Replacing multiple columns (or just one) with iloc does not work
#### Code Sample, a copy-pastable example if possible

```python
import pandas

columns = pandas.DataFrame({'a2': [11, 12, 13], 'b2': [14, 15, 16]})
inputs = pandas.DataFrame({'a1': [1, 2, 3], 'b1': [4, 5, 6], 'c1': [7, 8, 9]})

inputs.iloc[:, [1]] = columns.iloc[:, [0]]

print(inputs)
```

#### Problem description

I have a code which is replacing a set of columns with another set of columns, based on column indices. To make things done without a special case, I assumes I could just use `iloc` to both select and set columns in a DataFrame. But it seems that this not work and fails in strange ways.

#### Expected Output

```
   a1  b1  c1
0   1  11   7
1   2  12   8
2   3  13   9
```

But in reality, you get:

```
    a1  b1   c1
0  1.0 NaN  7.0
1  2.0 NaN  8.0
2  3.0 NaN  9.0
```

See how values converted to float and how column is `NaN`s?

But, if I do the following I get expected results:

```
inputs.iloc[:, [1]] = [[11], [12], [13]]
```

This also works:

```
inputs.iloc[:, [1]] = columns.iloc[:, [0]].values
```

So if it works with lists and ndarrays, one would assume it would also work with DataFrames themselves. But it does not.

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.13.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.23.3
pytest: None
pip: 18.0
setuptools: 40.0.0
Cython: None
numpy: 1.15.0
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
