Attributes

dtype : dtype

Data type of the array

shape : 2-tuple

Shape of the array

ndim : int

Number of dimensions (this is always 2)

nnz :

Number of stored values, including explicit zeros

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 indices are sorted

This can be instantiated in several ways:

csr_array(D)

with a dense array or rank-2 ndarray D

csr_array(S)

with another sparse array 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.

Notes

Sparse arrays can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and array power.

Advantages of the CSR format

• efficient arithmetic operations CSR + CSR, CSR * CSR, etc.

• efficient row slicing

• fast array vector products

Disadvantages of the CSR format

• slow column slicing operations (consider CSC)

• changes to the sparsity structure are expensive (consider LIL or DOK)

Compressed Sparse Row array

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]])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

networkx.convert_matrix.from_scipy_sparse_array scipy.sparse._arrays.csr_array

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