code: def detect_outliers(in_arr, thresh=3.0):
      
  
      mean = np.nanmean(in_arr)
      std = np.nanstd(in_arr)
  
      # calculate cutoffs 
      uppthresh = mean + thresh * std
      lowthresh = mean - thresh * std
  
      return np.logical_or(in_arr < lowthresh, in_arr > uppthresh)
docs: Detects outliers more than X standard deviations from mean. 
   
      Parameters 
      ----------   
      in_list: ndarray
          An array of measures for which outliers need to be detected. 
   
      thresh: float (optional)
          Threshold number of standard deviations before a measure is considered
          an outlier. Default = 3.0
   
      Returns 
      ------- 
      outlier_idx: ndarray of boolean  
          An array indicating if a measure is an outlier (True) or not (False).