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\         


        

Answer 1

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

Answer 2

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 3

Option 1

from itertools import product
import pandas as pd
DF=pd.DataFrame(list(product(df.Country, df.Country)), columns=['l1', 'l2'])
df=df.set_index('Country')
DF['v1']=DF.l1.map(df['Values'])
DF['v2']=DF.l2.map(df['Values'])
DF['DIFF']=DF['v2']-DF['v1']
DF.pivot(index='l1', columns='l2', values='DIFF').fillna(0).rename_axis(None).rename_axis(None,1)
Out[94]: 
      GB    JP    US
GB   0.0 -30.7 -14.5
JP  30.7   0.0  16.2
US  14.5 -16.2   0.0

---

Option 2 using apply

A=df['Values'].apply(lambda x : df['Values']-x)
A.columns=df.Country
A['Country']=df.Country


A
Out[124]: 
Country    GB    JP    US Country
0         0.0 -30.7 -14.5      GB
1        30.7   0.0  16.2      JP
2        14.5 -16.2   0.0      US