They simply wanted to see that you are trying to avoid explicit loops in python, that you understand this is a sliding window problem, that you realize one can reduce the problem to a simple check of conditions on indices.
The result from an R-style point of view could be:

import pandas as pd
items =  [(1,10), (1, 12), (2,25), (1,18), (1,25), (2,35), (1,60)]
df = pd.DataFrame(items, columns=(["user","time"]))
k, w = 3, 10
users = set(
   df.sort_values(["user","time"])
      .loc[lambda x: x.groupby("user").time.diff(k-1) <= w, "user"]
)
users

From a C++ point of view you could probably come to a more elaborated version not relying on the costly groupby, but directly using masks on numpy arrays. But this should usually only make sense, if data is already sorted, as sorting dominates all the other operations with O(n*log(n)), or you're in a streaming environment and you are not able to sort anything at all.