BUG: can't handle df.groupby('a').agg(c=('b', 'mean'), d=('b', 'mean'))

[MarcoGorelli]

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• I have confirmed this bug exists on the latest released version of Modin.

• I have confirmed this bug exists on the main branch of Modin. (In order to do this you can follow this guide.)

Reproducible Example

In [1]: import modin.pandas as mpd

In [2]: import pandas as pd

In [3]: df = pd.DataFrame({'a': [1,1,2], 'b': [4,5,6]})

In [4]: df.groupby('a').agg(c=('b', 'mean'), d=('b', 'mean'))
Out[4]: 
     c    d
a          
1  4.5  4.5
2  6.0  6.0

In [5]: mpd.DataFrame(df).groupby('a').agg(c=('b', 'mean'), d=('b', 'mean'))
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/algebra/groupby.py:783, in GroupByReduce.build_map_reduce_functions.<locals>._reduce(df, **call_kwargs)
    782 try:
--> 783     result = wrapper(df)
    784 # This will happen with Arrow buffer read-only errors. We don't want to copy
    785 # all the time, so this will try to fast-path the code first.

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/algebra/groupby.py:769, in GroupByReduce.build_map_reduce_functions.<locals>._reduce.<locals>.wrapper(df)
    768 def wrapper(df: pandas.DataFrame):
--> 769     return cls.reduce(
    770         df,
    771         axis=axis,
    772         groupby_kwargs=groupby_kwargs,
    773         reduce_func=reduce_func,
    774         agg_args=agg_args,
    775         agg_kwargs=agg_kwargs,
    776         drop=drop,
    777         method=method,
    778         finalizer_fn=finalizer_fn,
    779         **call_kwargs,
    780     )

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/algebra/groupby.py:274, in GroupByReduce.reduce(cls, df, reduce_func, axis, groupby_kwargs, agg_args, agg_kwargs, partition_idx, drop, method, finalizer_fn)
    273 apply_func = cls.get_callable(reduce_func, df)
--> 274 result = apply_func(
    275     df.groupby(axis=axis, **groupby_kwargs), *agg_args, **agg_kwargs
    276 )
    278 if not as_index:

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/algebra/groupby.py:666, in GroupByReduce._build_callable_for_dict.<locals>.aggregate_on_dict(grp_obj, *args, **kwargs)
    665 if preserve_aggregation_order and len(custom_aggs):
--> 666     result = result.reindex(result_columns, axis=1)
    667 return result

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/pandas/core/frame.py:5378, in DataFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
   5359 @doc(
   5360     NDFrame.reindex,
   5361     klass=_shared_doc_kwargs["klass"],
   (...)
   5376     tolerance=None,
   5377 ) -> DataFrame:
-> 5378     return super().reindex(
   5379         labels=labels,
   5380         index=index,
   5381         columns=columns,
   5382         axis=axis,
   5383         method=method,
   5384         copy=copy,
   5385         level=level,
   5386         fill_value=fill_value,
   5387         limit=limit,
   5388         tolerance=tolerance,
   5389     )

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/pandas/core/generic.py:5610, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
   5609 # perform the reindex on the axes
-> 5610 return self._reindex_axes(
   5611     axes, level, limit, tolerance, method, fill_value, copy
   5612 ).__finalize__(self, method="reindex")

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/pandas/core/generic.py:5633, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
   5632 ax = self._get_axis(a)
-> 5633 new_index, indexer = ax.reindex(
   5634     labels, level=level, limit=limit, tolerance=tolerance, method=method
   5635 )
   5637 axis = self._get_axis_number(a)

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py:4426, in Index.reindex(self, target, method, level, limit, tolerance)
   4425 elif self._is_multi:
-> 4426     raise ValueError("cannot handle a non-unique multi-index!")
   4427 elif not self.is_unique:
   4428     # GH#42568

ValueError: cannot handle a non-unique multi-index!

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
Cell In[5], line 1
----> 1 mpd.DataFrame(df).groupby('a').agg(c=('b', 'mean'), d=('b', 'mean'))

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/logging/logger_decorator.py:144, in enable_logging.<locals>.decorator.<locals>.run_and_log(*args, **kwargs)
    129 """
    130 Compute function with logging if Modin logging is enabled.
    131 
   (...)
    141 Any
    142 """
    143 if LogMode.get() == "disable":
--> 144     return obj(*args, **kwargs)
    146 logger = get_logger()
    147 logger.log(log_level, start_line)

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/pandas/groupby.py:989, in DataFrameGroupBy.aggregate(self, func, engine, engine_kwargs, *args, **kwargs)
    986     if callable(agg_func):
    987         return agg_func(*args, **kwargs)
--> 989 result = self._wrap_aggregation(
    990     qc_method=type(self._query_compiler).groupby_agg,
    991     numeric_only=False,
    992     agg_func=func,
    993     agg_args=args,
    994     agg_kwargs=kwargs,
    995     how="axis_wise",
    996 )
    997 return do_relabel(result) if do_relabel else result

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/logging/logger_decorator.py:144, in enable_logging.<locals>.decorator.<locals>.run_and_log(*args, **kwargs)
    129 """
    130 Compute function with logging if Modin logging is enabled.
    131 
   (...)
    141 Any
    142 """
    143 if LogMode.get() == "disable":
--> 144     return obj(*args, **kwargs)
    146 logger = get_logger()
    147 logger.log(log_level, start_line)

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/pandas/groupby.py:1646, in DataFrameGroupBy._wrap_aggregation(self, qc_method, numeric_only, agg_args, agg_kwargs, **kwargs)
   1642 else:
   1643     groupby_qc = self._query_compiler
   1645 return type(self._df)(
-> 1646     query_compiler=qc_method(
   1647         groupby_qc,
   1648         by=self._by,
   1649         axis=self._axis,
   1650         groupby_kwargs=self._kwargs,
   1651         agg_args=agg_args,
   1652         agg_kwargs=agg_kwargs,
   1653         drop=self._drop,
   1654         **kwargs,
   1655     )
   1656 )

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/logging/logger_decorator.py:144, in enable_logging.<locals>.decorator.<locals>.run_and_log(*args, **kwargs)
    129 """
    130 Compute function with logging if Modin logging is enabled.
    131 
   (...)
    141 Any
    142 """
    143 if LogMode.get() == "disable":
--> 144     return obj(*args, **kwargs)
    146 logger = get_logger()
    147 logger.log(log_level, start_line)

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/storage_formats/pandas/query_compiler_caster.py:157, in apply_argument_cast.<locals>.cast_args(*args, **kwargs)
    155     kwargs = cast_nested_args_to_current_qc_type(kwargs, current_qc)
    156     args = cast_nested_args_to_current_qc_type(args, current_qc)
--> 157 return obj(*args, **kwargs)

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/storage_formats/pandas/query_compiler.py:4191, in PandasQueryCompiler.groupby_agg(self, by, agg_func, axis, groupby_kwargs, agg_args, agg_kwargs, how, drop, series_groupby)
   4188             ErrorMessage.warn(message)
   4190 if isinstance(agg_func, dict) and GroupbyReduceImpl.has_impl_for(agg_func):
-> 4191     return self._groupby_dict_reduce(
   4192         by, agg_func, axis, groupby_kwargs, agg_args, agg_kwargs, drop
   4193     )
   4195 is_transform_method = how == "transform" or (
   4196     isinstance(agg_func, str) and agg_func in transformation_kernels
   4197 )
   4199 original_agg_func = agg_func

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/logging/logger_decorator.py:144, in enable_logging.<locals>.decorator.<locals>.run_and_log(*args, **kwargs)
    129 """
    130 Compute function with logging if Modin logging is enabled.
    131 
   (...)
    141 Any
    142 """
    143 if LogMode.get() == "disable":
--> 144     return obj(*args, **kwargs)
    146 logger = get_logger()
    147 logger.log(log_level, start_line)

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/storage_formats/pandas/query_compiler_caster.py:157, in apply_argument_cast.<locals>.cast_args(*args, **kwargs)
    155     kwargs = cast_nested_args_to_current_qc_type(kwargs, current_qc)
    156     args = cast_nested_args_to_current_qc_type(args, current_qc)
--> 157 return obj(*args, **kwargs)

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/storage_formats/pandas/query_compiler.py:3868, in PandasQueryCompiler._groupby_dict_reduce(self, by, agg_func, axis, groupby_kwargs, agg_args, agg_kwargs, drop, **kwargs)
   3866         reduce_dict[reduced_col_name] = reduce_fn
   3867     map_dict[col] = map_fns
-> 3868 return GroupByReduce.register(map_dict, reduce_dict, **kwargs)(
   3869     query_compiler=self,
   3870     by=by,
   3871     axis=axis,
   3872     groupby_kwargs=groupby_kwargs,
   3873     agg_args=agg_args,
   3874     agg_kwargs=agg_kwargs,
   3875     drop=drop,
   3876 )

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/algebra/groupby.py:100, in GroupByReduce.register.<locals>.<lambda>(*args, **kwargs)
     92     reduce_func = build_fn(reduce_func)
     94 assert not (
     95     isinstance(map_func, dict) ^ isinstance(reduce_func, dict)
     96 ) and not (
     97     callable(map_func) ^ callable(reduce_func)
     98 ), "Map and reduce functions must be either both dict or both callable."
--> 100 return lambda *args, **kwargs: cls.caller(
    101     *args, map_func=map_func, reduce_func=reduce_func, **kwargs, **call_kwds
    102 )

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/algebra/groupby.py:440, in GroupByReduce.caller(cls, query_compiler, by, map_func, reduce_func, axis, groupby_kwargs, agg_args, agg_kwargs, drop, method, default_to_pandas_func, finalizer_fn)
    438 else:
    439     new_index = None
--> 440 new_modin_frame = query_compiler._modin_frame.groupby_reduce(
    441     axis,
    442     broadcastable_by,
    443     map_fn,
    444     reduce_fn,
    445     apply_indices=apply_indices,
    446     new_index=new_index,
    447 )
    449 result = query_compiler.__constructor__(new_modin_frame)
    450 return result

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/logging/logger_decorator.py:144, in enable_logging.<locals>.decorator.<locals>.run_and_log(*args, **kwargs)
    129 """
    130 Compute function with logging if Modin logging is enabled.
    131 
   (...)
    141 Any
    142 """
    143 if LogMode.get() == "disable":
--> 144     return obj(*args, **kwargs)
    146 logger = get_logger()
    147 logger.log(log_level, start_line)

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/pandas/dataframe/utils.py:753, in lazy_metadata_decorator.<locals>.decorator.<locals>.run_f_on_minimally_updated_metadata(self, *args, **kwargs)
    751     elif apply_axis == "rows":
    752         obj._propagate_index_objs(axis=0)
--> 753 result = f(self, *args, **kwargs)
    754 if apply_axis is None and not transpose:
    755     result._deferred_index = self._deferred_index

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/pandas/dataframe/dataframe.py:4554, in PandasDataframe.groupby_reduce(self, axis, by, map_func, reduce_func, new_index, new_columns, apply_indices)
   4552     if by_parts.shape[axis] != self._partitions.shape[axis]:
   4553         self._filter_empties(compute_metadata=False)
-> 4554 new_partitions = self._partition_mgr_cls.groupby_reduce(
   4555     axis, self._partitions, by_parts, map_func, reduce_func, apply_indices
   4556 )
   4557 return self.__constructor__(
   4558     new_partitions,
   4559     index=new_index,
   4560     columns=new_columns,
   4561     pandas_backend=self._pandas_backend,
   4562 )

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/logging/logger_decorator.py:144, in enable_logging.<locals>.decorator.<locals>.run_and_log(*args, **kwargs)
    129 """
    130 Compute function with logging if Modin logging is enabled.
    131 
   (...)
    141 Any
    142 """
    143 if LogMode.get() == "disable":
--> 144     return obj(*args, **kwargs)
    146 logger = get_logger()
    147 logger.log(log_level, start_line)

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/pandas/partitioning/partition_manager.py:351, in PandasDataframePartitionManager.groupby_reduce(cls, axis, partitions, by, map_func, reduce_func, apply_indices)
    348 # Assuming, that the output will not be larger than the input,
    349 # keep the current number of partitions.
    350 num_splits = min(len(partitions), NPartitions.get())
--> 351 return cls.map_axis_partitions(
    352     axis,
    353     mapped_partitions,
    354     reduce_func,
    355     enumerate_partitions=True,
    356     num_splits=num_splits,
    357 )

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/logging/logger_decorator.py:144, in enable_logging.<locals>.decorator.<locals>.run_and_log(*args, **kwargs)
    129 """
    130 Compute function with logging if Modin logging is enabled.
    131 
   (...)
    141 Any
    142 """
    143 if LogMode.get() == "disable":
--> 144     return obj(*args, **kwargs)
    146 logger = get_logger()
    147 logger.log(log_level, start_line)

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/pandas/partitioning/partition_manager.py:869, in PandasDataframePartitionManager.map_axis_partitions(cls, axis, partitions, map_func, keep_partitioning, num_splits, lengths, enumerate_partitions, **kwargs)
    817 @classmethod
    818 def map_axis_partitions(
    819     cls,
   (...)
    827     **kwargs,
    828 ):
    829     """
    830     Apply `map_func` to every partition in `partitions` along given `axis`.
    831 
   (...)
    867     some global information about the axis.
    868     """
--> 869     return cls.broadcast_axis_partitions(
    870         axis=axis,
    871         left=partitions,
    872         apply_func=map_func,
    873         keep_partitioning=keep_partitioning,
    874         num_splits=num_splits,
    875         right=None,
    876         lengths=lengths,
    877         enumerate_partitions=enumerate_partitions,
    878         **kwargs,
    879     )

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/logging/logger_decorator.py:144, in enable_logging.<locals>.decorator.<locals>.run_and_log(*args, **kwargs)
    129 """
    130 Compute function with logging if Modin logging is enabled.
    131 
   (...)
    141 Any
    142 """
    143 if LogMode.get() == "disable":
--> 144     return obj(*args, **kwargs)
    146 logger = get_logger()
    147 logger.log(log_level, start_line)

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/pandas/partitioning/partition_manager.py:74, in wait_computations_if_benchmark_mode.<locals>.wait(cls, *args, **kwargs)
     71 @wraps(func)
     72 def wait(cls, *args, **kwargs):
     73     """Wait for computation results."""
---> 74     result = func(cls, *args, **kwargs)
     75     if BenchmarkMode.get():
     76         if isinstance(result, tuple):

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/pandas/partitioning/partition_manager.py:595, in PandasDataframePartitionManager.broadcast_axis_partitions(cls, axis, apply_func, left, right, keep_partitioning, num_splits, apply_indices, broadcast_all, enumerate_partitions, lengths, apply_func_args, **kwargs)
    590 if apply_indices is None:
    591     apply_indices = np.arange(len(left_partitions))
    593 result_blocks = np.array(
    594     [
--> 595         left_partitions[i].apply(
    596             preprocessed_map_func,
    597             *(apply_func_args if apply_func_args else []),
    598             other_axis_partition=(
    599                 right_partitions if broadcast_all else right_partitions[i]
    600             ),
    601             **kw,
    602             **({"partition_idx": idx} if enumerate_partitions else {}),
    603             **kwargs,
    604         )
    605         for idx, i in enumerate(apply_indices)
    606     ]
    607 )
    608 # If we are mapping over columns, they are returned to use the same as
    609 # rows, so we need to transpose the returned 2D NumPy array to return
    610 # the structure to the correct order.
    611 return result_blocks.T if not axis else result_blocks

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/logging/logger_decorator.py:144, in enable_logging.<locals>.decorator.<locals>.run_and_log(*args, **kwargs)
    129 """
    130 Compute function with logging if Modin logging is enabled.
    131 
   (...)
    141 Any
    142 """
    143 if LogMode.get() == "disable":
--> 144     return obj(*args, **kwargs)
    146 logger = get_logger()
    147 logger.log(log_level, start_line)

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/pandas/partitioning/axis_partition.py:288, in PandasDataframeAxisPartition.apply(self, func, num_splits, other_axis_partition, maintain_partitioning, lengths, manual_partition, *args, **kwargs)
    259     other_shape = np.cumsum(
    260         [0] + [len(o.list_of_blocks) for o in other_axis_partition]
    261     )
    263     return self._wrap_partitions(
    264         self.deploy_func_between_two_axis_partitions(
    265             self.axis,
   (...)
    285         )
    286     )
    287 result = self._wrap_partitions(
--> 288     self.deploy_axis_func(
    289         self.axis,
    290         func,
    291         args,
    292         kwargs,
    293         num_splits,
    294         maintain_partitioning,
    295         *self.list_of_blocks,
    296         min_block_size=(
    297             MinRowPartitionSize.get()
    298             if self.axis == 0
    299             else MinColumnPartitionSize.get()
    300         ),
    301         lengths=lengths,
    302         manual_partition=manual_partition,
    303     )
    304 )
    305 if self.full_axis:
    306     return result

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/logging/logger_decorator.py:144, in enable_logging.<locals>.decorator.<locals>.run_and_log(*args, **kwargs)
    129 """
    130 Compute function with logging if Modin logging is enabled.
    131 
   (...)
    141 Any
    142 """
    143 if LogMode.get() == "disable":
--> 144     return obj(*args, **kwargs)
    146 logger = get_logger()
    147 logger.log(log_level, start_line)

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/pandas/partitioning/axis_partition.py:462, in PandasDataframeAxisPartition.deploy_axis_func(***failed resolving arguments***)
    460             result = func(dataframe.copy(), *f_args, **f_kwargs)
    461         else:
--> 462             raise err
    464 # to reduce peak memory consumption
    465 del dataframe

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/pandas/partitioning/axis_partition.py:457, in PandasDataframeAxisPartition.deploy_axis_func(***failed resolving arguments***)
    455 warnings.filterwarnings("ignore", category=FutureWarning)
    456 try:
--> 457     result = func(dataframe, *f_args, **f_kwargs)
    458 except ValueError as err:
    459     if "assignment destination is read-only" in str(err):

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/algebra/groupby.py:787, in GroupByReduce.build_map_reduce_functions.<locals>._reduce(df, **call_kwargs)
    784 # This will happen with Arrow buffer read-only errors. We don't want to copy
    785 # all the time, so this will try to fast-path the code first.
    786 except ValueError:
--> 787     result = wrapper(df.copy())
    788 return result

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/algebra/groupby.py:769, in GroupByReduce.build_map_reduce_functions.<locals>._reduce.<locals>.wrapper(df)
    768 def wrapper(df: pandas.DataFrame):
--> 769     return cls.reduce(
    770         df,
    771         axis=axis,
    772         groupby_kwargs=groupby_kwargs,
    773         reduce_func=reduce_func,
    774         agg_args=agg_args,
    775         agg_kwargs=agg_kwargs,
    776         drop=drop,
    777         method=method,
    778         finalizer_fn=finalizer_fn,
    779         **call_kwargs,
    780     )

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/algebra/groupby.py:274, in GroupByReduce.reduce(cls, df, reduce_func, axis, groupby_kwargs, agg_args, agg_kwargs, partition_idx, drop, method, finalizer_fn)
    271 groupby_kwargs["level"] = list(range(len(df.index.names)))
    273 apply_func = cls.get_callable(reduce_func, df)
--> 274 result = apply_func(
    275     df.groupby(axis=axis, **groupby_kwargs), *agg_args, **agg_kwargs
    276 )
    278 if not as_index:
    279     idx = df.index

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/modin/core/dataframe/algebra/groupby.py:666, in GroupByReduce._build_callable_for_dict.<locals>.aggregate_on_dict(grp_obj, *args, **kwargs)
    664 # The order is naturally preserved if there's no custom aggregations
    665 if preserve_aggregation_order and len(custom_aggs):
--> 666     result = result.reindex(result_columns, axis=1)
    667 return result

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/pandas/core/frame.py:5378, in DataFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
   5359 @doc(
   5360     NDFrame.reindex,
   5361     klass=_shared_doc_kwargs["klass"],
   (...)
   5376     tolerance=None,
   5377 ) -> DataFrame:
-> 5378     return super().reindex(
   5379         labels=labels,
   5380         index=index,
   5381         columns=columns,
   5382         axis=axis,
   5383         method=method,
   5384         copy=copy,
   5385         level=level,
   5386         fill_value=fill_value,
   5387         limit=limit,
   5388         tolerance=tolerance,
   5389     )

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/pandas/core/generic.py:5610, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
   5607     return self._reindex_multi(axes, copy, fill_value)
   5609 # perform the reindex on the axes
-> 5610 return self._reindex_axes(
   5611     axes, level, limit, tolerance, method, fill_value, copy
   5612 ).__finalize__(self, method="reindex")

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/pandas/core/generic.py:5633, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
   5630     continue
   5632 ax = self._get_axis(a)
-> 5633 new_index, indexer = ax.reindex(
   5634     labels, level=level, limit=limit, tolerance=tolerance, method=method
   5635 )
   5637 axis = self._get_axis_number(a)
   5638 obj = obj._reindex_with_indexers(
   5639     {axis: [new_index, indexer]},
   5640     fill_value=fill_value,
   5641     copy=copy,
   5642     allow_dups=False,
   5643 )

File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py:4426, in Index.reindex(self, target, method, level, limit, tolerance)
   4422     indexer = self.get_indexer(
   4423         target, method=method, limit=limit, tolerance=tolerance
   4424     )
   4425 elif self._is_multi:
-> 4426     raise ValueError("cannot handle a non-unique multi-index!")
   4427 elif not self.is_unique:
   4428     # GH#42568
   4429     raise ValueError("cannot reindex on an axis with duplicate labels")

ValueError: cannot handle a non-unique multi-index!

Issue Description

it raises

Expected Behavior

it doesn't raise

spotted this in Narwhals because we are so awesome 😎

Error Logs

Replace this line with the error backtrace (if applicable).

Installed Versions

INSTALLED VERSIONS

commit : 3e951a6
python : 3.12.6
python-bits : 64
OS : Linux
OS-release : 5.15.167.4-microsoft-standard-WSL2
Version : #1 SMP Tue Nov 5 00:21:55 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : C.UTF-8

Modin dependencies

modin : 0.32.0
ray : None
dask : 2024.12.1
distributed : None

pandas dependencies

pandas : 2.2.3
numpy : 2.2.0
pytz : 2024.2
dateutil : 2.9.0.post0
pip : None
Cython : None
sphinx : None
IPython : 8.28.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.10.0
html5lib : None
hypothesis : 6.115.5
gcsfs : None
jinja2 : 3.1.4
lxml.etree : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 18.0.0
pyreadstat : None
pytest : 8.3.4
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.14.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2024.2
qtpy : None
pyqt5 : None