read_hdf or store.select returning incorrect results for a query
I have a large dataset (4 million rows, 50 columns) stored via pandas store.append.  When I use either store.select or read_hdf with a query for 2 columns being greater than a certain value (i.e. "(a > 10) & (b > 1)" I get 15,000 or so rows returned.

When I read in the entire table, as say df, and do df[(df.a > 10) & (df.b > 1)] I get 30,000 rows.  I narrowed down the problem - when I read in the entire table and do df.query("(a > 10) & (b > 1)") it's the same 15,000 rows, but when I set the engine to python ---> df.query("(a > 10) & (b > 1)", engine = 'python') I get the 30,000 rows.

I suspect it has something to do with the eval/numexpr method of querying in the HDF and Query methods.

The types are float64's in columns a and b, and even if I query with a float (ie. 1. instead of 1) the problem still persists.

I would appreciate any feedback or if others have the same problem we need to fix this.

Regards,
Neil
