The groupby part was fine, agg with a dict of functions is second nature at this point.
Start by clarifying the dataset schema and definitions (e.g., what constitutes a successful payment, how to handle refunds or partial payments). Then use pandas groupby aggregations to compute total amount paid and success rate per loan, and finally derive the boolean flag by comparing total paid to the $1,000 principal. Ensure the output is a clean DataFrame with one row per loan.
Pro tip: Always validate assumptions about the data, such as whether 'amount paid' includes only successful payments or all attempts, and whether the principal is fixed at $1,000 for all loans. Mentioning these checks shows attention to detail and business acumen.
Inspect the dataset to identify columns like loan_id, payment_amount, payment_status, and any other relevant fields. Clarify definitions: what counts as a successful payment? Is the principal always $1,000? Are there refunds or adjustments?
Filter to successful payments if necessary, then group by loan_id and sum the payment amounts. Use pandas groupby and agg to get total_paid per loan.
For each loan, compute the proportion of successful payments out of total payment attempts. This can be done by creating a boolean column for success, then grouping by loan_id and taking the mean.
Compare the total amount paid per loan to the $1,000 principal. Create a new column 'fully_repaid' that is True if total_paid >= 1000, else False.
Merge the aggregated metrics into a single DataFrame with one row per loan, containing loan_id, total_paid, success_rate, and fully_repaid. Ensure the output is clean and ready for analysis.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.