Converting a vertical values in a panda dataframe horizontal

Question

I currently have a dataframe that looks like this

DATE        ID      CHANGE
05-05-2020   333333      10
05-05-2020   333334       2
05-05-2020   333335       8
05-06-2020   333333      -2
05-06-2020   333334       4
05-06-2020   333335       5

and I want to be able to convert that dataframe horizontally by the date and separated by the ID to either

  DATE        ID      CHANGE      ID      CHANGE       ID      CHANGE
05-05-2020   333333      10      333334       2       333335       8       
05-06-2020   333333      -2      333334       4       333335       5  

or

 DATE     333333_CHANGE     333334_CHANGE     333335_CHANGE
05-05-2020       10                 2                 8
05-06-2020       -2                 4                 5    

I assume you have to do some kind of df.groupby() or pd.concat() though reading the documentations for them makes me confused as well.

Answer 1

If possible, output#2 is better, because duplicate column names can lead to many different issues in output#1.

For output #2, Use .unstack() and then do some cleanup on the multi-index columns for a better format:

df = df.set_index(['DATE', 'ID']).unstack(1).add_suffix('_CHANGE')
df.columns = df.columns.droplevel()
df = df.reset_index()
df
Out[1]: 
ID        DATE  333333_CHANGE  333334_CHANGE  333335_CHANGE
0   05-05-2020             10              2              8
1   05-06-2020             -2              4              5