SciPy the P-value and the _scipy of some statistics

There are a number of sub modules in the Scipy.stats module that involve computational statistics
Such as
Scipy.stats.uniform
Scipy.stats.norm
scipy.stats.t
Scipy.stats.chi2
Scipy.stats.f
More sub modules see here

The following methods are included in the SCIPY.STATS.F:

Come from here

These methods are similar in other child modules. In this paper, we introduce the P-value and the chi-square of f statistic, t statistic, the normal state, and the method of calculation is basically similar.

Example 1: Ask Pr (f4,58>1.67) =? , that is, a known critical value for P value

>>> from scipy.stats import F
>>> f.sf (1.67, 4)
0.16927935111708425
>>> 1-f . CDF (1.67, 4, 0.16927935111708448)

Example 2: Ask F (1−0.17) 4,58=? , that is, the known P value for the critical value

>>> from scipy.stats import F
>>> F.ISF (0.17, 4,)
1.666945416681088
>>> F.PPF (1-0.17, 4, 1.666945416681088)

Among them, ISF is the inverse of SF, PPF is the inverse of CDF. See above for specific explanation. Similar to other statistical methods, the difference is that fewer degrees of freedom are used as parameters.