Summarise the structure and quality of a local data file.

Call this whenever you need to understand a dataset — its columns, types, ranges, missing values, or quality problems — before analysing it, writing code against it, or answering questions about it. Prefer this over reading the file directly: it returns a compact summary instead of raw rows, so it works on files far too large to read, at a small fraction of the tokens.

Reports per column: dtype, null count and percentage, distinct count, sample values, quartiles for numbers, date ranges, and the most frequent values for categories. Flags likely problems: all-null and constant columns, probable ID columns, mixed types, and numbers or dates that were stored as text.

Args: path: Path to the file. Supports .csv, .tsv, .parquet, .json, .jsonl, .xlsx, and .xls. sample_rows: Profile at most this many rows. Pass null to read every row, which is slower on large files but makes all statistics exact. The result always states whether it was sampled. max_columns: Describe at most this many columns, so the response stays small on very wide tables. The true column count is always reported. top_k: How many of the most frequent values to list per categorical column. sheet: For Excel workbooks, the name of the sheet to profile. Defaults to the first sheet, which is often a title or notes page rather than the data. The result lists every available sheet, so if the one profiled looks empty or wrong, call again naming another.

Returns: A profile with file info, shape, per-column detail, and duplicate row count.