Efficient way to create dictionary of symmetric matrix with colum-row pair as key, and corresponding value in matrix as value - pandas

Question

I want to create a dictionary in the form of (row, column): value, from a symmetric matrix (like a distance matrix) as depicted below, whithout taking into account the NaN values or zeros (zeros is the diagonal). The matrix is a pandas dataframe.
Material 100051 100120 100138 100179 100253 100265 100281
100051 0.0 0.953488 0.959302 0.953488 0.959302 0.953488 0.953488
100120 NaN 0.000000 0.965116 0.953488 0.959302 0.959302 0.959302
100138 NaN NaN 0.000000 0.959302 0.970930 0.970930 0.970930
100179 NaN NaN NaN 0.000000 0.959302 0.953488 0.953488
100253 NaN NaN NaN NaN 0.000000 0.976744 0.976744
... ... ... ... ... ... ... ...
So a dictionary that looks like:
{((100120, 100051): 0.953488); ((1000138, 100051): 0.959302); ....}
For creating a dictionary, you can probably iterate over both rows and columns like:
jacsim_values = {}
for i in jacsim_matrix2:
for j in jacsim_matrix2:
if jacsim_matrix[i][j] != 0:
jacsim_values[i,j] = jacsim_matrix2[i][j]
But I am looking for something more efficient. This takes quite some time for the size of the matrix. However, I could not find how to do so. Is there somebody who can help me out?

Answer 1

IIUC, DataFrame.stack (row, column) or DataFrame.unstack (column, row) + DataFrame.to_dict
df.set_index('Material').rename(int, axis=1).unstack().to_dict()
{(100051, 100051): 0.0,
(100051, 100120): nan,
(100051, 100138): nan,
(100051, 100179): nan,
(100051, 100253): nan,
(100120, 100051): 0.9534879999999999,
(100120, 100120): 0.0,
(100120, 100138): nan,
(100120, 100179): nan,
(100120, 100253): nan,
(100138, 100051): 0.9593020000000001,
(100138, 100120): 0.965116,
(100138, 100138): 0.0,
(100138, 100179): nan,
(100138, 100253): nan,
(100179, 100051): 0.9534879999999999,
(100179, 100120): 0.9534879999999999,
(100179, 100138): 0.9593020000000001,
(100179, 100179): 0.0,
(100179, 100253): nan,
(100253, 100051): 0.9593020000000001,
(100253, 100120): 0.9593020000000001,
(100253, 100138): 0.97093,
(100253, 100179): 0.9593020000000001,
(100253, 100253): 0.0,
(100265, 100051): 0.9534879999999999,
(100265, 100120): 0.9593020000000001,
(100265, 100138): 0.97093,
(100265, 100179): 0.9534879999999999,
(100265, 100253): 0.9767440000000001,
(100281, 100051): 0.9534879999999999,
(100281, 100120): 0.9593020000000001,
(100281, 100138): 0.97093,
(100281, 100179): 0.9534879999999999,
(100281, 100253): 0.9767440000000001}