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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, (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]`.
    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

      >>> import numpy as np
      >>> from scipy.sparse import csr_matrix
      >>> csr_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, 0, 1, 2, 2, 2])
      >>> col = np.array([0, 2, 2, 0, 1, 2])
      >>> data = np.array([1, 2, 3, 4, 5, 6])
      >>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray()
      array([[1, 0, 2],
             [0, 0, 3],
             [4, 5, 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])
      >>> csr_matrix((data, indices, indptr), shape=(3, 3)).toarray()
      array([[1, 0, 2],
             [0, 0, 3],
             [4, 5, 6]])

  As an example of how to construct a CSR matrix incrementally, the following snippet builds a term-document matrix from texts:

      >>> docs = [["hello", "world", "hello"], ["goodbye", "cruel", "world"]]
      >>> indptr = [0]
      >>> indices = []
      >>> data = []
      >>> vocabulary = {}
      >>> for d in docs:
      ...     for term in d:
      ...         index = vocabulary.setdefault(term, len(vocabulary))
      ...         indices.append(index)
      ...         data.append(1)
      ...     indptr.append(len(indices))
      ...
      >>> csr_matrix((data, indices, indptr), dtype=int).toarray()
      array([[2, 1, 0, 0],
             [0, 1, 1, 1]])

  Attributes

  `nnz` | Get the count of explicitly-stored values (nonzeros)
  `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)
  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) | Ordinary dot product
  `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) CSR matrix (column vector).
  `getformat`() |
  `getmaxprint`() |
  `getnnz`([axis]) | Get the count of explicitly-stored values (nonzeros)
  `getrow`(i) | Returns a copy of row i of the matrix, as a (1 x n) CSR matrix (row vector).
  `log1p`() | Element-wise log1p.
  `max`([axis]) | Maximum of the elements of this matrix.
  `maximum`(other) |
  `mean`([axis]) | Average the matrix over the given axis.
  `min`([axis]) | Minimum of the elements of this matrix.
  `minimum`(other) |
  `multiply`(other) | Point-wise multiplication by another matrix, vector, or scalar.
  `nonzero`() | nonzero indices
  `power`(n[, dtype]) | This function performs element-wise power.
  `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]) | Set diagonal or off-diagonal elements of the array.
  `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
  `sqrt`() | Element-wise sqrt.
  `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.