# Buggy code
def _calc_properties(cls, x1, x2=None, axis="columns"):
    if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and (
        x2 is None or pd.api.types.is_scalar(x2) or isinstance(x2, TENSOR_TYPE)
    ):
        if x2 is None:
            dtypes = x1.dtypes
        elif pd.api.types.is_scalar(x2):
            dtypes = infer_dtypes(
                x1.dtypes, pd.Series(np.array(x2).dtype), cls._operator
            )
        elif x1.dtypes is not None and isinstance(x2, TENSOR_TYPE):
            dtypes = pd.Series(
                [infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
                index=x1.dtypes.index,
            )
        else:
            dtypes = x1.dtypes
        return {
            "shape": x1.shape,
            "dtypes": dtypes,
            "columns_value": x1.columns_value,
            "index_value": x1.index_value,
        }

    if isinstance(x1, (SERIES_TYPE, SERIES_CHUNK_TYPE)) and (
        x2 is None or pd.api.types.is_scalar(x2) or isinstance(x2, TENSOR_TYPE)
    ):
        x2_dtype = x2.dtype if hasattr(x2, "dtype") else type(x2)
        dtype = infer_dtype(x1.dtype, np.dtype(x2_dtype), cls._operator)
        return {"shape": x1.shape, "dtype": dtype, "index_value": x1.index_value}

    if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and isinstance(
        x2, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)
    ):
        index_shape, column_shape, dtypes, columns, index = (
            np.nan,
            np.nan,
            None,
            None,
            None,
        )

        if (
            x1.columns_value is not None
            and x2.columns_value is not None
            and x1.columns_value.key == x2.columns_value.key
        ):
            dtypes = pd.Series(
                [
                    infer_dtype(dt1, dt2, cls._operator)
                    for dt1, dt2 in zip(x1.dtypes, x2.dtypes)
                ],
                index=x1.dtypes.index,
            )
            columns = copy.copy(x1.columns_value)
            columns.value.should_be_monotonic = False
            column_shape = len(dtypes)
        elif x1.dtypes is not None and x2.dtypes is not None:
            dtypes = infer_dtypes(x1.dtypes, x2.dtypes, cls._operator)
            columns = parse_index(dtypes.index, store_data=True)
            columns.value.should_be_monotonic = True
            column_shape = len(dtypes)
        if x1.index_value is not None and x2.index_value is not None:
            if x1.index_value.key == x2.index_value.key:
                index = copy.copy(x1.index_value)
                index.value.should_be_monotonic = False
                index_shape = x1.shape[0]
            else:
                index = infer_index_value(x1.index_value, x2.index_value)
                index.value.should_be_monotonic = True
                if index.key == x1.index_value.key == x2.index_value.key and (
                    not np.isnan(x1.shape[0]) or not np.isnan(x2.shape[0])
                ):
                    index_shape = (
                        x1.shape[0] if not np.isnan(x1.shape[0]) else x2.shape[0]
                    )

        return {
            "shape": (index_shape, column_shape),
            "dtypes": dtypes,
            "columns_value": columns,
            "index_value": index,
        }

    if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and isinstance(
        x2, (SERIES_TYPE, SERIES_CHUNK_TYPE)
    ):
        if axis == "columns" or axis == 1:
            index_shape = x1.shape[0]
            index = x1.index_value
            column_shape, dtypes, columns = np.nan, None, None
            if x1.columns_value is not None and x1.index_value is not None:
                if x1.columns_value.key == x2.index_value.key:
                    dtypes = pd.Series(
                        [infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
                        index=x1.dtypes.index,
                    )
                    columns = copy.copy(x1.columns_value)
                    columns.value.should_be_monotonic = False
                    column_shape = len(dtypes)
                else:  # pragma: no cover
                    dtypes = x1.dtypes  # FIXME
                    columns = infer_index_value(x1.columns_value, x2.index_value)
                    columns.value.should_be_monotonic = True
                    column_shape = np.nan
        else:
            assert axis == "index" or axis == 0
            column_shape = x1.shape[1]
            columns = x1.columns_value
            dtypes = x1.dtypes
            index_shape, index = np.nan, None
            if x1.index_value is not None and x1.index_value is not None:
                if x1.index_value.key == x2.index_value.key:
                    dtypes = pd.Series(
                        [infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
                        index=x1.dtypes.index,
                    )
                    index = copy.copy(x1.index_value)
                    index.value.should_be_monotonic = False
                    index_shape = x1.shape[0]
                else:
                    if x1.dtypes is not None:
                        dtypes = pd.Series(
                            [
                                infer_dtype(dt, x2.dtype, cls._operator)
                                for dt in x1.dtypes
                            ],
                            index=x1.dtypes.index,
                        )
                    index = infer_index_value(x1.index_value, x2.index_value)
                    index.value.should_be_monotonic = True
                    index_shape = np.nan
        return {
            "shape": (index_shape, column_shape),
            "dtypes": dtypes,
            "columns_value": columns,
            "index_value": index,
        }

    if isinstance(x1, (SERIES_TYPE, SERIES_CHUNK_TYPE)) and isinstance(
        x2, (SERIES_TYPE, SERIES_CHUNK_TYPE)
    ):
        index_shape, dtype, index = np.nan, None, None

        dtype = infer_dtype(x1.dtype, x2.dtype, cls._operator)
        if x1.index_value is not None and x2.index_value is not None:
            if x1.index_value.key == x2.index_value.key:
                index = copy.copy(x1.index_value)
                index.value.should_be_monotonic = False
                index_shape = x1.shape[0]
            else:
                index = infer_index_value(x1.index_value, x2.index_value)
                index.value.should_be_monotonic = True
                if index.key == x1.index_value.key == x2.index_value.key and (
                    not np.isnan(x1.shape[0]) or not np.isnan(x2.shape[0])
                ):
                    index_shape = (
                        x1.shape[0] if not np.isnan(x1.shape[0]) else x2.shape[0]
                    )

        return {"shape": (index_shape,), "dtype": dtype, "index_value": index}

    raise NotImplementedError("Unknown combination of parameters")


# Traceback (ValueError)
import numpy as np
import pandas as pd
import mars.dataframe as md
rs = np.random.RandomState(0)
raw_df = rs.rand(20, 10)
raw_df = pd.DataFrame(np.where(raw_df > 0.4, raw_df, np.nan), columns=list('ABCDEFGHIJ'))
df = md.DataFrame(raw_df, chunk_size=6)
raw_df2 = rs.rand(20, 10)
raw_df2 = pd.DataFrame(np.where(raw_df2 > 0.4, raw_df2, np.nan), columns=list('ABCDEFGHIJ'))
df2 = md.DataFrame(raw_df2, chunk_size=4)
sumv = (df ** 2).mul(df2, axis=1, fill_value=0).sum(axis=0).execute()
Traceback (most recent call last):
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3417, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-16-f7addf55e6d4>", line 1, in <module>
sumv = (df ** 2).mul(df2, axis=1, fill_value=0).sum(axis=0).execute()
File "/Users/wenjun.swj/Code/mars/mars/dataframe/arithmetic/__init__.py", line 118, in call_df_fill
return func(df, other, axis=axis, level=level, fill_value=fill_value)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/arithmetic/multiply.py", line 48, in mul
return op(df, other)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/arithmetic/core.py", line 499, in __call__
return self._call(x1, x2)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/arithmetic/core.py", line 479, in _call
kw = self._calc_properties(df1, df2, axis=self.axis)
File "/Users/wenjun.swj/Code/mars/mars/dataframe/arithmetic/core.py", line 326, in _calc_properties
dtypes = pd.Series([infer_dtype(dt1, dt2, cls._operator) for dt1, dt2
File "/Users/wenjun.swj/miniconda3/lib/python3.8/site-packages/pandas/core/series.py", line 313, in __init__
raise ValueError(
ValueError: Length of passed values is 10, index implies 11.

# Fixed code
def _calc_properties(cls, x1, x2=None, axis="columns"):
    if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and (
        x2 is None or pd.api.types.is_scalar(x2) or isinstance(x2, TENSOR_TYPE)
    ):
        if x2 is None:
            dtypes = x1.dtypes
        elif pd.api.types.is_scalar(x2):
            dtypes = cls._operator(build_empty_df(x1.dtypes), x2).dtypes
        elif x1.dtypes is not None and isinstance(x2, TENSOR_TYPE):
            dtypes = pd.Series(
                [infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
                index=x1.dtypes.index,
            )
        else:
            dtypes = x1.dtypes
        return {
            "shape": x1.shape,
            "dtypes": dtypes,
            "columns_value": x1.columns_value,
            "index_value": x1.index_value,
        }

    if isinstance(x1, (SERIES_TYPE, SERIES_CHUNK_TYPE)) and (
        x2 is None or pd.api.types.is_scalar(x2) or isinstance(x2, TENSOR_TYPE)
    ):
        x2_dtype = x2.dtype if hasattr(x2, "dtype") else type(x2)
        dtype = infer_dtype(x1.dtype, np.dtype(x2_dtype), cls._operator)
        return {"shape": x1.shape, "dtype": dtype, "index_value": x1.index_value}

    if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and isinstance(
        x2, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)
    ):
        index_shape, column_shape, dtypes, columns, index = (
            np.nan,
            np.nan,
            None,
            None,
            None,
        )

        if (
            x1.columns_value is not None
            and x2.columns_value is not None
            and x1.columns_value.key == x2.columns_value.key
        ):
            dtypes = pd.Series(
                [
                    infer_dtype(dt1, dt2, cls._operator)
                    for dt1, dt2 in zip(x1.dtypes, x2.dtypes)
                ],
                index=x1.dtypes.index,
            )
            columns = copy.copy(x1.columns_value)
            columns.value.should_be_monotonic = False
            column_shape = len(dtypes)
        elif x1.dtypes is not None and x2.dtypes is not None:
            dtypes = infer_dtypes(x1.dtypes, x2.dtypes, cls._operator)
            columns = parse_index(dtypes.index, store_data=True)
            columns.value.should_be_monotonic = True
            column_shape = len(dtypes)
        if x1.index_value is not None and x2.index_value is not None:
            if x1.index_value.key == x2.index_value.key:
                index = copy.copy(x1.index_value)
                index.value.should_be_monotonic = False
                index_shape = x1.shape[0]
            else:
                index = infer_index_value(x1.index_value, x2.index_value)
                index.value.should_be_monotonic = True
                if index.key == x1.index_value.key == x2.index_value.key and (
                    not np.isnan(x1.shape[0]) or not np.isnan(x2.shape[0])
                ):
                    index_shape = (
                        x1.shape[0] if not np.isnan(x1.shape[0]) else x2.shape[0]
                    )

        return {
            "shape": (index_shape, column_shape),
            "dtypes": dtypes,
            "columns_value": columns,
            "index_value": index,
        }

    if isinstance(x1, (DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE)) and isinstance(
        x2, (SERIES_TYPE, SERIES_CHUNK_TYPE)
    ):
        if axis == "columns" or axis == 1:
            index_shape = x1.shape[0]
            index = x1.index_value
            column_shape, dtypes, columns = np.nan, None, None
            if x1.columns_value is not None and x1.index_value is not None:
                if x1.columns_value.key == x2.index_value.key:
                    dtypes = pd.Series(
                        [infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
                        index=x1.dtypes.index,
                    )
                    columns = copy.copy(x1.columns_value)
                    columns.value.should_be_monotonic = False
                    column_shape = len(dtypes)
                else:  # pragma: no cover
                    dtypes = x1.dtypes  # FIXME
                    columns = infer_index_value(x1.columns_value, x2.index_value)
                    columns.value.should_be_monotonic = True
                    column_shape = np.nan
        else:
            assert axis == "index" or axis == 0
            column_shape = x1.shape[1]
            columns = x1.columns_value
            dtypes = x1.dtypes
            index_shape, index = np.nan, None
            if x1.index_value is not None and x1.index_value is not None:
                if x1.index_value.key == x2.index_value.key:
                    dtypes = pd.Series(
                        [infer_dtype(dt, x2.dtype, cls._operator) for dt in x1.dtypes],
                        index=x1.dtypes.index,
                    )
                    index = copy.copy(x1.index_value)
                    index.value.should_be_monotonic = False
                    index_shape = x1.shape[0]
                else:
                    if x1.dtypes is not None:
                        dtypes = pd.Series(
                            [
                                infer_dtype(dt, x2.dtype, cls._operator)
                                for dt in x1.dtypes
                            ],
                            index=x1.dtypes.index,
                        )
                    index = infer_index_value(x1.index_value, x2.index_value)
                    index.value.should_be_monotonic = True
                    index_shape = np.nan
        return {
            "shape": (index_shape, column_shape),
            "dtypes": dtypes,
            "columns_value": columns,
            "index_value": index,
        }

    if isinstance(x1, (SERIES_TYPE, SERIES_CHUNK_TYPE)) and isinstance(
        x2, (SERIES_TYPE, SERIES_CHUNK_TYPE)
    ):
        index_shape, dtype, index = np.nan, None, None

        dtype = infer_dtype(x1.dtype, x2.dtype, cls._operator)
        if x1.index_value is not None and x2.index_value is not None:
            if x1.index_value.key == x2.index_value.key:
                index = copy.copy(x1.index_value)
                index.value.should_be_monotonic = False
                index_shape = x1.shape[0]
            else:
                index = infer_index_value(x1.index_value, x2.index_value)
                index.value.should_be_monotonic = True
                if index.key == x1.index_value.key == x2.index_value.key and (
                    not np.isnan(x1.shape[0]) or not np.isnan(x2.shape[0])
                ):
                    index_shape = (
                        x1.shape[0] if not np.isnan(x1.shape[0]) else x2.shape[0]
                    )

        return {"shape": (index_shape,), "dtype": dtype, "index_value": index}

    raise NotImplementedError("Unknown combination of parameters")