qcut: Using cut with IntervalIndex provided by qcut producing wrong NaN values
xref #17282 

#### Code Sample

```python
>>> x.isnull().sum()
0

>>> x.value_counts()
0.000000     693
12.561725      1
13.568112      1
12.521249      1
13.007628      1
6.993961       1
14.815512      1
6.017280       1
12.944714      1
Name: 0, dtype: int64

>>> categorized = pd.qcut(x, 10, duplicates='drop')
>>> categorized.isnull().sum()
0

>>> categorized.cat.categories  # Notice how all values of x are contained in the only interval
IntervalIndex([(-0.001, 14.816]]
              closed='right',
              dtype='interval[float64]')

>>> res = pd.cut(x, categorized.cat.categories)
>>> res.isnull().sum()
701
```
Copy pastable

```python
x = pd.read_csv('x.csv', header=None).iloc[:, 0]  # x.csv is provided in a comment below
categorized = pd.qcut(x, 10, duplicates='drop')
res = pd.cut(x, categorized.cat.categories)
res.isnull().sum()
```
#### Problem description

When I use `qcut` to get the `IntervalIndex` corresponding to the quantiles of a `float64` series, and than use this as the `bins` of `cut` on the same float64 series, it doesn't work. It produces a new series with a lot of NaN values, while the original series contained no NaN and all of its values are contained at the interval of `IntervalIndex`.

#### Expected Output

The result of both `qcut` and `cut` should also be the same, but they are not.

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-514.26.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.20.1
pytest: 3.0.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
xarray: None
IPython: 5.3.0
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.3.0
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

</details>
