# Buggy code
def agg(groupby, func, method="tree", *args, **kwargs):
    """
    Aggregate using one or more operations on grouped data.
    :param groupby: Groupby data.
    :param func: Aggregation functions.
    :param method: 'shuffle' or 'tree', 'tree' method provide a better performance, 'shuffle' is recommended
    if aggregated result is very large.
    :return: Aggregated result.
    """

    # When perform a computation on the grouped data, we won't shuffle
    # the data in the stage of groupby and do shuffle after aggregation.
    if not isinstance(groupby, GROUPBY_TYPE):
        raise TypeError("Input should be type of groupby, not %s" % type(groupby))

    if method not in ["shuffle", "tree"]:
        raise ValueError(
            "Method %s is not available, please specify 'tree' or 'shuffle" % method
        )

    if not _check_if_func_available(func):
        return groupby.transform(func, *args, _call_agg=True, **kwargs)

    agg_op = DataFrameGroupByAgg(
        func=func,
        method=method,
        raw_func=func,
        groupby_params=groupby.op.groupby_params,
    )
    return agg_op(groupby)


# Traceback (AttributeError)
In [25]: merge_df = parsing_df.append(pFold_4_df)

In [26]: mDf_g = merge_df.groupby(["query","template"])

In [27]: mDf_g.execute()
Out[27]: ---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
~/anaconda3/envs/py37/lib/python3.7/site-packages/IPython/core/formatters.py in __call__(self, obj)
700                 type_pprinters=self.type_printers,
701                 deferred_pprinters=self.deferred_printers)
--> 702             printer.pretty(obj)
703             printer.flush()
704             return stream.getvalue()

~/anaconda3/envs/py37/lib/python3.7/site-packages/IPython/lib/pretty.py in pretty(self, obj)
392                         if cls is not object \
393                                 and callable(cls.__dict__.get('__repr__')):
--> 394                             return _repr_pprint(obj, self, cycle)
395
396             return _default_pprint(obj, self, cycle)

~/anaconda3/envs/py37/lib/python3.7/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
698     """A pprint that just redirects to the normal repr function."""
699     # Find newlines and replace them with p.break_()
--> 700     output = repr(obj)
701     lines = output.splitlines()
702     with p.group():

~/anaconda3/envs/py37/lib/python3.7/site-packages/mars/dataframe/core.py in __repr__(self)
998
999     def __repr__(self):
-> 1000         return self._to_str(representation=True)
1001
1002     def _repr_html_(self):

~/anaconda3/envs/py37/lib/python3.7/site-packages/mars/dataframe/core.py in _to_str(self, representation)
968         else:
969             corner_data = fetch_corner_data(
--> 970                 self, session=self._executed_sessions[-1])
971
972             buf = StringIO()

~/anaconda3/envs/py37/lib/python3.7/site-packages/mars/dataframe/utils.py in fetch_corner_data(df_or_series, session)
768         return df_or_series.fetch(session=session)
769     else:
--> 770         head = iloc(df_or_series)[:index_size]
771         tail = iloc(df_or_series)[-index_size:]
772         head_data, tail_data = \

~/anaconda3/envs/py37/lib/python3.7/site-packages/mars/dataframe/indexing/iloc.py in __getitem__(self, indexes)
111         else:
112             op = SeriesIlocGetItem(indexes=process_iloc_indexes(self._obj, indexes))
--> 113         return op(self._obj)
114
115     def __setitem__(self, indexes, value):

~/anaconda3/envs/py37/lib/python3.7/site-packages/mars/dataframe/indexing/iloc.py in __call__(self, series)
485             index_value = indexing_index_value(series.index_value, self.indexes[0])
486             inputs = [series] + [index for index in self._indexes if isinstance(index, (Base, Entity))]
--> 487             return self.new_series(inputs, shape=shape, dtype=series.dtype,
488                                    index_value=index_value, name=series.name)
489

AttributeError: 'DataFrameGroupByData' object has no attribute 'dtype'

# Fixed code
def agg(groupby, func, method="auto", *args, **kwargs):
    """
    Aggregate using one or more operations on grouped data.
    :param groupby: Groupby data.
    :param func: Aggregation functions.
    :param method: 'shuffle' or 'tree', 'tree' method provide a better performance, 'shuffle' is recommended
    if aggregated result is very large, 'auto' will use 'shuffle' method in distributed mode and use 'tree'
    in local mode.
    :return: Aggregated result.
    """

    # When perform a computation on the grouped data, we won't shuffle
    # the data in the stage of groupby and do shuffle after aggregation.
    if not isinstance(groupby, GROUPBY_TYPE):
        raise TypeError("Input should be type of groupby, not %s" % type(groupby))

    if method not in ["shuffle", "tree", "auto"]:
        raise ValueError(
            "Method %s is not available, please specify 'tree' or 'shuffle" % method
        )

    if not _check_if_func_available(func):
        return groupby.transform(func, *args, _call_agg=True, **kwargs)

    agg_op = DataFrameGroupByAgg(
        func=func,
        method=method,
        raw_func=func,
        groupby_params=groupby.op.groupby_params,
    )
    return agg_op(groupby)