Modifying dataframe with float values using .iloc and boolean indexing raises ValueError
There seems to be inconsistent behavior between updating parts of a dataframe using `.iloc` depending on whether the index is accessed using an integer list or a boolean array. This does not seem to happen if all dataframe values are integer (but still happens if `np.nan` is replaced by `7.0`). Example:

#### Code Sample

```import pandas as pd
import numpy as np

data = [
    [0, 2, 13], 
    [1, 6, 21], 
    [2, np.nan, 0],
    [3, 5, 3]
]
df = pd.DataFrame(data, columns=("idx", "val1", "val2")).set_index("idx")

print("updating with ints")
df.iloc[np.nonzero(df.index >= 2)[0],0] *= 2
print("updating with bool")
try:
    df.iloc[df.index >= 2,0] *= 2 # raises ValueError
except ValueError as e:
    print("ValueError", e)
try:
    df.iloc[df.index < 2,0] *= 2 # raises ValueError
except ValueError as e:
    print("ValueError", e)
print("iloc with bool")
print(df.iloc[df.index >= 2,0])
print("iloc with ints")
print(df.iloc[np.nonzero(df.index >= 2)[0],0])
```
Output is:
```
updating with ints
updating with bool
ValueError Must have equal len keys and value when setting with an iterable
ValueError Must have equal len keys and value when setting with an iterable
iloc with bool
idx
2     NaN
3    10.0
Name: val1, dtype: float64
iloc with ints
idx
2     NaN
3    10.0
Name: val1, dtype: float64
```
#### Problem Description
Anticipated behavior is that they should be equivalent when updating, particuarly when one considers that the return value of `.iloc` is the same. Instead, we receieve `ValueError: Must have equal len keys and value when setting with an iterable`. 

#### Output of ``pd.show_versions()``
<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.15-1-ARCH
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: en_US.utf8
LOCALE: en_US.UTF-8

pandas: 0.22.0
pytest: None
pip: 9.0.3
setuptools: 38.5.2
Cython: 0.25.2
numpy: 1.14.2
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: None
patsy: None
dateutil: 2.7.2
pytz: 2018.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.0
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.2.5
pymysql: None
psycopg2: 2.7.4 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
