Repeated level indexes/names make reset_index() and others misbehave

[toobaz]

Code Sample, a copy-pastable example if possible

In [2]: s = pd.Series(range(4), name='foo', index=pd.MultiIndex.from_product([[0, 1]]*2, names=['a', 'b']))

In [3]: s.reset_index([0, 'a'])
Out[3]: 
   a  foo
0  0    0
1  0    1
2  1    2
3  1    3

In [4]: s.reset_index([0, 'b'])
Out[4]: 
   a  b  foo
0  0  0    0
1  0  1    1
2  1  0    2
3  1  1    3

In [5]: s.reset_index([0, 'a'], drop=True)
Out[5]: 
0    0
1    1
2    2
3    3
Name: foo, dtype: int64

In [6]: s.reset_index([0, 0], drop=True)
Out[6]: 
0    0
1    1
2    2
3    3
Name: foo, dtype: int64

In [7]: s.reset_index([0, 0])
Out[7]: 
   a  foo
0  0    0
1  0    1
2  1    2
3  1    3

Problem description

Passing the same level twice - even if referring to it once as label and once as index - should probably raise an error, and should certainly not drop another level (without re-adding it in the columns!).

Related to #21016, but not worth fixing there as the problem is deeper, e.g. also affects droplevel:

pd.MultiIndex.from_product([[0, 1]]*3, names=list('abc')).droplevel(['a', 'a'])

returns a flat index.

I guess the optimal fix involves having _get_level_number() accept a list, rather than calling it on each item separately.

Expected Output

ValueError

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None
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+5.g501f0418c
pytest: 3.5.0
pip: 9.0.1
setuptools: 39.0.1
Cython: 0.25.2
numpy: 1.14.1
scipy: 0.19.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.5.6
patsy: 0.5.0
dateutil: 2.7.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.0dev
tables: 3.3.0
numexpr: 2.6.1
feather: 0.3.1
matplotlib: 2.0.0
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