fix(pivot-wider): handle the case of empty id_cols (#9912)

ibis/backends/tests/test_generic.py

@@ -2453,3 +2453,51 @@ def test_union_generates_predictable_aliases(con):
     expr = ibis.union(sub1, sub2)
     df = con.execute(expr)
     assert len(df) == 2
+
+
+@pytest.mark.parametrize("id_cols", [s.none(), [], s.c()])
+def test_pivot_wider_empty_id_columns(con, backend, id_cols, monkeypatch):
+    monkeypatch.setattr(ibis.options, "default_backend", con)
+    data = pd.DataFrame(
+        {
+            "id": range(10),
+            "actual": [0, 1, 1, 0, 0, 1, 0, 0, 0, 1],
+            "prediction": [1, 0, 0, 1, 0, 0, 0, 0, 0, 1],
+        }
+    )
+    t = ibis.memtable(data)
+    expr = t.mutate(
+        outcome=(
+            ibis.case()
+            .when((_["actual"] == 0) & (_["prediction"] == 0), "TN")
+            .when((_["actual"] == 0) & (_["prediction"] == 1), "FP")
+            .when((_["actual"] == 1) & (_["prediction"] == 0), "FN")
+            .when((_["actual"] == 1) & (_["prediction"] == 1), "TP")
+            .end()
+        )
+    )
+    expr = expr.pivot_wider(
+        id_cols=id_cols,
+        names_from="outcome",
+        values_from="outcome",
+        values_agg=_.count(),
+        names_sort=True,
+    )
+    result = expr.to_pandas()
+    expected = pd.DataFrame({"FN": [3], "FP": [2], "TN": [4], "TP": [1]})
+    backend.assert_frame_equal(result, expected)
+
+
+@pytest.mark.notyet(
+    ["mysql", "risingwave", "impala", "mssql", "druid", "exasol", "oracle", "flink"],
+    raises=com.OperationNotDefinedError,
+    reason="backend doesn't support Arbitrary agg",
+)
+def test_simple_pivot_wider(con, backend, monkeypatch):
+    monkeypatch.setattr(ibis.options, "default_backend", con)
+    data = pd.DataFrame({"outcome": ["yes", "no"], "counted": [3, 4]})
+    t = ibis.memtable(data)
+    expr = t.pivot_wider(names_from="outcome", values_from="counted", names_sort=True)
+    result = expr.to_pandas()
+    expected = pd.DataFrame({"no": [4], "yes": [3]})
+    backend.assert_frame_equal(result, expected)

ibis/expr/types/relations.py

@@ -4075,6 +4075,27 @@ def pivot_wider(
         │     … │       … │     … │      … │     … │       … │     … │     … │ … │
         └───────┴─────────┴───────┴────────┴───────┴─────────┴───────┴───────┴───┘
 
+        You can do simple transpose-like operations using `pivot_wider`
+
+        >>> t = ibis.memtable(dict(outcome=["yes", "no"], counted=[3, 4]))
+        >>> t
+        ┏━━━━━━━━━┳━━━━━━━━━┓
+        ┃ outcome ┃ counted ┃
+        ┡━━━━━━━━━╇━━━━━━━━━┩
+        │ string  │ int64   │
+        ├─────────┼─────────┤
+        │ yes     │       3 │
+        │ no      │       4 │
+        └─────────┴─────────┘
+        >>> t.pivot_wider(names_from="outcome", values_from="counted", names_sort=True)
+        ┏━━━━━━━┳━━━━━━━┓
+        ┃ no    ┃ yes   ┃
+        ┡━━━━━━━╇━━━━━━━┩
+        │ int64 │ int64 │
+        ├───────┼───────┤
+        │     4 │     3 │
+        └───────┴───────┘
+
         Fill missing pivoted values using `values_fill`
 
         >>> fish_encounters.pivot_wider(
@@ -4411,7 +4432,13 @@ def pivot_wider(
                 key = names_sep.join(filter(None, key_components))
                 aggs[key] = arg if values_fill is None else arg.coalesce(values_fill)
 
-        return self.group_by(id_cols).aggregate(**aggs)
+        grouping_keys = id_cols.expand(self)
+
+        # no id columns, so do an ungrouped aggregation
+        if not grouping_keys:
+            return self.aggregate(**aggs)
+
+        return self.group_by(*grouping_keys).aggregate(**aggs)
 
     def relocate(
         self,