ENH/DOC: wide_to_long performance and docstring clarification
I had to massage some messy data recently, and a big bottleneck turned out to be [wide_to_long](https://github.com/pandas-dev/pandas/blob/4814823903b862c411caf527271e384df0d0d7e7/pandas/core/reshape.py#L878)


Some test data with many id variables and time variables

```python
import pandas as pd
import numpy as np
import string

vars = string.ascii_uppercase[0:4]
nyrs = 20
nidvars = 20
N = 5000

yrvars = []
for var in vars:
    for yr in range(1, nyrs + 1):
        yrvars.append( var + str(yr))
        
yearobs = dict(zip(yrvars, np.random.randn(len(yrvars), N)))
idobs = dict(zip(range(nidvars), np.random.rand(nidvars, N)))
        
frame = pd.concat([pd.DataFrame(idobs), pd.DataFrame(yearobs)], axis=1)

frame.shape
#(5000, 91)
```

Reshaping with `wide_to_long` takes around 2 secs.

```python
%%time
frame['id'] = frame.index
res1 = pd.wide_to_long(frame, list(vars), i='id', j='year')
#CPU times: user 1.89 s, sys: 210 ms, total: 2.1 s
#Wall time: 2.13 s
```

I modified `wide_to_long` slightly (regex on categorical column / avoid copying many "idvariables", postpone type coercion) and the runtime is now 

```python
%%time
res2 = wide_to_long2(frame, list(vars), i='id', j='year')
#CPU times: user 112 ms, sys: 10.8 ms, total: 123 ms
#Wall time: 125 ms
```

The result is the same

```python
res1.equals(res2)
#True
```

#### Docstring clarification

The `wide_to_long` docstring also contains an unused last parameter `stubend : str`, which should be removed. 

A docstring `Note` addtion about escaping special characters (with for example `re.escape`) in `stubnames` could also perhaps be informative, since if the user passes a dataframe with messy stubnames, the function fails with a pretty uninformative error message. 

I can send a PR for this if that is wanted?

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

<details>
INSTALLED VERSIONS
------------------
commit: 06f26b51e97a0e81e8bd7fca4bba18e57659d963
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.19.0+124.g06f26b5.dirty
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.24.1
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>
