获取对象在 Pandas 中处于某个状态的平均时间答案

【问题标题】：get the average time that an object stayed in a certain state in Pandas获取对象在 Pandas 中处于某个状态的平均时间
【发布时间】：2021-12-29 16:33:36
【问题描述】：

我有一个包含一些销售机会的大型 DF。此 opps 在其生命周期中多次更改阶段，我们可以看到这些更改是什么以及它们是何时进行的。可能的阶段是：

Closed Won        
Closed No Deal    
Propose           
Negotiate          
Qualify            
Closed Lost        
Invalid            
Identify           
Implemented        
Close Lost            
Close Won                         
Close No Deal

我也知道 opp 更改之前的 OldValue。我需要做的是有一个单独的 DF，我可以在其中获得机会在每个阶段停留多长时间的中值。我研究了网络和 SO，但找不到符合我标准的解决方案。

这是一些示例数据：

reatedDate                    OpportunityId     OldValue    NewValue
2020-05-05T12:04:32.000Z    0060N00000TbLneQAF  Propose     Qualify
2020-07-06T08:44:08.000Z    0060N00000TbLneQAF  Qualify     Identify
2020-08-05T08:59:45.000Z    0060N00000TbLneQAF  Identify    Qualify
2020-08-05T12:02:59.000Z    0060N00000TbLneQAF  Qualify     Propose
2020-09-22T06:47:16.000Z    0060N00000TbLneQAF  Propose     Qualify
2020-10-08T15:33:29.000Z    0060N00000TbLneQAF  Qualify     Identify
2020-10-08T15:40:21.000Z    0060N00000TbLneQAF  Identify    Closed No Deal
2021-07-29T07:57:28.000Z    0060N00000TbLohQAF  Identify    Closed No Deal
2021-10-17T03:07:24.000Z    0060N00000TbLtwQAF  Qualify     Closed No Deal
2021-07-27T13:57:34.000Z    0060N00000TbMhkQAF  Identify    Closed No Deal
2020-04-22T13:35:30.000Z    0060N00000TbMkjQAF  Negotiate   Closed Lost
2020-09-25T09:37:32.000Z    0060N00000TbN8qQAF  Qualify     Propose
2020-09-25T09:37:41.000Z    0060N00000TbN8qQAF  Propose     Negotiate
2021-09-06T14:31:05.000Z    0060N00000TbN8qQAF  Negotiate   Propose
2021-11-03T11:09:56.000Z    0060N00000TbNF8QAN  Identify    Qualify
2020-04-29T15:43:58.000Z    0060N00000TbNFSQA3  Identify    Invalid
2021-01-07T09:35:56.000Z    0060N00000TbNUDQA3  Identify    Closed No Deal
2020-12-03T08:53:12.000Z    0060N00000TbNUSQA3  Qualify     Identify
2021-09-08T09:41:54.000Z    0060N00000TbNUSQA3  Identify    Closed Lost
2021-04-14T07:31:49.000Z    0060N00000TbNg4QAF  Identify    Closed No Deal
2020-04-27T12:19:51.000Z    0060N00000TbNwCQAV  Qualify     Identify
2020-05-04T11:15:00.000Z    0060N00000TbNxPQAV  Identify    Closed No Deal
2021-05-24T03:13:10.000Z    0060N00000TbNywQAF  Qualify     Closed No Deal
2021-05-28T14:51:32.000Z    0060N00000TbO3SQAV  Identify    Invalid
2021-07-27T13:25:50.000Z    0060N00000TbOBlQAN  Identify    Closed No Deal
2021-07-27T13:25:50.000Z    0060N00000TbOCeQAN  Identify    Closed No Deal
2021-07-27T13:25:50.000Z    0060N00000TbOELQA3  Identify    Closed No Deal
2020-04-28T15:12:53.000Z    0060N00000TbOIrQAN  Qualify     Negotiate
2020-05-18T14:11:18.000Z    0060N00000TbOIrQAN  Negotiate   Closed Won
2021-07-27T13:22:09.000Z    0060N00000TbOJzQAN  Identify    Closed No Deal
2021-07-27T13:22:09.000Z    0060N00000TbOLbQAN  Identify    Closed No Deal
2020-08-13T05:04:36.000Z    0060N00000TbOOzQAN  Propose     Identify
2020-09-06T15:36:36.000Z    0060N00000TbOOzQAN  Identify    Invalid
2021-05-27T14:22:10.000Z    0060N00000TbOWKQA3  Qualify     Identify
2021-05-27T14:22:27.000Z    0060N00000TbOWKQA3  Identify    Closed Lost
2020-04-27T12:25:52.000Z    0060N00000TbOX3QAN  Qualify     Identify
2021-01-08T15:27:33.000Z    0060N00000TbOX3QAN  Identify    Qualify
2020-04-13T10:53:57.000Z    0060N00000TbOY6QAN  Qualify     Identify
2020-12-03T10:38:35.000Z    0060N00000TbOY6QAN  Identify    Closed Lost
2020-04-13T10:54:57.000Z    0060N00000TbOYaQAN  Qualify     Identify
2020-12-01T10:50:41.000Z    0060N00000TbOYaQAN  Identify    Closed No Deal
2021-07-27T13:57:34.000Z    0060N00000TbOb5QAF  Identify    Closed No Deal
2021-07-27T13:57:34.000Z    0060N00000TbOeYQAV  Identify    Closed No Deal
2020-05-29T12:28:44.000Z    0060N00000TbOgyQAF  Identify    Qualify
2020-12-18T07:34:18.000Z    0060N00000TbOgyQAF  Qualify     Identify
2020-12-18T07:34:43.000Z    0060N00000TbOgyQAF  Identify    Invalid
2021-07-27T13:22:09.000Z    0060N00000TbOhSQAV  Identify    Closed No Deal
2020-04-15T11:30:09.000Z    0060N00000TbOk7QAF  Identify    Invalid
2020-08-26T03:16:46.000Z    0060N00000TbOnVQAV  Qualify     Closed No Deal
2020-04-03T13:08:23.000Z    0060N00000TbOy4QAF  Identify    Closed Lost

到目前为止，我所做的是尝试使用 group_by 函数，然后尝试获取中位数，但我被卡住了，因为我需要查看 opp 更改的日期并将其与所花费的时间进行比较获得新的价值。我将查看以下结果：

Stage            Average Time
Closed Won        x days
Closed No Deal    x days
Propose           x days
Negotiate         x days 
Qualify           x days 
Closed Lost       x days 
Invalid           x days 
Identify          x days 
Implemented       x days 
Close Lost        x days 
Close Won         x days                
Close No Deal     x days

希望这是有道理的。顺便说一句，我是个菜鸟，所以谢谢你的帮助！

【问题讨论】：

在您的数据框中，您只能对以下状态有答案：“提议”、“资格”、“识别”和“协商”，因为从这个状态到另一个状态的转换（新值）。例如，为“Closed Lost”设置一个值是否有意义？
嗨@Corralien，是的，这是有道理的，因为 opp 可以从一个状态转到另一个状态，然后返回原始状态，然后转换到另一个状态。欢迎来到销售数据！......是精神！ :)
好的。我明白 :) 你可以手动计算 OppId '0060N00000TbLneQAF' 的输出（前 7 行）。这里只有 3 个状态：“Propose”、“Qualify”和“Identify”。我是对的？

标签： python python-3.x pandas dataframe

【解决方案1】：

我认为一个可能的解决方案是首先按OpportunityId 和reatedDate 对数据帧进行排序：

sorted_df = df.sort_values(by=['OpportunityId','reatedDate'])

然后你可以得到逐行的差异：

sorted_df['time_diff'] = sorted_df.reatedDate.diff()

您还需要确定 ID 中存在转换的情况。由于这些需要从统计中排除：

sorted_df['id_diff'] = sorted_df.OpportunityId.diff()
filtered_df=sorted_df[sorted_df.id_diff==0]

那么你应该能够做你的统计：

filtered_df.groupby(['OldValue'])[['time_diff']].median()

如果您只需要日期，您可以添加.dt.days

【讨论】：

【解决方案2】：

这对我有用，但你忘了提到“OldValue”代表 OPP 的创建。

df['difference'] = df.groupby('OpportunityId').reatedDate.diff()
df['aux'] = df['OldValue'] + ' - ' + df['NewValue']
df['days_diff'] = df['difference'].dt.days
df.groupby('aux')['days_diff'].mean()

输出：

aux                     days_difference
Identify - Closed Lost       170.666667
Identify - Closed No Deal    115.500000
Identify - Invalid            12.000000
Identify - Qualify           143.000000
Negotiate - Closed Lost             NaN
Negotiate - Closed Won        19.000000
Negotiate - Propose          346.000000
Propose - Identify                  NaN
Propose - Negotiate            0.000000
Propose - Qualify             47.000000
Qualify - Closed No Deal            NaN
Qualify - Identify            93.000000
Qualify - Negotiate                 NaN
Qualify - Propose              0.000000

【讨论】：