scipy.sparse.csr_matrix

class scipy.sparse.csr_matrix(arg1, shape=None, dtype=None, copy=False)[source]

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 Number of stored values, including explicit zeros. 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() count_nonzero() Number of non-zero entries, equivalent to 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]) Number of stored values, including explicit zeros. 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, out]) Return the maximum of the matrix or maximum along an axis. maximum(other) 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) 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[, order]) Gives a new shape to a sparse matrix without changing its data. 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, dtype, out]) Sum the matrix 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]) See the docstring for spmatrix.toarray. tobsr([blocksize, copy]) Convert this matrix to Block Sparse Row format. tocoo([copy]) Convert this matrix to COOrdinate format. tocsc([copy]) tocsr([copy]) Convert this matrix to Compressed Sparse Row format. todense([order, out]) Return a dense matrix representation of this matrix. todia([copy]) Convert this matrix to sparse DIAgonal format. todok([copy]) Convert this matrix to Dictionary Of Keys format. tolil([copy]) Convert this matrix to LInked List format. transpose([axes, copy]) Reverses the dimensions of the sparse matrix. trunc() Element-wise trunc.