| name | dataset-profiler |
| description | Use when first encountering a new dataset — produces a structured profile (schema, missingness, distributions, outliers, gotchas) before any analysis. |
| version | 0.1.0 |
| status | experimental |
| risk | low |
| tags | ["data","read-only","writes-files"] |
Dataset Profiler
When to use
- A new dataset arrives and you need to understand it before using it
- Before reproducing an analysis that referenced a dataset
- When data quality is suspect ("the chart looked wrong")
When NOT to use
- Streaming / online data (this is point-in-time)
- Sensitive PII without an explicit allow-list
Inputs
| Name | Type | Required | Notes |
|---|
path | path | yes | CSV / Parquet / JSONL |
target | string | no | column of interest (gets extra distribution detail) |
Outputs
profile.md with: Source, Schema, Missingness, Distributions, Outliers, Joins / keys, Gotchas, Open questions.
Workflow
- Load with the right reader (extension-detected); record row count, file size
- Schema: column → dtype → nullable → example value
- Missingness: % per column, top columns by missingness
- Distributions: numeric (min, p50, p95, max, std), categorical (top-k, cardinality)
- Outliers: flag rows beyond p99 + 3·IQR for numerics
- Identify potential keys (unique columns) and join candidates
- Gotchas: timezone columns, mixed encodings, suspicious all-zero rows, magic values (
-1, 9999-12-31)
- Open questions: ambiguous columns / values that need owner input
References
Success criteria
- Every column appears in Schema + Missingness
- Outliers section includes example rows
- Gotchas section is non-empty (real datasets always have some)
Failure modes
- File too large to read in memory → switch to streaming + sampled stats; flag prominently
- Encoding fails → try common alternatives; if all fail, surface and stop
