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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 get_correspondance_dict(self, classA, classB, restrict=None, replace_numeric=True):
 """ Returns a correspondance between classification A and B as dict Parameters classA: str Valid classification (column name of data) classB: str Valid classification (column name of data). restrict: boolean vector of size cc.data, optional where cc is the name of the CountryConverter instance. Used to restrict the data sheet if necessary. E.g. to convert to countries which were OECD members before 1970 use cc.get_correspondance_dict('ISO3', 'OECD', restrict=cc.data.OECD < 1970) replace_numeric: boolean, optional If True (default) replace numeric values with the column header. This can be used if get a correspondance to, for example, 'OECD' instead of to the OECD membership years. Set to False if the actual numbers are required (as for UNcode). Returns ------- dict with keys: based on classA items: list of correspoding entries in classB or None """

        result = {nn: None for nn in self.data[classA].values}

        if restrict is None:
            df = self.data.copy()
        else:
            df = self.data[restrict].copy()

        if replace_numeric and df[classB].dtype.kind in 'bifc':
            df.loc[~df[classB].isnull(), classB] = classB
            df.loc[df[classB].isnull(), classB] = None

        result.update(df.groupby(classA)
                        .aggregate(lambda x: list(x.unique()))
                        .to_dict()[classB])

        return result