def dispatch_to_series(left, right, func, str_rep=None, axis=None):
    """
    Evaluate the frame operation func(left, right) by evaluating
    column-by-column, dispatching to the Series implementation.

    Parameters
    ----------
    left : DataFrame
    right : scalar or DataFrame
    func : arithmetic or comparison operator
    str_rep : str or None, default None
    axis : {None, 0, 1, "index", "columns"}

    Returns
    -------
    DataFrame
    """
    # Note: we use iloc to access columns for compat with cases
    #       with non-unique columns.
    import pandas.core.computation.expressions as expressions

    right = lib.item_from_zerodim(right)
    if lib.is_scalar(right) or np.ndim(right) == 0:

        def column_op(a, b):
            return {i: func(a.iloc[:, i], b)
                    for i in range(len(a.columns))}

    elif isinstance(right, ABCDataFrame):
        assert right._indexed_same(left)

        def column_op(a, b):
            return {i: func(a.iloc[:, i], b.iloc[:, i])
                    for i in range(len(a.columns))}

    elif isinstance(right, ABCSeries) and axis == "columns":
        # We only get here if called via left._combine_match_columns,
        # in which case we specifically want to operate row-by-row
        assert right.index.equals(left.columns)

        def column_op(a, b):
            return {i: func(a.iloc[:, i], b.iloc[i])
                    for i in range(len(a.columns))}

    elif isinstance(right, ABCSeries):
        assert right.index.equals(left.index)  # Handle other cases later

        def column_op(a, b):
            return {i: func(a.iloc[:, i], b)
                    for i in range(len(a.columns))}

    else:
        # Remaining cases have less-obvious dispatch rules
        raise NotImplementedError(right)

    new_data = expressions.evaluate(column_op, str_rep, left, right)

    result = left._constructor(new_data, index=left.index, copy=False)
    # Pin columns instead of passing to constructor for compat with
    # non-unique columns case
    result.columns = left.columns
    return result
Evaluate the frame operation func(left, right) by evaluating
    column-by-column, dispatching to the Series implementation.

    Parameters
    ----------
    left : DataFrame
    right : scalar or DataFrame
    func : arithmetic or comparison operator
    str_rep : str or None, default None
    axis : {None, 0, 1, "index", "columns"}

    Returns
    -------
    DataFrame