sourcecode/scoring/pflip_model.py [373:390]:
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  def _get_helpfulness_ratings(self, notes: pd.DataFrame, ratings: pd.DataFrame) -> pd.DataFrame:
    """Return a DataFrame with one row per note and a column with a nested list of helpfulness ratings.

    Args:
      notes: pd.DataFrame used to determine full set of notes
      ratings: pd.DataFrame containing ratings that should contribute to model features.

    Returns:
      pd.DataFrame with two columns: noteIds and all user helpfulness ratings on the note.
    """
    raters = ratings[c.raterParticipantIdKey].astype(str)
    helpfulnessRatings = ratings[[c.noteIdKey]].copy()
    helpfulnessRatings.loc[:, _USER_HELPFULNESS_RATINGS] = (
      raters + ":" + ratings[c.helpfulnessLevelKey].astype(str)
    )
    helpfulnessRatings = (
      helpfulnessRatings[[c.noteIdKey, _USER_HELPFULNESS_RATINGS]]
      .groupby(c.noteIdKey)
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sourcecode/scoring/pflip_plus_model.py [675:692]:
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  def _get_helpfulness_ratings(self, notes: pd.DataFrame, ratings: pd.DataFrame) -> pd.DataFrame:
    """Return a DataFrame with one row per note and a column with a nested list of helpfulness ratings.

    Args:
      notes: pd.DataFrame used to determine full set of notes
      ratings: pd.DataFrame containing ratings that should contribute to model features.

    Returns:
      pd.DataFrame with two columns: noteIds and all user helpfulness ratings on the note.
    """
    raters = ratings[c.raterParticipantIdKey].astype(str)
    helpfulnessRatings = ratings[[c.noteIdKey]].copy()
    helpfulnessRatings.loc[:, _USER_HELPFULNESS_RATINGS] = (
      raters + ":" + ratings[c.helpfulnessLevelKey].astype(str)
    )
    helpfulnessRatings = (
      helpfulnessRatings[[c.noteIdKey, _USER_HELPFULNESS_RATINGS]]
      .groupby(c.noteIdKey)
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