header 中的展平 MultiIndex 交換列名

Question

我有以下帶有多個標題的數據框：

    Datetime            Value
id                      a           b           c           d           e
0   2017-01-01 00:00:00 0.774016    1.588788    270.06055   268.9109    93060.31
1   2017-01-01 00:10:00 0.774016    1.588788    270.06055   268.9109    93060.31
2   2017-01-01 00:20:00 0.774016    1.588788    270.06055   268.9109    93060.31
3   2017-01-01 00:30:00 0.774016    1.588788    270.06055   268.9109    93060.31
4   2017-01-01 00:40:00 0.774016    1.588788    270.06055   268.9109    93060.31

當我從多個標題傳遞到單個 header 時，有時列名被交換，我不知道如何修復它。

cols = ["a","b","c","d","e"]
df.columns = [col[1] if col[0] == '' else col[0] for col in df.columns]
cols.insert(0,"Datetime")
df.columns = cols

這給了我交換的列名：

    Datetime            a           b           d           e           c
0   2017-01-01 00:00:00 0.774016    1.588788    270.06055   268.9109    93060.31
1   2017-01-01 00:10:00 0.774016    1.588788    270.06055   268.9109    93060.31
2   2017-01-01 00:20:00 0.774016    1.588788    270.06055   268.9109    93060.31
3   2017-01-01 00:30:00 0.774016    1.588788    270.06055   268.9109    93060.31
4   2017-01-01 00:40:00 0.774016    1.588788    270.06055   268.9109    93060.31

我該如何解決？

更新：

{('Datetime', ''): {0: Timestamp('2017-01-01 00:00:00'),
  1: Timestamp('2017-01-01 00:10:00'),
  2: Timestamp('2017-01-01 00:20:00'),
  3: Timestamp('2017-01-01 00:30:00'),
  4: Timestamp('2017-01-01 00:40:00')},
 ('Value', 'a'): {0: 0.774016,
  1: 0.774016,
  2: 0.774016,
  3: 0.774016,
  4: 0.774016},
 ('Value', 'b'): {0: 1.588788,
  1: 1.588788,
  2: 1.588788,
  3: 1.588788,
  4: 1.588788},
 ('Value', 'c'): {0: 270.06055,
  1: 270.06055,
  2: 270.06055,
  3: 270.06055,
  4: 270.06055},
 ('Value', 'd'): {0: 268.9109,
  1: 268.9109,
  2: 268.9109,
  3: 268.9109,
  4: 268.9109},
 ('Value', 'e'): {0: 93060.31,
  1: 93060.31,
  2: 93060.31,
  3: 93060.31,
  4: 93060.31}}

Answer 1

蠻力方法

>>> pd.concat([df[['Datetime']].droplevel(1, axis=1), df["Value"]], axis=1)
               Datetime         a         b          c         d         e
id
0   2017-01-01 00:00:00  0.774016  1.588788  270.06055  268.9109  93060.31
1   2017-01-01 00:10:00  0.774016  1.588788  270.06055  268.9109  93060.31
2   2017-01-01 00:20:00  0.774016  1.588788  270.06055  268.9109  93060.31
3   2017-01-01 00:30:00  0.774016  1.588788  270.06055  268.9109  93060.31
4   2017-01-01 00:40:00  0.774016  1.588788  270.06055  268.9109  93060.31

Answer 2

嘗試使用set_index + droplevel + reset_index ：

df.set_index('Datetime', append=True).droplevel(0, 1).reset_index('Datetime')

              Datetime         a         b          c         d         e
id                                                                       
0  2017-01-01 00:00:00  0.774016  1.588788  270.06055  268.9109  93060.31
1  2017-01-01 00:10:00  0.774016  1.588788  270.06055  268.9109  93060.31
2  2017-01-01 00:20:00  0.774016  1.588788  270.06055  268.9109  93060.31
3  2017-01-01 00:30:00  0.774016  1.588788  270.06055  268.9109  93060.31
4  2017-01-01 00:40:00  0.774016  1.588788  270.06055  268.9109  93060.31

---

另外要修復您的實現，請不要插入列表並錯位 DataFrame 只需執行以下操作：

df.columns = [col[1] if col[1] else col[0] for col in df.columns]

              Datetime         a         b          c         d         e
id                                                                       
0  2017-01-01 00:00:00  0.774016  1.588788  270.06055  268.9109  93060.31
1  2017-01-01 00:10:00  0.774016  1.588788  270.06055  268.9109  93060.31
2  2017-01-01 00:20:00  0.774016  1.588788  270.06055  268.9109  93060.31
3  2017-01-01 00:30:00  0.774016  1.588788  270.06055  268.9109  93060.31
4  2017-01-01 00:40:00  0.774016  1.588788  270.06055  268.9109  93060.31