diff --git a/qcpanel/util.py b/qcpanel/util.py
index 3286c2f..01d3abc 100644
--- a/qcpanel/util.py
+++ b/qcpanel/util.py
@@ -13,7 +13,7 @@ valid_plot_modes = defaultdict(lambda: CompReport._default_modes)
 valid_plot_modes["avg"] = DatasetReport._default_dr_modes
 
 
-def create_plots(
+def create_plot(
     dr: DatasetReport,
     mode="delta",
     metric="acc",
@@ -24,28 +24,7 @@ def create_plots(
     estimators = CE.name[estimators]
     if mode is None:
         mode = valid_plot_modes[plot_view][0]
-    _dpi = 112
     match (plot_view, mode):
-        case ("avg", "train_table"):
-            _data = (
-                dr.data(metric=metric, estimators=estimators).groupby(level=1).mean()
-            )
-            return pn.pane.DataFrame(_data, align="center") if not _data.empty else None
-        case ("avg", "test_table"):
-            _data = (
-                dr.data(metric=metric, estimators=estimators).groupby(level=0).mean()
-            )
-            return pn.pane.DataFrame(_data, align="center") if not _data.empty else None
-        case ("avg", "shift_table"):
-            _data = (
-                dr.shift_data(metric=metric, estimators=estimators)
-                .groupby(level=0)
-                .mean()
-            )
-            return pn.pane.DataFrame(_data, align="center") if not _data.empty else None
-        case ("avg", "stats_table"):
-            _data = wilcoxon(dr, metric=metric, estimators=estimators)
-            return pn.pane.DataFrame(_data, align="center") if not _data.empty else None
         case ("avg", _ as plot_mode):
             _plot = dr.get_plots(
                 mode=mode,
@@ -54,36 +33,6 @@ def create_plots(
                 conf="panel",
                 save_fig=False,
             )
-            return (
-                pn.pane.Matplotlib(
-                    _plot,
-                    tight=True,
-                    format="png",
-                    # sizing_mode="scale_height",
-                    sizing_mode=_plot_sizing_mode,
-                    # sizing_mode="scale_both",
-                )
-                if _plot is not None
-                else None
-            )
-        case (_, "train_table"):
-            cr = dr.crs[_prevs.index(int(plot_view))]
-            _data = (
-                cr.data(metric=metric, estimators=estimators).groupby(level=0).mean()
-            )
-            return pn.pane.DataFrame(_data, align="center") if not _data.empty else None
-        case (_, "shift_table"):
-            cr = dr.crs[_prevs.index(int(plot_view))]
-            _data = (
-                cr.shift_data(metric=metric, estimators=estimators)
-                .groupby(level=0)
-                .mean()
-            )
-            return pn.pane.DataFrame(_data, align="center") if not _data.empty else None
-        case (_, "stats_table"):
-            cr = dr.crs[_prevs.index(int(plot_view))]
-            _data = wilcoxon(cr, metric=metric, estimators=estimators)
-            return pn.pane.DataFrame(_data, align="center") if not _data.empty else None
         case (_, _ as plot_mode):
             cr = dr.crs[_prevs.index(int(plot_view))]
             _plot = cr.get_plots(
@@ -93,18 +42,92 @@ def create_plots(
                 conf="panel",
                 save_fig=False,
             )
-            return (
-                pn.pane.Matplotlib(
-                    _plot,
-                    tight=True,
-                    format="png",
-                    sizing_mode=_plot_sizing_mode,
-                    # sizing_mode="scale_height",
-                    # sizing_mode="scale_both",
-                )
-                if _plot is not None
-                else None
+    if _plot is None:
+        return None
+
+    return pn.pane.Matplotlib(
+        _plot,
+        tight=True,
+        format="png",
+        # sizing_mode="scale_height",
+        sizing_mode=_plot_sizing_mode,
+        styles=dict(margin="0"),
+        # sizing_mode="scale_both",
+    )
+
+
+def create_table(
+    dr: DatasetReport,
+    mode="delta",
+    metric="acc",
+    estimators=None,
+    plot_view=None,
+):
+    _prevs = [round(cr.train_prev[1] * 100) for cr in dr.crs]
+    estimators = CE.name[estimators]
+    if mode is None:
+        mode = valid_plot_modes[plot_view][0]
+    match (plot_view, mode):
+        case ("avg", "train_table"):
+            _data = (
+                dr.data(metric=metric, estimators=estimators).groupby(level=1).mean()
             )
+        case ("avg", "test_table"):
+            _data = (
+                dr.data(metric=metric, estimators=estimators).groupby(level=0).mean()
+            )
+        case ("avg", "shift_table"):
+            _data = (
+                dr.shift_data(metric=metric, estimators=estimators)
+                .groupby(level=0)
+                .mean()
+            )
+        case ("avg", "stats_table"):
+            _data = wilcoxon(dr, metric=metric, estimators=estimators)
+        case (_, "train_table"):
+            cr = dr.crs[_prevs.index(int(plot_view))]
+            _data = (
+                cr.data(metric=metric, estimators=estimators).groupby(level=0).mean()
+            )
+        case (_, "shift_table"):
+            cr = dr.crs[_prevs.index(int(plot_view))]
+            _data = (
+                cr.shift_data(metric=metric, estimators=estimators)
+                .groupby(level=0)
+                .mean()
+            )
+        case (_, "stats_table"):
+            cr = dr.crs[_prevs.index(int(plot_view))]
+            _data = wilcoxon(cr, metric=metric, estimators=estimators)
+
+    return (
+        pn.Column(
+            pn.pane.DataFrame(
+                _data,
+                align="center",
+                float_format=lambda v: f"{v:6e}",
+                styles={"font-size-adjust": "0.62"},
+            ),
+            sizing_mode="stretch_both",
+            # scroll=True,
+        )
+        if not _data.empty
+        else None
+    )
+
+
+def create_result(
+    dr: DatasetReport,
+    mode="delta",
+    metric="acc",
+    estimators=None,
+    plot_view=None,
+):
+    match mode:
+        case m if m.endswith("table"):
+            return create_table(dr, mode, metric, estimators, plot_view)
+        case _:
+            return create_plot(dr, mode, metric, estimators, plot_view)
 
 
 def explore_datasets(root: Path | str):
diff --git a/qcpanel/viewer.py b/qcpanel/viewer.py
index 6d7abfa..76440d4 100644
--- a/qcpanel/viewer.py
+++ b/qcpanel/viewer.py
@@ -6,7 +6,7 @@ import pandas as pd
 import panel as pn
 import param
 
-from qcpanel.util import create_plots, explore_datasets, valid_plot_modes
+from qcpanel.util import create_result, explore_datasets, valid_plot_modes
 from quacc.evaluation.estimators import CE
 from quacc.evaluation.report import DatasetReport
 
@@ -365,7 +365,7 @@ class QuaccTestViewer(param.Parameterized):
             self.plot_pane = __svg
         else:
             _dr = self.datasets_[self.dataset]
-            __plot = create_plots(
+            __plot = create_result(
                 _dr,
                 mode=self.mode,
                 metric=self.metric,