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

    out_chunks = []

    if isinstance(op.mask, SERIES_TYPE):
        mask = op.inputs[1]

        nsplits, out_shape, df_chunks, mask_chunks = align_dataframe_series(
            in_df, mask, axis="index"
        )
        out_chunk_indexes = itertools.product(*(range(s) for s in out_shape))

        out_chunks = []
        for idx, df_chunk in zip(out_chunk_indexes, df_chunks):
            mask_chunk = mask_chunks[df_chunk.index[0]]
            out_chunk = (
                op.copy()
                .reset_key()
                .new_chunk(
                    [df_chunk, mask_chunk],
                    shape=(np.nan, df_chunk.shape[1]),
                    index=idx,
                    index_value=df_chunk.index_value,
                    columns_value=df_chunk.columns,
                )
            )
            out_chunks.append(out_chunk)

    else:
        nsplits_acc = np.cumsum((0,) + in_df.nsplits[0])
        for idx in range(in_df.chunk_shape[0]):
            for idxj in range(in_df.chunk_shape[1]):
                in_chunk = in_df.cix[idx, idxj]
                chunk_op = op.copy().reset_key()
                chunk_op._mask = op.mask.iloc[nsplits_acc[idx] : nsplits_acc[idx + 1]]
                out_chunk = chunk_op.new_chunk(
                    [in_chunk],
                    index=in_chunk.index,
                    shape=(np.nan, in_chunk.shape[1]),
                    dtypes=in_chunk.dtypes,
                    index_value=in_df.index_value,
                    columns_value=in_chunk.columns,
                )
                out_chunks.append(out_chunk)

        nsplits = ((np.nan,) * in_df.chunk_shape[0], in_df.nsplits[1])

    new_op = op.copy()
    return new_op.new_dataframes(
        op.inputs,
        shape=out_df.shape,
        dtypes=out_df.dtypes,
        index_value=out_df.index_value,
        columns_value=out_df.columns,
        chunks=out_chunks,
        nsplits=nsplits,
    )


# 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_with_mask(cls, op):
    in_df = op.inputs[0]
    out_df = op.outputs[0]

    out_chunks = []

    if isinstance(op.mask, SERIES_TYPE):
        mask = op.inputs[1]

        nsplits, out_shape, df_chunks, mask_chunks = align_dataframe_series(
            in_df, mask, axis="index"
        )
        out_chunk_indexes = itertools.product(*(range(s) for s in out_shape))

        out_chunks = []
        for idx, df_chunk in zip(out_chunk_indexes, df_chunks):
            mask_chunk = mask_chunks[df_chunk.index[0]]
            out_chunk = (
                op.copy()
                .reset_key()
                .new_chunk(
                    [df_chunk, mask_chunk],
                    shape=(np.nan, df_chunk.shape[1]),
                    index=idx,
                    index_value=df_chunk.index_value,
                    columns_value=df_chunk.columns_value,
                )
            )
            out_chunks.append(out_chunk)

    else:
        nsplits_acc = np.cumsum((0,) + in_df.nsplits[0])
        for idx in range(in_df.chunk_shape[0]):
            for idxj in range(in_df.chunk_shape[1]):
                in_chunk = in_df.cix[idx, idxj]
                chunk_op = op.copy().reset_key()
                chunk_op._mask = op.mask.iloc[nsplits_acc[idx] : nsplits_acc[idx + 1]]
                out_chunk = chunk_op.new_chunk(
                    [in_chunk],
                    index=in_chunk.index,
                    shape=(np.nan, in_chunk.shape[1]),
                    dtypes=in_chunk.dtypes,
                    index_value=in_df.index_value,
                    columns_value=in_chunk.columns_value,
                )
                out_chunks.append(out_chunk)

        nsplits = ((np.nan,) * in_df.chunk_shape[0], in_df.nsplits[1])

    new_op = op.copy()
    return new_op.new_dataframes(
        op.inputs,
        shape=out_df.shape,
        dtypes=out_df.dtypes,
        index_value=out_df.index_value,
        columns_value=out_df.columns_value,
        chunks=out_chunks,
        nsplits=nsplits,
    )