original_docstring: Extract the top k token from a dataframe both in regards to tf and tfidf score.
  
      Args:
          k: Determines how many tokens we show.
          df_papers: Dataframe with the papers to process.
          ngrams: The lower and upper bound of the n_grams, e.g. "1", "2", "1,2" or "1, 3".
  
      Returns:
          Dataframe with the top k entries by tf score.
          Dataframe with the top k entries by tf-idf score.
      
docstring: Extract the top k token from a dataframe both in regards to tf and tfidf score.
docstring_tokens: Extract the top k token from a dataframe both in regards to tf and tfidf score .
code: def top_k_tokens(k: int, df_papers: pd.DataFrame, ngrams: str) -> Tuple[pd.DataFrame, pd.DataFrame]:
      """Extract the top k token from a dataframe both in regards to tf and tfidf score.
  
      Args:
          k: Determines how many tokens we show.
          df_papers: Dataframe with the papers to process.
          ngrams: The lower and upper bound of the n_grams, e.g. "1", "2", "1,2" or "1, 3".
  
      Returns:
          Dataframe with the top k entries by tf score.
          Dataframe with the top k entries by tf-idf score.
      """
      df_freq = token_frequencies(df_papers, ngrams)
      count_top = df_freq.sort_values(by=["tf"], ascending=False).head(k)
      tfidf_top = df_freq.sort_values(by=["tfidf"], ascending=False).head(k)
      return count_top, tfidf_top
code_tokens: def top_k_tokens ( k : int , df_papers : pd . DataFrame , ngrams : str ) -> Tuple [ pd . DataFrame , pd . DataFrame ] : df_freq = token_frequencies ( df_papers , ngrams ) count_top = df_freq . sort_values ( by = [ "tf" ] , ascending = False ) . head ( k ) tfidf_top = df_freq . sort_values ( by = [ "tfidf" ] , ascending = False ) . head ( k ) return count_top , tfidf_top
short_docstring: Extract the top k token from a dataframe both in regards to tf and tfidf score.
short_docstring_tokens: Extract the top k token from a dataframe both in regards to tf and tfidf score .
comment: """Extract the top k token from a dataframe both in regards to tf and tfidf score.
  
      Args:
          k: Determines how many tokens we show.
          df_papers: Dataframe with the papers to process.
          ngrams: The lower and upper bound of the n_grams, e.g. "1", "2", "1,2" or "1, 3".
  
      Returns:
          Dataframe with the top k entries by tf score.
          Dataframe with the top k entries by tf-idf score.
      """