Use pandas for more nicely format numeric summary stats

extensions/positron-python/python_files/positron/positron_ipykernel/data_explorer.py

@@ -905,20 +905,22 @@ def _prof_summary_stats(self, column_index: int):
 
     @staticmethod
     def _summarize_number(col: "pd.Series"):
-        min_value = col.min()
-        max_value = col.max()
-        mean = col.mean()
-        median = col.median()
-        stdev = col.std()
+        import pandas.io.formats.format as fmt
+
+        minmax = pd_.Series([col.min(), col.max()], dtype=col.dtype)
+        numeric_stats = pd_.Series([col.mean(), col.median(), col.std()])
+
+        min_value, max_value = fmt.format_array(minmax.to_numpy(), None, leading_space=False)
+        mean, median, stdev = fmt.format_array(numeric_stats.to_numpy(), None, leading_space=False)
 
         return ColumnSummaryStats(
             type_display=ColumnDisplayType.Number,
             number_stats=SummaryStatsNumber(
-                min_value=str(min_value),
-                max_value=str(max_value),
-                mean=str(mean),
-                median=str(median),
-                stdev=str(stdev),
+                min_value=min_value,
+                max_value=max_value,
+                mean=mean,
+                median=median,
+                stdev=stdev,
             ),
         )
 

extensions/positron-python/python_files/positron/positron_ipykernel/tests/test_data_explorer.py

@@ -1380,7 +1380,7 @@ def _assert_close(expected, actual):
 
 def _assert_numeric_stats_equal(expected, actual):
     for attr, value in expected.items():
-        _assert_close(value, float(actual.get(attr)))
+        _assert_close(float(value), float(actual.get(attr)))
 
 
 def _assert_string_stats_equal(expected, actual):
@@ -1394,6 +1394,8 @@ def _assert_boolean_stats_equal(expected, actual):
 
 
 def test_pandas_profile_summary_stats(dxf: DataExplorerFixture):
+    import pandas.io.formats.format as fmt
+
     arr = np.random.standard_normal(100)
     arr_with_nulls = arr.copy()
     arr_with_nulls[::10] = np.nan
@@ -1420,27 +1422,30 @@ def test_pandas_profile_summary_stats(dxf: DataExplorerFixture):
     )
     dxf.register_table("df1", df1)
 
+    def _format_float(x):
+        return fmt.format_array(np.array([x], dtype="float64"), None, leading_space=False)[0]
+
     cases = [
         (
             "df1",
             0,
             {
-                "min_value": arr.min(),
-                "max_value": arr.max(),
-                "mean": df1["a"].mean(),
-                "stdev": df1["a"].std(),
-                "median": df1["a"].median(),
+                "min_value": _format_float(arr.min()),
+                "max_value": _format_float(arr.max()),
+                "mean": _format_float(df1["a"].mean()),
+                "stdev": _format_float(df1["a"].std()),
+                "median": _format_float(df1["a"].median()),
             },
         ),
         (
             "df1",
             1,
             {
-                "min_value": df1["b"].min(),
-                "max_value": df1["b"].max(),
-                "mean": df1["b"].mean(),
-                "stdev": df1["b"].std(),
-                "median": df1["b"].median(),
+                "min_value": _format_float(df1["b"].min()),
+                "max_value": _format_float(df1["b"].max()),
+                "mean": _format_float(df1["b"].mean()),
+                "stdev": _format_float(df1["b"].std()),
+                "median": _format_float(df1["b"].median()),
             },
         ),
         (