read_excel with multi-index columns and index_col=None, returns an index column
`read_excel()` seems to give the same results for a multi-index column file if `index_col=None` or `index_col=0`.  Using the file from /pandas/test/io/data:

```python
>>> df = pd.read_excel(
        "testmultiindex.xlsx", header=[0, 1], index_col=None,
        sheetname='mi_column_name')
       
>>> print(df.columns)
MultiIndex(levels=[[u'bar', u'foo'], [u'a', u'b']],
       labels=[[1, 1, 0, 0], [0, 1, 0, 1]],
       names=[u'c1', u'c2'])

>>> print(df)
```

```
c1 foo             bar       
c2   a    b          a      b
0    1  2.5 2015-01-01   True
1    2  3.5 2015-01-02  False
2    3  4.5 2015-01-03  False
3    4  5.5 2015-01-04   True
```

#### Expected Output

With `index_col=None` I would have expected the result to look something like:

```python
MultiIndex(levels=[[u'bar', u'c1', u'foo'], [u'a', u'b', u'c2']],
           labels=[[1, 2, 2, 0, 0], [2, 0, 1, 0, 1]],
           names=[u'', u''])
```

```
  c1 foo             bar       
  c2   a    b          a      b
0  0   1  2.5 2015-01-01   True
1  1   2  3.5 2015-01-02  False
2  2   3  4.5 2015-01-03  False
3  3   4  5.5 2015-01-04   True
```

#### Problem description

This behavior is due to `pandas.io.excel._pop_header_name()`.

```python

def _pop_header_name(row, index_col):
    """ (header, new_data) for header rows in MultiIndex parsing"""
    none_fill = lambda x: None if x == '' else x

    if index_col is None:
        # no index col specified, trim data for inference path
        return none_fill(row[0]), row[1:]
    else:
        # pop out header name and fill w/ blank
        i = index_col if not is_list_like(index_col) else max(index_col)
        return none_fill(row[i]), row[:i] + [''] + row[i + 1:]
```

There are two things in the function that I do not understand

1. `index_col is None` pulls out the first column and then treats it as an index.
2. `index_col == 0`  returns something similar but adds a column of `''` to the front of the return row.  Why are these almost the same?

#### Output of ``pd.show_versions()``

<details>
# Paste the output here pd.show_versions() here

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 30.3.0
Cython: 0.24
numpy: 1.12.0
scipy: 0.13.3
xarray: None
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: 1.3.2
bottleneck: 1.0.0
tables: 3.1.0
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.4
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: 3.6.0

pandas_datareader: 0.3.0.post

</details>
