addressed changes brought up in PR, converted test cases to not use non-1D EAs

Author: eicchen

pandas/core/frame.py

@@ -8444,12 +8444,7 @@ def _maybe_align_series_as_frame(self, series: Series, axis: AxisInt):
         """
         rvalues = series._values
         if not isinstance(rvalues, np.ndarray):
-            # TODO(EA2D): no need to special-case with 2D EAs
-            if rvalues.dtype in ("datetime64[ns]", "timedelta64[ns]"):
-                # We can losslessly+cheaply cast to ndarray
-                rvalues = np.asarray(rvalues)
-            else:
-                return series
+            rvalues = np.asarray(rvalues)
 
         if axis == 0:
             rvalues = rvalues.reshape(-1, 1)

pandas/tests/frame/test_arithmetic.py

@@ -2175,61 +2175,59 @@ def test_mixed_col_index_dtype(string_dtype_no_object):
     tm.assert_frame_equal(result, expected)
 
 
-@pytest.mark.parametrize("op", ["add", "sub", "mul", "div", "mod", "truediv", "pow"])
-def test_df_fill_value_operations(op):
-    # GH 61581
-    input_data = np.arange(50).reshape(10, 5)
-    fill_val = 5
-    columns = list("ABCDE")
-    df = DataFrame(input_data, columns=columns)
-    for i in range(5):
-        df.iat[i, i] = np.nan
-        df.iat[i + 1, i] = np.nan
-        df.iat[i + 4, i] = np.nan
-
-    df_base = df.iloc[:, :-1]
-    df_mult = df.iloc[:, -1]
-    mask = df.isna().values
-    mask = mask[:, :-1] & mask[:, [-1]]
-
-    df_result = getattr(df_base, op)(df_mult, axis=0, fill_value=fill_val)
-    df_expected = getattr(df_base.fillna(fill_val), op)(
-        df_mult.fillna(fill_val), axis=0
-    ).mask(mask, np.nan)
-
-    tm.assert_frame_equal(df_result, df_expected)
-
-
 dt_params = [
-    (tm.ALL_INT_NUMPY_DTYPES, 5),
-    (tm.ALL_INT_EA_DTYPES, 5),
-    (tm.FLOAT_NUMPY_DTYPES, 4.9),
-    (tm.FLOAT_EA_DTYPES, 4.9),
+    (tm.ALL_INT_NUMPY_DTYPES[0], 5),
+    (tm.ALL_INT_EA_DTYPES[0], 5),
+    (tm.FLOAT_NUMPY_DTYPES[0], 4.9),
+    (tm.FLOAT_EA_DTYPES[0], 4.9),
 ]
 
-dt_param_flat = [(dt, val) for lst, val in dt_params for dt in lst]
+axes = [0, 1]
 
 
-@pytest.mark.parametrize("data_type, fill_val", dt_param_flat)
-def test_df_fill_value_dtype(data_type, fill_val):
+@pytest.mark.parametrize(
+    "data_type,fill_val, axis",
+    [(dt, val, axis) for axis in axes for dt, val in dt_params],
+)
+def test_df_fill_value_dtype(data_type, fill_val, axis):
     # GH 61581
-    base_data = np.arange(50).reshape(10, 5)
-    df_data = pd.array(base_data, dtype=data_type)
+    base_data = np.arange(25).reshape(5, 5)
+    mult_list = [1, np.nan, 5, np.nan, 3]
+    np_int_flag = 0
+
+    try:
+        mult_data = pd.array(mult_list, dtype=data_type)
+    except ValueError as e:
+        # Numpy int type cannot represent NaN, it will end up here
+        if "cannot convert float NaN to integer" in str(e):
+            mult_data = np.asarray(mult_list)
+            np_int_flag = 1
+
     columns = list("ABCDE")
-    df = DataFrame(df_data, columns=columns)
-    for i in range(5):
-        df.iat[i, i] = np.nan
-        df.iat[i + 1, i] = pd.NA
-        df.iat[i + 4, i] = pd.NA
-
-    df_base = df.iloc[:, :-1]
-    df_mult = df.iloc[:, -1]
-    mask = df.isna().values
-    mask = mask[:, :-1] & mask[:, [-1]]
-
-    df_result = df_base.mul(df_mult, axis=0, fill_value=fill_val)
-    df_expected = (df_base.fillna(fill_val).mul(df_mult.fillna(fill_val), axis=0)).mask(
-        mask, np.nan
-    )
+    df = DataFrame(base_data, columns=columns)
+
+    for i in range(df.shape[0]):
+        try:
+            df.iat[i, i] = np.nan
+            df.iat[i + 1, i] = pd.NA
+            df.iat[i + 3, i] = pd.NA
+        except IndexError:
+            pass
+
+    mult_mat = np.broadcast_to(mult_data, df.shape)
+    if axis == 0:
+        mask = np.isnan(mult_mat).T
+    else:
+        mask = np.isnan(mult_mat)
+    mask = df.isna().values & mask
+
+    df_result = df.mul(mult_data, axis=axis, fill_value=fill_val)
+    if np_int_flag == 1:
+        mult_np = np.nan_to_num(mult_data, nan=fill_val)
+        df_expected = (df.fillna(fill_val).mul(mult_np, axis=axis)).mask(mask, np.nan)
+    else:
+        df_expected = (
+            df.fillna(fill_val).mul(mult_data.fillna(fill_val), axis=axis)
+        ).mask(mask, np.nan)
 
     tm.assert_frame_equal(df_result, df_expected)