BUG: EWM silently failed float32 (pandas-dev#42650)

* BUG: EWM silently failed float32

* added tests

* resolved mypy error

* added constant data in test

* added pytest.fixture & whatsnew

* parametrized expected df; removed float16

* added test for float32

* added tests on select_dtypes

Author: debnathshoham

doc/source/whatsnew/v1.4.0.rst

@@ -270,6 +270,7 @@ Groupby/resample/rolling
 - Bug in :meth:`Series.rolling.apply`, :meth:`DataFrame.rolling.apply`, :meth:`Series.expanding.apply` and :meth:`DataFrame.expanding.apply` with ``engine="numba"`` where ``*args`` were being cached with the user passed function (:issue:`42287`)
 - 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`)
 
 Reshaping
 ^^^^^^^^^

pandas/core/frame.py

@@ -4280,6 +4280,11 @@ def check_int_infer_dtype(dtypes):
                     # error: Argument 1 to "append" of "list" has incompatible type
                     # "Type[signedinteger[Any]]"; expected "Type[signedinteger[Any]]"
                     converted_dtypes.append(np.int64)  # type: ignore[arg-type]
+                elif dtype == "float" or dtype is float:
+                    # GH#42452 : np.dtype("float") coerces to np.float64 from Numpy 1.20
+                    converted_dtypes.extend(
+                        [np.float64, np.float32]  # type: ignore[list-item]
+                    )
                 else:
                     # error: Argument 1 to "append" of "list" has incompatible type
                     # "Union[dtype[Any], ExtensionDtype]"; expected

pandas/tests/frame/methods/test_select_dtypes.py

@@ -407,3 +407,37 @@ def test_select_dtypes_numeric_nullable_string(self, nullable_string_dtype):
         df = DataFrame(arr)
         is_selected = df.select_dtypes(np.number).shape == df.shape
         assert not is_selected
+
+    @pytest.mark.parametrize(
+        "expected, float_dtypes",
+        [
+            [
+                DataFrame(
+                    {"A": range(3), "B": range(5, 8), "C": range(10, 7, -1)}
+                ).astype(dtype={"A": float, "B": np.float64, "C": np.float32}),
+                float,
+            ],
+            [
+                DataFrame(
+                    {"A": range(3), "B": range(5, 8), "C": range(10, 7, -1)}
+                ).astype(dtype={"A": float, "B": np.float64, "C": np.float32}),
+                "float",
+            ],
+            [DataFrame({"C": range(10, 7, -1)}, dtype=np.float32), np.float32],
+            [
+                DataFrame({"A": range(3), "B": range(5, 8)}).astype(
+                    dtype={"A": float, "B": np.float64}
+                ),
+                np.float64,
+            ],
+        ],
+    )
+    def test_select_dtypes_float_dtype(self, expected, float_dtypes):
+        # GH#42452
+        dtype_dict = {"A": float, "B": np.float64, "C": np.float32}
+        df = DataFrame(
+            {"A": range(3), "B": range(5, 8), "C": range(10, 7, -1)},
+        )
+        df = df.astype(dtype_dict)
+        result = df.select_dtypes(include=float_dtypes)
+        tm.assert_frame_equal(result, expected)

pandas/tests/window/test_ewm.py

@@ -181,3 +181,51 @@ def test_ewma_times_adjust_false_raises():
         Series(range(1)).ewm(
             0.1, adjust=False, times=date_range("2000", freq="D", periods=1)
         )
+
+
+@pytest.mark.parametrize(
+    "func, expected",
+    [
+        [
+            "mean",
+            DataFrame(
+                {
+                    0: range(5),
+                    1: range(4, 9),
+                    2: [7.428571, 9, 10.571429, 12.142857, 13.714286],
+                },
+                dtype=float,
+            ),
+        ],
+        [
+            "std",
+            DataFrame(
+                {
+                    0: [np.nan] * 5,
+                    1: [4.242641] * 5,
+                    2: [4.6291, 5.196152, 5.781745, 6.380775, 6.989788],
+                }
+            ),
+        ],
+        [
+            "var",
+            DataFrame(
+                {
+                    0: [np.nan] * 5,
+                    1: [18.0] * 5,
+                    2: [21.428571, 27, 33.428571, 40.714286, 48.857143],
+                }
+            ),
+        ],
+    ],
+)
+def test_float_dtype_ewma(func, expected, float_dtype):
+    # GH#42452
+
+    df = DataFrame(
+        {0: range(5), 1: range(6, 11), 2: range(10, 20, 2)}, dtype=float_dtype
+    )
+    e = df.ewm(alpha=0.5, axis=1)
+    result = getattr(e, func)()
+
+    tm.assert_frame_equal(result, expected)

pandas/tests/window/test_rolling.py

@@ -1424,3 +1424,11 @@ def test_rolling_zero_window():
     result = s.rolling(0).min()
     expected = Series([np.nan])
     tm.assert_series_equal(result, expected)
+
+
+def test_rolling_float_dtype(float_dtype):
+    # GH#42452
+    df = DataFrame({"A": range(5), "B": range(10, 15)}, 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)