MultiIndex Bug Copying Values Incorrectly When Adding Values To Index
#### Code Sample, a copy-pastable example if possible

```python
df = pd.DataFrame(
    [
        ['A', np.nan, 1.23, 4.56],
        ['A', 'G', 1.23, 4.56],
        ['A', 'D', 9.87, 10.54],
    ],
    columns=['pivot_0', 'pivot_1', 'col_1', 'col_2'],
)
df.set_index(['pivot_0', 'pivot_1'], inplace=True)
pivot_0 = 'A'
necessary_pivot_1_values = ['D', 'E', 'F' ]
for necessary_value in necessary_pivot_1_values:
    if necessary_value not in df.index.get_level_values('pivot_1').tolist():
        print("Missing", necessary_value)
        
        df.at[(pivot_0, necessary_value), 'col_2'] = 0.0

assert df.loc[('A', 'F')]['col_2'] == 0.0  # Pass
assert pd.isnull(df.loc[('A', 'F')]['col_1'])  # Fails: value of 1.23 from the first row in the df is copied. As of v0.22.0 this was np.nan
```
#### Problem description

When using the MultiIndex features of pandas, when an `np.nan` is in the index when new values are added to the DF then the values are not `np.nan`, but  copied from the `np.nan` row.

This behavior shows for all versions v0.23.x, however is fine for 0.22.0.

#### Expected Output
```python
assert df.loc[('A', 'F')]['col_2'] == 0.0  # Pass
assert pd.isnull(df.loc[('A', 'F')]['col_1'])  # Pass, works in v0.22.0
```

Note this unexpected behavior does not show when the `np.nan` is not included in the index, nor for a single Index.

##### MultiIndex without `np.nan`
```python

df = pd.DataFrame(
    [
        #['A', np.nan, 1.23, 4.56],  # Comment out the np.nan
        ['A', 'G', 1.23, 4.56],
        ['A', 'D', 9.87, 10.54],
    ],
    columns=['pivot_0', 'pivot_1', 'col_1', 'col_2'],
)
df.set_index(['pivot_0', 'pivot_1'], inplace=True)

pivot_0 = 'A'
necessary_pivot_1_values = ['D', 'E', 'F' ]
for necessary_value in necessary_pivot_1_values:
    if necessary_value not in df.index.get_level_values('pivot_1').tolist():
        print "Missing", necessary_value
        
        df.at[(pivot_0, necessary_value), 'col_2'] = 0.0

assert df.loc[('A', 'F')]['col_2'] == 0.0  # Pass
assert pd.isnull(df.loc[('A', 'F')]['col_1'])  # Pass

```

##### Single Index with `np.nan`

```python

df = pd.DataFrame(
    [
        [np.nan, 1.23, 4.56],
        ['G', 1.23, 4.56],
        ['D', 9.87, 10.54],
    ],
    columns=['pivot_0', 'col_1', 'col_2'],
)
df.set_index(['pivot_0'], inplace=True)

necessary_pivot_0_values = ['D', 'E', 'F' ]
for necessary_value in necessary_pivot_0_values:
    if necessary_value not in df.index.get_level_values('pivot_0').tolist():
        print "Missing", necessary_value
        
        df.at[(necessary_value), 'col_2'] = 0.0

assert df.loc[('F')]['col_2'] == 0.0
assert pd.isnull(df.loc[('F')]['col_1'])

```

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 0, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.23.4
pytest: 3.0.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.26.1
numpy: 1.12.1
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 5.4.1
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.2.2
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 3.7.3
bs4: 4.6.0
html5lib: 0.999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
