code: def transform_standard_normal(df):
      
      import pandas as pd
      import scipy.stats as stats
      if type(df) == pd.core.frame.DataFrame:
          gc_ranks = df.rank(axis=1)
          gc_ranks = gc_ranks / (gc_ranks.shape[1] + 1)
          std_norm = stats.norm.ppf(gc_ranks)
          std_norm = pd.DataFrame(std_norm, index=gc_ranks.index, 
                                  columns=gc_ranks.columns)
      elif type(df) == pd.core.series.Series:
          gc_ranks = df.rank()
          gc_ranks = gc_ranks / (gc_ranks.shape[0] + 1)
          std_norm = stats.norm.ppf(gc_ranks)
          std_norm = pd.Series(std_norm, index=df.index)
      return std_norm
docs: Transform a series or the rows of a dataframe to the values of a standard
      normal based on rank.