创建转置和 GroupBy 矩阵 Dataframe Python Pandas

Question

I have the follow table with the columns names ID,STORE and PUSH available.

With the PUSH values for each row(in this case I just have a ID, but the table contains more), I would like to create the second table(yellow one in the picture).

Then the target is: create new columns for each STORE(listed 7 adh,ayc,maeg,rot,witz,mar,bud), where each store will receive the value from the PUSH column.

The result expected is the yellow table which I will add in the same dataframe used to generate the ID,STORE,PUSH tables.

Any help would be very appreciated !

the code I tried was:
df['ADH'] = combined_sf2.groupby('PNO') 
['Push'].transform() 
df['AYC'] = combined_sf2.groupby('PNO') 
['Push'].transform()
df['ADH'] = combined_sf2.groupby('PNO') 
['Push'].transform()
df['MAEG'] = combined_sf2.groupby('PNO') 
['Push'].transform()
df['ROT'] = combined_sf2.groupby('PNO') 
['Push'].transform()
df['WITZ'] = combined_sf2.groupby('PNO') 
['Push'].transform()
df['BUD'] = combined_sf2.groupby('PNO') 
['Push'].transform()
df['MAR'] = combined_sf2.groupby('PNO') 
['Push'].transform()

But is retuning just 1 value for all lines

Answer 1

# pivot to get the right table format (ID as index, STORE as column
# and PUSH as values).
# the second part (with the loc) is here to repeat the lines according
# to each ID.

pd.pivot(df, index='ID', columns='STORE', values='PUSH').loc[df.ID]