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 listtest_list = [6, 9, 1, 8, 4, 7]# initializing K, N K, N = 3, 4 # printing original listprint("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 resultprint("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 sampleimport itertools# initializing listtest_list = [6, 9, 1, 8, 4, 7]# initializing K, N K, N = 3, 4 # printing original listprint("The original list is : " + str(test_list))# get all permutationstemp = (idx for idx in itertools.product(test_list, repeat = K))# get Random N from themres = sample(list(temp), N)res = list(map(list, res))# printing resultprint("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 combinationsfrom random import randintdef 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_combostest_list = [6, 9, 1, 8, 4, 7]K, N = 3, 4res = random_lists(test_list, K, N)print("K sized N random lists : " + str(res)) |
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 listtest_list = [6, 9, 1, 8, 4, 7]# Initializing K, NK, N = 3, 4# Printing original listprint("The original list is : " + str(test_list))# Getting Random N lists of size Kres = []for i in range(N): res.append(random.sample(test_list, K))# Printing resultprint("K sized N random lists : " + str(res)) |
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 listtest_list = [6, 9, 1, 8, 4, 7]# Initializing K, NK, N = 3, 4# Printing original listprint("The original list is: " + str(test_list))# Get Random N lists of size K using NumPyarr = np.array(test_list)np.random.shuffle(arr) # Shuffle the array in-place# Adjust the number of elements in arr to ensure equal divisionnum_elements = N * Kif 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 listsres = [sublist.tolist() for sublist in res]# Printing resultprint("K-sized N random lists: " + str(res)) |
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.
