MultiLevelIndex sort_index already does support ascending as a list of booleans

[petrbel]

Code Sample, a copy-pastable example if possible

df.sort_index(level=[1, 0], ascending=[True, False])

Problem description

The documentation states that pandas.DataFrame.sort_index parameter accepts is a boolean. However, in the case of multi level index, it can (already) be a list of booleans indicating which level should ascending and which descending.

Both lexsort_indexer and sort_levels support this behavior.

I suggest updating both tests and documentation.

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None
python : 3.8.0.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.14-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF8
LOCALE : en_US.UTF-8

pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0
Cython : 0.29.14
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None