Python Code Doctor

Python Code Doctor: practical plan

Review plan

Reproduce safely

Capture Python and dependency versions, a small representative CSV, expected output and exact error. Create tests for blank customer IDs, missing amounts, malformed numbers, duplicate rows and encoding.

Correctness

Define whether blank IDs should be rejected, grouped as unknown or excluded. Decide how invalid amounts are reported. Avoid silently converting bad values to zero. Validate headers and input schema.

Large-file handling

Measure memory and runtime first. If the file exceeds memory, process chunks and aggregate incrementally, while preserving numeric precision and deterministic output. Use explicit encodings and newline handling.

Reliability

Write to a temporary file then atomically replace the destination after validation. Log row counts and rejected records without exposing unnecessary personal data.

Focused tests

Compare totals against a small hand-checked fixture and a larger synthetic file. Test interruption and rerun behavior.

Recommendation

Fix correctness and observability before optimizing. Benchmark with realistic data, pin dependencies and document how to reproduce the result.

Measurement and review

Record the baseline, source, date range, denominator and owner. Use one primary measure plus quality guardrails; compare like-for-like cohorts and disclose missing data. Treat estimates as scenarios, not observed outcomes.

Risks and controls

Check relevance, consent, disclosure, workload and downside before scaling. Keep an evidence trail, obtain human review for material commitments, and pause if trust, quality or compliance signals deteriorate.

Next steps

1. Confirm the goal, audience and available capacity.
2. Gather the minimum evidence for a first test.
3. Assign an owner, budget and review date.
4. Run a small pilot, record outcomes and decide whether to iterate, expand or stop.