diff --git a/python/cudf/cudf/core/column/column.py b/python/cudf/cudf/core/column/column.py
index 296fd6a41b0..583ad9b8f79 100644
--- a/python/cudf/cudf/core/column/column.py
+++ b/python/cudf/cudf/core/column/column.py
@@ -24,6 +24,7 @@
 import numpy as np
 import pandas as pd
 import pyarrow as pa
+import pyarrow.compute as pc
 from numba import cuda
 from typing_extensions import Self
 
@@ -2006,11 +2007,19 @@ def as_column(
         return col
 
     elif isinstance(arbitrary, (pa.Array, pa.ChunkedArray)):
-        if isinstance(arbitrary, pa.lib.HalfFloatArray):
+        if pa.types.is_float16(arbitrary.type):
             raise NotImplementedError(
                 "Type casting from `float16` to `float32` is not "
                 "yet supported in pyarrow, see: "
-                "https://issues.apache.org/jira/browse/ARROW-3802"
+                "https://github.com/apache/arrow/issues/20213"
+            )
+        elif (nan_as_null is None or nan_as_null) and pa.types.is_floating(
+            arbitrary.type
+        ):
+            arbitrary = pc.if_else(
+                pc.is_nan(arbitrary),
+                pa.nulls(len(arbitrary), type=arbitrary.type),
+                arbitrary,
             )
         col = ColumnBase.from_arrow(arbitrary)
 
diff --git a/python/cudf/cudf/tests/test_series.py b/python/cudf/cudf/tests/test_series.py
index 39da34fa89c..f05fb7c4849 100644
--- a/python/cudf/cudf/tests/test_series.py
+++ b/python/cudf/cudf/tests/test_series.py
@@ -2572,6 +2572,16 @@ def test_series_arrow_list_types_roundtrip():
             cudf.from_pandas(pdf)
 
 
+@pytest.mark.parametrize("klass", [cudf.Index, cudf.Series])
+@pytest.mark.parametrize(
+    "data", [pa.array([float("nan")]), pa.chunked_array([[float("nan")]])]
+)
+def test_nan_as_null_from_arrow_objects(klass, data):
+    result = klass(data, nan_as_null=True)
+    expected = klass(pa.array([None], type=pa.float64()))
+    assert_eq(result, expected)
+
+
 @pytest.mark.parametrize("reso", ["M", "ps"])
 @pytest.mark.parametrize("typ", ["M", "m"])
 def test_series_invalid_reso_dtype(reso, typ):