How to Generate Random Numbers Conforming to a Custom Numerical Distribution in Python?

Generating Random Numbers Based on a Given Numerical Distribution

Random number generation is a common task in programming, and it's often necessary to generate values according to a specific distribution. This can be a challenge, especially when the distribution is not standard or does not follow a known mathematical function.

Consider the scenario where you possess a file containing probabilities corresponding to different values:

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

The objective is to generate random numbers aligning with this distribution. While it's feasible to devise your own custom solution, leveraging existing libraries or modules can significantly simplify the task.

SciPy's Versatile Option

SciPy's scipy.stats.rv_discrete distribution provides an elegant solution to this problem. By specifying the probabilities via the values parameter, you can create a custom distribution tailored to your specific requirements. The rvs() method of the distribution object enables you to generate random numbers adhering to that distribution.

Numpy's Convenience

Numpy's numpy.random.choice() function also offers a convenient option for generating random values from a distribution. The p keyword parameter allows you to pass in a list of probabilities, enabling you to specify a custom distribution. For instance:

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

Python 3.6 and Beyond

Python 3.6 introduced the random.choices() function, which provides a straightforward method for generating random values based on a distribution. It accepts a sequence of possible values along with corresponding probabilities as arguments.

By utilizing these powerful libraries and functions, you can effortlessly generate random numbers adhering to a specified numerical distribution, paving the way for accurate simulations and data analysis that align with real-world scenarios.

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