code: def correlation_linear(
      values_1,
      values_2,
      printout = None
      ):
  
      
  
      r, p_value = scipy.stats.pearsonr(values_1, values_2)
      if printout is not True:
          return r, p_value
      else:
          text = (
              "Pearson linear correlation coefficient: {r}\n"
              "2-tailed p-value: {p_value}"
          ).format(
              r       = r,
              p_value = p_value
          )
          return text
docs: This function calculates the Pearson product-moment correlation coefficient.
      This is a measure of the linear collelation of two variables. The value can
      be between +1 and -1 inclusive, where 1 is total positive correlation, 0 is
      no correlation and -1 is total negative correlation. It is a measure of the
      linear dependence between two variables.
  
      This function also calculates the significance (2-tailed p-value) of the
      correlation coefficient given the sample size.