DataFrame[np.nan] raises TypeError with non-unique columns

[toobaz]

Code Sample, a copy-pastable example if possible

In [2]: pd.DataFrame(index=range(3), columns=[1, 2, float('nan'), 2])[float('nan')]
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-2-86ff82017f2a> in <module>()
----> 1 pd.DataFrame(index=range(3), columns=[1, 2, float('nan'), 2])[float('nan')]

/home/nobackup/repo/pandas/pandas/core/frame.py in __getitem__(self, key)
   2685             return self._getitem_multilevel(key)
   2686         else:
-> 2687             return self._getitem_column(key)
   2688 
   2689     def _getitem_column(self, key):

/home/nobackup/repo/pandas/pandas/core/frame.py in _getitem_column(self, key)
   2695 
   2696         # duplicate columns & possible reduce dimensionality
-> 2697         result = self._constructor(self._data.get(key))
   2698         if result.columns.is_unique:
   2699             result = result[key]

/home/nobackup/repo/pandas/pandas/core/internals.py in get(self, item, fastpath)
   4128 
   4129             if isna(item):
-> 4130                 raise TypeError("cannot label index with a null key")
   4131 
   4132             indexer = self.items.get_indexer_for([item])

TypeError: cannot label index with a null key

In [3]: pd.DataFrame(index=range(3), columns=[1, 2, float('nan'), 4])[float('nan')]
Out[3]: 
0    NaN
1    NaN
2    NaN
Name: nan, dtype: object

In [4]: pd.Series(index=[1, 2, float('nan'), 2])[float('nan')]
Out[4]: nan

Problem description

The behavior of DataFrame[np.nan] on non-unique columns makes no particular sense and deviates from the behavior of Series[np.nan].

This is mistakenly tested here:

pandas/pandas/tests/frame/test_constructors.py

Line 1665 in 636dd01

# multiple nans should fail

I will push a fix in few minutes.

Expected Output

Same as Out[3]:.

Output of pd.show_versions()

INSTALLED VERSIONS

commit: 415012f
python: 3.5.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.0-6-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.UTF-8
LOCALE: it_IT.UTF-8

pandas: 0.24.0.dev0+83.g415012f4f
pytest: 3.5.0
pip: 9.0.1
setuptools: 39.2.0
Cython: 0.25.2
numpy: 1.14.3
scipy: 0.19.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.5.6
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: 1.2.0dev
tables: 3.3.0
numexpr: 2.6.1
feather: 0.3.1
matplotlib: 2.2.2.post1153+gff6786446
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 1.3.0
xlsxwriter: 0.9.6
lxml: 4.1.1
bs4: 4.5.3
html5lib: 0.999999999
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.2.1