Start by clarifying the exact output format and edge cases (e.g., missing values, special characters) before writing any code. Then design a solution that parses the CSV, aggregates fees by status, and formats the output with precise control over alignment, separators, and decimal precision. Finally, validate the output against the provided example byte-for-byte.
Pro tip: Use a CSV library with proper quoting and escaping to handle edge cases, and format numbers using fixed-point arithmetic to avoid floating-point rounding errors. Always test with the provided example and compare byte-by-byte to catch subtle formatting issues.
Ask about the exact output format, including column order, separators, header text, alignment, decimal precision, and trailing newline. Discuss how to handle missing or malformed data, and whether fees should be summed or averaged.
Choose a robust CSV parsing approach (e.g., using a library) and decide on data structures to group fees by status. Consider using a dictionary mapping status to aggregated fee totals, and handle numeric precision carefully.
Construct the output string by explicitly formatting each column with the required alignment and decimal precision. Use string formatting functions that allow control over padding and rounding, and ensure the trailing newline is included.
Run your solution on the provided example and compare the output byte-by-byte with the expected result. Use diff tools or checksums to catch any discrepancies, and iterate until they match exactly.
Explain your choices regarding parsing libraries, memory usage, and performance. Mention how your solution would scale with larger files and whether streaming or batch processing is appropriate.
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