스태킹 MultiIndex DataFrame 및 병합 지수

Question

내 DataFrame은 다음과 같습니다, 나는 별도의 인덱스로 컬럼 (시간)을 얻을) (df.stack 사용하는 경우

     00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 
Code Alias Date                 
RO FI 05.07.2010 53.97 52.11 52.11 52.11 52.11 52.11 51.85 51.55 
    JY 05.07.2010 53.97 52.11 52.11 52.11 52.11 52.11 51.85 65.85 
    SE 05.07.2010 53.97 52.11 52.11 52.11 52.11 52.11 51.85 51.55 
    SJ 05.07.2010 53.97 52.11 52.11 52.11 51.49 52.11 51.85 51.55 

. 'Date'인덱스와 새로운 'hours'인덱스를 병합하여 DateTime 인덱스를 구성하는 방법은 무엇입니까? 이처럼

:

       Value 
Code Alias Date                 
RO FI 05.07.2010 00:00 53.97 
      05.07.2010 01:00 52.11 
      05.07.2010 02:00 52.11 

등

Answer 1

꽤 아니지만, 그것은

• 사용 strftime이 문자열로 Date를 켠 다음 Hour
• 와 연결하는 할거야 pd.to_datetime
개

---

s = df.rename_axis('Hour', 1).stack() 

d1 = s.reset_index(['Date', 'Hour'], name='Value') 

d1.set_index(
    pd.to_datetime(
     d1.Date.dt.strftime('%Y-%m-%d ') + 
     d1.Hour 
    ), append=True 
).Value 

Code Alias      
RO FI  2010-05-07 00:00:00 53.97 
      2010-05-07 01:00:00 52.11 
      2010-05-07 02:00:00 52.11 
      2010-05-07 03:00:00 52.11 
      2010-05-07 04:00:00 52.11 
      2010-05-07 05:00:00 52.11 
      2010-05-07 06:00:00 51.85 
      2010-05-07 07:00:00 51.55 
     JY  2010-05-07 00:00:00 53.97 
      2010-05-07 01:00:00 52.11 
      2010-05-07 02:00:00 52.11 
      2010-05-07 03:00:00 52.11 
      2010-05-07 04:00:00 52.11 
      2010-05-07 05:00:00 52.11 
      2010-05-07 06:00:00 51.85 
      2010-05-07 07:00:00 65.85 
     SE  2010-05-07 00:00:00 53.97 
      2010-05-07 01:00:00 52.11 
      2010-05-07 02:00:00 52.11 
      2010-05-07 03:00:00 52.11 
      2010-05-07 04:00:00 52.11 
      2010-05-07 05:00:00 52.11 
      2010-05-07 06:00:00 51.85 
      2010-05-07 07:00:00 51.55 
     SJ  2010-05-07 00:00:00 53.97 
      2010-05-07 01:00:00 52.11 
      2010-05-07 02:00:00 52.11 
      2010-05-07 03:00:00 52.11 
      2010-05-07 04:00:00 51.49 
      2010-05-07 05:00:00 52.11 
      2010-05-07 06:00:00 51.85 
      2010-05-07 07:00:00 51.55 
Name: Value, dtype: object