Reshaping a pandas correlation matrix

Question

I have the following correlation matrix which was created using pandas: df.corr()

symbol       aaa       bbb       ccc       ddd       eee
symbol                                                  
aaa     1.000000  0.346099  0.131874 -0.150910  0.177589
bbb     0.346099  1.000000  0.177308 -0.384893  0.301150
ccc     0.131874  0.177308  1.000000 -0.176995  0.258812
ddd    -0.150910 -0.384893 -0.176995  1.000000 -0.310137
eee     0.177589  0.301150  0.258812 -0.310137  1.000000

From the above dataframe, I need to transform it into a 3 column dataframe as follows:

aaa     aaa       1.000000
aaa     bbb       0.346099
aaa     ccc       0.131874
aaa     ddd      -0.150910
aaa     eee       0.177589
bbb     aaa       0.346099
bbb     bbb       1.000000
bbb     ccc       0.177308
bbb     ddd      -0.384893
bbb     eee       0.301150
ccc     aaa       0.131874
ccc     bbb       0.177308
ccc     ccc       1.000000
ccc     ddd      -0.176995
ccc     eee       0.258812
ddd     aaa      -0.150910
ddd     bbb      -0.384893
ddd     ccc      -0.176995
ddd     ddd       1.000000
ddd     eee      -0.310137
eee     aaa       0.177589
eee     bbb       0.301150
eee     ccc       0.258812
eee     ddd      -0.310137
eee     eee       1.000000

As shown, it is the same data, just presented differently. Each column/row pair from the original dataframe is simply grouped together into it's own row in the new dataframe.

Unfortunately I can't figure out how to get this done with the result being a dataframe. I have tried doing df.stack() but the the result of this is a Series. I need it to be a dataframe so that I can work with the columns. The other problem with df.stack() is that it does not fill in every row, here is a small sample of the issue:

aaa     aaa       1.000000
        bbb       0.346099
        ccc       0.131874
        ddd      -0.150910
        eee       0.177589
bbb     aaa       0.346099
        bbb       1.000000
        ccc       0.177308
        ddd      -0.384893
        eee       0.301150
etc...

Answer 1

You need add reset_index:

#reset columns and index names 
df = df.rename_axis(None).rename_axis(None, axis=1)

#if pandas version below 0.18.0
#df.columns.name = None
#df.index.name = None

print (df)
          aaa       bbb       ccc       ddd       eee
aaa  1.000000  0.346099  0.131874 -0.150910  0.177589
bbb  0.346099  1.000000  0.177308 -0.384893  0.301150
ccc  0.131874  0.177308  1.000000 -0.176995  0.258812
ddd -0.150910 -0.384893 -0.176995  1.000000 -0.310137
eee  0.177589  0.301150  0.258812 -0.310137  1.000000

df1 = df.stack().reset_index()
#set column names
df1.columns = ['a','b','c']
print (df1)
      a    b         c
0   aaa  aaa  1.000000
1   aaa  bbb  0.346099
2   aaa  ccc  0.131874
3   aaa  ddd -0.150910
4   aaa  eee  0.177589
5   bbb  aaa  0.346099
6   bbb  bbb  1.000000
7   bbb  ccc  0.177308
8   bbb  ddd -0.384893
9   bbb  eee  0.301150
10  ccc  aaa  0.131874
11  ccc  bbb  0.177308
12  ccc  ccc  1.000000
13  ccc  ddd -0.176995
14  ccc  eee  0.258812
15  ddd  aaa -0.150910
16  ddd  bbb -0.384893
17  ddd  ccc -0.176995
18  ddd  ddd  1.000000
19  ddd  eee -0.310137
20  eee  aaa  0.177589
21  eee  bbb  0.301150
22  eee  ccc  0.258812
23  eee  ddd -0.310137
24  eee  eee  1.000000

Answer 2

Use the code below to (a) reshape the correlation matrix, (b) remove duplicate rows (e.g., {aaa, bbb} and {bbb, aaa}), and (c) remove rows that contain the same variable in the first two columns (e.g., {aaa, aaa}):

# calculate the correlation matrix and reshape
df_corr = df.corr().stack().reset_index()

# rename the columns
df_corr.columns = ['FEATURE_1', 'FEATURE_2', 'CORRELATION']

# create a mask to identify rows with duplicate features as mentioned above
mask_dups = (df_corr[['FEATURE_1', 'FEATURE_2']].apply(frozenset, axis=1).duplicated()) | (df_corr['FEATURE_1']==df_corr['FEATURE_2']) 

# apply the mask to clean the correlation dataframe
df_corr = df_corr[~mask_dups]

This will generate an output like this:

    FEATURE_1  FEATURE_2  CORRELATION
0         aaa        bbb     0.346099
1         aaa        ccc     0.131874
2         aaa        ddd    -0.150910
3         aaa        eee     0.177589
4         bbb        ccc     0.177308
5         bbb        ddd    -0.384893
6         bbb        eee     0.301150
7         ccc        ddd    -0.176995
8         ccc        eee     0.258812
9         ddd        eee    -0.310137