Reading pandas DataFrame with categorical columns from an HDF file with 'where'
It seems pandas DataFrame's with categorical columns are read incorrectly from HDF files with a 'where' argument.

Here is what I see:

```python
>>> import pandas as pd
>>> d = pd.read_hdf(path, columns={'ex'}, where='(ex in ["LN","GY","FP"])')
>>> d['ex'].unique()
[LN, ID]
Categories (2, object): [LN, ID]
```

For the **same** data saved without conversion of data to Categorical, I get:

```python
>>> import pandas as pd
>>> d = pd.read_hdf(path, columns={'ex'}, where='(ex in ["LN","GY","FP"])')
>>> d['ex'].unique()
array(['LN'], dtype=object)
```

#### Problem description

I requested only `["LN","GY","FP"]` values of `'ex'` but got values with `d['ex']=='ID'`.

#### Expected Output

``` python
>>> d['ex'].unique()
```
should give some subset of `{"LN","GY","FP"}` values

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

<details>
>>> pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-693.17.1.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.utf-8
LANG: en_US.utf-8
LOCALE: en_US.UTF-8

pandas: 0.23.4
pytest: 3.3.2
pip: 18.0
setuptools: 39.0.1
Cython: 0.27.3
numpy: 1.14.5
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.6.6
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: None
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 3.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>


Thank you for your help!