Fitting empirical distribution to theoretical ones with Scipy (Python)?

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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?

Thank you!

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Fitting empirical distribution to theoretical ones with Scipy (Python)? __del__: Questions

import time
time.sleep(5)   # Delays for 5 seconds. You can also use a float value.

import time
while True:
    print("This prints once a minute.")
    time.sleep(60) # Delay for 1 minute (60 seconds).

from time import sleep
sleep(0.1) # Time in seconds

Fitting empirical distribution to theoretical ones with Scipy (Python)? __del__: Questions

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