Maybe this is because I have only worked at startups, but I am much more interested in if someone can read and understand code, where they feel logic is brittle, overly complex or badly designed. If they understand, even conceptually, how adding an optional field to an endpoint may be fine but removing one needs to be phased out or considered for active users. If they consider downstream risks, if they understand business goals and how to communicate limitations or opportunities.

Instead, every single tech interview seems to focus on how well you paid attention in your CS seminar which might be a reasonable screen for junior employees but is awfully irrelevant for anyone >3 years in the industry.

There are far too many corners of logic for everyone to know. Maybe someone has never dealt with data streams, or even forgets what a median is. You want to know if they are sharp with statistics? Great for some roles, wholly irrelevant for many others.

Engineers need to communicate, read and understand logic and how things connect. And the golden skill: willing and able to learn something new.

Honestly, when I ran this interview I didn’t care much about the specifics of what you memorized beforehand. I care if you can read and write code a bit. I care more whether we can have a productive conversation. If you learn something new from me or the problem, how does that look and feel? If I make a mistake, how do you react? Are we able to communicate technical ideas to each other? Are we able to productively work through conflict?

We’re not computing many medians day-to-day, but we’re doing all those other things constantly.

I was somewhat pained by this, as this is an interview question I've gotten, and I clearly annoyed the interviewer by knowing this isn't true, and you can avoid a full sort (which, at least two others have noted).

https://en.wikipedia.org/wiki/Quickselect

A fun follow up is asking a candidate how to compute the 25th and 75th percentiles or more broadly, the n-th percentile.

There is also an approximate algorithm that does not keep all the data in memory at the same time.

How about something like the beginnings of a spreadsheet engine?

Or.. count the number of distinctly shaped black regions in a bitmap image.

if you are not aware of quickselect algorithm.

Candidates who could implement an O(n) median algorithm but chose to implement an O(n log n) weighted median algorithm might be someone who rote remembered the O(n) algorithm. Truly excellent strong hires can adapt their O(n) algorithm to weighted median too.

  median = statistics.median(data)  # `import statistics` first

  median = (lambda x: (lambda s,n: s[n//2] if n%2 else (s[n//2-1]+s[n//2])/2)(sorted(x), len(x)))(data)

Computing a moving average with samples being pumped through an n-element buffer is easy. Doing so for the median requires more thought. It's also very useful e.g. for removing single-sample noise from an audio track, so it's not a meaningless exercise.

> # implications of sorted() vs numbers.sort()?

I thought references were passed by value in languages like Python? I am not particularly fond of Python, so my experience with and knowledge of the language are quite limited. But, I understand what the question is asking: mutation vs. the creation of a new object.

But they don't. I hope you, as an interviewer, have the grace to learn when one of your interviewees points out your mistake. :-) Median is O(n), not nlogn