熊猫塑造协变数据

Question

我需要在时间序列中进行简单的协方差分析。我的原始数据是这样的形状：

WEEK_END_DATE    TITLE_SHORT   SALES 
2012-02-25 00:00:00.000000 "Bob" (EBK)   1 
          "Bob" (EBK)   1 
2012-03-31 00:00:00.000000 "Bob" (EBK)   1 
          "Bob" (EBK)   1 
2012-03-03 00:00:00.000000 "Sally" (EBK)   1 
2012-03-10 00:00:00.000000 "Sally" (EBK)   1 
2012-03-17 00:00:00.000000 "Sally" (EBK)   1 
          "Sally" (EBK)   1 
2012-04-07 00:00:00.000000 "Sally" (EBK)   1 

正如你所看到的，有一些重复。除非我错过了某些东西，否则我需要这些数据成为每个标题的一组向量，以便我可以使用numpy.cov。

问：

如何查找日期和名称重复，并通过SUM聚合吗？我一直试图使用WEEK_END_DATE和TITTLE_SHORT来使用熊猫群，但它以我不明白的方式编制索引。

编辑： 具体而言，当我尝试df.groupby(["WEEK_END_DATE", "TITLE_SHORT"])，我得到这个：

>df.ix[0:3] 

WEEK_END_DATE    TITLE_SHORT    
2012-02-04 00:00:00.000000 'SALEM'S LOT (EBK)   <pandas.core.indexing._NDFrameIndexer object a... 
          'TIS THE SEASON! (EBK)  <pandas.core.indexing._NDFrameIndexer object a... 
          (NOT THAT YOU ASKED) (EBK) <pandas.core.indexing._NDFrameIndexer object a... 
dtype: object 

，并试图选择df.ix[1,]得到这个错误：

Traceback (most recent call last): 
    File "<stdin>", line 1, in <module> 
    File "/Library/Python/2.7/site-packages/pandas-0.11.0rc1_20130415-py2.7-macosx-10.8-intel.egg/pandas/core/series.py", line 613, in __getitem__ 
    return self.index.get_value(self, key) 
    File "/Library/Python/2.7/site-packages/pandas-0.11.0rc1_20130415-py2.7-macosx-10.8-intel.egg/pandas/core/index.py", line 1630, in get_value 
    loc = self.get_loc(key) 
    File "/Library/Python/2.7/site-packages/pandas-0.11.0rc1_20130415-py2.7-macosx-10.8-intel.egg/pandas/core/index.py", line 2285, in get_loc 
    result = slice(*self.slice_locs(key, key)) 
    File "/Library/Python/2.7/site-packages/pandas-0.11.0rc1_20130415-py2.7-macosx-10.8-intel.egg/pandas/core/index.py", line 2226, in slice_locs 
    start_slice = self._partial_tup_index(start, side='left') 
    File "/Library/Python/2.7/site-packages/pandas-0.11.0rc1_20130415-py2.7-macosx-10.8-intel.egg/pandas/core/index.py", line 2250, in _partial_tup_index 
    raise Exception('Level type mismatch: %s' % lab) 
Exception: Level type mismatch: 3 

Answer 1

我不能完全肯定我知道发生了什么，但这是我的开始。首先，获取数据（这看起来固定宽度给我）：

>>> df = pd.read_fwf("weekend.dat", widths=(26, 20, 9), parse_dates=[0]) 
>>> df = df.fillna(method="ffill") 
>>> df 
     WEEK_END_DATE TITLE_SHORT SALES 
0 2012-02-25 00:00:00 "Bob" (EBK)  1 
1 2012-02-25 00:00:00 "Bob" (EBK)  1 
2 2012-03-31 00:00:00 "Bob" (EBK)  1 
3 2012-03-31 00:00:00 "Bob" (EBK)  1 
4 2012-03-03 00:00:00 "Sally" (EBK)  1 
5 2012-03-10 00:00:00 "Sally" (EBK)  1 
6 2012-03-17 00:00:00 "Sally" (EBK)  1 
7 2012-03-17 00:00:00 "Sally" (EBK)  1 
8 2012-04-07 00:00:00 "Sally" (EBK)  1 

然后聚集的DUP：

>>> g = df.groupby(["WEEK_END_DATE", "TITLE_SHORT"]).sum().reset_index() 
>>> g 
     WEEK_END_DATE TITLE_SHORT SALES 
0 2012-02-25 00:00:00 "Bob" (EBK)  2 
1 2012-03-03 00:00:00 "Sally" (EBK)  1 
2 2012-03-10 00:00:00 "Sally" (EBK)  1 
3 2012-03-17 00:00:00 "Sally" (EBK)  2 
4 2012-03-31 00:00:00 "Bob" (EBK)  2 
5 2012-04-07 00:00:00 "Sally" (EBK)  1 

然后尽一切cov东西，你需要（注意：cov是一个系列/ DataFrame/GroupBy方法也是如此，所以你不需要特别地调用np.cov）。