scipy - Python p-value from t-statistic

Question

I have some t-values and degrees of freedom and want to find the p-values from them (it's two-tailed). In the real world I would use a t-test table in the back of a Statistics textbook; how do I do the equivalent in Python?

e.g.

t-lookup(5, 7) = 0.00245 or something like that.

I know in SciPy if I had arrays I could do scipy.stats.ttest_ind, but I don't. I just have t-statistics and degrees of freedom.

Answer 1

From http://docs.scipy.org/doc/scipy/reference/tutorial/stats.html

As an exercise, we can calculate our ttest also directly without using the provided function, which should give us the same answer, and so it does:

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