fix series tests

Signed-off-by: Dmitry Chigarev <[email protected]>

modin/core/dataframe/pandas/dataframe/utils.py

@@ -630,22 +630,15 @@ def split_partitions_using_pivots_for_sort(
         **kwargs: dict,
     ) -> "tuple[pandas.DataFrame, ...]":  # noqa
         def add_attr(df, timestamp):
-            if "dummy_timestamp" in df.attrs:
-                df.attrs["dummy_timestamp"] = (*df.attrs["dummy_timestamp"], timestamp)
+            if "bin_bounds" in df.attrs:
+                df.attrs["bin_bounds"] = (*df.attrs["bin_bounds"], timestamp)
             else:
-                df.attrs["dummy_timestamp"] = (timestamp,)
+                df.attrs["bin_bounds"] = (timestamp,)
             return df
 
         result = ShuffleSortFunctions.split_partitions_using_pivots_for_sort(
             df, columns_info, ascending, **kwargs
         )
-        lvl = columns_info[0].name
-        if isinstance(lvl, int):
-            to_drop = [i for i in range(result[0].index.nlevels) if i != lvl]
-        else:
-            to_drop = [i for i in result[0].index.names if i != lvl]
-        for rs in result:
-            rs.index = rs.index.droplevel(to_drop)
         for i, pivot in enumerate(columns_info[0].pivots):
             add_attr(result[i], pivot - pandas.Timedelta(1, unit="ns"))
             if i + 1 <= len(result):

modin/core/storage_formats/pandas/query_compiler.py

@@ -1066,15 +1066,21 @@ def map_func(df, resample_kwargs=resample_kwargs):  # pragma: no cover
                 if resample_kwargs["on"] is not None:
                     df = df.set_index(resample_kwargs["on"])
                 return df
-            if "dummy_timestamp" in df.attrs:
-                timestamps = df.attrs["dummy_timestamp"]
+            if "bin_bounds" in df.attrs:
+                timestamps = df.attrs["bin_bounds"]
+                if isinstance(df.index, pandas.MultiIndex):
+                    level_to_keep = resample_kwargs["level"]
+                    if isinstance(level_to_keep, int):
+                        to_drop = [lvl for lvl in range(df.index.nlevels) if lvl != level_to_keep]
+                    else:
+                        to_drop = [lvl for lvl in df.index.names if lvl != level_to_keep]
+                    df.index = df.index.droplevel(to_drop)
+                    resample_kwargs = resample_kwargs.copy()
+                    resample_kwargs["level"] = None
                 for ts in timestamps:
                     df.loc[ts] = np.NaN
             if df_op is not None:
                 df = df_op(df)
-            if use_range_impl:
-                resample_kwargs = resample_kwargs.copy()
-                resample_kwargs["level"] = None
             resampled_val = df.resample(**resample_kwargs)
             op = getattr(pandas.core.resample.Resampler, func_name)
             if callable(op):