Melt the Upper Triangular Matrix of a Pandas Dataframe

Question

Given a square pandas DataFrame of the following form:

   a  b  c
a  1 .5 .3
b .5  1 .4
c .3 .4  1

How can I melt only the upper triangle to get

 Row     Column    Value
  a        a       1
  a        b       .5 
  a        c       .3
  b        b       1
  b        c       .4
  c        c       1 

#Note the combination a,b is only listed once.  There is no b,a listing     

I'm more interested in an idiomatic pandas solution, a custom indexer would be easy enough to write by hand... Thank you in advance for your consideration and response.

Answer 1

First I convert lower values of df to NaN by where and numpy.triu and then stack, reset_index and set column names:

import numpy as np

print df
     a    b    c
a  1.0  0.5  0.3
b  0.5  1.0  0.4
c  0.3  0.4  1.0

print np.triu(np.ones(df.shape)).astype(np.bool)
[[ True  True  True]
 [False  True  True]
 [False False  True]]

df = df.where(np.triu(np.ones(df.shape)).astype(np.bool))
print df
    a    b    c
a   1  0.5  0.3
b NaN  1.0  0.4
c NaN  NaN  1.0

df = df.stack().reset_index()
df.columns = ['Row','Column','Value']
print df

  Row Column  Value
0   a      a    1.0
1   a      b    0.5
2   a      c    0.3
3   b      b    1.0
4   b      c    0.4
5   c      c    1.0

Answer 2

Building from solution by @jezrael, boolean indexing would be a more explicit approach:

import numpy
from pandas import DataFrame

df = DataFrame({'a':[1,.5,.3],'b':[.5,1,.4],'c':[.3,.4,1]},index=list('abc'))
print df,'\n'
keep = np.triu(np.ones(df.shape)).astype('bool').reshape(df.size)
print df.stack()[keep]

output:

     a    b    c
a  1.0  0.5  0.3
b  0.5  1.0  0.4
c  0.3  0.4  1.0 

a  a    1.0
   b    0.5
   c    0.3
b  b    1.0
   c    0.4
c  c    1.0
dtype: float64