scipy.sparse.csr_matrix

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

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, ij), [shape=(M, N)])
where data and ij satisfy the relationship a[ij[0, k], ij[1, 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]:indices[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

>>> from scipy.sparse import *
>>> from scipy import *
>>> csr_matrix( (3,4), dtype=int8 ).todense()
matrix([[0, 0, 0, 0],
        [0, 0, 0, 0],
        [0, 0, 0, 0]], dtype=int8)
>>> row = array([0,0,1,2,2,2])
>>> col = array([0,2,2,0,1,2])
>>> data = array([1,2,3,4,5,6])
>>> csr_matrix( (data,(row,col)), shape=(3,3) ).todense()
matrix([[1, 0, 2],
        [0, 0, 3],
        [4, 5, 6]])
>>> indptr = array([0,2,3,6])
>>> indices = array([0,2,2,0,1,2])
>>> data = array([1,2,3,4,5,6])
>>> csr_matrix( (data,indices,indptr), shape=(3,3) ).todense()
matrix([[1, 0, 2],
        [0, 0, 3],
        [4, 5, 6]])

Attributes

dtype
shape
ndim
nnz
has_sorted_indices
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

asformat
asfptype
astype
check_format
conj
conjugate
copy
diagonal
dot
eliminate_zeros
getH
get_shape
getcol
getformat
getmaxprint
getnnz
getrow
mean
multiply
nonzero
prune
reshape
set_shape
setdiag
sort_indices
sorted_indices
sum
sum_duplicates
toarray
tobsr
tocoo
tocsc
tocsr
todense
todia
todok
tolil
transpose

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