FIX-#7645: Stop raising an error for applying numpy ufuncs. (#7646)

Signed-off-by: sfc-gh-mvashishtha <mahesh.vashishtha@snowflake.com>

Author: sfc-gh-mvashishtha

modin/pandas/utils.py

@@ -101,10 +101,10 @@ def cast_function_modin2pandas(func):
     -------
     object
     """
-    if callable(func):
-        if func.__module__ == "modin.pandas.series":
+    if callable(func) and (module := getattr(func, "__module__", None)) is not None:
+        if module == "modin.pandas.series":
             func = getattr(pandas.Series, func.__name__)
-        elif func.__module__ in ("modin.pandas.dataframe", "modin.pandas.base"):
+        elif module in ("modin.pandas.dataframe", "modin.pandas.base"):
             # FIXME: when the method is defined in `modin.pandas.base` file, then the
             # type cannot be determined, in general there may be an error, but at the
             # moment it is better.

modin/tests/pandas/dataframe/test_udf.py

@@ -23,6 +23,7 @@
     _assert_casting_functions_wrap_same_implementation,
 )
 from modin.tests.pandas.utils import (
+    UNIVERSAL_UNARY_NUMPY_FUNCTIONS_FOR_FLOATS,
     agg_func_except_keys,
     agg_func_except_values,
     agg_func_keys,
@@ -318,6 +319,22 @@ def test_apply_modin_func_4635():
     )
 
 
+@pytest.mark.parametrize(
+    "apply_function",
+    (
+        lambda df, function: function(df),
+        lambda df, function: df.apply(function, axis=0),
+        lambda df, function: df.apply(function, axis=1),
+    ),
+)
+@pytest.mark.parametrize("function", UNIVERSAL_UNARY_NUMPY_FUNCTIONS_FOR_FLOATS)
+def test_apply_unary_numpy_universal_function_issue_7645(function, apply_function):
+    eval_general(
+        *create_test_dfs(test_data["float_nan_data"]),
+        lambda df: apply_function(df, function),
+    )
+
+
 def test_eval_df_use_case():
     frame_data = {"a": random_state.randn(10), "b": random_state.randn(10)}
     df = pandas.DataFrame(frame_data)

modin/tests/pandas/test_series.py

@@ -46,6 +46,7 @@
 from .utils import (
     RAND_HIGH,
     RAND_LOW,
+    UNIVERSAL_UNARY_NUMPY_FUNCTIONS_FOR_FLOATS,
     CustomIntegerForAddition,
     NonCommutativeMultiplyInteger,
     agg_func_except_keys,
@@ -5144,14 +5145,19 @@ def test_apply_return_df(data):
 
 
 @pytest.mark.parametrize(
-    "function",
-    [
-        np.abs,
-        np.sin,
-    ],
+    "apply_function",
+    (
+        lambda series, function: function(series),
+        lambda series, function: series.apply(function),
+        lambda series, function: series.map(function),
+    ),
 )
-def test_unary_numpy_universal_function_issue_6483(function):
-    eval_general(*create_test_series(test_data["float_nan_data"]), function)
+@pytest.mark.parametrize("function", UNIVERSAL_UNARY_NUMPY_FUNCTIONS_FOR_FLOATS)
+def test_unary_numpy_universal_function_issue_6483_and_7645(function, apply_function):
+    eval_general(
+        *create_test_series(test_data["float_nan_data"]),
+        lambda series: apply_function(series, function),
+    )
 
 
 def test_binary_numpy_universal_function_issue_6483():

modin/tests/pandas/utils.py

@@ -306,13 +306,65 @@
 join_type_keys = list(join_type.keys())
 join_type_values = list(join_type.values())
 
+
+UNIVERSAL_UNARY_NUMPY_FUNCTIONS_FOR_FLOATS = (
+    np.negative,
+    np.abs,
+    np.sin,
+    np.positive,
+    np.absolute,
+    np.fabs,
+    np.rint,
+    np.sign,
+    np.conj,
+    np.conjugate,
+    np.exp,
+    np.exp2,
+    np.log,
+    np.log2,
+    np.log10,
+    np.expm1,
+    np.log1p,
+    np.sqrt,
+    np.square,
+    np.cbrt,
+    np.reciprocal,
+    np.sin,
+    np.cos,
+    np.tan,
+    np.arcsin,
+    np.arccos,
+    np.arctan,
+    np.sinh,
+    np.cosh,
+    np.tanh,
+    np.arcsinh,
+    np.arccosh,
+    np.arctanh,
+    np.degrees,
+    np.radians,
+    np.deg2rad,
+    np.rad2deg,
+    np.logical_not,
+    np.isfinite,
+    np.isinf,
+    np.isnan,
+    np.fabs,
+    np.signbit,
+    np.spacing,
+    np.floor,
+    np.ceil,
+    np.trunc,
+)
+
 # Test functions for applymap
 test_func = {
     "plus one": lambda x: x + 1,
     "convert to string": str,
     "square": lambda x: x * x,
     "identity": lambda x: x,
     "return false": lambda x: False,
+    **{func.__name__: func for func in UNIVERSAL_UNARY_NUMPY_FUNCTIONS_FOR_FLOATS},
 }
 test_func_keys = list(test_func.keys())
 test_func_values = list(test_func.values())