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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def sasdata2dataframe(self, table: str, libref: str = '', dsopts: dict = None, method: str = 'MEMORY', **kwargs) -> 'pd.DataFrame': """ This method exports the SAS Data Set to a Pandas Data Frame, returning the Data Frame object. SASdata object that refers to the Sas Data Set you want to export to a Pandas Data Frame :param table: the name of the SAS Data Set you want to export to a Pandas Data Frame :param libref: the libref for the SAS Data Set. :param dsopts: a dictionary containing any of the following SAS data set options(where, drop, keep, obs, firstobs):
 - where is a string - keep are strings or list of strings. - drop are strings or list of strings. - obs is a numbers - either string or int - first obs is a numbers - either string or int - format is a string or dictionary { var: format } .. code-block:: python {'where' : 'msrp < 20000 and make = "Ford"' 'keep' : 'msrp enginesize Cylinders Horsepower Weight' 'drop' : ['msrp', 'enginesize', 'Cylinders', 'Horsepower', 'Weight'] 'obs' : 10 'firstobs' : '12' 'format' : {'money': 'dollar10', 'time': 'tod5.'} } :param method: defaults to MEMORY; the original method. CSV is the other choice which uses an intermediary csv file; faster for large data :param kwargs: dictionary :return: Pandas data frame """

        dsopts = dsopts if dsopts is not None else {}
        if self.exist(table, libref) == 0:
            print('The SAS Data Set ' + libref + '.' + table + ' does not exist')
            return None

        if self.nosub:
            print("too complicated to show the code, read the source :), sorry.")
            return None
        else:
            return self._io.sasdata2dataframe(table, libref, dsopts, method=method, **kwargs)