DataFrame.replace() overwrites when values are non-numeric
#### Code Sample, a copy-pastable example if possible

```python
In [1]: pd.Series([1,2,3]).replace({1 : 2, 2 : 3, 3 : 4})
Out[1]: 
0    2
1    3
2    4
dtype: int64

In [2]: pd.Series(['1','2','3']).replace({'1' : '2', '2' : '3', '3' : '4'})
Out[2]:
0    4
1    4
2    4
dtype: object

```
#### Problem description

I'd expect the replacement over values in a dataframe to be non-transitive. Suppose that we would like to replace `a` with `b`, and `b` with `c`. When this replacement is applied to an entry containing the value `a`, replacement rules are propagated and therefore `c` is returned instead of `b`. Same replacement is not transitive (as shown in example code) for numeric values. 

I think this default behavior should be mentioned explicitly in the documentation. It would also be nice to have a Boolean option to set the transitivity on/off. 

#### Expected Output
```python
Out[2]:
0    2
1    3
2    4
dtype: object
```

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

<details>
INSTALLED VERSIONS

------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.18.1
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
