ENH: Faster hashing of Period objects
I've noticed that a lot of manipulations using Periods are pretty slow. It [looks like it's hashing](https://github.com/pydata/pandas/blob/48f39af49a9ae76c2c7d8f519df6069e7af00755/pandas/src/period.pyx#L789) the tuple of the `ordinal` and `freq` attributes. I'm not sure what the mapping is between `freq` and `freqstr` is, but if `freqstr` can stand in for `freq`, it looks like hashing the string gives a decent speedup. Subclassing `Period` with this change speeds up operations like `drop_duplicates` a lot on my machine.

```
class Period_hsh(Period):
    def __hash__(self):
        return hash((self.ordinal, self.freqstr))

p = Period('2016', 'A-DEC')
ph = Period_hsh(p)

%timeit Series([p] * 100000).drop_duplicates()
%timeit Series([ph] * 100000).drop_duplicates()
"""
1 loops, best of 3: 1.14 s per loop
10 loops, best of 3: 171 ms per loop
"""
```

Does this change seem reasonable?
#### output of `pd.show_versions()`
## INSTALLED VERSIONS

commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.5
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 3.2.3
sphinx: None
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: 2.5.1
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
