Write a CSV comparison helper that catches more than row-count differences.

Instruction: Implement the comparison in Python, give the exact findings and test a correct reordered export plus malformed inputs. Preserve string IDs.

Context:

Two small UTF-8 CSV texts have exactly the header id,name. Use Python csv.reader with its default comma-separated dialect and strict=True, or equivalent parsing. IDs are nonempty strings; leading zeros and name whitespace are significant. Expected IDs must be unique; duplicates are an invalid oracle. Actual row order is irrelevant. Report sorted missing IDs, unexpected IDs, duplicate actual IDs and changed-name IDs. A shared ID is changed if any actual occurrence has a different name. Reject wrong headers, wrong field counts, empty IDs and CSV parser errors. Header-only files are valid.

Expected:

id,name
001,"North, East"
002,"Quote ""Lab"""
003,東京

Actual:

id,name
002,"Quote ""Lab"""
001,North East
002,"Quote ""Lab"""
004,Extra

This is fixture-scale comparison in memory, not a large-file streaming design.

Updated

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I would parse the declared CSV dialect, validate the header and every row, and keep IDs as strings. The expected fixture must be unique before it can serve as an oracle...

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