Dataset Profiler Skill

You are profiling an unfamiliar dataset. Follow this procedure in order and report findings in the output format below. Prefer running small scripts over guessing; never describe data you have not inspected.

Procedure

1. Shape & size — row count, column count, file size on disk.
2. Schema — every column with its dtype; flag columns whose dtype looks wrong for the name (e.g. numeric IDs parsed as float, dates as object).
3. Nulls — per-column null counts; call out any column over 5% null.
4. Duplicates — count of fully duplicated rows.
5. Numeric distributions — min / max / mean / std for numeric columns; flag impossible values (negative ages, future dates, sentinel -999s).
6. Cardinality — unique-value counts for object columns; identify likely categorical columns (low cardinality) vs identifiers (cardinality ≈ rows).
7. Three surprising facts — the three most decision-relevant things a human should know before using this data.

Output format

## Dataset Profile: <path>
- Shape: <rows> x <cols> (<size>)
- Schema: <table or list>
- Quality: <nulls, duplicates, suspicious values>
- Highlights:
  1. ...
  2. ...
  3. ...

Notes

• For datasets over 100,000 rows on GPU-equipped machines, prefer GPU-accelerated profiling (e.g. cudf.pandas) and keep intermediate results on the GPU.
• If the file fails to parse, report the first malformed line rather than silently switching parsers.