amend doc example

Author: TrevorBergeron

bigframes/core/blocks.py

@@ -1780,15 +1780,19 @@ def pivot(
         else:
             return result_block.with_column_labels(columns_values)
 
-    def stack(self, how="left", levels: int = 1):
+    def stack(
+        self, how="left", levels: int = 1, *, override_labels: Optional[pd.Index] = None
+    ):
         """Unpivot last column axis level into row axis"""
         if levels == 0:
             return self
 
         # These are the values that will be turned into rows
 
         col_labels, row_labels = utils.split_index(self.column_labels, levels=levels)
-        row_labels = row_labels.drop_duplicates()
+        row_labels = (
+            row_labels.drop_duplicates() if override_labels is None else override_labels
+        )
 
         if col_labels is None:
             result_index: pd.Index = pd.Index([None])

bigframes/operations/aggregations.py

@@ -251,12 +251,7 @@ def name(self):
     def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType:
         if not dtypes.is_orderable(input_types[0]):
             raise TypeError(f"Type {input_types[0]} is not orderable")
-        if pd.api.types.is_bool_dtype(input_types[0]) or pd.api.types.is_integer_dtype(
-            input_types[0]
-        ):
-            return dtypes.FLOAT_DTYPE
-        else:
-            return input_types[0]
+        return input_types[0]
 
 
 @dataclasses.dataclass(frozen=True)

bigframes/pandas/core/methods/describe.py

@@ -40,8 +40,25 @@ def describe(
     block = input._block
 
     describe_block = _describe(block, columns=block.value_columns, include=include)
+    # we override default stack behavior, because we want very specific ordering
+    stack_cols = pd.Index(
+        [
+            "count",
+            "nunique",
+            "top",
+            "freq",
+            "mean",
+            "std",
+            "min",
+            "25%",
+            "50%",
+            "75%",
+            "max",
+        ]
+    ).intersection(describe_block.column_labels.get_level_values(-1))
+    describe_block = describe_block.stack(override_labels=stack_cols)
 
-    return dataframe.DataFrame(describe_block).stack().droplevel(level=0)
+    return dataframe.DataFrame(describe_block).droplevel(level=0)
 
 
 def _describe(

third_party/bigframes_vendored/pandas/core/groupby/__init__.py

@@ -47,43 +47,25 @@ def describe(self, include: None | Literal["all"] = None):
             >>> import bigframes.pandas as bpd
             >>> bpd.options.display.progress_bar = None
 
-            >>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [0, 2, 8], "C": ["cat", "cat", "dog"]})
+            >>> df = bpd.DataFrame({"A": [1, 1, 1, 2, 2], "B": [0, 2, 8, 2, 7], "C": ["cat", "cat", "dog", "mouse", "cat"]})
             >>> df
-               A  B    C
-            0  3  0  cat
-            1  1  2  cat
-            2  2  8  dog
+               A  B      C
+            0  1  0    cat
+            1  1  2    cat
+            2  1  8    dog
+            3  2  2  mouse
+            4  2  7    cat
             <BLANKLINE>
-            [3 rows x 3 columns]
+            [5 rows x 3 columns]
 
-            >>> df.describe()
-                     A         B
-            count  3.0       3.0
-            mean   2.0  3.333333
-            std    1.0  4.163332
-            min    1.0       0.0
-            25%    1.0       0.0
-            50%    2.0       2.0
-            75%    3.0       8.0
-            max    3.0       8.0
-            <BLANKLINE>
-            [8 rows x 2 columns]
-
-
-        Using describe with include = "all":
-            >>> df.describe(include="all")
-                        A         B     C
-            count     3.0       3.0     3
-            nunique  <NA>      <NA>     2
-            mean      2.0  3.333333  <NA>
-            std       1.0  4.163332  <NA>
-            min       1.0       0.0  <NA>
-            25%       1.0       0.0  <NA>
-            50%       2.0       2.0  <NA>
-            75%       3.0       8.0  <NA>
-            max       3.0       8.0  <NA>
+            >>> df.groupby("A").describe(include="all")
+                  B                                             C
+              count      mean       std min 25% 50% 75% max count nunique
+            A
+            1     3  3.333333  4.163332   0   0   2   8   8     3       2
+            2     2       4.5  3.535534   2   2   2   7   7     2       2
             <BLANKLINE>
-            [9 rows x 3 columns]
+            [2 rows x 10 columns]
 
         Returns:
             bigframes.pandas.DataFrame: