List Cleaner & Deduplication Tool
High-performance client-side tool to clean, deduplicate, and sort text lists. Handles large datasets (100k+ lines) with options for trimming, case sensitivity, and natural sorting.
About
Data hygiene is the foundation of reliable database management and marketing analytics. Raw text lists exported from legacy systems or scraped from the web often contain irregularities that break downstream processes. Invisible whitespace characters cause exact-match lookup failures in SQL databases or API calls. Duplicate entries skew statistical analysis and waste budget in pay-per-click campaigns. This tool executes strict sanitization protocols directly in the browser memory. It ensures that sensitive customer data or proprietary key lists never transmit over the network. The logic handles massive arrays efficiently by leveraging hash-based sets for uniqueness and optimized sorting algorithms for ordering.
Formulas
The core deduplication process relies on Set Theory principles to filter the input vector L into a unique output set U. The cardinality of the output is always less than or equal to the input.
Reference Data
| Operation | Algorithmic Complexity | Input State | Transformation Logic | Result Utility |
|---|---|---|---|---|
| Deduplication | O(n) | Redundant entries | x ∈ S → SKIP | Unique Constraints |
| Trimming | O(n) | _User_ | trim(s) | String Matching |
| Empty Removal | O(n) | Null / Whitespace | if len(s) > 0 | Data Density |
| Natural Sort | O(n log n) | Item1, Item10, Item2 | compare(num) | Human Readability |
| Case Folding | O(n) | User, USER, user | lower(s) | Normalization |
| Reverse Order | O(n) | Ascending | swap(i, j) | LIFO Processing |
| Prefixing | O(n) | ID | p + s | SQL Formatting |
| Suffixing | O(n) | Value | s + e | CSV Generation |
| Randomization | O(n) | Ordered | Fisher-Yates | A/B Testing |
| Regex Filter | O(n) | Mixed Content | match(p) | Pattern Extraction |