scipy.sparse.

csr_array#

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

Compressed Sparse Row array.

This can be instantiated in several ways:
csr_array(D)

where D is a 2-D ndarray

csr_array(S)

with another sparse array or matrix S (equivalent to S.tocsr())

csr_array((M, N), [dtype])

to construct an empty array with shape (M, N) dtype is optional, defaulting to dtype=’d’.

csr_array((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_array((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 array dimensions are inferred from the index arrays.

Attributes:
dtypedtype

Data type of the array

shape2-tuple

Shape of the array

ndimint

Number of dimensions (this is always 2)

nnz

Number of stored values, including explicit zeros.

size

Number of stored values.

data

CSR format data array of the array

indices

CSR format index array of the array

indptr

CSR format index pointer array of the array

has_sorted_indices

Whether the indices are sorted

has_canonical_format

Whether the array/matrix has sorted indices and no duplicates

T

Transpose.

Methods

__getitem__(key)

__len__()

arcsin()

Element-wise arcsin.

arcsinh()

Element-wise arcsinh.

arctan()

Element-wise arctan.

arctanh()

Element-wise arctanh.

argmax([axis, out])

Return indices of maximum elements along an axis.

argmin([axis, out])

Return indices of minimum elements along an axis.

asformat(format[, copy])

Return this array/matrix in the passed format.

astype(dtype[, casting, copy])

Cast the array/matrix elements to a specified type.

ceil()

Element-wise ceil.

check_format([full_check])

Check whether the array/matrix respects the CSR or CSC format.

conj([copy])

Element-wise complex conjugation.

conjugate([copy])

Element-wise complex conjugation.

copy()

Returns a copy of this array/matrix.

count_nonzero()

Number of non-zero entries, equivalent to

deg2rad()

Element-wise deg2rad.

diagonal([k])

Returns the kth diagonal of the array/matrix.

dot(other)

Ordinary dot product

eliminate_zeros()

Remove zero entries from the array/matrix

expm1()

Element-wise expm1.

floor()

Element-wise floor.

log1p()

Element-wise log1p.

max([axis, out])

Return the maximum of the array/matrix or maximum along an axis.

maximum(other)

Element-wise maximum between this and another array/matrix.

mean([axis, dtype, out])

Compute the arithmetic mean along the specified axis.

min([axis, out])

Return the minimum of the array/matrix or maximum along an axis.

minimum(other)

Element-wise minimum between this and another array/matrix.

multiply(other)

Point-wise multiplication by array/matrix, vector, or scalar.

nanmax([axis, out])

Return the maximum of the array/matrix or maximum along an axis, ignoring any NaNs.

nanmin([axis, out])

Return the minimum of the array/matrix or minimum along an axis, ignoring any NaNs.

nonzero()

Nonzero indices of the array/matrix.

power(n[, dtype])

This function performs element-wise power.

prune()

Remove empty space after all non-zero elements.

rad2deg()

Element-wise rad2deg.

reshape(self, shape[, order, copy])

Gives a new shape to a sparse array/matrix without changing its data.

resize(*shape)

Resize the array/matrix in-place to dimensions given by shape

rint()

Element-wise rint.

setdiag(values[, k])

Set diagonal or off-diagonal elements of the array/matrix.

sign()

Element-wise sign.

sin()

Element-wise sin.

sinh()

Element-wise sinh.

sort_indices()

Sort the indices of this array/matrix in place

sorted_indices()

Return a copy of this array/matrix with sorted indices

sqrt()

Element-wise sqrt.

sum([axis, dtype, out])

Sum the array/matrix elements over a given axis.

sum_duplicates()

Eliminate duplicate entries by adding them together

tan()

Element-wise tan.

tanh()

Element-wise tanh.

toarray([order, out])

Return a dense ndarray representation of this sparse array/matrix.

tobsr([blocksize, copy])

Convert this array/matrix to Block Sparse Row format.

tocoo([copy])

Convert this array/matrix to COOrdinate format.

tocsc([copy])

Convert this array/matrix to Compressed Sparse Column format.

tocsr([copy])

Convert this array/matrix to Compressed Sparse Row format.

todense([order, out])

Return a dense representation of this sparse array/matrix.

todia([copy])

Convert this array/matrix to sparse DIAgonal format.

todok([copy])

Convert this array/matrix to Dictionary Of Keys format.

tolil([copy])

Convert this array/matrix to List of Lists format.

trace([offset])

Returns the sum along diagonals of the sparse array/matrix.

transpose([axes, copy])

Reverses the dimensions of the sparse array/matrix.

trunc()

Element-wise trunc.

__mul__

Notes

Sparse arrays 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_array
>>> csr_array((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_array((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_array((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_array((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 array incrementally, the following snippet builds a term-document array 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_array((data, indices, indptr), dtype=int).toarray()
array([[2, 1, 0, 0],
       [0, 1, 1, 1]])
On this page