swaplevel() does not need to always be slow?
#### Problem description

`pandas.DataFrame.swaplevel()` is slow (I observed 15 seconds on a 100,000 column MultiIndex), when in principle it could simply swap labels, levels and names in almost no time (right?).

Now, I see that this operation returns a copy of the whole data, [which seems to explain the slowness](https://stackoverflow.com/questions/51747470/why-is-swaplevel-slow-in-pandas). What about speeding up `swaplevel()` while maintaining backward compatibility, by adding an `inplace` argument for in-place modification?

<details>
INSTALLED VERSIONS

------------------ 

commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-642.6.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.21.1
pytest: 3.2.3
pip: 9.0.1
setuptools: 36.3.0
Cython: 0.27.3
numpy: 1.13.3
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.4
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: 3.2.1
numexpr: 2.6.5
feather: None
matplotlib: 2.1.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
