Combining rows based on matching columns pandas

Question

I have a csv file containing games and stats for each team for an entire season. I am wanting to move the away team into the same row as the home team it faced for that week.

Current dataframe:

      Week   Team   H/a   Opp   Pf   Pa   Pyards  
      1      A            C     3    14   100     
      1      B            D     7    21   200     
      1      C      @     A     14   3    300     
      1      D      @     B     21   7    400     

Desired dataframe:

  Week   HomeTeam   H-score   H-Pyards   AwayTeam   A-score   A-Pyards  
  1      A          3         100        C          14        300       
  1      B          7         200        D          21        400       

But I would have more stats for each team and mutliple weeks.

Answer 1

I believe the operation you are looking is self-join with some manipulation afterwards. As Quang Hoang stated, merging the same dataframe/table in different columns is called self-join. I believe this is an approach which gets the expected output:

df = pd.DataFrame({'Week':[1,1,1,1],
                   'Team':['A','B','C','D'],
                   'H/a':[np.nan,np.nan,'@','@'],
                   'Opp':['C','D','A','B'],
                   'Pf':[3,7,14,21],
                   'Pa':[14,21,3,7],
                   'Pyards':[100,200,300,400]})
print(df)
new_df = df.merge(df,how='inner',left_on=['Week','Team'],right_on=['Week','Opp'])
new_df = new_df[new_df['H/a_x'] != '@']
replacers = {'Team_x':'HomeTeam','Pf_x':'Pf','Pyards_x':'H-Pyards','Opp_x':'AwayTeam','Pa_x':'A-score','Pyards_y':'A-Pyards'}
new_df = new_df[['Week']+[x for x in replacers.keys()]]
new_df = new_df.rename(columns=replacers)
print(new_df)

Output:

   Week HomeTeam  Pf  H-Pyards AwayTeam  A-score  A-Pyards
0     1        A   3       100        C       14       300
1     1        B   7       200        D       21       400