BUG: DataFrame.min with skipna=True raises TypeError when column contains np.nan and datetime.date

pandas/core/frame.py

@@ -205,6 +205,8 @@
 )
 import pandas.plotting
 
+from pandas.core.tools.datetimes import to_datetime
+
 if TYPE_CHECKING:
     import datetime
 
@@ -11632,6 +11634,27 @@ def all(
             result = result.__finalize__(self, method="all")
         return result
 
+    def convert_date_na_columns(self):
+        """
+        Convert all columns containing only dates and nulls in a DataFrame to datetime objects.
+        """
+        for col in self.columns:
+            if self.is_date_or_null_column(self[col]):
+                try:
+                    self[col] = to_datetime(self[col])
+                except (ValueError, TypeError):
+                    continue
+            else:
+                continue
+
+    def is_date_or_null_column(self, column: Series) -> bool:
+        """Check if a pandas Series contains only datetime.date objects or nulls."""
+        for value in column:
+            from datetime import date
+            if not (isna(value) or isinstance(value, date)):
+                return False
+        return True
+
     @doc(make_doc("min", ndim=2))
     def min(
         self,
@@ -11640,6 +11663,7 @@ def min(
         numeric_only: bool = False,
         **kwargs,
     ):
+        self.convert_date_na_columns()
         result = super().min(axis, skipna, numeric_only, **kwargs)
         if isinstance(result, Series):
             result = result.__finalize__(self, method="min")
@@ -11653,6 +11677,7 @@ def max(
         numeric_only: bool = False,
         **kwargs,
     ):
+        self.convert_date_na_columns()
         result = super().max(axis, skipna, numeric_only, **kwargs)
         if isinstance(result, Series):
             result = result.__finalize__(self, method="max")

pandas/tests/frame/methods/test_minmax.py

@@ -0,0 +1,64 @@
+import pytest
+import pandas as pd
+import numpy as np
+import datetime
+
+
+def test_min_with_mixed_nan_and_dates():
+    """Test min() with mix of NaN and datetime.date objects"""
+    data = {
+        "dates": [
+            np.nan,
+            np.nan,
+            datetime.date(2025, 1, 3),
+            datetime.date(2025, 1, 4),
+        ],
+    }
+    df = pd.DataFrame(data)
+    result = df.min()
+    expected = pd.Series({"dates": datetime.date(2025, 1, 3)}, dtype="datetime64[ns]")
+    pd.testing.assert_series_equal(result, expected)
+
+
+def test_min_with_all_nan():
+    """Test min() with all NaN values (should return NaN)"""
+    data = {
+        "dates": [
+            np.nan,
+            np.nan,
+            np.nan,
+        ],
+    }
+    df = pd.DataFrame(data)
+    result = df.min()
+    expected = pd.Series({"dates": np.nan}, dtype="datetime64[ns]")
+    pd.testing.assert_series_equal(result, expected)
+
+
+def test_min_with_only_dates():
+    """Test min() with only datetime.date objects (no NaN)"""
+    data = {
+        "dates": [
+            datetime.date(2025, 1, 3),
+            datetime.date(2025, 1, 4),
+            datetime.date(2025, 1, 1),
+        ],
+    }
+    df = pd.DataFrame(data)
+    result = df.min()
+    expected = pd.Series({"dates": datetime.date(2025, 1, 1)}, dtype="datetime64[ns]")
+    pd.testing.assert_series_equal(result, expected)
+
+
+def test_min_with_mixed_types_error():
+    """Test min() with incompatible types (should raise TypeError)"""
+    data = {
+        "mixed": [
+            np.nan,
+            "string",
+            datetime.date(2025, 1, 3),
+        ],
+    }
+    df = pd.DataFrame(data)
+    with pytest.raises(TypeError):
+        df.max()