Convert a numpy float64 sparse matrix to a pandas data frame

Can you try toarray

pd.DataFrame(A.toarray())

This ipython session shows one way you could do it. The two steps are: convert the sparse matrix to COO format, and then create the Pandas DataFrame using the .row, .col and .data attributes of the COO matrix.

In [50]: data                                                                                                    Out[50]: <15x15 sparse matrix of type '<class 'numpy.float64'>'    with 11 stored elements in Compressed Sparse Row format>In [51]: print(data)                                                                                               (1, 12)   0.8581958095588134  (6, 12)   0.03828052946099181  (6, 14)   0.7908634838351427  (7, 1)    0.7995008873930302  (7, 11)   0.48477191537121145  (7, 13)   0.6226526443518743  (9, 4)    0.37242576669669103  (11, 1)   0.9604278557580955  (11, 5)   0.13285436036287313  (12, 11)  0.5631419223609928  (13, 8)   0.16481624650723847In [52]: import pandas as pd                                                                                     In [53]: c = data.tocoo()                                                                                        In [54]: df = pd.DataFrame({node1: c.row, node2: c.col, edge_weight: c.data})                                   In [55]: df                                                                                                      Out[55]:     node1  node2  edge_weight0       1     12     0.8581961       6     12     0.0382812       6     14     0.7908633       7      1     0.7995014       7     11     0.4847725       7     13     0.6226536       9      4     0.3724267      11      1     0.9604288      11      5     0.1328549      12     11     0.56314210     13      8     0.164816