tt = (sm-m)/np.sqrt(sv/float(n)) # t-statistic for mean
pval = stats.t.sf(np.abs(tt), n-1)*2 # two-sided pvalue = Prob(abs(t)>tt)
print 't-statistic = %6.3f pvalue = %6.4f' % (tt, pval)
t-statistic = 0.391 pvalue = 0.6955

# coding: utf-8
from __future__ import division
import numpy as np
from scipy import stats
 
means = [0.0539, 4,8,3,6,9,1]
stds = [5,4,8,3,6,7,9]
mu = [0, 4.1, 7, 2, 5, 8, 0]
n = 20
output = []
for sm, std, m in zip(means, stds, mu):
  # print("value:", sm, std)
  tt = (sm-m)/(std/np.sqrt(float(n)))   # t-statistic for mean
  pval = stats.t.sf(np.abs(tt), n-1)*2 # two-sided pvalue = Prob(abs(t)>tt)
  # print('t-statistic = %6.3f pvalue = %6.4f' % (tt, pval))
  output.append(format(pval))
print("\t".join(output))