# Buggy code
def tile(cls, op):
    in_df = build_concated_rows_frame(op.inputs[0])
    out_df = op.outputs[0]

    # First, perform groupby and aggregation on each chunk.
    agg_chunks = []
    for chunk in in_df.chunks:
        agg_op = op.copy().reset_key()
        agg_op._stage = Stage.agg
        agg_chunk = agg_op.new_chunk(
            [chunk],
            shape=out_df.shape,
            index=chunk.index,
            index_value=out_df.index_value,
            columns_value=out_df.columns,
        )
        agg_chunks.append(agg_chunk)

    # Shuffle the aggregation chunk.
    reduce_chunks = cls._gen_shuffle_chunks(op, in_df, agg_chunks)

    # Combine groups
    combine_chunks = []
    for chunk in reduce_chunks:
        combine_op = op.copy().reset_key()
        combine_op._stage = Stage.combine
        combine_chunk = combine_op.new_chunk(
            [chunk],
            shape=out_df.shape,
            index=chunk.index,
            index_value=out_df.index_value,
            columns_value=out_df.columns,
        )
        combine_chunks.append(combine_chunk)

    new_op = op.copy()
    return new_op.new_dataframes(
        [in_df],
        shape=out_df.shape,
        index_value=out_df.index_value,
        columns_value=out_df.columns,
        chunks=combine_chunks,
        nsplits=((np.nan,) * len(combine_chunks), (out_df.shape[1],)),
    )


# Traceback (Exception)
In [28]: df = md.DataFrame(np.random.rand(10, 2))

In [29]: df + np.random.rand(10, 2)
---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
<ipython-input-29-4a212df011a8> in <module>
----> 1 df + np.random.rand(10, 2)

~/Documents/mars_dev/mars/mars/dataframe/utils.py in wrapper(*args, **kwargs)
613     def wrapper(*args, **kwargs):
614         try:
--> 615             return func(*args, **kwargs)
616         except NotImplementedError:
617             return NotImplemented

~/Documents/mars_dev/mars/mars/dataframe/arithmetic/add.py in add(df, other, axis, level, fill_value)
33
34 def add(df, other, axis='columns', level=None, fill_value=None):
---> 35     other = wrap_sequence(other)
36     op = DataFrameAdd(axis=axis, level=level, fill_value=fill_value, lhs=df, rhs=other)
37     return op(df, other)

~/Documents/mars_dev/mars/mars/dataframe/utils.py in wrap_sequence(seq)
606
607     if isinstance(seq, (list, tuple, np.ndarray, TENSOR_TYPE)):
--> 608         seq = Series(seq)
609     return seq
610

~/Documents/mars_dev/mars/mars/dataframe/initializer.py in __init__(self, data, index, dtype, name, copy, chunk_size, gpu, sparse)
46             series = data
47         else:
---> 48             pd_series = pd.Series(data, index=index, dtype=dtype, name=name, copy=copy)
49             series = from_pandas_series(pd_series, chunk_size=chunk_size, gpu=gpu, sparse=sparse)
50         super(Series, self).__init__(series.data)

~/miniconda3/lib/python3.7/site-packages/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
312                     data = data.copy()
313             else:
--> 314                 data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)
315
316                 data = SingleBlockManager(data, index, fastpath=True)

~/miniconda3/lib/python3.7/site-packages/pandas/core/internals/construction.py in sanitize_array(data, index, dtype, copy, raise_cast_failure)
727     elif subarr.ndim > 1:
728         if isinstance(data, np.ndarray):
--> 729             raise Exception("Data must be 1-dimensional")
730         else:
731             subarr = com.asarray_tuplesafe(data, dtype=dtype)

Exception: Data must be 1-dimensional

# Fixed code
def tile(cls, op):
    in_df = build_concated_rows_frame(op.inputs[0])
    out_df = op.outputs[0]

    # First, perform groupby and aggregation on each chunk.
    agg_chunks = []
    for chunk in in_df.chunks:
        agg_op = op.copy().reset_key()
        agg_op._stage = Stage.agg
        agg_chunk = agg_op.new_chunk(
            [chunk],
            shape=out_df.shape,
            index=chunk.index,
            index_value=out_df.index_value,
            columns_value=out_df.columns_value,
        )
        agg_chunks.append(agg_chunk)

    # Shuffle the aggregation chunk.
    reduce_chunks = cls._gen_shuffle_chunks(op, in_df, agg_chunks)

    # Combine groups
    combine_chunks = []
    for chunk in reduce_chunks:
        combine_op = op.copy().reset_key()
        combine_op._stage = Stage.combine
        combine_chunk = combine_op.new_chunk(
            [chunk],
            shape=out_df.shape,
            index=chunk.index,
            index_value=out_df.index_value,
            columns_value=out_df.columns_value,
        )
        combine_chunks.append(combine_chunk)

    new_op = op.copy()
    return new_op.new_dataframes(
        [in_df],
        shape=out_df.shape,
        index_value=out_df.index_value,
        columns_value=out_df.columns_value,
        chunks=combine_chunks,
        nsplits=((np.nan,) * len(combine_chunks), (out_df.shape[1],)),
    )