Series.map updates all copies of a DataFrame
#### Code Sample

```python
df = pd.DataFrame({'foo':[0,1,2]})
new = df
new['foo'] = new['foo'].map({0:'zero', 1:'one', 2:'two'})

print(df)
```
output:
```
    foo
0  zero
1   one
2   two
```
#### Problem description
When using pd.Series.map on a column of a copied DataFrame, the copy is updated, but so are the original DataFrame and any other copies. This issue does not occur when map is applied to a copy of a Series, however.

#### Expected Output
The original DataFrame should remain unmodified:
```
   foo
0    0
1    1
2    2
```
#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.4.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-514.16.1.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.22.0
pytest: None
pip: 18.1
setuptools: 40.2.0
Cython: 0.28.5
numpy: 1.15.1
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
