Compute window averages and merge intervals

Q: Compute window averages and merge intervals

This question evaluates proficiency in pandas-based data manipulation—specifically windowed aggregations with strict boundary conditions and group-wise temporal interval merging—demonstrating skills in rolling/window operations, grouping, sorting, and handling time-based overlaps.

Q: How do I approach Data Manipulation (SQL/Python) interview questions?

Data Manipulation (SQL/Python) questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master data manipulation (sql/python) interviews.

Q: What difficulty level is this interview question?

This is a medium difficulty Data Manipulation (SQL/Python) question, commonly asked during Technical Screen rounds at WeRide.

Q: What role is this question designed for?

This question is commonly asked for Data Scientist candidates at WeRide during technical interviews.

Question

You are given two independent pandas tasks.

Sliding-window average
- Input DataFrame: df
- Schema:
  - row_id INT — unique row order key, already sorted ascending
  - value FLOAT
- Given an integer k >= 0 , compute for each row the average of the values from the previous k rows, the current row, and the next k rows.
- If a row does not have at least k previous rows and k next rows, set the output to -1 for that row.
- Return a DataFrame with columns: row_id , value , window_avg .
Merge overlapping autonomous-driving intervals
- Input DataFrame: segments
- Schema:
  - vehicle_id STRING
  - event_type STRING
  - start_ts TIMESTAMP
  - end_ts TIMESTAMP
- Assume all timestamps are in the same timezone and start_ts <= end_ts for every row.
- For each (vehicle_id, event_type) independently, merge intervals that overlap or touch, where a new interval should be merged into the current one if next.start_ts <= current.end_ts .
- Return the merged result with columns: vehicle_id , event_type , merged_start_ts , merged_end_ts , sorted by vehicle_id , event_type , merged_start_ts .

Write pandas code for both tasks.

Compute window averages and merge intervals

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