df[df.columns.unique()] returns duplicate columns when concatanating dataframes which contain identical columns
#### Code Sample

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

dates = pd.date_range('1/1/2000', periods=8)
data1 = {'A': np.arange(8), 
         'B': np.random.randn(8)
        }
df1 = pd.DataFrame(data1, index=dates)

data2 = {'A': np.arange(8),
         'C': np.random.randn(8)
        }
df2 = pd.DataFrame(data2, index=dates)

df = pd.concat([df1, df2], axis=1, ignore_index=False)
```

```
print(df1)
            A         B
2000-01-01  0  0.756225
2000-01-02  1  0.537196
2000-01-03  2  0.813128
2000-01-04  3  0.819868
2000-01-05  4 -0.653656
2000-01-06  5  1.246314
2000-01-07  6 -0.703276
2000-01-08  7 -0.743086
```

```
print(df2)
            A         C
2000-01-01  0 -1.584052
2000-01-02  1 -0.804060
2000-01-03  2 -1.350531
2000-01-04  3 -0.762914
2000-01-05  4 -0.589009
2000-01-06  5  0.395458
2000-01-07  6 -0.124446
2000-01-08  7  0.100183
```

df1 and df2 both contain column A (which is not an index column), so the unique column names are:

```
print(df.columns.unique())
['A' 'B' 'C']
```

When applying the below selection (assuming that columns with the same name contain the same values - which I know is true in this use case however may not always be true), the duplicate columns are kept.

```
print(df[df.columns.unique()])
            A  A         B         C
2000-01-01  0  0  0.086386 -2.558181
2000-01-02  1  1  0.217020 -0.964484
2000-01-03  2  2 -0.426538 -0.579129
2000-01-04  3  3  0.620989 -1.908091
2000-01-05  4  4 -1.463473 -0.345109
2000-01-06  5  5 -0.460185  0.670077
2000-01-07  6  6 -1.353535  0.226513
2000-01-08  7  7  0.329263  2.274627
```
#### Expected Output

A drop_duplicates for columns where for example the first duplicate column name is kept.

```
print(df.T.drop_duplicates().T)
              A         B         C
2000-01-01  0.0  0.086386 -2.558181
2000-01-02  1.0  0.217020 -0.964484
2000-01-03  2.0 -0.426538 -0.579129
2000-01-04  3.0  0.620989 -1.908091
2000-01-05  4.0 -1.463473 -0.345109
2000-01-06  5.0 -0.460185  0.670077
2000-01-07  6.0 -1.353535  0.226513
2000-01-08  7.0  0.329263  2.274627
```
#### output of `pd.show_versions()`

```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-59-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8

pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: 0.7.3.None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: None
pandas_datareader: None
```
