如何将熊猫列移动到第一列的最后一行

Question

我正在尝试操作我的pandas数据帧，以便：

1. 创建一个名为"Ticker"的新列
2. 将列"AAL"移动到列"A"下方
3. 在新的"Ticker"列中将列"A"的所有元素标记为A，并将新移动的"AAL"列的AAL标记为
4. 将列"A"重命名为"Adj Close">
5. 将"AAL"行的索引值复制到列"Adj Close"的左侧

实际数据帧输出：

Adj Close   Adj Close
A           AAL
Date            
1/11/19 80.22673035 28.54166412
1/12/19 84.7361908  28.57440376
1/1/20  82.17785645 26.74117851

所需数据帧输出：

Ticker      Adj Close
Date            
1/11/19    A        80.22673035
1/12/19    A        84.7361908
1/1/20     A        82.17785645     
1/11/19   AAL       28.54166412
1/12/19   AAL       28.57440376
1/1/20    AAL       26.74117851

这可能吗？如果可能，最好的方法是什么？我试过使用groupby函数和pivot，但没有取得任何进展。我是python的新手，所以我可能做错了什么。

感谢您的帮助并保持安全：(

EDIT(请求输出(print(df.to_dict())

{('Adj Close', 'A'): 
{Timestamp('2019-10-01 00:00:00'): nan, 
Timestamp('2019-11-01 00:00:00'): 80.22673034667969, 
Timestamp('2019-12-01 00:00:00'): 84.73619079589844, 
Timestamp('2020-01-01 00:00:00'): 82.1778564453125, 
Timestamp('2020-02-01 00:00:00'): 76.71327209472656, 
Timestamp('2020-03-01 00:00:00'): 71.28850555419922, 
Timestamp('2020-04-01 00:00:00'): 76.4993667602539, 
Timestamp('2020-05-01 00:00:00'): 87.95530700683594, 
Timestamp('2020-06-01 00:00:00'): 88.18482971191406, 
Timestamp('2020-07-01 00:00:00'): 96.33000183105469, 
Timestamp('2020-08-01 00:00:00'): 100.41999816894531, 
Timestamp('2020-09-01 00:00:00'): 100.94000244140625, 
Timestamp('2020-10-01 00:00:00'): 100.01000213623047, 
Timestamp('2020-10-02 00:00:00'): 100.01000213623047}, 
('Adj Close', 'AAL'): 
{Timestamp('2019-10-01 00:00:00'): nan, 
Timestamp('2019-11-01 00:00:00'): 28.541664123535156, 
Timestamp('2019-12-01 00:00:00'): 28.574403762817383, 
Timestamp('2020-01-01 00:00:00'): 26.741178512573242, 
Timestamp('2020-02-01 00:00:00'): 18.9798583984375, 
Timestamp('2020-03-01 00:00:00'): 12.1899995803833, 
Timestamp('2020-04-01 00:00:00'): 12.010000228881836, 
Timestamp('2020-05-01 00:00:00'): 10.5, 
Timestamp('2020-06-01 00:00:00'): 13.069999694824219, 
Timestamp('2020-07-01 00:00:00'): 11.119999885559082, 
Timestamp('2020-08-01 00:00:00'): 13.050000190734863, 
Timestamp('2020-09-01 00:00:00'): 12.289999961853027, 
Timestamp('2020-10-01 00:00:00'): 13.0, 
Timestamp('2020-10-02 00:00:00'): 13.0}}

Answer 1

如果您的列标题是multiIndex:，请尝试此操作

df.stack(1).reset_index().rename(columns={'level_1': 'Ticker'})

输出：

Date Ticker  Adj Close
0  1/11/19      A  80.226730
1  1/11/19    AAL  28.541664
2  1/12/19      A  84.736191
3  1/12/19    AAL  28.574404
4   1/1/20      A  82.177856
5   1/1/20    AAL  26.741179