The everyday conversion: a spreadsheet export needs to become something a program can read. This converter handles the parts that trip up naive implementations — quoted commas, embedded newlines, inconsistent row widths — and gives you properly typed JSON.
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 JSON
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.
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Work with Parquet as well?See ParquetReader with ParquetReader — opens Parquet files directly.Plain text rows that open anywhere, with no types and no schema. Nested, self-describing records that every language can read.
| CSV | JSON | |
|---|---|---|
| Type information | None — every value is text | Strings, numbers, booleans, null |
| Structure | Flat rows and columns | Nested objects and arrays |
| Compression | None | None |
| Typical file size | Baseline (100%) | Largest — keys repeat on every record |
| Schema | Header row at best | Implicit, per record |
| Human-readable | Yes, in any text editor | Yes, though verbose |
| Tool support | Universal | Every language, natively |
A postcode of 01234 stays the string "01234" rather than becoming the number 1234. This is the single most common way CSV to JSON conversions corrupt data, so the rule here is deliberate and not configurable per column.
JSON objects cannot have two keys with the same name. A second column called id becomes id_2, and the conversion warns you rather than silently dropping one of them.
Excel writes semicolon-delimited CSVs in locales where the comma is the decimal separator. Detection handles this, but if the numbers look wrong, check whether decimals are written as 1,5 rather than 1.5.
Product and content teams maintain data in a spreadsheet; developers need it as JSON. This is the bridge.
Most APIs accept a JSON array of objects, and almost none accept CSV.
JSON parses natively in both, with no CSV parsing edge cases to get wrong.
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