  `todia`() |
  `todok`() |
  `tolil`() |
  `transpose`([copy]) |
  `trunc`() | Element-wise trunc.

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