python - 从 HDFStore 检索多索引 Pandas DataFrame 时遇到问题(以表格式)

Question

我有一些代码，简化为下面包含的示例，它获取一些原始数据，从中创建一个数据透视表，然后将其与另一个数据帧合并，最后将结果存储在 HDFStore 对象中。如果它以固定格式存储，则可以很好地检索。但是，如果以表格式存储，则会产生错误。我需要表格格式，以便我可以一次提取 block (总数据集有数千万行)。

知道问题是什么吗？

代码示例:

import pandas as pd

def createFrame():
    data = {
             'colA':[1,1,1,2,2,2,3,3,3,4,4,4,4],
             'colB':[5,5,5,5,5,5,5,5,5,5,5,5,5],
             'colC':['a','b','c','a','b','c','a','b','c','d','e','f','g'],
             'colD':['ap','ap','ap','bp','bp','bp','cp','cp','cp','dp','dp','dp','dp']
           }
    frame = pd.DataFrame(data)
    return frame

def createOtherFrame():
    data = {
             'colD':['ap','bp','cp','dp'],
             'colE':[100,200,300,400]
           }
    frame = pd.DataFrame(data)
    return frame 

if __name__ == '__main__':
    pd.set_option('display.width', 120) # default is 80
    pd.set_option('io.hdf.default_format','table')    

    pivotStore = pd.HDFStore('test.h5')
    frame = createFrame()
    otherFrame = createOtherFrame()
    pivoted = frame.pivot_table(['colB'],
                                index=['colA'],
                                columns='colC',
                                aggfunc='sum'
                                )
    print(pivoted)
    print('')    
#    print(pivoted.info(),end='\n\n')

    mergedFrameA = pd.merge(frame[['colA','colD']].drop_duplicates(),
                        otherFrame, 
                        how = 'left',
                        on='colD'
                       ).set_index('colA')
#    print(mergedFrameA.info())
    print(mergedFrameA)

    mergedFrameB = pd.merge(pivoted,mergedFrameA,how='left',left_index=True,right_index=True)
#    print(mergedFrameB.info())
    print(mergedFrameB)

    pivotStore['bob'] = mergedFrameB
    pivotStore.close()
    store = pd.HDFStore('test.h5')
    extracted = store.select('bob',start=0,stop=5)
    print(extracted)
    store.close()

产生的输出(有错误):

     colB                        
colC    a   b   c   d   e   f   g
colA                             
1       5   5   5 NaN NaN NaN NaN
2       5   5   5 NaN NaN NaN NaN
3       5   5   5 NaN NaN NaN NaN
4     NaN NaN NaN   5   5   5   5

     colD  colE
colA           
1      ap   100
2      bp   200
3      cp   300
4      dp   400
      (colB, a)  (colB, b)  (colB, c)  (colB, d)  (colB, e)  (colB, f)  (colB, g) colD  colE
colA                                                                                        
1             5          5          5        NaN        NaN        NaN        NaN   ap   100
2             5          5          5        NaN        NaN        NaN        NaN   bp   200
3             5          5          5        NaN        NaN        NaN        NaN   cp   300
4           NaN        NaN        NaN          5          5          5          5   dp   400
Traceback (most recent call last):
  File "C:\multiindextest.py", line 52, in <module>
    extracted = store.select('bob',start=0,stop=5)
  File "C:\Anaconda3\envs\py34\lib\site-packages\pandas\io\pytables.py", line 665, in select
    return it.get_result()
  File "C:\Anaconda3\envs\py34\lib\site-packages\pandas\io\pytables.py", line 1359, in get_result
    results = self.func(self.start, self.stop, where)
  File "C:\Anaconda3\envs\py34\lib\site-packages\pandas\io\pytables.py", line 658, in func
    columns=columns, **kwargs)
  File "C:\Anaconda3\envs\py34\lib\site-packages\pandas\io\pytables.py", line 3999, in read
    cols.set_names(names, inplace=True)
  File "C:\Anaconda3\envs\py34\lib\site-packages\pandas\core\index.py", line 529, in set_names
    idx._set_names(names, level=level)
  File "C:\Anaconda3\envs\py34\lib\site-packages\pandas\core\index.py", line 3274, in _set_names
    'Length of names must match number of levels in MultiIndex.')
ValueError: Length of names must match number of levels in MultiIndex.
Closing remaining open files:test.h5...done

Answer 1

你不能存储这样的索引，即部分元组和部分字符串。它不是真正的多索引(也没有任何用处)。

我会简单地使用常规索引。您也可以使用 MultiIndex 来执行此操作，但每一列都必须是其中的一部分。

In [67]: pivoted = frame.pivot_table('colB',index='colA',columns='colC',aggfunc='sum')

In [68]: pivoted
Out[68]: 
colC   a   b   c   d   e   f   g
colA                            
1      5   5   5 NaN NaN NaN NaN
2      5   5   5 NaN NaN NaN NaN
3      5   5   5 NaN NaN NaN NaN
4    NaN NaN NaN   5   5   5   5

In [69]: df = pd.concat([pivoted,mergedFrameA],axis=1)

In [70]: df
Out[70]: 
colC   a   b   c   d   e   f   g colD  colE
colA                                       
1      5   5   5 NaN NaN NaN NaN   ap   100
2      5   5   5 NaN NaN NaN NaN   bp   200
3      5   5   5 NaN NaN NaN NaN   cp   300
4    NaN NaN NaN   5   5   5   5   dp   400