CLN: get_flattened_iterator

pandas/core/groupby/ops.py

@@ -50,7 +50,7 @@
 from pandas.core.sorting import (
     compress_group_index,
     decons_obs_group_ids,
-    get_flattened_iterator,
+    get_flattened_list,
     get_group_index,
     get_group_index_sorter,
     get_indexer_dict,
@@ -153,7 +153,7 @@ def _get_group_keys(self):
             comp_ids, _, ngroups = self.group_info
 
             # provide "flattened" iterator for multi-group setting
-            return get_flattened_iterator(comp_ids, ngroups, self.levels, self.codes)
+            return get_flattened_list(comp_ids, ngroups, self.levels, self.codes)
 
     def apply(self, f: F, data: FrameOrSeries, axis: int = 0):
         mutated = self.mutated

pandas/core/sorting.py

@@ -1,5 +1,6 @@
 """ miscellaneous sorting / groupby utilities """
-from typing import Callable, Optional
+from collections import defaultdict
+from typing import Callable, Dict, List, Optional, Tuple
 
 import numpy as np
 
@@ -440,36 +441,18 @@ def ensure_key_mapped(values, key: Optional[Callable], levels=None):
     return result
 
 
-class _KeyMapper:
-    """
-    Map compressed group id -> key tuple.
-    """
-
-    def __init__(self, comp_ids, ngroups: int, levels, labels):
-        self.levels = levels
-        self.labels = labels
-        self.comp_ids = comp_ids.astype(np.int64)
-
-        self.k = len(labels)
-        self.tables = [hashtable.Int64HashTable(ngroups) for _ in range(self.k)]
-
-        self._populate_tables()
-
-    def _populate_tables(self):
-        for labs, table in zip(self.labels, self.tables):
-            table.map(self.comp_ids, labs.astype(np.int64))
-
-    def get_key(self, comp_id):
-        return tuple(
-            level[table.get_item(comp_id)]
-            for table, level in zip(self.tables, self.levels)
-        )
-
-
-def get_flattened_iterator(comp_ids, ngroups, levels, labels):
-    # provide "flattened" iterator for multi-group setting
-    mapper = _KeyMapper(comp_ids, ngroups, levels, labels)
-    return [mapper.get_key(i) for i in range(ngroups)]
+def get_flattened_list(
+    comp_ids: np.ndarray, ngroups: int, levels, labels: List[np.ndarray]
+) -> List[Tuple]:
+    """Map compressed group id -> key tuple."""
+    comp_ids = comp_ids.astype(np.int64, copy=False)
+    arrays: Dict[int, List] = defaultdict(list)
+    for labs, level in zip(labels, levels):
+        table = hashtable.Int64HashTable(ngroups)
+        table.map(comp_ids, labs.astype(np.int64, copy=False))
+        for i in range(ngroups):
+            arrays[i].append(level[table.get_item(i)])
+    return [tuple(array) for array in arrays.values()]
 
 
 def get_indexer_dict(label_list, keys):