class_docstr: Wilcoxon Rank Sum (inspired by https://github.com/Zeta-and-Company/pydistinto/blob/main/scripts/measures/wilcoxon_ranksum.py)
  Pseudo counts of 0.0001 are added to each document.
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_score_df(self, *args):
      """
      In this case, args aren't used, since this information is taken
      directly from the corpus categories.
  
      Returns
      -------
      np.array, scores
      """
      X = self._get_X() > 0
      cat_X, ncat_X = self._get_cat_and_ncat(X)
      cat_X = cat_X.todense().T
      cat_X = cat_X / cat_X.sum(axis=0)
      ncat_X = ncat_X.todense().T
      ncat_X = ncat_X / ncat_X.sum(axis=0)
  
      it = range(cat_X.shape[0])
      if self.verbose:
          from tqdm.auto import tqdm
  
          it = tqdm(range(cat_X.shape[0]), total=cat_X.shape[0])
      scores = []
      for i in it:
          a = cat_X[i].A1 + [0.0001]
          b = ncat_X[i].A1 + [0.0001]
          stat = mannwhitneyu(a, b)
          scores.append([stat.statistic, stat.pvalue, a.sum(), b.sum()])
      return (
          pd.DataFrame(
              scores, columns=["Score", "PValue", "Base", "Counter"]
          )
          .assign(
              Term=self.corpus_.get_terms(),
              Z=lambda df: norm.ppf(df.PValue),
          )
          .set_index("Term")
      )
  
file_path: scattertext/scattertext/termscoring/rank_sum.py
imports: import pandas
  from scipy.stats import norm, mannwhitneyu
  from scattertext.termscoring.CorpusBasedTermScorer import CorpusBasedTermScorer
label_desc: Use image node_id 3 for calling the RankSum obj's underlying member method code with example usage: obj.get_score_df() and returns: pd