Pandas - 根据条件复制行-Python问题

Pandas - Duplicate Row based on condition(Pandas - 根据条件复制行)

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问题描述

如果行满足条件，我正在尝试创建重复行.在下表中，我根据 groupby 创建了一个累积计数，然后再计算 groupby 的 MAX.

I'm trying to create a duplicate row if the row meets a condition. In the table below, I created a cumulative count based on a groupby, then another calculation for the MAX of the groupby.

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

在这种情况下，我只想复制 2/1/17 的记录，因为该日期只有一个实例(即 MaxPathID == 1).

In this case, I want to duplicate only the record for 2/1/17 since there is only one instance for that date (i.e. where the 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！

推荐答案

我认为你需要通过 Date Completed 获取 unique 行，然后 concat 行到原始:

I think you need get unique rows by Date Completed and then concat rows to original:

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

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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