**INTRODUCTION**: I have a list of more than 30,000 integer values ranging from 0 to 47, inclusive, e.g.`[0,0,0,0,..,1,1,1,1,...,2,2,2,2,...,47,47,47,...]` sampled from some continuous distribution. The values in the list are not necessarily in order, but order doesn't matter for this problem.

**PROBLEM**: Based on my distribution I would like to calculate p-value (the probability of seeing greater values) for any given value. For example, as you can see p-value for 0 would be approaching 1 and p-value for higher numbers would be tending to 0.

I don't know if I am right, but to determine probabilities I think I need to fit my data to a theoretical distribution that is the most suitable to describe my data. I assume that some kind of goodness of fit test is needed to determine the best model.

Is there a way to implement such an analysis in Python (`Scipy` or `Numpy`)?
Could you present any examples?