我有這樣的 DateFrame： period payor variance_charges6/1/2018 LIABILITY PLANS 4631.66677/1/2018 LIABILITY PLANS -1125.83338/1/2018 LIABILITY PLANS -12688.33339/1/2018 LIABILITY PLANS -1657.510/1/2018 LIABILITY PLANS -14806.666711/1/2018 LIABILITY PLANS 13910.833312/1/2018 LIABILITY PLANS 12154.16676/1/2018 MEDICAID CMO -39174.58177/1/2018 MEDICAID CMO 59504.57678/1/2018 MEDICAID CMO 13967.48839/1/2018 MEDICAID CMO -158103.4910/1/2018 MEDICAID CMO -71191.966711/1/2018 MEDICAID CMO -405366.121712/1/2018 MEDICAID CMO -21637.05在對付款人（列）進(jìn)行匯總后，我想檢查每個(gè)窗口（每個(gè)窗口上 3 行）有多少負(fù)值：period payor variance_charges count_neg6/1/2018 LIABILITY PLANS 4631.6667 07/1/2018 LIABILITY PLANS -1125.8333 18/1/2018 LIABILITY PLANS -12688.3333 29/1/2018 LIABILITY PLANS -1657.5 310/1/2018 LIABILITY PLANS -14806.6667 311/1/2018 LIABILITY PLANS 13910.8333 212/1/2018 LIABILITY PLANS 12154.1667 16/1/2018 MEDICAID CMO -39174.5817 17/1/2018 MEDICAID CMO 59504.5767 18/1/2018 MEDICAID CMO 13967.4883 19/1/2018 MEDICAID CMO -158103.49 110/1/2018 MEDICAID CMO -71191.9667 211/1/2018 MEDICAID CMO -405366.12 312/1/2018 MEDICAID CMO -21637.05 3我試過下面的代碼df.sort_values(by = 'period', ascending=True)df['count_neg'] = df.groupby(['payor'])['variance_charges'].transform(lambda x: x.rolling(6, min_periods=1).apply(lambda n: sum(n < 0 for n in x), raw = False))使用上面的代碼，我可以檢查整個(gè)聚合有多少負(fù)值，而不考慮窗口。我得到的錯誤結(jié)果如下所示：period payor variance_charges count_neg6/1/2018 LIABILITY PLANS 4631.6667 47/1/2018 LIABILITY PLANS -1125.8333 48/1/2018 LIABILITY PLANS -12688.3333 49/1/2018 LIABILITY PLANS -1657.5 410/1/2018 LIABILITY PLANS -14806.6667 411/1/2018 LIABILITY PLANS 13910.8333 412/1/2018 LIABILITY PLANS 12154.1667 46/1/2018 MEDICAID CMO -39174.5817 57/1/2018 MEDICAID CMO 59504.5767 58/1/2018 MEDICAID CMO 13967.4883 5請幫助解決這個(gè)問題。
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TA貢獻(xiàn)1836條經(jīng)驗(yàn) 獲得超13個(gè)贊
您可以通過 remove 簡化您的功能for n in x：
f = lambda x: x.rolling(3, min_periods=1).apply(lambda n: sum(n < 0), raw = False)
df['count_neg1'] = df.groupby(['payor'])['variance_charges'].transform(f).astype(int)
print (df)
period payor variance_charges count_neg count_neg1
0 6/1/2018 LIABILITY PLANS 4631.6667 0 0
1 7/1/2018 LIABILITY PLANS -1125.8333 1 1
2 8/1/2018 LIABILITY PLANS -12688.3333 2 2
3 9/1/2018 LIABILITY PLANS -1657.5000 3 3
4 10/1/2018 LIABILITY PLANS -14806.6667 3 3
5 11/1/2018 LIABILITY PLANS 13910.8333 2 2
6 12/1/2018 LIABILITY PLANS 12154.1667 1 1
7 6/1/2018 MEDICAID CMO -39174.5817 1 1
8 7/1/2018 MEDICAID CMO 59504.5767 1 1
9 8/1/2018 MEDICAID CMO 13967.4883 1 1
10 9/1/2018 MEDICAID CMO -158103.4900 1 1
11 10/1/2018 MEDICAID CMO -71191.9667 2 2
12 11/1/2018 MEDICAID CMO -405366.1200 3 3
13 12/1/2018 MEDICAID CMO -21637.0500 3 3
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