[SPARK-36388][SPARK-36386][PYTHON] Fix DataFrame groupby-rolling and groupby-expanding to follow pandas 1.3
This PR proposes to fix `RollingGroupBy` and `ExpandingGroupBy` to follow latest pandas behavior.
`RollingGroupBy` and `ExpandingGroupBy` no longer returns grouped-by column in values from pandas 1.3.
Before:
```python
>>> df = pd.DataFrame({"A": [1, 1, 2, 3], "B": [0, 1, 2, 3]})
>>> df.groupby("A").rolling(2).sum()
A B
A
1 0 NaN NaN
1 2.0 1.0
2 2 NaN NaN
3 3 NaN NaN
```
After:
```python
>>> df = pd.DataFrame({"A": [1, 1, 2, 3], "B": [0, 1, 2, 3]})
>>> df.groupby("A").rolling(2).sum()
B
A
1 0 NaN
1 1.0
2 2 NaN
3 3 NaN
```
We should follow the behavior of pandas as much as possible.
Yes, the result of `RollingGroupBy` and `ExpandingGroupBy` is changed as described above.
Unit tests.
Closes #33646 from itholic/SPARK-36388.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit b8508f4876)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
This commit is contained in:
itholic
Hyukjin Kwon
parent
f2f09e4cdb
commit
0fc8c393b4
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@ -125,7 +125,7 @@ class GroupBy(Generic[FrameLike], metaclass=ABCMeta):
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groupkeys: List[Series],
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as_index: bool,
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dropna: bool,
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column_labels_to_exlcude: Set[Label],
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column_labels_to_exclude: Set[Label],
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agg_columns_selected: bool,
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agg_columns: List[Series],
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):
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@ -133,7 +133,7 @@ class GroupBy(Generic[FrameLike], metaclass=ABCMeta):
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self._groupkeys = groupkeys
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self._as_index = as_index
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self._dropna = dropna
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self._column_labels_to_exlcude = column_labels_to_exlcude
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self._column_labels_to_exclude = column_labels_to_exclude
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self._agg_columns_selected = agg_columns_selected
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self._agg_columns = agg_columns
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@ -1175,7 +1175,7 @@ class GroupBy(Generic[FrameLike], metaclass=ABCMeta):
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agg_columns = [
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psdf._psser_for(label)
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for label in psdf._internal.column_labels
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if label not in self._column_labels_to_exlcude
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if label not in self._column_labels_to_exclude
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]
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psdf, groupkey_labels, groupkey_names = GroupBy._prepare_group_map_apply(
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@ -1372,7 +1372,7 @@ class GroupBy(Generic[FrameLike], metaclass=ABCMeta):
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agg_columns = [
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psdf._psser_for(label)
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for label in psdf._internal.column_labels
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if label not in self._column_labels_to_exlcude
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if label not in self._column_labels_to_exclude
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]
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data_schema = (
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@ -1890,7 +1890,7 @@ class GroupBy(Generic[FrameLike], metaclass=ABCMeta):
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agg_columns = [
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psdf._psser_for(label)
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for label in psdf._internal.column_labels
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if label not in self._column_labels_to_exlcude
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if label not in self._column_labels_to_exclude
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]
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psdf, groupkey_labels, _ = GroupBy._prepare_group_map_apply(
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@ -2708,17 +2708,17 @@ class DataFrameGroupBy(GroupBy[DataFrame]):
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(
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psdf,
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new_by_series,
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column_labels_to_exlcude,
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column_labels_to_exclude,
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) = GroupBy._resolve_grouping_from_diff_dataframes(psdf, by)
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else:
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new_by_series = GroupBy._resolve_grouping(psdf, by)
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column_labels_to_exlcude = set()
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column_labels_to_exclude = set()
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return DataFrameGroupBy(
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psdf,
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new_by_series,
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as_index=as_index,
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dropna=dropna,
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column_labels_to_exlcude=column_labels_to_exlcude,
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column_labels_to_exclude=column_labels_to_exclude,
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)
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def __init__(
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@ -2727,20 +2727,20 @@ class DataFrameGroupBy(GroupBy[DataFrame]):
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by: List[Series],
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as_index: bool,
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dropna: bool,
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column_labels_to_exlcude: Set[Label],
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column_labels_to_exclude: Set[Label],
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agg_columns: List[Label] = None,
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):
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agg_columns_selected = agg_columns is not None
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if agg_columns_selected:
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for label in agg_columns:
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if label in column_labels_to_exlcude:
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if label in column_labels_to_exclude:
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raise KeyError(label)
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else:
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agg_columns = [
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label
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for label in psdf._internal.column_labels
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if not any(label == key._column_label and key._psdf is psdf for key in by)
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and label not in column_labels_to_exlcude
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and label not in column_labels_to_exclude
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]
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super().__init__(
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@ -2748,7 +2748,7 @@ class DataFrameGroupBy(GroupBy[DataFrame]):
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groupkeys=by,
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as_index=as_index,
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dropna=dropna,
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column_labels_to_exlcude=column_labels_to_exlcude,
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column_labels_to_exclude=column_labels_to_exclude,
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agg_columns_selected=agg_columns_selected,
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agg_columns=[psdf[label] for label in agg_columns],
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)
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@ -2788,7 +2788,7 @@ class DataFrameGroupBy(GroupBy[DataFrame]):
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self._groupkeys,
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as_index=self._as_index,
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dropna=self._dropna,
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column_labels_to_exlcude=self._column_labels_to_exlcude,
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column_labels_to_exclude=self._column_labels_to_exclude,
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agg_columns=item,
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)
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@ -2932,7 +2932,7 @@ class SeriesGroupBy(GroupBy[Series]):
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groupkeys=by,
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as_index=True,
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dropna=dropna,
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column_labels_to_exlcude=set(),
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column_labels_to_exclude=set(),
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agg_columns_selected=True,
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agg_columns=[psser],
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)
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@ -146,10 +146,8 @@ class ExpandingTest(PandasOnSparkTestCase, TestUtils):
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pdf = pd.DataFrame({"a": [1.0, 2.0, 3.0, 2.0], "b": [4.0, 2.0, 3.0, 1.0]})
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psdf = ps.from_pandas(pdf)
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# The behavior of GroupBy.expanding is changed from pandas 1.3.
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if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
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# TODO(SPARK-36367): Fix the behavior to follow pandas >= 1.3
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pass
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else:
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self.assert_eq(
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getattr(psdf.groupby(psdf.a).expanding(2), f)().sort_index(),
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getattr(pdf.groupby(pdf.a).expanding(2), f)().sort_index(),
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@ -162,6 +160,19 @@ class ExpandingTest(PandasOnSparkTestCase, TestUtils):
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getattr(psdf.groupby(psdf.a + 1).expanding(2), f)().sort_index(),
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getattr(pdf.groupby(pdf.a + 1).expanding(2), f)().sort_index(),
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)
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else:
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self.assert_eq(
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getattr(psdf.groupby(psdf.a).expanding(2), f)().sort_index(),
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getattr(pdf.groupby(pdf.a).expanding(2), f)().drop("a", axis=1).sort_index(),
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)
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self.assert_eq(
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getattr(psdf.groupby(psdf.a).expanding(2), f)().sum(),
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getattr(pdf.groupby(pdf.a).expanding(2), f)().sum().drop("a"),
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)
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self.assert_eq(
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getattr(psdf.groupby(psdf.a + 1).expanding(2), f)().sort_index(),
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getattr(pdf.groupby(pdf.a + 1).expanding(2), f)().drop("a", axis=1).sort_index(),
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)
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self.assert_eq(
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getattr(psdf.b.groupby(psdf.a).expanding(2), f)().sort_index(),
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@ -181,10 +192,8 @@ class ExpandingTest(PandasOnSparkTestCase, TestUtils):
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pdf.columns = columns
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psdf.columns = columns
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# The behavior of GroupBy.expanding is changed from pandas 1.3.
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if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
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# TODO(SPARK-36367): Fix the behavior to follow pandas >= 1.3
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pass
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else:
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self.assert_eq(
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getattr(psdf.groupby(("a", "x")).expanding(2), f)().sort_index(),
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getattr(pdf.groupby(("a", "x")).expanding(2), f)().sort_index(),
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@ -194,6 +203,20 @@ class ExpandingTest(PandasOnSparkTestCase, TestUtils):
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getattr(psdf.groupby([("a", "x"), ("a", "y")]).expanding(2), f)().sort_index(),
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getattr(pdf.groupby([("a", "x"), ("a", "y")]).expanding(2), f)().sort_index(),
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)
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else:
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self.assert_eq(
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getattr(psdf.groupby(("a", "x")).expanding(2), f)().sort_index(),
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getattr(pdf.groupby(("a", "x")).expanding(2), f)()
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.drop(("a", "x"), axis=1)
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.sort_index(),
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)
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self.assert_eq(
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getattr(psdf.groupby([("a", "x"), ("a", "y")]).expanding(2), f)().sort_index(),
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getattr(pdf.groupby([("a", "x"), ("a", "y")]).expanding(2), f)()
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.drop([("a", "x"), ("a", "y")], axis=1)
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.sort_index(),
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)
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def test_groupby_expanding_count(self):
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# The behaviour of ExpandingGroupby.count are different between pandas>=1.0.0 and lower,
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@ -112,10 +112,8 @@ class RollingTest(PandasOnSparkTestCase, TestUtils):
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pdf = pd.DataFrame({"a": [1.0, 2.0, 3.0, 2.0], "b": [4.0, 2.0, 3.0, 1.0]})
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psdf = ps.from_pandas(pdf)
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# The behavior of GroupBy.rolling is changed from pandas 1.3.
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if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
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# TODO(SPARK-36367): Fix the behavior to follow pandas >= 1.3
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pass
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else:
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self.assert_eq(
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getattr(psdf.groupby(psdf.a).rolling(2), f)().sort_index(),
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getattr(pdf.groupby(pdf.a).rolling(2), f)().sort_index(),
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@ -128,6 +126,19 @@ class RollingTest(PandasOnSparkTestCase, TestUtils):
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getattr(psdf.groupby(psdf.a + 1).rolling(2), f)().sort_index(),
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getattr(pdf.groupby(pdf.a + 1).rolling(2), f)().sort_index(),
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)
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else:
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self.assert_eq(
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getattr(psdf.groupby(psdf.a).rolling(2), f)().sort_index(),
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getattr(pdf.groupby(pdf.a).rolling(2), f)().drop("a", axis=1).sort_index(),
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)
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self.assert_eq(
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getattr(psdf.groupby(psdf.a).rolling(2), f)().sum(),
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getattr(pdf.groupby(pdf.a).rolling(2), f)().sum().drop("a"),
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)
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self.assert_eq(
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getattr(psdf.groupby(psdf.a + 1).rolling(2), f)().sort_index(),
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getattr(pdf.groupby(pdf.a + 1).rolling(2), f)().drop("a", axis=1).sort_index(),
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)
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self.assert_eq(
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getattr(psdf.b.groupby(psdf.a).rolling(2), f)().sort_index(),
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@ -147,10 +158,8 @@ class RollingTest(PandasOnSparkTestCase, TestUtils):
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pdf.columns = columns
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psdf.columns = columns
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# The behavior of GroupBy.rolling is changed from pandas 1.3.
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if LooseVersion(pd.__version__) >= LooseVersion("1.3"):
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# TODO(SPARK-36367): Fix the behavior to follow pandas >= 1.3
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pass
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else:
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self.assert_eq(
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getattr(psdf.groupby(("a", "x")).rolling(2), f)().sort_index(),
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getattr(pdf.groupby(("a", "x")).rolling(2), f)().sort_index(),
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@ -160,6 +169,20 @@ class RollingTest(PandasOnSparkTestCase, TestUtils):
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getattr(psdf.groupby([("a", "x"), ("a", "y")]).rolling(2), f)().sort_index(),
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getattr(pdf.groupby([("a", "x"), ("a", "y")]).rolling(2), f)().sort_index(),
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)
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else:
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self.assert_eq(
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getattr(psdf.groupby(("a", "x")).rolling(2), f)().sort_index(),
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getattr(pdf.groupby(("a", "x")).rolling(2), f)()
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.drop(("a", "x"), axis=1)
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.sort_index(),
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)
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self.assert_eq(
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getattr(psdf.groupby([("a", "x"), ("a", "y")]).rolling(2), f)().sort_index(),
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getattr(pdf.groupby([("a", "x"), ("a", "y")]).rolling(2), f)()
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.drop([("a", "x"), ("a", "y")], axis=1)
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.sort_index(),
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)
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def test_groupby_rolling_count(self):
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self._test_groupby_rolling_func("count")
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@ -36,7 +36,7 @@ from pyspark.pandas.missing.window import (
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# For running doctests and reference resolution in PyCharm.
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from pyspark import pandas as ps # noqa: F401
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from pyspark.pandas._typing import FrameLike
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from pyspark.pandas.groupby import GroupBy
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from pyspark.pandas.groupby import GroupBy, DataFrameGroupBy
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from pyspark.pandas.internal import NATURAL_ORDER_COLUMN_NAME, SPARK_INDEX_NAME_FORMAT
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from pyspark.pandas.spark import functions as SF
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from pyspark.pandas.utils import scol_for
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@ -706,10 +706,15 @@ class RollingGroupby(RollingLike[FrameLike]):
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if groupby._agg_columns_selected:
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agg_columns = groupby._agg_columns
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else:
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# pandas doesn't keep the groupkey as a column from 1.3 for DataFrameGroupBy
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column_labels_to_exclude = groupby._column_labels_to_exclude.copy()
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if isinstance(groupby, DataFrameGroupBy):
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for groupkey in groupby._groupkeys: # type: ignore
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column_labels_to_exclude.add(groupkey._internal.column_labels[0])
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agg_columns = [
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psdf._psser_for(label)
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for label in psdf._internal.column_labels
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if label not in groupby._column_labels_to_exlcude
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if label not in column_labels_to_exclude
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]
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applied = []
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@ -777,19 +782,19 @@ class RollingGroupby(RollingLike[FrameLike]):
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>>> df = ps.DataFrame({"A": s.to_numpy(), "B": s.to_numpy() ** 2})
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>>> df.groupby(df.A).rolling(2).count().sort_index() # doctest: +NORMALIZE_WHITESPACE
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A B
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B
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A
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2 0 1.0 1.0
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1 2.0 2.0
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3 2 1.0 1.0
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3 2.0 2.0
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4 2.0 2.0
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4 5 1.0 1.0
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6 2.0 2.0
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7 2.0 2.0
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8 2.0 2.0
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5 9 1.0 1.0
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10 2.0 2.0
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2 0 1.0
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1 2.0
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3 2 1.0
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3 2.0
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4 2.0
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4 5 1.0
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6 2.0
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7 2.0
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8 2.0
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5 9 1.0
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10 2.0
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"""
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return super().count()
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@ -831,19 +836,19 @@ class RollingGroupby(RollingLike[FrameLike]):
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>>> df = ps.DataFrame({"A": s.to_numpy(), "B": s.to_numpy() ** 2})
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>>> df.groupby(df.A).rolling(2).sum().sort_index() # doctest: +NORMALIZE_WHITESPACE
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A B
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B
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A
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2 0 NaN NaN
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1 4.0 8.0
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3 2 NaN NaN
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3 6.0 18.0
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4 6.0 18.0
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4 5 NaN NaN
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6 8.0 32.0
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7 8.0 32.0
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8 8.0 32.0
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5 9 NaN NaN
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10 10.0 50.0
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2 0 NaN
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1 8.0
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3 2 NaN
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3 18.0
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4 18.0
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4 5 NaN
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6 32.0
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7 32.0
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8 32.0
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5 9 NaN
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10 50.0
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"""
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return super().sum()
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@ -885,19 +890,19 @@ class RollingGroupby(RollingLike[FrameLike]):
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>>> df = ps.DataFrame({"A": s.to_numpy(), "B": s.to_numpy() ** 2})
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>>> df.groupby(df.A).rolling(2).min().sort_index() # doctest: +NORMALIZE_WHITESPACE
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A B
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B
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A
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2 0 NaN NaN
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1 2.0 4.0
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3 2 NaN NaN
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3 3.0 9.0
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4 3.0 9.0
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4 5 NaN NaN
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6 4.0 16.0
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7 4.0 16.0
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8 4.0 16.0
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5 9 NaN NaN
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10 5.0 25.0
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2 0 NaN
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1 4.0
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3 2 NaN
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3 9.0
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4 9.0
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4 5 NaN
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6 16.0
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7 16.0
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8 16.0
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5 9 NaN
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10 25.0
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"""
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return super().min()
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@ -939,19 +944,19 @@ class RollingGroupby(RollingLike[FrameLike]):
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>>> df = ps.DataFrame({"A": s.to_numpy(), "B": s.to_numpy() ** 2})
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>>> df.groupby(df.A).rolling(2).max().sort_index() # doctest: +NORMALIZE_WHITESPACE
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A B
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B
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A
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2 0 NaN NaN
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1 2.0 4.0
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3 2 NaN NaN
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3 3.0 9.0
|
||||
4 3.0 9.0
|
||||
4 5 NaN NaN
|
||||
6 4.0 16.0
|
||||
7 4.0 16.0
|
||||
8 4.0 16.0
|
||||
5 9 NaN NaN
|
||||
10 5.0 25.0
|
||||
2 0 NaN
|
||||
1 4.0
|
||||
3 2 NaN
|
||||
3 9.0
|
||||
4 9.0
|
||||
4 5 NaN
|
||||
6 16.0
|
||||
7 16.0
|
||||
8 16.0
|
||||
5 9 NaN
|
||||
10 25.0
|
||||
"""
|
||||
return super().max()
|
||||
|
||||
|
|
@ -993,19 +998,19 @@ class RollingGroupby(RollingLike[FrameLike]):
|
|||
|
||||
>>> df = ps.DataFrame({"A": s.to_numpy(), "B": s.to_numpy() ** 2})
|
||||
>>> df.groupby(df.A).rolling(2).mean().sort_index() # doctest: +NORMALIZE_WHITESPACE
|
||||
A B
|
||||
B
|
||||
A
|
||||
2 0 NaN NaN
|
||||
1 2.0 4.0
|
||||
3 2 NaN NaN
|
||||
3 3.0 9.0
|
||||
4 3.0 9.0
|
||||
4 5 NaN NaN
|
||||
6 4.0 16.0
|
||||
7 4.0 16.0
|
||||
8 4.0 16.0
|
||||
5 9 NaN NaN
|
||||
10 5.0 25.0
|
||||
2 0 NaN
|
||||
1 4.0
|
||||
3 2 NaN
|
||||
3 9.0
|
||||
4 9.0
|
||||
4 5 NaN
|
||||
6 16.0
|
||||
7 16.0
|
||||
8 16.0
|
||||
5 9 NaN
|
||||
10 25.0
|
||||
"""
|
||||
return super().mean()
|
||||
|
||||
|
|
@ -1478,19 +1483,19 @@ class ExpandingGroupby(ExpandingLike[FrameLike]):
|
|||
|
||||
>>> df = ps.DataFrame({"A": s.to_numpy(), "B": s.to_numpy() ** 2})
|
||||
>>> df.groupby(df.A).expanding(2).count().sort_index() # doctest: +NORMALIZE_WHITESPACE
|
||||
A B
|
||||
B
|
||||
A
|
||||
2 0 NaN NaN
|
||||
1 2.0 2.0
|
||||
3 2 NaN NaN
|
||||
3 2.0 2.0
|
||||
4 3.0 3.0
|
||||
4 5 NaN NaN
|
||||
6 2.0 2.0
|
||||
7 3.0 3.0
|
||||
8 4.0 4.0
|
||||
5 9 NaN NaN
|
||||
10 2.0 2.0
|
||||
2 0 NaN
|
||||
1 2.0
|
||||
3 2 NaN
|
||||
3 2.0
|
||||
4 3.0
|
||||
4 5 NaN
|
||||
6 2.0
|
||||
7 3.0
|
||||
8 4.0
|
||||
5 9 NaN
|
||||
10 2.0
|
||||
"""
|
||||
return super().count()
|
||||
|
||||
|
|
@ -1532,19 +1537,19 @@ class ExpandingGroupby(ExpandingLike[FrameLike]):
|
|||
|
||||
>>> df = ps.DataFrame({"A": s.to_numpy(), "B": s.to_numpy() ** 2})
|
||||
>>> df.groupby(df.A).expanding(2).sum().sort_index() # doctest: +NORMALIZE_WHITESPACE
|
||||
A B
|
||||
B
|
||||
A
|
||||
2 0 NaN NaN
|
||||
1 4.0 8.0
|
||||
3 2 NaN NaN
|
||||
3 6.0 18.0
|
||||
4 9.0 27.0
|
||||
4 5 NaN NaN
|
||||
6 8.0 32.0
|
||||
7 12.0 48.0
|
||||
8 16.0 64.0
|
||||
5 9 NaN NaN
|
||||
10 10.0 50.0
|
||||
2 0 NaN
|
||||
1 8.0
|
||||
3 2 NaN
|
||||
3 18.0
|
||||
4 27.0
|
||||
4 5 NaN
|
||||
6 32.0
|
||||
7 48.0
|
||||
8 64.0
|
||||
5 9 NaN
|
||||
10 50.0
|
||||
"""
|
||||
return super().sum()
|
||||
|
||||
|
|
@ -1586,19 +1591,19 @@ class ExpandingGroupby(ExpandingLike[FrameLike]):
|
|||
|
||||
>>> df = ps.DataFrame({"A": s.to_numpy(), "B": s.to_numpy() ** 2})
|
||||
>>> df.groupby(df.A).expanding(2).min().sort_index() # doctest: +NORMALIZE_WHITESPACE
|
||||
A B
|
||||
B
|
||||
A
|
||||
2 0 NaN NaN
|
||||
1 2.0 4.0
|
||||
3 2 NaN NaN
|
||||
3 3.0 9.0
|
||||
4 3.0 9.0
|
||||
4 5 NaN NaN
|
||||
6 4.0 16.0
|
||||
7 4.0 16.0
|
||||
8 4.0 16.0
|
||||
5 9 NaN NaN
|
||||
10 5.0 25.0
|
||||
2 0 NaN
|
||||
1 4.0
|
||||
3 2 NaN
|
||||
3 9.0
|
||||
4 9.0
|
||||
4 5 NaN
|
||||
6 16.0
|
||||
7 16.0
|
||||
8 16.0
|
||||
5 9 NaN
|
||||
10 25.0
|
||||
"""
|
||||
return super().min()
|
||||
|
||||
|
|
@ -1639,19 +1644,19 @@ class ExpandingGroupby(ExpandingLike[FrameLike]):
|
|||
|
||||
>>> df = ps.DataFrame({"A": s.to_numpy(), "B": s.to_numpy() ** 2})
|
||||
>>> df.groupby(df.A).expanding(2).max().sort_index() # doctest: +NORMALIZE_WHITESPACE
|
||||
A B
|
||||
B
|
||||
A
|
||||
2 0 NaN NaN
|
||||
1 2.0 4.0
|
||||
3 2 NaN NaN
|
||||
3 3.0 9.0
|
||||
4 3.0 9.0
|
||||
4 5 NaN NaN
|
||||
6 4.0 16.0
|
||||
7 4.0 16.0
|
||||
8 4.0 16.0
|
||||
5 9 NaN NaN
|
||||
10 5.0 25.0
|
||||
2 0 NaN
|
||||
1 4.0
|
||||
3 2 NaN
|
||||
3 9.0
|
||||
4 9.0
|
||||
4 5 NaN
|
||||
6 16.0
|
||||
7 16.0
|
||||
8 16.0
|
||||
5 9 NaN
|
||||
10 25.0
|
||||
"""
|
||||
return super().max()
|
||||
|
||||
|
|
@ -1693,19 +1698,19 @@ class ExpandingGroupby(ExpandingLike[FrameLike]):
|
|||
|
||||
>>> df = ps.DataFrame({"A": s.to_numpy(), "B": s.to_numpy() ** 2})
|
||||
>>> df.groupby(df.A).expanding(2).mean().sort_index() # doctest: +NORMALIZE_WHITESPACE
|
||||
A B
|
||||
B
|
||||
A
|
||||
2 0 NaN NaN
|
||||
1 2.0 4.0
|
||||
3 2 NaN NaN
|
||||
3 3.0 9.0
|
||||
4 3.0 9.0
|
||||
4 5 NaN NaN
|
||||
6 4.0 16.0
|
||||
7 4.0 16.0
|
||||
8 4.0 16.0
|
||||
5 9 NaN NaN
|
||||
10 5.0 25.0
|
||||
2 0 NaN
|
||||
1 4.0
|
||||
3 2 NaN
|
||||
3 9.0
|
||||
4 9.0
|
||||
4 5 NaN
|
||||
6 16.0
|
||||
7 16.0
|
||||
8 16.0
|
||||
5 9 NaN
|
||||
10 25.0
|
||||
"""
|
||||
return super().mean()
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue