Python program to randomly create N Lists of K size

When you need to generate multiple random lists of a specific size from a larger dataset, Python's random.shuffle() combined with generator functions provides an efficient solution. This approach creates N lists, each containing K randomly selected elements.

Using Generator Function with Shuffle

Here's how to create a generator that produces random sublists ?

from random import shuffle

def gen_random_list(my_val, K):
    while True:
        shuffle(my_val)
        yield my_val[:K]

my_list = [12, 45, 76, 32, 45, 88, 99, 0, 1]
print("The original list is:")
print(my_list)

K, N = 4, 5
print(f"Creating {N} lists of size {K}")

my_result = []
for elem in range(0, N):
    my_result.append(next(gen_random_list(my_list, K)))

print("The result is:")
print(my_result)

The original list is:
[12, 45, 76, 32, 45, 88, 99, 0, 1]
Creating 5 lists of size 4
The result is:
[[88, 76, 99, 12], [12, 99, 32, 76], [32, 76, 12, 99], [32, 45, 0, 12], [76, 0, 1, 45]]

Alternative Method Using Random Sampling

For cases where you want sampling without replacement, use random.sample() ?

import random

def create_random_lists(data, K, N):
    result = []
    for _ in range(N):
        random_sublist = random.sample(data, K)
        result.append(random_sublist)
    return result

my_list = [12, 45, 76, 32, 45, 88, 99, 0, 1]
K, N = 4, 5

random_lists = create_random_lists(my_list, K, N)

print("Original list:", my_list)
print(f"Generated {N} random lists of size {K}:")
for i, sublist in enumerate(random_lists, 1):
    print(f"List {i}: {sublist}")

Original list: [12, 45, 76, 32, 45, 88, 99, 0, 1]
Generated 5 random lists of size 4:
List 1: [76, 0, 45, 88]
List 2: [32, 99, 45, 12]
List 3: [1, 76, 45, 99]
List 4: [88, 32, 0, 45]
List 5: [76, 1, 12, 99]

How It Works

The generator function approach:

• shuffle(my_val) − Randomly reorders the original list in-place

• yield my_val[:K] − Returns the first K elements as a slice

• while True − Creates an infinite generator for continuous list generation

• next() − Gets the next value from the generator

Comparison

Conclusion

Use the generator approach with shuffle() for memory-efficient random list generation. For simpler cases where preserving the original list matters, random.sample() provides a cleaner solution without side effects.