ENH: HDFStore enhancements
- will retain index attributes (freq,tz,name) on recreation #3499
- support datelike columns with a timezone as data_columns #2852
- will raise if trying to append with a different timezone than existing

will warn if the existing frequency of an index is different than an appended one
(thought raising was too strict)

```
In [5]: df  = DataFrame(dict(A = Series(xrange(3), index=date_range('2000-1-1',periods=3,freq='H'))))

In [6]: df2 = DataFrame(dict(A = Series(xrange(3), index=date_range('2002-1-1',periods=3,freq='D'))))

In [9]: df.index
Out[9]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-01 00:00:00, ..., 2000-01-01 02:00:00]
Length: 3, Freq: H, Timezone: None

In [10]: df2.index
Out[10]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2002-01-01 00:00:00, ..., 2002-01-03 00:00:00]
Length: 3, Freq: D, Timezone: None

In [12]: df.to_hdf('test.h5','data',mode='w',append=True)

In [13]: df2.to_hdf('test.h5','data',append=True)
pandas/io/pytables.py:1148: FrequencyWarning: 
the frequency of the existing index is [<1 Hour>] which conflicts with the new freq [<1 Day>],
resetting the frequency to None

  warnings.warn(ws, FrequencyWarning)

```

this is technically an API change, so anyone have an issue with it?

or should I allow it, and maybe do a warning?

as this is technically ok, but I think with an HDFStore it  is just wrong....

@wesm ?

```
In [1]: date_range('20130101',periods=3,freq='D')
Out[1]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00, ..., 2013-01-03 00:00:00]
Length: 3, Freq: D, Timezone: None

In [2]: date_range('20130101',periods=3,freq='H')
Out[2]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00, ..., 2013-01-01 02:00:00]
Length: 3, Freq: H, Timezone: None

In [3]: date_range('20130101',periods=3,freq='D')+date_range('20130101',periods=3,freq='H')
Out[3]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00, ..., 2013-01-03 00:00:00]
Length: 5, Freq: None, Timezone: None
```
 I changed to a warning (see description) if attempting to append with a different frequency than what exists in the store (so it allows you to catch the warning if you really want to do this)

appending a timezone that is different will raise, however
 This is great, but I think there is a problem with the tz storage.  If you run the test with tz set in date_range, the resulting date_range retrieved from the store is shifted 5 hours.

df  = DataFrame(dict(A = Series(xrange(3), index=date_range('2000-1-1',periods=3,freq='H', tz='US/Eastern')))) will fail the equal test.
 works in master (this is not released yet btw), its in 0.12

what version are you trying on?

```
In [13]: df
Out[13]: 
                           A
2000-01-01 00:00:00-05:00  0
2000-01-01 01:00:00-05:00  1
2000-01-01 02:00:00-05:00  2

In [14]: df.index
Out[14]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-01 00:00:00, ..., 2000-01-01 02:00:00]
Length: 3, Freq: H, Timezone: US/Eastern

In [15]: df.to_hdf('tz.h5','df',mode='w',table=True)

In [16]: pd.read_hdf('tz.h5','df')
Out[16]: 
                           A
2000-01-01 05:00:00-05:00  0
2000-01-01 06:00:00-05:00  1
2000-01-01 07:00:00-05:00  2

In [17]: pd.read_hdf('tz.h5','df').index
Out[17]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2000-01-01 05:00:00, ..., 2000-01-01 07:00:00]
Length: 3, Freq: H, Timezone: US/Eastern

In [18]: pd.__version__
Out[18]: '0.12.0.dev-f09a03c'
```
 never mind it is NOT workg...thought my test was catching it...thanks!
 see #4098; this was tested as a column (and not an index), I'll see if I can fix this
 Seems to me like the table mode breaks which I think is consistent with what you're saying (not clear to me in the first example without table mode if the DatetimeIndex is being stored as such rather than a column).  Also, I'm curious to see if 'select' will be impacted at all by this change. 
 @rockg thanks for the report...fixed in master
