Staaarter

CSV Data Finder

Searches all cells of a CSV for a query (a case-insensitive-by-default substring, or a regular expression) and reports every matching cell's (row, column) coordinate along with the full rows those matches belong to. A free online tool from Staaarter, right in your browser.

Runs locallyUpdated 2026-07-29
By Staaarter Team
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Overview

Introduction

Finding a specific value buried in a large CSV by scrolling through a spreadsheet is slow and error-prone. This tool searches every cell at once and tells you exactly where each match lives.

It supports both a simple substring search for quick lookups and a full regular expression for pattern-based searches.

What Is CSV Data Finder?

A whole-CSV cell search tool: it parses the input into a grid and tests every single cell against your query, reporting the (row, column) coordinate of each match.

Matching is a case-insensitive substring by default, or a regular expression when regex mode is enabled.

How CSV Data Finder Works

The CSV is parsed with the shared RFC 4180-aware grid parser. Every cell in every row (including the header) is then tested against the query using either String.includes (substring mode) or RegExp.test (regex mode).

Every match is collected with its row and column index and original value, then grouped so the full content of each matching row is also shown alongside the individual cell coordinates.

When To Use CSV Data Finder

Use it to quickly locate every occurrence of a value across an entire CSV, without knowing which column it's in ahead of time.

Regex mode is useful for pattern searches, like finding every cell that looks like an email address or a specific ID format.

Often used alongside CSV Cell Filter and CSV Comparator.

Features

Advantages

  • Searches every column simultaneously, so you don't need to know in advance where a value lives.
  • Reports exact (row, column) coordinates for each match, not just which row matched.
  • Supports both simple substring search and full regular expressions.

Limitations

  • An invalid regular expression is rejected with an error rather than silently falling back to substring search.
  • On very large CSVs, searching every cell can be slower than a targeted column-only search.

Examples

Finding every cell containing 'ada'

Input

id,name,manager
1,Ada Lovelace,Grace Hopper
2,Alan Turing,Ada Lovelace

Output

row 1, column 1: "Ada Lovelace"
row 2, column 2: "Ada Lovelace"

The case-insensitive substring "ada" matches "Ada Lovelace" wherever it appears, whether in the name column or the manager column.

Best Practices & Notes

Best Practices

  • Use regex mode with anchors (^, $) when you need to match a whole cell's value rather than a substring anywhere inside it.
  • Enable case-sensitive mode if your data legitimately distinguishes case, e.g. product SKUs.
  • Combine with CSV Cutter afterward if you only want to keep the columns where matches were found.

Developer Notes

Regex compilation happens once up front and is wrapped in a try/catch so an invalid pattern produces a clear error message rather than throwing mid-search across thousands of cells.

CSV Data Finder Use Cases

  • Locating every row that mentions a specific customer, product, or ID across an entire export
  • Auditing a CSV for cells matching a pattern, like malformed email addresses
  • Quickly confirming whether a value exists anywhere in a large dataset before writing a more targeted query

Common Mistakes

  • Forgetting that regex mode applies per-cell, not per-row, so a pattern spanning multiple columns won't match anything.
  • Leaving case-sensitive mode off when searching for something that's only meaningfully distinct by case.

Tips

  • Start with substring search for a quick check, then switch to regex only if you need pattern matching.
  • If you get too many matches, narrow the query or switch on case-sensitive mode to cut down noise.

References

Frequently Asked Questions