pandas: how to compute correlation of between one column with multiple other columns?

Question

import pandas as pd
import numpy as np

df = pd.DataFrame({'group': ['a'] * 5 + ['b'] * 5, 'x1': np.random.normal(0, 1, 10), 'x2': np.random.normal(0, 1, 10), 'y': np.random.normal(0, 1, 10)})

df
Out[4]: 
  group        x1        x2         y
0     a -0.468746  1.254817 -1.629483
1     a -1.849347 -2.776032  1.413563
2     a  1.186306  0.766866  0.163395
3     a -0.314397 -0.531984  0.473665
4     a  0.278961  0.510429  1.484343
5     b  2.240489  0.856263  0.369464
6     b  2.029284  1.020894 -0.042139
7     b  1.571930 -0.415627  0.865577
8     b  0.609133  1.370543  0.450230
9     b -1.820421 -0.211467  0.704480

I would like to calculate the correlations between y and some specific(not all) columns of the same dataframe by group to produce an output dataframe that looks like:

Out[5]: 
         x1        x2
a -0.168390 -0.622155
b -0.467561 -0.771757

I had tried to use one-liner like:

df.groupby('group')[['x1', 'x2']].apply(...some function here that takes y as argument...)

However, I am having difficulty on how to write the function so that it will iterate through the specified columns( x1 and x2 ) and how to specify y as the fixed column.

Does anyone know an elegant one-liner that can achieve this?

Answer 1

use groupby + corrwith

df.groupby('group').apply(lambda d: d.filter(like='x').corrwith(d.y))

             x1        x2
group                    
a      0.127141  0.434080
b     -0.892755  0.524215