# Buggy code
def execute(cls, ctx, op):
    def _base_concat(chunk, inputs):
        # auto generated concat when executing a DataFrame, Series or Index
        if chunk.op.object_type == ObjectType.dataframe:
            return _auto_concat_dataframe_chunks(chunk, inputs)
        elif chunk.op.object_type == ObjectType.series:
            return _auto_concat_series_chunks(chunk, inputs)
        else:
            raise TypeError(
                "Only DataFrameChunk, SeriesChunk and IndexChunk "
                "can be automatically concatenated"
            )

    def _auto_concat_dataframe_chunks(chunk, inputs):
        if chunk.op.axis is not None:
            return pd.concat(inputs, axis=op.axis)
        # auto generated concat when executing a DataFrame
        n_rows = max(inp.index[0] for inp in chunk.inputs) + 1
        n_cols = int(len(inputs) // n_rows)
        assert n_rows * n_cols == len(inputs)

        xdf = pd if isinstance(inputs[0], pd.DataFrame) else cudf

        concats = []
        for i in range(n_rows):
            concat = xdf.concat([inputs[i * n_cols + j] for j in range(n_cols)], axis=1)
            concats.append(concat)

        if xdf is pd:
            # The `sort=False` is to suppress a `FutureWarning` of pandas, when the index or column of chunks to
            # concatenate is not aligned, which may happens for certain ops.
            #
            # See also Note [Columns of Left Join] in test_merge_execution.py.
            ret = xdf.concat(concats, sort=False)
        else:
            ret = xdf.concat(concats)
        if getattr(chunk.index_value, "should_be_monotonic", False):
            ret.sort_index(inplace=True)
        if getattr(chunk.columns, "should_be_monotonic", False):
            ret.sort_index(axis=1, inplace=True)
        return ret

    def _auto_concat_series_chunks(chunk, inputs):
        # auto generated concat when executing a Series
        if all(np.isscalar(inp) for inp in inputs):
            return pd.Series(inputs)
        else:
            xdf = pd if isinstance(inputs[0], pd.Series) else cudf
            if chunk.op.axis is not None:
                concat = xdf.concat(inputs, axis=chunk.op.axis)
            else:
                concat = xdf.concat(inputs)
            if getattr(chunk.index_value, "should_be_monotonic", False):
                concat.sort_index(inplace=True)
            return concat

    chunk = op.outputs[0]
    inputs = [ctx[input.key] for input in op.inputs]

    if isinstance(inputs[0], tuple):
        ctx[chunk.key] = tuple(
            _base_concat(chunk, [input[i] for input in inputs])
            for i in range(len(inputs[0]))
        )
    else:
        ctx[chunk.key] = _base_concat(chunk, inputs)


# 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 execute(cls, ctx, op):
    def _base_concat(chunk, inputs):
        # auto generated concat when executing a DataFrame, Series or Index
        if chunk.op.object_type == ObjectType.dataframe:
            return _auto_concat_dataframe_chunks(chunk, inputs)
        elif chunk.op.object_type == ObjectType.series:
            return _auto_concat_series_chunks(chunk, inputs)
        else:
            raise TypeError(
                "Only DataFrameChunk, SeriesChunk and IndexChunk "
                "can be automatically concatenated"
            )

    def _auto_concat_dataframe_chunks(chunk, inputs):
        if chunk.op.axis is not None:
            return pd.concat(inputs, axis=op.axis)
        # auto generated concat when executing a DataFrame
        n_rows = max(inp.index[0] for inp in chunk.inputs) + 1
        n_cols = int(len(inputs) // n_rows)
        assert n_rows * n_cols == len(inputs)

        xdf = pd if isinstance(inputs[0], pd.DataFrame) else cudf

        concats = []
        for i in range(n_rows):
            concat = xdf.concat([inputs[i * n_cols + j] for j in range(n_cols)], axis=1)
            concats.append(concat)

        if xdf is pd:
            # The `sort=False` is to suppress a `FutureWarning` of pandas, when the index or column of chunks to
            # concatenate is not aligned, which may happens for certain ops.
            #
            # See also Note [Columns of Left Join] in test_merge_execution.py.
            ret = xdf.concat(concats, sort=False)
        else:
            ret = xdf.concat(concats)
        if getattr(chunk.index_value, "should_be_monotonic", False):
            ret.sort_index(inplace=True)
        if getattr(chunk.columns_value, "should_be_monotonic", False):
            ret.sort_index(axis=1, inplace=True)
        return ret

    def _auto_concat_series_chunks(chunk, inputs):
        # auto generated concat when executing a Series
        if all(np.isscalar(inp) for inp in inputs):
            return pd.Series(inputs)
        else:
            xdf = pd if isinstance(inputs[0], pd.Series) else cudf
            if chunk.op.axis is not None:
                concat = xdf.concat(inputs, axis=chunk.op.axis)
            else:
                concat = xdf.concat(inputs)
            if getattr(chunk.index_value, "should_be_monotonic", False):
                concat.sort_index(inplace=True)
            return concat

    chunk = op.outputs[0]
    inputs = [ctx[input.key] for input in op.inputs]

    if isinstance(inputs[0], tuple):
        ctx[chunk.key] = tuple(
            _base_concat(chunk, [input[i] for input in inputs])
            for i in range(len(inputs[0]))
        )
    else:
        ctx[chunk.key] = _base_concat(chunk, inputs)