Series.replace() should raise an exception if invalid argument is given
Series.replace() should potentially raise an exception if an invalid argument is given? I am new to pandas and this is more of a question. **This is a user error on my part, but possible improvement to replace.**

```python
df = pd.DataFrame(
{
    'one': ['1','1 ','10'],
    'two': ['1 ', '20 ', '30 ']
})

# creates
     one  two
0    1    1
1    1    20
2    10    30

# I intentionally added a space in df.one. So df.one.value_counts() results in
1     1
10    1
1     1

# I want to strip the spaces around all values in df.one 
# so that value_counts() only has 1 and 10 with values 2 and 1 respectively. 
# The following does not work.

df.one.replace(lambda x: x.strip(), inplace=True)

```
#### Problem description

Series.replace() accepts `"str, regex, list, dict, Series, numeric, or None"`. So I understand why the above does not work. But as @toobaz pointed out on gitter, when a lambda is passed, replace should probably raise an error?

#### Expected Output
Raises TypeError

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

<details>

[paste the output of ``pd.show_versions()`` here below this line]

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.20.3
pytest: None
pip: 9.0.1
setuptools: 36.3.0
Cython: None
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: 6.1.0
sphinx: 1.6.3
patsy: None
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.8
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.8.0
bs4: None
html5lib: 0.999999999
sqlalchemy: 1.1.14
pymysql: 0.7.11.None
psycopg2: None
jinja2: 2.8.1
s3fs: None
pandas_gbq: None
pandas_datareader: None
</details>
