docstr: :param corpus: TermDocMatrix, should just have unigrams
  :param document_scores: np.array, continuous value for each document score
  :return: pd.DataFrame
docstr_tok: [":","param","corpus",":","TermDocMatrix",",","should","just","have","unigrams",":","param","document_scores",":","np.array",",","continuous","value","for","each","document","score",":","return",":","pd.DataFrame"]
code: def get_correlation_df(
      self, corpus: TermDocMatrix, document_scores: np.array
  ) -> pd.DataFrame:
      """
  
      :param corpus: TermDocMatrix, should just have unigrams
      :param document_scores: np.array, continuous value for each document score
      :return: pd.DataFrame
      """
      assert document_scores.shape == (corpus.get_num_docs(),)
      tdm = self._get_tdm(corpus)
  
      return (
          pd.DataFrame(
              [
                  self.__get_correlation_funct()(
                      tdm.T[i].todense().A1, document_scores
                  )
                  for i in range(tdm.shape[1])
              ],
              columns=self.cols_,
          )
          .assign(
              Term=self._get_terms(corpus),
              Frequency=(tdm > 0).sum(axis=0).A1,
          )
          .set_index("Term")
          .reindex(self._get_terms(corpus))
      )
  
file_path: scattertext/scattertext/continuous/correlations.py
imports: import numpy
  import pandas
  from scattertext.TermDocMatrix import TermDocMatrix
  from scipy.stats import pearsonr, spearmanr, kendalltau
  from scattertext.continuous.coefficientbase import CoefficientBase
label_desc: Use image node_id 5 for calling the Correlations obj's underlying member method code with example usage: obj.get_correlation_df(corpus, document_scores) and returns: pd