# LeetCode 346. Moving Average from Data Stream

## Description

https://leetcode.com/problems/moving-average-from-data-stream/

Given a stream of integers and a window size, calculate the moving average of all integers in the sliding window.

Implement the `MovingAverage` class:

• `MovingAverage(int size)` Initializes the object with the size of the window `size`.
• `double next(int val)` Returns the moving average of the last `size` values of the stream.

Example 1:

```Input
["MovingAverage", "next", "next", "next", "next"]
[[3], [1], [10], [3], [5]]
Output
```

[null, 1.0, 5.5, 4.66667, 6.0]

Explanation MovingAverage movingAverage = new MovingAverage(3); movingAverage.next(1); // return 1.0 = 1 / 1 movingAverage.next(10); // return 5.5 = (1 + 10) / 2 movingAverage.next(3); // return 4.66667 = (1 + 10 + 3) / 3 movingAverage.next(5); // return 6.0 = (10 + 3 + 5) / 3

Constraints:

• `1 <= size <= 1000`
• `-105 <= val <= 105`
• At most `104` calls will be made to `next`.

## Explanation

Track the numbers added and size of numbers.

## Python Solution

``````class MovingAverage:

def __init__(self, size: int):
"""
"""
self.numbers = []
self.size = size

def next(self, val: int) -> float:
self.numbers.append(val)

if len(self.numbers) > self.size:
return sum(self.numbers[-(self.size):]) / self.size

return sum(self.numbers) / len(self.numbers)

# Your MovingAverage object will be instantiated and called as such:
# obj = MovingAverage(size)
# param_1 = obj.next(val)``````
• Time Complexity: O(N).
• Space Complexity: O(N).