Series with MultiIndex gives surprising iloc results
Several puzzling behaviors appear below wherein calling `Series.iloc[int]` for a Series with a MultiIndex gives confusing results.  Namely, I see unexpected indices mutated.  Unexpected indices read and then added to others.  As well as more than one index being mutated.  Contrast all of the behaviors below with indexing a vanilla numpy array where none of the mystery occurs.  Full recreates below along with why I find them surprising.  I've seen similar behavior back in the Pandas 0.19.x series.

#### Code Sample 1

The length-2 case with duplicated keys at the first level.

```python
In [1]: import pandas as pd

In [2]: z = pd.DataFrame()
   ...: z['i'] = [1, 1]
   ...: z['j'] = [22, 33]
   ...: z['k'] = [555, 666]
   ...: z.set_index(['i', 'j'], inplace=True)
   ...: v = z.k
   ...: 

In [3]: v
Out[3]: 
i  j 
1  22    555
   33    666
Name: k, dtype: int64

In [4]: v.values
Out[4]: array([555, 666])

In [5]: v.iloc[0] += 200; v
Out[5]: 
i  j 
1  22    755
   33    666
Name: k, dtype: int64

In [6]: v.iloc[-1] += 300; v
Out[6]: 
i  j 
1  22    755
   33    966
Name: k, dtype: int64

In [7]: v.iloc[1] += 100; v
Out[7]: 
i  j 
1  22    1066
   33    1066
Name: k, dtype: int64
```

#### Code Sample 1 Problem / Expected Result

Final statement incremented key (1, 33) then wrote the result to be (1, 22) and (1, 33)?
Expected (1, 22) to remain 755 and (1, 33) to become 1066.

#### Code Sample 2

The length-2 case with unique keys at each level.

```python
In [1]: import pandas as pd

In [2]: z = pd.DataFrame()
   ...: z['i'] = [1, 2]
   ...: z['j'] = [3, 4]
   ...: z['k'] = [7, 8]
   ...: z.set_index(['i', 'j'], inplace=True)
   ...: v = z.k
   ...: 

In [3]: v
Out[3]: 
i  j
1  3    7
2  4    8
Name: k, dtype: int64

In [4]: v.values
Out[4]: array([7, 8])

In [5]: v.iloc[0] += 10; v
Out[5]: 
i  j
1  3    17
2  4     8
Name: k, dtype: int64

In [6]: v.iloc[-1] += 10; v
Out[6]: 
i  j
1  3    17
2  4    18
Name: k, dtype: int64

In [7]: v.iloc[1] += 1000; v
Out[7]: 
i  j
1  3    1018
2  4      18
Name: k, dtype: int64
```

#### Code Sample 2 Problem / Expected Result

Above iloc[0] incremented key (1, 3) but then iloc[1] does?
Do not expect 2 different iloc indices differing by 1 to point to the same location in a length 2 array.

#### Code Sample 3

The length-3 case.

```python
In [1]: import pandas as pd

In [2]: x = pd.DataFrame()
   ...: x['i'] = [1, 2, 3]
   ...: x['j'] = [11, 22, 33]
   ...: x['k'] = [4, 5, 6]
   ...: x.set_index(['i', 'j'], inplace=True)
   ...: v = x.k
   ...: 

In [3]: v
Out[3]: 
i  j 
1  11    4
2  22    5
3  33    6
Name: k, dtype: int64

In [4]: v.values
Out[4]: array([4, 5, 6])

In [5]: v.iloc[0] += 4; v
Out[5]: 
i  j 
1  11    8
2  22    5
3  33    6
Name: k, dtype: int64

In [6]: # Expected (3, 33) to be incremented but instead (2, 22) is?
   ...: v.iloc[2] += 7; v
   ...: 
Out[6]: 
i  j 
1  11     8
2  22    13
3  33     6
Name: k, dtype: int64

In [7]: # Expected (2, 22) to be incremented but instead (1, 11) is set to (2, 22) + increment?
   ...: v.iloc[1] += 10; v
   ...: 
Out[7]: 
i  j 
1  11    23
2  22    13
3  33     6
Name: k, dtype: int64
```

#### Code Sample 3 Problem / Expected Result

Comments inline above

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.35-pv-ts2
machine: x86_64
processor: 
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.utf8
LOCALE: en_US.UTF-8

pandas: 0.20.3
pytest: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.13.1
scipy: None
xarray: None
IPython: 6.1.0
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2017.2
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: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None
</details>
