本文以csr_matrix为例来说明sparse矩阵的使用方法，其他类型的sparse矩阵可以参考https://docs.scipy.org/doc/scipy/reference/sparse.html

csr_matrix是Compressed Sparse Row matrix的缩写组合，下面介绍其两种初始化方法

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.

import numpy as np
from scipy.sparse import csr_matrix
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])

a = csr_matrix((data, (row, col)), shape=(3, 3)).toarray()

print(a)

运行结果：

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])
a = csr_matrix((data, indices, indptr), shape=(3, 3)).toarray()
print(a)

运行结果：

array([[1, 0, 2],
       [0, 0, 3],
       [4, 5, 6]])

See https://www.cnblogs.com/leebxo/p/11897727.html