diff --git a/doc/source/whatsnew/v1.4.0.rst b/doc/source/whatsnew/v1.4.0.rst
index 6763c3043b102..c49dc859d324b 100644
--- a/doc/source/whatsnew/v1.4.0.rst
+++ b/doc/source/whatsnew/v1.4.0.rst
@@ -271,6 +271,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrame.groupby.rolling.var` would calculate the rolling variance only on the first group (:issue:`42442`)
 - Bug in :meth:`GroupBy.shift` that would return the grouping columns if ``fill_value`` was not None (:issue:`41556`)
 - Bug in :meth:`pandas.DataFrame.ewm`, where non-float64 dtypes were silently failing (:issue:`42452`)
+- Bug in :meth:`pandas.DataFrame.rolling` operation along rows (``axis=1``) incorrectly omits columns containing ``float16`` and ``float32`` (:issue:`41779`)
 
 Reshaping
 ^^^^^^^^^
diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py
index 8a253726ab0b6..01e11ff4b008d 100644
--- a/pandas/core/window/rolling.py
+++ b/pandas/core/window/rolling.py
@@ -226,7 +226,7 @@ def _create_data(self, obj: FrameOrSeries) -> FrameOrSeries:
             # GH: 20649 in case of mixed dtype and axis=1 we have to convert everything
             # to float to calculate the complete row at once. We exclude all non-numeric
             # dtypes.
-            obj = obj.select_dtypes(include=["integer", "float"], exclude=["timedelta"])
+            obj = obj.select_dtypes(include=["number"], exclude=["timedelta"])
             obj = obj.astype("float64", copy=False)
             obj._mgr = obj._mgr.consolidate()
         return obj
diff --git a/pandas/tests/window/test_rolling.py b/pandas/tests/window/test_rolling.py
index f446eb494d5fa..aedaadb46abc6 100644
--- a/pandas/tests/window/test_rolling.py
+++ b/pandas/tests/window/test_rolling.py
@@ -1432,3 +1432,38 @@ def test_rolling_float_dtype(float_dtype):
     expected = DataFrame({"A": [np.nan] * 5, "B": range(10, 20, 2)}, dtype=float_dtype)
     result = df.rolling(2, axis=1).sum()
     tm.assert_frame_equal(result, expected, check_dtype=False)
+
+
+def test_rolling_numeric_dtypes():
+    # GH#41779
+    df = DataFrame(np.arange(40).reshape(4, 10), columns=list("abcdefghij")).astype(
+        {
+            "a": "float16",
+            "b": "float32",
+            "c": "float64",
+            "d": "int8",
+            "e": "int16",
+            "f": "int32",
+            "g": "uint8",
+            "h": "uint16",
+            "i": "uint32",
+            "j": "uint64",
+        }
+    )
+    result = df.rolling(window=2, min_periods=1, axis=1).min()
+    expected = DataFrame(
+        {
+            "a": range(0, 40, 10),
+            "b": range(0, 40, 10),
+            "c": range(1, 40, 10),
+            "d": range(2, 40, 10),
+            "e": range(3, 40, 10),
+            "f": range(4, 40, 10),
+            "g": range(5, 40, 10),
+            "h": range(6, 40, 10),
+            "i": range(7, 40, 10),
+            "j": range(8, 40, 10),
+        },
+        dtype="float64",
+    )
+    tm.assert_frame_equal(result, expected)