Pandas：无法查询

Question

当我尝试查询数据帧对象时，会发生以下情况：

df2.query('a==1')

.conda/envs/myenv2/lib/python2.7/site-packages/pandas/computation/align.pyc in _align_core(terms)
     96                 reindexer_size = len(reindexer)
     97 
---> 98                 ordm = np.log10(abs(reindexer_size - term_axis_size))
     99                 if ordm >= 1 and reindexer_size >= 10000:
    100                     warnings.warn('Alignment difference on axis {0} is larger '

FloatingPointError: divide by zero encountered in log10

以下是我的数据框的外观：

In [16]: df2.head()
Out[16]: 
                     S         t        S         t    S-elas     t-elas
y                  0.9       0.9      1.0       1.0                     
T a   k   c                                                             
1 0.1 0.2 0.4      NaN       NaN  49.9547  0.935831       NaN        NaN
          0.5  48.4641   0.91747  51.6021  0.893594 -0.595826   0.250475
      0.3 0.4  43.3879  0.930355  44.7448  0.935127 -0.292537 -0.0486025
          0.5  43.1652  0.915278  45.5227  0.888028 -0.505053   0.287115
  1.0 0.2 0.4  7.82282  0.922999  7.95204   0.93587 -0.155631  -0.131551

怎么回事？无论我选择't==1'、'a==1'm还是其他，它都不起作用。如何根据MultiIndex的部分值选择行？我使用的是pandas版本0.17.0，我不愿意升级，因为conda还不支持0.18

---

数据帧创建

我从这个数据帧开始

     a    c    k    y  T        S         t
0    5  0.4  0.2  0.9  1  1.98521   0.90772
1  0.1  0.4  0.2    1  1  49.9547  0.935831
2    5  0.4  0.2    1  1  2.00426  0.905521
3    5  0.4  0.2    1  5  0.54526   4.02065
4  0.1  0.4  0.2    1  5  16.4644   4.26893

然后我想用一种方式，在同一行中，不同的y有S和t：

df.set_index(['T', 'a', 'k', 'c', 'y'], inplace=True)
df = df.stack().unstack(4)

df2 = df.unstack().swaplevel(0, 1, axis=1)

这就是它现在的样子（加上一些附加列）

数据帧创建需要2

或者，这里是df2.reset_index()：

In [36]: df2.reset_index()
Out[36]: 
     T    a    k    c         S         t         S         t       s-diff  \
y                           0.9       0.9       1.0       1.0                
0    1  0.1  0.2  0.4       NaN       NaN   49.9547  0.935831          NaN   
1    1  0.1  0.2  0.5   48.4641   0.91747   51.6021  0.893594       -3.138   
2    1  0.1  0.3  0.4   43.3879  0.930355   44.7448  0.935127     -1.35695   
3    1  0.1  0.3  0.5   43.1652  0.915278   45.5227  0.888028     -2.35748   
4    1  1.0  0.2  0.4   7.82282  0.922999   7.95204   0.93587    -0.129213   
5    1  1.0  0.2  0.5   7.82921  0.910954   7.95381  0.908296    -0.124604   
6    1  1.0  0.3  0.4    7.3131  0.923834   7.41049  0.929179   -0.0973891   
7    1  1.0  0.3  0.5   7.31753  0.911348   7.45975  0.907871    -0.142221   
8    1  5.0  0.2  0.4   1.98521   0.90772   2.00426  0.905521   -0.0190501   
9    1  5.0  0.2  0.5   2.11855    0.9082   2.02325  0.891608    0.0953023   
10   1  5.0  0.3  0.4   1.87945  0.908802   1.90995  0.937631   -0.0305015   
11   1  5.0  0.3  0.5   1.88057   0.89712   1.91215  0.868879   -0.0315846   
12   5  0.1  0.2  0.4   16.4244   3.52755   16.4644   4.26893   -0.0400153   
13   5  0.1  0.3  0.4       NaN       NaN   16.0271   3.61533          NaN   
14   5  0.1  0.3  0.5   15.2237   3.80595       NaN       NaN          NaN   
15   5  1.0  0.2  0.4   2.35366   3.80512    2.3596   3.79693   -0.0059349   
16   5  1.0  0.2  0.5   2.34645    3.8176   2.51691   3.04467    -0.170462   
17   5  1.0  0.3  0.4   2.19598   3.78134   2.21581   3.74906    -0.019823   
18   5  1.0  0.3  0.5   2.34471   3.26898   2.34466   3.26885  4.97027e-05   
19   5  5.0  0.2  0.4  0.541352   3.79937   0.54526   4.02065  -0.00390801   
20   5  5.0  0.2  0.5  0.566614   3.87528  0.567599    3.8671 -0.000984482   
21   5  5.0  0.3  0.4  0.520307    4.2113   0.51744   3.71401   0.00286746   
22   5  5.0  0.3  0.5  0.554546   3.47341   0.55858   3.44815  -0.00403361   
23  10  0.1  0.3  0.5    9.3711   6.70258       NaN       NaN          NaN   
24  10  1.0  0.2  0.5   1.35114   6.84992   1.35207   6.84496 -0.000936299   

         t-diff       S-elas       t-elas  
y                                          
0           NaN          NaN          NaN  
1     0.0238751    -0.595826     0.250475  
2   -0.00477195    -0.292537   -0.0486025  
3     0.0272504    -0.505053     0.287115  
4    -0.0128704    -0.155631    -0.131551  
5    0.00265812    -0.150001     0.027761  
6   -0.00534445    -0.125675   -0.0547997  
7    0.00347693    -0.182862    0.0363132  
8    0.00219903   -0.0907268    0.0230424  
9     0.0165924     0.437187     0.175161  
10    -0.028829    -0.152934    -0.296654  
11    0.0282413    -0.158226     0.303842  
12    -0.741378   -0.0231171     -1.80674  
13          NaN          NaN          NaN  
14          NaN          NaN          NaN  
15   0.00818596   -0.0239247    0.0204594  
16     0.772931    -0.665956      2.14006  
17    0.0322827   -0.0853706    0.0814527  
18  0.000129179  0.000201381  0.000375417  
19     -0.22128   -0.0683336    -0.537635  
20    0.0081767   -0.0164918    0.0200658  
21     0.497293       0.0525       1.1922  
22    0.0252581   -0.0688499    0.0693348  
23          NaN          NaN          NaN  
24    0.0049623  -0.00658096    0.0068846

将此数据帧称为test。然后test.set_index(['T', 'a', 'k', 'c'], inplace=True).query('a == 1')给出与上面相同的错误。你知道吗