optimize group access

packages/python/plotly/plotly/express/_core.py

@@ -1904,44 +1904,42 @@ def infer_config(args, constructor, trace_patch, layout_patch):
     return trace_specs, grouped_mappings, sizeref, show_colorbar
 
 
-def get_orderings(args, grouper):
+def get_groups_and_orders(args, grouper):
     """
     `orders` is the user-supplied ordering with the remaining data-frame-supplied
     ordering appended if the column is used for grouping. It includes anything the user
     gave, for any variable, including values not present in the dataset. It's a dict
     where the keys are e.g. "x" or "color"
 
-    `sorted_group_names` is the set of groups, ordered by the order above. It's a list
-    of tuples like [("value1", ""), ("value2", "")] where each tuple contains the name
+    `groups` is the dicts of groups, ordered by the order above. Its keys are
+    tuples like [("value1", ""), ("value2", "")] where each tuple contains the name
     of a single dimension-group
     """
     orders = {} if "category_orders" not in args else args["category_orders"].copy()
 
     # figure out orders and what the single group name would be if there were one
     single_group_name = []
+    unique_cache = dict()
     for col in grouper:
         if col == one_group:
             single_group_name.append("")
         else:
-            uniques = list(args["data_frame"][col].unique())
+            if col not in unique_cache:
+                unique_cache[col] = list(args["data_frame"][col].unique())
+            uniques = unique_cache[col]
             if len(uniques) == 1:
                 single_group_name.append(uniques[0])
             if col not in orders:
                 orders[col] = uniques
             else:
                 orders[col] = list(OrderedDict.fromkeys(list(orders[col]) + uniques))
-
+    df = args["data_frame"]
     if len(single_group_name) == len(grouper):
         # we have a single group, so we can skip all group-by operations!
-        grouped = None
-        sorted_group_names = [tuple(single_group_name)]
+        groups = {tuple(single_group_name): df}
     else:
-        grouped = args["data_frame"].groupby(grouper, sort=False)
-        sorted_group_names = []
-        for group_name in grouped.groups:
-            if len(grouper) == 1:
-                group_name = (group_name,)
-            sorted_group_names.append(group_name)
+        group_indices = df.groupby(grouper, sort=False).indices
+        sorted_group_names = [g if len(grouper) != 1 else (g,) for g in group_indices]
 
         for i, col in reversed(list(enumerate(grouper))):
             if col != one_group:
@@ -1951,7 +1949,9 @@ def get_orderings(args, grouper):
                     if g[i] in orders[col]
                     else -1,
                 )
-    return grouped, orders, sorted_group_names
+
+        groups = {s: df.iloc[group_indices[s]] for s in sorted_group_names}
+    return groups, orders
 
 
 def make_figure(args, constructor, trace_patch=None, layout_patch=None):
@@ -1970,7 +1970,7 @@ def make_figure(args, constructor, trace_patch=None, layout_patch=None):
         args, constructor, trace_patch, layout_patch
     )
     grouper = [x.grouper or one_group for x in grouped_mappings] or [one_group]
-    grouped, orders, sorted_group_names = get_orderings(args, grouper)
+    groups, orders = get_groups_and_orders(args, grouper)
 
     col_labels = []
     row_labels = []
@@ -1999,13 +1999,7 @@ def make_figure(args, constructor, trace_patch=None, layout_patch=None):
     trendline_rows = []
     trace_name_labels = None
     facet_col_wrap = args.get("facet_col_wrap", 0)
-    for group_name in sorted_group_names:
-        if grouped is not None:
-            group = grouped.get_group(
-                group_name if len(group_name) > 1 else group_name[0]
-            )
-        else:
-            group = args["data_frame"]
+    for group_name, group in groups.items():
         mapping_labels = OrderedDict()
         trace_name_labels = OrderedDict()
         frame_name = ""