ENH: add .loc attribute to provide location-based indexing

Pandas provides the .loc method in order to get purely integer based indexing. The semantics follow closely python and numpy slicing. These are 0-based indexing. When slicing, the start bounds is included, while the upper bound is excluded. Invalid selections will raise with an IndexError. Trying to use a non-integer, even a valid label will raise a ValueError

Should close #1052 (at least partially)

Includes docs and test coverage. 

```
In [285]: s1 = Series(np.random.randn(8),index=range(0,16,2))

In [286]: s1
Out[286]: 
0     0.341852
2    -0.547515
4    -0.180901
6    -0.355934
8    -1.313090
10    0.784062
12   -0.329752
14   -1.819607
Dtype: float64

In [287]: s1.loc[:5]
Out[287]: 
0    0.341852
2   -0.547515
4   -0.180901
6   -0.355934
8   -1.313090
Dtype: float64

In [288]: s1.loc[5]
Out[288]: 0.78406202900317212

Note that setting works as well:

In [289]: s1.loc[:5] = 0

In [290]: s1
Out[290]: 
0     0.000000
2     0.000000
4     0.000000
6     0.000000
8     0.000000
10    0.784062
12   -0.329752
14   -1.819607
Dtype: float64

With a DataFrame

In [291]: df1 = DataFrame(np.random.randn(8,4),index=range(0,16,2),columns=range(0,8,2))

In [292]: df1
Out[292]: 
           0         2         4         6
0   0.334582  1.936465 -1.120100 -0.378014
2   0.097167  0.534742  0.891384 -0.438346
4  -1.269083  0.649958 -0.350875  1.498596
6   0.088944  0.175965  0.315016  2.593186
8  -0.336016  0.153366  0.402341  0.157685
10 -0.120190 -0.997170  0.178300 -0.625006
12 -0.372354 -0.765653 -1.468988 -0.620692
14 -0.721084 -0.622696  0.648250 -1.944616

In [293]: df1.loc[:3]
Out[293]: 
          0         2         4         6
0  0.334582  1.936465 -1.120100 -0.378014
2  0.097167  0.534742  0.891384 -0.438346
4 -1.269083  0.649958 -0.350875  1.498596

In [294]: df1.loc[5,2]
Out[294]: 0.17829955220403318

In [295]: df1.loc[1:5,2:4]
Out[295]: 
          4         6
2  0.891384 -0.438346
4 -0.350875  1.498596
6  0.315016  2.593186
8  0.402341  0.157685
```