googleapis · gcf-merge-on-green · Mar 26, 2024 · Mar 22, 2024 · Mar 22, 2024 · Mar 23, 2024
@@ -190,7 +190,7 @@ def _(
        .else_(magnitude * pow(-1, negative_count_parity))
        .end()
    )
-    return float_result.cast(column.type())  # type: ignore
+    return float_result


 @compile_unary_agg.register

@@ -139,10 +139,7 @@ class ProductOp(UnaryAggregateOp):
    name: ClassVar[str] = "product"

    def output_type(self, *input_types: dtypes.ExpressionType):
-        if pd.api.types.is_bool_dtype(input_types[0]):
-            return dtypes.INT_DTYPE
-        else:
-            return input_types[0]
+        return dtypes.FLOAT_DTYPE


 @dataclasses.dataclass(frozen=True)

@@ -228,8 +228,7 @@ def test_dataframe_groupby_multi_sum(
        (lambda x: x.cumsum(numeric_only=True)),
        (lambda x: x.cummax(numeric_only=True)),
        (lambda x: x.cummin(numeric_only=True)),
-        # pandas 2.2 uses floating point for cumulative product even for
-        # integer inputs.
+        # Pre-pandas 2.2 doesn't always proeduce float.
        (lambda x: x.cumprod().astype("Float64")),
        (lambda x: x.shift(periods=2)),
    ],

@@ -1481,7 +1481,7 @@ def test_groupby_prod(scalars_dfs):
    bf_series = scalars_df[col_name].groupby(scalars_df["int64_col"]).prod()
    pd_series = (
        scalars_pandas_df[col_name].groupby(scalars_pandas_df["int64_col"]).prod()
-    )
+    ).astype(pd.Float64Dtype())
    # TODO(swast): Update groupby to use index based on group by key(s).
    bf_result = bf_series.to_pandas()
    assert_series_equal(

@@ -4416,10 +4416,10 @@ def cumprod(self) -> DataFrame:
            [3 rows x 2 columns]

            >>> df.cumprod()
-                A	B
-            0	3	1
-            1	3	2
-            2	6	6
+                 A    B
+            0  3.0  1.0
+            1  3.0  2.0
+            2  6.0  6.0
            <BLANKLINE>
            [3 rows x 2 columns]