PERF difference between concat and merge on simple 1-to-1 index-to-index merge
Simple case of concatting two frames with equal (or at least partially overlapping indices) index seems to be quicker when using merge instead of concat:

```
In [98]: df1 = pd.DataFrame(np.random.randn(10000,2), columns=['a', 'b'])

In [99]: df2 = pd.DataFrame(np.random.randn(10000,2), columns=['c', 'd']) 

In [100]: %timeit pd.concat([df1, df2], axis=1) 
1000 loops, best of 3: 1.46 ms per loop 

In [101]: %timeit pd.merge(df1, df2, left_index=True, right_index=True) 
The slowest run took 5.32 times longer than the fastest. This could mean that an intermediate result is being cached 
1000 loops, best of 3: 519 µs per loop
```
