You cannot do a selection if the format is 'fixed'. That would raise an exception (and access times actually would be much faster). That said, you can directly index a fixed format.
In [39]: df = DataFrame(np.random.randn(1000000,10))
In [40]: df.to_hdf('test.h5','df',mode='w',format='table')
In [41]: def f():
df = pd.read_hdf('test.h5','df')
return df.loc[50001:,0]
....:
In [42]: def g():
df = pd.read_hdf('test.h5','df')
return df.loc[df.index>50000,0]
....:
In [43]: def h():
return pd.read_hdf('test.h5','df',where='index>50000')[0]
....:
In [44]: f().equals(g())
Out[44]: True
In [46]: f().equals(h())
Out[46]: True
In [47]: %timeit f()
10 loops, best of 3: 159 ms per loop
In [48]: %timeit g()
10 loops, best of 3: 127 ms per loop
In [49]: %timeit h()
1 loops, best of 3: 499 ms per loop
sure it slower. but you are doing a lot more work. This is comparing the boolean indexer vs the entire array. If you read in the entire frame then it has quite a number of advantages (e.g. caching, locality).
Of course if you are just selecting a contiguous slice, then just do
In [59]: def i():
return pd.read_hdf('test.h5','df',start=50001)[0]
....:
In [60]: i().equals(h())
Out[60]: True
In [61]: %timeit i()
10 loops, best of 3: 86.6 ms per loop