My dataframe looks like this:

SKU #    GRP    CATG   PRD
0   54995  9404000  4040  99999
1   54999  9404000  4040  99999
2   55037  9404000  4040  1556894
3   55148  9404000  4040  1556894
4   55254  9404000  4040  1556894
5   55291  9404000  4040  1556894
6   55294  9404000  4040  1556895
7   55445  9404000  4040  1556895
8   55807  9404001  4040  1556896
9   49021  9404002  4040  1556897
10  49035  9404002  4040  1556897
11  27538  9404000  4040  1556898
12  27539  9404000  4040  1556899
13  27540  9404000  4040  1556894
14  27542  9404000  4040  1556900
15  27543  9404000  4040  1556900
16  27544  9404003  4040  1556901
17  27546  9404004  4040  1556902
18  99111  9404005  4040  1556903
19  99112  9404006  4040  1556904
20  99113  9404007  4040  1556905
21  99116  9404008  4040  1556906
22  99119  9404009  4040  1556907
23  99122  94040010 4040  1556908
24  99125  94040011 4040  1556909
25  86007  94040012 4040  1556910
26  86010  94040013 4040  1556911 

And when I try to perform a group by operation on the above dataframe, I get the "cannot reindex from a duplicate axis" error.

df.groupby(['GRP','CATG'],as_index=False)['PRD'].min()

I tried to find out the duplicate indices using:

df[df.index.duplicated()]

But didn't return any thing. How can I go about resolving this issue?

This error is often thrown due to duplications in your column names (not necessarily values)

First, just check if there is any duplication in your column names using the code: df.columns.duplicated().any()

If it's true, then remove the duplicated columns

df.loc[:,~df.columns.duplicated()]

After you remove the duplicated columns, you should be able to run your groupby operation.