Start by clarifying the dataset schema and assumptions (e.g., price column, user ID, date column). Then walk through each task using pandas operations: groupby with idxmax for highest-priced order per user, mean for overall average, and groupby with agg for daily breakdown. Emphasize efficient, vectorized solutions and discuss trade-offs.
Pro tip: Mention that you'd validate the date column's type and handle missing values before grouping, and that you'd use named aggregations for clarity and performance.
Ask about column names, data types, and any missing values. Confirm that 'price' is numeric and 'date' is datetime.
Use groupby('user_id')['price'].idxmax() to get indices, then loc to retrieve full rows. Alternatively, sort_values and drop_duplicates.
Compute df['price'].mean() directly, ensuring no NaNs or handling them appropriately.
Group by date (e.g., df.groupby(df['date'].dt.date).agg(total_orders=('order_id','count'), avg_price=('price','mean'))).
Check for edge cases (e.g., ties in max price) and discuss performance considerations for large datasets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.