PERF: improves performance in SeriesGroupBy.count
BUG: closes bug in Series.count when index has nulls

``` python
In [4]: ts
Out[4]:
a  1      0
   2      1
b  2      2
   NaN    3
c  1      4
   2      5
dtype: int64

In [5]: ts.count(level=1)
Out[5]:
1    2
2    4          # <<< BUG!
dtype: int64

In [6]: from string import ascii_lowercase

In [7]: np.random.seed(2718281)

In [8]: n = 1 << 21

In [9]: df = DataFrame({
   ...:     '1st':np.random.choice(list(ascii_lowercase), n),
   ...:     '2nd':np.random.randint(0, n // 100, n),
   ...:     '3rd':np.random.randn(n).round(3)})

In [10]: df.loc[np.random.choice(n, n // 10), '3rd'] = np.nan

In [11]:

In [11]: gr = df.groupby(['1st', '2nd'])['3rd']

In [12]: %timeit gr.count()
The slowest run took 6.67 times longer than the fastest. This could mean that an intermediate result is being cached
1 loops, best of 3: 86.4 ms per loop

In [13]: %timeit gr.count()
10 loops, best of 3: 87 ms per loop
```

on branch:

``` python
In [5]: ts.count(level=1)
Out[5]:
 1     2
 2     3
NaN    1
dtype: int64

...

In [12]: %timeit gr.count()
The slowest run took 12.29 times longer than the fastest. This could mean that an intermediate result is being cached
1 loops, best of 3: 43.1 ms per loop

In [13]: %timeit gr.count()
10 loops, best of 3: 43.5 ms per loop
```

@behzadnouri pls rebase and make the changes as above
 @behzadnouri can you update according to comments
 @jreback the added test will fail if i change it what u suggest.
 @behzadnouri I just don't think its worth it to support this kind of behavior in Series.count which is inconsitent with all other stat functions on how they handle levels, they just dispatch to groupby.
 then plz go ahead and change it
 https://github.com/pydata/pandas/issues/443 would fix this, e.g. we need an option like:

`s.count(level=1, skipna=False)`
 hmm, looks like my way would break some existing tests....ok will merge as is, and when we eventually add nan group handling in groupby can simplify this code.
 need rebase?
 I just did it. thxs.
 merged via https://github.com/pydata/pandas/commit/33723f9577d451c20db68b4d10ed90e81f54e03e
