Python Pandas-使用逻辑过滤数据帧

我有一个像这样的熊猫数据框

Employee ID     ActionCode     ActionReason      ConcatenatedOutput
1                  TER              DEA                TER_DEA                
1                  RET              ABC                RET_ABC
1                  RET              DEF                RET_DEF
2                  TER              DEA                TER_DEA
2                  ABC              ABC                ABC_ABC
2                  DEF              DEF                DEF_DEF
3                  RET              FGH                RET_FGH
3                  RET              EFG                RET_EFG
4                  PLA              ABC                PLA_ABC
4                  TER              DEA                TER_DEA                

Employee ID          ConcatenatedOutput       Context
1                     RET_ABC                 RET or TER Found
2                     TER_DEA                 RET or TER Found
3                     RET_FGH                 RET or TER Found
4                     PLA_ABC                 RET or TER Not Found

我想用下面的逻辑对它进行过滤，然后把它改成这样

Employee ID     ActionCode     ActionReason      ConcatenatedOutput
1                  TER              DEA                TER_DEA                
1                  RET              ABC                RET_ABC
1                  RET              DEF                RET_DEF
2                  TER              DEA                TER_DEA
2                  ABC              ABC                ABC_ABC
2                  DEF              DEF                DEF_DEF
3                  RET              FGH                RET_FGH
3                  RET              EFG                RET_EFG
4                  PLA              ABC                PLA_ABC
4                  TER              DEA                TER_DEA                

Employee ID          ConcatenatedOutput       Context
1                     RET_ABC                 RET or TER Found
2                     TER_DEA                 RET or TER Found
3                     RET_FGH                 RET or TER Found
4                     PLA_ABC                 RET or TER Not Found

逻辑：- 1如果员工的第一条记录是TERU DEA，那么我们进入该员工，查看该员工是否有任何其他记录，如果该员工有另一条RET记录，那么我们选择第一条可用的RET记录，否则我们坚持TERU DEA记录

2如果员工的第一条记录不是Teru DEA，那么我们坚持该记录

3上下文是有条件的，如果它有一个RET或TER，那么我们说RET或TER已找到，否则它未找到

注意：-对于员工ID，最终输出只有一条记录

下面的数据

employee_id = [1,1,1,2,2,2,3,3,4,4]
action_code = ['TER','RET','RET','TER','ABC','DEF','RET','RET','PLA','TER']
action_reason = ['DEA','ABC','DEF','DEA','ABC','DEF','FGH','EFG','ABC','DEA']
concatenated_output = ['TER_DEA', 'RET_ABC', 'RET_DEF', 'TER_DEA', 'ABC_ABC', 'DEF_DEF', 'RET_FGH', 'RET_EFG', 'PLA_ABC', 'TER_DEA']

df = pd.DataFrame({
    'Employee ID': employee_id,
    'ActionCode': action_code,
    'ActionReason': action_reason,
    'ConcatenatedOutput': concatenated_output,
})

---

我建议你在那一领域与一个笨蛋合作。为了获得测试数据，我使用了以下方法：

import pandas as pd

employee_id = [1,1,1,2,2,2,3,3,4,4]
action_code = ['TER','RET','RET','TER','ABC','DEF','RET','RET','PLA','TER']
action_reason = ['DEA','ABC','DEF','DEA','ABC','DEF','FGH','EFG','ABC','DEA']
concatenated_output = ['TER_DEA', 'RET_ABC', 'RET_DEF', 'TER_DEA', 'ABC_ABC', 'DEF_DEF', 'RET_FGH', 'RET_EFG', 'PLA_ABC', 'TER_DEA']

df = pd.DataFrame({
    'Employee ID': employee_id,
    'ActionCode': action_code,
    'ActionReason': action_reason,
    'ConcatenatedOutput': concatenated_output,
})

然后，您可以根据员工ID进行分组，并在其中应用一个函数来执行特定的程序逻辑

def myfunc(data):
    if data.iloc[0]['ConcatenatedOutput'] == 'TER_DEA':
        if len(data.loc[data['ActionCode'] == 'RET']) > 0:
            located_record = data.loc[data['ActionCode'] == 'RET'].iloc[[0]]
        else:
            located_record = data.iloc[[0]]
    else:
        located_record = data.iloc[[0]]
    located_record['RET or TER Context'] = data['ActionCode'].str.contains('|'.join(['RET', 'TER']))
    return located_record

df.groupby(['Employee ID']).apply(myfunc)

---

同样，这里的问题不是条件列，而是基于LogicsHanks消除重复记录。但是你能帮我做这个功能吗。我不确定，如何更改函数以查找TERU DEA，然后查找下一个RET记录。正在忙，1秒钟。正在检查。请给我10分钟。谢谢你的帮助：