How to Generate Random Numbers with a Specified Numerical Distribution in Python?

Generating Random Numbers with Specified Numerical Distribution

When faced with the task of generating random numbers that adhere to a certain distribution, one may ponder the existence of preexisting modules capable of handling such a task. After all, this is a prevalent problem with a potential solution that has been addressed by numerous programmers.

Consider the following example:

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

Here, we have a file containing values and their corresponding probabilities. To generate random numbers based on this distribution, we could utilize scipy.stats.rv_discrete. By supplying our probabilities through the values parameter, we can create a distribution object. Subsequenly, we can employ the rvs() method of the distribution object to generate random numbers.

However, another viable option is to use numpy.random.choice(). This function accepts a p keyword parameter, allowing us to specify our probabilities directly.

For instance:

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

And finally, for those using Python 3.6 or later, random.choices() from the standard library provides a convenient solution.

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