Why does pandas.read_csv converts strings to NaN?
#### Code Sample, a copy-pastable example if possible

```python
def get_char_vocab(words: Sequence[str], size: int = 200) -> Dict[str, int]:
    return get_vocab("".join(words), size)

vocab = pd.read_csv(data_file, index_col=['id'], header=0, delimiter='\t', quoting=csv.QUOTE_NONE) 
char_vocab = get_char_vocab(vocab["word"])
```
#### Problem description

data_file is a huge csv file with `\t` delimited 8 columns. 
This file contains the following several rows.

```csv
b1338cd0-feed-11e8-946e-e5be960a5987	<para>	<para>	73.557	688.67	0	0	0
b1338cd0-feed-11e8-946e-e5be960a5987	test/ok	test/ok	73.557	689.059	1	0	0
b1338cd0-feed-11e8-946e-e5be960a5987	n/a	N/A	108.195	688.865	1	1	0
b1338cd0-feed-11e8-946e-e5be960a5987	n/a	N/A	130.378	688.865	1	1	0
b1338cd0-feed-11e8-946e-e5be960a5987	test2	test2	154.508	688.865	1	0	0
b1338cd0-feed-11e8-946e-e5be960a5987	test3	test3	170.27	688.865	1	0	0
b1338cd0-feed-11e8-946e-e5be960a5987	<unk>	Szx	200.043	688.67	1	0	0
```

Once pandas read this csv the data the second column starts to look the following way:

```csv
b1338cd0-feed-11e8-946e-e5be960a5987         <para>
b1338cd0-feed-11e8-946e-e5be960a5987        test/ok
b1338cd0-feed-11e8-946e-e5be960a5987            NaN
b1338cd0-feed-11e8-946e-e5be960a5987            NaN
b1338cd0-feed-11e8-946e-e5be960a5987           test2
b1338cd0-feed-11e8-946e-e5be960a5987         test3
b1338cd0-feed-11e8-946e-e5be960a5987          <unk>
```

Why on earth does pandas convert two "n/a" strings into `NaN`?


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

<details>

[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.14.91-1-MANJARO
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.23.4
pytest: 4.0.0
pip: 18.1
setuptools: 40.4.3
Cython: 0.29
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.0.1
sphinx: 1.8.2
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
