#0284

Running Median Stream

HardDSA40 min25 XP

Problem

Given nums as a stream, return a list of medians after each insertion. Use floats for .5 medians.

Why This Matters

Running median is the strongest everyday use case for two heaps.

Function Signature

def running_medians(nums):

Examples

Example 1

Inputnums = [5, 15, 1, 3]

Output[5.0, 10.0, 5.0, 4.0]

The sorted prefixes have medians 5, 10, 5, and 4.

Constraints

Return the exact requested value.
Handle edge cases cleanly.
Use the intended DSA pattern when brute force would scale poorly.

CodePython

def running_medians(nums):
    """
    Input example: nums = [5, 15, 1, 3]
    Return: [5.0, 10.0, 5.0, 4.0]
    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 running_medians(nums):
    """
    Input example: nums = [5, 15, 1, 3]
    Return: [5.0, 10.0, 5.0, 4.0]
    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

Four inserts

Input[5,15,1,3]

Expected[5.0, 10.0, 5.0, 4.0]

Each median comes from the two heap tops.

Increasing stream

Input[1,2,3]

Expected[1.0, 1.5, 2.0]

Even prefixes average the two middle values.

Hidden Test Categories

negative valuesduplicatessingle item