BUG/CLN: Allow the BlockManager to have a non-unique items (axis 0)

- Non-unique index support clarified #3092
   - Fix assigning a new index to a duplicate index in a DataFrame would fail #3468
   - Fix construction of a DataFrame with a duplicate index
   - ref_locs support to allow duplicative indices across dtypes,
     allows iget support to always find the index (even across dtypes) #2194
   - applymap on a DataFrame with a non-unique index now works
     (removed warning) #2786, and fix #3230
   - Fix to_csv to handle non-unique columns #3495
   - Modification to cache_readonly to allow you to pass an argument (allow_setting), to 'set'
     this value (useful in order to avoid a computation you know to be true, e.g. is_unique = True
     for a default index


partially fixes #3468

This would previously raise (same dtype assignment to a non-multi dtype frame with dup indicies)
````
In [6]: df = DataFrame([[1,2]], columns=['a','a'])

In [7]: df.columns = ['a','a.1']

In [8]: df
Out[8]: 
   a  a.1
0  1    2

```
construction of a multi-dtype frame with a dup index (#2194) is fixed
```
In [18]: DataFrame([[1,2,1.,2.,3.,'foo','bar']], columns=list('aaaaaaa'))
Out[18]: 
   a  a  a  a  a    a    a
0  1  2  1  2  3  foo  bar
```

This was also previously would raise
```
In [3]: df_float  = DataFrame(np.random.randn(10, 3),dtype='float64')

In [4]: df_int    = DataFrame(np.random.randn(10, 3),dtype='int64')

In [5]: df_bool   = DataFrame(True,index=df_float.index,columns=df_float.columns)

In [6]: df_object = DataFrame('foo',index=df_float.index,columns=df_float.columns)

In [7]: df_dt     = DataFrame(Timestamp('20010101'),index=df_float.index,columns=df_float.columns)

In [9]: df        = pan.concat([ df_float, df_int, df_bool, df_object, df_dt ], axis=1)

In [14]: cols = []

In [15]: for i in range(5):
   ....:     cols.extend([0,1,2])
   ....:     

In [16]: df.columns = cols

In [17]: df
Out[17]: 
          0         1         2  0  1  2     0     1     2    0    1    2                   0                   1                   2
0  0.586610  0.369944  1.341337  1  1  1  True  True  True  foo  foo  foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
1 -1.944284 -0.813987  0.061306  0  0  1  True  True  True  foo  foo  foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
2 -1.688694  1.644802  0.659083  0  0  0  True  True  True  foo  foo  foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
3  1.422893  0.712382  0.749263 -1  0 -1  True  True  True  foo  foo  foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
4 -0.453802  0.228886 -0.339753  2  0 -2  True  True  True  foo  foo  foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
5 -0.189643  1.309407 -0.386121  0  0  0  True  True  True  foo  foo  foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
6  0.455658  0.822050 -0.741014  0  0  0  True  True  True  foo  foo  foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
7 -0.484678 -1.089146  0.774849  0  1  0  True  True  True  foo  foo  foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
8  0.720365  1.696400 -0.604040 -1  0  0  True  True  True  foo  foo  foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00
9 -0.344480  0.886489  0.274428  1  0  0  True  True  True  foo  foo  foo 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01 00:00:00

```

For those of you interested.....here is the new ref_loc indexer for duplicate columns
its by necessity a block oriented indexer, returns the column map (by column number) to a tuple of the block and the index in the block, only created when needed (e.g. when trying to get a column via iget and the index is non-unique, and the results are cached), this is #3092
```
In [1]: df = pd.DataFrame(np.random.randn(8,4),columns=['a']*4)

In [2]: df._data.blocks
Out[2]: [FloatBlock: [a, a, a, a], 4 x 8, dtype float64]

In [3]: df._data.blocks[0]._ref_locs

In [4]: df._data._set_ref_locs()
Out[4]: 
array([(FloatBlock: [a, a, a, a], 4 x 8, dtype float64, 0),
       (FloatBlock: [a, a, a, a], 4 x 8, dtype float64, 1),
       (FloatBlock: [a, a, a, a], 4 x 8, dtype float64, 2),
       (FloatBlock: [a, a, a, a], 4 x 8, dtype float64, 3)], dtype=object)
```

Fixed the #2786, #3230 bug that caused applymap to not work (we temp worked around by raising a ValueError; removed that check)
```
n [3]: In [3]: df = pd.DataFrame(np.random.random((3,4)))

In [4]: In [4]: cols = pd.Index(['a','a','a','a'])

In [5]: In [5]: df.columns = cols

In [6]: In [6]: df.applymap(str)
Out[6]: 
                a                a               a               a
0  0.494204195164   0.534601503195  0.471870025143  0.880092879641
1  0.860369768954  0.0472931994392  0.775532754792  0.822046777859
2  0.478775855962   0.623584943227  0.932012693593  0.739502590395

```
Finally, to_csv writing has been fixed to use a single column mapper (which is derived from the ref_locs if the index is non-unique or the column numbering if it is unique)