大熊貓日期時間切片：junkdf.ix [ '2015年8月3日'： '2015年8月6日']不工作

Question

junkdf：

  rev 
dtime 
2015-08-03 20.45 
2015-08-04 -2.57 
2015-08-05 12.53 
2015-08-06 -8.16 
2015-08-07 -4.41 

junkdf.reset_index（）to_dict（」 REC「）

[{'dtime': datetime.date(2015, 8, 3), 'rev': 20.45}, 
{'dtime': datetime.date(2015, 8, 4), 'rev': -2.5699999999999994}, 
{'dtime': datetime.date(2015, 8, 5), 'rev': 12.53}, 
{'dtime': datetime.date(2015, 8, 6), 'rev': -8.16}, 
{'dtime': datetime.date(2015, 8, 7), 'rev': -4.41}] 

junkdf.set_index('dtime',inplace=True) 

爲什麼我不能做任何的日期時間切片一樣，在描述：

python-pandas-dataframe-slicing-by-date-conditions

time series datetime slicing

junkdf [ '2015年8月3日'：]

C:\Users\blah\Anaconda3\lib\site-packages\pandas\core\base.py in searchsorted(self, key, side, sorter) 
    1112  def searchsorted(self, key, side='left', sorter=None): 
    1113   # needs coercion on the key (DatetimeIndex does already) 
-> 1114   return self.values.searchsorted(key, side=side, sorter=sorter) 
    1115 
    1116  _shared_docs['drop_duplicates'] = (

TypeError: unorderable types: datetime.date() > str() 

junkdf.ix [ '2015年8月3日'： '2015年8月6日']

C:\Users\blah\Anaconda3\lib\site-packages\pandas\core\base.py in searchsorted(self, key, side, sorter) 
    1112  def searchsorted(self, key, side='left', sorter=None): 
    1113   # needs coercion on the key (DatetimeIndex does already) 
-> 1114   return self.values.searchsorted(key, side=side, sorter=sorter) 
    1115 
    1116  _shared_docs['drop_duplicates'] = (

TypeError: unorderable types: datetime.date() > str() 

開始= junkdf.index.searchsorted（dt.datetime（2015，8,4））

C:\Users\blah\Anaconda3\lib\site-packages\pandas\core\base.py in searchsorted(self, key, side, sorter) 
    1112  def searchsorted(self, key, side='left', sorter=None): 
    1113   # needs coercion on the key (DatetimeIndex does already) 
-> 1114   return self.values.searchsorted(key, side=side, sorter=sorter) 
    1115 
    1116  _shared_docs['drop_duplicates'] = (

TypeError: can't compare datetime.datetime to datetime.date)) 

但是，下面的作品，如果我用dt.date（）：

start = junkdf.index.searchsorted(dt.date(2015, 8, 4)) 
end = junkdf.index.searchsorted(dt.date(2015, 8, 6)) 

junkdf.ix[start:end] 

       rev 
    dtime 
    2015-08-04 -2.57 
    2015-08-05 12.53 

UPDATE：

junkdf = df[['dtime','rev']].groupby((df.dtime).dt.date).sum().copy() 

其中df[['dtime','rev']]樣子：

dtime rev 
0 2015-08-03 07:59:59 -0.18 
1 2015-08-03 08:59:59 -0.11 
2 2015-08-03 09:59:59 -0.29 
3 2015-08-03 10:59:59 -0.08 
4 2015-08-03 11:59:59 0.69 

UPDATE2：

我想：

df[['dtime','rev']].head() 

dtime rev 
0 2015-08-03 07:59:59 -0.18 
1 2015-08-03 08:59:59 -0.11 
2 2015-08-03 09:59:59 -0.29 
3 2015-08-03 10:59:59 -0.08 
4 2015-08-03 11:59:59 0.69 

df[['dtime','rev']].groupby(pd.TimeGrouper('D', key=df.dtime)).sum() 

C:\Users\blah\Anaconda3\lib\site-packages\pandas\core\generic.py in __hash__(self) 
    804  def __hash__(self): 
    805   raise TypeError('{0!r} objects are mutable, thus they cannot be' 
--> 806       ' hashed'.format(self.__class__.__name__)) 
    807 
    808  def __iter__(self): 

TypeError: 'Series' objects are mutable, thus they cannot be hashed 

Answer 1

假設您有以下來源DF（我把它從你剛纔的問題，並進行了更改，所以我們必須多天的數據）：

In [85]: df 
Out[85]: 
       datetime hour rev 
0 2016-05-01 01:00:00  1 -0.02 
1 2016-05-01 02:00:00  2 -0.01 
2 2016-05-01 03:00:00  3 -0.02 
3 2016-05-01 04:00:00  4 -0.02 
4 2016-05-01 05:00:00  5 -0.01 
5 2016-05-02 06:00:00  6 -0.03 
6 2016-05-02 07:00:00  7 -0.10 
7 2016-05-02 08:00:00  8 -0.09 
8 2016-05-03 09:00:00  9 -0.08 
9 2016-05-03 10:00:00 10 -0.10 
10 2016-05-03 11:00:00 11 -0.12 
11 2016-05-04 12:00:00 12 -0.14 
12 2016-05-04 13:00:00 13 -0.17 
13 2016-05-04 14:00:00 14 -0.16 
14 2016-05-05 15:00:00 15 -0.15 
15 2016-05-05 16:00:00 16 -0.15 
16 2016-05-05 17:00:00 17 -0.17 
17 2016-05-06 18:00:00 18 -0.16 
18 2016-05-06 19:00:00 19 -0.18 
19 2016-05-06 20:00:00 20 -0.17 
20 2016-05-07 21:00:00 21 -0.14 
21 2016-05-07 22:00:00 22 -0.16 
22 2016-05-08 23:00:00 23 -0.08 
23 2016-05-08 00:00:00 24 -0.06 

讓我們按天計算並計算sum：

In [89]: rslt = (df.assign(t=df.datetime - pd.Timedelta(hours=1)) 
    ....:   .groupby(pd.TimeGrouper('D', key='t'))['rev'] 
    ....:   .sum()) 

In [90]: rslt 
Out[90]: 
t 
2016-05-01 -0.08 
2016-05-02 -0.22 
2016-05-03 -0.30 
2016-05-04 -0.47 
2016-05-05 -0.47 
2016-05-06 -0.51 
2016-05-07 -0.36 
2016-05-08 -0.08 
Freq: D, Name: rev, dtype: float64 

In [92]: rslt.index.dtype 
Out[92]: dtype('<M8[ns]') 

現在切片應該正常工作（因爲該指數已datetime D型）：

In [91]: rslt.ix['2016-05-03':'2016-05-06'] 
Out[91]: 
t 
2016-05-03 -0.30 
2016-05-04 -0.47 
2016-05-05 -0.47 
2016-05-06 -0.51 
Freq: D, Name: rev, dtype: float64