API fixes

Signed-off-by: Anatoly Myachev <anatoly.myachev@intel.com>

modin/pandas/base.py

@@ -1106,11 +1106,26 @@ def _deprecate_downcast(self, downcast, method_name: str):
         return downcast
 
     def bfill(
-        self, *, axis=None, inplace=False, limit=None, downcast=lib.no_default
+        self,
+        *,
+        axis=None,
+        inplace=False,
+        limit=None,
+        limit_area=None,
+        downcast=lib.no_default,
     ):  # noqa: PR01, RT01, D200
         """
         Synonym for `DataFrame.fillna` with ``method='bfill'``.
         """
+        if limit_area is not None:
+            return self._default_to_pandas(
+                "bfill",
+                axis=axis,
+                inplace=inplace,
+                limit=limit,
+                limit_area=limit_area,
+                downcast=downcast,
+            )
         downcast = self._deprecate_downcast(downcast, "bfill")
         with warnings.catch_warnings():
             warnings.filterwarnings(
@@ -1599,11 +1614,26 @@ def expanding(
         )
 
     def ffill(
-        self, *, axis=None, inplace=False, limit=None, downcast=lib.no_default
+        self,
+        *,
+        axis=None,
+        inplace=False,
+        limit=None,
+        limit_area=None,
+        downcast=lib.no_default,
     ):  # noqa: PR01, RT01, D200
         """
         Synonym for `DataFrame.fillna` with ``method='ffill'``.
         """
+        if limit_area is not None:
+            return self._default_to_pandas(
+                "ffill",
+                axis=axis,
+                inplace=inplace,
+                limit=limit,
+                limit_area=limit_area,
+                downcast=downcast,
+            )
         downcast = self._deprecate_downcast(downcast, "ffill")
         with warnings.catch_warnings():
             warnings.filterwarnings(
@@ -2489,8 +2519,8 @@ def resample(
         axis: Axis = lib.no_default,
         closed: Optional[str] = None,
         label: Optional[str] = None,
-        convention: str = "start",
-        kind: Optional[str] = None,
+        convention: str = lib.no_default,
+        kind: Optional[str] = lib.no_default,
         on: Level = None,
         level: Level = None,
         origin: Union[str, TimestampConvertibleTypes] = "start_day",

modin/pandas/dataframe.py

@@ -394,12 +394,14 @@ def apply(
         result_type=None,
         args=(),
         by_row="compat",
+        engine="python",
+        engine_kwargs=None,
         **kwargs,
     ):  # noqa: PR01, RT01, D200
         """
         Apply a function along an axis of the ``DataFrame``.
         """
-        if by_row != "compat":
+        if by_row != "compat" or engine != "python":
             # TODO: add test
             return self._default_to_pandas(
                 pandas.DataFrame.apply,
@@ -409,6 +411,8 @@ def apply(
                 result_type=result_type,
                 args=args,
                 by_row=by_row,
+                engine=engine,
+                engine_kwargs=engine_kwargs,
                 **kwargs,
             )
 
@@ -1446,7 +1450,7 @@ def pivot_table(
         margins=False,
         dropna=True,
         margins_name="All",
-        observed=False,
+        observed=lib.no_default,
         sort=True,
     ):  # noqa: PR01, RT01, D200
         """

modin/pandas/resample.py

@@ -71,13 +71,14 @@ def _get_groups(self):
             Groups as specified by resampling arguments.
         """
         df = self._dataframe if self.axis == 0 else self._dataframe.T
+        convention = self.resample_kwargs["convention"]
         groups = df.groupby(
             pandas.Grouper(
                 key=self.resample_kwargs["on"],
                 freq=self.resample_kwargs["rule"],
                 closed=self.resample_kwargs["closed"],
                 label=self.resample_kwargs["label"],
-                convention=self.resample_kwargs["convention"],
+                convention=convention if convention is not lib.no_default else "start",
                 level=self.resample_kwargs["level"],
                 origin=self.resample_kwargs["origin"],
                 offset=self.resample_kwargs["offset"],

modin/pandas/series_utils.py

@@ -33,7 +33,7 @@
 
 @_inherit_docstrings(pandas.core.arrays.arrow.ListAccessor)
 class ListAccessor(ClassLogger):
-    def __init__(self, data):
+    def __init__(self, data=None):
         self._series = data
         self._query_compiler = data._query_compiler
 
@@ -71,7 +71,7 @@ def _default_to_pandas(self, op, *args, **kwargs):
 
 @_inherit_docstrings(pandas.core.arrays.arrow.StructAccessor)
 class StructAccessor(ClassLogger):
-    def __init__(self, data):
+    def __init__(self, data=None):
         self._series = data
         self._query_compiler = data._query_compiler
 

modin/pandas/test/dataframe/test_default.py

@@ -789,7 +789,7 @@ def test_replace():
     df_equals(modin_df, pandas_df)
 
 
-@pytest.mark.parametrize("rule", ["5T", pandas.offsets.Hour()])
+@pytest.mark.parametrize("rule", ["5min", pandas.offsets.Hour()])
 @pytest.mark.parametrize("axis", [0])
 def test_resampler(rule, axis):
     data, index = (
@@ -808,7 +808,7 @@ def test_resampler(rule, axis):
     )
 
 
-@pytest.mark.parametrize("rule", ["5T"])
+@pytest.mark.parametrize("rule", ["5min"])
 @pytest.mark.parametrize("axis", ["index", "columns"])
 @pytest.mark.parametrize(
     "method",
@@ -833,7 +833,7 @@ def test_resampler_functions(rule, axis, method):
     )
 
 
-@pytest.mark.parametrize("rule", ["5T"])
+@pytest.mark.parametrize("rule", ["5min"])
 @pytest.mark.parametrize("axis", ["index", "columns"])
 @pytest.mark.parametrize(
     "method_arg",
@@ -861,7 +861,7 @@ def test_resampler_functions_with_arg(rule, axis, method_arg):
     )
 
 
-@pytest.mark.parametrize("rule", ["5T"])
+@pytest.mark.parametrize("rule", ["5min"])
 @pytest.mark.parametrize("closed", ["left", "right"])
 @pytest.mark.parametrize("label", ["right", "left"])
 @pytest.mark.parametrize(
@@ -955,7 +955,7 @@ def test_resample_getitem(columns):
     }
     eval_general(
         *create_test_dfs(data, index=index),
-        lambda df: df.resample("3T")[columns].mean(),
+        lambda df: df.resample("3min")[columns].mean(),
     )
 
 

modin/pandas/test/test_series.py

@@ -2936,7 +2936,7 @@ def test_replace():
 @pytest.mark.parametrize("level", [None, 1])
 @pytest.mark.exclude_in_sanity
 def test_resample(closed, label, level):
-    rule = "5T"
+    rule = "5min"
     freq = "h"
 
     index = pandas.date_range("1/1/2000", periods=12, freq=freq)