diff --git a/pointblank/datascan.py b/pointblank/datascan.py
index 11db84748..4e067cd6e 100644
--- a/pointblank/datascan.py
+++ b/pointblank/datascan.py
@@ -345,68 +345,41 @@ def get_tabular_report(self, *, show_sample_data: bool = False) -> GT:
         )
 
         if "freqs" in formatted_data.columns:  # TODO: don't love this arbitrary check
-            # Extract HTML freqs:
-            try:
-                formatted_data = formatted_data.with_columns(
-                    __freq_true=nw.col("freqs").struct.field("True"),
-                    __freq_false=nw.col("freqs").struct.field("False"),
-                )
-            except Exception:  # TODO: should be narrowed if possible
-                # if no struct implimentation exists, it must be done manually
-                freq_ser: nw.Series = formatted_data["freqs"]
-                trues: list[int | None] = []
-                falses: list[int | None] = []
-                for freq in freq_ser:
-                    try:
-                        trues.append(freq["True"])
-                        falses.append(freq["False"])
-                    except (KeyError, TypeError):
-                        trues.append(None)
-                        falses.append(None)
-                true_ser: nw.Series = nw.new_series(
-                    name="__freq_true", values=trues, backend=_backend
+            # Build the T/F summary for boolean columns straight from each column's profile.
+            # The `freqs` dict only has keys for the values present (e.g., `{"True": 3}` for an
+            # all-`True` column), so a missing key is a count of zero. The rows of `data` follow
+            # the order of `column_profiles`.
+            bool_unique_html: list[str | None] = []
+            for prof in self.profile.column_profiles:
+                freqs = next((stat.val for stat in prof.statistics if stat.name == "freqs"), None)
+                if not isinstance(freqs, dict):
+                    bool_unique_html.append(None)
+                    continue
+                pct_true, pct_false = _fmt_frac(
+                    [
+                        freqs.get("True", 0) / self.profile.row_count,
+                        freqs.get("False", 0) / self.profile.row_count,
+                    ]
                 )
-                false_ser: nw.Series = nw.new_series(
-                    name="__freq_false", values=falses, backend=_backend
+                bool_unique_html.append(
+                    f"<span style='font-weight: bold;'>T</span>{pct_true}"
+                    f"<br><span style='font-weight: bold;'>F</span>{pct_false}"
                 )
-                formatted_data = formatted_data.with_columns(
-                    __freq_true=true_ser, __freq_false=false_ser
-                )
-
-            ## format pct true values
-            formatted_data = formatted_data.with_columns(
-                # for bools, UQs are represented as percentages
-                __pct_true=nw.col("__freq_true") / self.profile.row_count,
-                __pct_false=nw.col("__freq_false") / self.profile.row_count,
+            bool_ser: nw.Series = nw.new_series(
+                name="__bool_unique_html",
+                values=bool_unique_html,
+                dtype=nw.String(),
+                backend=_backend,
             )
-            for _fmt_col in ("__pct_true", "__pct_false"):
-                _formatted: list[str | None] = _fmt_frac(formatted_data[_fmt_col])
-                formatted = nw.new_series(name=_fmt_col, values=_formatted, backend=_backend)
-                formatted_data = formatted_data.drop(_fmt_col)
-                formatted_data = formatted_data.with_columns(formatted.alias(_fmt_col))
 
             formatted_data = (
-                formatted_data.with_columns(
-                    __bool_unique_html=nw.concat_str(
-                        nw.lit("<span style='font-weight: bold;'>T</span>"),
-                        nw.col("__pct_true"),
-                        nw.lit("<br><span style='font-weight: bold;'>F</span>"),
-                        nw.col("__pct_false"),
-                    ),
-                )
+                formatted_data.with_columns(__bool_unique_html=bool_ser)
                 .with_columns(
                     n_unique=nw.when(~nw.col("__bool_unique_html").is_null())
                     .then(nw.col("__bool_unique_html"))
                     .otherwise(nw.col("n_unique"))
                 )
-                .drop(
-                    "__freq_true",
-                    "__freq_false",
-                    "__bool_unique_html",
-                    "freqs",
-                    "__pct_true",
-                    "__pct_false",
-                )
+                .drop("__bool_unique_html", "freqs")
             )
 
         ## Determine Value Formatting Selectors:
diff --git a/tests/test_datascan.py b/tests/test_datascan.py
index c4fd67917..7ffcc23c4 100644
--- a/tests/test_datascan.py
+++ b/tests/test_datascan.py
@@ -216,6 +216,41 @@ def test_col_summary_tbl(df):
     assert isinstance(col_summary, GT)
 
 
+def _bool_summary_cells(col_summary: GT) -> list[str]:
+    tag = "<span style='font-weight: bold;'>"
+    return [
+        str(v).replace(tag, "").replace("</span>", "")
+        for v in nw.from_native(col_summary._tbl_data)["n_unique"]
+    ]
+
+
+@pytest.mark.parametrize(
+    ("data", "expected"),
+    [
+        ({"flag": [True, True, True]}, ["T1<br>F0"]),
+        ({"flag": [True]}, ["T1<br>F0"]),
+        ({"flag": [True, None, True]}, ["T0.67<br>F0"]),
+        ({"flag": [False, False]}, ["T0<br>F1"]),
+        ({"a": [True, True, False], "b": [True, True, True]}, ["T0.67<br>F0.33", "T1<br>F0"]),
+        ({"a": [True, True, True], "b": [True, False, None]}, ["T1<br>F0", "T0.33<br>F0.33"]),
+    ],
+)
+@pytest.mark.parametrize("backend", ["polars", "pandas", "duckdb"])
+def test_col_summary_tbl_bool_missing_true_or_false(data, expected, backend):
+    if backend == "pandas":
+        pytest.importorskip("pandas")
+        if any(None in vals for vals in data.values()):
+            pytest.skip("pandas stores booleans with missing values as `object`")
+        tbl = pl.DataFrame(data).to_pandas()
+    elif backend == "duckdb":
+        ibis = pytest.importorskip("ibis")
+        tbl = ibis.memtable(pl.DataFrame(data).to_arrow())
+    else:
+        tbl = pl.DataFrame(data)
+
+    assert _bool_summary_cells(col_summary_tbl(tbl)) == expected
+
+
 def test_col_summary_tbl_polars_categorical_column():
     log_levels = pl.Enum(["debug", "info", "warning", "error"])