BUG: fillna with inplace does not work with multiple columns selection by loc
#### Code Sample, a copy-pastable example if possible

```python
df = pd.DataFrame(np.random.randn(3, 4), columns=list('ABCD'))
df.iloc[1, 2:4] = np.nan
df.loc[:, ['C', 'D']].fillna(-1, inplace=True)
display(df)
```
Output:
```
A	B	C	D
0	1.387547	-1.299578	0.360015	1.290783
1	-0.395182	-0.112581	NaN	NaN
2	-0.649372	-1.831869	-0.103746	0.533153
```

#### Problem description

It's expected to modify the Nan to -1 but it does **NOT**.

Please see the following comparisons.

#### Comparison (1)

On contrary, the following codes behave as expected.
(The **only difference** is selection by **iloc** or by **loc**)

```python
df = pd.DataFrame(np.random.randn(3, 4), columns=list('ABCD'))
df.iloc[1, 2:4] = np.nan
df.iloc[:, 2:4].fillna(-1, inplace=True)
display(df)
```
Output:
```
	A	B	C	D
0	-0.522821	-1.600520	-1.468871	0.715790
1	0.493071	0.722474	-1.000000	-1.000000
2	0.545852	-0.877946	0.993169	-0.582661
```

#### Comparison (2)

When only **one** column is selected with loc, it behaves properly.

```python
df = pd.DataFrame(np.random.randn(3, 4), columns=list('ABCD'))
df.iloc[1, 2:4] = np.nan
df.loc[:, 'C'].fillna(-1, inplace=True)
display(df)
```
Output:
```
A	B	C	D
0	-0.549106	0.261093	-1.278554	2.017178
1	-1.424498	0.439482	-1.000000	NaN
2	-1.281520	1.190736	0.356319	0.416363
```

#### Expected Output of the first code sample

```
A	B	C	D
0	1.181106	1.101231	-0.198445	0.295238
1	-0.654265	-1.129840	-1.000000	-1.000000
2	-1.070404	0.096556	0.499020	-1.835347
```

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

<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-358.14.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: zh_TW.big5
LOCALE: zh_TW.big5

pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
