Modify matrix df format

Question

Having a df as:

    14       15         16
14  10.1166  18.2331    65.0185
15  18.2331  6.664      57.5195
16  65.3499  57.851     20.9907

What is a different more efficient way to modify the df to look as

   a   b   c
0  14  14  10.1166
1  14  15  18.2331
2  14  16  65.0185
3  15  14  18.2331
4  15  15  6.664 
etc.

I wrote this code, but i dont like the fact that i need to use a loop for it.

for row in tt.index:
    row_vals = tt[tt.index==row]
    col_vals = row_vals.T
    col_vals['from_zone'] = row
    col_vals['to_zone'] = tt.index
    col_vals['travel_time'] = col_vals[row].astype('int')
    col_vals = col_vals.drop(row, axis=1)
    travel_data = pd.concat([travel_data,col_vals])

Answer 1

In [58]: df.stack().reset_index().rename(columns={'level_0':'a','level_1':'b',0:'c'})
Out[58]:
    a   b        c
0  14  14  10.1166
1  14  15  18.2331
2  14  16  65.0185
3  15  14  18.2331
4  15  15   6.6640
5  15  16  57.5195
6  16  14  65.3499
7  16  15  57.8510
8  16  16  20.9907

Step by step:

In [59]: df.stack()
Out[59]:
14  14    10.1166
    15    18.2331
    16    65.0185
15  14    18.2331
    15     6.6640
    16    57.5195
16  14    65.3499
    15    57.8510
    16    20.9907
dtype: float64

In [60]: df.stack().reset_index()
Out[60]:
   level_0 level_1        0
0       14      14  10.1166
1       14      15  18.2331
2       14      16  65.0185
3       15      14  18.2331
4       15      15   6.6640
5       15      16  57.5195
6       16      14  65.3499
7       16      15  57.8510
8       16      16  20.9907