PERF: much faster to_datetime performance with a format of '%Y%m%d', equiv of YYYYMMDD (which is also accepted)


```
In [1]: rng = date_range('1/1/2000', periods=10000, freq='D')

In [2]: strings = Series(rng.year*10000+rng.month*100+rng.day,dtype=np.int64).apply(str)

In [3]: %timeit pandas.tslib.array_strptime(strings.values,"%Y%m%d")
10 loops, best of 3: 42.9 ms per loop

In [4]: %timeit pd.to_datetime(strings,format="%Y%m%d")
100 loops, best of 3: 9.21 ms per loop

In [5]: (pd.to_datetime(strings,format="%Y%m%d") == pandas.tslib.array_strptime(strings.values,"%Y%m%d")).all()
Out[5]: True
```