Arrays from iterable scalars are now supported.

graphique/core.py

@@ -211,8 +211,7 @@ def quantile(self, **options) -> pa.Array:
 
     def index(self, **options) -> pa.Array:
         """index for first occurrence of each list scalar"""
-        values = [pc.index(value, **options) for value in ListChunk.scalars(self)]
-        return Column.from_scalars(values)
+        return pa.array(pc.index(value, **options) for value in ListChunk.scalars(self))
 
     @register
     def list_all(ctx, self: pa.list_(pa.bool_())) -> pa.bool_():  # type: ignore
@@ -228,10 +227,6 @@ def list_any(ctx, self: pa.list_(pa.bool_())) -> pa.bool_():  # type: ignore
 class Column(pa.ChunkedArray):
     """Chunked array interface as a namespace of functions."""
 
-    def from_scalars(values: Sequence) -> pa.Array:
-        """Return array from arrow scalars."""
-        return pa.array((value.as_py() for value in values), values[0].type)
-
     def scalar_type(self):
         return self.type.value_type if pa.types.is_dictionary(self.type) else self.type
 

graphique/interface.py

@@ -291,9 +291,7 @@ def fragments(self, info: Info, counts: str = '', aggregate: HashAggregates = {}
             for name in row:
                 columns[name].append(row[name])
         for name, values in columns.items():
-            if isinstance(values[0], pa.Scalar):
-                columns[name] = C.from_scalars(values)
-            elif isinstance(values[0], pa.Array):
+            if isinstance(values[0], pa.Array):
                 columns[name] = ListChunk.from_scalars(values)
         columns |= {field.name: pa.array(columns[field.name], field.type) for field in schema}
         return self.add_metric(info, pa.table(columns), mode='fragment')