A metrics DataFrame has user_id, metric, value. The same (user, metric) can repeat (multiple measurements), and a value can be NaN (a failed reading).
Produce one row per user_id with two columns per metric: - <metric>_mean — the mean of that user's non-null readings (NaN if there are none) - <metric>_n — how many non-null readings the user has, as an integer (0 when there are none)
Columns: user_id first, then each metric's _mean and _n side by side, metrics in alphabetical order — e.g. user_id, x_mean, x_n, y_mean, y_n. Every metric that appears in the input gets both columns. Rows sorted by user_id, with a clean 0..n-1 index.
What the tests check
4 on Run · 5 on Submit01columns are user_id then <metric>_mean, <metric>_n per metric, metrics alphabetical
02mean and count over repeated measurements
03no readings: count is 0 (an int), mean stays NaN
04a failed (NaN) reading counts toward neither the mean nor n
Submit runs these and one more check you don’t see, so code written for the visible tests alone can still miss.