如何在转移数据框架后将所有列与同一标签合并

Question

我有一个df，在'transpoting'df：

之后看起来像这样

                     1     2                     3            4           5  
level_1     questionId  type                 value  exportLabel  questionId   
0        participantId    id  -Ll4truw3KbSjVRtXmJy       Viewed    viewTime   
            6                         7            8  
level_1  type                     value  exportLabel  
0        time  2019-07-31T02:41:34.063Z      Started  

如果名称匹配并将其制成列？

如何合并值level_1索引行？

新的DF应该看起来像这样：

    questionId       type          value            exportLabel
  0  participantId    id    -Ll4truw3KbSjVRtXmJy         Viewed    
  1  viewTime         time  2019-07-31T02:41:34.063Z    Started

我尝试了groupby，但是数据将转换为串联，索引变成列，并使用此代码：

df = df.groupby(df.loc['level_1'])

Answer 1

df (your data).. Generated using below
temp = StringIO("""  
                     1     2                     3            4           5   6          7            8  
level_1     questionId  type                 value  exportLabel  questionId  type      value  exportLabel   
0        participantId    id  -Ll4truw3KbSjVRtXmJy       Viewed    viewTime   time  2019-07-31T02:41:34.063Z      Started  
""")
df = pd.read_csv(temp, sep='s+')
##df

                     1     2                     3            4           5  
level_1     questionId  type                 value  exportLabel  questionId   
0        participantId    id  -Ll4truw3KbSjVRtXmJy       Viewed    viewTime   
            6                         7            8  
level_1  type                     value  exportLabel  
0        time  2019-07-31T02:41:34.063Z      Started  

---

df = df.T.groupby('level_1')['0'].apply(lambda x: pd.Series(list(x))).unstack().T
del df.columns.name
print(df[['questionId','type','value','exportLabel']])

---

      questionId  type                     value exportLabel
0  participantId    id      -Ll4truw3KbSjVRtXmJy      Viewed
1       viewTime  time  2019-07-31T02:41:34.063Z     Started