如果该行满足条件，我试图创建一个重复的行。在下表中，我根据 groupby 创建了累积计数，然后对 groupby 的 MAX 进行了另一个计算。
df['PathID'] = df.groupby(DateCompleted).cumcount() + 1
df['MaxPathID'] = df.groupby(DateCompleted)['PathID'].transform(max)
Date Completed PathID MaxPathID
1/31/17 1 3
1/31/17 2 3
1/31/17 3 3
2/1/17 1 1
2/2/17 1 2
2/2/17 2 2
在本例中，我只想复制 2017 年 2 月 1 日的记录，因为该日期只有一个实例（即 MaxPathID == 1）。
期望的输出：
Date Completed PathID MaxPathID
1/31/17 1 3
1/31/17 2 3
1/31/17 3 3
2/1/17 1 1
2/1/17 1 1
2/2/17 1 2
2/2/17 2 2
提前致谢！
我想你需要得到unique行按Date Completed进而concat原始行：
df1 = df.loc[~df['Date Completed'].duplicated(keep=False), ['Date Completed']]
print (df1)
Date Completed
3 2/1/17
df = pd.concat([df,df1], ignore_index=True).sort_values('Date Completed')
df['PathID'] = df.groupby('Date Completed').cumcount() + 1
df['MaxPathID'] = df.groupby('Date Completed')['PathID'].transform(max)
print (df)
Date Completed PathID MaxPathID
0 1/31/17 1 3
1 1/31/17 2 3
2 1/31/17 3 3
3 2/1/17 1 2
6 2/1/17 2 2
4 2/2/17 1 2
5 2/2/17 2 2
EDIT:
print (df)
Date Completed a b
0 1/31/17 4 5
1 1/31/17 3 5
2 1/31/17 6 3
3 2/1/17 7 9
4 2/2/17 2 0
5 2/2/17 6 7
df1 = df[~df['Date Completed'].duplicated(keep=False)]
#alternative - boolean indexing by numpy array
#df1 = df[~df['Date Completed'].duplicated(keep=False).values]
print (df1)
Date Completed a b
3 2/1/17 7 9
df = pd.concat([df,df1], ignore_index=True).sort_values('Date Completed')
print (df)
Date Completed a b
0 1/31/17 4 5
1 1/31/17 3 5
2 1/31/17 6 3
3 2/1/17 7 9
6 2/1/17 7 9
4 2/2/17 2 0
5 2/2/17 6 7
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