将 pandas 数据框作为数据集插入 HDFStore

Question

我遇到了 pandas HDFStore 方法的问题，我无法以我使用 h5py.File 方法检索的方式访问数据。这是代码sn-p：

In [1]: import pandas as pd  

In [2]: import numpy as np  

In [3]: import h5py as h5

In [4]: hdf = pd.HDFStore("tmp.h5")

In [5]: hdf.put('tables/t1', pd.DataFrame(np.random.rand(20,5)))

In [6]: hdf.put('t2', pd.DataFrame(np.random.rand(10,5)))

In [7]: 

In [7]: hdf.close() 

In [8]: 

In [8]: ############ Read using pd.HDFStore ############

In [9]: 

In [9]: data = pd.HDFStore ("tmp.h5") 

In [10]: data["tables/t1"] 
Out[10]: 
           0         1         2         3         4
0   0.384926  0.712066  0.022438  0.686217  0.942678
1   0.079548  0.466799  0.575394  0.276646  0.514414
2   0.672582  0.828567  0.801799  0.296046  0.124042
3   0.568058  0.931348  0.225348  0.547913  0.736184
4   0.496768  0.419699  0.724118  0.313427  0.353825
5   0.771868  0.963346  0.523821  0.793295  0.052085
6   0.358478  0.845149  0.334389  0.674448  0.239096
7   0.454559  0.604438  0.183654  0.027641  0.186922
8   0.776586  0.155783  0.253801  0.123986  0.560601
9   0.201239  0.932080  0.040997  0.119049  0.154076
10  0.753566  0.770133  0.123285  0.112419  0.353622
11  0.040959  0.384800  0.806119  0.247106  0.013442
12  0.739205  0.100547  0.855418  0.774874  0.710557
13  0.865856  0.565094  0.815860  0.816869  0.834415
14  0.251312  0.624995  0.976317  0.854855  0.744861
15  0.179678  0.435902  0.602303  0.118516  0.386935
16  0.452009  0.973729  0.067736  0.097811  0.292619
17  0.285994  0.569845  0.584602  0.001671  0.422877
18  0.727996  0.291086  0.736912  0.960595  0.132891
19  0.356397  0.747693  0.458485  0.100849  0.072220

In [11]: ## Success 

In [12]: data ["tables"]["t1"] 
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-12-c7599d16a7b6> in <module>()
----> 1 data ["tables"]["t1"]

/usr/conda/lib/python2.7/site-packages/pandas/io/pytables.py in __getitem__(self, key)
    415 
    416     def __getitem__(self, key):
--> 417         return self.get(key)
    418 
    419     def __setitem__(self, key, value):

/usr/conda/lib/python2.7/site-packages/pandas/io/pytables.py in get(self, key)
    632         if group is None:
    633             raise KeyError('No object named %s in the file' % key)
--> 634         return self._read_group(group)
    635 
    636     def select(self, key, where=None, start=None, stop=None, columns=None,

/usr/conda/lib/python2.7/site-packages/pandas/io/pytables.py in _read_group(self, group, **kwargs)
   1268 
   1269     def _read_group(self, group, **kwargs):
-> 1270         s = self._create_storer(group)
   1271         s.infer_axes()
   1272         return s.read(**kwargs)

/usr/conda/lib/python2.7/site-packages/pandas/io/pytables.py in _create_storer(self, group, format, value, append, **kwargs)
   1151                 else:
   1152                     raise TypeError(
-> 1153                         "cannot create a storer if the object is not existing "
   1154                         "nor a value are passed")
   1155             else:

TypeError: cannot create a storer if the object is not existing nor a value are passed

In [13]: 

In [13]: data.close() 

In [14]: 

In [14]: ########### Read using h5py.File ############## 

In [15]: 

In [15]: data = h5.File("tmp.h5","r") 

In [16]: 

In [16]: data["tables"]
Out[16]: <HDF5 group "/tables" (1 members)>

In [17]: 

In [17]: data["tables"]["t1"]
Out[17]: <HDF5 group "/tables/t1" (4 members)>

In [18]: 

In [18]: data['tables']['t1'].keys ()
Out[18]: [u'axis0', u'axis1', u'block0_items', u'block0_values']

In [19]: [u'axis0', u'axis1', u'block0_items', u'block0_values']
Out[19]: [u'axis0', u'axis1', u'block0_items', u'block0_values']

In [20]: 

In [20]: data['tables']['t1']['block0_values'].value
Out[20]: 
array([[ 0.38492571,  0.71206567,  0.02243773,  0.68621713,  0.9426783 ],
       [ 0.07954806,  0.4667994 ,  0.57539433,  0.27664603,  0.51441446],
       [ 0.67258161,  0.82856681,  0.80179916,  0.29604625,  0.12404214],
       [ 0.56805845,  0.93134797,  0.22534757,  0.54791294,  0.73618366],
       [ 0.49676792,  0.41969943,  0.72411835,  0.31342698,  0.35382463],
       [ 0.77186804,  0.96334586,  0.52382094,  0.7932945 ,  0.05208528],
       [ 0.3584784 ,  0.84514863,  0.33438851,  0.6744483 ,  0.23909552],
       [ 0.45455901,  0.6044383 ,  0.18365449,  0.02764097,  0.18692162],
       [ 0.77658631,  0.15578276,  0.25380109,  0.12398617,  0.56060138],
       [ 0.20123928,  0.93207974,  0.04099724,  0.11904895,  0.15407568],
       [ 0.75356644,  0.77013349,  0.12328475,  0.11241904,  0.35362213],
       [ 0.04095888,  0.38480023,  0.80611853,  0.24710571,  0.01344193],
       [ 0.73920528,  0.1005474 ,  0.85541761,  0.7748739 ,  0.71055697],
       [ 0.86585587,  0.5650938 ,  0.81586031,  0.81686915,  0.83441517],
       [ 0.25131205,  0.62499501,  0.97631707,  0.85485518,  0.74486096],
       [ 0.17967805,  0.43590236,  0.60230302,  0.11851596,  0.38693535],
       [ 0.4520091 ,  0.97372923,  0.0677363 ,  0.09781059,  0.29261929],
       [ 0.28599448,  0.56984462,  0.5846021 ,  0.00167063,  0.42287738],
       [ 0.72799625,  0.29108631,  0.7369122 ,  0.96059508,  0.13289119],
       [ 0.35639696,  0.7476934 ,  0.45848456,  0.10084881,  0.07221995]])

In [21]: 

In [21]: ######################## End ############### 

In [22]: 

In [22]: 

我想用data['tables']['t1']的方式访问数据。由于这个问题，我被困住了。我观察到的是，熊猫将 hd5 中的每个数据帧作为组插入。我想将它作为数据集插入，以便我可以轻松访问数据。

Answer 1

根据documentation 为HDFStore。

警告：对于存储在根节点下的项目，无法将分层键检索为如上所述的点（属性）访问。相反，请使用基于字符串的显式键。

所以这只是模块加载 hdfs 的方式不同。