BUG: GH3468 Fix assigning a new index to a duplicate index in a DataFrame would fail

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 [1]: DataFrame([[1,2,1.,2.,3.,'foo','bar']], columns=list('aaaaaaa'))
Out[1]: 
   a  a  a  a  a    a    a
0  1  2  3  1  2  foo  bar
```

This was also previously would raise
```
In [2]: %cpaste
Pasting code; enter '--' alone on the line to stop or use Ctrl-D.
:        df_float  = DataFrame(np.random.randn(10, 3),dtype='float64')
:        df_int    = DataFrame(np.random.randn(10, 3),dtype='int64')
:        df_bool   = DataFrame(True,index=df_float.index,columns=df_float.columns)
:        df_object = DataFrame('foo',index=df_float.index,columns=df_float.columns)
:        df_dt     = DataFrame(Timestamp('20010101'),index=df_float.index,columns=df_float.columns)
:        df        = pan.concat([ df_float, df_int, df_bool, df_object, df_dt ], axis=1)
:--

In [3]: df
Out[3]: 
      0     1     2                   0                   1  \
0  True  True  True 2001-01-01 00:00:00 2001-01-01 00:00:00   
1  True  True  True 2001-01-01 00:00:00 2001-01-01 00:00:00   
2  True  True  True 2001-01-01 00:00:00 2001-01-01 00:00:00   
3  True  True  True 2001-01-01 00:00:00 2001-01-01 00:00:00   
4  True  True  True 2001-01-01 00:00:00 2001-01-01 00:00:00   
5  True  True  True 2001-01-01 00:00:00 2001-01-01 00:00:00   
6  True  True  True 2001-01-01 00:00:00 2001-01-01 00:00:00   
7  True  True  True 2001-01-01 00:00:00 2001-01-01 00:00:00   
8  True  True  True 2001-01-01 00:00:00 2001-01-01 00:00:00   
9  True  True  True 2001-01-01 00:00:00 2001-01-01 00:00:00   

                    2    0    1    2  0  1  2         0         1         2  
0 2001-01-01 00:00:00  foo  foo  foo  0  0  0  0.431857 -0.131747 -1.039563  
1 2001-01-01 00:00:00  foo  foo  foo  0  0 -1  0.516910 -0.683163  0.736468  
2 2001-01-01 00:00:00  foo  foo  foo  0  0  0 -0.147417 -0.305452  0.006213  
3 2001-01-01 00:00:00  foo  foo  foo  0 -1  0  1.443031  0.082710 -0.335054  
4 2001-01-01 00:00:00  foo  foo  foo  1  0  0 -1.349293  0.645316  0.305524  
5 2001-01-01 00:00:00  foo  foo  foo -1  0  0  0.571095  0.756571 -0.773880  
6 2001-01-01 00:00:00  foo  foo  foo  0  0  0 -0.285091  1.196018  0.882786  
7 2001-01-01 00:00:00  foo  foo  foo  2  0  0  0.003610  0.549072 -0.823217  
8 2001-01-01 00:00:00  foo  foo  foo -1  1  0 -0.348279 -0.728958 -0.397435  
9 2001-01-01 00:00:00  foo  foo  foo -1  0  0  0.363489  2.154132  0.494673  

```

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

```