How to calculate probability in a normal distribution given mean & standard deviation?

Question

How to calculate probability in normal distribution given mean, std in Python? I can always explicitly code my own function according to the definition like the OP in this question did: Calculating Probability of a Random Variable in a Distribution in Python

Just wondering if there is a library function call will allow you to do this. In my imagine it would like this:

nd = NormalDistribution(mu=100, std=12) p = nd.prob(98) 

There is a similar question in Perl: How can I compute the probability at a point given a normal distribution in Perl?. But I didn't see one in Python.

Numpy has a random.normal function, but it's like sampling, not exactly what I want.

Answer 1

There's one in scipy.stats:

>>> import scipy.stats >>> scipy.stats.norm(0, 1) <scipy.stats.distributions.rv_frozen object at 0x928352c> >>> scipy.stats.norm(0, 1).pdf(0) 0.3989422804014327 >>> scipy.stats.norm(0, 1).cdf(0) 0.5 >>> scipy.stats.norm(100, 12) <scipy.stats.distributions.rv_frozen object at 0x928352c> >>> scipy.stats.norm(100, 12).pdf(98) 0.032786643008494994 >>> scipy.stats.norm(100, 12).cdf(98) 0.43381616738909634 >>> scipy.stats.norm(100, 12).cdf(100) 0.5 

[One thing to beware of -- just a tip -- is that the parameter passing is a little broad. Because of the way the code is set up, if you accidentally write scipy.stats.norm(mean=100, std=12) instead of scipy.stats.norm(100, 12) or scipy.stats.norm(loc=100, scale=12), then it'll accept it, but silently discard those extra keyword arguments and give you the default (0,1).]