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fix: DataFrameGroupby.agg now works with unnamed tuples #985

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Merged
merged 3 commits into from
Sep 13, 2024
Merged

fix: DataFrameGroupby.agg now works with unnamed tuples #985

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c8f1918
fix: DataFrameGroupby.agg no works with unnamed tuples
TrevorBergeron Sep 13, 2024
8f9f55f
Merge remote-tracking branch 'github/main' into more_groupby
TrevorBergeron Sep 13, 2024
bb2e7cf
add tests for invalid inputs
TrevorBergeron Sep 13, 2024
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fix: DataFrameGroupby.agg no works with unnamed tuples
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@TrevorBergeron
TrevorBergeron committed Sep 13, 2024
commit c8f191852294322cd5bc01741101ce2690378df6
10 changes: 4 additions & 6 deletions bigframes/core/groupby/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -414,12 +414,10 @@ def _agg_named(self, **kwargs) -> df.DataFrame:
raise NotImplementedError(
f"Only string aggregate names supported. {constants.FEEDBACK_LINK}"
)
if not hasattr(v, "column") or not hasattr(v, "aggfunc"):
import bigframes.pandas as bpd

raise TypeError(f"kwargs values must be {bpd.NamedAgg.__qualname__}")
col_id = self._resolve_label(v.column)
aggregations.append((col_id, agg_ops.lookup_agg_func(v.aggfunc)))
if not isinstance(v, tuple) or (len(v) != 2):
raise TypeError("kwargs values must be 2-tuples of column, aggfunc")
col_id = self._resolve_label(v[0])
aggregations.append((col_id, agg_ops.lookup_agg_func(v[1])))
column_labels.append(k)
agg_block, _ = self._block.aggregate(
by_column_ids=self._by_col_ids,
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20 changes: 20 additions & 0 deletions tests/system/small/test_groupby.py
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Maybe we could also add test cases for:

  • v is not a tuple
  • v is a tuple but its length is not 2
    ?

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added

Original file line number Diff line number Diff line change
Expand Up @@ -247,6 +247,26 @@ def test_dataframe_groupby_agg_named(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_kw_tuples(scalars_df_index, scalars_pandas_df_index):
col_names = ["int64_too", "float64_col", "int64_col", "bool_col", "string_col"]
bf_result = (
scalars_df_index[col_names]
.groupby("string_col")
.agg(
agg1=("int64_too", "sum"),
agg2=("float64_col", "max"),
)
)
pd_result = (
scalars_pandas_df_index[col_names]
.groupby("string_col")
.agg(agg1=("int64_too", "sum"), agg2=("float64_col", "max"))
)
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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