解决方法
使用HDFStore文档是
here,压缩文档是
here
In [26]: df = DataFrame(np.random.randn(100,2),columns=['A','B'])
In [27]: df.to_hdf('test.h5','df',mode='w',format='table')
In [28]: store = pd.HDFStore('test.h5')
In [29]: nrows = store.get_storer('df').nrows
In [30]: nrows
Out[30]: 100
In [32]: r = np.random.randint(0,nrows,size=10)
In [33]: r
Out[33]: array([69,28,8,2,14,51,92,25,82,64])
In [34]: pd.read_hdf('test.h5',where=pd.Index(r))
Out[34]:
A B
69 -0.370739 -0.325433
28 0.155775 0.961421
8 0.101041 -0.047499
2 0.204417 0.470805
14 0.599348 1.174012
51 0.634044 -0.769770
92 0.240077 -0.154110
25 0.367211 -1.027087
82 -0.698825 -0.084713
64 -1.029897 -0.796999
[10 rows x 2 columns]
要包含其他条件,您可以这样做：
# make sure that we have indexable columns
df.to_hdf('test.h5',format='table',data_columns=True)
# select where the index (an integer index) matches r and A > 0
In [14]: r
Out[14]: array([33,33,95,69,21,43,58,58])
In [13]: pd.read_hdf('test.h5',where='index=r & A>0')
Out[13]:
A B
21 1.456244 0.173443
43 0.174464 -0.444029
[2 rows x 2 columns]