Simplify old logic

python/cudf/cudf/_lib/groupby.pyx

@@ -94,41 +94,17 @@ def _(dtype: DecimalDtype):
     return _DECIMAL_AGGS
 
 
-cdef _agg_result_from_pylibcudf_tables(
-    pylibcudf_results,
-    set column_included,
-    int n_input_columns
-):
-    """Construct the list of result columns from libcudf result. The result
-    contains the same number of lists as the number of input columns. Result
-    for an input column that has no applicable aggregations is an empty list.
-    """
-    cdef:
-        int i
-        int result_index = 0
-    result_columns = []
-    for i in range(n_input_columns):
-        if i in column_included:
-            result_columns.append(
-                columns_from_pylibcudf_table(pylibcudf_results[result_index])
-            )
-            result_index += 1
-        else:
-            result_columns.append([])
-    return result_columns
-
-
 cdef class GroupBy:
     cdef dict __dict__
 
     def __init__(self, keys, dropna=True):
-        self._groupby = pylibcudf.groupby.GroupBy(
-            pylibcudf.table.Table([c.to_pylibcudf(mode="read") for c in keys]),
-            pylibcudf.types.NullPolicy.EXCLUDE if dropna
-            else pylibcudf.types.NullPolicy.INCLUDE
-        )
-
         with acquire_spill_lock() as spill_lock:
+            self._groupby = pylibcudf.groupby.GroupBy(
+                pylibcudf.table.Table([c.to_pylibcudf(mode="read") for c in keys]),
+                pylibcudf.types.NullPolicy.EXCLUDE if dropna
+                else pylibcudf.types.NullPolicy.INCLUDE
+            )
+
             # We spill lock the columns while this GroupBy instance is alive.
             self._spill_lock = spill_lock
 
@@ -180,45 +156,36 @@ cdef class GroupBy:
         -------
         Frame of aggregated values
         """
-        alg = "scan" if _is_all_scan_aggregate(aggregations) else "aggregate"
-
-        allow_empty = all(len(v) == 0 for v in aggregations)
-
         included_aggregations = []
-        column_included = set()
+        column_included = []
         requests = []
         for i, (col, aggs) in enumerate(zip(values, aggregations)):
-            dtype = col.dtype
-
-            valid_aggregations = get_valid_aggregation(dtype)
+            valid_aggregations = get_valid_aggregation(col.dtype)
             included_aggregations_i = []
-
             col_aggregations = []
             for agg in aggs:
                 agg_obj = make_groupby_aggregation(agg)
-                if (valid_aggregations == "ALL"
-                        or agg_obj.kind in valid_aggregations):
+                if valid_aggregations == "ALL" or agg_obj.kind in valid_aggregations:
                     included_aggregations_i.append((agg, agg_obj.kind))
                     col_aggregations.append(agg_obj.c_obj)
             included_aggregations.append(included_aggregations_i)
             if col_aggregations:
                 requests.append(pylibcudf.groupby.GroupByRequest(
                     col.to_pylibcudf(mode="read"), col_aggregations
                 ))
-                column_included.add(i)
-        if not requests and not allow_empty:
-            raise DataError("All requested aggregations are unsupported.")
+                column_included.append(i)
 
-        keys, results = self._groupby.aggregate(requests) if alg == "aggregate" \
-            else self._groupby.scan(requests)
+        if not requests and any(len(v) > 0 for v in aggregations):
+            raise DataError("All requested aggregations are unsupported.")
 
-        grouped_keys = columns_from_pylibcudf_table(keys)
+        keys, results = self._groupby.scan(requests) if \
+            _is_all_scan_aggregate(aggregations) else self._groupby.aggregate(requests)
 
-        result_columns = _agg_result_from_pylibcudf_tables(
-            results, column_included, len(values)
-        )
+        result_columns = [[] for _ in range(len(values))]
+        for i, result in zip(column_included, results):
+            result_columns[i] = columns_from_pylibcudf_table(result)
 
-        return result_columns, grouped_keys, included_aggregations
+        return result_columns, columns_from_pylibcudf_table(keys), included_aggregations
 
     def shift(self, list values, int periods, list fill_values):
         keys, shifts = self._groupby.shift(