This is where I spent most of my mental energy.
Break the problem into three parts: first, compute daily Likes-per-DAU for each country and post type using the provided DAU table; second, for each date, compute the median Likes-per-DAU for the same weekday over the prior 8 weeks, excluding outage dates; third, calculate the percent change from that median. Use window functions like PERCENTILE_CONT for the median and ensure proper filtering and partitioning.
Pro tip: Always verify that your DAU source is consistent with the metric definition—using the DailyActiveUsers table avoids double-counting and ensures alignment with company-wide standards. Also, consider edge cases like missing data or insufficient history for the 8-week median.
Join the likes table with DailyActiveUsers on date, country, and post type (if applicable) to compute Likes divided by DAU for each country, post type, and date. Ensure you use the provided DAU values, not a count of users.
Exclude any dates flagged with an outage from the analysis. This ensures that anomalies due to outages do not skew the median calculation.
For each country, post type, and date, use a window function like PERCENTILE_CONT(0.5) to compute the median Likes-per-DAU over the same weekday in the prior 8 weeks, excluding outage dates. Ensure the window is correctly partitioned and ordered.
For each date, compute the percent change between the current Likes-per-DAU and the median from step 3. Handle cases where the median is zero or null to avoid division errors.
Check for anomalies, ensure the output includes country, post type, date, Likes-per-DAU, median, and percent change. Consider aggregating or visualizing trends if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the metric definitions and data sources, then write a SQL query to compute Likes-per-DAU by country and post type, filtering for declines of at least 10%. For each flagged country, decompose the decline into new vs. existing user contributions using a weighted average or cohort-based analysis, and present the top 3 with the required fields.
Pro tip: Always validate your decomposition by checking that the sum of new and existing user contributions equals the total decline, and be prepared to explain any assumptions about user classification or time windows.
Confirm what 'Likes-per-DAU' means (e.g., total likes divided by daily active users), how new vs. existing users are defined (e.g., first session in last 30 days), and the time period for comparison (e.g., week-over-week).
Write a SQL query to calculate Likes-per-DAU for each country and post type over two periods, compute the percent change, and filter for declines at or below -10%.
For each flagged country, break down the total decline into contributions from new and existing users by calculating each group's Likes-per-DAU change and weighting by their share of DAU.
Sort the flagged countries by percent decline, select the top 3, and for each, report country, post type, percent change, and the share of decline attributable to new users.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The 'no correlated subqueries' constraint is the real constraint here.
First, clarify the definition of acceptance rate and the flagged countries, then design a SQL query that aggregates daily friend requests and acceptances per country, uses window functions to compute rolling 14-day sums for the current and prior windows, and finally calculates absolute and relative changes. Ensure the query is efficient by avoiding correlated subqueries and leveraging window functions like SUM() OVER with ROWS BETWEEN.
Pro tip: Mention that you would validate the 14-day windows by checking for data completeness and edge cases like countries with zero requests in the prior window, and consider using a calendar table to handle missing dates.
Confirm what 'flagged countries' means, how acceptance rate is defined (e.g., accepted requests / total requests), and the exact date ranges for the two 14-day windows.
Write a subquery or CTE that groups by country and date, summing total friend requests and accepted requests to get daily counts.
Use SUM() OVER (PARTITION BY country ORDER BY date ROWS BETWEEN 13 PRECEDING AND CURRENT ROW) to calculate the 14-day rolling totals for requests and acceptances, then compute the acceptance rate for each day.
For each country, identify the most recent 14-day window and the prior 14-day window, then calculate the absolute change (current rate - prior rate) and relative change (absolute change / prior rate).
Address scenarios like zero requests in the prior window (avoid division by zero) and ensure the output includes country, current rate, prior rate, absolute change, and relative change.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly a bit of a relief after the SQL gauntlet.
Start by restating the SQL logic in plain terms, then map each SQL clause to its pandas equivalent, emphasizing groupby, merge, rolling/expanding windows, and quantile operations. Walk through a concrete example with sample data, explaining how you would handle time-based windows and quantile calculations efficiently.
Pro tip: Mention that for rolling quantiles, pandas' rolling().quantile() can be slow on large datasets, so consider using expanding().quantile() or vectorized approaches like numpy's percentile with stride tricks for performance. Also, highlight the importance of sorting by time before rolling operations.
Restate the SQL query in plain English, identifying the grouping keys, join conditions, window frame (e.g., rolling 7-day), and quantile thresholds. This ensures alignment before diving into pandas.
Use df.groupby() to replicate GROUP BY, and pd.merge() for JOINs. Explain how to handle multiple keys and join types (inner, left, etc.) to mirror the SQL logic.
After sorting by time, use df.rolling(window=...) or df.expanding() to compute windowed aggregates. Discuss window types (time-based vs. count-based) and how to handle missing data.
Apply .quantile(q) on the rolling/expanding object to get quantile values. For multiple quantiles, use a list and handle the resulting MultiIndex. Mention alternatives like numpy.percentile for performance.
Compare results with the SQL output on a small dataset to ensure correctness. Discuss performance optimizations like using categorical dtypes, avoiding loops, and leveraging parallel processing if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.