How to Generate Random Numbers with a Custom Distribution in Python?

Generating Random Numbers Using a Specific Distribution

You possess a file containing probabilities for distinct values:

1 0.1
2 0.05
3 0.05
4 0.2
5 0.4
6 0.2

Your goal is to generate random numbers based on this distribution. You seek an existing module capable of handling this task, recognizing that it is a prevalent issue that may have already prompted the development of specialized functions or modules.

Solution

The Python ecosystem provides several options for generating random numbers with a specific distribution:

1. scipy.stats.rv_discrete:

   • Supply your probabilities within the values parameter.
   • Utilize the rvs() method of the distribution object to generate random numbers.

2. numpy.random.choice() with p parameter:

   • Pass the probabilities as the p keyword parameter.

   • Example:

     numpy.random.choice(numpy.arange(1, 7), p=[0.1, 0.05, 0.05, 0.2, 0.4, 0.2])

3. random.choices() (Python 3.6 ):

   • This function from the standard library allows for specifying probabilities.

   • Example:

     import random
     random.choices(population=list(range(1, 7)), weights=[0.1, 0.05, 0.05, 0.2, 0.4, 0.2])

These modules provide efficient and straightforward ways to generate random numbers with a given distribution, eliminating the need to implement custom algorithms.

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