BUG: Strange behavior when modifying part of a dataframe(indexed by tz-aware DateTimeIndex) via .loc
#### Code Sample, a copy-pastable example if possible

```
import pytz
import pandas as pd
from datetime import datetime

px=[100.0, 101.0, 102.0, 103.0]
idx = pd.date_range('2017-01-01', periods=4, tz="UTC")
df = pd.DataFrame({'px': px}, index=idx)

cutoff = datetime(2017, 1, 3, 8, 0, 0)
cutoff = pytz.UTC.localize(cutoff)
df.loc[df.index < cutoff, ['px']] += 88.8 

print(df)
```

#### Problem description
Running the code above, you will find that all affected rows in df becomes NaN:

```
px
2017-01-01 00:00:00+00:00    NaN
2017-01-02 00:00:00+00:00    NaN
2017-01-03 00:00:00+00:00    NaN
2017-01-04 00:00:00+00:00  103.0
```

What’s probably equally strange is, the problem goes away with either one of the following two modifications:
- Make idx timezone-unaware i.e. remove the tz=”UTC” in the definition of idx, and the NaNs will go away
- Make px a list of integers instead of floats: px=[100, 101, 102, 103], and the NaNs will go away!


#### Expected Output
The expected output is df with all rows before cutoff time incremented by given value (88.8)
 
```
px
2017-01-01 00:00:00+00:00  188.8
2017-01-02 00:00:00+00:00  189.8
2017-01-03 00:00:00+00:00  190.8
2017-01-04 00:00:00+00:00  103.0
```
#### Output of ``pd.show_versions()``

<details>

[paste the output of ``pd.show_versions()`` here below this line]
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.35-pv-ts2
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.20.3
pytest: 3.0.3
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
feather: None
matplotlib: 1.5.3
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.3
lxml: None
bs4: 4.5.1
html5lib: None
sqlalchemy: 1.1.0
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_gbq: None
pandas_datareader: None
```
</details>
