`scipy.sparse._csc:csc_array`

source: /scipy/sparse/_csc.py:188

Signature

def csc_array(arg1, shape = None, dtype = None, copy = False, *, maxprint = None)

Summary

Compressed Sparse Column array.

Extended Summary

This can be instantiated in several ways:

csc_array(D): where D is a 2-D ndarray
csc_array(S): with another sparse array or matrix 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.

Attributes

data : ndarray: CSC format data array of the array
indices : ndarray: CSC format index array of the array
indptr : ndarray: CSC format index pointer array of the array
has_sorted_indices : bool: Whether indices are sorted
has_canonical_format : bool: Whether indices are sorted and no duplicate entries exist
dtype : dtype: Data type of the array
shape : 2-tuple: Shape of the array
ndim : int: Number of dimensions (this is always 2)
format : str: Three letter code for the format of the array storage, e.g. 'csc'
nnz : int: Number of values stored in the array
size : int: Number of values stored in the array
T : csc_array: The transpose of the array
mT : csc_array: The matrix transpose of the array

Notes

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

Advantages of the CSC format

efficient arithmetic operations CSC + CSC, CSC * CSC, etc.
efficient column slicing
fast matrix 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)

Canonical format

Within each column, indices are sorted by row.
There are no duplicate entries.

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]])

Aliases

scipy.sparse.csc_array