All conversions
20 dedicated pages across 6 formats. The converter below handles any combination, including pairs that do not have their own page yet.
CSVParquet
Runs on your deviceOptions
Reading CSV
Auto-detect reads the first rows and picks the separator that splits them consistently.
Turn off when the file starts straight into data. Columns are then named column_1, column_2, and so on.
Turns "42" into a number and "true" into a boolean. Values with leading zeros stay text so IDs and zip codes survive.
Which worksheet to convert. Filled in once the workbook is read.
Dotted path to the array of records, such as data.items. Leave empty to use the whole document.
Turns {"user":{"id":7}} into a user.id column. Arrays are kept as JSON text.
Pick the encoding the file was written in. Wrong encoding shows up as garbled accents.
Comma-separated words that should become null instead of text.
Writing Parquet
Snappy is the default across Spark, DuckDB and pandas. Gzip is smaller but slower to read.
Turns a user.id column back into {"user":{"id":7}}.
Excel needs a byte order mark to open UTF-8 files with accents correctly.
Rows per row group. Larger groups compress better; smaller groups let readers skip more.
conversion-preroll
This short ad is what keeps the converter free.
Parquet is unreadable in a text editor. That is by design.Check your new Parquet file with ParquetReader — opens Parquet files directly.From CSV
Plain text rows that open anywhere, with no types and no schema.
From TSV
Like CSV, but tab-delimited, so commas inside values stay harmless.
From JSON
Nested, self-describing records that every language can read.
From JSONL
One JSON object per line, built for streaming and appending.
From Parquet
Typed, compressed, columnar storage built for analytical scans.
From Excel
Spreadsheet workbooks with sheets, formatting and cell types.