pd.to_datetime much slower with supplied format than when format is inferred
It is **much slower** when converting a date string to supply a date format for a column than for it to be inferred. I would've though there should be less work to do when the format is known (and supplied)

To test

``` python
df = DataFrame({'date_text':["2015-05-18" for i in range(10**6)]})
%timeit pd.to_datetime(df['date_text'],infer_datetime_format=True, box=False).values.view('i8')/10**9
10 loops, best of 3: 115 ms per loop
#Top line from %prun of same command:
ncalls  tottime  percall  cumtime  percall filename:lineno(function)
1       0.095    0.095    0.095  0.095   {pandas.tslib.array_to_datetime} 

%timeit pd.to_datetime(df['date_text'],format="%Y-%m-%d", box=False).values.view('i8')/10**9
1 loops, best of 3: 2.27 s per loop
#Top line from %prun of same command:
ncalls  tottime  percall  cumtime  percall filename:lineno(function)
1       2.282    2.282    2.282  2.282   {pandas.tslib.array_strptime}
```

This plot is taken from this [S/O post](http://stackoverflow.com/questions/30330428/converting-string-date-to-epoch-time-not-working-with-cython-and-posix-c-librari) which shows the difference over a larger range of sizes (and compared to other methods).

![perf_comparison](https://cloud.githubusercontent.com/assets/1150727/7725765/6d9cb844-fef5-11e4-9051-35a0d983d54e.png)
## INSTALLED VERSIONS

commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-52-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8

pandas: 0.15.2
nose: 1.3.4
Cython: 0.22
numpy: 1.9.2
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.10
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.0
openpyxl: 2.2.0-b1
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: 0.9
apiclient: 1.4.0
rpy2: None
sqlalchemy: 1.0.0
pymysql: None
psycopg2: None
