diff --git a/numerblox/evaluation.py b/numerblox/evaluation.py
index af761e5a..7ae841db 100644
--- a/numerblox/evaluation.py
+++ b/numerblox/evaluation.py
@@ -148,7 +148,8 @@ def per_era_corrs(
         return dataf.groupby(dataf[self.era_col]).apply(
             lambda d: self._normalize_uniform(d[pred_col].fillna(0.5)).corr(
                 d[target_col]
-            )
+            ),
+            include_groups=False,
         )
 
     def mean_std_sharpe(
@@ -207,7 +208,8 @@ def max_feature_exposure(
     ) -> np.float64:
         """Maximum exposure over all features."""
         max_per_era = dataf.groupby(self.era_col).apply(
-            lambda d: d[dataf.feature_cols].corrwith(d[pred_col]).abs().max()
+            lambda d: d[dataf.feature_cols].corrwith(d[pred_col]).abs().max(),
+            include_groups=False,
         )
         max_feature_exposure = max_per_era.mean(skipna=True)
         return max_feature_exposure
diff --git a/numerblox/postprocessing.py b/numerblox/postprocessing.py
index 1960b3b7..285831bc 100644
--- a/numerblox/postprocessing.py
+++ b/numerblox/postprocessing.py
@@ -184,7 +184,8 @@ def __init__(
     def transform(self, dataf: NumerFrame) -> NumerFrame:
         feature_names = self.feature_names if self.feature_names else dataf.feature_cols
         neutralized_preds = dataf.groupby(dataf.meta.era_col).apply(
-            lambda x: self.normalize_and_neutralize(x, [self.pred_name], feature_names)
+            lambda x: self.normalize_and_neutralize(x, [self.pred_name], feature_names),
+            include_groups=False,
         )
         dataf.loc[:, self.new_col_name] = MinMaxScaler().fit_transform(
             neutralized_preds