rusackas commented on code in PR #41184:
URL: https://github.com/apache/superset/pull/41184#discussion_r3565611545
##########
superset-frontend/plugins/plugin-chart-pivot-table/src/plugin/buildQuery.ts:
##########
@@ -54,6 +59,39 @@ export default function buildQuery(formData:
PivotTableQueryFormData) {
}
return col;
});
+}
+
+export default function buildQuery(formData: PivotTableQueryFormData) {
+ const { extra_form_data } = formData;
+ const time_grain_sqla =
+ extra_form_data?.time_grain_sqla || formData.time_grain_sqla;
+
+ // The full set of dimensions (the leaf level) is always queried; the rollup
+ // levels are derived from it. Apply transposePivot here exactly as
+ // buildGroupbyCombinations does, so the column order matches the rollup
+ // levels. See SIP.md.
+ const [fullRows, fullColumnDims] = formData.transposePivot
+ ? [formData.groupbyColumns, formData.groupbyRows]
+ : [formData.groupbyRows, formData.groupbyColumns];
+ const fullGroupby: Groupby = {
+ rows: ensureIsArray<QueryFormColumn>(fullRows),
+ columns: ensureIsArray<QueryFormColumn>(fullColumnDims),
+ };
+ const fullColumns = getQueryColumns(fullGroupby, formData, time_grain_sqla);
+
+ // A single query always suffices:
+ // - additive metrics (SUM/COUNT/MIN/MAX): a full-detail query whose
+ // subtotals/grand totals transformProps derives by client-side reduction.
+ // - non-additive metrics: a GROUPING SETS query (one `grouping_sets` level
+ // per displayed total/subtotal) so the database computes every level; the
+ // backend falls back to per-level queries on engines without native
+ // support. transformProps splits the combined result by level.
+ const additive = allMetricsAdditive(ensureIsArray(formData.metrics));
+ const groupingSets = additive
+ ? undefined
+ : buildGroupbyCombinations(formData).map(level =>
+ [...level.rows, ...level.columns].map(getColumnLabel),
Review Comment:
Good catch, deduped the labels per level. A dimension on both axes would
otherwise land twice in the same GROUPING SETS tuple.
##########
superset-frontend/plugins/plugin-chart-pivot-table/test/plugin/utilities.test.ts:
##########
@@ -0,0 +1,446 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+import { TimeGranularity } from '@superset-ui/core';
+import buildGroupbyCombinations, {
+ isAdditiveMetric,
+ allMetricsAdditive,
+ additiveReducerFor,
+ synthesizeAdditiveLevels,
+ splitGroupingSetsResult,
+ groupingMarkerLabel,
+} from '../../src/plugin/utilities';
+import { PivotTableQueryFormData, MetricsLayoutEnum } from '../../src/types';
+
+const baseFormData = {
+ groupbyRows: ['row1', 'row2'],
+ groupbyColumns: ['col1', 'col2'],
+ metrics: ['metric1', 'metric2'],
+ tableRenderer: 'Table With Subtotal',
+ colOrder: 'key_a_to_z',
+ rowOrder: 'key_a_to_z',
+ aggregateFunction: 'Sum',
+ metricsLayout: MetricsLayoutEnum.ROWS,
+ transposePivot: false,
+ rowSubtotalPosition: true,
+ colSubtotalPosition: true,
+ colTotals: true,
+ colSubTotals: true,
+ rowTotals: true,
+ rowSubTotals: true,
+ valueFormat: 'SMART_NUMBER',
+ datasource: '5__table',
+ viz_type: 'my_chart',
+ width: 800,
+ height: 600,
+ combineMetric: false,
+ verboseMap: {},
+ columnFormats: {},
+ currencyFormats: {},
+ metricColorFormatters: [],
+ dateFormatters: {},
+ setDataMask: () => {},
+ legacy_order_by: 'count',
+ order_desc: true,
+ margin: 0,
+ time_grain_sqla: TimeGranularity.MONTH,
+ temporal_columns_lookup: { col1: true },
+ currencyFormat: { symbol: 'USD', symbolPosition: 'prefix' },
+} as unknown as PivotTableQueryFormData;
+
+test('should build all combinations for basic pivot table', () => {
+ const combinations = buildGroupbyCombinations(baseFormData);
+
+ expect(combinations).toEqual([
+ { rows: [], columns: [] },
+ { rows: [], columns: ['col1'] },
+ { rows: [], columns: ['col1', 'col2'] },
+ { rows: ['row1'], columns: [] },
+ { rows: ['row1'], columns: ['col1'] },
+ { rows: ['row1'], columns: ['col1', 'col2'] },
+ { rows: ['row1', 'row2'], columns: [] },
+ { rows: ['row1', 'row2'], columns: ['col1'] },
+ { rows: ['row1', 'row2'], columns: ['col1', 'col2'] },
+ ]);
+
+ expect(combinations).toHaveLength(9);
+});
+
+test('should handle transposed pivot correctly', () => {
+ const modifiedFormData = {
+ ...baseFormData,
+ transposePivot: true,
+ };
+
+ const combinations = buildGroupbyCombinations(modifiedFormData);
+
+ expect(combinations).toEqual([
+ { rows: [], columns: [] },
+ { rows: [], columns: ['row1'] },
+ { rows: [], columns: ['row1', 'row2'] },
+ { rows: ['col1'], columns: [] },
+ { rows: ['col1'], columns: ['row1'] },
+ { rows: ['col1'], columns: ['row1', 'row2'] },
+ { rows: ['col1', 'col2'], columns: [] },
+ { rows: ['col1', 'col2'], columns: ['row1'] },
+ { rows: ['col1', 'col2'], columns: ['row1', 'row2'] },
+ ]);
+});
+
+test('should filter combinations when combineMetric is true with ROWS layout',
() => {
+ const modifiedFormData = {
+ ...baseFormData,
+ combineMetric: true,
+ metricsLayout: MetricsLayoutEnum.ROWS,
+ };
+
+ const combinations = buildGroupbyCombinations(modifiedFormData);
+
+ expect(combinations).toEqual([
+ { rows: ['row1', 'row2'], columns: [] },
+ { rows: ['row1', 'row2'], columns: ['col1'] },
+ { rows: ['row1', 'row2'], columns: ['col1', 'col2'] },
+ ]);
+
+ expect(combinations).toHaveLength(3);
+});
+
+test('should filter combinations when combineMetric is true with COLUMNS
layout', () => {
+ const modifiedFormData = {
+ ...baseFormData,
+ combineMetric: true,
+ metricsLayout: MetricsLayoutEnum.COLUMNS,
+ };
+
+ const combinations = buildGroupbyCombinations(modifiedFormData);
+
+ expect(combinations).toEqual([
+ { rows: [], columns: ['col1', 'col2'] },
+ { rows: ['row1'], columns: ['col1', 'col2'] },
+ { rows: ['row1', 'row2'], columns: ['col1', 'col2'] },
+ ]);
+
+ expect(combinations).toHaveLength(3);
+});
+
+test('should handle single dimension in rows only', () => {
+ const modifiedFormData = {
+ ...baseFormData,
+ groupbyRows: ['row'],
+ groupbyColumns: [],
+ };
+
+ const combinations = buildGroupbyCombinations(modifiedFormData);
+
+ expect(combinations).toEqual([
+ { rows: [], columns: [] },
+ { rows: ['row'], columns: [] },
+ ]);
+
+ expect(combinations).toHaveLength(2);
+});
+
+test('should handle single dimension in columns only', () => {
+ const modifiedFormData = {
+ ...baseFormData,
+ groupbyRows: [],
+ groupbyColumns: ['col'],
+ };
+
+ const combinations = buildGroupbyCombinations(modifiedFormData);
+
+ expect(combinations).toEqual([
+ { rows: [], columns: [] },
+ { rows: [], columns: ['col'] },
+ ]);
+
+ expect(combinations).toHaveLength(2);
+});
+
+test('should handle empty groupby arrays', () => {
+ const modifiedFormData = {
+ ...baseFormData,
+ groupbyRows: [],
+ groupbyColumns: [],
+ };
+
+ const combinations = buildGroupbyCombinations(modifiedFormData);
+
+ expect(combinations).toEqual([{ rows: [], columns: [] }]);
+
+ expect(combinations).toHaveLength(1);
+});
+
+test('should work with combineMetric and transposed pivot', () => {
+ const modifiedFormData = {
+ ...baseFormData,
+ transposePivot: true,
+ combineMetric: true,
+ metricsLayout: MetricsLayoutEnum.COLUMNS,
+ };
+
+ const combinations = buildGroupbyCombinations(modifiedFormData);
+
+ expect(combinations).toEqual([
+ { rows: [], columns: ['row1', 'row2'] },
+ { rows: ['col1'], columns: ['row1', 'row2'] },
+ { rows: ['col1', 'col2'], columns: ['row1', 'row2'] },
+ ]);
+});
+
+test('should handle combineMetric with empty arrays correctly', () => {
+ const modifiedFormData = {
+ ...baseFormData,
+ groupbyRows: [],
+ groupbyColumns: ['col'],
+ combineMetric: true,
+ metricsLayout: MetricsLayoutEnum.ROWS,
+ };
+
+ const combinations = buildGroupbyCombinations(modifiedFormData);
+
+ expect(combinations).toEqual([
+ { rows: [], columns: [] },
+ { rows: [], columns: ['col'] },
+ ]);
+});
+
+test('should work with large number of dimensions', () => {
+ const modifiedFormData = {
+ ...baseFormData,
+ groupbyRows: ['r1', 'r2', 'r3', 'r4'],
+ groupbyColumns: ['c1', 'c2', 'c3'],
+ };
+
+ const combinations = buildGroupbyCombinations(modifiedFormData);
+
+ expect(combinations).toHaveLength(20);
+
+ expect(combinations).toContainEqual({ rows: [], columns: [] });
+ expect(combinations).toContainEqual({
+ rows: ['r1', 'r2', 'r3', 'r4'],
+ columns: ['c1', 'c2', 'c3'],
+ });
+});
+
+test('isAdditiveMetric: SIMPLE metrics with additive aggregates are additive',
() => {
+ expect(
+ isAdditiveMetric({
+ expressionType: 'SIMPLE',
+ aggregate: 'SUM',
+ column: { column_name: 'num' },
+ label: 'sum_num',
+ } as any),
+ ).toBe(true);
+ expect(
+ isAdditiveMetric({
+ expressionType: 'SIMPLE',
+ aggregate: 'COUNT',
+ column: { column_name: 'num' },
+ label: 'count_num',
+ } as any),
+ ).toBe(true);
+});
+
+test('isAdditiveMetric: non-additive aggregates, SQL, and saved metrics are
not additive', () => {
+ expect(
+ isAdditiveMetric({
+ expressionType: 'SIMPLE',
+ aggregate: 'AVG',
+ column: { column_name: 'num' },
+ label: 'avg_num',
+ } as any),
+ ).toBe(false);
+ expect(
+ isAdditiveMetric({
+ expressionType: 'SIMPLE',
+ aggregate: 'COUNT_DISTINCT',
+ column: { column_name: 'name' },
+ label: 'distinct_names',
+ } as any),
+ ).toBe(false);
+ expect(
+ isAdditiveMetric({
+ expressionType: 'SQL',
+ sqlExpression: 'SUM(a) / SUM(b)',
+ label: 'ratio',
+ } as any),
+ ).toBe(false);
+ // saved-metric reference: aggregate unknown from form data -> non-additive
+ expect(isAdditiveMetric('count' as any)).toBe(false);
Review Comment:
Fixed, `isAdditiveMetric` already accepts a string so the cast was unneeded.
##########
superset/common/grouping_sets.py:
##########
@@ -0,0 +1,117 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+SQL building blocks for the pivot-table non-additive totals optimization
+(SIP.md, phase 3b). When a datasource engine reports
+``supports_grouping_sets``, the N per-rollup-level queries can be collapsed
into
+a single ``GROUPING SETS`` query: the database computes every level in one
scan,
+and each returned row is attributed to its level via ``GROUPING()`` markers.
+
+These are the engine-agnostic SQL primitives. Wiring them into the query
context
+(emitting one query and splitting the result back into per-level results) is
the
+remaining integration; see SIP.md.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import Final
+
+import pandas as pd
+from sqlalchemy import func, tuple_
+from sqlalchemy.sql.elements import ColumnElement
+
+# Suffix for the per-column GROUPING() marker columns added to a GROUPING SETS
+# query. Chosen to be unlikely to collide with a real metric/column label.
+GROUPING_MARKER_SUFFIX: Final = "__superset_grouping"
+
+
+def grouping_marker_label(column_label: str) -> str:
+ """The output label of the GROUPING() marker for a groupby column."""
+ return f"{column_label}{GROUPING_MARKER_SUFFIX}"
+
+
+def grouping_sets_clause(
+ groups: Sequence[Sequence[ColumnElement]],
+) -> ColumnElement:
+ """
+ Build a ``GROUP BY GROUPING SETS (...)`` clause from rollup column groups.
+
+ Each group is the set of columns grouped at one rollup level; the empty
+ group ``()`` is the grand total. For example, groups ``[[a, b], [a], []]``
+ produce ``GROUPING SETS ((a, b), (a), ())``.
+
+ :param groups: one column list per rollup level
+ :return: a clause element suitable for ``select(...).group_by(...)``
+ """
+ return func.grouping_sets(*[tuple_(*group) for group in groups])
+
+
+def grouping_id_column(column: ColumnElement, label: str) -> ColumnElement:
+ """
+ Build a ``GROUPING(col) AS label`` marker column.
+
+ In a ``GROUPING SETS`` result, ``GROUPING(col)`` is ``0`` when ``col`` is
+ part of the row's grouping level and ``1`` when it has been rolled up
+ (aggregated away). Selecting one marker per groupby column lets the caller
+ attribute each returned row to its rollup level when splitting the single
+ result back into per-level results.
+
+ :param column: the groupby column to probe
+ :param label: the output label for the marker (see
``grouping_marker_label``)
+ :return: the labelled ``GROUPING(col)`` column
+ """
+ return func.grouping(column).label(label)
+
+
+def split_grouping_sets_result(
+ df: pd.DataFrame,
+ levels: Sequence[Sequence[str]],
+ groupby_columns: Sequence[str],
+) -> list[pd.DataFrame]:
+ """
+ Split a combined ``GROUPING SETS`` result into one DataFrame per rollup
+ level, the inverse of {@link grouping_sets_clause}.
+
+ Each row of ``df`` carries a ``GROUPING()`` marker per groupby column
(named
+ by ``grouping_marker_label``): ``0`` if the column is grouped at that row's
+ level, ``1`` if it was rolled up. A row belongs to a level iff its markers
+ are ``0`` exactly for that level's columns. Marker columns are dropped from
+ the returned frames so each looks like an ordinary per-level query result.
+
+ :param df: the combined query result, including marker columns
+ :param levels: the grouped-column list for each rollup level (same order as
+ passed to ``grouping_sets_clause``)
+ :param groupby_columns: every groupby column that has a marker
+ :return: one DataFrame per level, in ``levels`` order
+ """
+ markers = [grouping_marker_label(col) for col in groupby_columns]
Review Comment:
Added the type hint.
##########
superset/common/grouping_sets.py:
##########
@@ -0,0 +1,117 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+SQL building blocks for the pivot-table non-additive totals optimization
+(SIP.md, phase 3b). When a datasource engine reports
+``supports_grouping_sets``, the N per-rollup-level queries can be collapsed
into
+a single ``GROUPING SETS`` query: the database computes every level in one
scan,
+and each returned row is attributed to its level via ``GROUPING()`` markers.
+
+These are the engine-agnostic SQL primitives. Wiring them into the query
context
+(emitting one query and splitting the result back into per-level results) is
the
+remaining integration; see SIP.md.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import Final
+
+import pandas as pd
+from sqlalchemy import func, tuple_
+from sqlalchemy.sql.elements import ColumnElement
+
+# Suffix for the per-column GROUPING() marker columns added to a GROUPING SETS
+# query. Chosen to be unlikely to collide with a real metric/column label.
+GROUPING_MARKER_SUFFIX: Final = "__superset_grouping"
+
+
+def grouping_marker_label(column_label: str) -> str:
+ """The output label of the GROUPING() marker for a groupby column."""
+ return f"{column_label}{GROUPING_MARKER_SUFFIX}"
+
+
+def grouping_sets_clause(
+ groups: Sequence[Sequence[ColumnElement]],
+) -> ColumnElement:
+ """
+ Build a ``GROUP BY GROUPING SETS (...)`` clause from rollup column groups.
+
+ Each group is the set of columns grouped at one rollup level; the empty
+ group ``()`` is the grand total. For example, groups ``[[a, b], [a], []]``
+ produce ``GROUPING SETS ((a, b), (a), ())``.
+
+ :param groups: one column list per rollup level
+ :return: a clause element suitable for ``select(...).group_by(...)``
+ """
+ return func.grouping_sets(*[tuple_(*group) for group in groups])
+
+
+def grouping_id_column(column: ColumnElement, label: str) -> ColumnElement:
+ """
+ Build a ``GROUPING(col) AS label`` marker column.
+
+ In a ``GROUPING SETS`` result, ``GROUPING(col)`` is ``0`` when ``col`` is
+ part of the row's grouping level and ``1`` when it has been rolled up
+ (aggregated away). Selecting one marker per groupby column lets the caller
+ attribute each returned row to its rollup level when splitting the single
+ result back into per-level results.
+
+ :param column: the groupby column to probe
+ :param label: the output label for the marker (see
``grouping_marker_label``)
+ :return: the labelled ``GROUPING(col)`` column
+ """
+ return func.grouping(column).label(label)
+
+
+def split_grouping_sets_result(
+ df: pd.DataFrame,
+ levels: Sequence[Sequence[str]],
+ groupby_columns: Sequence[str],
+) -> list[pd.DataFrame]:
+ """
+ Split a combined ``GROUPING SETS`` result into one DataFrame per rollup
+ level, the inverse of {@link grouping_sets_clause}.
+
+ Each row of ``df`` carries a ``GROUPING()`` marker per groupby column
(named
+ by ``grouping_marker_label``): ``0`` if the column is grouped at that row's
+ level, ``1`` if it was rolled up. A row belongs to a level iff its markers
+ are ``0`` exactly for that level's columns. Marker columns are dropped from
+ the returned frames so each looks like an ordinary per-level query result.
+
+ :param df: the combined query result, including marker columns
+ :param levels: the grouped-column list for each rollup level (same order as
+ passed to ``grouping_sets_clause``)
+ :param groupby_columns: every groupby column that has a marker
+ :return: one DataFrame per level, in ``levels`` order
+ """
+ markers = [grouping_marker_label(col) for col in groupby_columns]
+ results: list[pd.DataFrame] = []
+ for level in levels:
+ grouped = set(level)
Review Comment:
Added the type hint.
##########
superset/common/grouping_sets.py:
##########
@@ -0,0 +1,117 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+SQL building blocks for the pivot-table non-additive totals optimization
+(SIP.md, phase 3b). When a datasource engine reports
+``supports_grouping_sets``, the N per-rollup-level queries can be collapsed
into
+a single ``GROUPING SETS`` query: the database computes every level in one
scan,
+and each returned row is attributed to its level via ``GROUPING()`` markers.
+
+These are the engine-agnostic SQL primitives. Wiring them into the query
context
+(emitting one query and splitting the result back into per-level results) is
the
+remaining integration; see SIP.md.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import Final
+
+import pandas as pd
+from sqlalchemy import func, tuple_
+from sqlalchemy.sql.elements import ColumnElement
+
+# Suffix for the per-column GROUPING() marker columns added to a GROUPING SETS
+# query. Chosen to be unlikely to collide with a real metric/column label.
+GROUPING_MARKER_SUFFIX: Final = "__superset_grouping"
+
+
+def grouping_marker_label(column_label: str) -> str:
+ """The output label of the GROUPING() marker for a groupby column."""
+ return f"{column_label}{GROUPING_MARKER_SUFFIX}"
+
+
+def grouping_sets_clause(
+ groups: Sequence[Sequence[ColumnElement]],
+) -> ColumnElement:
+ """
+ Build a ``GROUP BY GROUPING SETS (...)`` clause from rollup column groups.
+
+ Each group is the set of columns grouped at one rollup level; the empty
+ group ``()`` is the grand total. For example, groups ``[[a, b], [a], []]``
+ produce ``GROUPING SETS ((a, b), (a), ())``.
+
+ :param groups: one column list per rollup level
+ :return: a clause element suitable for ``select(...).group_by(...)``
+ """
+ return func.grouping_sets(*[tuple_(*group) for group in groups])
+
+
+def grouping_id_column(column: ColumnElement, label: str) -> ColumnElement:
+ """
+ Build a ``GROUPING(col) AS label`` marker column.
+
+ In a ``GROUPING SETS`` result, ``GROUPING(col)`` is ``0`` when ``col`` is
+ part of the row's grouping level and ``1`` when it has been rolled up
+ (aggregated away). Selecting one marker per groupby column lets the caller
+ attribute each returned row to its rollup level when splitting the single
+ result back into per-level results.
+
+ :param column: the groupby column to probe
+ :param label: the output label for the marker (see
``grouping_marker_label``)
+ :return: the labelled ``GROUPING(col)`` column
+ """
+ return func.grouping(column).label(label)
+
+
+def split_grouping_sets_result(
+ df: pd.DataFrame,
+ levels: Sequence[Sequence[str]],
+ groupby_columns: Sequence[str],
+) -> list[pd.DataFrame]:
+ """
+ Split a combined ``GROUPING SETS`` result into one DataFrame per rollup
+ level, the inverse of {@link grouping_sets_clause}.
+
+ Each row of ``df`` carries a ``GROUPING()`` marker per groupby column
(named
+ by ``grouping_marker_label``): ``0`` if the column is grouped at that row's
+ level, ``1`` if it was rolled up. A row belongs to a level iff its markers
+ are ``0`` exactly for that level's columns. Marker columns are dropped from
+ the returned frames so each looks like an ordinary per-level query result.
+
+ :param df: the combined query result, including marker columns
+ :param levels: the grouped-column list for each rollup level (same order as
+ passed to ``grouping_sets_clause``)
+ :param groupby_columns: every groupby column that has a marker
+ :return: one DataFrame per level, in ``levels`` order
+ """
+ markers = [grouping_marker_label(col) for col in groupby_columns]
+ results: list[pd.DataFrame] = []
+ for level in levels:
+ grouped = set(level)
+ mask = pd.Series(True, index=df.index)
Review Comment:
Added the type hint.
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