Pandas: Unmelt Dataframe To Add Arbitrary Number Of Columns?

I've got a dataframe df in Pandas that looks like this: stores product discount Westminster 102141 T Westminster 102142 F City o

Solution 1:

Use DataFrame.unstack for reshape, only necessary create counter by GroupBy.cumcount, last change ordering of second level and flatten MultiIndex in columns by map:

df = (df.set_index(['stores', df.groupby('stores').cumcount().add(1)])
        .unstack()
        .sort_index(axis=1, level=1))
df.columns = df.columns.map('{0[0]}_{0[1]}'.format)
df = df.reset_index()
print (df)
           stores discount_1  product_1 discount_2  product_2 discount_3  \
0  City of London          T   102141.0          F   102142.0          T   
1     Westminster          T   102141.0          F   102142.0        NaN   

   product_3  
0   102143.0  
1        NaN