E5EB fix: correct index labels in multiple aggregations for DataFrameGroupBy by chelsea-lin · Pull Request #723 · googleapis/python-bigquery-dataframes · GitHub
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fix: correct index labels in multiple aggregations for DataFrameGroupBy #723

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Merged
gcf-merge-on-green merged 3 commits into from
May 29, 2024
Merged

fix: correct index labels in multiple aggregations for DataFrameGroupBy #723

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5247771
fix: multi-index label in DataFrameGroupBy
chelsea-lin May 24, 2024
d1238f3
fix: correct index labels with multi-index DataFrameGroupBy
chelsea-lin May 28, 2024
e3a8d9f
add tests
chelsea-lin May 29, 2024
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27 changes: 24 additions & 3 deletions bigframes/core/groupby/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -339,9 +339,30 @@ def _agg_list(self, func: typing.Sequence) -> df.DataFrame:
for col_id in self._aggregated_columns()
for f in func
]
column_labels = [
(col_id, f) for col_id in self._aggregated_columns() for f in func
]

if self._block.column_labels.nlevels > 1:
# Restructure MultiIndex for proper format: (idx1, idx2, func)
# rather than ((idx1, idx2), func).
aggregated_columns = pd.MultiIndex.from_tuples(
[
self._block.col_id_to_label[col_id]
for col_id in self._aggregated_columns()
],
names=[*self._block.column_labels.names],
).to_frame(index=False)

column_labels = [
tuple(col_id) + (f,)
for col_id in aggregated_columns.to_numpy()
for f in func
]
else:
column_labels = [
(self._block.col_id_to_label[col_id], f)
for col_id in self._aggregated_columns()
for f in func
]

agg_block, _ = self._block.aggregate(
by_column_ids=self._by_col_ids,
aggregations=aggregations,
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17 changes: 17 additions & 0 deletions tests/system/small/test_groupby.py
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Expand Up @@ -144,6 +144,23 @@ def test_dataframe_groupby_agg_list(scalars_df_index, scalars_pandas_df_index):
pd.testing.assert_frame_equal(pd_result, bf_result_computed, check_dtype=False)


def test_dataframe_groupby_agg_list_w_column_multi_index(
scalars_df_index, scalars_pandas_df_index
):
columns = ["int64_too", "string_col", "bool_col"]
multi_columns = pd.MultiIndex.from_tuples(zip(["a", "b", "a"], columns))
bf_df = scalars_df_index[columns].copy()
bf_df.columns = multi_columns
pd_df = scalars_pandas_df_index[columns].copy()
pd_df.columns = multi_columns

bf_result = bf_df.groupby(level=0).agg(["count", "min"])
pd_result = pd_df.groupby(level=0).agg(["count", "min"])

bf_result_computed = bf_result.to_pandas()
pd.testing.assert_frame_equal(pd_result, bf_result_computed, check_dtype=False)


@pytest.mark.parametrize(
("as_index"),
[
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22 changes: 22 additions & 0 deletions third_party/bigframes_vendored/pandas/core/groupby/__init__.py
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Expand Up @@ -1092,6 +1092,17 @@ def agg(self, func, **kwargs):
<BLANKLINE>
[2 rows x 2 columns]

Multiple aggregations

>>> df.groupby('A').agg(['min', 'max'])
B C
min max min max
A
1 1 2 0.227877 0.362838
2 3 4 -0.56286 1.267767
<BLANKLINE>
[2 rows x 4 columns]

Args:
func (function, str, list, dict or None):
Function to use for aggregating the data.
Expand Down Expand Up @@ -1140,6 +1151,17 @@ def aggregate(self, func, **kwargs):
<BLANKLINE>
[2 rows x 2 columns]

Multiple aggregations

>>> df.groupby('A').agg(['min', 'max'])
B C
min max min max
A
1 1 2 0.227877 0.362838
2 3 4 -0.56286 1.267767
<BLANKLINE>
[2 rows x 4 columns]

Args:
func (function, str, list, dict or None):
Function to use for aggregating the data.
Expand Down
0