Pivot Multiple Pandas Rows into Columns Based on Groupby Max

Question

I'm fairly new with Python and pandas and have a problem I'm not quite sure how to solve. I have a pandas DataFrame that contains hockey players who have played for multiple teams in the same year:

Player         Season      Team      GP        G      A       TP      
Player A        2020        A        10        8      3       11
Player A        2020        B        25        10     5       15
Player A        2020        C        6         4      7       11
Player B        2020        A        30        20     6       26
Player B        2020        B        25        18     5       23

I want to be able to combine rows that contain the same player from the same year, and arrange the columns by the team that player played the most for. In the above example all of Team B's numbers would be first because Player A has played the most games for Team B, followed by Team A and then Team C. If a player hasn't played for multiple teams or less than three, I'd like NA to be filled in for the given column.

For example the df above would turn into (Team1 stands for highest team):

Player        Season      Team1      GP1    G1      A1     TP1     Team2      GP2        G2      A2       TP2    Team3    GP3   G3   A3  TP3
Player A      2020          B        25     10      5      15       A         10         8       3        11       C       6     4   7    11
Player B      2020          A        30     20      6      26       B         25         18      5        23       NA     NA     NA  NA   NA

The initial way I can think of attacking this problem is by using a series of groupby max but I'm not sure if that will achieve the desired outcome. Any help would be greatly appreciated!

Answer 1

You could sort, then pivot:

a=(df.sort_values('GP')
   .assign(col=df.groupby(['Player','Season']).cumcount()+1)
   .pivot_table(index=['Player','Season'], columns='col', aggfunc='first')
)

# rename:
a.columns = [f'{x}{y}' for x,y in a.columns]