How can I use row index values as column for dataframe?

So, I collected data from 21 participants with 16 EEG channels and I extracted the Gamma band. My current dataframe looks like this ([336 rows x 2 columns]):

Channels	Gamma
Fp1	0.345908
Fp2	0.121232
F3	0.213212
.....	....

Now I want to transpose it in such a way, that I have the gamma values for each channel in one column. Like this:

Fp1	Fp2	F3	....	Oz
0.067005	0.345908	0.207540	....	0.013512
0.137292	0.121232	0.121210	....	0.121111
0.112121	0.213212	0.123443	....	0.432233

when I just transpose the dataframe, then I get one row with all channels next to each other:

Fp1	Fp1	Fp1	....	Oz	Oz	Oz
0.067005	0.345908	0.207540	....	0.013512	0.12123	0.112423

I looked at pd.melt but I can't figure it out. Can someone help?

Thank you in advance!

Solution

One approach is to group by the Channels and then set these groups as columns of your new dataframe. Assuming following dataframe:

  Channels     Gamma
0      Fp1  0.345908
1      Fp2  0.121232
2      Fp1  0.455908
3      Fp2  0.213212

Then apply this code to the dataframe:

pd.concat(
        {k: g.reset_index(drop=True) 
            for k, g in df.groupby('Channels')['Gamma']}, axis=1)

and receive the following output:

        Fp1       Fp2
0  0.345908  0.121232
1  0.455908  0.213212