hdf5 loading performance issue for non-sorted data in pandas 1.0.1
#### Problem Description
compared to the legacy pandas 0.2x.x, the most recent 1.0.1 version doesnt perform well in HDF5 file loading unless the conditioned column was sorted beforehand

```python
import os
import numpy as np
import pandas as pd
row = 6000
col = 2
start_date = '20000101'
freq = 'B'
ind = pd.date_range(start_date,periods=row,freq=freq)
a = pd.DataFrame(np.random.rand(row*col).reshape((row,-1)),index=ind)
a = a.reset_index(drop=False)
a.columns = ['date','x','y']
a = pd.concat([a]*2000)
a.sort_values('y').to_hdf(os.path.join(os.path.expanduser('~'),'hdf_test.h5'),key='df',data_columns=True,format='table')
a.sort_values('date').to_hdf(os.path.join(os.path.expanduser('~'),'hdf_test_sorted.h5'),key='df',data_columns=True,format='table')

# significantly slower in pandas 1.0.1 (this is not an issue in pandas 0.2x.x)
store = pd.HDFStore(os.path.join(os.path.expanduser('~'),'hdf_test.h5'),mode='r')
result = store.select('df',where="{0}>=20150101 & {0}<=20160730".format('date'),)
store.close()

# as fast as pandas 0.2x.x if data was sorted on the 'term' column
store = pd.HDFStore(os.path.join(os.path.expanduser('~'),'hdf_test_sorted.h5'),mode='r')
result = store.select('df',where="{0}>=20150101 & {0}<=20160730".format('date'),)
store.close()
```