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BUG: MultiIndex sort with ascending as list #16937

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Jul 15, 2017
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.21.0.txt
Original file line number Diff line number Diff line change
Expand Up @@ -156,6 +156,7 @@ Indexing
- When called on an unsorted ``MultiIndex``, the ``loc`` indexer now will raise ``UnsortedIndexError`` only if proper slicing is used on non-sorted levels (:issue:`16734`).
- Fixes regression in 0.20.3 when indexing with a string on a ``TimedeltaIndex`` (:issue:`16896`).
- Fixed ``TimedeltaIndex.get_loc`` handling of ``np.timedelta64`` inputs (:issue:`16909`).
- Fix MultiIndex ``sort_index`` ordering when ``ascending`` argument is a list but not all levels are specified, or are in a different order (:issue:`16934`).
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Nit: add a comma before the "...but not all levels"

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@gfyoung gfyoung Jul 15, 2017

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Also, I might reword the beginning as follows:

Fix ``.sort_index()`` ordering with a `MultiIndex` index when...

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:meth:`MultiIndex.sort_index`

should link to the method.


I/O
^^^
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3 changes: 2 additions & 1 deletion pandas/core/indexes/multi.py
Original file line number Diff line number Diff line change
Expand Up @@ -1697,7 +1697,8 @@ def sortlevel(self, level=0, ascending=True, sort_remaining=True):
raise ValueError("level must have same length as ascending")

from pandas.core.sorting import lexsort_indexer
indexer = lexsort_indexer(self.labels, orders=ascending)
indexer = lexsort_indexer([self.labels[lev] for lev in level],
orders=ascending)

# level ordering
else:
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17 changes: 17 additions & 0 deletions pandas/tests/test_multilevel.py
Original file line number Diff line number Diff line change
Expand Up @@ -2781,3 +2781,20 @@ def test_sort_index_nan(self):
result = s.sort_index(na_position='first')
expected = s.iloc[[1, 2, 3, 0]]
tm.assert_series_equal(result, expected)

def test_sort_ascending_list(self):
# GH: 16934

# Set up a Series with a three level MultiIndex
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'],
[4, 3, 2, 1, 4, 3, 2, 1]]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples,
names=['first', 'second', 'third'])
s = pd.Series(range(8), index=index)

result = s.sort_index(level=['third', 'first'],
ascending=[False, True])

assert np.array_equal(result, [0, 4, 1, 5, 2, 6, 3, 7])
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As a general rule of thumb, we try to avoid using numpy comparisons in tests because we have our own custom comparison functions, which you can find under pandas.util.testing (e.g. we have our own ndarray comparison function with assert_numpy_array_equal)

Secondly, you should be constructing a Series as your expected output, not a list, since the output of sort_index is a Series. Then use the function assert_series_equal from pandas.util.testing.

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can you also both cases from the original issue (in same tests)

IOW assert first one works as well

# Sort descending works
s.sort_index(level=['third', 'first'], ascending=False)

# But sort descending on third level, ascending on first does not
s.sort_index(level=['third', 'first'], ascending=[False, True])