← Two Sigma Interview Insights
The filtering part seemed easy but I almost forgot to convert duration to hours before computing speed.
Start by clarifying the schema and join keys, then outline a step-by-step pipeline: join trips to zones on pickup location ID, compute speed as distance/duration with unit conversion, filter invalid trips, and aggregate median speed by borough and hour. Finally, sort and select the top 3 with the specified tie-breaking rules, and discuss how you would validate the results.
Pro tip: Mention that median is more robust to outliers than mean, and explicitly state your unit conversions (e.g., miles per hour from distance in miles and duration in minutes). Also, note that hour of day should be extracted from the pickup timestamp, not drop-off, to align with the pickup zone join.
Confirm the column names and data types in both datasets, especially the pickup zone ID in trips and the zone ID in zones. Ensure the join is on pickup zone to get the borough for each trip.
Calculate speed as trip_distance divided by (duration_minutes / 60) to get mph. Filter trips to duration between 1 and 120 minutes and speed between 1 and 80 mph, handling any nulls or invalid values.
Extract the hour of day from the pickup timestamp. Group by borough and hour, then compute the median speed for each group.
Sort the aggregated results by median speed descending, then by borough ascending, then by hour ascending. Select the first 3 rows.
Check for missing boroughs, empty groups, or unexpected values. Discuss how you would handle ties beyond the top 3 and whether the results make sense.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said I'd use numpy's percentile with interpolation='linear' which is the pandas default for quantile(0.95).
First, clarify the definition of 95th percentile (e.g., linear interpolation) and confirm the time window and location. Then, filter trips to Manhattan pickups between midnight and 5am, group by taxi_id, compute the 95th percentile of trip duration for each taxi, and select the top 3 by percentile value, breaking ties by taxi_id ascending.
Pro tip: Mention that you would validate the percentile calculation with a small sample or use a built-in function to ensure consistency, and discuss how to handle taxis with very few trips (e.g., requiring a minimum number of trips to avoid noise).
Confirm the definition of 'Manhattan pickups' (e.g., using pickup location IDs or coordinates) and the exact time window (midnight to 5am, inclusive/exclusive). Explicitly state the method for computing the 95th percentile (e.g., linear interpolation as in numpy's default).
Filter the dataset to include only trips with Manhattan pickups and pickup times between 00:00 and 05:00. Ensure trip duration is in minutes and handle any missing or invalid values.
Group the filtered data by taxi_id and compute the 95th percentile of trip duration for each taxi. Consider setting a minimum trip count threshold to ensure statistical reliability.
Sort the taxis by their 95th percentile duration in descending order. For ties, sort by taxi_id ascending. Select the top 3 taxi IDs.
Validate the results by checking for anomalies (e.g., extremely high percentiles due to outliers) and present the final list with the computed percentiles.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The categorical dtype requirement caught me a bit flat-footed.
First, restate the analyses and confirm the constraints (no row-level loops, parsed datetimes, single join, categorical borough, indexed pickup_ts). Then outline a vectorized pandas pipeline: load and parse, set index, join once, and compute aggregations using groupby/merge operations. Finally, write the code with comments explaining how each requirement is met and the time complexity.
Pro tip: Mention that setting the datetime index before filtering enables fast time-based slicing and that using categorical dtype for borough reduces memory and speeds up groupby operations. Also, note that a single join should be done after all necessary filtering to minimize data movement.
Confirm the analyses needed, the input data columns, and the constraints. Identify the join key and the time column for indexing.
Read data with appropriate dtypes, parse datetime columns, convert borough to categorical, and set pickup_ts as the index.
Use the datetime index to filter rows for the required time period efficiently, avoiding row-wise operations.
Merge the filtered data with the other dataset once, using an appropriate join type and ensuring the join key is optimized.
Use groupby, aggregation, and other vectorized pandas methods to compute the required metrics, ensuring no Python loops.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with downcasting int64 columns to int32 where values were small, and doing the quantile aggregation in a single groupby pass rather than computing mean and median separately.
Choose two optimizations that you actually implemented, and for each, explain the problem, the optimization, and the measured impact. Focus on the trade-offs and why the optimization was worth it in your specific context.
Pro tip: Quantify the impact with concrete numbers (e.g., 'reduced memory by 60%' or 'sped up runtime by 3x') and mention any trade-offs you accepted, such as increased code complexity or reduced readability.
Briefly describe the problem or dataset that motivated the optimization, including scale and performance bottlenecks.
Detail the specific optimization, such as downcasting numeric columns to smaller dtypes or combining multiple groupby-agg operations into a single pass.
Provide concrete metrics showing the improvement, such as memory reduction percentage or speedup factor.
Acknowledge any downsides, like potential precision loss from downcasting or increased code complexity, and why the trade-off was acceptable.
Summarize when and why this optimization is applicable, showing broader understanding.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.