PyTables dates don't work when you switch to a different time zone
Create a dataframe with a date (not datetime) column as the index. Save that dataframe to the HDFStore. Now open that same file on a computer in a time zone that's behind you. (IE, write the file in New York and then read the file from Dallas). Result: all the dates are off by one.
Here is a code snippet to demonstrate the problem, and also a proposed fix to the pandas code. (But someone more qualified than me can review this fix).

To repro the problem:
# write a file in New York time zone

import pandas
import datetime

def writeToFile(fileName) :
    dates = [ datetime.date.today() , datetime.date.today(), datetime.date.today() ]
    numbers = [ 1, 2, 3 ]
    p = pandas.DataFrame({ "date" : dates, "number": numbers}, columns = ['date', 'number'])
    p.set_index('date', inplace=True)
    store = pandas.HDFStore(fileName)
    store['obj1'] = p
    store.close()
# Then go to a dallas time zone and read that file:

def readFromFile(fileName) :
    store = pandas.HDFStore(fileName)
    return store['obj1']
# Notice that the dates on the dataframe now show YESTERDAY when they ought to show today.

Proposed fix in pandas.io.pytables.py:

def _convert_index(index):
   ...
   elif inferred_type == 'date':
        #OLD LINE: CHANGE THIS LINE
        #converted = np.array([time.mktime(v.timetuple()) for v in values],
        #                     dtype=np.int32)
        #NEW LINE:
        converted = np.array([v.toordinal() for v in values],  dtype=np.int32)        

and then also change:

def _unconvert_index(data, kind):
    ...
    elif kind == 'date':
        # here we'll try from ordinal first.  If the date was saved with the old
        # mktime mechanism it'll throw an exception as it'll be out of bounds.
        # in those cases we'll convert using the old method (with the bug!)  
        try: 
            index = np.array([date.fromordinal(v) for v in data], dtype=object)
        except ValueError:
            index = np.array([date.fromtimestamp(v) for v in data], dtype=object)
