from scipy.sparse import *
row = [0,0,0,1,1,1,2,2,2]#行指标
col = [0,1,2,0,1,2,0,1,2]#列指标
data = [1,0,1,0,1,1,1,1,0]#在行指标列指标下的数字
team = csr_matrix((data,(row,col)),shape=(3,3))
print(team)
print(team.todense())
输出结果:
(0, 0) 1
(0, 1) 0
(0, 2) 1
(1, 0) 0
(1, 1) 1
(1, 2) 1
(2, 0) 1
(2, 1) 1
(2, 2) 0
[[1 0 1]
[0 1 1]
[1 1 0]]
Process finished with exit code 0
row = [0,0,0,0,1,1,1,1,2,2,2,2]#行指标
col = [0,1,2,3,0,1,2,3,0,1,2,3]#列指标
data = [1,0,1,1,0,1,1,1,1,0,1,1]#在行指标列指标下的数字
team = csr_matrix((data,(row,col)),shape=(3,4))
# print(team)
# print(team.todense())
team_dok = team.todok()
# print(team_dok)
team_coo = team_dok.tocoo()
item =list(team_coo.col.reshape(-1))
# print(type(item))
print(item)
输出:
[0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3]
team_coo = team_dok.tocoo()
item =list(team_coo.col.reshape(-1))
user =list(team_coo.row.reshape(-1))
print("col:",item)
print("row:",user)
输出:
col: [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3]
row: [0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2]
Process finished with exit code 0