← Boston Consulting Group Interview Insights
This was a lot to hold in your head at once.
Start by normalizing column names across all seven dataframes (lowercase, strip whitespace, replace spaces with underscores) and mapping them to a fixed target schema. Then perform left joins from the orders table to preserve all orders, using outer joins only where necessary, and apply a tiered imputation strategy (e.g., fill from related tables, then defaults) for missing values. Finally, validate the output schema and row count to ensure no orders are lost.
Pro tip: Emphasize that you would build a reusable merge function with a configuration dictionary for column mappings and imputation rules, making it easy to adapt when schemas change. Also, mention that you would add logging and assertions to catch data quality issues early, which is crucial in consulting where data often comes from disparate sources.
Standardize column names across all dataframes by stripping whitespace, lowercasing, and replacing spaces with underscores. Create a mapping dictionary to align each dataframe's columns to the target schema.
Identify the primary table (orders) and use left joins to preserve all orders. For tables that may be missing (shipper, payment), use left joins and handle missing keys appropriately.
For missing values, first attempt to fill from related tables (e.g., customer info from orders), then apply statistical imputation (mean/median) or business defaults (e.g., 'Unknown') based on column type and importance.
After merging, ensure the final dataframe has exactly the target columns in the correct order. Check row count matches the orders table and handle any duplicates introduced by joins.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I talked about setting indexes on the join keys before merging so pandas uses a hash join rather than a nested loop.
Start by framing the pipeline as a sequence of operations where each step's complexity is analyzed, focusing on index alignment and avoiding unnecessary sorting. Then, describe specific pandas functions and index choices that achieve O(N log N) or better, such as using merge with sorted indexes or join on indexed columns. Finally, discuss trade-offs and alternatives like using categorical data or partitioning for large datasets.
Pro tip: Mention that setting and verifying indexes before merging can turn O(N log N) sorts into O(N) hash joins, but be aware that pandas' merge on non-unique indexes may still require sorting. Also, consider using `pd.merge_asof` for time-series merges if applicable.
Ask about data size, key uniqueness, and whether the merge is many-to-one or many-to-many. This determines the optimal strategy and expected complexity.
Set the join key as the index for both DataFrames using `set_index` if not already, and ensure indexes are sorted. This enables efficient index-based joins.
Use `DataFrame.join` for index-based joins (O(N) hash join) or `pd.merge` with `left_index=True` and `right_index=True`. For sorted merges, use `pd.merge_ordered` or `merge_asof`.
Perform groupby on the indexed DataFrame using `groupby(level=0)` to avoid resetting index, and use built-in aggregation functions that are optimized in Cython.
Check performance with `%timeit` or `df.info(memory_usage='deep')`, and consider alternatives like using categorical dtypes or partitioning if data is large.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
order 10 has two items: product 501 with quantity 2 and price 30, and product 502 with null quantity and price 10.
First, restate the expected aggregation logic for order_id 10: total_amount is the sum of quantity * unit_price across line items, product_count is the number of distinct products, and category_list is the distinct set of categories. Then walk through the sample data row by row, explicitly handling missing quantity and unit_price by treating them as NULL/zero and excluding them from calculations, and finally compare the computed values to the expected ones.
Pro tip: Mention that missing quantity or unit_price should be treated as NULL and excluded from aggregation, but flag them as data quality issues that could bias results if not addressed upstream.
State that total_amount = SUM(quantity * unit_price), product_count = COUNT(DISTINCT product_id), and category_list = DISTINCT category values for order_id 10.
List each line item, noting any missing quantity or unit_price values and how they affect the calculations.
Explain that missing quantity or unit_price should be treated as NULL and excluded from the sum, but note that this may undercount total_amount and product_count if the row is otherwise valid.
Calculate total_amount by summing quantity * unit_price for rows with both values present, count distinct product_ids for product_count, and collect distinct categories for category_list.
Compare computed values to the expected ones, and discuss how missing data could impact downstream analytics or business decisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The key is that medians for imputation should be computed only on training data and then applied to the test set, not computed on the full dataset before the split.
Start by clarifying that imputation must be fit only on the training data and then applied to the test data to avoid leakage. Explain that when splitting by date, the training set is earlier in time, so any statistics used for imputation (e.g., mean, median, mode) must be computed solely from that earlier period. Emphasize that this mimics real-world deployment where future data is unseen.
Pro tip: Mention that even seemingly harmless global operations like using the full dataset's mean for imputation can leak future information, and that time-aware imputation (e.g., forward-fill) can be a safer alternative when appropriate.
Explain that the dataset is split by date into training (earlier) and test (later) sets to simulate temporal order.
Describe how imputation parameters (e.g., mean, median, mode) are calculated exclusively from the training set.
Show that the same imputation parameters are then used to fill missing values in the test set without recomputing.
Highlight that this prevents any information from the future (test set) from influencing the training process.
Optionally mention that for time-series data, methods like forward-fill or rolling statistics can further respect temporal order.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.