Normalization & Cleaning
Remove noise and unify formats for better matching
Normalization is the prerequisite of fuzzy dedup: standardize first, match second.
Key Highlights
Related Landing Pages
Featured Tutorials
Excel Email Deduplication: Case, Dots & Plus Aliases All Covered
Professional guide for marketing email list cleaning: handle case differences, Gmail dot rules, +alias tags. Supports complex scenarios like ZHANG.SAN@EXAMPLE.COM vs zhangsan@example.com, alice+ads@gmail.com vs alice@gmail.com.
How to Remove Duplicate Phone Numbers in Excel (with Country Codes & Format Standardization)
Essential skills for marketing/customer service list cleaning: standardize phone number formats, handle country codes, and keep the latest records. Supports standardization of various formats like +86 138-0000-1234, 13800001234, 086-13800001234.
CSV Batch Deduplication: Big Data & Cross-Platform
Learn how to batch process multiple CSV files, handle encoding, delimiters and other common issues, supporting deduplication of tens of millions of records. Includes Python script examples.
Excel Million-Row Deduplication Without Crashing: Performance Optimization & Crash Prevention Guide
Professional guide for Excel big data processing: 64-bit environment setup, CSV optimization, batch processing strategies. Solve million-row data freezing, memory issues, program crashes, provides SheetCleaner, Power Query, Python script high-performance solutions.
Understanding Your Deduplication Results with Visual Analytics
Learn how to use SheetCleaner built-in charts to analyze your data cleaning results. Understand data composition with pie charts and identify which columns have the most duplicates using bar charts.
Research/Survey Data Batch Deduplication: Email/Organization/Time Window Composite Key Practice
Professional guide for academic research and survey data cleaning: handle duplicate responses, cross-batch merging, organization name standardization, time window deduplication. Support privacy compliance, retention rule configuration, audit tracking, suitable for academic research, market research, user feedback and other scenarios.