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 :

Data array of the array

indices :

CSC format index array

indptr :

CSC format index pointer array

has_sorted_indices :

Whether indices are sorted

This can be instantiated in several ways:

csc_array(D)

with a dense array or rank-2 ndarray D

csc_array(S)

with another sparse array S (equivalent to S.tocsc())

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

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

csc_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] .

csc_array((data, indices, indptr), [shape=(M, N)])

is the standard CSC representation where the row indices for column 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 CSC format

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

• efficient column slicing

• fast array vector products (CSR, BSR may be faster)

Disadvantages of the CSC format

• slow row slicing operations (consider CSR)

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

Compressed Sparse Column array

Examples

>>> import numpy as np
... from scipy.sparse import csc_array
... csc_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, 2, 2, 0, 1, 2])
... col = np.array([0, 0, 1, 2, 2, 2])
... data = np.array([1, 2, 3, 4, 5, 6])
... csc_array((data, (row, col)), shape=(3, 3)).toarray()
array([[1, 0, 4],
       [0, 0, 5],
       [2, 3, 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])
... csc_array((data, indices, indptr), shape=(3, 3)).toarray()
array([[1, 0, 4],
       [0, 0, 5],
       [2, 3, 6]])

See :

Back References

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

scipy.sparse._arrays.csc_array

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