# scipy.sparse.csr_array

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

  Compressed Sparse Row matrix

  This can be instantiated in several ways:
    csr_array(D)

      with a dense matrix or rank-2 ndarray D

    csr_array(S)

      with another sparse matrix S (equivalent to S.tocsr())

    csr_array((M, N), [dtype])

      to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype=’d’.

    csr_array((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_array((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)

  Canonical Format

    - Within each row, indices are sorted by column.

    - There are no duplicate entries.

  Examples

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

  Duplicate entries are summed together:

      >>> row = np.array([0, 1, 2, 0])
      >>> col = np.array([0, 1, 1, 0])
      >>> data = np.array([1, 2, 4, 8])
      >>> csr_array((data, (row, col)), shape=(3, 3)).toarray()
      array([[9, 0, 0],
             [0, 2, 0],
             [0, 4, 0]])

  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_array((data, indices, indptr), dtype=int).toarray()
      array([[2, 1, 0, 0],
             [0, 1, 1, 1]])

  Attributes:
    dtype dtype

      Data type of the matrix

    `shape` 2-tuple

      The shape of the array.

    ndim int

      Number of dimensions (this is always 2)

    `nnz`

      Number of stored values, including explicit zeros.

    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

    `has_sorted_indices`

      Determine whether the matrix has sorted indices

  Methods

  `__len__`() |
  `arcsin`() | Element-wise arcsin.
  `arcsinh`() | Element-wise arcsinh.
  `arctan`() | Element-wise arctan.
  `arctanh`() | Element-wise arctanh.
  `argmax`([axis, out]) | Return indices of maximum elements along an axis.
  `argmin`([axis, out]) | Return indices of minimum elements along an axis.
  `asformat`(format[, copy]) | Return this array in the passed format.
  `asfptype`() | Upcast array to a floating point format (if necessary)
  `astype`(dtype[, casting, copy]) | Cast the array elements to a specified type.
  `ceil`() | Element-wise ceil.
  `check_format`([full_check]) | check whether the matrix format is valid
  `conj`([copy]) | Element-wise complex conjugation.
  `conjugate`([copy]) | Element-wise complex conjugation.
  `copy`() | Returns a copy of this array.
  `count_nonzero`() | Number of non-zero entries, equivalent to
  `deg2rad`() | Element-wise deg2rad.
  `diagonal`([k]) | Returns the kth diagonal of the array.
  `dot`(other) | Ordinary dot product
  `eliminate_zeros`() | Remove zero entries from the matrix
  `expm1`() | Element-wise expm1.
  `floor`() | Element-wise floor.
  `getH`() | Return the Hermitian transpose of this array.
  `get_shape`() | Get shape of a sparse array.
  `getcol`(j) | Returns a copy of column j of the array, as an (m x 1) sparse array (column vector).
  `getformat`() | Matrix storage format.
  `getmaxprint`() | Maximum number of elements to display when printed.
  `getnnz`([axis]) | Number of stored values, including explicit zeros.
  `getrow`(i) | Returns a copy of row i of the array, as a (1 x n) sparse array (row vector).
  `log1p`() | Element-wise log1p.
  `max`([axis, out]) | Return the maximum of the matrix or maximum along an axis.
  `maximum`(other) | Element-wise maximum between this and another array.
  `mean`([axis, dtype, out]) | Compute the arithmetic mean along the specified axis.
  `min`([axis, out]) | Return the minimum of the matrix or maximum along an axis.
  `minimum`(other) | Element-wise minimum between this and another array.
  `multiply`(other) | Point-wise multiplication by another matrix, vector, or scalar.
  `nanmax`([axis, out]) | Return the maximum of the matrix or maximum along an axis, ignoring any NaNs.
  `nanmin`([axis, out]) | Return the minimum of the matrix or minimum along an axis, ignoring any NaNs.
  `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`(self, shape[, order, copy]) | Gives a new shape to a sparse array without changing its data.
  `resize`(*shape) | Resize the array in-place to dimensions given by `shape`
  `rint`() | Element-wise rint.
  `set_shape`(shape) | See `reshape`.
  `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, dtype, out]) | Sum the array elements over a given axis.
  `sum_duplicates`() | Eliminate duplicate matrix entries by adding them together
  `tan`() | Element-wise tan.
  `tanh`() | Element-wise tanh.
  `toarray`([order, out]) | Return a dense ndarray representation of this sparse array.
  `tobsr`([blocksize, copy]) | Convert this array to Block Sparse Row format.
  `tocoo`([copy]) | Convert this array to COOrdinate format.
  `tocsc`([copy]) | Convert this array to Compressed Sparse Column format.
  `tocsr`([copy]) | Convert this array to Compressed Sparse Row format.
  `todense`([order, out]) | Return a dense matrix representation of this sparse array.
  `todia`([copy]) | Convert this array to sparse DIAgonal format.
  `todok`([copy]) | Convert this array to Dictionary Of Keys format.
  `tolil`([copy]) | Convert this array to List of Lists format.
  `trace`([offset]) | Returns the sum along diagonals of the sparse array.
  `transpose`([axes, copy]) | Reverses the dimensions of the sparse array.
  `trunc`() | Element-wise trunc.

  __getitem__ |
  __mul__ |