code: def merge_chromosome_dfs(df_tuple):
      # type: (Tuple[pd.DataFrame, pd.DataFrame]) -> pd.DataFrame
      
  
      plus_df, minus_df = df_tuple
      index_cols = "Chromosome Bin".split()
      count_column = plus_df.columns[0]
  
      if plus_df.empty:
          return return_other(minus_df, count_column, index_cols)
      if minus_df.empty:
          return return_other(plus_df, count_column, index_cols)
  
      # sum duplicate bins
      # TODO: why are there duplicate bins here in the first place?
      plus_df = plus_df.groupby(index_cols).sum()
      minus_df = minus_df.groupby(index_cols).sum()
  
      # first sum the two bins from each strand
      df = pd.concat([plus_df, minus_df], axis=1).fillna(0).sum(axis=1)
      df = df.reset_index().sort_values(by="Bin")
  
      df.columns = ["Chromosome", "Bin", count_column]
  
      df = df.sort_values(["Chromosome", "Bin"])
      df[["Bin", count_column]] = df[["Bin", count_column]].astype(int32)
      df = df[[count_column, "Chromosome", "Bin"]]
      return df.reset_index(drop=True)
docs: Merges data from the two strands into strand-agnostic counts.