SmartQueryTools

Find & Replace in JSON Files Online

Find and replace text values in JSON files directly in your browser. Supports plain text and regex patterns across any column — no upload required.

How to find & Replace in JSON files

  1. Drop your file onto the upload area. The tool shows the row count, how many text columns it found, and the first 200 rows.
  2. In Apply to column, keep All text columns or pick one column. Number, date and boolean columns are never changed.
  3. Type the text to find and the replacement. Leave Replace with empty to delete every match.
  4. Tick Use regular expression if the Find box holds a pattern rather than literal text, then click Find & Replace.
  5. Check the result preview and download the file in the same format.

Your file is processed locally in your browser and is never uploaded. The free limit is 50 MB per file; larger files work if your device has the memory for them.

Worked example

A contractor list was typed in by hand, so phone numbers mix brackets, spaces, dashes and a country prefix. The SMS gateway wants digits only.

Input (JSON)

[
  {
    "name": "Aroha Ngata",
    "phone": "(021) 555-0143",
    "city": "Auckland"
  },
  {
    "name": "Liam Park",
    "phone": "021 555 0198",
    "city": "Hamilton"
  },
  {
    "name": "Mele Tupou",
    "phone": "+64 21 555 0112",
    "city": "Auckland"
  },
  {
    "name": "Sam Reid",
    "phone": null,
    "city": "Tauranga"
  }
]

Settings

  • Apply to column: phone
  • Find: [^0-9]
  • Replace with: (empty)
  • Use regular expression: ticked

Result

namephonecity
Aroha Ngata0215550143Auckland
Liam Park0215550198Hamilton
Mele Tupou64215550112Auckland
Sam ReidNULLTauranga

The pattern [^0-9] matches any character that is not a digit, and every match in the cell is replaced, not just the first. Brackets, spaces, dashes and the plus sign are removed. The phone column stays text, so the leading zero survives. Sam's empty phone stays empty. Limiting the run to the phone column keeps the other columns untouched.

Working with JSON files

Each top-level key becomes a column, and only string-typed keys are offered. A field that holds numbers in some objects and strings in others is loaded with a generic JSON type and is not offered. Objects that do not have the key are loaded as null and stay null after the run, so the key is written back with a null value rather than left out. Number and boolean values are never changed, so replacing 1 with yes only touches string fields.

Nested objects and arrays are loaded as struct and list columns, and replacement does not reach inside them. They are not offered in the column list, and All text columns leaves them unchanged. To edit a nested value such as address.city, flatten the JSON first. Replacement text is written as a normal JSON string, with quotes and backslashes escaped for you.

Frequently Asked Questions

Can I find and replace inside nested JSON objects?

No. Only top-level string keys are searched. Flatten the JSON so nested fields become their own columns, run the replacement, and keep the flat result or rebuild it as needed.

Does All text columns touch nested JSON objects?

No. Only top-level string keys are changed. Nested objects and arrays, even ones that hold strings, are written back unchanged.

Is the search case-sensitive?

Yes, in both modes. With Use regular expression ticked, you can start the pattern with (?i) to ignore case, for example (?i)limited.

Can I use capture groups in the replacement?

Yes. In regex mode, write \1, \2 and so on in Replace with. For example, find (\d{4})-(\d{2}) and replace with \2/\1 to turn 2026-04 into 04/2026. The syntax is RE2, so lookaheads and lookbehinds are not supported.

Does it replace every match or only the first one in each cell?

Every match. Both plain text and regex mode replace all occurrences in each cell of the targeted columns.

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