Start by identifying the necessary tables and joins: events (impressions and clicks), users (signup date and country), and pins (format). Filter for US users whose signup date is within 30 days before the event date, then aggregate by pin format to compute impressions, clicks, CTR, and distinct new users. Use conditional aggregation to count impressions and clicks separately, and ensure CTR is calculated as clicks divided by impressions, rounded to 4 decimal places.
Pro tip: Clarify the definition of 'new user'—it could mean users who signed up within 30 days of the event, or users whose first event is within that window. Also, consider whether to use COUNT(DISTINCT user_id) for new users or a separate metric, and handle potential division by zero in CTR.
Determine which tables contain event data (impressions, clicks), user data (signup date, country), and pin data (format). Join them on user_id and pin_id as appropriate.
Apply a WHERE clause to include only users with country = 'US' and whose signup date is within 30 days before the event date (e.g., event_date BETWEEN signup_date AND signup_date + INTERVAL '30 days').
Use GROUP BY pin_format and compute SUM(CASE WHEN event_type = 'impression' THEN 1 ELSE 0 END) for impressions, similarly for clicks, and COUNT(DISTINCT user_id) for new users.
Compute CTR as clicks divided by impressions, using NULLIF to avoid division by zero, and round to 4 decimal places with ROUND(..., 4).
Ensure the query returns the required columns in the correct order. Consider if there are multiple event types or if clicks and impressions are in separate tables, requiring UNION or JOIN.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The case-insensitive filter tripped me up slightly.
Start by filtering the DataFrame to video pins and dropping rows with nulls in the relevant columns. Then normalize the category IDs for case-insensitive matching, map them to names, compute the average time spent per category using groupby, and finally select the category with the highest average, breaking ties by lexicographic order.
Pro tip: Mention that you would validate the mapping dictionary for missing or duplicate keys and handle them appropriately, as this shows attention to data quality and edge cases.
Filter the DataFrame to include only video pins and drop any rows with null values in the category ID or time spent columns.
Convert category IDs to a consistent case (e.g., lowercase) and map them to category names using the provided dictionary, handling any unmapped IDs.
Group the filtered DataFrame by category name and calculate the mean time spent for each category.
Sort the resulting averages in descending order and then by category name in ascending lexicographic order, and pick the first category.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward conceptually but the complexity discussion is where they actually wanted detail.
Start by clarifying the problem and edge cases, then propose an efficient solution that iterates through the dictionary once, using a set to count unique pins per user. Discuss time and space complexity, and mention how to handle very large inputs (e.g., streaming or distributed processing).
Pro tip: Emphasize that the mean should be computed over all users, including those with zero pins, and discuss how to handle division by zero if the dictionary is empty. Also, mention that using a set per user is memory-efficient if lists are small, but for very large lists, consider alternative approaches like sorting and counting.
Ask about input size, whether users with empty lists should be included, and if the mean should be over all users or only those with pins. Confirm that duplicates should be ignored.
Iterate through each user's list, convert to a set to get unique pins, and count the size. Accumulate the total unique pins and the number of users.
Divide the total unique pins by the number of users. If there are no users, return 0 or handle appropriately. Ensure empty lists contribute 0 to the total.
Time complexity is O(N) where N is total number of pin entries across all users. Space complexity is O(U + M) where U is number of users and M is max unique pins per user (for sets).
For inputs too large for memory, suggest streaming or distributed approaches (e.g., MapReduce) to compute unique counts per user and then aggregate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.