ENH: fix eval scoping issues
closes #5987
closes #5087

Relevant user-facing changes:

```
In [9]: df = DataFrame(randn(5, 2), columns=list('ab'))

In [10]: a, b = 1, 2

In [11]: # column names take precedence

In [12]: df.query('a < b')
Out[12]:
          a         b
0 -0.805549 -0.090572
1 -1.782325 -1.594079
2 -0.984364  0.934457
3 -1.963798  1.122112

[4 rows x 2 columns]

In [13]: # we must use @ whenever we want a local variable

In [14]: df.query('@a < b')
Out[14]:
          a         b
3 -1.963798  1.122112

[1 rows x 2 columns]

In [15]: # we cannot use @ in eval calls

In [16]: pd.eval('@a + b')
  File "<string>", line unknown
SyntaxError: The '@' prefix is not allowed in top-level eval calls, please refer to your variables by name without the '@' prefix

In [17]: pd.eval('@a + b', parser='python')
  File "<string>", line unknown
SyntaxError: The '@' prefix is only supported by the pandas parser
```
- [x] update query/eval docstrings/indexing/perfenhancing
- [x] make sure docs build
- [x] release notes
- [x] pytables
- [x] make the `repr` of `Scope` objects work or revert to previous version
- [x] more tests for new local variable scoping API
- [x] disallow (and provide a useful error message for) locals in expressions like `pd.eval('@a + b')`
- [x] Raise when your variables have the same name as the [builtin math functions that `numexpr` supports](https://code.google.com/p/numexpr/wiki/UsersGuide#Supported_functions), since you cannot override them in `numexpr.evaluate`, even when explicitly passing them. For example

``` python
import numexpr as ne
sin = randn(10)
d = {'sin': sin}
result = ne.evaluate('sin > 1', local_dict=d, global_dict=d)
result == array(True)
```

For reference, after this PR local variables are given lower precedence than column names. For example

``` python
a, b = 1, 2
df = DataFrame(randn(10, 2), columns=list('ab'))
res = df.query('a > b')
```

will no longer raise an exception about overlapping variable names. If you want the local `a` (as opposed to the column `a`) you must do

``` python
df.query('@a > b')
```
