Random Sampling Implementation | Dataford Interview Questions

How would you implement a function to generate a random sample from a specific distribution? Implement categorical sampling using the inverse transform method. The function receives outcome values, matching probabilities, a sample size, and a random seed, then returns a reproducible list of sampled outcomes. Inputs are values, probabilities, n, and seed; probabilities are nonnegative and sum to 1, and n is nonnegative. Return exactly n values, preserving the supplied outcome types.

Constraints

1 <= len(values) = len(probabilities) <= 10^4
0 <= n <= 1000
0 <= probabilities[i] <= 1
The probabilities sum to 1
All values are valid Python values
The seed is an integer

Function Signature

def sample_distribution(values, probabilities, n, seed):

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Problem

Constraints

1 <= len(values) = len(probabilities) <= 10^4
0 <= n <= 1000
0 <= probabilities[i] <= 1
The probabilities sum to 1
All values are valid Python values
The seed is an integer

Function Signature

def sample_distribution(values, probabilities, n, seed):

Your question is Random Sampling Implementation. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.