JSON to CSV Converter
Turn a JSON file into a CSV you can open, filter, and share. Upload the file, Gigasheet flattens the nested structure into rows and columns, and you export the result.
- Nested JSON becomes flat columns: Objects and arrays are unpacked into a spreadsheet-style table. No parsing script, no code.
- Big files welcome: Files that stall a browser converter or crash Excel load here. 500GB and 1 billion rows come standard, and both expand.
- Working with an MRF? Price transparency files usually need a viewer rather than a converter. Start with MRF Viewer instead.
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How to Convert JSON to CSV Online
- Create a Gigasheet account and upload your .json file. Zip it first if it is large.
- Gigasheet reads the structure and flattens nested objects and arrays into rows and columns.
- Open the table to search, filter, group, and calculate across the whole file.
- Export what you need as CSV for Excel, a BI tool, or a pricing model.
That covers the ordinary case: a JSON file in, a CSV out. Healthcare price transparency files are a different animal, and the section below explains why converting one is usually the wrong first move.
Gigasheet handles the common JSON shapes: an array of objects, newline-delimited JSON, deeply nested records, and inconsistent keys from record to record. Nested paths become dotted column names so you can see where every value came from, and you can drop the columns you do not need before exporting. Upload from your computer, from cloud storage such as Google Drive, OneDrive, Box, or AWS S3, or query the source directly from a warehouse.
If Your JSON Is an MRF, Converting It to CSV Is Usually the Wrong Move
Payer Transparency in Coverage files and hospital price transparency files are JSON, so the instinct is to convert one to CSV and open it in Excel. That plan breaks in three predictable ways.
- The CSV comes out bigger than the JSON. Flattening a TiC file repeats the payer, plan, provider, and code context on every single rate row. A single payer file routinely lands past what Excel will open, so the conversion succeeds and the file still will not.
- Flatten at the wrong level and the rate disappears. A negotiated rate sits four levels deep, under
in_network, thennegotiated_rates, thenprovider_groupsandnegotiated_prices. Convert at the top level and you get one row per billing code with the rates collapsed into unusable text. - Most of the rows do not matter. Zombie rates and duplicates can account for as much as 90 percent of an MRF. Converting them to CSV just converts the noise along with the signal.
MRF Viewer is built for exactly this case. It opens the JSON directly, flattens it at the rate level, and gives you filters, pivots, and export without a conversion step in between. Narrow to the payer, provider, billing code, or geography you care about first, then export a CSV small enough to actually use. If you want to see the structure before you commit, read how to open and view MRFs, which includes a sample TiC file.
When You Want the Data, Not the File
Converting files one at a time is a reasonable way to answer a single question. It is a poor way to run a benchmarking program. If you need negotiated rates that are already cleansed of zombie rates, deduplicated, and enriched with NPI, TIN, and Medicare reference context, see Gigasheet Price Transparency Data, or query them from your own systems with the Price Transparency API.
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Inspect Before You Export
Use the online JSON viewer to open and explore a large JSON file first, then export only the records you need as CSV.
Working With MRFs? Start in MRF Viewer
Payer and hospital price transparency files are too large and too nested to convert blind. MRF Viewer opens them directly, flattens to the rate level, and exports only the slice you filtered to.
Skip the Files Entirely
Cleansed negotiated-rate data with provider and payer enrichment, Medicare reference context, and out-of-the-box benchmarks, available as datasets or through the Price Transparency API.
