Convert a numpy float64 sparse matrix to a pandas data frame

I have an n x n numpy float64 sparse matrix (data, where n = 44), where the rows and columns are graph nodes and the values are edge weights:

>>> 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

I’d like to convert it to a pandas data frame, in order to write it to a file, with the columns: node1, node2, edge_weight, which will therefore give:

node1, node2, edge_weight
0, 7, 0.11793236293516568
0, 9, 0.10992000939300195
:, :, :
43, 36, 0.3381986531986532

Any idea how to do that?

Note that:

>>> pandas.DataFrame(data)

gives:

                                                    0
0     (0, 7)t0.11793236293516568n  (0, 9)t0.109...
1     (0, 6)t0.015070325434709386n  (0, 10)t9.3...

And

>>> pandas.DataFrame(print(data))

Gives:

  (0, 7)    0.11793236293516568
  (0, 9)    0.10992000939300195

So I guess pandas.DataFrame(print(data)) is close to what I’m looking for.