The moving average can be calculated using a sliding window approach.

1. Using a Sliding Window:
   • For each window of size n, compute the average.
2. Code Example:
   
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   def moving_average(numbers, window_size):
       averages = []
       for i in range(len(numbers) - window_size + 1):
           window = numbers[i:i+window_size]
           average = sum(window) / window_size
           averages.append(average)
       return averages
   
   my_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
   print(moving_average(my_list, 3))  # Output: [2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
   

   
   
3. Using the deque from collections:
   • Maintains a moving window efficiently.
4. Alternative Code:
   
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   from collections import deque
   
   def moving_average(numbers, window_size):
       window = deque(maxlen=window_size)
       averages = []
       for number in numbers:
           window.append(number)
           if len(window) == window_size:
               average = sum(window) / window_size
               averages.append(average)
       return averages
   
   my_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
   print(moving_average(my_list, 3))  # Output: [2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
   

   
   
5. Explanation:
   • The deque approach efficiently maintains the current window of numbers.
   • Both methods provide the same result but with different approaches to window management.