pandas correlation matrix between each pair groupby item

Question

I have a csv file like this:

date,sym,close
2014.01.01,A,10
2014.01.02,A,11
2014.01.03,A,12
2014.01.04,A,13
2014.01.01,B,20
2014.01.02,B,22
2014.01.03,B,23
2014.01.01,C,33
2014.01.02,C,32
2014.01.03,C,31

Then, I get a dateframe named df via read_csv function

import numpy as np
import pandas as pd
df=pd.read_csv('daily.csv',index_col=[0])
groups=df.groupby('sym')[['close']].apply(lambda x:func(x['close'].values))

The groups look like this:

sym
A    [nan,1.00,2.00,...]
B    [nan,1.00,2.00,...]
C    [nan,1.00,2.00,...]

How to calculate the correlation between each pair of sym?

AA,AB,AC,BB,BA,BC,CA,CB,CC

BTW, the item numbers of each sym may be NOT the same.

Answer 1

With df as above, make a pivot table:

dfp = df.pivot('date','sym')
print(dfp)

           close        
sym            A   B   C
date                    
2014-01-01    10  20  33
2014-01-02    11  22  32
2014-01-03    12  23  31
2014-01-04    13 NaN  30

pandas will calculate the pairwise coefficients:

print(dfp.corr())

              close                    
sym               A         B         C
      sym                              
close A    1.000000  0.981981 -1.000000
      B    0.981981  1.000000 -0.981981
      C   -1.000000 -0.981981  1.000000

But if you want to prettify it, check out seaborn:

import seaborn as sns
sns.corrplot(dfp, annot=True)

result:

Answer 2

After get groups:

sym
A    [nan,1.00,2.00,...]
B    [nan,1.00,2.00,...]
C    [nan,1.00,2.00,...]

I created a DataFrame df2

df2=DataFrame()
df2['A']=groups['A']
df2['B']=groups['B']
df2['C']=groups['C']

df2.corr()

This method can get the correlation via data of groups. However, not perfect. How to convert a groups to a DataFrame like this? Loop keys of groups? I need to continue to try.