python - 如何从一天的索引匹配中获取全天数据

Question

我有一个数据框 df1 由 datetime 索引，每分钟有一个条目，持续数周示例:

           SAMPLE_TIME       Bottom     Top      Out     state                                                                    
0  2015-07-15 16:41:56      48.625   55.812   43.875        1       
1  2015-07-15 16:42:55      48.750   55.812   43.875        1     
2  2015-07-15 16:43:55      48.937   55.812   43.875        1       
3  2015-07-15 16:44:56      49.125   55.812   43.812        1      
4  2015-07-15 16:45:55      49.312   55.812   43.812        1     

我想找到 Avg(TempBottom，TempTop)最低的那一天，然后按分钟获取全天数据，这样我就可以绘制那一天，我试过了:

df2 = df1.groupby(pd.TimeGrouper('D')).agg(min) \
.sort(['TempTop','TempBottom'], ascending=[True,True])

这给了我订购的最低温度天数。示例:

SAMPLE_TIME       Bottom     Top      Out     state                                                                    
2015-10-17       19.994   25.840   21.875        0       
2015-08-29       26.182   28.777   25.937        0       
2015-11-19       19.244   33.027   28.937        0        
2015-11-07       19.744   33.527   28.125        0           

然后我虽然我需要的只是从 df2 中获取第一个条目的索引:

 df1[df2.index[1]]

但是我得到一个错误:

KeyError: Timestamp('2015-08-29 00:00:00')

Answer 1

来自 docs :

Warning

The following selection will raise a KeyError; otherwise this selection methodology would be inconsistent with other selection methods in pandas (as this is not a slice, nor does it resolve to one)

dft['2013-1-15 12:30:00']

To select a single row, use .loc

In [71]: dft.loc['2013-1-15 12:30:00']
Out[71]: 
A    0.193284
Name: 2013-01-15 12:30:00, dtype: float64

所以你需要在你的情况下使用 loc 方法:

In [103]: df1.loc[df2.index[0]]
Out[103]: 
           SAMPLE_TIME  TempBottom  TempTop  TempOut  State  Bypass
2015-07-15    16:41:56      48.625   55.812   43.875      1       1
2015-07-15    16:42:55      48.750   55.812   43.875      1       1
2015-07-15    16:43:55      48.937   55.812   43.875      1       1
2015-07-15    16:44:56      49.125   55.812   43.812      1       1
2015-07-15    16:45:55      49.312   55.812   43.812      1       1

编辑

当您传递单个参数时，它会尝试使用标签进行访问。但是，当您通过间隔时，它将用作切片。您可以使用技巧来传递值 + 1 天:

In [276]: df2.index[0]
Out[276]: Timestamp('2015-07-15 00:00:00', offset='D')

In [277]: df2.index[0] + 1
Out[277]: Timestamp('2015-07-16 00:00:00', offset='D')

In [278]: df1.loc[df2.index[0]: df2.index[0] + 1]
Out[278]: 
                     TempBottom  TempTop  TempOut  State  Bypass
SAMPLE_TIME                                                     
2015-07-15 16:41:56      48.625   55.812   43.875      1       1
2015-07-15 16:42:55      48.750   55.812   43.875      1       1
2015-07-15 16:43:55      48.937   55.812   43.875      1       1
2015-07-15 16:44:56      49.125   55.812   43.812      1       1
2015-07-15 16:45:55      49.312   55.812   43.812      1       1

EDIT2

或者您可以将 Timestamp 的 date 转换为 str:

In [355]: df2.index[0]
Out[355]: Timestamp('2015-07-15 00:00:00', offset='D')

In [356]: df2.index[0].date()
Out[356]: datetime.date(2015, 7, 15)

In [357]: str(df2.index[0].date())
Out[357]: '2015-07-15'

In [359]: df1[str(df2.index[0].date())]
Out[359]: 
                     TempBottom  TempTop  TempOut  State  Bypass
2015-07-15 16:41:56      48.625   55.812   43.875      1       1
2015-07-15 16:42:55      48.750   55.812   43.875      1       1
2015-07-15 16:43:55      48.937   55.812   43.875      1       1
2015-07-15 16:44:56      49.125   55.812   43.812      1       1
2015-07-15 16:45:55      49.312   55.812   43.812      1       1