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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 set_categories(self,
                       category_name,
                       not_category_names=[],
                       neutral_category_names=[]):
        '''
        Specify the category to score. Optionally, score against a specific set of categories.
        '''
        tdf = self.term_ranker_.get_ranks()
        d = {'cat': tdf[category_name + ' freq']}
        if not_category_names == []:
            not_category_names = [c + ' freq' for c in self.corpus_.get_categories()
                                  if c != category_name]
        else:
            not_category_names = [c + ' freq' for c in not_category_names]
        d['ncat'] = tdf[not_category_names].sum(axis=1)
        if neutral_category_names == []:
            # neutral_category_names = [c + ' freq' for c in self.corpus.get_categories()
            #                          if c != category_name and c not in not_category_names]
            pass
        else:
            neutral_category_names = [c + ' freq' for c in neutral_category_names]
        for i, c in enumerate(neutral_category_names):
            d['neut%s' % (i)] = tdf[c]
        self.tdf_ = pd.DataFrame(d)
        self.category_name = category_name
        self.not_category_names = [c[:-5] for c in not_category_names]
        self.neutral_category_names = [c[:-5] for c in neutral_category_names]
        self.category_name_is_set_ = True
        return self