[英]Reshaping Pandas groupby data row values into column headers
I am trying to extract grouped row data from a pandas groupby object so that the primary group data ('course' in the example below) act as a row index, the secondary grouped row values act as column headers ('student') and the aggregate values as the corresponding row data ('score'). 我试图从pandas groupby对象中提取分组行数据,以便主要组数据(下面示例中的“course”)充当行索引,次要分组行值充当列标题('student')和聚合值作为相应的行数据('得分')。
So, for example, I would like to transform: 所以,例如,我想改造:
import pandas as pd
import numpy as np
data = {'course_id':[101,101,101,101,102,102,102,102] ,
'student_id':[1,1,2,2,1,1,2,2],
'score':[80,85,70,60,90,65,95,80]}
df = pd.DataFrame(data, columns=['course_id', 'student_id','score'])
Which I have grouped by course_id and student_id: 我按course_id和student_id分组:
group = df.groupby(['course_id', 'student_id']).aggregate(np.mean)
g = pd.DataFrame(group)
Into something like this: 进入这样的事情:
data = {'course':[101,102],'1':[82.5,77.5],'2':[65.0,87.5]}
g3 = pd.DataFrame(data, columns=['course', '1', '2'])
I have spent some time looking through the groupby documentation and I have trawled stack overflow and the like but I'm still not sure how to approach the problem. 我花了一些时间查看groupby文档 ,我已经拖网堆栈溢出等,但我仍然不知道如何解决问题。 I would be very grateful if anyone would suggest a sensible way of achieving this for a largish dataset. 如果有人建议采用合理的方法为大型数据集实现这一点,我将非常感激。
Many thanks! 非常感谢!
>>> g.reset_index().pivot('course_id', 'student_id', 'score')
student_id 1 2
course_id
101 82.5 65.0
102 77.5 87.5
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