Adding a new DataFrame row using dict() gives unexpected behaviour
#### Code Sample, a copy-pastable example if possible
[Link to StackOverflow example](https://stackoverflow.com/questions/38383127/updating-a-pandas-dataframe-row-with-a-dictionary)

Using pd.series works fine:
```
df = pd.DataFrame(np.random.randint(1, 10, (3, 3)), index=['one', 'one', 'two'], columns=['col1', 'col2', 'col3'])
new_data = pd.Series({'col1': 'new', 'col2': 'new', 'col3': 'new'})
df.iloc[0] = new_data

# resulting df looks like:

#       col1    col2    col3
#one    new     new     new
#one    9       6       1
#two    8       3       7
```

But if I try to add a dictionary instead, I get this:

```
new_data = {'col1': 'new', 'col2': 'new', 'col3': 'new'}
df.iloc[0] = new_data
#
#         col1  col2    col3
#one      col2  col3    col1
#one      2     1       7
#two      5     8       6
```

#### Problem description

Considering that a dataframe can be created using a dictionary, it seems odd that adding to a dataframe using a dictionary would result in shuffled columns when they have been explicitly labeled.

This seems like an unexpected behaviour which, although may not be a bug, doesn't seem like the optimal way of making this process function.

#### Expected Output

Adding a row to a dataframe using a dictionary which gives column headers results in the same thing as adding a row using pd.series

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-83-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: en_GB.UTF-8
LOCALE: None.None

pandas: 0.20.2
pytest: None
pip: 9.0.1
setuptools: 33.1.1.post20170320
Cython: None
numpy: 1.12.1
scipy: 0.19.0
xarray: None
IPython: 5.3.0
sphinx: 1.6.1
patsy: None
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: None
lxml: 3.7.3
bs4: None
html5lib: 0.999
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.5
s3fs: None
pandas_gbq: None
pandas_datareader: None
</details>
