docstr: In this case, args aren't used, since this information is taken
  directly from the corpus categories.
  
  Returns
  -------
  np.array, scores
docstr_tok: ["In","this","case",",","args","are","n't","used",",","since","this","information","is","taken","directly","from","the","corpus","categories",".","Returns","--","--","--","-","np.array",",","scores"]
code: def get_scores(self, *args):
      """
      In this case, args aren't used, since this information is taken
      directly from the corpus categories.
  
      Returns
      -------
      np.array, scores
      """
      if self.tdf_ is None:
          raise Exception(
              "Use set_categories('category name', ['not category name', ...]) "
              + "to set the category of interest"
          )
  
      dense_rank_difference = self._dense_rank_difference(
          self.tdf_["cat"], self.tdf_["ncat"]
      )
  
      if type(self.tdf_["cat"]) == pd.Series:
          return pd.Series(
              dense_rank_difference, index=self.tdf_["cat"].index
          )
      return dense_rank_difference
  
file_path: scattertext/scattertext/termscoring/RankDifferenceScorer.py
imports: import numpy
  import pandas
  from scattertext.termscoring.CorpusBasedTermScorer import CorpusBasedTermScorer
  from scipy.stats import rankdata
label_desc: Use image node_id 2 for calling the RankDifferenceScorer obj's underlying member method code with example usage: obj.get_scores() and returns: dense_rank_difference, pd