DataFrame with PeriodIndex written in Python2 gets an Int64Index when read back in Python3
#### Code Sample, a copy-pastable example if possible

```
df = pd.DataFrame([1.,2,3], index=pd.PeriodIndex(['2015-01-01', '2015-01-02', '2015-01-05'], freq='B'))
df.to_hdf('pind2.7', 'p')
```
#### Problem description

Writing this DataFrame out via HDF5 in Python2.7, then reading back in Python3.6 causes the DataFrame to have an Int64Index rather than a PeriodIndex:

**in python3.6:**
```
In [6]: pd.read_hdf('pind2.7').index
Out[6]: Int64Index([11740, 11741, 11742], dtype='int64')
```

#### Expected Output

The deserialized index should look like this:

**in python2.7**:
```
In [5]: pd.read_hdf('pind2.7').index
Out[5]: PeriodIndex(['2015-01-01', '2015-01-02', '2015-01-05'], dtype='period[B]', freq='B')
```

The index is correctly deserialized when reading it back in from Python2.7.  The index is also correctly deserialized when reading a file that was written in Python3.6.  I don't know if we guarantee 2.7 -> 3.6 compatibility for an on-disk HDF file?

I've attached an HDF file written in Python2.7 and a similar HDF file written in Python3.6.

[pind.zip](https://github.com/pandas-dev/pandas/files/1106157/pind.zip)


#### Output of ``pd.show_versions()``

<details>
pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Darwin
OS-release: 16.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.20.1
pytest: 3.0.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
xarray: None
IPython: 5.3.0
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.3.0
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.6.0
html5lib: 0.999
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None
</details>
