如何标记列值集匹配的DataFrame行？

Question

I have a DataFrame containing columns that overlap in a sense:

import pandas as pd

df = pd.DataFrame({
    'Date': ['2017-04-02', '2017-04-02', '2017-04-02', '2017-04-02', '2017-04-02', '2017-04-02'],
    'Team': ['CHC', 'ARI', 'NYY', 'TBR', 'STL', 'SFG'],
    'Home': [True, False, True, False, False, True],
    'Opp': ['STL', 'SFG', 'TBR', 'NYY', 'CHC', 'ARI'],
    'Rslt': ['L', 'W', 'L', 'W', 'W', 'L']
})

df['Date'] = pd.to_datetime(df['Date'])

print(df)

OUTPUT:

        Date Team   Home  Opp Rslt
0 2017-04-02  CHC   True  STL    L
1 2017-04-02  ARI  False  SFG    W
2 2017-04-02  NYY   True  TBR    L
3 2017-04-02  TBR  False  NYY    W
4 2017-04-02  STL  False  CHC    W
5 2017-04-02  SFG   True  ARI    L

For the date of 2017-04-01 , there were 3 games played. The DataFrame contains the game results for each day for each team. This results in 6 results. Take row 2 and 3 , this is a game between NYY and TBR:

• Row 2 gives the NYY result of L , meaning they lost
• Row 3 gives the TBR result of W , meaning they won

---

What I'm trying to do is group all row pairs that relate to the same game. My initial idea was to create a new column that would act as a label for the pair and then use that to group on or set MultiIndex . I thought about it and considered concatenating the three columns into a single string for each row and then, using sets, look through all rows for each date in Date and find the other row that contains the same characters:

df['Match'] = df['Date'].dt.strftime('%Y-%m-%d') + ',' + df['Team'] + ',' + df['Opp']

print(df)

OUTPUT:

        Date Team   Home  Opp Rslt               Match
0 2017-04-02  CHC   True  STL    L  2017-04-02,CHC,STL
1 2017-04-02  ARI  False  SFG    W  2017-04-02,ARI,SFG
2 2017-04-02  NYY   True  TBR    L  2017-04-02,NYY,TBR
3 2017-04-02  TBR  False  NYY    W  2017-04-02,TBR,NYY
4 2017-04-02  STL  False  CHC    W  2017-04-02,STL,CHC
5 2017-04-02  SFG   True  ARI    L  2017-04-02,SFG,ARI

From here, I'm not sure how to proceed. I have a method in mind using sets that I've used in the past. If we focus on row 2 and 3 again, subtracting the sets of the string, split using the , , and taking the bool() will return False for two sets containing the same string elements and True for anything else (different sets):

print(
    bool(set('2017-04-02,NYY,TBR'.split(',')) - set('2017-04-02,TBR,NYY'.split(',')))
)
print(
    bool(set('2017-04-02,NYY,TBR'.split(',')) - set('2017-04-02,CHC,STL'.split(',')))
)

OUTPUT:

False
True

Is there a better way to take a row value in a column and lookup all other row values in that same column and label the rows where they are related? The kind of label I would like to have is creating a unique numbering of games. Since these three games happen on the same day, labelling the pairs as 1, 2, 3 would be great so that each game pair for each day has a unique ID.

PS I've also seen this post that kinda looks like what I'm trying to do... I've tried using .isin() but kept running into errors so scrapped that approach. I thought about pd.DataFrame.lookup but I'm not quite sure if that's the right approach either. Just need a way to group up each pair of rows.

Answer 1

Merge the DataFrame on itself, swap values where it's not a home game, take the information you want, and then drop the duplicates:

df2 = df.merge(df, left_on=['Date', 'Team'], right_on=['Date', 'Opp'], suffixes=['_Home', '_Away'])
swap_cols = ['Team', 'Opp', 'Rslt', 'Home']
for col in swap_cols:
    df2[f'{col}_Home'], df2[f'{col}_Away'] = np.where(df2.Home_Home, [df2[f'{col}_Home'],df2[f'{col}_Away']], [df2[f'{col}_Away'],df2[f'{col}_Home']])

df2 = df2[['Date','Team_Home', 'Rslt_Home', 'Team_Away', 'Rslt_Away']].drop_duplicates()
print(df2)

Output:

        Date Team_Home Rslt_Home Team_Away Rslt_Away
0 2017-04-02       CHC         L       STL         W
1 2017-04-02       SFG         L       ARI         W
2 2017-04-02       NYY         L       TBR         W

Answer 2

IIUC,

You can do it this way using merge and query.

df_match = df.merge(df, 
                    left_on=['Date', 'Team'], 
                    right_on=['Date', 'Opp'], 
                    suffixes=('','_opp'))

df_match.query('Home')

Output:

        Date Team  Home  Opp Rslt Team_opp  Home_opp Opp_opp Rslt_opp
0 2017-04-02  CHC  True  STL    L      STL     False     CHC        W
2 2017-04-02  NYY  True  TBR    L      TBR     False     NYY        W
5 2017-04-02  SFG  True  ARI    L      ARI     False     SFG        W

Answer 3

To match the other answers better, you can deduplicate by selecting only the rows where the team was at home and then creating new columns for Result_home and Result_away.

I haven't tested, but I think this should be faster than approaches that merge the table onto itself

I'm not sure what you want your final output to look like but here's one option

rename_cols = {
    'Team':'Home_Team',
    'Opp':'Away_Team',
    'Rslt':'Result_home',
}

df = df[df['Home']]
df['Result_away'] = df['Rslt'].replace({'L':'W','W':'L'})
df = df.rename(columns=rename_cols).drop(columns=['Home'])
df