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sort_index does not work with levels not aligned with index #25775

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@ClementWalter

Description

@ClementWalter

Code Sample, a copy-pastable example if possible

# Your code here
import pandas as pd

pd.np.random.seed(0)
(
    pd.DataFrame(pd.np.random.rand(10, 5), columns=['a', 'b', 'c', 'd', 'e'])
    .assign(b=lambda df: (df.b*10).astype(int))
    .set_index(['a', 'b', 'c'])
    .sort_index(axis=0, level=['b', 'a'])
)

Out[96]: 
                            d         e
a        b c                           
0.087129 0 0.832620  0.778157  0.870012
0.639921 1 0.944669  0.521848  0.414662
0.670638 2 0.128926  0.315428  0.363711
0.359508 4 0.697631  0.060225  0.666767
0.645894 4 0.891773  0.963663  0.383442
0.791725 5 0.568045  0.925597  0.071036
0.617635 6 0.616934  0.943748  0.681820
0.264556 7 0.456150  0.568434  0.018790
0.978618 7 0.461479  0.780529  0.118274
0.548814 7 0.602763  0.544883  0.423655

Problem description

I don't understand why the a index is not sorted (see the b=7 rows).
Indeed, set_index does not seem to take into account levels not aligned with dataframe indexes. Since index is ['a', 'b', 'c'] it can only sort with levels=['a'], levels=['a', 'b'], levels=['a', 'b', 'c'], levels=['b'], levels=['b', 'c'], levels=['c'].

Note: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!

Note: Many problems can be resolved by simply upgrading pandas to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if master addresses this issue, but that is not necessary.

For documentation-related issues, you can check the latest versions of the docs on master here:

https://pandas-docs.github.io/pandas-docs-travis/

If the issue has not been resolved there, go ahead and file it in the issue tracker.

Expected Output

pd.np.random.seed(0)
(
    pd.DataFrame(pd.np.random.rand(10, 5), columns=['a', 'b', 'c', 'd', 'e'])
    .assign(b=lambda df: (df.b*10).astype(int))
    .sort_values(['b', 'a'])
    .set_index(['a', 'b', 'c'])
)

Out[104]: 
                            d         e
a        b c                           
0.087129 0 0.832620  0.778157  0.870012
0.639921 1 0.944669  0.521848  0.414662
0.670638 2 0.128926  0.315428  0.363711
0.359508 4 0.697631  0.060225  0.666767
0.645894 4 0.891773  0.963663  0.383442
0.791725 5 0.568045  0.925597  0.071036
0.617635 6 0.616934  0.943748  0.681820
0.264556 7 0.456150  0.568434  0.018790
0.548814 7 0.602763  0.544883  0.423655
0.978618 7 0.461479  0.780529  0.118274

Output of pd.show_versions()

pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: fr_FR.UTF-8
pandas: 0.23.2
pytest: 3.6.1
pip: 18.0
setuptools: 40.4.3
Cython: None
numpy: 1.14.3
scipy: 1.0.1
pyarrow: None
xarray: None
IPython: 7.0.1
sphinx: None
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

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