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, (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	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

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()
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])	Get the count of explicitly-stored values (nonzeros)
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])	Maximum of the elements of this matrix.
maximum(other)
mean([axis])	Average the matrix over the given axis.
min([axis])	Minimum of the elements of this matrix.
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)
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])	Sum the matrix over the 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])
tocoo([copy])	Return a COOrdinate representation of this matrix
tocsc()
tocsr([copy])
todense([order, out])	Return a dense matrix representation of this matrix.
todia()
todok()
tolil()
transpose([copy])
trunc()	Element-wise trunc.