[BUG]: Rolling.sum() calculated wrong values when axis is one and dtypes are mixed

doc/source/whatsnew/v1.2.0.rst

@@ -336,7 +336,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrameGroupby.tshift` failing to raise ``ValueError`` when a frequency cannot be inferred for the index of a group (:issue:`35937`)
 - Bug in :meth:`DataFrame.groupby` does not always maintain column index name for ``any``, ``all``, ``bfill``, ``ffill``, ``shift`` (:issue:`29764`)
 - Bug in :meth:`DataFrameGroupBy.apply` raising error with ``np.nan`` group(s) when ``dropna=False`` (:issue:`35889`)
--
+- Bug in :meth:`Rolling.sum()` returned wrong values when dtypes where mixed between float and integer and axis was equal to one (:issue:`20649`)
 
 Reshaping
 ^^^^^^^^^

pandas/core/window/rolling.py

@@ -25,6 +25,7 @@
 from pandas._typing import ArrayLike, Axis, FrameOrSeries, FrameOrSeriesUnion
 from pandas.compat._optional import import_optional_dependency
 from pandas.compat.numpy import function as nv
+from pandas.core.dtypes.cast import infer_dtype_from
 from pandas.util._decorators import Appender, Substitution, cache_readonly, doc
 
 from pandas.core.dtypes.common import (
@@ -490,6 +491,9 @@ def _apply_blockwise(
             return self._apply_series(homogeneous_func)
 
         obj = self._create_data(self._selected_obj)
+        if self.axis == 1:
+            obj = obj.astype(infer_dtype_from(obj.values)[0], copy=False)
+            obj._mgr = obj._mgr.consolidate()
         mgr = obj._mgr
 
         def hfunc(bvalues: ArrayLike) -> ArrayLike:

pandas/tests/window/test_rolling.py

@@ -771,3 +771,18 @@ def test_rolling_numerical_too_large_numbers():
         index=dates,
     )
     tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    ("func", "value"),
+    [("sum", 2.0), ("max", 1.0), ("min", 1.0), ("mean", 1.0), ("median", 1.0)],
+)
+def test_rolling_mixed_dtypes_axis_1(func, value):
+    # GH: 20649
+    df = pd.DataFrame(1, index=[1, 2], columns=["a", "b", "c"])
+    df["c"] = 1.0
+    result = getattr(df.rolling(window=2, min_periods=1, axis=1), func)()
+    expected = pd.DataFrame(
+        {"a": [1.0, 1.0], "b": [value, value], "c": [value, value]}, index=[1, 2]
+    )
+    tm.assert_frame_equal(result, expected)