HDFStore Exception: cannot find the correct atom type with pandas to_hdf bug
I posted this on [StackOverflow](https://stackoverflow.com/questions/47186347/hdfstore-exception-cannot-find-the-correct-atom-type-with-pandas-to-hdf), but I'm increasingly convinced this is actually a bug, not just a user issue, so moving here. 

[EDIT:] If it helps, the table I read in required I set `encoding='latin-1'` in `read_table`.


#### Code Sample, a copy-pastable example if possible

```python
df['street_address_2010'] = df['street_address_2010'].astype('str')
df[['street_address_2010']].to_hdf('preprocessed.h5', 
                                   key='key', complevel=9, complib='blosc', 
                                   fletcher32=True,
                                   format='table')
```
#### Problem description

    Exception: cannot find the correct atom type -> [dtype->object,items ->Index(['street_address_2010'], dtype='object')] 

#### Expected Output

(none)

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.5.4.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.22.0.dev0+61.gbc69dc69b
pytest: 3.2.3
pip: 9.0.1
setuptools: 36.6.0
Cython: 0.27.2
numpy: 1.13.3
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.6.5
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.1.0
openpyxl: 2.5.0b1
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.1.0
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: 2.7.3.1 (dt dec pq3 ext lo64)
jinja2: 2.9.6
s3fs: 0.1.2
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>

#### Things I've tried

Checked all strings:


    dtypes = df.street_address_2010.apply(lambda x: type(x), convert_dtype=False)

    dtypes.value_counts(dropna=False)
    Out[120]: 
    <class 'str'>    7537988
    Name: street_address_2010, dtype: int64


Saved in chunks, no problems:

    for i in range(0, len(df), 10000):
        df['street_address_2008'].iloc[i:i+10005].to_hdf('preprocessed.h5',
                  key='key', complevel=9, complib='blosc', fletcher32=True,
                  format='table')

