Pandas - Creating Difference Matrix from Data Frame

Question

I'm trying to create a matrix to show the differences between the rows in a Pandas data frame.

import pandas as pd

data = {'Country':['GB','JP','US'],'Values':[20.2,-10.5,5.7]}
df = pd.DataFrame(data)

I would like this:

  Country  Values
0      GB    20.2
1      JP   -10.5
2      US     5.7

To become something like this (differences going vertically):

  Country     GB     JP     US
0      GB    0.0  -30.7   14.5
1      JP   30.7    0.0   16.2
2      US   14.5  -16.2    0.0

Is this achievable with built-in function or would I need to build a loop to get the desired output? Thanks for your help!

Answer 1

This is a standard use case for numpy's broadcasting:

df['Values'].values - df['Values'].values[:, None]
Out: 
array([[  0. , -30.7, -14.5],
       [ 30.7,   0. ,  16.2],
       [ 14.5, -16.2,   0. ]])

We access the underlying numpy array with the values attribute and [:, None] introduces a new axis so the result is two dimensional.

You can concat this with your original Series:

arr = df['Values'].values - df['Values'].values[:, None]
pd.concat((df['Country'], pd.DataFrame(arr, columns=df['Country'])), axis=1)
Out: 
  Country    GB    JP    US
0      GB   0.0 -30.7 -14.5
1      JP  30.7   0.0  16.2
2      US  14.5 -16.2   0.0

The array can also be generated with the following, thanks to @Divakar:

arr = np.subtract.outer(*[df.Values]*2).T

Here we are calling .outer on the subtract ufunc and it applies it to all pair of its inputs.

Answer 2

I try improve Divakar comment:

a = np.column_stack([df['Country'], np.subtract.outer(*[-df.Values]*2)])

df = pd.DataFrame(a, columns=['Country'] + df['Country'].tolist())
print (df)
  Country    GB    JP    US
0      GB     0 -30.7 -14.5
1      JP  30.7     0  16.2
2      US  14.5 -16.2     0