The rolling uniqueness part tripped me up.
First, compute daily active users per device by grouping events by date and device, then aggregating unique user IDs. Next, for the rolling 2-day unique user count, use a self-merge on consecutive dates (or a rolling window with a set-based aggregation) to combine user sets, then count distinct users. Emphasize that uniqueness across days requires set union, not sum, and that pandas' nunique on concatenated groups ensures deduplication.
Pro tip: Mention that while pandas' rolling().apply() can work, it's slow for large data; a self-merge on date and date-1 followed by groupby nunique is more efficient and scalable. Also, clarify that 'aligned to day end' means the window includes the current day and the previous day, so the rolling count for day D is the unique users from D-1 and D.
Group the events DataFrame by date and device, then aggregate unique user IDs using nunique. This yields a DataFrame with one row per date-device pair and the DAU count.
To compute a 2-day rolling unique user count, you need the actual sets of users per day, not just counts. Create a DataFrame where each row contains the date, device, and a set (or list) of unique user IDs for that day.
Perform a self-merge on device where the right date equals the left date minus one day (or use a rolling window). This pairs each day with its previous day for the same device.
For each merged pair, combine the user sets (e.g., using set union) and count the distinct users. This gives the 2-day rolling unique user count aligned to the current day (day end).
For the first day, the rolling count is just the DAU of that day. Ensure the output is sorted by device and date, and that the rolling count is correctly aligned to day end.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty clean once you get the first-order aggregation right.
Start by outlining the SQL logic: use a LEFT JOIN from users to orders, then filter to each user's first order using a window function or subquery. Address the same-day edge case by clarifying that days_to_first_order should be 0.0 (not NaN) when signup and first order occur on the same date, and explain how to compute the difference in days as a float.
Pro tip: Mention that you would validate the join by checking for duplicate first orders per user and confirm that users with no orders correctly show NaN, as this demonstrates attention to data quality and edge cases.
Confirm that days_to_first_order is the difference in days between signup and first order, and that same-day signup and order should yield 0.0 days, not NaN. Also confirm that users with no orders should have NaN for both first_order_ts and days_to_first_order.
Use a subquery with ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY order_ts) = 1, or a correlated subquery with MIN(order_ts), to select the earliest order per user.
LEFT JOIN the users table to the first-order subquery on user_id, ensuring all users are retained. Users without orders will have NULL for first_order_ts.
Calculate the difference in days between first_order_ts and signup_ts, casting to float. For same-day signup and order, the difference is 0.0; for users with no orders, it remains NaN (NULL).
Check that each user has at most one first order, and that same-day cases produce 0.0. Ensure NaN is represented correctly (e.g., NULL in SQL, NaN in pandas) and consider timezone consistency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem: we need to aggregate spending per user and category, rank categories by total amount descending with alphabetical tie-breaking, and pivot to get the top two. Then outline an efficient solution using window functions or groupby with rank, and handle edge cases like users with fewer than two categories by filling with NaN.
Pro tip: Mention that you would verify the solution on edge cases such as users with exactly two categories, ties in spending, and users with only one category, and discuss how to scale the approach for large datasets using distributed computing if needed.
Confirm the definition of 'top categories' (by total spending), tie-breaking rule (alphabetical), and output format (NaN for missing). Ask about data size and distribution to guide implementation choices.
Group the data by user and category, summing the spending amounts to get total per user-category pair.
For each user, rank categories by total spending descending, with alphabetical order as tie-breaker. Use window functions (e.g., ROW_NUMBER or RANK) or sort and enumerate.
Filter to ranks 1 and 2, then pivot so each user has one row with columns top_cat_1 and top_cat_2. Ensure users with fewer than 2 categories get NaN in the missing column.
Test on edge cases (ties, single category, no spending). Discuss how the approach can be adapted for large-scale data using tools like Spark or by optimizing with window functions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward groupby-agg to get the ALL rows, set device to the string 'ALL', then pd.concat.
Start by outlining the SQL steps: compute daily totals with GROUP BY, label them as 'ALL', then UNION ALL with the original table. Emphasize that column types must match exactly, so explicitly cast the device column to string and ensure revenue is numeric in both parts.
Pro tip: Mention that using UNION ALL instead of UNION avoids unnecessary deduplication and preserves performance, and always verify types with a DESCRIBE or information_schema query after the operation.
Use a GROUP BY on day to sum revenue across all devices, and add a literal 'ALL' as the device value.
Explicitly cast the device column in the original table to STRING (or VARCHAR) and confirm revenue is the same numeric type in both parts.
Use UNION ALL to concatenate the original table with the summary rows, preserving all rows and avoiding deduplication overhead.
Check the resulting schema and sample rows to confirm column types and that the 'ALL' rows are correctly placed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.