Dataset Clean Tools

Clean rows and missing values

Use these pages when the issue is visible across rows or when an empty value needs a stated replacement rule.

• Data Cleaner detects CSV, TSV, Excel, JSON, JSONL, XML, or YAML. It applies shared cleanup, previews the changes, and exports the result with normalized extensions, including XLSX for Excel input.
• Remove Duplicate Rows from CSV, Excel, JSON & Tables compares full rows or selected key columns. Keep the first or last match, review matches manually, and export the retained and removed records.
• Fill Blank Cells fills selected columns with a constant, the previous or next row, a group value, a sequence, or a formula. The changed table is exported as CSV.
• Normalize Null Values replaces selected missing-value markers in CSV, TSV, or TXT tables with a target for each column and a preview of the result.

Standardize values by field

Choose a field-specific page when the value type sets the cleanup rule. A date such as 2026/10/17 follows a different rule from categories such as NYC and New York City.

Input:  2026/10/17
Output: 2026-10-17

• Email List Cleaner normalizes whitespace and casing, removes duplicate email entries, and separates malformed values for review.
• Phone Number List Cleaner treats phone values as text. Choose a default country or require plus prefixes, then export E.164, national, or display-format values with flagged records.
• Date Format Standardizer detects common date formats in one column and converts them to ISO 8601, US, EU, or long form. Ambiguous and unparseable values are flagged.
• Address Format Standardizer maps fields to address roles and applies your chosen order, separators, casing, line breaks, and regional layout. Incomplete records are flagged.
• Number & Currency Normalizer normalizes number and currency values in CSV, TSV, JSON, or pasted table data. Ambiguous values can be reviewed before you choose the output precision.
• Value Clusterer & Standardizer groups inconsistent categorical or text values using selected matching methods. Approve or edit a canonical value before export.

What each tool leaves for review

Some pages separate uncertain records instead of choosing an interpretation. Email List Cleaner and Phone Number List Cleaner export malformed or flagged records. Date Format Standardizer flags values it cannot parse or distinguish, while Address Format Standardizer flags incomplete records. Value Clusterer & Standardizer waits for your approval before it replaces a cluster with its canonical value.

Remove Duplicate Rows from CSV, Excel, JSON & Tables includes original-row references in its removed export and adds a JSON report. Address Format Standardizer changes presentation; it does not geocode an address or check deliverability. Phone Number List Cleaner applies formatting rules to phone text, rather than treating it as numeric data.

Common dataset cleanup jobs

A sign-up export contains repeated email addresses and inconsistent casing. Email List Cleaner produces a clean list, a duplicate list, and a malformed list for separate follow-up.

Several monthly files use blank cells, NULL, and N/A for missing values. Normalize Null Values handles the selected markers. Fill Blank Cells is for blanks that need a constant, row value, group value, sequence, or formula.

A report combines date and currency columns. Date Format Standardizer gives the date column one output format. Number & Currency Normalizer produces locale-neutral numeric values and leaves ambiguous entries for review.

Choosing a starting page

Use Data Cleaner when the file format or cleanup scope is unclear. Choose a field-specific tool when the problem is already known, such as duplicate rows, null markers, phone formatting, address presentation, or inconsistent category values.

Frequently asked questions