From a91b718a211544b23d54152c2d97aac41d5138ae Mon Sep 17 00:00:00 2001
From: tchalla61 <148000459+Bharath-970@users.noreply.github.com>
Date: Fri, 3 Jul 2026 14:41:06 +0530
Subject: [PATCH] [BUGFIX] Fix crash rendering results with
 unexpected_index_column_names and no domain column

_convert_unexpected_indices_to_df computes domain_column_name_list as
whatever keys of an unexpected_index_list entry aren't in
unexpected_index_column_names. When every key IS an ID/PK column (no
separate domain/value column present -- e.g. result_format=COMPLETE with
unexpected_index_column_names covering all tracked keys), that list is
empty and pandas.DataFrame.groupby([]) raises:

    ValueError: No group keys passed!

aborting Data Docs rendering for the affected result.

With no domain column to group by, each unexpected row already stands on
its own -- there's nothing to merge across rows. Skip the groupby/agg in
that case and wrap each row's values in a single-item list directly,
matching the same output shape .groupby().agg() would otherwise produce.

Added a regression test exercising the previously-crashing input shape;
confirmed as a true guard (fails without the fix, passes with it). Full
tests/render/ suite: 168 passed.

Closes #11933.
---
 great_expectations/render/util.py | 14 +++++++++++---
 tests/render/test_util.py         | 25 +++++++++++++++++++++++++
 2 files changed, 36 insertions(+), 3 deletions(-)

diff --git a/great_expectations/render/util.py b/great_expectations/render/util.py
index 6cdfc23c46ed..36ee5b6f510c 100644
--- a/great_expectations/render/util.py
+++ b/great_expectations/render/util.py
@@ -445,9 +445,17 @@ def _convert_unexpected_indices_to_df(
     def _agg_func(y: pd.Series[T]) -> list[T]:  # type: ignore[type-var]  # not yet supported by pandas-stubs
         return list(y)
 
-    all_unexpected_indices: pd.DataFrame = unexpected_index_df.groupby(domain_column_name_list).agg(
-        _agg_func
-    )
+    all_unexpected_indices: pd.DataFrame
+    if domain_column_name_list:
+        all_unexpected_indices = unexpected_index_df.groupby(domain_column_name_list).agg(_agg_func)
+    else:
+        # No domain column is present to group by (every key in each
+        # `unexpected_index_list` entry is itself one of
+        # `unexpected_index_column_names`), so there is nothing to aggregate across
+        # rows -- `.groupby([])` raises `ValueError: No group keys passed!` here.
+        # Each row already stands on its own; wrap its values in a single-item list
+        # to match the shape `.groupby().agg(_agg_func)` would otherwise produce.
+        all_unexpected_indices = unexpected_index_df.map(lambda x: [x])
 
     # 2. add count
     col_to_count: str = unexpected_index_column_names[0]
diff --git a/tests/render/test_util.py b/tests/render/test_util.py
index 0214071021b8..42d602fe8571 100644
--- a/tests/render/test_util.py
+++ b/tests/render/test_util.py
@@ -157,6 +157,31 @@ def test_convert_unexpected_indices_to_df():
     assert res.iloc[2].tolist() == ["5", "five", 1]
 
 
+def test_convert_unexpected_indices_to_df_no_domain_column():
+    # Regression test: when every key in each `unexpected_index_list` entry is
+    # itself one of `unexpected_index_column_names` (i.e. no separate domain/value
+    # column is present), there is nothing to group by. This previously raised
+    # `ValueError: No group keys passed!` from `.groupby([])`. See GH#11933.
+    unexpected_index_list = [{"pk_1": 3}, {"pk_1": 4}, {"pk_1": 5}]
+    unexpected_index_column_names = ["pk_1"]
+
+    partial_unexpected_counts = [
+        {"count": 1, "value": None},
+        {"count": 1, "value": None},
+        {"count": 1, "value": None},
+    ]
+
+    res = _convert_unexpected_indices_to_df(
+        unexpected_index_list=unexpected_index_list,
+        unexpected_index_column_names=unexpected_index_column_names,
+        partial_unexpected_counts=partial_unexpected_counts,
+    )
+    assert list(res) == ["pk_1", "Count"]
+    assert res.iloc[0].tolist() == ["3", 1]
+    assert res.iloc[1].tolist() == ["4", 1]
+    assert res.iloc[2].tolist() == ["5", 1]
+
+
 def test_convert_unexpected_indices_to_df_multiple():
     result = [
         {"animals": "giraffe", "pk_1": 3},