Pandas: how to make pivot table with new columns?

Question

I have a table from several files with the same names, but different values:

I need to make new columns, based on the number of repeating names with the saved number of participants, so it looks like this:

I tried to make the pivot table without division of points using

df.pivot(index='Name', columns='numbers of participants')

But I got an error.

Is it possible to make new columns using the pivot table function?

Answer 1

Use GroupBy.cumcount for counter and pass to DataFrame.pivot with DataFrame.add_prefix :

df['g'] = df.groupby(['Name', 'numbers of participants']).cumcount().add(1)
df1 = (df.pivot(index=['Name','numbers of participants'], columns='g', values='points')
         .add_prefix('points_')
         .reset_index()
         .rename_axis(None, axis=1))

print (df1)
  Name  numbers of participants  points_1  points_2
0    A                       20      7679       124
1    B                       30        23       231
2    C                       40       341      4124

---

df1['numbers of participants'] = df1.pop('numbers of participants')
print (df1)
  Name  points_1  points_2  numbers of participants
0    A      7679       124                       20
1    B        23       231                       30
2    C       341      4124                       40