Basically a sanity check step, nothing tricky.
Start by importing pandas and reading both CSV files into DataFrames using pd.read_csv. Then use .head(2) to display the first two rows and .tail(1) to display the last row of the trips DataFrame, ensuring the output is clear and verifiable.
Pro tip: Mention that you would also check the shape and data types to ensure the data loaded correctly, and consider using a random sample or summary statistics for a more thorough validation.
Import the pandas library to access DataFrame functionality.
Use pd.read_csv to load both CSV files into separate DataFrames, assigning them to variables like df1 and df2 or more descriptive names.
Use the .head(2) method on the trips DataFrame to display the first two rows.
Use the .tail(1) method on the trips DataFrame to display the last row.
Explain that this quick check confirms the data loaded correctly and gives a sense of the structure and content.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The whitespace-stripping part is where I almost messed up.
Start by inspecting the data to understand the extent of missing values and the current data types, especially for fare and route columns. Then apply a cleaning pipeline that drops rows with missing fare or empty/whitespace route strings, and coerces the fare column to numeric, handling any conversion errors. Finally, validate the cleaned dataset to ensure no missing values remain and fare is numeric.
Pro tip: Always document the number of rows dropped and the reasons, as this demonstrates awareness of data loss impact and helps with reproducibility. Consider whether dropping missing fares could introduce bias, and mention alternative imputation strategies if appropriate.
Check the shape, data types, and missing value counts for fare and route columns. Identify empty strings or whitespace in route and non-numeric entries in fare.
Remove rows where route is missing, empty, or contains only whitespace. Optionally, strip whitespace and standardize route strings.
Drop rows with missing fare values. Convert fare to numeric using pd.to_numeric with errors='coerce', then drop any rows that failed conversion (became NaN).
Verify that no missing fares or empty routes remain and that fare is numeric. Record the number of rows dropped and any assumptions made.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
str.split with expand=True is clean but I forgot to set n=1 initially, which would've caused issues if any route had more than one pipe.
Start by clarifying the data format and edge cases, then outline a step-by-step transformation using pandas: split the route column on the pipe, expand into two columns, trim whitespace, and filter out rows with fewer than two parts. Emphasize handling missing or malformed data and validating the result.
Pro tip: Mention that you would first inspect the distribution of split counts to understand data quality, and use vectorized string operations for efficiency rather than apply, which is slower on large datasets.
Inspect the route column to see typical values, delimiters, and potential edge cases like missing pipes or extra pipes. Clarify whether to keep only rows with exactly two parts or at least two parts.
Use pandas' str.split method with the pipe delimiter and expand=True to create separate columns. This efficiently handles the split in a vectorized manner.
Apply str.strip to both resulting columns to remove leading and trailing whitespace, ensuring clean origin and destination values.
Drop rows where the split did not produce at least two parts. This can be done by checking the number of non-null values in the split columns or by using a mask based on the original string containing the delimiter.
Check the resulting DataFrame for correct column names, data types, and no unexpected nulls. Optionally, rename columns to 'origin' and 'destination' and reset the index.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Left join then filter felt a bit redundant to me, like why not just do an inner join.
Walk through the join and filter logic step by step, explaining why a left join is used first and then why filtering to matched rows effectively converts it to an inner join. Emphasize the importance of validating the join keys and understanding the business context for why unmatched rows are dropped.
Pro tip: Mention that filtering after a left join to keep only matched rows is equivalent to an inner join, but the explicit left join + filter pattern can be useful for debugging or when you want to inspect unmatched rows before discarding them.
Verify that both datasets have a driver ID column, check for data types and potential duplicates, and understand the grain of each table.
Use a left join to retain all trips and attach driver information where available, ensuring that trips without a matching driver get nulls for driver columns.
Apply a filter to keep only rows where the driver ID from the drivers table is not null, effectively removing trips without a driver record.
Check row counts and confirm that the output contains only trips with valid driver information, and discuss the equivalence to an inner join.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the part that took the most time.
Start by clarifying the schema and edge cases, then outline a SQL solution using window functions to compute per-driver aggregates and a separate window to rank trips by timestamp. Combine the two using a join or conditional aggregation, ensuring correct rounding and handling of drivers with fewer than three trips.
Pro tip: Mention that you would validate the results by checking a few drivers manually and consider performance implications for large datasets, such as using appropriate indexes or partitioning.
Ask about the table structure, column names, data types, and any constraints. Confirm definitions: trip count, average fare, and 'last three trips' based on timestamp.
Use GROUP BY driver_id to calculate trip count and average fare, rounding to two decimals. Ensure handling of NULLs or invalid fares.
Use a window function like ROW_NUMBER() OVER (PARTITION BY driver_id ORDER BY timestamp DESC) to rank trips, then filter for rank <= 3.
For each driver, calculate the average fare of the selected last three trips (or fewer if not available), rounding to two decimals.
Join the overall metrics with the last-three-trip average, ensuring all drivers are included. Output driver_id, trip_count, avg_fare, and avg_last_3_fare.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
sort_values with multiple columns and mixed ascending/descending is one of those things I always have to look up.
Start by clarifying the data schema and assumptions (e.g., whether 'fare' is per trip or total, and how to handle missing values). Then outline the aggregation and sorting logic: group by driver, compute average fare and trip count, sort by average fare descending, trip count descending, and driver name ascending, and take the top two. Finally, mention how to inspect the resulting DataFrame's column types, such as using df.dtypes or df.info().
Pro tip: Explicitly state that you would validate the result by checking for ties at the cutoff and confirming that the sorting order matches the business requirement, since ranking logic is a common source of subtle bugs.
Ask about the data schema, what 'fare' represents, and how to handle missing or invalid values. Confirm that 'driver' is a unique identifier and that 'trip count' means the number of trips per driver.
Group the data by driver and compute the average fare and the number of trips for each driver. Ensure that the average is calculated correctly (e.g., mean of fare column).
Sort the aggregated DataFrame by average fare descending, then by trip count descending, then by driver name ascending. Use a stable sort or specify all keys to ensure correct tie-breaking.
Take the first two rows after sorting to get the top two drivers. Optionally, reset the index or keep driver as a column for clarity.
Print the DataFrame's column types using df.dtypes or df.info() to show the data types of each column in the final result.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.