Remove Duplicate Lines

Paste text and instantly remove all duplicate lines. Supports case-insensitive matching, line trimming, and blank line removal.

Input
Options

Frequently Asked Questions

Yes. The first occurrence of each unique line is kept and its position is preserved. Subsequent duplicate occurrences are removed. The output order matches the order of first appearance in the input.
When enabled, Apple and apple are treated as the same line and the first occurrence is kept. When disabled (default), case differences are significant — Apple and apple would both be retained.
When enabled, leading and trailing spaces are stripped from each line before comparison and from the output. This means   apple and apple  would be treated as duplicates of apple.
A duplicate is any line whose content is identical to a previously seen line after applying your chosen options (trimming and case folding). Only the first occurrence is kept; all later occurrences are removed. If case-insensitive is off, "Apple" and "apple" are not duplicates. If trimming is off, "apple " (with trailing space) is not a duplicate of "apple".
In data cleaning workflows, duplicate rows inflate counts, skew statistics, and cause double-billing or double-sending issues. Removing duplicates from email lists prevents sending the same message twice. Removing duplicate IDs from exports ensures accurate record counts. Enable case-insensitive and trim options to catch near-duplicates introduced by inconsistent data entry.
This tool keeps the first occurrence and removes all subsequent duplicates — matching what SQL's DISTINCT, Python's dict.fromkeys(), and spreadsheet deduplication tools do. To keep the last occurrence instead, reverse the list first, remove duplicates, then reverse again.
No. This tool performs exact matching only (with optional case-folding and trimming). Near-duplicates like "John Smith" and "John Smit" (a typo) would not be flagged. Fuzzy deduplication requires a similarity algorithm such as Levenshtein distance or Jaro-Winkler, typically available in Python via rapidfuzz.
When Remove blank lines is checked (the default), all empty lines are removed from the output. When unchecked, blank lines are treated as a line with empty content — meaning multiple blank lines would be deduplicated down to a single blank line. Uncheck this option to preserve intentional paragraph spacing.
In case-sensitive mode (default), "Apple", "apple", and "APPLE" are three distinct lines — all three would be kept. In case-insensitive mode, all three are considered duplicates of the same value and only the first occurrence is kept. Use case-insensitive mode when cleaning user-submitted data where the same value may have been entered with different capitalisation.
Not exactly. The Unix command sort -u sorts the lines alphabetically and then removes duplicates — it does not preserve the original order. This tool removes duplicates while preserving the original line order (keeping the first occurrence). If you also want alphabetical sorting, use the Sort Lines tool after deduplication.

About This Duplicate Line Remover

This free duplicate line remover deletes repeated lines from any text, keeping only the first occurrence of each line. Options include case-insensitive matching and trimming whitespace before comparing — all processing happens in your browser.

When to use this tool

  • Deduplicating a list of URLs, email addresses, or keywords
  • Cleaning up log output with repeated identical lines
  • Removing duplicate entries from a CSV column before importing
  • Normalising a word list before further text processing

Standards & References

Related Articles

In-depth guides and technical articles.

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PCR Duplicates, Git's Content Hashing, and Cross-Language Plagiarism — How "Duplicate" Means Something Different in Every Technical Domain
PCR duplicates in DNA sequencing aren't defined by identical sequence text — they're defined by identical mapping position, because sequencing errors mean true duplicates may differ by a single base. Here's how Git's content-addressable storage deduplicates by exact SHA hash, why CMS media libraries need perceptual hashing to catch visually-identical-but-byte-different logo uploads, and why cross-lingual plagiarism detection requires semantic embeddings rather than text matching.
What Does "Duplicate" Actually Mean? The Normalization and Merge Decisions Behind Effective Deduplication
Deduplication is a business logic decision masquerading as a technical one — "duplicate" means different things for contact lists (same email, different fields) vs URL lists (case sensitivity, trailing slashes) vs product catalogs (same SKU, different descriptions). Here's the exact vs near-duplicate distinction, normalization as the preprocessing step that determines deduplication quality, CRM merge strategies, and URL-specific case sensitivity rules.
The Algorithm Behind "Remove Duplicates": Sort-Then-Scan vs Hash-Set, and When Each Is Right
"Remove duplicates" in a sorted list is a different operation than in an unsorted list — and which you need determines whether you must sort first, and whether you can process in a single pass. Here's the sort-then-scan vs hash-set trade-off (O(n log n) memory-efficient vs O(n) order-preserving), the "which occurrence to keep" question, and two edge cases most people don't think about until they hit them: blank lines and case sensitivity.
Email List Deduplication and GDPR: Why "We Have This Person Eleven Times" Is a Compliance Question, Not Just a Mess
Three different "subscriber" CSV exports often contain the same person eleven times — with case variations, plus-addressing tags, and provider-specific formatting quirks. Under GDPR, duplicate contact records aren't just messy — they're a compliance question for "right to erasure" and access requests. Here's why email lists accumulate duplicates, the normalization steps before exact-match deduplication, and the consent-tracking implications of un-reconciled duplicates.
Database Deduplication at Scale: Fuzzy Matching, Master Data Management, and Building a Deduplication Pipeline
Duplicate database records cost businesses in wasted marketing spend and GDPR violations — and simple string matching misses "St" vs "Street" or "Smyth" vs "Smith." Here's the deduplication spectrum from exact to fuzzy matching, master data management golden records, and building a Python deduplication pipeline.