我想在日期时间索引数据框的每一天开始时添加一行。例子:date = ['2015-02-03 23:00:00','2015-02-03 23:30:00','2015-02-04 00:00:00','2015-02-04 00:30:00','2015-02-04 01:00:00','2015-02-04 01:30:00','2015-02-04 02:00:00','2015-02-04 02:30:00','2015-02-04 03:00:00','2015-02-04 03:30:00','2015-02-04 04:00:00','2015-02-04 04:30:00','2015-02-04 05:00:00','2015-02-04 05:30:00','2015-02-04 06:00:00','2015-02-04 06:30:00','2015-02-04 07:00:00','2015-02-04 07:30:00','2015-02-04 08:00:00','2015-02-04 08:30:00','2015-02-04 09:00:00','2015-02-04 09:30:00','2015-02-04 10:00:00','2015-02-04 10:30:00','2015-02-04 11:00:00','2015-02-04 11:30:00','2015-02-04 12:00:00','2015-02-04 12:30:00','2015-02-04 13:00:00','2015-02-04 13:30:00','2015-02-04 14:00:00','2015-02-04 14:30:00','2015-02-04 15:00:00','2015-02-04 15:30:00','2015-02-04 16:00:00','2015-02-04 16:30:00','2015-02-04 17:00:00','2015-02-04 17:30:00','2015-02-04 18:00:00','2015-02-04 18:30:00','2015-02-04 19:00:00','2015-02-04 19:30:00','2015-02-04 20:00:00','2015-02-04 20:30:00','2015-02-04 21:00:00','2015-02-04 21:30:00','2015-02-04 22:00:00','2015-02-04 22:30:00','2015-02-04 23:00:00','2015-02-04 23:30:00']value = [33.24 , 31.71 , 34.39 , 34.49 , 34.67 , 34.46 , 34.59 , 34.83 , 35.78 , 33.03 , 35.49 , 33.79 , 36.12 , 37.09 , 39.54 , 41.19 , 45.99 , 50.23 , 46.72 , 47.47 , 48.46 , 48.38 , 48.40 , 48.13 , 38.35 , 38.19 , 38.12 , 38.05 , 38.06 , 37.83 , 37.49 , 37.41 , 41.84 , 42.26 , 44.09 , 48.85 , 50.07 , 50.94 , 51.09 , 50.60 , 47.39 , 45.57 , 45.03 , 44.98 , 41.32 , 40.37 , 41.12 , 39.33 , 35.38 , 33.44 ]df = pd.DataFrame({'value':value,'index':date})df.index = pd.to_datetime(df['index'],format='%Y-%m-%d %H:%M')df.drop(['index'],axis=1,inplace=True)print(df) valueindex 2015-02-03 23:00:00 33.242015-02-03 23:30:00 31.712015-02-04 00:00:00 34.392015-02-04 00:30:00 34.492015-02-04 01:00:00 34.672015-02-04 01:30:00 34.46我怎样才能有效地做到这一点?在此示例中,我在每天开始时添加了值 10。
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慕后森
TA贡献1802条经验 获得超5个赞
你可以试试
s=df.groupby(df.index.date).first()
s.iloc[:,0]=10
s.index=pd.to_datetime(s.index,format='%Y-%m-%d %H:%M:%S')
df=pd.concat([df,s]).sort_index()
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