如何优化数据框，以便每次使用日志都不会打印相同的输出？

Question

我有一个 python 程序，可以打印从各个博彩公司那里获取的赔率差异。这是通过将赔率附加到熊猫数据框来实现的。我想使用记录数据帧输出的日志，每次我运行程序时，程序都不会打印重复的赔率差异。日志将记录数据框的“马”列。当程序打印数据框时，它会参考日志以查看“马”列中是否有重复的名称。

以下是数据框输出的示例：

            Race             Horse      Bookmaker   Odds  AvgOdds
13     Mackay R1        Which Lily  SportsBetting   2.45     2.04
15     Mackay R1        Which Lily         Bet365   2.40     2.04
17     Mackay R1  Molongle Drifter           Ubet   9.00     7.26
18     Mackay R1  Molongle Drifter        BetEasy   8.50     7.26
19     Mackay R1  Molongle Drifter           Neds   8.50     7.26
...          ...               ...            ...    ...      ...
1545  Mackay R10        Cold Power  SportsBetting   8.10     6.39
1547  Mackay R10        Cold Power         Bet365   8.00     6.39
1548  Mackay R10   All Star Rocket           Ubet   7.20     2.98
1560  Mackay R10           Dawlish      Sportsbet  14.00    11.65
1561  Mackay R10           Dawlish  SportsBetting  15.20    11.65

这是我的代码中与数据框相关的部分：

cols1 = ['Race', 'Horse', 'Bookmaker', 'Odds']
df1 = pd.DataFrame(data=data, columns=cols1)
cols2 = ['Race', 'Horse', 'Bookmaker', 'AvgOdds']
df2 = pd.DataFrame(data=data, columns=cols2)
df3 = df2.groupby(by='Horse', sort=False).mean()
df3 = df3.reset_index()
df4 = round(df3,2)
dfmerge = pd.merge(df1,df4,on='Horse',how='inner')
dfmerge2 = dfmerge[dfmerge['Odds']>dfmerge['AvgOdds']*1.15]
dfmerge3 = dfmerge2['Horse']

Answer 1

我建议您扩展您的初始数据框以包含以前报告的事件，这些事件会随着报告而更新。

然后，当您向数据集中添加更多行时，您可以重新运行程序，而不会看到以前报告的数据。

因此，鉴于此数据（请注意额外的列，您最初必须将其设置为 'N'）：

            Race             Horse      Bookmaker   Odds  Reported
13     Mackay R1        Which Lily  SportsBetting   2.45         N
15     Mackay R1        Which Lily         Bet365   2.40         N
17     Mackay R1  Molongle Drifter           Ubet   9.00         N
18     Mackay R1  Molongle Drifter        BetEasy   8.50         N
19     Mackay R1  Molongle Drifter           Neds   8.50         N
...          ...               ...            ...    ...       ...
1545  Mackay R10        Cold Power  SportsBetting   8.10         N
1547  Mackay R10        Cold Power         Bet365   8.00         N
1548  Mackay R10   All Star Rocket           Ubet   7.20         N
1560  Mackay R10           Dawlish      Sportsbet  14.00         N
1561  Mackay R10           Dawlish  SportsBetting  27.20         N

并使用此代码：

# Previously...
base_data = pd.DataFrame(...)

# Refactored code from example given
cols_raw = ['Race', 'Horse', 'Bookmaker', 'Odds']
raw_data = base_data[cols_raw]

cols_mean = ['Race', 'Horse', 'Odds']
mean_data = (
    base_data[cols_mean]
    # I assume this was meant to be by race and horse...
    .groupby(by=['Race', 'Horse'], sort=False)  
    .mean()
    .reset_index()
    .rename(columns={'Odds': 'AvgOdds'})
)
mean_data = round(mean_data)
report = pd.merge(raw_data, mean_data, on=['Race', 'Horse'], how='inner')
report = (
    report[report['Odds'] > report['AvgOdds'] * 1.15]
    ['Horse']
)

# Filter out any horses that have already been reported on:
pre_reported_horses = base_data[base_data['Reported'] == 'Y']['Horse'].unique()
report = report[~report['Horse'].isin(pre_reported_horses)]

# And then update the Reported column for next time you run the code
reported_horses = pre_reported_horses | set(report['Horse'].unique())
base_data.loc[base_data['Horse'].isin(reported_horses), 'Reported'] = 'Y'

然后可以将新数据附加到基础数据数据框中，并将Reported 设置为'N'，然后重新运行报告，而不会看到重复的异常赔率报告。

例如，如果您报告了马“Dawlish”，那么您更新后的 base_data 数据框现在应该如下所示：

            Race             Horse      Bookmaker   Odds  Reported
13     Mackay R1        Which Lily  SportsBetting   2.45         N
15     Mackay R1        Which Lily         Bet365   2.40         N
17     Mackay R1  Molongle Drifter           Ubet   9.00         N
18     Mackay R1  Molongle Drifter        BetEasy   8.50         N
19     Mackay R1  Molongle Drifter           Neds   8.50         N
...          ...               ...            ...    ...       ...
1545  Mackay R10        Cold Power  SportsBetting   8.10         N
1547  Mackay R10        Cold Power         Bet365   8.00         N
1548  Mackay R10   All Star Rocket           Ubet   7.20         N
1560  Mackay R10           Dawlish      Sportsbet  14.00         Y
1561  Mackay R10           Dawlish  SportsBetting  27.20         Y