这是一个广泛的问题,但我不确定如何获得有关如何改进此代码的指导。
我有一个包含投注赔率和比赛结果的数据框,我想计算投资某个团队的支出。
我现在的代码可以工作,但我觉得它仅仅依靠 apply
方法并放入 Python 中,就忽略了 Pandas 可以做的很多事情。
这是我的代码:
def compute_payout(odds, amount=1):
if odds < 0:
return amount/(-1.0 * odds/100.0)
elif odds > 0:
return amount/(100.0/odds)
def game_payout(row, team_name):
if row['home_team'] == team_name:
if row['home_score'] > row['away_score']:
return compute_payout(row['home_odds'])
else:
return -1
elif row['away_team'] == team_name:
if row['away_score'] > row['home_score']:
return compute_payout(row['away_odds'])
else:
return -1
payout = df.apply(lambda row: game_payout(row, team_name), axis=1)
非常感谢任何建议!
最佳答案
使用numpy.select
条件由 &
链接用于 按位 AND
和 ~
用于反转 bool 掩码:
m11 = df['home_team'] == team_name
m21 = df['away_team'] == team_name
m12 = df['home_score'] > df['away_score']
m22 = df['home_score'] < df['away_score']
vals = [df['home_odds'].apply(compute_payout), -1, df['away_odds'].apply(compute_payout), -1]
payout = np.select([m11 & m12, m11 & ~m12, m21 & m22, m21 & ~m22], vals, default=np.nan)
关于pandas - 我怎样才能使这段代码变成更惯用的 Pandas ?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/54177011/