read_csv returns different float values for same number
#### Code Sample, a copy-pastable example if possible
`test.csv`
```
-15.361
-15.361000
```

```
>>> import pandas as pd
>>> x = pd.read_csv('test.csv', header=None)
>>> x.loc[0, 0] == x.loc[1, 0]
False
```
#### Problem description / Expected output

The expected output of the code above is 
```
>>> x.loc[0, 0] == x.loc[1, 0]
True
```
We should expect both `-15.361` and `-15.361000` to be converted to the same `np.float64` representation. However, they are converted to different float values, differing in exactly the last bit of their floating point representation. For some reason, `-15.361` gets converted incorrectly to `0xC02EB8D4FDF3B645` whereas `-15.361000` is correctly to `0xC02EB8D4FDF3B646`.

For completeness, here are some more comparisons
`x.loc[1, 0]` is equal (`==`) to `np.float64('-15.361')`, `np.float64('-15.361000')`, and `float('-15.361000')`.
`x.loc[0, 0]` is not equal to any of those.

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

<details>

[paste the output of ``pd.show_versions()`` here below this line]

INSTALLED VERSIONS
------------------
commit: None
python: 3.5.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-73-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.20.3
pytest: None
pip: 9.0.1
setuptools: 36.2.4
Cython: None
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
pandas_gbq: None
pandas_datareader: None