Backport PR #40878: REGR: ufunc with DataFrame input not passing all kwargs (#40895)

Co-authored-by: Matthew Zeitlin <37011898+mzeitlin11@users.noreply.github.com>

Author: meeseeksmachine

doc/source/whatsnew/v1.2.4.rst

@@ -21,6 +21,7 @@ Fixed regressions
 - Fixed regression in :meth:`DataFrame.where` not returning a copy in the case of an all True condition (:issue:`39595`)
 - Fixed regression in :meth:`DataFrame.replace` raising ``IndexError`` when ``regex`` was a multi-key dictionary (:issue:`39338`)
 - Fixed regression in repr of floats in an ``object`` column not respecting ``float_format`` when printed in the console or outputted through :meth:`DataFrame.to_string`, :meth:`DataFrame.to_html`, and :meth:`DataFrame.to_latex` (:issue:`40024`)
+- Fixed regression in NumPy ufuncs such as ``np.add`` not passing through all arguments for :class:`DataFrame` (:issue:`40662`)
 
 .. ---------------------------------------------------------------------------
 

pandas/core/arraylike.py

@@ -351,15 +351,17 @@ def reconstruct(result):
         # * len(inputs) > 1 is doable when we know that we have
         #   aligned blocks / dtypes.
         inputs = tuple(np.asarray(x) for x in inputs)
-        result = getattr(ufunc, method)(*inputs)
+        result = getattr(ufunc, method)(*inputs, **kwargs)
     elif self.ndim == 1:
         # ufunc(series, ...)
         inputs = tuple(extract_array(x, extract_numpy=True) for x in inputs)
         result = getattr(ufunc, method)(*inputs, **kwargs)
     else:
         # ufunc(dataframe)
-        if method == "__call__":
+        if method == "__call__" and not kwargs:
             # for np.<ufunc>(..) calls
+            # kwargs cannot necessarily be handled block-by-block, so only
+            # take this path if there are no kwargs
             mgr = inputs[0]._mgr
             result = mgr.apply(getattr(ufunc, method))
         else:

pandas/tests/frame/test_ufunc.py

@@ -1,3 +1,5 @@
+from functools import partial
+
 import numpy as np
 import pytest
 
@@ -55,6 +57,42 @@ def test_binary_input_dispatch_binop(dtype):
     tm.assert_frame_equal(result, expected)
 
 
+@pytest.mark.parametrize(
+    "func,arg,expected",
+    [
+        (np.add, 1, [2, 3, 4, 5]),
+        (
+            partial(np.add, where=[[False, True], [True, False]]),
+            np.array([[1, 1], [1, 1]]),
+            [0, 3, 4, 0],
+        ),
+        (np.power, np.array([[1, 1], [2, 2]]), [1, 2, 9, 16]),
+        (np.subtract, 2, [-1, 0, 1, 2]),
+        (
+            partial(np.negative, where=np.array([[False, True], [True, False]])),
+            None,
+            [0, -2, -3, 0],
+        ),
+    ],
+)
+def test_ufunc_passes_args(func, arg, expected, request):
+    # GH#40662
+    arr = np.array([[1, 2], [3, 4]])
+    df = pd.DataFrame(arr)
+    result_inplace = np.zeros_like(arr)
+    # 1-argument ufunc
+    if arg is None:
+        result = func(df, out=result_inplace)
+    else:
+        result = func(df, arg, out=result_inplace)
+
+    expected = np.array(expected).reshape(2, 2)
+    tm.assert_numpy_array_equal(result_inplace, expected)
+
+    expected = pd.DataFrame(expected)
+    tm.assert_frame_equal(result, expected)
+
+
 @pytest.mark.parametrize("dtype_a", dtypes)
 @pytest.mark.parametrize("dtype_b", dtypes)
 def test_binary_input_aligns_columns(dtype_a, dtype_b):