visits +-----------+-----------+------+ | person_id | timestamp | page | +-----------+-----------+------+ | 1 | 100 | A | | 1 | 110 | B | | 1 | 150 | C | | 2 | 100 | B | | 2 | 120 | C | +-----------+-----------+------+ ##### Scenario You receive a TSV file in which each line contains a user’s chronological page-visit history formatted as timestamp,page and separated by “/t”. Business wants insights on usage and performance optimizations. ##### Question Parse the file and return the page with the highest total visit count. 2) For every visit, compute the residence time (current timestamp – next timestamp). Return the page with the greatest total residence time across all users. 3) Treat each user’s ordered page sequence as a path. Return the most frequent complete path (e.g., "A→B→C"). 4) #2 can be slow with explicit loops. Rewrite the residence-time computation so it can execute in parallel / vectorized form (e.g., time[i] – time[i-1]) and explain the performance benefit. ##### Hints Load into pandas, sort by person_id & timestamp, use groupby + diff/shift, Counter or groupby agg, and vectorized numpy operations for parallelism.