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Analyze Loan Payments Using Pandas for Key Insights

AffirmData Scientist · Technical Screen
MediumData ScientistTechnical ScreenData Manipulation (SQL/Python)

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This question evaluates data manipulation and aggregation skills using pandas, focusing on computing per-loan payment totals, payment success rates, and a repayment-status flag. It is commonly asked to assess applied data wrangling and numeric summarization abilities in the Data Manipulation (SQL/Python) domain, testing practical application rather than purely conceptual understanding.

loan_payments

+------------+------------+-------------+-----------+---------+ | loan_id | payment_id | payment_date | amount | status | +------------+------------+-------------+-----------+---------+ | L1 | P1 | 2023-01-15 | 250.00 | success | | L1 | P2 | 2023-02-15 | 250.00 | success | | L1 | P3 | 2023-03-15 | 250.00 | success | | L1 | P4 | 2023-04-15 | 250.00 | success | | L2 | P5 | 2023-01-20 | 500.00 | failed | +------------+------------+-------------+-----------+---------+

Scenario

Credit Risk – Analyze loan-payment data with pandas

Question

Using pandas, load the loan_payments table below and compute: (a) total amount paid per loan_id, (b) payment success rate per loan_id, and (c) a flag indicating whether the loan has been fully repaid ($1,000 principal). Return the resulting DataFrame.

Hints

Use groupby, agg, and boolean logic.