我有一个n x n numpy float64 sparse matrix (data, where n = 44），其中行和列是图节点，值是边权重：
>>> data
<44x44 sparse matrix of type '<class 'numpy.float64'>'
with 668 stored elements in Compressed Sparse Row format>
>>> type(data)
<class 'scipy.sparse.csr.csr_matrix'>
>>> print(data)
(0, 7) 0.11793236293516568
(0, 9) 0.10992000939300195
(0, 21) 0.7422196678913772
(0, 23) 0.0630039712667936
(0, 24) 0.027037442463504143
(0, 27) 0.16908845414214152
(0, 28) 0.6109227233402952
(0, 32) 0.0514765253537568
(0, 33) 0.016341754080557713
(1, 6) 0.015070325434709386
(1, 10) 9.346673769086203e-05
(1, 11) 0.2471018034781923
(1, 14) 0.0020684269551621776
(1, 18) 0.015258704502643251
(1, 20) 0.021798149289490358
(1, 22) 0.0087026831764125
(1, 24) 0.1454235884185166
(1, 25) 0.022060777594183015
(1, 29) 0.9117391202819067
(1, 30) 0.018557883854566116
(1, 31) 0.001876070225734826
(1, 32) 0.025841354399637764
(1, 33) 0.014766488228364438
(1, 39) 0.002791226433410351
(1, 43) 1.0
: :
(41, 7) 0.8922099840113696
(41, 10) 0.015776226631920767
(41, 12) 1.0
(41, 15) 0.1839408706622038
(41, 18) 0.5151025641025642
(41, 20) 0.4599130036630037
(41, 22) 0.29378473237788827
(41, 33) 0.47474890700697153
(41, 39) 1.0
(42, 2) 1.0
(42, 10) 0.023305789342610222
(42, 11) 0.011349136164776494
(42, 12) 1.0
(42, 17) 0.886081346522542
(42, 18) 1.0
(42, 30) 1.0
(42, 40) 1.0
(43, 1) 1.0
(43, 6) 1.0
(43, 11) 0.039948959300013256
(43, 13) 1.0
(43, 14) 0.02669811947637717
(43, 29) 1.0
(43, 30) 1.0
(43, 36) 0.3381986531986532
我想将其转换为pandas data frame，为了将其写入文件，其中包含以下列：node1, node2, edge_weight，因此将给出：
node1, node2, edge_weight
0, 7, 0.11793236293516568
0, 9, 0.10992000939300195
:, :, :
43, 36, 0.3381986531986532
知道该怎么做吗？
注意：
>>> pandas.DataFrame(data)
gives:
0
0 (0, 7)\t0.11793236293516568\n (0, 9)\t0.109...
1 (0, 6)\t0.015070325434709386\n (0, 10)\t9.3...
And
>>> pandas.DataFrame(print(data))
Gives:
(0, 7) 0.11793236293516568
(0, 9) 0.10992000939300195
所以我猜pandas.DataFrame(print(data))接近我正在寻找的东西。
