# Python program to randomly create N Lists of K size

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:

1. Import the required modules:
2. Define the function to generate K sized N random lists:
3. 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

1. Import random module to use random.sample() method.
2. Initialize the list of integers test_list.
3. Initialize variables K and N.
4. Print the original list.
5. Use random.sample() method to get a random sample of K integers from test_list.
6. Repeat the above step N times using a loop and append the result to a list.
7. 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.

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