BUG: quantile ignores axis kwarg
Closes https://github.com/pydata/pandas/issues/7306

We may want to wait on this till later today while I figure out what will happen in https://github.com/pydata/pandas/issues/7308

This should be ok once Travis passes.

One odd thing is that `numeric_only` is _always_ considered by column, even when `axis=1`.
So if you have something like

``` python
[[1,    2,  3],
 ['a', 'b', 4]]
```

It will exclude the first two columns since they aren't numeric.  But this is the same as the previous behavior.
 Just to expand on that last comment. As before, we get:

``` python
In [34]: df
Out[34]: 
   0  1  2
0  1  2  3
1  a  b  4

In [35]: df.quantile(.5, axis=1)
Out[35]: 
0    3
1    4
dtype: float64
```

So the first two columns are excluded since they're non-numeric, so we're doing quantile on just the last column:

``` python
In [38]: df[2]
Out[38]: 
0    3
1    4
Name: 2, dtype: int64
```

Since `axis=1`, we take the median of each length 1 array and get the same thing back.

What I think should happen is we transpose _first_, and see that the first col is numeric and calculate the quantile on that column:

```
In [52]: df.T
Out[52]: 
   0  1
0  1  a
1  2  b
2  3  4
```

So `df.quantile(.5, axis=1)` should be equivalent to `df.T.quantlie(.5, axis=0)`

But this would potentially break existing code so I'll hold off on that till the future.
 I added one more test covering this; I'll merge when that's green.
 gr8 looks good
 thanks @TomAugspurger 
