如何将 3 个 Pandas 数据帧合并到第 4 个数据帧以匹配列值名称？

Question

I have one main DataFrame titled nflLineups.

I am looking to merge 3 more DataFrames: dfPass, dfRush, dfReceive with the first DF, nflLineups.

So far nothing I have tried has worked. Tried appending, concatenation, and merging -- merge how='left', how='outer', on = 'Name', etc.

My goal is to have one large output that merges the data on Name but maintains all of the columns and their respective values.

The main output should have the following columns: Name, Team, Position, passYrds, rushYrds, recYrds. I would just like the stat data (pass, rush, rec) to fill in their respective rows next to the player's name in nflLineups. Not every player has data in every category so those values should be left blank (n/a).

I see that there are some merging examples on Stack but have yet to find code that I can use successfully. Spent the last 2 days messing with this and could use some help if possible. Still learning how to merge data and consider myself a relatively new with Python.

Any help would be greatly appreciated.

Here is my code so far:

import pandas as pd

nflLineups = pd.DataFrame([{'Name': 'Teddy', 'Team': 'DEN', 'Position': 'QB'},
                        {'Name': 'Melvin', 'Team': 'DEN', 'Position': 'RB'},
                        {'Name': 'Courtland', 'Team': 'DEN', 'Position': 'WR'},
                        {'Name': 'Tim', 'Team': 'DEN', 'Position': 'WR'},
                        {'Name': 'Kendal', 'Team': 'DEN', 'Position': 'WR'},
                        {'Name': 'Noah', 'Team': 'DEN', 'Position': 'TE'},
                        
                        {'Name': 'Case', 'Team': 'CLE', 'Position': 'QB'},
                        {'Name': 'D Ernest', 'Team': 'CLE', 'Position': 'RB'},
                        {'Name': 'Odell', 'Team': 'CLE', 'Position': 'WR'},
                        {'Name': 'Jarvis', 'Team': 'CLE', 'Position': 'WR'},
                        {'Name': 'Donovan', 'Team': 'CLE', 'Position': 'WR'},
                        {'Name': 'Austin', 'Team': 'CLE', 'Position': 'TE'},])


dfPass = pd.DataFrame([{'Name': 'Teddy', 'Team': 'DEN', 'Position': 'QB', 'passYrds': 1500},
                        {'Name': 'Case', 'Team': 'CLE', 'Position': 'QB', 'passYrds': 1350}])


dfRun = pd.DataFrame([{'Name': 'Teddy', 'Team': 'DEN', 'Position': 'QB', 'rushYrds': 45},
                        {'Name': 'D Ernest', 'Team': 'CLE', 'Position': 'RB', 'rushYrds': 350}])


dfReceive = pd.DataFrame([{'Name': 'D Ernest', 'Team': 'CLE', 'Position': 'RB', 'recYrds': 68},
                        {'Name': 'Jarvis', 'Team': 'CLE', 'Position': 'WR', 'recYrds': 250}])

Answer 1

IIUC, one way using pandas.DataFrame.groupby.first after pandas.concat .

Note that I assumed Team and Position are same for each Name .

df = pd.concat([nflLineups, dfPass, dfRun, dfReceive])
df = df.groupby("Name", sort=False).first()

Output:

          Team Position  passYrds  rushYrds  recYrds
Name                                                
Teddy      DEN       QB    1500.0      45.0      NaN
Melvin     DEN       RB       NaN       NaN      NaN
Courtland  DEN       WR       NaN       NaN      NaN
Tim        DEN       WR       NaN       NaN      NaN
Kendal     DEN       WR       NaN       NaN      NaN
Noah       DEN       TE       NaN       NaN      NaN
Case       CLE       QB    1350.0       NaN      NaN
D Ernest   CLE       RB       NaN     350.0     68.0
Odell      CLE       WR       NaN       NaN      NaN
Jarvis     CLE       WR       NaN       NaN    250.0
Donovan    CLE       WR       NaN       NaN      NaN
Austin     CLE       TE       NaN       NaN      NaN

Answer 2

Or with merge:

df_main = nflLineups.merge(dfPass, how='left', on=['Name', 'Team', 'Position']).merge(dfRun, how='left', on=['Name', 'Team', 'Position']).merge(dfReceive, how='left', on=['Name', 'Team', 'Position'])

Output:

         Name Team Position  passYrds  rushYrds  recYrds
0       Teddy  DEN       QB    1500.0      45.0      NaN
1      Melvin  DEN       RB       NaN       NaN      NaN
2   Courtland  DEN       WR       NaN       NaN      NaN
3         Tim  DEN       WR       NaN       NaN      NaN
4      Kendal  DEN       WR       NaN       NaN      NaN
5        Noah  DEN       TE       NaN       NaN      NaN
6        Case  CLE       QB    1350.0       NaN      NaN
7    D Ernest  CLE       RB       NaN     350.0     68.0
8       Odell  CLE       WR       NaN       NaN      NaN
9      Jarvis  CLE       WR       NaN       NaN    250.0
10    Donovan  CLE       WR       NaN       NaN      NaN
11     Austin  CLE       TE       NaN       NaN      NaN