Fix pandas compat and add aggregate/summary test (round 5)

- P1: Replace pandas-2.2-only groupby.apply(include_groups=False) with
  pandas-1.3-compatible pd.Series.groupby().sum() in OLS zero-weight guard
- P2: Add test for survey aggregate() with df_survey inference and
  summary() survey block display

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: igerber

diff_diff/wooldridge.py

@@ -652,11 +652,9 @@ def _fit_ols(
 
         # Guard: zero-weight unit/time groups cause 0/0 in within_transform
         if survey_weights is not None and np.any(survey_weights == 0):
+            sw_series = pd.Series(survey_weights, index=sample.index)
             for grp_col, grp_label in [(unit, "unit"), (time, "time period")]:
-                grp_sums = sample.groupby(grp_col).apply(
-                    lambda g: survey_weights[g.index].sum(),
-                    include_groups=False,
-                )
+                grp_sums = sw_series.groupby(sample[grp_col]).sum()
                 zero_grps = grp_sums[grp_sums == 0].index.tolist()
                 if zero_grps:
                     raise ValueError(

tests/test_wooldridge.py

@@ -1573,3 +1573,23 @@ def test_ols_survey_non_range_index(self, survey_panel):
                 df, outcome="y", unit="unit", time="time",
                 cohort="cohort", survey_design=sd,
             )
+
+    def test_survey_aggregate_and_summary(self, survey_panel):
+        """Survey aggregate() uses df_survey and summary() shows survey block."""
+        from diff_diff.survey import SurveyDesign
+        sd = SurveyDesign(weights="weight", strata="stratum", psu="unit")
+        r = WooldridgeDiD().fit(
+            survey_panel, outcome="y", unit="unit", time="time",
+            cohort="cohort", survey_design=sd,
+        )
+        # aggregate() should use t-distribution with survey df
+        r.aggregate("group")
+        assert r.group_effects is not None
+        assert r._df_survey is not None
+        for eff in r.group_effects.values():
+            assert np.isfinite(eff["p_value"])
+
+        # summary() should include survey design block
+        s = r.summary()
+        assert "Survey Design" in s
+        assert "pweight" in s