Fix case

mars/dataframe/base/bloom_filter.py

@@ -175,7 +175,7 @@ def _build_dataframe_filter(cls, in_data: pd.DataFrame, op: "DataFrameBloomFilte
     def _convert_to_hashable_dtypes(cls, dtypes: pd.Series):
         dtypes = dict(
             (name, dtype) if np.issubdtype(dtype, int) else (name, str)
-            for name, dtype in dtypes.iteritems()
+            for name, dtype in dtypes.items()
         )
         return dtypes
 

mars/dataframe/reduction/aggregation.py

@@ -139,6 +139,15 @@ def _calc_result_shape(self, df):
             self.output_types = [OutputType.scalar]
             return np.array(result_df).dtype, None, 0
 
+    def _get_reduced_dim_unit(self, in_ndim, out_ndim):
+        """
+        If rows can be reduced into multiple columns, return nan,
+        otherwise returns 1
+        """
+        if not isinstance(self.raw_func, str) and isinstance(self.raw_func, Iterable):
+            return 1
+        return 1 if in_ndim != out_ndim else np.nan
+
     def __call__(self, df, output_type=None, dtypes=None, index=None):
         self._output_types = df.op.output_types
         normalize_reduction_funcs(self, ndim=df.ndim)
@@ -154,9 +163,7 @@ def __call__(self, df, output_type=None, dtypes=None, index=None):
             else:
                 out_ndim = 0
 
-        reduced_len = (
-            1 if df.ndim != out_ndim or isinstance(self.raw_func, list) else np.nan
-        )
+        reduced_len = self._get_reduced_dim_unit(df.ndim, out_ndim)
         if self.output_types[0] == OutputType.dataframe:
             if self.axis == 0:
                 new_shape = (len(index) * reduced_len, len(dtypes))
@@ -213,7 +220,7 @@ def _safe_append(d, key, val):
     @classmethod
     def _gen_map_chunks(
         cls,
-        op,
+        op: "DataFrameAggregate",
         in_df,
         out_df,
         func_infos: List[ReductionSteps],
@@ -232,9 +239,7 @@ def _gen_map_chunks(
 
         agg_chunks = np.empty(agg_chunks_shape, dtype=object)
         dtypes_cache = dict()
-        reduced_len = (
-            1 if in_df.ndim != out_df.ndim or isinstance(op.raw_func, list) else np.nan
-        )
+        reduced_len = op._get_reduced_dim_unit(in_df.ndim, out_df.ndim)
         for chunk in in_df.chunks:
             input_index = chunk.index[1 - axis] if len(chunk.index) > 1 else 0
             if input_index not in input_index_to_output:
@@ -438,9 +443,7 @@ def _tile_tree(cls, op: "DataFrameAggregate"):
         chunks = cls._gen_map_chunks(
             op, in_df, out_df, axis_func_infos, input_index_to_output
         )
-        reduced_len = (
-            1 if in_df.ndim != out_df.ndim or isinstance(op.raw_func, list) else np.nan
-        )
+        reduced_len = op._get_reduced_dim_unit(in_df.ndim, out_df.ndim)
         while chunks.shape[axis] > combine_size:
             if axis == 0:
                 new_chunks_shape = (
@@ -715,6 +718,8 @@ def _do_predefined_agg(cls, op: "DataFrameAggregate", input_obj, func_name, kwds
                     raise NotImplementedError("numeric_only not implemented under cudf")
             if isinstance(input_obj, pd.Index):
                 kwds.pop("skipna", None)
+            if getattr(input_obj, "ndim", 0) > 1:
+                kwds["axis"] = op.axis
             return getattr(input_obj, func_name)(**kwds)
 
     @classmethod
@@ -865,7 +870,19 @@ def _execute_agg(cls, ctx, op: "DataFrameAggregate"):
             ser_index = None
             if agg_series_ndim < out.ndim:
                 ser_index = [func_name]
-            aggs.append(cls._wrap_df(op, agg_series, index=ser_index))
+            if (
+                isinstance(agg_series, np.ndarray)
+                and getattr(func_inputs[0], "ndim", 0) >= 1
+                and hasattr(func_inputs[0], "index")
+            ):
+                agg_series = cls._wrap_df(op, agg_series, index=ser_index)
+                if op.axis == 0:
+                    agg_series.columns = func_inputs[0].index
+                else:
+                    agg_series.index = func_inputs[0].index
+            else:
+                agg_series = cls._wrap_df(op, agg_series, index=ser_index)
+            aggs.append(agg_series)
 
         # concatenate to produce final result
         concat_df = xdf.concat(aggs, axis=axis)
@@ -931,7 +948,7 @@ def execute(cls, ctx, op: "DataFrameAggregate"):
                 ):
                     result = op.func[0](in_data)
                 else:
-                    result = in_data.agg(op.raw_func, axis=op.axis)
+                    result = in_data.agg(op.raw_func, axis=op.axis, **op.raw_func_kw)
                     if op.outputs[0].ndim == 1:
                         result = result.astype(op.outputs[0].dtype, copy=False)
 

mars/dataframe/reduction/mode.py

@@ -41,14 +41,20 @@ def __init__(
         self._numeric_only = numeric_only
         self._dropna = dropna
 
+    @staticmethod
+    def _explode_dict_series(s: pd.Series) -> pd.DataFrame:
+        exploded = s.apply(pd.Series)
+        # if exploded.columns.hasnans:
+        return exploded
+
     def pre(self, in_data):  # noqa: W0221  # pylint: disable=arguments-differ
         xdf = cudf if self.is_gpu() else pd
         if isinstance(in_data, xdf.Series):
             return in_data.value_counts(dropna=self._dropna)
         else:
             if self._axis == 0:
                 data = dict()
-                for d, v in in_data.iteritems():
+                for d, v in in_data.items():
                     data[d] = [v.value_counts(dropna=self._dropna).to_dict()]
                 df = xdf.DataFrame(data)
             else:
@@ -64,7 +70,7 @@ def agg(self, in_data):  # noqa: W0221  # pylint: disable=arguments-differ
         else:
             if self._axis == 0:
                 data = dict()
-                for d, v in in_data.iteritems():
+                for d, v in in_data.items():
                     data[d] = [v.apply(pd.Series).sum().to_dict()]
                 df = xdf.DataFrame(data)
             else:
@@ -85,9 +91,7 @@ def _handle_series(s):
         if isinstance(in_data, xdf.Series):
             return _handle_series(in_data)
         else:
-            in_data_iter = (
-                in_data.iteritems() if self._axis == 0 else in_data.iterrows()
-            )
+            in_data_iter = in_data.items() if self._axis == 0 else in_data.iterrows()
             s_list = []
             for d, v in in_data_iter:
                 if isinstance(v.dtype, ArrowListDtype):

mars/dataframe/reduction/nunique.py

@@ -59,7 +59,7 @@ def pre(self, in_data):  # noqa: W0221  # pylint: disable=arguments-differ
         else:
             if self._axis == 0:
                 data = dict()
-                for d, v in in_data.iteritems():
+                for d, v in in_data.items():
                     if not self._use_arrow_dtype or xdf is cudf:
                         data[d] = [v.drop_duplicates().to_list()]
                     else:
@@ -82,7 +82,7 @@ def agg(self, in_data):  # noqa: W0221  # pylint: disable=arguments-differ
         else:
             if self._axis == 0:
                 data = dict()
-                for d, v in in_data.iteritems():
+                for d, v in in_data.items():
                     if not self._use_arrow_dtype or xdf is cudf:
                         data[d] = [v.explode().drop_duplicates().to_list()]
                     else:
@@ -103,9 +103,7 @@ def post(self, in_data):  # noqa: W0221  # pylint: disable=arguments-differ
         if isinstance(in_data, xdf.Series):
             return in_data.explode().nunique(dropna=self._dropna)
         else:
-            in_data_iter = (
-                in_data.iteritems() if self._axis == 0 else in_data.iterrows()
-            )
+            in_data_iter = in_data.items() if self._axis == 0 else in_data.iterrows()
             data = dict()
             for d, v in in_data_iter:
                 if isinstance(v.dtype, ArrowListDtype):

mars/dataframe/reduction/tests/test_reduction_execution.py

@@ -36,6 +36,7 @@
 cp = lazy_import("cupy", rename="cp")
 _agg_size_as_series = pd_release_version >= (1, 3)
 _support_kw_agg = pd_release_version >= (1, 1)
+_drop_level_reduction = pd_release_version >= (2, 0)
 
 
 @pytest.fixture
@@ -119,6 +120,9 @@ def compute(data, **kwargs):
     np.testing.assert_equal(r.execute().fetch(), compute(data))
 
 
+@pytest.mark.skipif(
+    _drop_level_reduction, reason="Level reduction not supported for pandas>=2.0"
+)
 @pytest.mark.parametrize("func_name,func_opts", reduction_functions)
 def test_series_level_reduction(setup, func_name, func_opts: FunctionOptions):
     def compute(data, **kwargs):
@@ -162,6 +166,9 @@ def compute(data, **kwargs):
         )
 
 
+@pytest.mark.skipif(
+    _drop_level_reduction, reason="Level reduction not supported for pandas>=2.0"
+)
 @pytest.mark.parametrize("func_name,func_opts", reduction_functions)
 def test_dataframe_reduction(
     setup, check_ref_counts, func_name, func_opts: FunctionOptions
@@ -255,6 +262,9 @@ def compute(data, **kwargs):
     )
 
 
+@pytest.mark.skipif(
+    _drop_level_reduction, reason="Level reduction not supported for pandas>=2.0"
+)
 @pytest.mark.parametrize("func_name,func_opts", reduction_functions)
 def test_dataframe_level_reduction(
     setup, check_ref_counts, func_name, func_opts: FunctionOptions
@@ -403,6 +413,9 @@ def compute(data, **kwargs):
     assert r.execute().fetch() is True
 
 
+@pytest.mark.skipif(
+    _drop_level_reduction, reason="Level reduction not supported for pandas>=2.0"
+)
 @pytest.mark.parametrize("func_name", bool_reduction_functions)
 def test_series_bool_level_reduction(setup, check_ref_counts, func_name):
     def compute(data, **kwargs):
@@ -510,6 +523,9 @@ def compute(data, **kwargs):
     )
 
 
+@pytest.mark.skipif(
+    _drop_level_reduction, reason="Level reduction not supported for pandas>=2.0"
+)
 @pytest.mark.parametrize("func_name", bool_reduction_functions)
 def test_dataframe_bool_level_reduction(setup, check_ref_counts, func_name):
     def compute(data, **kwargs):
@@ -685,18 +701,20 @@ def test_mode(setup, check_ref_counts):
     config_kw = {
         "extra_config": {
             "check_shape": False,
+            "check_dtypes": False,
+            "check_columns_value": False,
             "check_index_value": False,
         }
     }
     data1 = pd.Series(np.random.randint(0, 5, size=(20,)))
 
     series = md.Series(data1)
-    result = series.mode().execute().fetch()
+    result = series.mode().execute(**config_kw).fetch(**config_kw)
     expected = data1.mode()
     pd.testing.assert_series_equal(result, expected)
 
     series = md.Series(data1, chunk_size=6)
-    result = series.mode().execute().fetch()
+    result = series.mode().execute(**config_kw).fetch(**config_kw)
     expected = data1.mode()
     pd.testing.assert_series_equal(result, expected)
 
@@ -705,7 +723,7 @@ def test_mode(setup, check_ref_counts):
     data2[[2, 9, 18]] = np.nan
 
     series = md.Series(data2)
-    result = series.mode().execute().fetch()
+    result = series.mode().execute(**config_kw).fetch(**config_kw)
     expected = data2.mode()
     pd.testing.assert_series_equal(result, expected)
 
@@ -720,7 +738,7 @@ def test_mode(setup, check_ref_counts):
         columns=["c" + str(i) for i in range(20)],
     )
     df = md.DataFrame(data1)
-    result = df.mode().execute().fetch()
+    result = df.mode().execute(**config_kw).fetch(**config_kw)
     expected = data1.mode()
     pd.testing.assert_frame_equal(result, expected)
 
@@ -730,7 +748,7 @@ def test_mode(setup, check_ref_counts):
     pd.testing.assert_frame_equal(result, expected)
 
     df = md.DataFrame(data1)
-    result = df.mode(axis=1).execute().fetch()
+    result = df.mode(axis=1).execute(**config_kw).fetch(**config_kw)
     expected = data1.mode(axis=1)
     pd.testing.assert_frame_equal(result, expected)
 
@@ -744,7 +762,7 @@ def test_mode(setup, check_ref_counts):
     data2.iloc[[2, 9, 18], [2, 9, 18]] = np.nan
 
     df = md.DataFrame(data2)
-    result = df.mode().execute().fetch()
+    result = df.mode().execute(**config_kw).fetch(**config_kw)
     expected = data2.mode()
     pd.testing.assert_frame_equal(result, expected)
 
@@ -1008,7 +1026,10 @@ def test_dataframe_aggregate(setup, check_ref_counts):
             mean_9=NamedAgg(9, "mean"),
         )
         result = df.agg(**agg_kw)
-        pd.testing.assert_frame_equal(result.execute().fetch(), data.agg(**agg_kw))
+        pd.testing.assert_frame_equal(
+            result.execute().fetch(extra_config={"check_shape": False}),
+            data.agg(**agg_kw),
+        )
 
 
 def test_series_aggregate(setup, check_ref_counts):

mars/deploy/oscar/session.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import asyncio
+import copy
 import itertools
 import logging
 import json
@@ -355,6 +356,7 @@ async def _run_in_background(
     async def execute(self, *tileables, **kwargs) -> ExecutionInfo:
         if self._closed:
             raise RuntimeError("Session closed already")
+        kwargs = copy.deepcopy(kwargs)
         fuse_enabled: bool = kwargs.pop("fuse_enabled", None)
         extra_config: dict = kwargs.pop("extra_config", None)
         warn_duplicated_execution: bool = kwargs.pop("warn_duplicated_execution", False)

mars/deploy/oscar/tests/session.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import asyncio
+import copy
 import inspect
 import os
 import uuid
@@ -62,6 +63,7 @@ def _process_result(self, tileable, result):
         return super()._process_result(tileable, result)
 
     async def fetch(self, *tileables, **kwargs):
+        kwargs = copy.deepcopy(kwargs)
         extra_config = kwargs.pop("extra_config", dict())
         if kwargs:
             unexpected_keys = ", ".join(list(kwargs.keys()))

mars/tests/test_session.py

@@ -301,7 +301,7 @@ def test_iter(setup):
     raw_data = pd.DataFrame(np.random.randint(1000, size=(20, 10)))
     df = md.DataFrame(raw_data, chunk_size=5)
 
-    for col, series in df.iteritems():
+    for col, series in df.items():
         pd.testing.assert_series_equal(series.execute().fetch(), raw_data[col])
 
     for i, batch in enumerate(df.iterbatch(batch_size=15)):
@@ -331,9 +331,7 @@ def test_iter(setup):
         pd.testing.assert_series_equal(batch, raw_data.iloc[i * 15 : (i + 1) * 15])
 
     i = 0
-    for result_item, expect_item in zip(
-        s.iteritems(batch_size=15), raw_data.iteritems()
-    ):
+    for result_item, expect_item in zip(s.items(batch_size=15), raw_data.items()):
         assert result_item[0] == expect_item[0]
         assert result_item[1] == expect_item[1]
         i += 1