human
What is a Python code to fix = cycle([prefix]) given the following:
I wrote the below code

    from pandas.core.reshape.concat import concat
    from itertools import cycle
    if isinstance(data, DataFrame):
        if (columns is None):
            columns_to_encode = data.select_dtypes(include=['object', 'category']).columns
        else:
            columns_to_encode = columns
        def check_len(item, name):
            length_msg = "Length    of    '{0}'    ({1})    did    not    match    the    length    of    the    columns    being    encoded    ({2})."
            if is_list_like(item):
                if (not (len(item) == len(columns_to_encode))):
                    raise ValueError(length_msg.format(name, len(item), len(columns_to_encode)))
        check_len(prefix, 'prefix')
        check_len(prefix_sep, 'prefix_sep')
        if isinstance(prefix, compat.string_types):
            prefix = cycle([prefix])
        if isinstance(prefix, dict):
            prefix = [prefix[col] for col in columns_to_encode]
        if (prefix is None):
            prefix = columns_to_encode
        if isinstance(prefix_sep, compat.string_types):
            prefix_sep = cycle([prefix_sep])
        elif isinstance(prefix_sep, dict):
            prefix_sep = [prefix_sep[col] for col in columns_to_encode]
        if (set(columns_to_encode) == set(data.columns)):
            with_dummies = []
        else:
            with_dummies = [data.drop(columns_to_encode, axis=1)]
        for (col, pre, sep) in zip(columns_to_encode, prefix, prefix_sep):
            dummy = _get_dummies_1d(data[col], prefix=pre, prefix_sep=sep, dummy_na=dummy_na, sparse=sparse, drop_first=drop_first)
            with_dummies.append(dummy)
        result = concat(with_dummies, axis=1)
    else:
        result = _get_dummies_1d(data, prefix, prefix_sep, dummy_na, sparse=sparse, drop_first=drop_first)
    return result

What's a good function header?
assistant
#Here is Python code to fix = cycle([prefix])
.

def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False):