英文:
combine rows with the same IDs into the same row
问题
I understand your request. Here's the translated code part without any additional content:
我明白你的请求。以下是翻译好的代码部分，没有任何额外内容：我有一个包含600行的数据集。数据有一个主要的ID=版本(Version)和第二个ID=任务(Task)。数据如下所示：我想要更改格式，使得属于同一个版本(Version)的“任务”(Task)在同一行中，如下所示：
请注意，这只是代码的翻译部分，不包含任何其他内容。如果需要更多帮助，请告诉我。
英文:
I have a dataset with 600 rows. the data has one main ID= Version and second ID= Task. Data looks like this:
Version Task Concept Att 1 - Att 2 -1 1 1 3 21 1 2 1 11 2 1 2 31 2 2 1 21 3 1 2 31 3 2 3 12 1 1 2 12 1 2 3 22 2 1 2 22 2 2 1 32 3 1 3 12 3 2 1 3
I would like to change the format, so to have "Task" which belongs to the same "Version" in the same row like this:
Version Task Concept Att 1 - Att 2 - Version Task Concept Att 1 - Att 2 -1 1 1 3 2 1 1 2 1 11 2 1 2 3 1 2 2 1 21 3 1 2 3 1 3 2 3 12 1 1 2 1 2 1 2 3 22 2 1 2 2 2 2 2 1 32 3 1 3 1 2 3 2 1 3
I have tried different things like groupby, pivot but I cannot find the right solution
答案1
得分: 0
I think a pivot is the clean way to reshape (df.pivot(index=['Version', 'Task'], columns='Concept'), optionally with flattening the columns MultiIndex).
That said if you really want to duplicate the columns, you could combine a groupby and concat:
out = (pd.concat([g.set_index(['Version', 'Task'], drop=False)for k, g in df.groupby('Concept')], axis=1).reset_index(drop=True))
Output:
Version Task Concept Att 1 - Att 2 - Version Task Concept Att 1 - Att 2 -0 1 1 1 3 2 1 1 2 1 11 1 2 1 2 3 1 2 2 1 22 1 3 1 2 3 1 3 2 3 13 2 1 1 2 1 2 1 2 3 24 2 2 1 2 2 2 2 2 1 35 2 3 1 3 1 2 3 2 1 3
英文:
I think a pivot is the clean way to reshape (df.pivot(index=['Version', 'Task'], columns='Concept'), optionally with flattening the columns MultiIndex).
That said if you really want to duplicate the columns, you could combine a groupby and concat:
out = (pd.concat([g.set_index(['Version', 'Task'], drop=False)for k, g in df.groupby('Concept')], axis=1).reset_index(drop=True))
Output:
Version Task Concept Att 1 - Att 2 - Version Task Concept Att 1 - Att 2 -0 1 1 1 3 2 1 1 2 1 11 1 2 1 2 3 1 2 2 1 22 1 3 1 2 3 1 3 2 3 13 2 1 1 2 1 2 1 2 3 24 2 2 1 2 2 2 2 2 1 35 2 3 1 3 1 2 3 2 1 3
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