Python – Get Random Range Average

Given range and Size of elements, extract random numbers within a range, and perform average of it.

Input : N = 3, strt_num = 10, end_num = 15 Output : 13.58 Explanation : Random elements extracted between 10 and 15, averaging out to 13.58. Input : N = 2, strt_num = 13, end_num = 18 Output : 15.82 Explanation : 2 elements average to 15.82 in this case.

Method #1 : Using loop + uniform() The combination of above functions can be used to solve this problem. In this, we perform the task of extracting numbers using uniform() and loop is used to perform addition of numbers. The average is computed at end by dividing by size. 

Python3

# Python3 code to demonstrate working of 
# Random Range Average
# Using loop + uniform()
import random

# initializing N
num = 4

# Initialize strt_num
strt_num = 15

# Initialize end_num
end_num = 60

# Using loop + uniform()
res = 0.0
for _ in range(num):     
    
    # performing summation of range elements
    res += random.uniform(strt_num, end_num)

# performing average
res = res / num

# printing result 
print("The average value : " + str(res)) 

Output

The average value : 42.980287235196116

Method #2 : Using sum() + uniform() + generator expression The combination of above functions can be used to solve this problem. In this, we perform the task of performing average using sum() to compute sum() and whole logic is encapsulated in one-liner using generator expression. 

Python3

# Python3 code to demonstrate working of 
# Random Range Average
# Using sum() + uniform() + generator expression
import random

# initializing N
num = 4

# Initialize strt_num
strt_num = 15

# Initialize end_num
end_num = 60

# Using sum() + uniform() + generator expression
# shorthand, using generator expression to form sum and division by Size
res = sum(random.uniform(strt_num, end_num) for _ in range(num)) / num

# printing result 
print("The average value : " + str(res))

Output

The average value : 42.980287235196116

Method #3 : Using recursion + uniform()

This approach uses NumPy’s random.uniform function to generate an array of N random numbers between strt_num and end_num. It then uses NumPy’s mean function to calculate the average of the array.

Python3

import random

def random_range_average(N, strt_num, end_num):
    # Generate a list of N random floating-point numbers between strt_num and end_num using a list comprehension
    random_nums = [random.uniform(strt_num, end_num) for _ in range(N)]
    
    # Calculate the sum of the random numbers using the sum function, and then divide by N to get the average
    return sum(random_nums) / N

# Example usage
N = 2
strt_num = 13
end_num = 18
print(random_range_average(N, strt_num, end_num))  # Prints the average of 2 random numbers between 13 and 18

Output

13.573741284818514

Time complexity: O(N)

Auxiliary space: O(N)

Method #3 : Using numpy():

Algorithm :

1.Initialize the number of random numbers to generate (num), the starting point of the range (strt_num), and the ending point of the range (end_num).
2.Use numpy to generate a sequence of num random numbers within the range of strt_num to end_num.
3.Calculate the average of the generated numbers using numpy’s mean function.
4.Print the calculated average.

Python3

import numpy as np

num = 4
strt_num = 15
end_num = 60

np.random.seed(0)  # set the random seed to reproduce the same sequence of random numbers
random_numbers = np.random.uniform(strt_num, end_num, size=num)
average = np.mean(random_numbers)

print("The average value : " + str(average))
#This code is contributed by Jyothi pinjala.

Output:

The average value : 41.473505114621176

The time complexity : O(num), as it takes a constant amount of time to generate each random number and to calculate the average of the generated numbers.

The space complexity : O(num), as it needs to store the generated numbers in memory before calculating the average. However, the space complexity can be reduced by generating the numbers one by one and calculating the average on-the-fly, which would result in a space complexity of O(1).

Approach#4: Using lambda

The lambda function takes three arguments, N for the number of random elements to generate, strt_num as the starting range, and end_num as the ending range. It generates N random numbers using random.uniform() function and calculates their mean using statistics.mean() function. Finally, it returns the mean of the randomly generated numbers.

Algorithm

1. Input three parameters, N, strt_num, and end_num.
2. Generate N random numbers using random.uniform() function.
3. Calculate the mean of the generated numbers using statistics.mean() function.
4. Return the calculated mean.

Python3

import random
import statistics as stats

result = lambda N, strt_num, end_num: stats.mean(random.uniform(strt_num, end_num) for _ in range(N))

print(result(3, 10, 15)) 

print(result(2, 13, 18)) 

Output

13.05643776832048
15.221901001123129

Time complexity: O(N). The time complexity of the random.uniform() function is O(1) as it generates a single random number at a time. The time complexity of the statistics.mean() function is O(n) as it calculates the sum of the numbers and then divides by the length of the list.

Auxiliary Space: O(N), The lambda function generates a list of N random numbers and calculates the mean of the generated numbers.