Question

我正在转换一些申请人的交易数据，我需要创建一个新的标志列（在我的例子中标记为＆＃34; DESIRED FLAG＆＃34;）。但是，我无法弄清楚正确的循环/应用方法，因为下面的逻辑可能有很多不同的变化。

在一个完美的世界中，顺序申请人流程历史将如下所示，所有＆＃34;状态＆＃34;设置为＆＃34;已完成＆＃34;：

现场采访开始 - ＆gt;安排面试 - ＆gt;决策; OR
电话采访开始 - ＆gt;安排面试 - ＆gt;决定

当然，申请人可以在申请过程中通过多次电话访问和现场访问。

如下面的例子所示，有时会有＆＃34;日程访谈＆＃34;被取消了。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＆＃34;日程访谈，＆＃34;决定＆＃34;和＆＃34;现场采访开始＆＃34;或＆＃34;电话采访开始＆＃34;。此外，有时可能会有其他＆＃34;事件＆＃34;就像我们看到手动跳过的一样。

我还需要为其创建标记的其他类型的场景，因此我需要将原始数据帧保留为新列。

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

Answer 1

我认为以下代码可以解决您的问题

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)

您将获得以下结果

   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

在这种情况下，最好的Pandas应用/循环方法是什么？

1 个答案: