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Drop multiple columns matching regex, wildcard prefix, or substring patterns in one click

Delete batches of columns matching regex or text patterns from wide exports.

🛡️ Your rows never leave your browser.

Why Onfyt: Provides instant interactive preview highlighting all columns slated for deletion before applying the schema change.

Interactive Sample Data

id	name	temp_score	temp_rank	notes	sync_status
1	Alpha	92	1	Verified	OK
2	Beta	78	4	Pending	QUEUED

How this tool works

Modern data exports from platforms like Salesforce, HubSpot, or big data query engines often produce tables with dozens or hundreds of columns. Frequently, large blocks of these columns share naming conventions that identify them as temporary calculations, internal system metadata, or deprecated properties—such as 'temp_score', 'temp_rank', 'sync_v1_timestamp', or 'internal_guid'. Deleting these columns one by one in Excel or manual table editors is slow, repetitive, and prone to accidentally removing production columns. If a table has 150 columns and you need to discard 40 columns starting with 'analytics_', point-and-click column deletion becomes a major time sink. DataFix lets you specify pattern matching rules—including wildcards (e.g. 'temp_*'), suffixes ('*_raw'), substrings ('audit'), or full regular expressions—to identify and drop matching columns in a single operation. The tool previews exactly which column headers matched the rule before removing them, ensuring you never inadvertently delete vital business data. Filtering out columns matching specific regex or prefix patterns (such as temporary staging fields or metadata tags) cleans wide tables before production export.

Things that usually go wrong

Accidentally matching the primary key column and removing it breaks all downstream joins.
Test the pattern in preview mode before applying to the full file.

Edge cases we tested

🧪 Case-insensitive pattern matching ('Temp_' vs 'temp_')

Matched correctly when case-insensitivity toggle is active.

🧪 Patterns matching all columns in the file

Alerts user and prevents generating an invalid zero-column empty file.

🧪 Headers containing special regex characters (brackets, dots)

Properly escaped in literal wildcard mode to avoid syntax errors.

Frequently Asked Questions

What is the difference from Keep Columns by Pattern?

This tool removes matching columns; Keep Columns removes non-matching columns. They are inverses.

Can I remove multiple patterns at once?

Yes — enter patterns separated by commas, or use a regex alternation (pattern1|pattern2).

Can I preview which columns will be removed before applying?

Yes — the preview panel shows exactly which columns match your pattern and would be removed, before you click Apply.

Can I use multiple patterns in one pass?

Yes — enter comma-separated patterns (internal_, sys_, _temp) and all matching columns are removed in one step.

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