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BUG/PERF: algos.union_with_duplicates losing EA dtypes #48900

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Merged
merged 11 commits into from
Oct 6, 2022
Merged

BUG/PERF: algos.union_with_duplicates losing EA dtypes #48900

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6760a19
perf: union_with_duplicates
lukemanley Oct 1, 2022
0d8237f
whatsnew
lukemanley Oct 1, 2022
5d32385
mypy
lukemanley Oct 1, 2022
eadf5f1
preserve ea dtypes
lukemanley Oct 2, 2022
3b0adbe
mypy
lukemanley Oct 2, 2022
5180e1a
fix
lukemanley Oct 2, 2022
8df7a38
push type checking into union_with_duplicates
lukemanley Oct 2, 2022
335614d
mypy
lukemanley Oct 2, 2022
51bc249
add test for duplicate NA
lukemanley Oct 4, 2022
e466ade
Merge remote-tracking branch 'upstream/main' into union-with-duplicates
lukemanley Oct 6, 2022
9c2a2a8
add gh ref
lukemanley Oct 6, 2022
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9 changes: 9 additions & 0 deletions asv_bench/benchmarks/index_object.py
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Expand Up @@ -65,6 +65,15 @@ def time_datetime_difference_disjoint(self):
self.datetime_left.difference(self.datetime_right)


class UnionWithDuplicates:
def setup(self):
self.left = Index(np.repeat(np.arange(1000), 100))
self.right = Index(np.tile(np.arange(500, 1500), 50))

def time_union_with_duplicates(self):
self.left.union(self.right)


class Range:
def setup(self):
self.idx_inc = RangeIndex(start=0, stop=10**6, step=3)
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.6.0.rst
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Expand Up @@ -140,6 +140,7 @@ Performance improvements
- Performance improvement in :meth:`MultiIndex.difference` (:issue:`48606`)
- Performance improvement in :meth:`.DataFrameGroupBy.mean`, :meth:`.SeriesGroupBy.mean`, :meth:`.DataFrameGroupBy.var`, and :meth:`.SeriesGroupBy.var` for extension array dtypes (:issue:`37493`)
- Performance improvement in :meth:`MultiIndex.isin` when ``level=None`` (:issue:`48622`)
- Performance improvement in :meth:`Index.union` and :meth:`MultiIndex.union` when index contains duplicates (:issue:`48900`)
- Performance improvement for :meth:`Series.value_counts` with nullable dtype (:issue:`48338`)
- Performance improvement for :class:`Series` constructor passing integer numpy array with nullable dtype (:issue:`48338`)
- Performance improvement for :class:`DatetimeIndex` constructor passing a list (:issue:`48609`)
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19 changes: 11 additions & 8 deletions pandas/core/algorithms.py
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Expand Up @@ -1970,7 +1970,9 @@ def _sort_tuples(
return original_values[indexer]


def union_with_duplicates(lvals: ArrayLike, rvals: ArrayLike) -> ArrayLike:
def union_with_duplicates(
lvals: ArrayLike | Index, rvals: ArrayLike | Index
) -> ArrayLike:
"""
Extracts the union from lvals and rvals with respect to duplicates and nans in
both arrays.
Expand All @@ -1991,13 +1993,14 @@ def union_with_duplicates(lvals: ArrayLike, rvals: ArrayLike) -> ArrayLike:
-----
Caller is responsible for ensuring lvals.dtype == rvals.dtype.
"""
indexer = []
l_count = value_counts(lvals, dropna=False)
r_count = value_counts(rvals, dropna=False)
l_count, r_count = l_count.align(r_count, fill_value=0)
unique_array = unique(concat_compat([lvals, rvals]))
unique_array = ensure_wrapped_if_datetimelike(unique_array)

for i, value in enumerate(unique_array):
indexer += [i] * int(max(l_count.at[value], r_count.at[value]))
return unique_array.take(indexer)
final_count = np.maximum(l_count, r_count).astype("int", copy=False)
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You've got 2 MultiIndex checks now. I'd rather handle this on the _union level. Is there any performance benefit of doing it like this?

Additionally, I think you can use unique_vals.take(repeats)
That's more in line with what we are doing elsewhere

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I think we need the MultiIndex checks within union_with_duplicates, otherwise we lose dtypes in there. I'm not sure how how this could be done only on the _union level. Let me know if you have any suggestions.

The repeat logic is not quite a simple take:

np.repeat(unique_vals, repeats)

is equivalent to:

indexer = np.arange(len(unique_vals))
indexer = np.repeat(indexer, repeats)
unique_vals.take(indexer)

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You could simply return final_count and handle the rest In the _union level. Alternatively, you could just pass in self and other and handle everything on the lower level. Or add another argument unique_vals to the function

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Thx, makes sense. I pushed the type checking down into union_with_duplicates.

if isinstance(lvals, ABCMultiIndex) and isinstance(rvals, ABCMultiIndex):
unique_vals = lvals.append(rvals).unique()
else:
unique_vals = unique(concat_compat([lvals, rvals]))
unique_vals = ensure_wrapped_if_datetimelike(unique_vals)
repeats = final_count.reindex(unique_vals).values # type: ignore[attr-defined]
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Could you add the mypy error as a comment?

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I fixed/removed the mypy ignore

return np.repeat(unique_vals, repeats)
6 changes: 5 additions & 1 deletion pandas/core/indexes/base.py
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Expand Up @@ -3444,7 +3444,11 @@ def _union(self, other: Index, sort):

elif not other.is_unique:
# other has duplicates
result_dups = algos.union_with_duplicates(lvals, rvals)
if self._is_multi:
# pass MultiIndex objects to preserve dtypes
result_dups = algos.union_with_duplicates(self, other)
else:
result_dups = algos.union_with_duplicates(lvals, rvals)
return _maybe_try_sort(result_dups, sort)

# Self may have duplicates; other already checked as unique
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24 changes: 24 additions & 0 deletions pandas/tests/indexes/multi/test_setops.py
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Expand Up @@ -578,6 +578,30 @@ def test_union_keep_ea_dtype(any_numeric_ea_dtype, val):
tm.assert_index_equal(result, expected)


def test_union_with_duplicates_keep_ea_dtype(any_numeric_ea_dtype):
# GH48900
mi1 = MultiIndex.from_arrays(
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What happens if theres duplicate NAs?

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Works the same as non-NA. I added the dupe NA case to the test.

[
Series([1, 3, 2], dtype=any_numeric_ea_dtype),
Series([1, 3, 2], dtype=any_numeric_ea_dtype),
]
)
mi2 = MultiIndex.from_arrays(
[
Series([2, 3, 3], dtype=any_numeric_ea_dtype),
Series([2, 3, 3], dtype=any_numeric_ea_dtype),
]
)
result = mi1.union(mi2)
expected = MultiIndex.from_arrays(
[
Series([1, 2, 3, 3], dtype=any_numeric_ea_dtype),
Series([1, 2, 3, 3], dtype=any_numeric_ea_dtype),
]
)
tm.assert_index_equal(result, expected)


def test_union_duplicates(index, request):
# GH#38977
if index.empty or isinstance(index, (IntervalIndex, CategoricalIndex)):
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