Online Tool Store
CSV to Parquet Preview
Paste a CSV sample to preview the column names, inferred types, and an estimated compressed size if it were converted to Parquet, a sanity check before running a real conversion pipeline. Runs entirely in your browser.
🔒 This tool runs entirely in your browser. Your files are never uploaded to a server.
id,name,signup_date,plan,mrr 1,Jane Doe,2026-01-14,pro,49.00 2,Alex Kim,2026-02-02,free,0.00 3,Sam Patel,2026-02-19,enterprise,499.00
Example shown — paste your own CSV to preview.
| Column | Inferred type |
|---|---|
| id | int64 |
| name | string |
| signup_date | date |
| plan | string (categorical) |
| mrr | double |
Estimated size: ~2.1 KB CSV → ~0.9 KB Parquet (est. with Snappy compression)
How it works
- Paste a CSV sample, including its header row.
- Each column's type is inferred from its values.
- An estimated Parquet size is shown alongside the schema.
FAQ
Does this produce a real .parquet file?
No — it previews the column schema Parquet would infer and estimates the compressed size, so you can sanity-check a dataset before running it through a real conversion pipeline (pandas, DuckDB, Spark).
How are column types inferred?
Each column is sampled and matched to the closest type — integer, double, date, or string — the same way most CSV-to-Parquet tools infer a schema before writing.
Why would Parquet be smaller than the same data as CSV?
Parquet is a columnar, compressed binary format — similar values stored together compress better than CSV's repeated text, especially for numeric and low-cardinality columns.
How we compare
| Feature | Online Tool Store | Running a full pandas/DuckDB conversion just to check | Guessing column types by eye |
|---|---|---|---|
| No pipeline setup for a quick check | Yes | No | Yes |
| Consistent type inference | Yes | Yes | Error-prone |
| Instant, in-browser preview | Yes | No | Yes |
Before wiring up a real conversion pipeline, this gives you a fast sanity check on schema and expected size.