我有 csv 文件，其中包含來自不同足球比賽的大量結果。數據類似于下面的示例。該result列可以包含 3 個可能的值：H -> 主隊獲勝（主隊獲得+3分）A -> 客隊獲勝（客場將獲得 + 3 分）D -> 平局（兩隊均獲得 +1 分） HomeTeam AwayTeam Result0 FC_Fake ABC_United H1 Team_123 FC_Berlin A2 FC_FAKE TEAM_123 D我想更新文件，以便每一行都包含每支球隊的總積分as they are at the start of the match（因此尚未更新該行本身的比賽結果）我使用以下代碼更新數據框，因此它包含points_[TEAM_NAME]每個團隊的虛擬列。# Teams is a python list I extracted earlierfor team in teams: df['points_' + team] = 0目標是轉換數據幀，使上面的例子變成下面的例子。（同樣，分數應該代表比賽開始時的情況。所以即使FC_FAKE在第一行贏得比賽，Points_FC_FAKE列也是 0 ）HomeTeam | AwayTeam | Result Points_FC_FAKE | Points_TEAM_123 | Points_FC_Berlin | etc------------------------------------------------------------------------------- FC_Fake ABC_United H 0 0 0 Team_123 FC_Berlin A 3 0 0 FC_FAKE Team_123 D 3 0 3我創建了以下 python 函數，如果它遍歷數據幀中的所有行，則應解析結果并將正確的點數獎勵給正確的團隊。def point_updater(x): if x['Result'] == 'H': home = x['HomeTeam'] x.shift(-1)['points_' + home] += 3 return x elif x['Result'] == 'A': away = x['AwayTeam'] x.shift(-1)['points_' + away] += 3 return x elif x['Result'] == 'D': home = x['AwayTeam'] away = x['AwayTeam'] x.shift(-1)['points_' + home] += 1 x.shift(-1)['points_' + away] += 1 return x問題是當我將此函數應用于數據幀時，點不會改變（全部保持為 0）df = df.apply(point_counter, axis=1)df['points_FC_Fake'].value_counts()----0 2691有誰知道我做錯了什么？
3 回答
千巷貓影
TA貢獻1829條經驗 獲得超7個贊
在某些例外情況下，我們可以使用iterrows它。另外，在開始計算之前，我通過進行一些清理使您的代碼更具故障證明性和通用性：
# Convert to uppercase letters
df['HomeTeam'] = df['HomeTeam'].str.upper()
df['AwayTeam'] = df['AwayTeam'].str.upper()
# get a list off all the teams in competition
lst_teams = list(set(list(df.HomeTeam.unique()) + list(df.AwayTeam.unique())))
# Create columns for each team
for team in lst_teams:
df[team] = 0
# Iterate over each row and assign correct points
for idx, r in df.iterrows():
if r['Result'] == 'H':
df.loc[[idx], [r['HomeTeam']]] = 3
if r['Result'] == 'A':
df.loc[[idx], [r['AwayTeam']]] = 3
if r['Result'] == 'D':
df.loc[[idx], [r['AwayTeam']]] = 1
df.loc[[idx], [r['HomeTeam']]] = 1
# Shift the rows one down, since points are only available at start of match
df.iloc[:, 3:] = df.iloc[:, 3:].cumsum().shift(1).fillna(0).astype(int)
輸出
print(df)
HomeTeam AwayTeam Result ABC_UNITED TEAM_123 FC_FAKE FC_BERLIN
0 FC_FAKE ABC_UNITED H 0 0 0 0
1 TEAM_123 FC_BERLIN A 0 0 3 0
2 FC_FAKE TEAM_123 D 0 0 3 3
慕的地8271018
TA貢獻1796條經驗 獲得超4個贊
可能有一種更簡潔的方式來執行這些操作，但現在應該足夠了。您可以使用df.replace()將Result鍵映射到它們的關聯值，然后使用pd.concat()和pd.DataFrame.pivot()實現您想要的結果：
import pandas as pd
df = pd.DataFrame({'HomeTeam': ['FC_Fake','Team_123','FC_Fake'], 'AwayTeam': ['ABC_United','FC_Berlin','Team_123'], 'Result': ['H','A','D']})
remap = df.replace({'H': 3, 'A': 3, 'D': 1})
new = pd.concat([remap.pivot(columns='HomeTeam', values='Result'), remap.pivot(columns='AwayTeam', values='Result')], axis=1).shift(1).fillna(0).astype(int).cumsum()
final = pd.concat([df, new], axis=1)
產量：
HomeTeam AwayTeam Result FC_Fake Team_123 ABC_United FC_Berlin \
0 FC_Fake ABC_United H 0 0 0 0
1 Team_123 FC_Berlin A 3 0 3 0
2 FC_Fake Team_123 D 3 3 3 3
Team_123
0 0
1 0
2 0
慕妹3242003
TA貢獻1824條經驗 獲得超6個贊
將您的功能更改為：
def point_updater(x):
if x['Result'] == 'H':
home = x['HomeTeam']
x['points_' + home] += 3
return x
elif x['Result'] == 'A':
away = x['AwayTeam']
x['points_' + away] += 3
return x
elif x['Result'] == 'D':
home = x['HomeTeam']
away = x['AwayTeam']
x['points_' + home] += 1
x['points_' + away] += 1
return x
然后在代碼的末尾添加：
df = df.apply(point_updater,axis=1)
for team in teams:
df["points_" + team]= df["points_" + team].cumsum()
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