Sliding Window Average with Deque in Python

Computes the running average of a sliding window over streaming numbers using a collections.deque for O(1) pop-left operations.

Easy Python 3.9+ Aug 9, 2026 Streaming & messaging 13 views 0 copies

sliding-window deque streaming average

Python code

26 lines

Python 3.9+

from collections import deque

class SlidingAverage:
    def __init__(self, window_size):
        self.window_size = window_size
        self.window = deque()
        self.total = 0

    def add(self, value):
        self.window.append(value)
        self.total += value
        if len(self.window) > self.window_size:
            self.total -= self.window.popleft()
        return self.average()

    def average(self):
        if not self.window:
            return 0.0
        return self.total / len(self.window)


if __name__ == "__main__":
    sa = SlidingAverage(3)
    for v in [5, 10, 15, 20, 25]:
        avg = sa.add(v)
        print(f"after adding {v:>3}: window={list(sa.window):>15} avg={avg:.2f}")

Output

stdout

after adding   5: window=[5]             avg=5.00
after adding  10: window=[5, 10]        avg=7.50
after adding  15: window=[5, 10, 15]    avg=10.00
after adding  20: window=[10, 15, 20]   avg=15.00
after adding  25: window=[15, 20, 25]   avg=20.00

How it works

The deque maintains the elements in the current window; when the window exceeds the specified size, the oldest value is popped from the left. The running total is updated incrementally, so the average is computed in O(1) time per add operation. The average method handles an empty window by returning 0.0. This pattern is suitable for streaming data where new values arrive continuously and you need a moving average.

Common mistakes

Using a list and popping the first element, which is O(n) and slow for large windows.
Forgetting to update the total when removing the oldest element, leading to a wrong average.
Assuming the window is always full; the average is only over the current elements in the window.

Variations

Use a fixed-length list with an index pointer to emulate a ring buffer.
Use numpy's rolling window or pandas' rolling mean for batch data.

Real-world use cases

Monitoring server response times to compute a rolling latency average for health checks.
Processing real-time sensor readings to smooth noisy data with a moving average.
Calculating rolling connection rate in a message queue consumer to detect spikes.

Sliding Window Average with Deque in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

More from Streaming & messaging

Tutorials

Quizzes

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

More from Streaming & messaging

Keep learning

Tutorials

Quizzes