HDF5 Select with Filter gives incorrect results when using Iteration
Linked Issue: https://github.com/PyTables/PyTables/issues/319

Hi,

I have a dataframe saved in HDF5 with 6.7 million records (about 425MB). As you can see below, it gives an incorrect result when it has to do multiple iterations (which is a serious issue for me actually). ->

```
# prepare
fact_hdf = pd.HDFStore('iteration_test3.h5', mode='r')
store_name = 'store_0'
column_list = ['o', 'm101']
where = [('o', '=', [-15534, -16280, -17113, -14786, -14790, -18074])]

# describe
fact_hdf[store_name]
'''
<class 'pandas.core.frame.DataFrame'>
Int64Index: 6708525 entries, 0 to 6708524
Columns: 27 entries, a1007 to m105_b
dtypes: float64(11), int64(16)
'''

# non iterated
fact_hdf.select(store_name, columns=column_list, where=where)['o'].unique()
'''
array([-14790, -16280, -15534, -14786, -18074, -17113])
'''

# iterated by 1mln
chunksize = 1000000 # 1 mln
for sub_df in fact_hdf.select(store_name, columns=column_list, where=where, iterator=True, chunksize=chunksize):
    sub_df['o'].unique()

'''
array([-14790, -16280, -15534, -14786, -18074, -17113])
array([-14786, -14790, -15534, -18074, -16280, -17113])
array([-14790, -18074, -15534, -14786, -16280, -17113])
array([-16280, -14790, -15534, -18074, -14786, -17113])
array([-14790, -14786, -15534, -18074, -16280, -17113])
array([-18511, -17074, -17099, -16876, -18060, -15965, -14733, -16300,
       -15534, -14790, -16280, -14786, -18074, -17113])
array([-16280, -14790, -18074, -15534, -14786, -17113])
'''

# iterated by 10mln
chunksize = 10000000 # 1 mln
for sub_df in fact_hdf.select(store_name, columns=column_list, where=where, iterator=True, chunksize=chunksize):
    sub_df['o'].unique()

'''
array([-14790, -16280, -15534, -14786, -18074, -17113])
'''
```

Reproducing the bug can be hard (not all files have the same issue), but this specific case happens again (it's not a corrupted HDF5 file, if you write away the dataframe it happens again).

I can share the file with pandas/pytables developers if needed (it has to be personal though / not uploaded to a public location; message me for a sftp link)

Some technical stuff
- Pandas 0.12
- Pytables 3.0
- Ubuntu 12.04LTS 64bit
- File is saved with BLOSC, complevel=9

Kind regards,

Carst
