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scipy.sparse.

# csr_matrix

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

  Compressed Sparse Row matrix.

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

      where D is a 2-D ndarray

    csr_matrix(S)

      with another sparse array or 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.

  Attributes:
    dtypedtype

      Data type of the matrix

    `shape` 2-tuple

      Shape of the matrix

    ndimint

      Number of dimensions (this is always 2)

    `nnz`

      Number of stored values, including explicit zeros.

    `size`

      Number of stored values.

    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`

      Whether the indices are sorted

    `has_canonical_format`

      Whether the array/matrix has sorted indices and no duplicates

    `T`

      Transpose.

  Methods

  `__len__`() |
  `__mul__`(other) |
  `arcsin`() | Element-wise arcsin.
  `arcsinh`() | Element-wise arcsinh.
  `arctan`() | Element-wise arctan.
  `arctanh`() | Element-wise arctanh.
  `argmax`([axis, out, explicit]) | Return indices of maximum elements along an axis.
  `argmin`([axis, out, explicit]) | Return indices of minimum elements along an axis.
  `asformat`(format[, copy]) | Return this array/matrix in the passed format.
  `asfptype`() | Upcast matrix to a floating point format (if necessary)
  `astype`(dtype[, casting, copy]) | Cast the array/matrix elements to a specified type.
  `ceil`() | Element-wise ceil.
  `check_format`([full_check]) | Check whether the array/matrix respects the CSR or CSC format.
  `conj`([copy]) | Element-wise complex conjugation.
  `conjugate`([copy]) | Element-wise complex conjugation.
  `copy`() | Returns a copy of this array/matrix.
  `count_nonzero`([axis]) | Number of non-zero entries, equivalent to
  `deg2rad`() | Element-wise deg2rad.
  `diagonal`([k]) | Returns the kth diagonal of the array/matrix.
  `dot`(other) | Ordinary dot product
  `eliminate_zeros`() | Remove zero entries from the array/matrix
  `expm1`() | Element-wise expm1.
  `floor`() | Element-wise floor.
  `getH`() | Return the Hermitian transpose of this matrix.
  `get_shape`() | Get the shape of the matrix
  `getcol`(j) | Returns a copy of column j of the matrix, as an (m x 1) sparse matrix (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 matrix, as a (1 x n) sparse matrix (row vector).
  `log1p`() | Element-wise log1p.
  `max`([axis, out, explicit]) | Return the maximum of the array/matrix or maximum along an axis.
  `maximum`(other) | Element-wise maximum between this and another array/matrix.
  `mean`([axis, dtype, out]) | Compute the arithmetic mean along the specified axis.
  `min`([axis, out, explicit]) | Return the minimum of the array/matrix or maximum along an axis.
  `minimum`(other) | Element-wise minimum between this and another array/matrix.
  `multiply`(other) | Element-wise multiplication by another array/matrix.
  `nanmax`([axis, out, explicit]) | Return the maximum, ignoring any Nans, along an axis.
  `nanmin`([axis, out, explicit]) | Return the minimum, ignoring any Nans, along an axis.
  `nonzero`() | Nonzero indices of the array/matrix.
  `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/matrix without changing its data.
  `resize`(*shape) | Resize the array/matrix in-place to dimensions given by `shape`
  `rint`() | Element-wise rint.
  `set_shape`(shape) | Set the shape of the matrix in-place
  `setdiag`(values[, k]) | Set diagonal or off-diagonal elements of the array/matrix.
  `sign`() | Element-wise sign.
  `sin`() | Element-wise sin.
  `sinh`() | Element-wise sinh.
  `sort_indices`() | Sort the indices of this array/matrix in place
  `sorted_indices`() | Return a copy of this array/matrix with sorted indices
  `sqrt`() | Element-wise sqrt.
  `sum`([axis, dtype, out]) | Sum the array/matrix elements over a given axis.
  `sum_duplicates`() | Eliminate duplicate 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/matrix.
  `tobsr`([blocksize, copy]) | Convert this array/matrix to Block Sparse Row format.
  `tocoo`([copy]) | Convert this array/matrix to COOrdinate format.
  `tocsc`([copy]) | Convert this array/matrix to Compressed Sparse Column format.
  `tocsr`([copy]) | Convert this array/matrix to Compressed Sparse Row format.
  `todense`([order, out]) | Return a dense representation of this sparse matrix.
  `todia`([copy]) | Convert this array/matrix to sparse DIAgonal format.
  `todok`([copy]) | Convert this array/matrix to Dictionary Of Keys format.
  `tolil`([copy]) | Convert this array/matrix to List of Lists format.
  `trace`([offset]) | Returns the sum along diagonals of the sparse array/matrix.
  `transpose`([axes, copy]) | Reverses the dimensions of the sparse array/matrix.
  `trunc`() | Element-wise trunc.

  __getitem__ |

  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_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]])

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