Dataset Clean Tools
Clean up datasets locally in your browser, with no upload.
Email List Cleaner
Clean a list of email addresses: normalize whitespace and casing, remove exact duplicates, and separate malformed entries out for review, with CSV/TXT export of clean, duplicate, and malformed lists.
Phone Number List Cleaner
Clean a list of phone numbers: pick a default country or require explicit + prefixes, normalize whitespace, punctuation, trunk prefixes, and extensions under a visible rule set, then output E.164, national, or display format with ambiguous records flagged for review.
Data Cleaner
Format-aware cleanup entry point that auto-detects CSV, TSV, Excel, JSON, JSONL, XML or YAML, applies safe shared cleanup (whitespace, blank rows/lines, BOM, line endings), shows a before/after preview and change report, exports with normalized extensions (.ndjson to .jsonl, .yml to .yaml, .txt to .csv, and Excel input to .xlsx), and routes you to the right format- or domain-specific workspace for advanced controls.
Fill Blank Cells
Fill blank cells in selected table columns using an explicit rule you choose - constant value, previous/next row, group value, sequence, or formula - then review the 200-row preview and change log before export.
Normalize Null Values
Standardize inconsistent missing-value markers - blank, NULL, N/A, NA, unknown, dash, and more - into one chosen representation, with per-column overrides and a preview before export.
Remove Duplicate Rows
Remove duplicate records from CSV, Excel, JSON, and pasted tables by full-row or key-column matching. Choose keep-first, keep-last, or manual review, preview duplicate groups before export, and download retained-versus-removed records with original row references.
Address Format Standardizer
Normalize supplied address fields into a consistent presentation: map your columns to address roles, choose component order, separators, casing, line breaks, and a regional layout rule, then write normalized fields alongside (or in place of) the originals with incomplete or ambiguous records flagged for review.
Date Format Standardizer
Detect common date formats in a selected column and convert them to one of four output formats: ISO 8601, US style, EU style, or long form. Flags values that are ambiguous or unparseable for review. Normalizes representation only - it does not validate whether a date is logically valid beyond what's needed to parse it.
Value Clusterer & Standardizer
Cluster inconsistent text values within a column - typos, casing, abbreviations - and standardize them to an approved canonical value. Nothing is replaced without your explicit approval.
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