#0053

Compute Sliding Window Averages

MediumPython18 min15 XP

Problem

Given a list of numbers and an integer k, return a list of averages for every contiguous window of length k. If k is invalid or larger than the list length, return an empty list.

Why This Matters

Sliding windows appear in time-series smoothing, rolling metrics, log analytics, signal processing, and DSA interviews.

Function Signature

def window_averages(values, k):

Examples

Example 1

Inputvalues = [2, 4, 6, 8, 10], k = 3

Output[4.0, 6.0, 8.0]

The windows are [2,4,6], [4,6,8], and [6,8,10].

Constraints

k may be invalid.
Values can be integers or floats.
Use O(n) time.

CodePython

def window_averages(values, k):
    """
    Input example: values = [2, 4, 6, 8, 10], k = 3
    Return: [4.0, 6.0, 8.0]
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return []

def window_averages(values, k):
    """
    Input example: values = [2, 4, 6, 8, 10], k = 3
    Return: [4.0, 6.0, 8.0]
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return []

Visible browser tests run here when available.

Testcases2 visible / 3 hidden categories

Three-value windows

Input[2, 4, 6, 8, 10], 3

Expected[4.0, 6.0, 8.0]

Each average reuses most of the previous window.

Window size one

Input[5, -1, 3], 1

Expected[5.0, -1.0, 3.0]

A size-one window average is the value itself as a float.

Hidden Test Categories

k larger than list lengthk equal to list lengthnegative and decimal values