Compute Euclidean distance between rows of two pandas dataframes

Question

I have two pandas dataframes d1 and d2 that look like these:

d1 looks like:

  output   value1   value2   value2
    1           100     103      87
    1           201     97.5     88.9
    1           144     54       85

d2 looks like:

 output   value1   value2   value2
    0           100     103      87
    0           201     97.5     88.9
    0           144     54       85
    0           100     103      87
    0           201     97.5     88.9
    0           144     54       85

The column output has a value of 1 for all rows in d1 and 0 for all rows in d2. It's a grouping variable. I need to find euclidean distance between each rows of d1 and d2 (not within d1 or d2). If d1 has m rows and d2 has n rows, then the distance matrix will have m rows and n columns

Answer 1

By using scipy.spatial.distance.cdist:

from scipy.spatial.distance import cdist

ary = cdist(d1.iloc[:,1:], d2.iloc[:,1:], metric='euclidean')

pd.DataFrame(ary)
Out[1274]: 
            0           1          2           3           4          5
0    0.000000  101.167485  65.886266    0.000000  101.167485  65.886266
1  101.167485    0.000000  71.808495  101.167485    0.000000  71.808495
2   65.886266   71.808495   0.000000   65.886266   71.808495   0.000000