Given a list of lists. The task is to extract a random element from it.
Examples:
Input : test_list = [[4, 5, 5], [2, 7, 4], [8, 6, 3]] Output : 7 Explanation : Random number extracted from Matrix.
Input : test_list = [[4, 5, 5], [2, 7, 4], [8, 6, 3]], r_no = 2 Output : 6 Explanation : Random number extracted from 2nd row from Matrix.
Method #1: Using chain.from_iterable() + random.choice()
In this, we flatten the Matrix to list using from_iterable() and choice() is used to get a random number from the list.
Python3
# Python3 code to demonstrate working of# Random Matrix Element# Using chain.from_iterables() + random.choice()from itertools import chainimport random# Initializing listtest_list = [[4, 5, 5], [2, 7, 4], [8, 6, 3]]# Printing original listprint("The original list is : " + str(test_list))# choice() for random number, from_iterables for flatteningres = random.choice(list(chain.from_iterable(test_list)))# Printing resultprint("Random number from Matrix : " + str(res)) |
Output
The original list is : [[4, 5, 5], [2, 7, 4], [8, 6, 3]] Random number from Matrix : 6
Method #2 : Using choice() to get element from particular row
If a row is mentioned, the choice() method can be used to get a random element from that row.
Python3
# Python3 code to demonstrate working of# Random Matrix Element# Using random.choice() [if row number given]import random# initializing listtest_list = [[4, 5, 5], [2, 7, 4], [8, 6, 3]]# printing original listprint("The original list is : " + str(test_list))# initializing Row numberr_no = [0, 1, 2]# choice() for random number, from_iterables for flatteningres = random.choice(test_list[random.choice(r_no)])# printing resultprint("Random number from Matrix Row : " + str(res)) |
Output
The original list is : [[4, 5, 5], [2, 7, 4], [8, 6, 3]] Random number from Matrix Row : 7
Method 3: Using the numpy package.
Approach:
- Import the numpy package.
- Initialize the 2D matrix using the numpy array() method.
- Use the numpy random.choice() method to generate a random number from the matrix.
- Print the result.
Python3
import numpy as npimport random# initializing listtest_list = [[4, 5, 5], [2, 7, 4], [8, 6, 3]]# printing original listprint("The original list is : " + str(test_list))# initializing Row numberr_no = [0, 1, 2]# Converting list to numpy arrayarr = np.array(test_list)# Generating random number from matrix# using numpy random.choice() methodres = np.random.choice(arr[random.choice(r_no)])# Printing resultprint("Random number from Matrix Row : " + str(res)) |
Output
The original list is : [[4, 5, 5], [2, 7, 4], [8, 6, 3]] Random number from Matrix Row : 7
Time Complexity: O(1)
Auxiliary Space: O(1)
Method 3 : Using list comprehension and random.sample()
Approach:
- Import the random module
- Initialize the list and print the original list
- Generate a random number for the row index using random.randint() method
- Use list comprehension to extract the row corresponding to the random index generated in step 3
- Use the random.sample() method to get a sample of elements from the row, with a sample size of 1.
- Get the randomly selected element from the sample and store it in a variable.
- Print the randomly selected element from the matrix row.
Python3
import random# initializing listtest_list = [[4, 5, 5], [2, 7, 4], [8, 6, 3]]# printing original listprint("The original list is : " + str(test_list))# generating random row indexrow_index = random.randint(0, len(test_list)-1)# extracting the row corresponding to the random index using list comprehensionrow = [test_list[row_index][i] for i in range(len(test_list[row_index]))]# getting a sample of elements from the row using random.sample()sample = random.sample(row, k=1)# getting the randomly selected element from the sampleres = sample[0]# printing resultprint("Random number from Matrix Row : " + str(res)) |
Output
The original list is : [[4, 5, 5], [2, 7, 4], [8, 6, 3]] Random number from Matrix Row : 7
Time Complexity: O(n) – linear time complexity as we are traversing the matrix once using list comprehension to extract the row.
Auxiliary Space: O(n) – linear auxiliary space as we are creating a new list to store the extracted row.
