code: def _ros_group_rank(df, dl_idx, censorship):
      """
      Ranks each observation within the data groups.
  
      In this case, the groups are defined by the record's detection
      limit index and censorship status.
  
      Parameters
      ----------
      df : DataFrame
  
      dl_idx : str
          Name of the column in the dataframe the index of the
          observations' corresponding detection limit in the `cohn`
          dataframe.
  
      censorship : str
          Name of the column in the dataframe that indicates that a
          observation is left-censored. (i.e., True -> censored,
          False -> uncensored)
  
      Returns
      -------
      ranks : ndarray
          Array of ranks for the dataset.
      """
  
      # (editted for pandas 0.14 compatibility; see commit 63f162e
      #  when `pipe` and `assign` are available)
      ranks = df.copy()
      ranks.loc[:, "rank"] = 1
      ranks = ranks.groupby(by=[dl_idx, censorship])["rank"].transform(
          lambda g: g.cumsum()
      )
      return ranks
  
file_path: statsmodels/statsmodels/imputation/ros.py
imports: import warnings
  import numpy
  import pandas
  from scipy import stats
label_desc: Use image node_id 4 for calling a global function with example usage: _ros_group_rank(df, dl_idx, censorship) and returns: ranks