Duplicate Line Remover

Remove duplicate lines and keep only unique lines.

Input

Output

Examples

Example 1

apple
banana
apple
orange
banana
grape

Example 2

Line 1
Line 2
Line 1
Line 3
Line 2

How Duplicate Line Removal Works

Duplicate removal tracks seen lines and filters subsequent occurrences. First instance of each unique line passes through, while repeated lines are discarded. 'apple\nbanana\napple' becomes 'apple\nbanana' with second apple removed.

Line-by-line comparison uses exact string matching. 'Apple' and 'apple' are different lines unless case-insensitive mode is enabled. Whitespace differences also create distinct lines—leading/trailing spaces prevent matching.

Preservation order maintains first occurrence position. If line A appears on lines 5, 12, and 20, output includes line A at position 5 only. Original order is preserved for non-duplicated lines and first instances.

Practical Deduplication Applications

Data cleaning removes duplicate entries from lists. Email lists, username collections, and inventory records often accumulate duplicates—removal ensures one entry per unique item for accurate counting and processing.

Log file analysis benefits from unique event extraction. Server logs repeat common messages—deduplicating reveals distinct events and error types without noise from repeated identical entries.

Combining multiple sources creates duplicates. Merging contact lists, aggregating search results, or consolidating data from different systems produces overlapping entries—deduplication merges sources cleanly.

Testing and validation uses unique values for coverage. Generating test inputs or checking distinct scenarios requires unique values—duplicate removal from generated datasets ensures comprehensive testing without redundancy.

Duplicate Removal Challenges

Case sensitivity creates false non-duplicates. 'Product A' and 'product a' are treated as unique unless normalized. Case-insensitive deduplication requires lowercasing before comparison to match human perception of duplicates.

Whitespace variations prevent matching. Lines differing only in spaces, tabs, or line endings are considered distinct. Trim and normalize whitespace before comparison for accurate duplicate detection.

Ordering information is lost. If line order matters semantically (chronological logs, ranked lists), deduplication while preserving original order may remove later occurrences that have temporal or priority significance.

Near-duplicates are not detected. 'Contact: John Smith' and 'Contact: John Smith ' (trailing space) are different lines. Fuzzy matching or normalization is needed to catch similar but not identical duplicates.

Best Practices for Deduplication

Normalize before deduplication: Trim whitespace, convert to lowercase, remove punctuation—ensure that intended duplicates actually match exactly after preprocessing.

Decide on case sensitivity explicitly: For user-facing data (names, addresses), case-insensitive deduplication is often appropriate. For technical data (identifiers, keys), preserve case sensitivity.

Sort before or after deduplication: Sorting before deduplication groups duplicates for efficient processing. Sorting after creates alphabetically ordered unique lists for easier review.

Count duplicates before removing: Report how many instances of each line existed. Statistics like '10 unique lines (from 47 total)' inform users about deduplication impact and data quality.

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