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<p>我有一个<code>n x n</code><code>numpy</code><code>float64</code><code>sparse matrix</code>(<code>data</code>,其中<code>n = 44</code>),其中的行和列是图节点,值是边权重:</p>
<pre><code>>>> 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
</code></pre>
<p>我想将它转换成<code>pandas</code><code>data frame</code>,以便将其写入一个文件,列为:<code>node1, node2, edge_weight</code>,因此它将给出:</p>
^{pr2}$
<p>你知道怎么做吗?在</p>
<p>请注意:</p>
^{3}$
<p>给出:</p>
<pre><code> 0
0 (0, 7)\t0.11793236293516568\n (0, 9)\t0.109...
1 (0, 6)\t0.015070325434709386\n (0, 10)\t9.3...
</code></pre>
<p>以及</p>
<pre><code>>>> pandas.DataFrame(print(data))
</code></pre>
<p>给出:</p>
<pre><code> (0, 7) 0.11793236293516568
(0, 9) 0.10992000939300195
</code></pre>
<p>所以我想<code>pandas.DataFrame(print(data))</code>很接近我要找的东西。在</p>