csr_matrix — SciPy v1.16.0 Manual (original) (raw)
scipy.sparse.
class scipy.sparse.csr_matrix(arg1, shape=None, dtype=None, copy=False, *, maxprint=None)[source]#
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
shape2-tuple
Shape of the matrix
ndimint
Number of dimensions (this is always 2)
Number of stored values, including explicit zeros.
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
Whether the indices are sorted
Whether the array/matrix has sorted indices and no duplicates
Transpose.
Methods
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]])