diff --git a/src/ydata_profiling/model/pandas/describe_date_pandas.py b/src/ydata_profiling/model/pandas/describe_date_pandas.py
index 1ff64a50f..72b25a697 100644
--- a/src/ydata_profiling/model/pandas/describe_date_pandas.py
+++ b/src/ydata_profiling/model/pandas/describe_date_pandas.py
@@ -11,6 +11,13 @@
     series_handle_nulls,
     series_hashable,
 )
+from ydata_profiling.model.typeset_relations import is_pandas_1
+
+
+def to_datetime(series: pd.Series) -> pd.Series:
+    if is_pandas_1():
+        return pd.to_datetime(series, errors="coerce")
+    return pd.to_datetime(series, format="mixed", errors="coerce")
 
 
 @describe_date_1d.register
@@ -29,6 +36,12 @@ def pandas_describe_date_1d(
     Returns:
         A dict containing calculated series description values.
     """
+    og_series = series.dropna()
+    series = to_datetime(og_series)
+    invalid_values = og_series[series.isna()]
+
+    series = series.dropna()
+
     if summary["value_counts_without_nan"].empty:
         values = series.values
         summary.update(
@@ -53,5 +66,12 @@ def pandas_describe_date_1d(
     if config.vars.num.chi_squared_threshold > 0.0:
         summary["chi_squared"] = chi_square(values)
 
-    summary.update(histogram_compute(config, values, summary["n_distinct"]))
+    summary.update(histogram_compute(config, values, series.nunique()))
+    summary.update(
+        {
+            "invalid_dates": invalid_values.nunique(),
+            "n_invalid_dates": len(invalid_values),
+            "p_invalid_dates": len(invalid_values) / summary["n"],
+        }
+    )
     return config, values, summary
diff --git a/src/ydata_profiling/model/pandas/summary_pandas.py b/src/ydata_profiling/model/pandas/summary_pandas.py
index bbb401fd0..5d15b2d3c 100644
--- a/src/ydata_profiling/model/pandas/summary_pandas.py
+++ b/src/ydata_profiling/model/pandas/summary_pandas.py
@@ -44,6 +44,7 @@ def pandas_describe_1d(
         and series.name in typeset.type_schema
     ):
         vtype = typeset.type_schema[series.name]
+
     elif config.infer_dtypes:
         # Infer variable types
         vtype = typeset.infer_type(series)
diff --git a/src/ydata_profiling/report/structure/variables/render_date.py b/src/ydata_profiling/report/structure/variables/render_date.py
index c75a80a5e..1f142daae 100644
--- a/src/ydata_profiling/report/structure/variables/render_date.py
+++ b/src/ydata_profiling/report/structure/variables/render_date.py
@@ -62,6 +62,16 @@ def render_date(config: Settings, summary: Dict[str, Any]) -> Dict[str, Any]:
         [
             {"name": "Minimum", "value": fmt(summary["min"]), "alert": False},
             {"name": "Maximum", "value": fmt(summary["max"]), "alert": False},
+            {
+                "name": "Invalid dates",
+                "value": fmt(summary["n_invalid_dates"]),
+                "alert": False,
+            },
+            {
+                "name": "Invalid dates (%)",
+                "value": fmt_percent(summary["p_invalid_dates"]),
+                "alert": False,
+            },
         ],
         style=config.html.style,
     )
diff --git a/tests/unit/test_describe.py b/tests/unit/test_describe.py
index 0eb10b7b7..0918be08e 100644
--- a/tests/unit/test_describe.py
+++ b/tests/unit/test_describe.py
@@ -582,3 +582,19 @@ def test_describe_list(summarizer, typeset):
 
     with pytest.raises(NotImplementedError):
         describe(config, "", [1, 2, 3], summarizer, typeset)
+
+
+def test_decribe_series_type_schema(config, summarizer):
+    "Test describe with invalid date types."
+    typeset = ProfilingTypeSet(config, type_schema={"date": "datetime"})
+    data = {
+        "value": [1, 2, 3, 4],
+        "date": ["0001-01-01", "9999-12-31", "2022-10-03", "2022-10-04"],
+    }
+    df = pd.DataFrame(data)
+    result = describe(config, df, summarizer, typeset)
+
+    assert result.variables["date"]["type"] == "DateTime"
+    assert result.variables["date"]["n_missing"] == 0
+    assert result.variables["date"]["n_invalid_dates"] == 2
+    assert result.variables["date"]["p_invalid_dates"] == 0.5