docstr: Parameters
  ----------
  corpus
  
  Returns
  -------
  (float, pd.Series)
  float: point on x-axis at even characteristicness
  pd.Series: term -> value between 0 and 1, sorted by score in a descending manner
          Background scores from corpus
docstr_tok: ["Parameters","--","--","--","--","--","corpus","Returns","--","--","--","-","(","float",",","pd.Series",")","float",":","point","on","x-axis","at","even","characteristicness","pd.Series",":","term","-",">","value","between","0","and","1",",","sorted","by","score","in","a","descending","manner","Background","scores","from","corpus"]
code: def get_scores(self, corpus):
      """
      Parameters
      ----------
      corpus
  
      Returns
      -------
      (float, pd.Series)
      float: point on x-axis at even characteristicness
      pd.Series: term -> value between 0 and 1, sorted by score in a descending manner
              Background scores from corpus
      """
      term_ranks = self.term_ranker(corpus).get_ranks()
  
      freq_df = pd.DataFrame(
          {
              "corpus": term_ranks.sum(axis=1),
              "standard": self.background_frequencies.get_background_frequency_df()[
                  "background"
              ],
          }
      )
  
      freq_df = freq_df.loc[freq_df["corpus"].dropna().index].fillna(0)
  
      corpus_rank = rankdata(freq_df.corpus, "dense")
      standard_rank = rankdata(freq_df.standard, "dense")
      scores = (
          corpus_rank / corpus_rank.max()
          - standard_rank / standard_rank.max()
      )
  
      if self.rerank_ranks:
          rank_scores, zero_marker = self._rerank_scores(scores)
          freq_df["score"] = pd.Series(rank_scores, index=freq_df.index)
      else:
          if scores.min() < 0 and scores.max() > 0:
              zero_marker = -scores.min() / (
                  scores.max() - scores.min()
              )
          elif scores.min() > 0:
              zero_marker = 0
          else:
              zero_marker = 1
          freq_df["score"] = scale(scores)
      return (
          zero_marker,
          freq_df.sort_values(by="score", ascending=False)["score"],
      )
  
file_path: scattertext/scattertext/characteristic/DenseRankCharacteristicness.py
imports: import pandas
  from scipy.stats import rankdata
  from scattertext.Scalers import scale
  from scattertext.frequencyreaders.DefaultBackgroundFrequencies import DefaultBackgroundFrequencies
  from scattertext.termranking import AbsoluteFrequencyRanker
  from scattertext.termscoring.RankDifference import RankDifference
label_desc: Use image node_id 1 for calling the DenseRankCharacteristicness obj's underlying member method code with example usage: obj.get_scores(corpus) and returns: zero_marker,  freq_df