def __init__(self, order, name, l, normal_type=InvalidType, blanks_as_nulls=True, infer_types=True):
        """
        Construct a column from a sequence of values.

        If normal_type is not InvalidType, inference will be skipped and values assumed to have already been normalized.
        If infer_types is False, type inference will be skipped and the type assumed to be unicode.
        """
        if normal_type in InvalidType:
            t = normal_type
            data = l
        elif not infer_types:
            t = six.text_type
            data = l
        else:
            t, data = typeinference.normalize_column_type(l, blanks_as_nulls=blanks_as_nulls)

        list.__init__(self, data)
        self.order = order
        self.name = name or '_unnamed'  # empty column names don't make sense
        self.type = t
dataset/ETHPy150Open wireservice/csvkit/csvkit/table.py/Column.__init__/operator `!=` -> `in`