COMPAT: cross-interpret compat for HDF5
My work environment uses Windows 10, Python 2.7, and Python 3.6. Today I noticed a strange issue that seems to be caused by VC version.

First, I created two `conda` environments:

```
conda create --name test27 python==2.7.13 pandas pytables
bzip2:           1.0.6-vc9_3       [vc9]
hdf5:            1.8.17-vc9_0      [vc9]
mkl:             2017.0.3-0
numexpr:         2.6.2-np113py27_0
numpy:           1.13.1-py27_0
pandas:          0.20.3-py27_0
pip:             9.0.1-py27_1
pytables:        3.4.2-np113py27_0
python:          2.7.13-1
python-dateutil: 2.6.1-py27_0
pytz:            2017.2-py27_0
setuptools:      27.2.0-py27_1
six:             1.10.0-py27_0
vs2008_runtime:  9.00.30729.5054-0
wheel:           0.29.0-py27_0
zlib:            1.2.11-vc9_0      [vc9]
```

```
conda create --name test27 python==3.6.2 pandas pytables
bzip2:           1.0.6-vc14_3      [vc14]
hdf5:            1.8.17-vc14_0     [vc14]
mkl:             2017.0.3-0
numexpr:         2.6.2-np113py36_0
numpy:           1.13.1-py36_0
pandas:          0.20.3-py36_0
pip:             9.0.1-py36_1
pytables:        3.4.2-np113py36_0
python:          3.6.2-0
python-dateutil: 2.6.1-py36_0
pytz:            2017.2-py36_0
setuptools:      27.2.0-py36_1
six:             1.10.0-py36_0
vs2015_runtime:  14.0.25420-0
wheel:           0.29.0-py36_0
zlib:            1.2.11-vc14_0     [vc14]
```

The only difference between the two environments is the VC version, 9 vs 14.

Under both environments, I created an artificial series, and wrote with Pandas in compressed mode:

```
(test27) C:\Users\libo.yin>python
Python 2.7.13 |Continuum Analytics, Inc.| (default, May 11 2017, 13:17:26) [MSC v.1500 64 bit (AMD64)] on win32
>>> import pandas as pd
>>> s = pd.Series(pd.to_datetime(['0:00:01.123456', '0:00:02.654321']))
>>> s
0   2017-08-21 00:00:01.123456
1   2017-08-21 00:00:02.654321
dtype: datetime64[ns]
>>> s.to_hdf('test27_compressed.h5', 'data', mode='w', format='f', complevel=9, complib='blosc:lz4')
```

```
(test36) C:\Users\libo.yin>python
Python 3.6.2 |Continuum Analytics, Inc.| (default, Jul 20 2017, 12:30:02) [MSC v.1900 64 bit (AMD64)] on win32
>>> import pandas as pd
>>> s = pd.Series(pd.to_datetime(['0:00:01.123456', '0:00:02.654321']))
>>> s
0   2017-08-21 00:00:01.123456
1   2017-08-21 00:00:02.654321
dtype: datetime64[ns]
>>> s.to_hdf('test36_compressed.h5', 'data', mode='w', format='f', complevel=9, complib='blosc:lz4')
```

Then I tried to load the HDF5 files in both interpreters:

```
(test27) C:\Users\libo.yin>python
Python 2.7.13 |Continuum Analytics, Inc.| (default, May 11 2017, 13:17:26) [MSC v.1500 64 bit (AMD64)] on win32
>>> import pandas as pd
>>> pd.read_hdf('test27_compressed.h5').dtypes
dtype('<M8[ns]')
>>> pd.read_hdf('test36_compressed.h5').dtypes
dtype('<M8[ns]')
```

```
(test36) C:\Users\libo.yin>python
Python 3.6.2 |Continuum Analytics, Inc.| (default, Jul 20 2017, 12:30:02) [MSC v.1900 64 bit (AMD64)] on win32
>>> import pandas as pd
>>> pd.read_hdf('test27_compressed.h5').dtypes  # this is not right!
dtype('int64')
>>> pd.read_hdf('test36_compressed.h5').dtypes
dtype('<M8[ns]')
```

Is this a bug, or is this a feature that I do not quite understand? Which package caused this issue?

Thanks in advance!