import random
# Initial list of candidates or prizes
participants = ["Alexey", "Maria", "Ivan", "Olga", "Dmitry"]
# 1. Selecting 3 unique winners (sample without replacement)
winners = random.sample(participants, k=3)
print(f"Winners: {winners}")
# The result is different each time, but there will be no repetitions within the list of winners!
# 2. Shuffling an entire string (creating an anagram)
word = "python"
shuffled_word = "".join(random.sample(word, len(word)))
print(f"Anagram: {shuffled_word}")
# 3. Important difference: random.choices allows repetitions
print(f"With repetitions: {random.choices(participants, k=3)}")
✨ Honest selection and generation of unique sets
When it's necessary to implement the logic of prize draws, random task distribution, or generating test questions, developers often use
random.choice() in a loop. But this approach requires manually ensuring that the same element is not selected twice. The random.sample function takes on this routine.— Guarantee of uniqueness: The main property of
random.sample is "without replacement". The extracted element no longer participates in the next selection cycle, which completely eliminates duplicates in the resulting list.— Safety of the original: The function does not modify the original list (unlike
random.shuffle()), but creates a completely new array with the results. This allows the structure of the original data to remain intact.— Strict control of size: If you pass a parameter
k (the number of elements) that exceeds the length of the original list, Python will not start duplicating elements and will immediately throw an ValueError error. This protects the program logic from incorrect data.#Python #Random #Coding #NoRepetition #DataScience #UniqueSets
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