diff --git a/tests/test_sql.py b/tests/test_sql.py
index b4e2bfd..66abe41 100644
--- a/tests/test_sql.py
+++ b/tests/test_sql.py
@@ -1,6 +1,7 @@
 """SQL functionality tests for xarray-sql using pytest."""
 
 import numpy as np
+import pandas as pd
 import pytest
 import xarray as xr
 
@@ -146,3 +147,44 @@ def test_string_coordinates():
   assert "student" in result.columns
   assert "subject" in result.columns
   assert "score" in result.columns
+
+
+class TestNanAsNull:
+  """NaN in float columns should become Arrow nulls so SQL aggregates work."""
+
+  @pytest.fixture
+  def nan_ds(self):
+    data = np.array([[[1.0, 2.0], [np.nan, 4.0]], [[5.0, np.nan], [7.0, 8.0]]])
+    return xr.Dataset(
+        {"temp": (["time", "x", "y"], data)},
+        coords={
+            "time": pd.date_range("2020-01-01", periods=2),
+            "x": [0, 1],
+            "y": [0, 1],
+        },
+    ).chunk({"time": 1})
+
+  def test_nan_aggregates(self, nan_ds):
+    ctx = XarrayContext()
+    ctx.from_dataset("data", nan_ds)
+
+    # Test multiple aggregates at once:
+    # MAX/MIN/AVG should ignore NaN, COUNT(col) should exclude NaN,
+    # and WHERE col IS NULL should match NaN.
+    query = """
+        SELECT
+            MAX(temp) AS mx,
+            MIN(temp) AS mn,
+            AVG(temp) AS avg,
+            COUNT(temp) AS cnt,
+            COUNT(*) FILTER (WHERE temp IS NULL) AS null_cnt
+        FROM data
+    """
+    result = ctx.sql(query).to_pandas().iloc[0]
+
+    assert result["mx"] == 8.0
+    assert result["mn"] == 1.0
+    expected_avg = np.nanmean([1.0, 2.0, 4.0, 5.0, 7.0, 8.0])
+    assert abs(result["avg"] - expected_avg) < 1e-6
+    assert result["cnt"] == 6
+    assert result["null_cnt"] == 2
diff --git a/xarray_sql/df.py b/xarray_sql/df.py
index d08207d..353a3db 100644
--- a/xarray_sql/df.py
+++ b/xarray_sql/df.py
@@ -207,7 +207,11 @@ def dataset_to_record_batch(
       arrays.append(pa.array(arr, type=field.type))
     else:
       # Data variable: ravel to 1-D (zero-copy for C-contiguous arrays).
-      arrays.append(pa.array(ds[name].values.ravel(), type=field.type))
+      # from_pandas=True maps NaN → Arrow null inside the C++ copy kernel,
+      # so SQL aggregates (MAX, MIN, AVG) skip missing values correctly.
+      arrays.append(
+          pa.array(ds[name].values.ravel(), type=field.type, from_pandas=True)
+      )
 
   return pa.RecordBatch.from_arrays(arrays, schema=schema)
 
@@ -282,7 +286,11 @@ def iter_record_batches(
         arrays.append(pa.array(coord_values[name][coord_idx], type=field.type))
       else:
         arrays.append(
-            pa.array(data_arrays[name][row_start:row_end], type=field.type)
+            pa.array(
+                data_arrays[name][row_start:row_end],
+                type=field.type,
+                from_pandas=True,
+            )
         )
 
     yield pa.RecordBatch.from_arrays(arrays, schema=schema)