Python 什么'；在这种情况下，应用/循环的最佳方法是什么？

我正在转换一些申请者事务数据，我需要创建一个新的标志列（在我的示例中标记为“所需标志”）。然而，我无法找出正确的循环/应用方法，因为下面的逻辑中可能有很多不同的变化

在一个完美的世界中，连续的申请人流程历史将如下所示，所有“状态”都设置为“已完成”：

• 现场面试开始-->安排面试-->决策；或
• 电话面试开始-->安排面试-->决策

当然，在申请过程中，申请人可以通过许多电话面试和现场面试

如下面的示例所示，有时会取消“计划面试”。在这些情况下，我需要删除该步骤以及与之相关的后续步骤。这些包括“安排面试”、“决定”和“现场面试开始”或“电话面试开始”。此外，有时也会有其他“事件”，如我们看到的手动跳过的事件

我还需要为其他类型的场景创建标志，因此我需要仅使用新列保留原始数据帧

import pandas as pd

data = {'Employee ID': ["100","100", "100", "100","100","100","100","100","100","100","200", "200", "200","200","200","200","200","300","300", "300", "300","300","300","300"],
        'Completed On Date': ["2009-01-01","2010-01-01","2011-06-05","2012-07-01","2013-01-01","2014-01-01","2015-01-01","2016-01-01","2017-01-01","2018-01-01","2010-01-01","2011-06-05","2012-07-01","2012-08-15","2013-01-01","2014-01-01","2015-01-01","2009-01-01","2010-01-01","2011-06-05","2012-07-01","2013-01-01","2014-01-01","2015-01-01"],
        'Event': ["Decision","On-Site Interview Kick Off","Schedule Interviews","Decision","On-Site Interview Kick Off","Schedule Interviews","Decision","Phone Interview Kick Off","Schedule Interviews","Decision","On-Site Interview Kick Off","Schedule Interviews","Decision","Decision","Phone Interview Kick Off","Schedule Interviews","Decision","Job Apply","Phone Interview Kick Off","Schedule Interviews","Decision","On-Site Interview Kick Off","Schedule Interviews","Decision"],
        'Event Status': ["Completed","Completed","CANCELED","Completed","Completed","Completed","Completed","Completed","Completed","Completed","Completed","CANCELED","Manually Skipped","Completed","Completed","Completed","Completed","Completed","Completed","CANCELED","Completed","Completed","Completed","Completed"],
        'DESIRED FLAG': ["Keep","Keep","Remove","Remove","Remove","Keep","Keep","Keep","Keep","Keep","Keep","Remove","Remove","Remove","Remove","Keep","Keep","Keep","Keep","Remove","Remove","Remove","Keep","Keep"]}
df = pd.DataFrame(data, columns=['Employee ID','Completed On Date','Event','Event Status','DESIRED FLAG'])
df = df.sort_values(by=(['Employee ID','Completed On Date']))

df

---

我认为下面的代码解决了您的问题

import pandas as pd

data = {'Employee ID': ["100","100", "100", "100","100","100","100","100","100","100","200", "200", "200","200","200","200","200","300","300", "300", "300","300","300","300"],
        'Completed On Date': ["2009-01-01","2010-01-01","2011-06-05","2012-07-01","2013-01-01","2014-01-01","2015-01-01","2016-01-01","2017-01-01","2018-01-01","2010-01-01","2011-06-05","2012-07-01","2012-08-15","2013-01-01","2014-01-01","2015-01-01","2009-01-01","2010-01-01","2011-06-05","2012-07-01","2013-01-01","2014-01-01","2015-01-01"],
        'Event': ["Decision","On-Site Interview Kick Off","Schedule Interviews","Decision","On-Site Interview Kick Off","Schedule Interviews","Decision","Phone Interview Kick Off","Schedule Interviews","Decision","On-Site Interview Kick Off","Schedule Interviews","Decision","Decision","Phone Interview Kick Off","Schedule Interviews","Decision","Job Apply","Phone Interview Kick Off","Schedule Interviews","Decision","On-Site Interview Kick Off","Schedule Interviews","Decision"],
        'Event Status': ["Completed","Completed","CANCELED","Completed","Completed","Completed","Completed","Completed","Completed","Completed","Completed","CANCELED","Manually Skipped","Completed","Completed","Completed","Completed","Completed","Completed","CANCELED","Completed","Completed","Completed","Completed"],
        'DESIRED FLAG': ["Keep","Keep","Remove","Remove","Remove","Keep","Keep","Keep","Keep","Keep","Keep","Remove","Remove","Remove","Remove","Keep","Keep","Keep","Keep","Remove","Remove","Remove","Keep","Keep"]}
df = pd.DataFrame(data, columns=['Employee ID','Completed On Date','Event','Event Status','DESIRED FLAG'])
df = df.sort_values(by=(['Employee ID','Completed On Date']))


index_list_delete = []
start_deleting = False
for i in range(0, len(df)):
    if start_deleting == False:
        # whenever I see a "CANCELED", i know some following rows need to be deleted
        if df.iloc[i]['Event Status'] == 'CANCELED':
            index_list_delete += [i]
            start_deleting = True
    else:
        # whenever i see a "Schedule Interviews", i need to stop deleting. 
        # otherwise keep track of the rows that need to be deleted
        if df.iloc[i]['Event'] == 'Schedule Interviews':
            start_deleting = False
        else:
            index_list_delete += [i]

# deleting rows
df = df.drop(df.index[index_list_delete])
# reseting index
df = df.reset_index(drop = True)

您将得到以下结果

   Employee ID Completed On Date                       Event Event Status DESIRED FLAG
0          100        2009-01-01                    Decision    Completed         Keep
1          100        2010-01-01  On-Site Interview Kick Off    Completed         Keep
2          100        2014-01-01         Schedule Interviews    Completed         Keep
3          100        2015-01-01                    Decision    Completed         Keep
4          100        2016-01-01    Phone Interview Kick Off    Completed         Keep
5          100        2017-01-01         Schedule Interviews    Completed         Keep
6          100        2018-01-01                    Decision    Completed         Keep
7          200        2010-01-01  On-Site Interview Kick Off    Completed         Keep
8          200        2014-01-01         Schedule Interviews    Completed         Keep
9          200        2015-01-01                    Decision    Completed         Keep
10         300        2009-01-01                   Job Apply    Completed         Keep
11         300        2010-01-01    Phone Interview Kick Off    Completed         Keep
12         300        2014-01-01         Schedule Interviews    Completed         Keep
13         300        2015-01-01                    Decision    Completed         Keep

---

我认为下面的代码解决了您的问题

import pandas as pd

data = {'Employee ID': ["100","100", "100", "100","100","100","100","100","100","100","200", "200", "200","200","200","200","200","300","300", "300", "300","300","300","300"],
        'Completed On Date': ["2009-01-01","2010-01-01","2011-06-05","2012-07-01","2013-01-01","2014-01-01","2015-01-01","2016-01-01","2017-01-01","2018-01-01","2010-01-01","2011-06-05","2012-07-01","2012-08-15","2013-01-01","2014-01-01","2015-01-01","2009-01-01","2010-01-01","2011-06-05","2012-07-01","2013-01-01","2014-01-01","2015-01-01"],
        'Event': ["Decision","On-Site Interview Kick Off","Schedule Interviews","Decision","On-Site Interview Kick Off","Schedule Interviews","Decision","Phone Interview Kick Off","Schedule Interviews","Decision","On-Site Interview Kick Off","Schedule Interviews","Decision","Decision","Phone Interview Kick Off","Schedule Interviews","Decision","Job Apply","Phone Interview Kick Off","Schedule Interviews","Decision","On-Site Interview Kick Off","Schedule Interviews","Decision"],
        'Event Status': ["Completed","Completed","CANCELED","Completed","Completed","Completed","Completed","Completed","Completed","Completed","Completed","CANCELED","Manually Skipped","Completed","Completed","Completed","Completed","Completed","Completed","CANCELED","Completed","Completed","Completed","Completed"],
        'DESIRED FLAG': ["Keep","Keep","Remove","Remove","Remove","Keep","Keep","Keep","Keep","Keep","Keep","Remove","Remove","Remove","Remove","Keep","Keep","Keep","Keep","Remove","Remove","Remove","Keep","Keep"]}
df = pd.DataFrame(data, columns=['Employee ID','Completed On Date','Event','Event Status','DESIRED FLAG'])
df = df.sort_values(by=(['Employee ID','Completed On Date']))


index_list_delete = []
start_deleting = False
for i in range(0, len(df)):
    if start_deleting == False:
        # whenever I see a "CANCELED", i know some following rows need to be deleted
        if df.iloc[i]['Event Status'] == 'CANCELED':
            index_list_delete += [i]
            start_deleting = True
    else:
        # whenever i see a "Schedule Interviews", i need to stop deleting. 
        # otherwise keep track of the rows that need to be deleted
        if df.iloc[i]['Event'] == 'Schedule Interviews':
            start_deleting = False
        else:
            index_list_delete += [i]

# deleting rows
df = df.drop(df.index[index_list_delete])
# reseting index
df = df.reset_index(drop = True)

您将得到以下结果

   Employee ID Completed On Date                       Event Event Status DESIRED FLAG
0          100        2009-01-01                    Decision    Completed         Keep
1          100        2010-01-01  On-Site Interview Kick Off    Completed         Keep
2          100        2014-01-01         Schedule Interviews    Completed         Keep
3          100        2015-01-01                    Decision    Completed         Keep
4          100        2016-01-01    Phone Interview Kick Off    Completed         Keep
5          100        2017-01-01         Schedule Interviews    Completed         Keep
6          100        2018-01-01                    Decision    Completed         Keep
7          200        2010-01-01  On-Site Interview Kick Off    Completed         Keep
8          200        2014-01-01         Schedule Interviews    Completed         Keep
9          200        2015-01-01                    Decision    Completed         Keep
10         300        2009-01-01                   Job Apply    Completed         Keep
11         300        2010-01-01    Phone Interview Kick Off    Completed         Keep
12         300        2014-01-01         Schedule Interviews    Completed         Keep
13         300        2015-01-01                    Decision    Completed         Keep

---

如果您可以发布所需输出的样子，这将非常有帮助。请参阅“所需标志”列。这就是输出的样子。谢谢！明白了。以数据帧的形式将其可视化会有帮助，但可能这只是me.Np。我从未想过如何在这个论坛中输出DF！：如果您可以发布，这将非常有帮助期望的输出是什么样子的。请参阅“期望的标志”列。这就是输出应该是什么样子的。谢谢！明白了。以数据帧的形式将其可视化很有帮助，但这可能只是我。Np。我从来没有在这个论坛中弄明白如何输出DF！：OI使用真实数据进行了一些额外的测试，这种逻辑并没有限制它的功能f到员工ID…它应该只在每个相应的员工ID集内执行您的解决方案。下面是一个不雅观的部分解决方案。在后续步骤中，我仍然必须筛选出最后一步是安排面试团队的解决方案…如果（df.iloc[I]['Event Status']='cancelled'）和（df.iloc[I]['Employee ID']==df.iloc[i+1]['Employee ID']）：我用真实数据做了一些额外的测试，这个逻辑并不局限于员工ID。它应该只在每个员工ID集中执行您的解决方案。下面是一个不雅观的部分解决方案。在接下来的步骤中，我仍然必须筛选出最后一步是安排面试团队的解决方案。。。。如果（df.iloc[i]['Event Status']=='cancelled'）和（df.iloc[i]['Employee ID']==df.iloc[i+1]['Employee ID']）：