revert for legend scroll

superset/common/query_context_processor.py

@@ -250,36 +250,13 @@ def get_query_result(self, query_object: QueryObject) -> QueryResult:
 
                 query += ";\n\n".join(queries)
                 query += ";\n\n"
-            timeseries_limit_metric = None
-            x_axis_index = None
-            sort_order = None
-            # Here we extract the x axis as its being used as index
-            # in the pivot operation
-            if self._query_context and self._query_context.form_data:
-                x_axis_index = self._query_context.form_data.get("x_axis")
-                if isinstance(x_axis_index, dict):
-                    x_axis_index = x_axis_index.get("label")
-                timeseries_limit_metric = self._query_context.form_data.get(
-                    "timeseries_limit_metric"
-                )
-                if timeseries_limit_metric and x_axis_index is not None:
-                    sort_order = df[x_axis_index].unique().tolist()
-                    sort_order = [item for item in sort_order if item is not None]
+
             # Re-raising QueryObjectValidationError
             try:
                 df = query_object.exec_post_processing(df)
             except InvalidPostProcessingError as ex:
                 raise QueryObjectValidationError(ex.message) from ex
-            if (
-                timeseries_limit_metric
-                and x_axis_index is not None
-                and sort_order is not None
-            ):
-                df[x_axis_index] = pd.Categorical(
-                    df[x_axis_index], categories=sort_order, ordered=True
-                )
-                df = df.sort_values(x_axis_index)
-                df = df.reset_index(drop=True)
+
         result.df = df
         result.query = query
         result.from_dttm = query_object.from_dttm