Original numpy array getting altered when changes made to dataframe 
#### Code Sample
```python
a = np.array([1,2,3, np.nan])
b = pd.DataFrame(a)
b.fillna(4, inplace=True)
print b
print a
```
Output
```python
     0
0  1.0
1  2.0
2  3.0
3  4.0
[ 1.  2.  3.  4.]
```

#### Problem description

When a dataframe is created from a numpy array the changes to the dataframe are altering the original numpy array. I did not expect this to happen and I'm not sure if this is an expected behaviour or a known issue.

I do know how to work around this, but my question is whether I have to.

#### Expected Output
```python
     0
0  1.0
1  2.0
2  3.0
3  4.0
[  1.   2.   3.  nan]
```

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.8-040408-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8

pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.6.1
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0

</details>
