FIX-#1906: fixed incorrect behaviour of 'groupby.__getattr__'

modin/pandas/groupby.py

@@ -110,7 +110,7 @@ def __getattr__(self, key):
             return object.__getattribute__(self, key)
         except AttributeError as e:
             if key in self._columns:
-                return self._default_to_pandas(lambda df: df.__getitem__(key))
+                return self.__getitem__(key)
             raise e
 
     @property

modin/pandas/test/test_groupby.py

@@ -16,7 +16,7 @@
 import numpy as np
 import modin.pandas as pd
 from modin.utils import try_cast_to_pandas, get_current_backend
-from modin.pandas.utils import from_pandas
+from modin.pandas.utils import from_pandas, is_scalar
 from .utils import (
     df_equals,
     check_df_columns_have_nans,
@@ -49,6 +49,17 @@ def eval_aggregation(md_df, pd_df, operation=None, by=None, *args, **kwargs):
     )
 
 
+def build_types_asserter(comparator):
+    def wrapper(obj1, obj2, *args, **kwargs):
+        error_str = f"obj1 and obj2 has incorrect types: {type(obj1)} and {type(obj2)}"
+        assert not (is_scalar(obj1) ^ is_scalar(obj2)), error_str
+        assert obj1.__module__.split(".")[0] == "modin", error_str
+        assert obj2.__module__.split(".")[0] == "pandas", error_str
+        comparator(obj1, obj2, *args, **kwargs)
+
+    return wrapper
+
+
 @pytest.mark.parametrize("as_index", [True, False])
 def test_mixed_dtypes_groupby(as_index):
     frame_data = np.random.randint(97, 198, size=(2 ** 6, 2 ** 4))
@@ -1038,16 +1049,18 @@ def eval_quantile(modin_groupby, pandas_groupby):
 
 
 def eval___getattr__(modin_groupby, pandas_groupby, item):
-    try:
-        pandas_groupby = pandas_groupby[item]
-        pandas_result = pandas_groupby.count()
-    except Exception as e:
-        with pytest.raises(type(e)):
-            modin_groupby[item].count()
-    else:
-        modin_groupby = modin_groupby[item]
-        modin_result = modin_groupby.count()
-        df_equals(modin_result, pandas_result)
+    eval_general(
+        modin_groupby,
+        pandas_groupby,
+        lambda grp: grp[item].count(),
+        comparator=build_types_asserter(df_equals),
+    )
+    eval_general(
+        modin_groupby,
+        pandas_groupby,
+        lambda grp: getattr(grp, item).count(),
+        comparator=build_types_asserter(df_equals),
+    )
 
 
 def eval_groups(modin_groupby, pandas_groupby):