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
andcol_ind
satisfy the relationshipa[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 indata[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