Python program to randomly create N Lists of K size

Last Updated : 18 May, 2023

Given a List, the task is to write a Python program to randomly generate N lists of size K.

Examples:

Input : test_list = [6, 9, 1, 8, 4, 7], K, N = 3, 4

Output : [[8, 7, 6], [8, 6, 9], [8, 1, 6], [7, 8, 9]]

Explanation : 4 rows of 3 length are randomly extracted.

Input : test_list = [6, 9, 1, 8, 4, 7], K, N = 2, 3

Output : [[7, 6], [7, 9], [1, 9]]

Explanation : 3 rows of 2 length are randomly extracted.

Method 1 : Using generator + shuffle()

In this, getting random elements is done using shuffle(), and yield with slicing is used to get K size of shuffled list.

Python3

# Python3 code to demonstrate working of
# K sized N random elements
# Using generator + shuffle()
from random import shuffle

# get random list 
def random_list(sub, K):
    while True:
        shuffle(sub)
        yield sub[:K]

# initializing list
test_list = [6, 9, 1, 8, 4, 7]

# initializing K, N 
K, N = 3, 4
             
# printing original list
print("The original list is : " + str(test_list))

res = []
# getting N random elements 
for idx in range(0, N):
    res.append(next(random_list(test_list, K)))

# printing result
print("K sized N random lists : " + str(res))

Output:

The original list is : [6, 9, 1, 8, 4, 7]
K sized N random lists : [[7, 1, 8], [8, 6, 1], [4, 9, 6], [6, 9, 1]]

Time Complexity: O(n)
Auxiliary Space: O(n)

Method 2 : Using product() + sample()

In this, all the possible permutations of K elements are extracted using product(), and from that random sampling of N lists are done.

Python3

# Python3 code to demonstrate working of
# K sized N random elements
# Using product() + sample()
from random import sample
import itertools

# initializing list
test_list = [6, 9, 1, 8, 4, 7]

# initializing K, N 
K, N = 3, 4
             
# printing original list
print("The original list is : " + str(test_list))

# get all permutations
temp = (idx for idx in itertools.product(test_list, repeat = K))

# get Random N from them
res = sample(list(temp), N)
res = list(map(list, res))

# printing result
print("K sized N random lists : " + str(res))

Output:

The original list is : [6, 9, 1, 8, 4, 7]
K sized N random lists : [[1, 1, 1], [6, 9, 4], [8, 7, 6], [4, 8, 8]]

Time Complexity: O(n) where n is the number of elements in the list “test_list”. The product() + sample() is used to perform the task and it takes O(n) time.
Auxiliary Space: O(n), new list of size O(n) is created where n is the number of elements in the list

Method 3: Using combinations() and randint()

We can use the combinations() function from the itertools module to generate all possible combinations of K elements from the input list, and then use the randint() function from the random module to select N random combinations.

steps to implement this approach:

Import the required modules:
Define the function to generate K sized N random lists:
Call the function with the input list, K and N:

Python3

from itertools import combinations
from random import randint

def random_lists(lst, K, N):
    combos = list(combinations(lst, K))
    rand_combos = [combos[randint(0, len(combos) - 1)] for i in range(N)]
    return rand_combos

test_list = [6, 9, 1, 8, 4, 7]
K, N = 3, 4

res = random_lists(test_list, K, N)

print("K sized N random lists : " + str(res))

Output

K sized N random lists : [(9, 1, 8), (6, 8, 7), (6, 9, 4), (1, 8, 4)]

Time complexity: O(1)
Auxiliary space: O(N)

Method 5: Using random.sample() and slicing

Import random module to use random.sample() method.
Initialize the list of integers test_list.
Initialize variables K and N.
Print the original list.
Use random.sample() method to get a random sample of K integers from test_list.
Repeat the above step N times using a loop and append the result to a list.
Print the final result.

Python3

import random

# Initializing list
test_list = [6, 9, 1, 8, 4, 7]

# Initializing K, N
K, N = 3, 4

# Printing original list
print("The original list is : " + str(test_list))

# Getting Random N lists of size K
res = []
for i in range(N):
    res.append(random.sample(test_list, K))

# Printing result
print("K sized N random lists : " + str(res))

Output

The original list is : [6, 9, 1, 8, 4, 7]
K sized N random lists : [[4, 6, 8], [1, 7, 4], [6, 9, 1], [1, 8, 7]]

Time Complexity: O(NK)
Auxiliary Space: O(NK)

Method 6: Using NumPy library

Python3

import numpy as np

# Initializing list
test_list = [6, 9, 1, 8, 4, 7]

# Initializing K, N
K, N = 3, 4

# Printing original list
print("The original list is: " + str(test_list))

# Get Random N lists of size K using NumPy
arr = np.array(test_list)
np.random.shuffle(arr)  # Shuffle the array in-place

# Adjust the number of elements in arr to ensure equal division
num_elements = N * K
if num_elements > len(arr):
    arr = np.tile(arr, num_elements // len(arr) + 1)[:num_elements]

res = np.split(arr, N)

# Converting NumPy arrays to nested lists
res = [sublist.tolist() for sublist in res]

# Printing result
print("K-sized N random lists: " + str(res))

Output

The original list is : [6, 9, 1, 8, 4, 7]
K sized N random lists : [[4, 6, 8], [1, 7, 4], [6, 9, 1], [1, 8, 7]]

Time Complexity: O(N * K) since we still shuffle the array once and take the first N * K elements.
Auxiliary Space: O(N * K) as we need to store the shuffled array and the N subarrays.