added support for selecting multiple nth values
Fix DataFrame.to_latex() with MultiIndex columns

Currently, the positioning of \midrule is determined by the number of index
levels, not columns levels:

	>>> print pd.DataFrame({('x', 'y'): ['a']}).to_latex()
	\begin{tabular}{ll}
	\toprule
	{} &  x \\
	\midrule
	{} &  y \\
	0 &  a \\
	\bottomrule
	\end{tabular}

The fix is simple: use the number of column levels instead of the number
of index levels.
BUG: rolling/expanding_* treamtment of center
ENH: plot functions accept multiple axes and layout kw
bug fix for 7987
CLN/DOC/TST: Categorical fixups (GH7768)

Categorical: preserve ints when NaN are present

  `Categorical([1, np.nan])` would end up with a single `1.` float level.
  This commit ensures that if `values` is a list of ints and contains np.nan,
  the float conversation does not take place.

Categorical: fix describe with np.nan

Categorical: ensure that one can assign np.nan

Categorical: fix assigning NaN if NaN in levels

API: change default Categorical.from_codes() to ordered=False

  In the normal constructor `ordered=True` is only assumed if the levels
  are given or the values are sortable (which is most of the cases), but
  in `from_codes(...)` we can't asssume this so the default should be
  `False`.

Categorical: add some links to Categorical in the other docs

Categorical: use s.values when calling private methods

  s.values is the underlying Categorical object, s.cat will be changed
  to only expose the API methods/properties.

Categorical: Change series.cat to only expose the API

Categorical: Fix order and na_position

Categorical: Fix comparison of Categoricals and Series|Categorical|np.array

  Categorical can only be comapred to another Categorical with the same levels
  and the same ordering or to a scalar value.

  If the Categorical has no order defined (cat.ordered == False), only equal
  (and not equal) are defined.

Categorical: Tab completition tests

Categorical: Fix for NA handling/float converting in levels

Categorical: make sure fillna gets both missing values and NA-levels

Categorical: add back labels as deprecated property

Categorical: Fix assigment to a Series(Categorical)) and remove Series.cat.codes

Categorical: declare most methods in Categorical NON-API
API: Timetamp.tz_localize and tz_convert raises TypeError rathar
Merge pull request #8036 from sinhrks/tz_except

API: Timetamp.tz_localize and tz_convert raises TypeError rathar
Update README to use pydata instead of pystatsmodels google group

I'm pretty sure PyData is the main mailing list for pandas now, e.g., as seen on the website:
http://pandas.pydata.org/community.html
Merge pull request #8047 from shoyer/patch-1

Update README to use pydata instead of pystatsmodels google group
BUG: stack with datetimes

Just take from index itself, not levels.
TST: catch invalid options data parsing
Merge pull request #8053 from jreback/test_data

TST: catch invalid options data parsing
BUG: Bug in DataFrameGroupby.transform when transforming with a passed non-sorted key (GH8046)
Merge pull request #8043 from TomAugspurger/stack-datetime

BUG: stack with datetimes
Don't call np.roll on empty arrays.

On some versions of numpy (such as 1.7, but not 1.8), this throws a
ZeroDivisionError.

Fixes #8019.
Merge pull request #8049 from jreback/transform_order

BUG: Bug in DataFrameGroupby.transform when transforming with a passed non-sorted key (GH8046)
Merge pull request #8041 from ahlmss/slice_empty

BUG: Don't call np.roll on empty arrays.
PERF: StataReader is slow due to excessive lookups for missing vales

Previous versions of StataReader did not correctly check for missing values.
This was fixed in a previous PR, but these checks had previously been
implemented on a value-by-value basis.  This has now been changed to a
vectorized version that is orders of magnitude faster.

Adds support for preserving Stata missing values when importing data using
the convert_missing argument.  When True, missing values will be returned as
StataMissingValue objects.  Test cases for missing value conversion have been
included.

Additionally, a benchmark was added to monitor performance issues in the
future.

Closes #8040
Merge pull request #8045 from bashtage/stata_unpack_performance

PERF: StataReader is slow due to excessive lookups for missing vales